Package: a4 Version: 1.59.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo, Rgraphviz, GOstats, hgu95av2.db License: GPL-3 MD5sum: 48a38d87a395d7ca1c8cf4826e947890 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Umbrella Package Description: Umbrella package is available for the entire Automated Affymetrix Array Analysis suite of package. biocViews: Microarray Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4 git_branch: devel git_last_commit: ac91744 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/a4_1.59.0.tar.gz vignettes: vignettes/a4/inst/doc/a4vignette.pdf vignetteTitles: a4vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4/inst/doc/a4vignette.R dependencyCount: 84 Package: a4Base Version: 1.59.0 Depends: a4Preproc, a4Core Imports: methods, graphics, grid, Biobase, annaffy, mpm, genefilter, limma, multtest, glmnet, gplots Suggests: Cairo, ALL, hgu95av2.db, nlcv Enhances: gridSVG, JavaGD License: GPL-3 MD5sum: 1a032921a56fdf2e5d6dcc6d99a384c6 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Base Package Description: Base utility functions are available for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray Author: Willem Talloen [aut], Tine Casneuf [aut], An De Bondt [aut], Steven Osselaer [aut], Hinrich Goehlmann [aut], Willem Ligtenberg [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4Base git_branch: devel git_last_commit: 2ae8314 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/a4Base_1.59.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 suggestsMe: epimutacions dependencyCount: 75 Package: a4Classif Version: 1.59.0 Depends: a4Core, a4Preproc Imports: methods, Biobase, ROCR, pamr, glmnet, varSelRF, utils, graphics, stats Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 0a74184c78691850d5df517189c7d8a7 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Classification Package Description: Functionalities for classification of Affymetrix microarray data, integrating within the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, GeneExpression, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Classif git_branch: devel git_last_commit: 45e0ead git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/a4Classif_1.59.0.tar.gz vignettes: vignettes/a4Classif/inst/doc/a4Classif-vignette.html vignetteTitles: a4Classif package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Classif/inst/doc/a4Classif-vignette.R dependsOnMe: a4 dependencyCount: 33 Package: a4Core Version: 1.59.0 Imports: Biobase, glmnet, methods, stats Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 7343e6c57b5dc16baa47fd97a02ab573 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Core Package Description: Utility functions for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Core git_branch: devel git_last_commit: c04f1a7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/a4Core_1.59.0.tar.gz vignettes: vignettes/a4Core/inst/doc/a4Core-vignette.html vignetteTitles: a4Core package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Core/inst/doc/a4Core-vignette.R dependsOnMe: a4, a4Base, a4Classif, nlcv dependencyCount: 20 Package: a4Preproc Version: 1.59.0 Imports: BiocGenerics, Biobase Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 04c9b9e3f0271862f2fc2ec951a22a3e NeedsCompilation: no Title: Automated Affymetrix Array Analysis Preprocessing Package Description: Utility functions to pre-process data for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Preprocessing Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Preproc git_branch: devel git_last_commit: 921a89b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/a4Preproc_1.59.0.tar.gz vignettes: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.html vignetteTitles: a4Preproc package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.R dependsOnMe: a4, a4Base, a4Classif suggestsMe: graphite dependencyCount: 7 Package: a4Reporting Version: 1.59.0 Imports: methods, xtable Suggests: knitr, rmarkdown License: GPL-3 MD5sum: b839918daa08bec247665e6f4e0cd8a9 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Reporting Package Description: Utility functions to facilitate the reporting of the Automated Affymetrix Array Analysis Reporting set of packages. biocViews: Microarray Author: Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Reporting git_branch: devel git_last_commit: 999bf0e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/a4Reporting_1.59.0.tar.gz vignettes: vignettes/a4Reporting/inst/doc/a4reporting-vignette.html vignetteTitles: a4Reporting package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Reporting/inst/doc/a4reporting-vignette.R dependsOnMe: a4 dependencyCount: 4 Package: ABarray Version: 1.79.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: 7b6d87fc3423732041a39c790a11f872 NeedsCompilation: no Title: Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data. Description: Automated pipline to perform gene expression analysis for Applied Biosystems Genome Survey Microarray (AB1700) data format. Functions include data preprocessing, filtering, control probe analysis, statistical analysis in one single function. A GUI interface is also provided. The raw data, processed data, graphics output and statistical results are organized into folders according to the analysis settings used. biocViews: Microarray, OneChannel, Preprocessing Author: Yongming Andrew Sun Maintainer: Yongming Andrew Sun git_url: https://git.bioconductor.org/packages/ABarray git_branch: devel git_last_commit: 839d701 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ABarray_1.79.0.tar.gz vignettes: vignettes/ABarray/inst/doc/ABarray.pdf, vignettes/ABarray/inst/doc/ABarrayGUI.pdf vignetteTitles: ABarray gene expression, ABarray gene expression GUI interface hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 17 Package: abseqR Version: 1.29.0 Depends: R (>= 3.5.0) Imports: ggplot2, RColorBrewer, circlize, reshape2, VennDiagram, plyr, flexdashboard, BiocParallel (>= 1.1.25), png, grid, gridExtra, rmarkdown, knitr, vegan, ggcorrplot, ggdendro, plotly, BiocStyle, stringr, utils, methods, grDevices, stats, tools, graphics Suggests: testthat License: GPL-3 | file LICENSE MD5sum: 7f5a6c9572605c30ae8cf169c9dbfe50 NeedsCompilation: no Title: Reporting and data analysis functionalities for Rep-Seq datasets of antibody libraries Description: AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output. biocViews: Sequencing, Visualization, ReportWriting, QualityControl, MultipleComparison Author: JiaHong Fong [cre, aut], Monther Alhamdoosh [aut] Maintainer: JiaHong Fong URL: https://github.com/malhamdoosh/abseqR SystemRequirements: pandoc (>= 1.19.2.1) VignetteBuilder: knitr BugReports: https://github.com/malhamdoosh/abseqR/issues git_url: https://git.bioconductor.org/packages/abseqR git_branch: devel git_last_commit: 89ae70b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/abseqR_1.29.0.tar.gz vignettes: vignettes/abseqR/inst/doc/abseqR.pdf vignetteTitles: Introduction to abseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/abseqR/inst/doc/abseqR.R dependencyCount: 109 Package: ABSSeq Version: 1.65.0 Depends: R (>= 2.10), methods Imports: locfit, limma Suggests: edgeR License: GPL (>= 3) MD5sum: 67e7dd4a4a45c63c6699d8b652fdc516 NeedsCompilation: no Title: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences Description: Inferring differential expression genes by absolute counts difference between two groups, utilizing Negative binomial distribution and moderating fold-change according to heterogeneity of dispersion across expression level. biocViews: DifferentialExpression Author: Wentao Yang Maintainer: Wentao Yang git_url: https://git.bioconductor.org/packages/ABSSeq git_branch: devel git_last_commit: 3e159b8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ABSSeq_1.65.0.tar.gz vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf vignetteTitles: ABSSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABSSeq/inst/doc/ABSSeq.R importsMe: metaseqR2 dependencyCount: 10 Package: acde Version: 1.41.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: 186f3acaebc1bac5fb87dd0287e74aeb NeedsCompilation: no Title: Artificial Components Detection of Differentially Expressed Genes Description: This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR). The methods on this package are described in the vignette or in the article 'Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine and S. Restrepo (2015, pending publication). biocViews: DifferentialExpression, TimeCourse, PrincipalComponent, GeneExpression, Microarray, mRNAMicroarray Author: Juan Pablo Acosta, Liliana Lopez-Kleine Maintainer: Juan Pablo Acosta git_url: https://git.bioconductor.org/packages/acde git_branch: devel git_last_commit: 631d7e5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/acde_1.41.0.tar.gz vignettes: vignettes/acde/inst/doc/acde.pdf vignetteTitles: Identification of Differentially Expressed Genes with Artificial Components hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/acde/inst/doc/acde.R dependencyCount: 3 Package: ACE Version: 1.29.0 Depends: R (>= 3.4) Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods, grDevices, GenomicRanges Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 532f501205d2a217ef6e9dc58bfb42bb NeedsCompilation: no Title: Absolute Copy Number Estimation from Low-coverage Whole Genome Sequencing Description: Uses segmented copy number data to estimate tumor cell percentage and produce copy number plots displaying absolute copy numbers. biocViews: CopyNumberVariation, DNASeq, Coverage, WholeGenome, Visualization, Sequencing Author: Jos B Poell Maintainer: Jos B Poell URL: https://github.com/tgac-vumc/ACE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ACE git_branch: devel git_last_commit: e5dde82 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ACE_1.29.0.tar.gz vignettes: vignettes/ACE/inst/doc/ACE_vignette.html vignetteTitles: ACE vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACE/inst/doc/ACE_vignette.R dependencyCount: 66 Package: aCGH Version: 1.89.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, grDevices, graphics, methods, stats, splines, utils License: GPL-2 MD5sum: 277bcce2294559a3b95546c7f6450c61 NeedsCompilation: yes Title: Classes and functions for Array Comparative Genomic Hybridization data Description: Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects. biocViews: CopyNumberVariation, DataImport, Genetics Author: Jane Fridlyand , Peter Dimitrov Maintainer: Peter Dimitrov git_url: https://git.bioconductor.org/packages/aCGH git_branch: devel git_last_commit: bbcc194 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/aCGH_1.89.0.tar.gz vignettes: vignettes/aCGH/inst/doc/aCGH.pdf vignetteTitles: aCGH Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aCGH/inst/doc/aCGH.R dependsOnMe: CRImage importsMe: ADaCGH2 dependencyCount: 17 Package: ACME Version: 2.67.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) MD5sum: 0b1d4f0e764cc071d4df226b7c2e2405 NeedsCompilation: yes Title: Algorithms for Calculating Microarray Enrichment (ACME) Description: ACME (Algorithms for Calculating Microarray Enrichment) is a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". It does not rely on a specific array technology (although the array should be a "tiling" array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory. biocViews: Technology, Microarray, Normalization Author: Sean Davis Maintainer: Sean Davis URL: http://watson.nci.nih.gov/~sdavis git_url: https://git.bioconductor.org/packages/ACME git_branch: devel git_last_commit: 299474e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ACME_2.67.0.tar.gz vignettes: vignettes/ACME/inst/doc/ACME.pdf vignetteTitles: ACME hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACME/inst/doc/ACME.R suggestsMe: oligo dependencyCount: 7 Package: ADaCGH2 Version: 2.51.3 Depends: R (>= 3.2.0), parallel, ff Imports: bit, DNAcopy, tilingArray, waveslim, cluster, aCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi, GLAD License: GPL (>= 3) MD5sum: ee0898120cac71e65aedfefc5b14242c NeedsCompilation: yes Title: Analysis of Big Data from aCGH Experiments using Parallel Computing and ff Objects Description: Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data. biocViews: Microarray, CopyNumberVariation, aCGH Author: Ramon Diaz-Uriarte [aut, cre] (ORCID: ), Oscar M. Rueda [aut], Li Hsu [ctb] (Wavelet-based aCGH smoothing code), Douglas Grove [ctb] (Wavelet-based aCGH smoothing code), Barry Rowlingson [ctb] (Imagemap code), Erez Ben-Yaacov [ctb] (HaarSeg code), Edwin de Jonge [ctb] (Code from ffbase), Jan Wijffels [ctb] (Code from ffbase), Jan van der Laan [ctb] (Code from ffbase) Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/adacgh2 BugReports: https://github.com/rdiaz02/adacgh2 git_url: https://git.bioconductor.org/packages/ADaCGH2 git_branch: devel git_last_commit: de39939 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/ADaCGH2_2.51.3.tar.gz vignettes: vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf, vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf, vignettes/ADaCGH2/inst/doc/benchmarks.pdf vignetteTitles: ADaCGH2-long-examples.pdf, ADaCGH2 Overview, benchmarks.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADaCGH2/inst/doc/ADaCGH2.R dependencyCount: 84 Package: ADAM Version: 1.27.0 Depends: R(>= 3.5), stats, utils, methods Imports: Rcpp (>= 0.12.18), GO.db (>= 3.6.0), KEGGREST (>= 1.20.2), knitr, pbapply (>= 1.3-4), dplyr (>= 0.7.6), DT (>= 0.4), stringr (>= 1.3.1), SummarizedExperiment (>= 1.10.1) LinkingTo: Rcpp Suggests: testthat, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: 65c52a03362fc794a339fe31f6d7e51b NeedsCompilation: yes Title: ADAM: Activity and Diversity Analysis Module Description: ADAM is a GSEA R package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions (Gene Ontology and KEGG pathways as default) and show their significance by calculating p-values referring togene diversity and activity. Each group of genes is called GFAG (Group of Functionally Associated Genes). biocViews: GeneSetEnrichment, Pathways, KEGG, GeneExpression, Microarray Author: André Luiz Molan [aut], Giordano Bruno Sanches Seco [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAM git_branch: devel git_last_commit: 75224e2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ADAM_1.27.0.tar.gz vignettes: vignettes/ADAM/inst/doc/ADAM.html vignetteTitles: "Using ADAM" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAM/inst/doc/ADAM.R dependsOnMe: ADAMgui dependencyCount: 89 Package: ADAMgui Version: 1.27.0 Depends: R(>= 3.6), stats, utils, methods, ADAM Imports: GO.db (>= 3.5.0), dplyr (>= 0.7.6), shiny (>= 1.1.0), stringr (>= 1.3.1), stringi (>= 1.2.4), varhandle (>= 2.0.3), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), ggpubr (>= 0.1.8), ggsignif (>= 0.4.0), reshape2 (>= 1.4.3), RColorBrewer (>= 1.1-2), colorRamps (>= 2.3), DT (>= 0.4), data.table (>= 1.11.4), gridExtra (>= 2.3), shinyjs (>= 1.0), knitr, testthat Suggests: markdown, BiocStyle License: GPL (>= 2) MD5sum: 61b1848c34f8f46d5f20967a0ab67263 NeedsCompilation: no Title: Activity and Diversity Analysis Module Graphical User Interface Description: ADAMgui is a Graphical User Interface for the ADAM package. The ADAMgui package provides 2 shiny-based applications that allows the user to study the output of the ADAM package files through different plots. It's possible, for example, to choose a specific GFAG and observe the gene expression behavior with the plots created with the GFAGtargetUi function. Features such as differential expression and foldchange can be easily seen with aid of the plots made with GFAGpathUi function. biocViews: GeneSetEnrichment, Pathways, KEGG Author: Giordano Bruno Sanches Seco [aut], André Luiz Molan [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho URL: TBA VignetteBuilder: knitr BugReports: https://github.com/jrybarczyk/ADAMgui/issues git_url: https://git.bioconductor.org/packages/ADAMgui git_branch: devel git_last_commit: fe6a43e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ADAMgui_1.27.0.tar.gz vignettes: vignettes/ADAMgui/inst/doc/ADAMgui.html vignetteTitles: "Using ADAMgui" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAMgui/inst/doc/ADAMgui.R dependencyCount: 169 Package: ADAPT Version: 1.5.0 Depends: R (>= 4.1.0) Imports: Rcpp (>= 1.0.8), RcppArmadillo (>= 0.10.8), RcppParallel (>= 5.1.5), phyloseq (>= 1.39.0), methods, stats, ggplot2 (>= 3.4.1), ggrepel (>= 0.9.1) LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: rmarkdown (>= 2.11), knitr (>= 1.37), testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: e8b56ec731338d6eaafe09dafc170642 NeedsCompilation: yes Title: Analysis of Microbiome Differential Abundance by Pooling Tobit Models Description: ADAPT carries out differential abundance analysis for microbiome metagenomics data in phyloseq format. It has two innovations. One is to treat zero counts as left censored and use Tobit models for log count ratios. The other is an innovative way to find non-differentially abundant taxa as reference, then use the reference taxa to find the differentially abundant ones. biocViews: DifferentialExpression, Microbiome, Normalization, Sequencing, Metagenomics, Software, MultipleComparison Author: Mukai Wang [aut, cre] (ORCID: ), Simon Fontaine [ctb], Hui Jiang [ctb], Gen Li [aut, ctb] Maintainer: Mukai Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAPT git_branch: devel git_last_commit: f78af6b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ADAPT_1.5.0.tar.gz vignettes: vignettes/ADAPT/inst/doc/ADAPT-manual.html vignetteTitles: ADAPT Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ADAPT/inst/doc/ADAPT-manual.R dependencyCount: 69 Package: ADImpute Version: 1.21.0 Depends: R (>= 4.0) Imports: checkmate, BiocParallel, data.table, DrImpute, kernlab, MASS, Matrix, methods, rsvd, S4Vectors, SAVER, SingleCellExperiment, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 + file LICENSE MD5sum: 5c5009c8274044eb9e809e327d76cfeb NeedsCompilation: no Title: Adaptive Dropout Imputer (ADImpute) Description: Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble. biocViews: GeneExpression, Network, Preprocessing, Sequencing, SingleCell, Transcriptomics Author: Ana Carolina Leote [cre, aut] (ORCID: ) Maintainer: Ana Carolina Leote VignetteBuilder: knitr BugReports: https://github.com/anacarolinaleote/ADImpute/issues git_url: https://git.bioconductor.org/packages/ADImpute git_branch: devel git_last_commit: 8a9fa73 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ADImpute_1.21.0.tar.gz vignettes: vignettes/ADImpute/inst/doc/ADImpute_tutorial.html vignetteTitles: ADImpute tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ADImpute/inst/doc/ADImpute_tutorial.R dependencyCount: 54 Package: adSplit Version: 1.81.0 Depends: R (>= 2.1.0), methods (>= 2.1.0) Imports: AnnotationDbi, Biobase (>= 1.5.12), cluster (>= 1.9.1), GO.db (>= 1.8.1), graphics, grDevices, KEGGREST (>= 1.30.1), multtest (>= 1.6.0), stats (>= 2.1.0) Suggests: golubEsets (>= 1.0), vsn (>= 1.5.0), hu6800.db (>= 1.8.1) License: GPL (>= 2) MD5sum: 007398219e2a94786a3491aa18b9a2f5 NeedsCompilation: yes Title: Annotation-Driven Clustering Description: This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined. biocViews: Microarray, Clustering Author: Claudio Lottaz, Joern Toedling Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/adSplit.shtml git_url: https://git.bioconductor.org/packages/adSplit git_branch: devel git_last_commit: a2e95b4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/adSplit_1.81.0.tar.gz vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf vignetteTitles: Annotation-Driven Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adSplit/inst/doc/tr_2005_02.R dependencyCount: 51 Package: adverSCarial Version: 1.9.0 Depends: R (>= 3.5.0) Imports: gtools, S4Vectors, methods, DelayedArray Suggests: knitr, RUnit, BiocGenerics, TENxPBMCData, CHETAH, stringr, LoomExperiment License: MIT + file LICENSE MD5sum: 152e84a85c0ebbd02791565e30700b22 NeedsCompilation: no Title: adverSCarial, generate and analyze the vulnerability of scRNA-seq classifier to adversarial attacks Description: adverSCarial is an R Package designed for generating and analyzing the vulnerability of scRNA-seq classifiers to adversarial attacks. The package is versatile and provides a format for integrating any type of classifier. It offers functions for studying and generating two types of attacks, single gene attack and max change attack. The single-gene attack involves making a small modification to the input to alter the classification. The max-change attack involves making a large modification to the input without changing its classification. The CGD attack is based on an estimated gradient descent. against adversarial attacks. The package provides a comprehensive solution for evaluating the robustness of scRNA-seq classifiers against adversarial attacks. biocViews: Software, SingleCell, Transcriptomics, Classification Author: Ghislain FIEVET [aut, cre] (ORCID: ), Sébastien HERGALANT [aut] (ORCID: ) Maintainer: Ghislain FIEVET VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/adverSCarial git_branch: devel git_last_commit: a19db67 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/adverSCarial_1.9.0.tar.gz vignettes: vignettes/adverSCarial/inst/doc/vign01_adverSCarial.html, vignettes/adverSCarial/inst/doc/vign02_overView_analysis.html, vignettes/adverSCarial/inst/doc/vign03_adapt_classifier.html, vignettes/adverSCarial/inst/doc/vign04_advRandWalkMinChange.html vignetteTitles: Vign01_adverSCarial, Vign02_overView_analysis, Vign03_adapt_classifiers, Vign04_advRandWalkMinChange hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/adverSCarial/inst/doc/vign01_adverSCarial.R, vignettes/adverSCarial/inst/doc/vign02_overView_analysis.R dependencyCount: 22 Package: AffiXcan Version: 1.29.0 Depends: R (>= 3.6), SummarizedExperiment Imports: MultiAssayExperiment, BiocParallel, crayon Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: ed50f639490de493d384594a525e0c1e NeedsCompilation: no Title: A Functional Approach To Impute Genetically Regulated Expression Description: Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known. biocViews: GeneExpression, Transcription, GeneRegulation, DimensionReduction, Regression, PrincipalComponent Author: Alessandro Lussana [aut, cre] Maintainer: Alessandro Lussana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AffiXcan git_branch: devel git_last_commit: a4562bc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AffiXcan_1.29.0.tar.gz vignettes: vignettes/AffiXcan/inst/doc/AffiXcan.html vignetteTitles: AffiXcan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffiXcan/inst/doc/AffiXcan.R dependencyCount: 56 Package: affxparser Version: 1.83.0 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles License: LGPL (>= 2) MD5sum: f30506050d007b3c584fa8c09dcd0935 NeedsCompilation: yes Title: Affymetrix File Parsing SDK Description: Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms, OneChannel Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman [ctb], Kasper Daniel Hansen [aut, cre], Jim Hester [ctb], Martin Morgan [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/affxparser BugReports: https://github.com/HenrikBengtsson/affxparser/issues git_url: https://git.bioconductor.org/packages/affxparser git_branch: devel git_last_commit: e8726cc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/affxparser_1.83.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder importsMe: affyILM, cn.farms, EventPointer, GeneRegionScan, ITALICS, oligo suggestsMe: TIN, aroma.affymetrix, aroma.apd dependencyCount: 0 Package: affy Version: 1.89.0 Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5) Imports: affyio (>= 1.13.3), BiocManager, graphics, grDevices, methods, preprocessCore, stats, utils LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools, hgu95av2cdf License: LGPL (>= 2.0) MD5sum: c1fd6ce881db139e41874980bd1aa3c7 NeedsCompilation: yes Title: Methods for Affymetrix Oligonucleotide Arrays Description: The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it. biocViews: Microarray, OneChannel, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Benjamin Milo Bolstad , and Crispin Miller with contributions from Magnus Astrand , Leslie M. Cope , Robert Gentleman, Jeff Gentry, Conrad Halling , Wolfgang Huber, James MacDonald , Benjamin I. P. Rubinstein, Christopher Workman , John Zhang Maintainer: Robert D. Shear URL: https://bioconductor.org/packages/affy BugReports: https://github.com/rafalab/affy/issues git_url: https://git.bioconductor.org/packages/affy git_branch: devel git_last_commit: b2e5a66 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/affy_1.89.0.tar.gz vignettes: vignettes/affy/inst/doc/affy.pdf, vignettes/affy/inst/doc/builtinMethods.pdf, vignettes/affy/inst/doc/customMethods.pdf, vignettes/affy/inst/doc/vim.pdf vignetteTitles: 1. Primer, 2. Built-in Processing Methods, 3. Custom Processing Methods, 4. Import Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affy/inst/doc/affy.R, vignettes/affy/inst/doc/builtinMethods.R, vignettes/affy/inst/doc/customMethods.R, vignettes/affy/inst/doc/vim.R dependsOnMe: affyContam, affyPLM, AffyRNADegradation, altcdfenvs, arrayMvout, Cormotif, DrugVsDisease, ExiMiR, frmaTools, gcrma, maskBAD, panp, prebs, qpcrNorm, RPA, SCAN.UPC, webbioc, affydata, ALLMLL, AmpAffyExample, bronchialIL13, CLL, curatedBladderData, ecoliLeucine, Hiiragi2013, MAQCsubset, mvoutData, PREDAsampledata, SpikeIn, SpikeInSubset, XhybCasneuf importsMe: affycoretools, affyILM, affylmGUI, arrayQualityMetrics, bnem, CAFE, ChIPXpress, Cormotif, Doscheda, ffpe, frma, gcrma, GEOsubmission, HTqPCR, iCheck, lumi, makecdfenv, mimager, MSnbase, PECA, plier, puma, pvac, Rnits, STATegRa, tilingArray, TurboNorm, vsn, rat2302frmavecs, DeSousa2013, signatureSearchData, bapred suggestsMe: AnnotationForge, ArrayExpress, autonomics, beadarray, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, factDesign, GeneRegionScan, limma, made4, piano, PREDA, qcmetrics, runibic, siggenes, TCGAbiolinks, ath1121501frmavecs, estrogen, ffpeExampleData, arrays, aroma.affymetrix, hexbin, isatabr, maGUI dependencyCount: 11 Package: affycomp Version: 1.87.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: aaf4d88360d29f3b54eca5daf79675d1 NeedsCompilation: no Title: Graphics Toolbox for Assessment of Affymetrix Expression Measures Description: The package contains functions that can be used to compare expression measures for Affymetrix Oligonucleotide Arrays. biocViews: OneChannel, Microarray, Preprocessing Author: Rafael A. Irizarry and Zhijin Wu with contributions from Simon Cawley Maintainer: Robert D. Shear URL: https://bioconductor.org/packages/affycomp BugReports: https://github.com/rafalab/affycomp/issues git_url: https://git.bioconductor.org/packages/affycomp git_branch: devel git_last_commit: ec5ab1b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/affycomp_1.87.0.tar.gz vignettes: vignettes/affycomp/inst/doc/affycomp.pdf vignetteTitles: affycomp primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycomp/inst/doc/affycomp.R dependsOnMe: affycompData dependencyCount: 7 Package: affyContam Version: 1.69.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata Suggests: hgu95av2cdf License: Artistic-2.0 MD5sum: b238fe052ca117be3785f8e3de54b317 NeedsCompilation: no Title: structured corruption of affymetrix cel file data Description: structured corruption of cel file data to demonstrate QA effectiveness biocViews: Infrastructure Author: V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/affyContam git_branch: devel git_last_commit: 87d41e7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/affyContam_1.69.0.tar.gz vignettes: vignettes/affyContam/inst/doc/affyContam.pdf vignetteTitles: affy contamination tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyContam/inst/doc/affyContam.R importsMe: arrayMvout dependencyCount: 14 Package: affyILM Version: 1.63.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, Biobase Suggests: AffymetrixDataTestFiles, hgfocusprobe License: GPL-3 MD5sum: 8f069a9247836ae9933c1e106f08f172 NeedsCompilation: no Title: Linear Model of background subtraction and the Langmuir isotherm Description: affyILM is a preprocessing tool which estimates gene expression levels for Affymetrix Gene Chips. Input from physical chemistry is employed to first background subtract intensities before calculating concentrations on behalf of the Langmuir model. biocViews: Microarray, OneChannel, Preprocessing Author: K. Myriam Kroll, Fabrice Berger, Gerard Barkema, Enrico Carlon Maintainer: Myriam Kroll and Fabrice Berger git_url: https://git.bioconductor.org/packages/affyILM git_branch: devel git_last_commit: bd4e9e3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/affyILM_1.63.0.tar.gz vignettes: vignettes/affyILM/inst/doc/affyILM.pdf vignetteTitles: affyILM1.3.0 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyILM/inst/doc/affyILM.R dependencyCount: 23 Package: affyio Version: 1.81.0 Depends: R (>= 2.6.0) Imports: methods License: LGPL (>= 2) MD5sum: a8ae545cf6964c527a26a9d75223fe81 NeedsCompilation: yes Title: Tools for parsing Affymetrix data files Description: Routines for parsing Affymetrix data files based upon file format information. Primary focus is on accessing the CEL and CDF file formats. biocViews: Microarray, DataImport, Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyio git_url: https://git.bioconductor.org/packages/affyio git_branch: devel git_last_commit: 51aea4e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/affyio_1.81.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: makecdfenv, SCAN.UPC importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods dependencyCount: 1 Package: affylmGUI Version: 1.85.0 Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma, affy, affyio, affyPLM, gcrma, BiocGenerics, AnnotationDbi, BiocManager, R2HTML, xtable License: GPL (>=2) MD5sum: a09474e0fa13d9b365d4f35c7ba42fda NeedsCompilation: no Title: GUI for limma Package with Affymetrix Microarrays Description: A Graphical User Interface (GUI) for analysis of Affymetrix microarray gene expression data using the affy and limma packages. biocViews: GUI, GeneExpression, Transcription, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [cre,aut], Gordon Smyth [aut], Ken Simpson [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/affylmGUI/ git_url: https://git.bioconductor.org/packages/affylmGUI git_branch: devel git_last_commit: 6df4f72 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/affylmGUI_1.85.0.tar.gz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf, vignettes/affylmGUI/inst/doc/about.html, vignettes/affylmGUI/inst/doc/CustMenu.html, vignettes/affylmGUI/inst/doc/index.html, vignettes/affylmGUI/inst/doc/windowsFocus.html vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files, about.html, CustMenu.html, index.html, windowsFocus.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R dependencyCount: 55 Package: affyPLM Version: 1.87.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS, hgu95av2cdf License: GPL (>= 2) MD5sum: 55499f0f2c19e4dc7c108229ee5f3858 NeedsCompilation: yes Title: Methods for fitting probe-level models Description: A package that extends and improves the functionality of the base affy package. Routines that make heavy use of compiled code for speed. Central focus is on implementation of methods for fitting probe-level models and tools using these models. PLM based quality assessment tools. biocViews: Microarray, OneChannel, Preprocessing, QualityControl Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyPLM git_url: https://git.bioconductor.org/packages/affyPLM git_branch: devel git_last_commit: 53a03e9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/affyPLM_1.87.0.tar.gz vignettes: vignettes/affyPLM/inst/doc/AffyExtensions.pdf, vignettes/affyPLM/inst/doc/MAplots.pdf, vignettes/affyPLM/inst/doc/QualityAssess.pdf, vignettes/affyPLM/inst/doc/ThreeStep.pdf vignetteTitles: affyPLM: Fitting Probe Level Models, affyPLM: Advanced use of the MAplot function, affyPLM: Model Based QC Assessment of Affymetrix GeneChips, affyPLM: the threestep function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPLM/inst/doc/AffyExtensions.R, vignettes/affyPLM/inst/doc/MAplots.R, vignettes/affyPLM/inst/doc/QualityAssess.R, vignettes/affyPLM/inst/doc/ThreeStep.R dependsOnMe: bapred importsMe: affylmGUI, arrayQualityMetrics, mimager suggestsMe: arrayMvout, BiocGenerics, frmaTools, metahdep, piano, aroma.affymetrix dependencyCount: 22 Package: AffyRNADegradation Version: 1.57.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample, hgu133acdf License: GPL-2 MD5sum: aa8d6b8b66f572dd828b3b48b07dbb98 NeedsCompilation: no Title: Analyze and correct probe positional bias in microarray data due to RNA degradation Description: The package helps with the assessment and correction of RNA degradation effects in Affymetrix 3' expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation. biocViews: GeneExpression, Microarray, OneChannel, Preprocessing, QualityControl Author: Mario Fasold Maintainer: Mario Fasold git_url: https://git.bioconductor.org/packages/AffyRNADegradation git_branch: devel git_last_commit: a547cf1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AffyRNADegradation_1.57.0.tar.gz vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf vignetteTitles: AffyRNADegradation Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyRNADegradation/inst/doc/vignette.R dependencyCount: 12 Package: AGDEX Version: 1.59.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: 6961b1b2e16a12554f0db9a098ca08b4 NeedsCompilation: no Title: Agreement of Differential Expression Analysis Description: A tool to evaluate agreement of differential expression for cross-species genomics biocViews: Microarray, Genetics, GeneExpression Author: Stan Pounds ; Cuilan Lani Gao Maintainer: Cuilan lani Gao git_url: https://git.bioconductor.org/packages/AGDEX git_branch: devel git_last_commit: 8634f30 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AGDEX_1.59.0.tar.gz vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf vignetteTitles: AGDEX.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AGDEX/inst/doc/AGDEX.R dependencyCount: 47 Package: aggregateBioVar Version: 1.21.0 Depends: R (>= 4.0) Imports: stats, methods, S4Vectors, SummarizedExperiment, SingleCellExperiment, Matrix, tibble, rlang Suggests: BiocStyle, magick, knitr, rmarkdown, testthat, BiocGenerics, DESeq2, magrittr, dplyr, ggplot2, cowplot, ggtext, RColorBrewer, pheatmap, viridis License: GPL-3 MD5sum: 79faba081c7f0e960a7c130725e08465 NeedsCompilation: no Title: Differential Gene Expression Analysis for Multi-subject scRNA-seq Description: For single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates), this package contains tools to summarize single cell gene expression profiles at the level of subject. A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools. biocViews: Software, SingleCell, RNASeq, Transcriptomics, Transcription, GeneExpression, DifferentialExpression Author: Jason Ratcliff [aut, cre] (ORCID: ), Andrew Thurman [aut], Michael Chimenti [ctb], Alejandro Pezzulo [ctb] Maintainer: Jason Ratcliff URL: https://github.com/jasonratcliff/aggregateBioVar VignetteBuilder: knitr BugReports: https://github.com/jasonratcliff/aggregateBioVar/issues git_url: https://git.bioconductor.org/packages/aggregateBioVar git_branch: devel git_last_commit: b0e2560 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/aggregateBioVar_1.21.0.tar.gz vignettes: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.html vignetteTitles: Multi-subject scRNA-seq Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.R dependencyCount: 36 Package: agilp Version: 3.43.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: d696f0d03f71428e0f14e16b33d225cf NeedsCompilation: no Title: Agilent expression array processing package Description: More about what it does (maybe more than one line) Author: Benny Chain Maintainer: Benny Chain git_url: https://git.bioconductor.org/packages/agilp git_branch: devel git_last_commit: 527412b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/agilp_3.43.0.tar.gz vignettes: vignettes/agilp/inst/doc/agilp_manual.pdf vignetteTitles: An R Package for processing expression microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/agilp/inst/doc/agilp_manual.R dependencyCount: 0 Package: AIMS Version: 1.43.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 2fe89d457b6ed7bc31b9bb1b3456c3b3 NeedsCompilation: no Title: AIMS : Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype Description: This package contains the AIMS implementation. It contains necessary functions to assign the five intrinsic molecular subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like, Normal-like). Assignments could be done on individual samples as well as on dataset of gene expression data. biocViews: ImmunoOncology, Classification, RNASeq, Microarray, Software, GeneExpression Author: Eric R. Paquet, Michael T. Hallett Maintainer: Eric R Paquet URL: http://www.bci.mcgill.ca/AIMS git_url: https://git.bioconductor.org/packages/AIMS git_branch: devel git_last_commit: 50f1469 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AIMS_1.43.0.tar.gz vignettes: vignettes/AIMS/inst/doc/AIMS.pdf vignetteTitles: AIMS An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AIMS/inst/doc/AIMS.R dependsOnMe: genefu dependencyCount: 12 Package: airpart Version: 1.19.0 Depends: R (>= 4.1) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, scater, stats, smurf, apeglm (>= 1.13.3), emdbook, mclust, clue, dynamicTreeCut, matrixStats, dplyr, plyr, ggplot2, ComplexHeatmap, forestplot, RColorBrewer, rlang, lpSolve, grid, grDevices, graphics, utils, pbapply Suggests: knitr, rmarkdown, roxygen2 (>= 6.0.0), testthat (>= 3.0.0), gplots, tidyr License: GPL-2 MD5sum: cca021ed1edc47d69f6ab04bcc6e51f4 NeedsCompilation: no Title: Differential cell-type-specific allelic imbalance Description: Airpart identifies sets of genes displaying differential cell-type-specific allelic imbalance across cell types or states, utilizing single-cell allelic counts. It makes use of a generalized fused lasso with binomial observations of allelic counts to partition cell types by their allelic imbalance. Alternatively, a nonparametric method for partitioning cell types is offered. The package includes a number of visualizations and quality control functions for examining single cell allelic imbalance datasets. biocViews: SingleCell, RNASeq, ATACSeq, ChIPSeq, Sequencing, GeneRegulation, GeneExpression, Transcription, TranscriptomeVariant, CellBiology, FunctionalGenomics, DifferentialExpression, GraphAndNetwork, Regression, Clustering, QualityControl Author: Wancen Mu [aut, cre] (ORCID: ), Michael Love [aut, ctb] (ORCID: ) Maintainer: Wancen Mu URL: https://github.com/Wancen/airpart VignetteBuilder: knitr BugReports: https://github.com/Wancen/airpart/issues git_url: https://git.bioconductor.org/packages/airpart git_branch: devel git_last_commit: b69681a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/airpart_1.19.0.tar.gz vignettes: vignettes/airpart/inst/doc/airpart.html vignetteTitles: Differential allelic imbalance with airpart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/airpart/inst/doc/airpart.R dependencyCount: 134 Package: alabaster.base Version: 1.11.4 Imports: alabaster.schemas, methods, utils, S4Vectors, rhdf5 (>= 2.47.6), jsonlite, jsonvalidate, Rcpp LinkingTo: Rcpp, assorthead (>= 1.1.2), Rhdf5lib Suggests: BiocStyle, rmarkdown, knitr, testthat, digest, Matrix, alabaster.matrix License: MIT + file LICENSE MD5sum: 1af0fed35dbb219f435694edce6a8f6f NeedsCompilation: yes Title: Save Bioconductor Objects to File Description: Save Bioconductor data structures into file artifacts, and load them back into memory. This is a more robust and portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataRepresentation, DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/ArtifactDB/alabaster.base SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/ArtifactDB/alabaster.base/issues git_url: https://git.bioconductor.org/packages/alabaster.base git_branch: devel git_last_commit: 5424d3b git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/alabaster.base_1.11.4.tar.gz vignettes: vignettes/alabaster.base/inst/doc/userguide.html vignetteTitles: Saving and loading artifacts hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.base/inst/doc/userguide.R dependsOnMe: alabaster, alabaster.bumpy, alabaster.mae, alabaster.matrix, alabaster.ranges, alabaster.sce, alabaster.se, alabaster.sfe, alabaster.spatial, alabaster.string, alabaster.vcf importsMe: chevreulShiny, celldex, scRNAseq dependencyCount: 23 Package: alabaster.bumpy Version: 1.11.0 Depends: BumpyMatrix, alabaster.base Imports: methods, rhdf5, Matrix, BiocGenerics, S4Vectors, IRanges Suggests: BiocStyle, rmarkdown, knitr, testthat, jsonlite License: MIT + file LICENSE MD5sum: 4427d9db821204e6720303a350a0ea45 NeedsCompilation: no Title: Save and Load BumpyMatrices to/from file Description: Save BumpyMatrix objects into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.bumpy git_branch: devel git_last_commit: 08f8924 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/alabaster.bumpy_1.11.0.tar.gz vignettes: vignettes/alabaster.bumpy/inst/doc/userguide.html vignetteTitles: Saving and loading BumpyMatrices hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.bumpy/inst/doc/userguide.R importsMe: alabaster dependencyCount: 30 Package: alabaster.files Version: 1.9.0 Depends: alabaster.base, Imports: methods, S4Vectors, BiocGenerics, Rsamtools Suggests: BiocStyle, rmarkdown, knitr, testthat, VariantAnnotation, rtracklayer, Biostrings License: MIT + file LICENSE MD5sum: 7cc455c1b9b84ffc8268b8ffb7282521 NeedsCompilation: no Title: Wrappers to Save Common File Formats Description: Save common bioinformatics file formats within the alabaster framework. This includes BAM, BED, VCF, bigWig, bigBed, FASTQ, FASTA and so on. We save and load additional metadata for each file, and we support linkage between each file and its corresponding index. biocViews: DataRepresentation, DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.files git_branch: devel git_last_commit: 32a02ee git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/alabaster.files_1.9.0.tar.gz vignettes: vignettes/alabaster.files/inst/doc/userguide.html vignetteTitles: Saving common file formats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.files/inst/doc/userguide.R dependencyCount: 44 Package: alabaster.ranges Version: 1.11.0 Depends: GenomicRanges, alabaster.base Imports: methods, S4Vectors, BiocGenerics, IRanges, Seqinfo, rhdf5 Suggests: testthat, knitr, BiocStyle, jsonlite License: MIT + file LICENSE MD5sum: 211d3755eef0cde736dd91a933e2f98d NeedsCompilation: no Title: Load and Save Ranges-related Artifacts from File Description: Save GenomicRanges, IRanges and related data structures into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.ranges git_branch: devel git_last_commit: 283afd5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/alabaster.ranges_1.11.0.tar.gz vignettes: vignettes/alabaster.ranges/inst/doc/userguide.html vignetteTitles: Saving and loading genomic ranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.ranges/inst/doc/userguide.R importsMe: alabaster, alabaster.se dependencyCount: 27 Package: alabaster.schemas Version: 1.11.0 Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 85cf2f32b822e73c339cf57b815f1c95 NeedsCompilation: no Title: Schemas for the Alabaster Framework Description: Stores all schemas required by various alabaster.* packages. No computation should be performed by this package, as that is handled by alabaster.base. We use a separate package instead of storing the schemas in alabaster.base itself, to avoid conflating management of the schemas with code maintenence. biocViews: DataRepresentation, DataImport Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.schemas git_branch: devel git_last_commit: 89d35bc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/alabaster.schemas_1.11.0.tar.gz vignettes: vignettes/alabaster.schemas/inst/doc/userguide.html vignetteTitles: Metadata schemas for Bioconductor hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: alabaster.base dependencyCount: 0 Package: alabaster.string Version: 1.11.0 Depends: Biostrings, alabaster.base Imports: utils, methods, S4Vectors Suggests: BiocStyle, rmarkdown, knitr, testthat License: MIT + file LICENSE MD5sum: d9431449471e17b19056f9174705d46c NeedsCompilation: no Title: Save and Load Biostrings to/from File Description: Save Biostrings objects to file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.string git_branch: devel git_last_commit: d83f418 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/alabaster.string_1.11.0.tar.gz vignettes: vignettes/alabaster.string/inst/doc/userguide.html vignetteTitles: Saving and loading XStringSets hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.string/inst/doc/userguide.R importsMe: alabaster, alabaster.vcf dependencyCount: 30 Package: ALDEx2 Version: 1.43.0 Depends: methods, stats, zCompositions, lattice, latticeExtra Imports: Rfast, BiocParallel, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, multtest, directlabels Suggests: testthat, BiocStyle, knitr, rmarkdown, purrr, ggpattern, ggplot2, cowplot, tidyverse, magick License: GPL (>=3) MD5sum: a4ee58c89e7b87efa39bbf86e65d8662 NeedsCompilation: no Title: Analysis Of Differential Abundance Taking Sample and Scale Variation Into Account Description: A differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a Kruskal-Wallis test (via aldex.kw), a generalized linear model (via aldex.glm), or a correlation test (via aldex.corr). All tests report predicted p-values and posterior Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs. ALDEx2 can now be used to estimate the effect of scale on the results and report on the scale-dependent robustness of results. biocViews: DifferentialExpression, RNASeq, Transcriptomics, GeneExpression, DNASeq, ChIPSeq, Bayesian, Sequencing, Software, Microbiome, Metagenomics, ImmunoOncology, Scale simulation, Posterior p-value Author: Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert, Matt Links, Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon Lieng, Michelle Nixon Maintainer: Greg Gloor URL: https://github.com/ggloor/ALDEx_bioc VignetteBuilder: knitr BugReports: https://github.com/ggloor/ALDEx_bioc/issues git_url: https://git.bioconductor.org/packages/ALDEx2 git_branch: devel git_last_commit: 298f21d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ALDEx2_1.43.0.tar.gz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html, vignettes/ALDEx2/inst/doc/scaleSim_vignette.html vignetteTitles: ANOVA-Like Differential Expression tool for high throughput sequencing data, Incorporating Scale Uncertainty into ALDEx2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R, vignettes/ALDEx2/inst/doc/scaleSim_vignette.R dependsOnMe: omicplotR importsMe: benchdamic, aIc suggestsMe: dar, ggpicrust2, pctax dependencyCount: 55 Package: alevinQC Version: 1.27.1 Depends: R (>= 4.0) Imports: rmarkdown (>= 2.5), tools, methods, ggplot2 (>= 3.4.0), GGally, dplyr, rjson, shiny, shinydashboard, DT, stats, utils, tximport (>= 1.17.4), cowplot, rlang, Rcpp LinkingTo: Rcpp Suggests: knitr, BiocStyle, testthat (>= 3.0.0), BiocManager License: MIT + file LICENSE MD5sum: e2c657029cb7d56b7354f330ced24fd6 NeedsCompilation: yes Title: Generate QC Reports For Alevin Output Description: Generate QC reports summarizing the output from an alevin, alevin-fry, or simpleaf run. Reports can be generated as html or pdf files, or as shiny applications. biocViews: QualityControl, SingleCell Author: Charlotte Soneson [aut, cre] (ORCID: ), Avi Srivastava [aut], Rob Patro [aut], Dongze He [aut] Maintainer: Charlotte Soneson URL: https://github.com/csoneson/alevinQC VignetteBuilder: knitr BugReports: https://github.com/csoneson/alevinQC/issues git_url: https://git.bioconductor.org/packages/alevinQC git_branch: devel git_last_commit: 725aff6 git_last_commit_date: 2026-01-27 Date/Publication: 2026-04-20 source.ver: src/contrib/alevinQC_1.27.1.tar.gz vignettes: vignettes/alevinQC/inst/doc/alevinqc.html vignetteTitles: alevinQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alevinQC/inst/doc/alevinqc.R dependencyCount: 82 Package: AllelicImbalance Version: 1.49.0 Depends: R (>= 4.0.0), grid, GenomicRanges (>= 1.31.8), SummarizedExperiment (>= 0.2.0), GenomicAlignments (>= 1.15.6) Imports: methods, BiocGenerics, AnnotationDbi, BSgenome (>= 1.47.3), VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Rsamtools (>= 1.99.3), GenomicFeatures (>= 1.31.3), Gviz, lattice, latticeExtra, gridExtra, seqinr, GenomeInfoDb, nlme Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: fea1083235a3d547757316dca6b4ee8c NeedsCompilation: no Title: Investigates Allele Specific Expression Description: Provides a framework for allelic specific expression investigation using RNA-seq data. biocViews: Genetics, Infrastructure, Sequencing Author: Jesper R Gadin, Lasse Folkersen Maintainer: Jesper R Gadin URL: https://github.com/pappewaio/AllelicImbalance VignetteBuilder: knitr BugReports: https://github.com/pappewaio/AllelicImbalance/issues git_url: https://git.bioconductor.org/packages/AllelicImbalance git_branch: devel git_last_commit: d78bb64 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AllelicImbalance_1.49.0.tar.gz vignettes: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf vignetteTitles: AllelicImbalance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.R dependencyCount: 158 Package: AlphaBeta Version: 1.25.0 Depends: R (>= 3.6.0) Imports: dplyr (>= 0.7), data.table (>= 1.10), stringr (>= 1.3), utils (>= 3.6.0), gtools (>= 3.8.0), optimx (>= 2018-7.10), expm (>= 0.999-4), stats (>= 3.6), BiocParallel (>= 1.18), igraph (>= 1.2.4), graphics (>= 3.6), ggplot2 (>= 3.2), grDevices (>= 3.6), plotly (>= 4.9) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 8fd54c9fafde6ebe0bf7bfc1645f1ebc NeedsCompilation: no Title: Computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants Description: AlphaBeta is a computational method for estimating epimutation rates and spectra from high-throughput DNA methylation data in plants. The method has been specifically designed to: 1. analyze 'germline' epimutations in the context of multi-generational mutation accumulation lines (MA-lines). 2. analyze 'somatic' epimutations in the context of plant development and aging. biocViews: Epigenetics, FunctionalGenomics, Genetics, MathematicalBiology Author: Yadollah Shahryary Dizaji [cre, aut], Frank Johannes [aut], Rashmi Hazarika [aut] Maintainer: Yadollah Shahryary Dizaji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AlphaBeta git_branch: devel git_last_commit: 2602838 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AlphaBeta_1.25.0.tar.gz vignettes: vignettes/AlphaBeta/inst/doc/AlphaBeta.pdf vignetteTitles: AlphaBeta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlphaBeta/inst/doc/AlphaBeta.R dependencyCount: 89 Package: AlpsNMR Version: 4.13.0 Depends: R (>= 4.2) Imports: utils, generics, graphics, stats, grDevices, cli, magrittr (>= 1.5), dplyr (>= 1.1.0), signal (>= 0.7-6), rlang (>= 0.3.0.1), scales (>= 1.2.0), stringr (>= 1.3.1), tibble(>= 1.3.4), tidyr (>= 1.0.0), tidyselect, readxl (>= 1.1.0), purrr (>= 0.2.5), glue (>= 1.2.0), reshape2 (>= 1.4.3), mixOmics (>= 6.22.0), matrixStats (>= 0.54.0), fs (>= 1.2.6), rmarkdown (>= 1.10), speaq (>= 2.4.0), htmltools (>= 0.3.6), pcaPP (>= 1.9-73), ggplot2 (>= 3.1.0), baseline (>= 1.2-1), vctrs (>= 0.3.0), BiocParallel (>= 1.34.0) Suggests: ASICS, BiocStyle, ChemoSpec, cowplot, curl, DT (>= 0.5), GGally (>= 1.4.0), ggrepel (>= 0.8.0), gridExtra, knitr, NMRphasing, plotly (>= 4.7.1), progressr, SummarizedExperiment, S4Vectors, testthat (>= 2.0.0), writexl (>= 1.0), zip (>= 2.0.4) License: MIT + file LICENSE MD5sum: e0dae246ac035755ee549b14d5abedac NeedsCompilation: no Title: Automated spectraL Processing System for NMR Description: Reads Bruker NMR data directories both zipped and unzipped. It provides automated and efficient signal processing for untargeted NMR metabolomics. It is able to interpolate the samples, detect outliers, exclude regions, normalize, detect peaks, align the spectra, integrate peaks, manage metadata and visualize the spectra. After spectra proccessing, it can apply multivariate analysis on extracted data. Efficient plotting with 1-D data is also available. Basic reading of 1D ACD/Labs exported JDX samples is also available. biocViews: Software, Preprocessing, Visualization, Classification, Cheminformatics, Metabolomics, DataImport Author: Ivan Montoliu Roura [aut], Sergio Oller Moreno [aut, cre] (ORCID: ), Francisco Madrid Gambin [aut] (ORCID: ), Luis Fernandez [aut] (ORCID: ), Laura López Sánchez [ctb], Héctor Gracia Cabrera [aut], Santiago Marco Colás [aut] (ORCID: ), Nestlé Institute of Health Sciences [cph], Institute for Bioengineering of Catalonia [cph], Miller Jack [ctb] (ORCID: , Autophase wrapper, ASICS export) Maintainer: Sergio Oller Moreno URL: https://sipss.github.io/AlpsNMR/, https://github.com/sipss/AlpsNMR VignetteBuilder: knitr BugReports: https://github.com/sipss/AlpsNMR/issues git_url: https://git.bioconductor.org/packages/AlpsNMR git_branch: devel git_last_commit: 4ce0fcd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AlpsNMR_4.13.0.tar.gz vignettes: vignettes/AlpsNMR/inst/doc/Vig01-introduction-to-alpsnmr.pdf, vignettes/AlpsNMR/inst/doc/Vig01b-introduction-to-alpsnmr-old-api.pdf, vignettes/AlpsNMR/inst/doc/Vig02-handling-metadata-and-annotations.pdf vignetteTitles: Vignette 01: Introduction to AlpsNMR (start here), Older Introduction to AlpsNMR (soft-deprecated API), Vignette 02: Handling metadata and annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlpsNMR/inst/doc/Vig01-introduction-to-alpsnmr.R, vignettes/AlpsNMR/inst/doc/Vig01b-introduction-to-alpsnmr-old-api.R, vignettes/AlpsNMR/inst/doc/Vig02-handling-metadata-and-annotations.R dependencyCount: 126 Package: altcdfenvs Version: 2.73.0 Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.25), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings, hypergraph Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db, hgu133acdf, Rgraphviz, RColorBrewer License: GPL (>= 2) MD5sum: 46ee0140953fb09fde51bedc8b1da00d NeedsCompilation: no Title: alternative CDF environments (aka probeset mappings) Description: Convenience data structures and functions to handle cdfenvs biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Annotation, ProprietaryPlatforms, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/altcdfenvs git_branch: devel git_last_commit: 2798b4f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/altcdfenvs_2.73.0.tar.gz vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf, vignettes/altcdfenvs/inst/doc/modify.pdf, vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf vignetteTitles: altcdfenvs, Modifying existing CDF environments to make alternative CDF environments, Alternative CDF environments for 2(or more)-genomes chips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/altcdfenvs/inst/doc/altcdfenvs.R, vignettes/altcdfenvs/inst/doc/modify.R, vignettes/altcdfenvs/inst/doc/ngenomeschips.R dependencyCount: 23 Package: AMARETTO Version: 1.27.0 Depends: R (>= 3.6), impute, doParallel, grDevices, dplyr, methods, ComplexHeatmap Imports: callr (>= 3.0.0.9001), Matrix, Rcpp, BiocFileCache, DT, MultiAssayExperiment, circlize, curatedTCGAData, foreach, glmnet, httr, limma, matrixStats, readr, reshape2, tibble, rmarkdown, graphics, grid, parallel, stats, knitr, ggplot2, gridExtra, utils LinkingTo: Rcpp Suggests: testthat, MASS, knitr, BiocStyle License: Apache License (== 2.0) + file LICENSE MD5sum: f95d4090335921da5d75359ed0b3578a NeedsCompilation: no Title: Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression Description: Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways. biocViews: StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,Transcription,Preprocessing,BatchEffect,DataImport,mRNAMicroarray,MicroRNAArray,Regression,Clustering,RNASeq,CopyNumberVariation,Sequencing,Microarray,Normalization,Network,Bayesian,ExonArray,OneChannel,TwoChannel,ProprietaryPlatforms,AlternativeSplicing,DifferentialExpression,DifferentialSplicing,GeneSetEnrichment,MultipleComparison,QualityControl,TimeCourse Author: Jayendra Shinde, Celine Everaert, Shaimaa Bakr, Mohsen Nabian, Jishu Xu, Vincent Carey, Nathalie Pochet and Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMARETTO git_branch: devel git_last_commit: 87c41a4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AMARETTO_1.27.0.tar.gz vignettes: vignettes/AMARETTO/inst/doc/amaretto.html vignetteTitles: "1. Introduction" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AMARETTO/inst/doc/amaretto.R dependencyCount: 149 Package: AMOUNTAIN Version: 1.37.0 Depends: R (>= 3.3.0) Imports: stats Suggests: BiocStyle, qgraph, knitr, rmarkdown License: GPL (>= 2) MD5sum: 21a6d4ed337216200ab6a42d135a81be NeedsCompilation: yes Title: Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach Description: A pure data-driven gene network, weighted gene co-expression network (WGCN) could be constructed only from expression profile. Different layers in such networks may represent different time points, multiple conditions or various species. AMOUNTAIN aims to search active modules in multi-layer WGCN using a continuous optimization approach. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, Shan He, Zhisong Pan and Guyu Hu Maintainer: Dong Li SystemRequirements: gsl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMOUNTAIN git_branch: devel git_last_commit: 96cb01a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AMOUNTAIN_1.37.0.tar.gz vignettes: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.R importsMe: MODA dependencyCount: 1 Package: amplican Version: 1.33.7 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.22.0), Biostrings (>= 2.44.2), pwalign Imports: Rcpp, utils (>= 3.4.1), S4Vectors (>= 0.14.3), ShortRead (>= 1.34.0), IRanges (>= 2.10.2), GenomicRanges (>= 1.61.1), Seqinfo, BiocParallel (>= 1.10.1), gtable (>= 0.2.0), gridExtra (>= 2.2.1), ggplot2 (>= 3.3.4), ggthemes (>= 3.4.0), stringr (>= 1.2.0), stats (>= 3.4.1), matrixStats (>= 0.52.2), Matrix (>= 1.2-10), data.table (>= 1.10.4-3), rmarkdown (>= 1.6), knitr (>= 1.16), cluster (>= 2.1.4) LinkingTo: Rcpp Suggests: testthat, BiocStyle, GenomicAlignments License: GPL-3 MD5sum: 764f61210067f9a3f421ea72fd73958d NeedsCompilation: yes Title: Automated analysis of CRISPR experiments Description: `amplican` performs alignment of the amplicon reads, normalizes gathered data, calculates multiple statistics (e.g. cut rates, frameshifts) and presents results in form of aggregated reports. Data and statistics can be broken down by experiments, barcodes, user defined groups, guides and amplicons allowing for quick identification of potential problems. biocViews: ImmunoOncology, Technology, Alignment, qPCR, CRISPR Author: Kornel Labun [aut], Eivind Valen [cph, cre] Maintainer: Eivind Valen URL: https://github.com/valenlab/amplican VignetteBuilder: knitr BugReports: https://github.com/valenlab/amplican/issues git_url: https://git.bioconductor.org/packages/amplican git_branch: devel git_last_commit: b983282 git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/amplican_1.33.7.tar.gz vignettes: vignettes/amplican/inst/doc/amplicanFAQ.html, vignettes/amplican/inst/doc/amplicanOverview.html, vignettes/amplican/inst/doc/example_amplicon_report.html, vignettes/amplican/inst/doc/example_barcode_report.html, vignettes/amplican/inst/doc/example_group_report.html, vignettes/amplican/inst/doc/example_guide_report.html, vignettes/amplican/inst/doc/example_id_report.html, vignettes/amplican/inst/doc/example_index.html vignetteTitles: amplican FAQ, amplican overview, example amplicon_report report, example barcode_report report, example group_report report, example guide_report report, example id_report report, example index report hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/amplican/inst/doc/amplicanOverview.R, vignettes/amplican/inst/doc/example_amplicon_report.R, vignettes/amplican/inst/doc/example_barcode_report.R, vignettes/amplican/inst/doc/example_group_report.R, vignettes/amplican/inst/doc/example_guide_report.R, vignettes/amplican/inst/doc/example_id_report.R, vignettes/amplican/inst/doc/example_index.R dependencyCount: 102 Package: anansi Version: 1.1.0 Depends: R (>= 4.5.0) Imports: S7, stats, methods, igraph, Matrix, forcats, S4Vectors, SummarizedExperiment, MultiAssayExperiment, SingleCellExperiment, TreeSummarizedExperiment, rlang, ggplot2, ggforce, patchwork, ggraph, tidygraph Suggests: BiocStyle, dplyr, tidyr, graph, mia, KEGGREST, testthat (>= 3.0.0), knitr, rmarkdown License: GPL-3 MD5sum: e033c9f56dd3a9971dcda35693cbd36c NeedsCompilation: no Title: Annotation-Based Analysis of Specific Interactions Description: Studies including both microbiome and metabolomics data are becoming more common. Often, it would be helpful to integrate both datasets in order to see if they corroborate each others patterns. All vs all association is imprecise and likely to yield spurious associations. This package takes a knowledge-based approach to constrain association search space, only considering metabolite-function pairs that have been recorded in a pathway database. This package also provides a framework to assess differential association. biocViews: Microbiome, Metabolomics, Regression, Pathways, KEGG Author: Thomaz Bastiaanssen [aut, cre] (ORCID: ), Thomas Quinn [aut] (ORCID: ), Giulio Benedetti [aut] (ORCID: ), Tuomas Borman [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ) Maintainer: Thomaz Bastiaanssen URL: https://github.com/thomazbastiaanssen/anansi, https://thomazbastiaanssen.github.io/anansi VignetteBuilder: knitr BugReports: https://github.com/thomazbastiaanssen/anansi/issues git_url: https://git.bioconductor.org/packages/anansi git_branch: devel git_last_commit: d5ff74a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/anansi_1.1.0.tar.gz vignettes: vignettes/anansi/inst/doc/adjacency_matrices.html, vignettes/anansi/inst/doc/anansi.html, vignettes/anansi/inst/doc/differential_associations.html vignetteTitles: 2. Working with (bi)adjacency matrices, 1. Getting started with anansi, 3. Differential associations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anansi/inst/doc/adjacency_matrices.R, vignettes/anansi/inst/doc/anansi.R, vignettes/anansi/inst/doc/differential_associations.R dependencyCount: 98 Package: Anaquin Version: 2.35.0 Depends: R (>= 3.3), ggplot2 (>= 2.2.0) Imports: ggplot2, ROCR, knitr, qvalue, locfit, methods, stats, utils, plyr, DESeq2 Suggests: RUnit, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: a5e39086104eaa719ada890efa5cfb14 NeedsCompilation: no Title: Statistical analysis of sequins Description: The project is intended to support the use of sequins (synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard open source library for quantitative analysis, modelling and visualization of spike-in controls. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, GeneExpression, Software Author: Ted Wong Maintainer: Ted Wong URL: www.sequin.xyz VignetteBuilder: knitr BugReports: https://github.com/student-t/RAnaquin/issues git_url: https://git.bioconductor.org/packages/Anaquin git_branch: devel git_last_commit: b243e73 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Anaquin_2.35.0.tar.gz vignettes: vignettes/Anaquin/inst/doc/Anaquin.pdf vignetteTitles: Anaquin - Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Anaquin/inst/doc/Anaquin.R dependencyCount: 73 Package: ANF Version: 1.33.0 Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat License: GPL-3 MD5sum: cf16734a66701668d4c9f5f98c581854 NeedsCompilation: no Title: Affinity Network Fusion for Complex Patient Clustering Description: This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion. biocViews: Clustering, GraphAndNetwork, Network Author: Tianle Ma, Aidong Zhang Maintainer: Tianle Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ANF git_branch: devel git_last_commit: 9a47e06 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ANF_1.33.0.tar.gz vignettes: vignettes/ANF/inst/doc/ANF.html vignetteTitles: Cancer Patient Clustering with ANF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANF/inst/doc/ANF.R suggestsMe: HarmonizedTCGAData dependencyCount: 24 Package: anglemania Version: 1.1.0 Depends: R (>= 4.5.0) Imports: bigparallelr, bigstatsr, checkmate, digest, dplyr, Matrix, pbapply, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tidyr, withr LinkingTo: Rcpp, rmio, bigstatsr Suggests: batchelor, BiocStyle, bluster, knitr, magick, matrixStats, patchwork, RcppArmadillo, rmarkdown, scater, scran, Seurat, splatter, testthat (>= 3.0.0), UpSetR License: GPL (>= 3) MD5sum: d4ccabfce65117609f7e6329409511a7 NeedsCompilation: yes Title: Feature Extraction for scRNA-seq Dataset Integration Description: anglemania extracts genes from multi-batch scRNA-seq experiments for downstream dataset integration. It shows improvement over the conventional usage of highly-variable genes for many integration tasks. We leverage gene-gene correlations that are stable across batches to identify biologically informative genes which are less affected by batch effects. Currently, its main use is for single-cell RNA-seq dataset integration, but it can be applied for other multi-batch downstream analyses such as NMF. biocViews: SingleCell, BatchEffect, MultipleComparison, FeatureExtraction Author: Aaron Kollotzek [aut, cre] (ORCID: ), Vedran Franke [aut] (ORCID: ), Artem Baranovskii [aut], Altuna Akalin [aut], SFB1588 [fnd] (Funded by the DFG – Deutsche Forschungsgemeinschaft) Maintainer: Aaron Kollotzek URL: https://github.com/BIMSBbioinfo/anglemania/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/anglemania/issues git_url: https://git.bioconductor.org/packages/anglemania git_branch: devel git_last_commit: fee34a3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/anglemania_1.1.0.tar.gz vignettes: vignettes/anglemania/inst/doc/anglemania_tutorial.html vignetteTitles: anglemania tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anglemania/inst/doc/anglemania_tutorial.R dependencyCount: 78 Package: animalcules Version: 1.27.1 Depends: R (>= 4.3.0) Imports: ape, assertthat, caret, covr, DESeq2, dplyr, DT, forcats, ggforce, ggplot2, GUniFrac, lattice, limma, magrittr, Matrix, methods, MultiAssayExperiment, plotly, rentrez, reshape2, ROCit, S4Vectors (>= 0.23.19), scales, shiny, shinyjs, stats, SummarizedExperiment, tibble, tidyr, tsne, umap, utils, vegan, XML Suggests: BiocStyle, biomformat, devtools, glmnet, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: 2e4f66d7eddbcaf304f32e801d80d57b NeedsCompilation: no Title: Interactive microbiome analysis toolkit Description: animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights. biocViews: Microbiome, Metagenomics, Coverage, Visualization Author: W. Evan Johnson [aut, cre] (ORCID: ), Yue Zhao [aut] (ORCID: ), Anthony Federico [aut] (ORCID: ), Jessica Anderson [ctr] (ORCID: ) Maintainer: W. Evan Johnson URL: https://github.com/wejlab/animalcules VignetteBuilder: knitr BugReports: https://github.com/wejlab/animalcules/issues git_url: https://git.bioconductor.org/packages/animalcules git_branch: devel git_last_commit: c13c0a5 git_last_commit_date: 2026-02-10 Date/Publication: 2026-04-20 source.ver: src/contrib/animalcules_1.27.1.tar.gz vignettes: vignettes/animalcules/inst/doc/animalcules.html vignetteTitles: animalcules hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/animalcules/inst/doc/animalcules.R importsMe: LegATo suggestsMe: MetaScope dependencyCount: 189 Package: annaffy Version: 1.83.0 Depends: R (>= 2.5.0), methods, Biobase, BiocManager, GO.db Imports: AnnotationDbi (>= 0.1.15), DBI Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: 3dbf522627fdc7665e9cd54688cfc374 NeedsCompilation: no Title: Annotation tools for Affymetrix biological metadata Description: Functions for handling data from Bioconductor Affymetrix annotation data packages. Produces compact HTML and text reports including experimental data and URL links to many online databases. Allows searching biological metadata using various criteria. biocViews: OneChannel, Microarray, Annotation, GO, Pathways, ReportWriting Author: Colin A. Smith Maintainer: Colin A. Smith git_url: https://git.bioconductor.org/packages/annaffy git_branch: devel git_last_commit: 926f56c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/annaffy_1.83.0.tar.gz vignettes: vignettes/annaffy/inst/doc/annaffy.pdf vignetteTitles: annaffy Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annaffy/inst/doc/annaffy.R dependsOnMe: webbioc importsMe: a4Base suggestsMe: metaMA dependencyCount: 44 Package: anndataR Version: 1.1.2 Depends: R (>= 4.5.0) Imports: cli, lifecycle, Matrix, methods, purrr, R6 (>= 2.4.0), reticulate (>= 1.41.1), rlang, stats Suggests: BiocFileCache, BiocStyle, knitr, processx, rhdf5 (>= 2.52.1), rmarkdown, S4Vectors, Seurat, SeuratObject, SingleCellExperiment, spelling, SummarizedExperiment, testthat (>= 3.0.0), vctrs, withr, yaml License: MIT + file LICENSE MD5sum: f026e684c60101b9c51062b4dec5bc20 NeedsCompilation: no Title: AnnData interoperability in R Description: Bring the power and flexibility of AnnData to the R ecosystem, allowing you to effortlessly manipulate and analyse your single-cell data. This package lets you work with backed h5ad and zarr files, directly access various slots (e.g. X, obs, var), or convert the data into SingleCellExperiment and Seurat objects. biocViews: SingleCell, DataImport, DataRepresentation Author: Robrecht Cannoodt [aut, cre] (ORCID: , github: rcannood), Luke Zappia [aut] (ORCID: , github: lazappi), Martin Morgan [aut] (ORCID: , github: mtmorgan), Louise Deconinck [aut] (ORCID: , github: LouiseDck), Danila Bredikhin [ctb] (ORCID: , github: gtca), Isaac Virshup [ctb] (ORCID: , github: ivirshup), Brian Schilder [ctb] (ORCID: , github: bschilder), Chananchida Sang-aram [ctb] (ORCID: , github: csangara), Data Intuitive [fnd], Chan Zuckerberg Initiative [fnd], scverse consortium [spn] Maintainer: Robrecht Cannoodt URL: https://anndataR.scverse.org/, https://github.com/scverse/anndataR VignetteBuilder: knitr BugReports: https://github.com/scverse/anndataR/issues git_url: https://git.bioconductor.org/packages/anndataR git_branch: devel git_last_commit: aa1d2a0 git_last_commit_date: 2026-02-26 Date/Publication: 2026-04-20 source.ver: src/contrib/anndataR_1.1.2.tar.gz vignettes: vignettes/anndataR/inst/doc/anndataR.html, vignettes/anndataR/inst/doc/usage_python.html, vignettes/anndataR/inst/doc/usage_seurat.html, vignettes/anndataR/inst/doc/usage_singlecellexperiment.html vignetteTitles: Using anndataR to read and convert, Python integration with anndataR, Read/write Seurat objects using anndataR, Read/write SingleCellExperiment objects using anndataR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/anndataR/inst/doc/anndataR.R, vignettes/anndataR/inst/doc/usage_python.R, vignettes/anndataR/inst/doc/usage_seurat.R, vignettes/anndataR/inst/doc/usage_singlecellexperiment.R importsMe: OSTA suggestsMe: HistoImagePlot, imageFeatureTCGA, imageTCGAutils, dyngen dependencyCount: 25 Package: annmap Version: 1.53.0 Depends: R (>= 2.15.0), methods, GenomicRanges Imports: DBI, RMySQL (>= 0.6-0), digest, Biobase, grid, lattice, Rsamtools, genefilter, IRanges, BiocGenerics Suggests: RUnit, rjson, Gviz License: GPL-2 MD5sum: 6053b4409d218aed00e691b48bceecb3 NeedsCompilation: no Title: Genome annotation and visualisation package pertaining to Affymetrix arrays and NGS analysis. Description: annmap provides annotation mappings for Affymetrix exon arrays and coordinate based queries to support deep sequencing data analysis. Database access is hidden behind the API which provides a set of functions such as genesInRange(), geneToExon(), exonDetails(), etc. Functions to plot gene architecture and BAM file data are also provided. Underlying data are from Ensembl. The annmap database can be downloaded from: https://figshare.manchester.ac.uk/account/articles/16685071 biocViews: Annotation, Microarray, OneChannel, ReportWriting, Transcription, Visualization Author: Tim Yates Maintainer: Chris Wirth URL: https://github.com/cruk-mi/annmap git_url: https://git.bioconductor.org/packages/annmap git_branch: devel git_last_commit: d64521e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/annmap_1.53.0.tar.gz vignettes: vignettes/annmap/inst/doc/annmap.pdf, vignettes/annmap/inst/doc/cookbook.pdf, vignettes/annmap/inst/doc/INSTALL.pdf vignetteTitles: annmap primer, The Annmap Cookbook, annmap installation instruction hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 68 Package: annotate Version: 1.89.0 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8), httr Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges, rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db, org.Mm.eg.db, humanCHRLOC, Rgraphviz, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 7e02a58dc51e130bf7787df13ce3f491 NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/annotate git_branch: devel git_last_commit: a7eff2c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/annotate_1.89.0.tar.gz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf, vignettes/annotate/inst/doc/chromLOC.html, vignettes/annotate/inst/doc/useDataPkgs.html vignetteTitles: Annotation Overview, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Affymetrix Probe Level Data, HowTo: Build and use chromosomal information, Using Bioconductor's Annotation Libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotate/inst/doc/annotate.R, vignettes/annotate/inst/doc/chromLOC.R, vignettes/annotate/inst/doc/GOusage.R, vignettes/annotate/inst/doc/prettyOutput.R, vignettes/annotate/inst/doc/query.R, vignettes/annotate/inst/doc/useDataPkgs.R, vignettes/annotate/inst/doc/useProbeInfo.R dependsOnMe: ChromHeatMap, geneplotter, GSEABase, idiogram, MLInterfaces, phenoTest, PREDA, sampleClassifier, SemDist, Neve2006, PREDAsampledata importsMe: CAFE, Category, categoryCompare, CNEr, codelink, debrowser, DrugVsDisease, genefilter, GlobalAncova, globaltest, GOstats, lumi, methylumi, MGFR, phenoTest, qpgraph, signatureSearch, tigre, UMI4Cats, geneExpressionFromGEO suggestsMe: BiocGenerics, GenomicRanges, GSAR, GSEAlm, hmdbQuery, metagenomeSeq, MLP, pageRank, PhosR, RnBeads, siggenes, SummarizedExperiment, systemPipeR, adme16cod.db, ag.db, ath1121501.db, bovine.db, canine.db, canine2.db, celegans.db, chicken.db, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, drosgenome1.db, drosophila2.db, ecoli2.db, GGHumanMethCancerPanelv1.db, h10kcod.db, h20kcod.db, hcg110.db, hgfocus.db, hgu133a.db, hgu133a2.db, hgu133b.db, hgu133plus2.db, hgu219.db, hgu95a.db, hgu95av2.db, hgu95b.db, hgu95c.db, hgu95d.db, hgu95e.db, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133b.db, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, htmg430a.db, htmg430b.db, htmg430pm.db, htrat230pm.db, htratfocus.db, hu35ksuba.db, hu35ksubb.db, hu35ksubc.db, hu35ksubd.db, hu6800.db, huex10stprobeset.db, huex10sttranscriptcluster.db, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiMouseAll.db, lumiRatAll.db, m10kcod.db, m20kcod.db, mgu74a.db, mgu74av2.db, mgu74b.db, mgu74bv2.db, mgu74c.db, mgu74cv2.db, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, miRBaseVersions.db, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430b.db, moex10stprobeset.db, moex10sttranscriptcluster.db, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse4302.db, mouse430a2.db, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubb.db, Mu15v1.db, mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db, mwgcod.db, Norway981.db, nugohs1a520180.db, nugomm1a520177.db, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hbacteriophora.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, POCRCannotation.db, porcine.db, r10kcod.db, rae230a.db, rae230b.db, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rgu34a.db, rgu34b.db, rgu34c.db, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, ri16cod.db, RnAgilentDesign028282.db, rnu34.db, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rwgcod.db, SHDZ.db, SomaScan.db, u133x3p.db, xlaevis.db, yeast2.db, ygs98.db, zebrafish.db, clValid, limorhyde, maGUI dependencyCount: 44 Package: AnnotationDbi Version: 1.73.1 Depends: R (>= 2.7.0), methods, stats4, BiocGenerics (>= 0.29.2), Biobase (>= 1.17.0), IRanges Imports: DBI, RSQLite, S4Vectors (>= 0.9.25), stats, KEGGREST Suggests: utils, hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr License: Artistic-2.0 MD5sum: cab45a511a729f05a253ffbc72d1d813 NeedsCompilation: no Title: Manipulation of SQLite-based annotations in Bioconductor Description: Implements a user-friendly interface for querying SQLite-based annotation data packages. biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation Author: Hervé Pagès, Marc Carlson, Seth Falcon, Nianhua Li Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationDbi VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik BugReports: https://github.com/Bioconductor/AnnotationDbi/issues git_url: https://git.bioconductor.org/packages/AnnotationDbi git_branch: devel git_last_commit: 96d4de7 git_last_commit_date: 2026-04-08 Date/Publication: 2026-04-20 source.ver: src/contrib/AnnotationDbi_1.73.1.tar.gz vignettes: vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: 1. Introduction To Bioconductor Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.R dependsOnMe: annotate, AnnotationForge, ASpli, attract, Category, ChromHeatMap, customProDB, DEXSeq, EGSEA, EpiTxDb, GenomicFeatures, goProfiles, GSReg, ipdDb, miRNAtap, OrganismDbi, pathRender, proBAMr, safe, SemDist, topGO, adme16cod.db, ag.db, agprobe, anopheles.db0, arabidopsis.db0, ath1121501.db, ath1121501probe, barley1probe, bovine.db, bovine.db0, bovineprobe, bsubtilisprobe, canine.db, canine.db0, canine2.db, canine2probe, canineprobe, celegans.db, celegansprobe, chicken.db, chicken.db0, chickenprobe, chimp.db0, citrusprobe, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottonprobe, DO.db, drosgenome1.db, drosgenome1probe, drosophila2.db, drosophila2probe, ecoli2.db, ecoli2probe, ecoliasv2probe, ecoliK12.db0, ecoliprobe, ecoliSakai.db0, fly.db0, GGHumanMethCancerPanelv1.db, GO.db, h10kcod.db, h20kcod.db, hcg110.db, hcg110probe, hgfocus.db, hgfocusprobe, hgu133a.db, hgu133a2.db, hgu133a2probe, hgu133aprobe, hgu133atagprobe, hgu133b.db, hgu133bprobe, hgu133plus2.db, hgu133plus2probe, hgu219.db, hgu219probe, hgu95a.db, hgu95aprobe, hgu95av2.db, hgu95av2probe, hgu95b.db, hgu95bprobe, hgu95c.db, hgu95cprobe, hgu95d.db, hgu95dprobe, hgu95e.db, hgu95eprobe, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, Homo.sapiens, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133aprobe, hthgu133b.db, hthgu133bprobe, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmprobe, htmg430a.db, htmg430aprobe, htmg430b.db, htmg430bprobe, htmg430pm.db, htmg430pmprobe, htrat230pm.db, htrat230pmprobe, htratfocus.db, htratfocusprobe, hu35ksuba.db, hu35ksubaprobe, hu35ksubb.db, hu35ksubbprobe, hu35ksubc.db, hu35ksubcprobe, hu35ksubd.db, hu35ksubdprobe, hu6800.db, hu6800probe, huex10stprobeset.db, huex10sttranscriptcluster.db, HuExExonProbesetLocation, HuExExonProbesetLocationHg18, HuExExonProbesetLocationHg19, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1probe, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, human.db0, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, IlluminaHumanMethylation450kprobe, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizeprobe, malaria.db0, medicagoprobe, mgu74a.db, mgu74aprobe, mgu74av2.db, mgu74av2probe, mgu74b.db, mgu74bprobe, mgu74bv2.db, mgu74bv2probe, mgu74c.db, mgu74cprobe, mgu74cv2.db, mgu74cv2probe, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirna10probe, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430aprobe, moe430b.db, moe430bprobe, moex10stprobeset.db, moex10sttranscriptcluster.db, MoExExonProbesetLocation, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1probe, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse.db0, mouse4302.db, mouse4302probe, mouse430a2.db, mouse430a2probe, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubaprobe, mu11ksubb.db, mu11ksubbprobe, Mu15v1.db, mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180probe, nugomm1a520177.db, nugomm1a520177probe, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hbacteriophora.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Mxanthus.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db, paeg1aprobe, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, PFAM.db, pig.db0, plasmodiumanophelesprobe, POCRCannotation.db, poplarprobe, porcine.db, porcineprobe, primeviewprobe, r10kcod.db, rae230a.db, rae230aprobe, rae230b.db, rae230bprobe, raex10stprobeset.db, raex10sttranscriptcluster.db, RaExExonProbesetLocation, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1probe, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat.db0, rat2302.db, rat2302probe, rattoxfxprobe, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34aprobe, rgu34b.db, rgu34bprobe, rgu34c.db, rgu34cprobe, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesus.db0, rhesusprobe, ri16cod.db, riceprobe, RnAgilentDesign028282.db, rnu34.db, rnu34probe, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34probe, rwgcod.db, saureusprobe, SHDZ.db, SomaScan.db, soybeanprobe, sugarcaneprobe, test3probe, tomatoprobe, u133x3p.db, u133x3pprobe, vitisviniferaprobe, wheatprobe, worm.db0, xenopus.db0, xenopuslaevisprobe, xlaevis.db, xlaevis2probe, xtropicalisprobe, yeast.db0, yeast2.db, yeast2probe, ygs98.db, ygs98probe, zebrafish.db, zebrafish.db0, zebrafishprobe, tinesath1probe, rnaseqGene, convertid importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, annoLinker, AnnotationHub, AnnotationHubData, annotatr, artMS, beadarray, BgeeCall, bioCancer, BiocSet, biomaRt, BioNAR, BioNet, biovizBase, bumphunter, BUSpaRse, categoryCompare, cellity, chimeraviz, chipenrich, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, Cogito, compEpiTools, CoSIA, crisprDesign, CrispRVariants, cTRAP, Damsel, debrowser, derfinder, DominoEffect, DOSE, DOTSeq, doubletrouble, EasyCellType, EDASeq, EnrichmentBrowser, ensembldb, EpiMix, epimutacions, epiSeeker, esATAC, FRASER, funOmics, GA4GHshiny, gage, gDNAx, genefilter, geneplotter, GeneTonic, geneXtendeR, GenomicInteractionNodes, GenVisR, ggbio, GlobalAncova, globaltest, GmicR, goatea, GOfan, GOfuncR, GOpro, GOSemSim, goseq, goSTAG, GOstats, goTools, graphite, GSEABase, GSEABenchmarkeR, Gviz, gwascat, ideal, isomiRs, IVAS, karyoploteR, keggorthology, LRBaseDbi, lumi, magpie, mastR, MCbiclust, MeSHDbi, meshes, MesKit, MetaboSignal, methylGSA, methylumi, MiRaGE, mirIntegrator, MIRit, miRNAmeConverter, missMethyl, mitology, MLP, MOSClip, mosdef, MSnID, multiGSEA, multiMiR, NanoMethViz, NetSAM, ORFik, OutSplice, PADOG, pathview, pcaExplorer, phantasus, phenoTest, proActiv, psichomics, qpgraph, QuasR, RAIDS, ReactomePA, REDseq, regutools, RFGeneRank, RFLOMICS, rGREAT, rgsepd, ribosomeProfilingQC, RNAAgeCalc, rrvgo, rTRM, SBGNview, scanMiRApp, scPipe, scruff, scTensor, SEMPLR, SGSeq, signatureSearch, signifinder, simplifyEnrichment, SMITE, SPICEY, SubCellBarCode, svaRetro, SVMDO, TCGAutils, tenXplore, TFutils, tigre, trackViewer, TRESS, tricycle, txcutr, txdbmaker, tximeta, UniProt.ws, VariantAnnotation, VariantFiltering, ViSEAGO, VISTA, adme16cod.db, ag.db, agcdf, ath1121501.db, ath1121501cdf, barley1cdf, bovine.db, bovinecdf, bsubtiliscdf, canine.db, canine2.db, canine2cdf, caninecdf, celegans.db, celeganscdf, chicken.db, chickencdf, citruscdf, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottoncdf, cyp450cdf, DO.db, drosgenome1.db, drosgenome1cdf, drosophila2.db, drosophila2cdf, ecoli2.db, ecoli2cdf, ecoliasv2cdf, ecolicdf, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState, GGHumanMethCancerPanelv1.db, gp53cdf, h10kcod.db, h20kcod.db, hcg110.db, hcg110cdf, HDO.db, hgfocus.db, hgfocuscdf, hgu133a.db, hgu133a2.db, hgu133a2cdf, hgu133acdf, hgu133atagcdf, hgu133b.db, hgu133bcdf, hgu133plus2.db, hgu133plus2cdf, hgu219.db, hgu219cdf, hgu95a.db, hgu95acdf, hgu95av2.db, hgu95av2cdf, hgu95b.db, hgu95bcdf, hgu95c.db, hgu95ccdf, hgu95d.db, hgu95dcdf, hgu95e.db, hgu95ecdf, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hivprtplus2cdf, Homo.sapiens, HPO.db, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, Hspec, hspeccdf, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133acdf, hthgu133b.db, hthgu133bcdf, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmcdf, htmg430a.db, htmg430acdf, htmg430b.db, htmg430bcdf, htmg430pm.db, htmg430pmcdf, htrat230pm.db, htrat230pmcdf, htratfocus.db, htratfocuscdf, hu35ksuba.db, hu35ksubacdf, hu35ksubb.db, hu35ksubbcdf, hu35ksubc.db, hu35ksubccdf, hu35ksubd.db, hu35ksubdcdf, hu6800.db, hu6800cdf, hu6800subacdf, hu6800subbcdf, hu6800subccdf, hu6800subdcdf, huex10stprobeset.db, huex10sttranscriptcluster.db, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1cdf, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizecdf, medicagocdf, mgu74a.db, mgu74acdf, mgu74av2.db, mgu74av2cdf, mgu74b.db, mgu74bcdf, mgu74bv2.db, mgu74bv2cdf, mgu74c.db, mgu74ccdf, mgu74cv2.db, mgu74cv2cdf, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, miRBaseVersions.db, mirna102xgaincdf, mirna10cdf, mirna20cdf, miRNAtap.db, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430acdf, moe430b.db, moe430bcdf, moex10stprobeset.db, moex10sttranscriptcluster.db, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1cdf, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse4302.db, mouse4302cdf, mouse430a2.db, mouse430a2cdf, mpedbarray.db, MPO.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubacdf, mu11ksubb.db, mu11ksubbcdf, Mu15v1.db, mu19ksuba.db, mu19ksubacdf, mu19ksubb.db, mu19ksubbcdf, mu19ksubc.db, mu19ksubccdf, Mu22v3.db, mu6500subacdf, mu6500subbcdf, mu6500subccdf, mu6500subdcdf, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180cdf, nugomm1a520177.db, nugomm1a520177cdf, OperonHumanV3.db, paeg1acdf, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, plasmodiumanophelescdf, POCRCannotation.db, PolyPhen.Hsapiens.dbSNP131, poplarcdf, porcine.db, porcinecdf, primeviewcdf, r10kcod.db, rae230a.db, rae230acdf, rae230b.db, rae230bcdf, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1cdf, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rat2302cdf, rattoxfxcdf, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34acdf, rgu34b.db, rgu34bcdf, rgu34c.db, rgu34ccdf, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesuscdf, ri16cod.db, ricecdf, RmiR.Hs.miRNA, RmiR.hsa, RnAgilentDesign028282.db, rnu34.db, rnu34cdf, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34cdf, rwgcod.db, saureuscdf, SHDZ.db, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, soybeancdf, sugarcanecdf, test1cdf, test2cdf, test3cdf, tomatocdf, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Cfamiliaris.UCSC.canFam4.refGene, TxDb.Cfamiliaris.UCSC.canFam5.refGene, TxDb.Cfamiliaris.UCSC.canFam6.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg19.refGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg38.refGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, u133aaofav2cdf, u133x3p.db, u133x3pcdf, vitisviniferacdf, wheatcdf, xenopuslaeviscdf, xlaevis.db, xlaevis2cdf, xtropicaliscdf, ye6100subacdf, ye6100subbcdf, ye6100subccdf, ye6100subdcdf, yeast2.db, yeast2cdf, ygs98.db, ygs98cdf, zebrafish.db, zebrafishcdf, celldex, chipenrich.data, DeSousa2013, msigdb, scRNAseq, lisat, netgsa, pathfindR, PathwayVote, RCPA, SEMgraph, SurprisalAnalysis suggestsMe: APAlyzer, ASURAT, autonomics, bambu, BiocGenerics, CellTrails, cicero, cola, csaw, DAPAR, DEGreport, drugfindR, edgeR, eisaR, enrichplot, esetVis, FELLA, FGNet, fgsea, fishpond, GA4GHclient, gatom, gCrisprTools, GeDi, GeneRegionScan, GenomicPlot, GenomicRanges, ggkegg, gsean, hpar, imageFeatureTCGA, iNETgrate, iSEEu, limma, MutationalPatterns, NetActivity, oligo, ontoProc, OUTRIDER, pathlinkR, piano, Pigengene, plotgardener, pRoloc, ProteoDisco, quantiseqr, R3CPET, recount, scDotPlot, scGraphVerse, simona, SingleCellAlleleExperiment, sparrow, spatialHeatmap, SpliceImpactR, SpliceWiz, SummarizedExperiment, systemPipeR, TFEA.ChIP, tidybulk, topconfects, weitrix, wiggleplotr, BioPlex, BloodCancerMultiOmics2017, curatedAdipoChIP, RforProteomics, CALANGO, conos, DIscBIO, easylabel, enrichit, genekitr, goat, pagoda2, rliger, scITD, scPairs, WayFindR dependencyCount: 41 Package: AnnotationFilter Version: 1.35.0 Depends: R (>= 3.4.0) Imports: utils, methods, GenomicRanges, lazyeval Suggests: BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db, rmarkdown License: Artistic-2.0 MD5sum: 9f5b57383898518c51d01f700f6136e1 NeedsCompilation: no Title: Facilities for Filtering Bioconductor Annotation Resources Description: This package provides class and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters will be used by ensembldb, Organism.dplyr, and other packages. biocViews: Annotation, Infrastructure, Software Author: Martin Morgan [aut], Johannes Rainer [aut], Joachim Bargsten [ctb], Daniel Van Twisk [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/AnnotationFilter VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationFilter/issues git_url: https://git.bioconductor.org/packages/AnnotationFilter git_branch: devel git_last_commit: f5cf226 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AnnotationFilter_1.35.0.tar.gz vignettes: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.html vignetteTitles: Facilities for Filtering Bioconductor Annotation resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.R dependsOnMe: chimeraviz, CompoundDb, ensembldb importsMe: biovizBase, BUSpaRse, CleanUpRNAseq, drugTargetInteractions, ggbio, QFeatures, RAIDS, RITAN, scanMiRApp, TVTB, GenomicDistributionsData, locuszoomr, RNAseqQC suggestsMe: GenomicDistributions, GenomicFeatures, TFutils, wiggleplotr dependencyCount: 13 Package: AnnotationForge Version: 1.53.0 Depends: R (>= 3.5.0), methods, utils, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), AnnotationDbi (>= 1.33.14) Imports: DBI, RSQLite, XML, S4Vectors, RCurl Suggests: biomaRt, httr, GenomeInfoDb (>= 1.17.1), Biostrings, affy, hgu95av2.db, human.db0, org.Hs.eg.db, Homo.sapiens, GO.db, rmarkdown, BiocStyle, knitr, BiocManager, BiocFileCache, RUnit License: Artistic-2.0 MD5sum: 76b4bc6f8e46703238b171bcd2e03445 NeedsCompilation: no Title: Tools for building SQLite-based annotation data packages Description: Provides code for generating Annotation packages and their databases. Packages produced are intended to be used with AnnotationDbi. biocViews: Annotation, Infrastructure Author: Marc Carlson [aut], Hervé Pagès [aut], Madelyn Carlson [ctb] ('Creating probe packages' vignette translation from Sweave to Rmarkdown / HTML), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationForge VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationForge/issues git_url: https://git.bioconductor.org/packages/AnnotationForge git_branch: devel git_last_commit: a113c77 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AnnotationForge_1.53.0.tar.gz vignettes: vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf, vignettes/AnnotationForge/inst/doc/makeProbePackage.html, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html vignetteTitles: AnnotationForge: Creating select Interfaces for custom Annotation resources, SQLForge: An easy way to create a new annotation package with a standard database schema., Creating probe packages, Making New Organism Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationForge/inst/doc/makeProbePackage.R, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.R, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.R, vignettes/AnnotationForge/inst/doc/SQLForge.R importsMe: AnnotationHubData, GOstats, ViSEAGO, GGHumanMethCancerPanelv1.db suggestsMe: AnnotationDbi, AnnotationHub dependencyCount: 45 Package: AnnotationHub Version: 4.1.0 Depends: BiocGenerics (>= 0.15.10), BiocFileCache (>= 2.99.3) Imports: utils, methods, grDevices, RSQLite, BiocManager, BiocVersion, curl, rappdirs, AnnotationDbi (>= 1.31.19), S4Vectors, httr2, yaml, dplyr, BiocBaseUtils Suggests: IRanges, Seqinfo, GenomeInfoDb, GenomicRanges, VariantAnnotation, Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit, txdbmaker, MSnbase, mzR, Biostrings, CompoundDb, keras, ensembldb, SummarizedExperiment, ExperimentHub, gdsfmt, rmarkdown, HubPub Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: 563870d5305c1dc026f3e1325574a43d NeedsCompilation: yes Title: Client to access AnnotationHub resources Description: This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, Ensembl) can be discovered. The resource includes metadata about each resource, e.g., a textual description, tags, and date of modification. The client creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationHub/issues git_url: https://git.bioconductor.org/packages/AnnotationHub git_branch: devel git_last_commit: 027c4b7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AnnotationHub_4.1.0.tar.gz vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/TroubleshootingTheHubs.html vignetteTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub: Access the AnnotationHub Web Service, Troubleshooting The Hubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.R, vignettes/AnnotationHub/inst/doc/AnnotationHub.R, vignettes/AnnotationHub/inst/doc/TroubleshootingTheHubs.R dependsOnMe: adductomicsR, AnnotationHubData, ExperimentHub, ipdDb, LRcell, octad, AlphaMissense.v2023.hg19, AlphaMissense.v2023.hg38, cadd.v1.6.hg19, cadd.v1.6.hg38, EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB, GenomicState, hpAnnot, org.Mxanthus.db, phastCons30way.UCSC.hg38, phastCons35way.UCSC.mm39, phyloP35way.UCSC.mm39, rGenomeTracksData, synaptome.data, UCSCRepeatMasker, MetaGxBreast, MetaGxOvarian, NestLink, scMultiome, sesameData, tartare, annotation, sequencing, OSCA.basic, OSCA.workflows, scrapbook, SingleRBook importsMe: annotatr, atena, BiocHubsShiny, BUSpaRse, circRNAprofiler, coMethDMR, cTRAP, customCMPdb, damidBind, DeconvoBuddies, DMRcate, dmrseq, EpiCompare, EpiMix, epimutacions, epiregulon, gDNAx, GenomicScores, GRaNIE, GSEABenchmarkeR, gwascat, iSEEhub, knowYourCG, MACSr, meshes, MetaboAnnotation, methodical, MethReg, Moonlight2R, MSnID, OGRE, ontoProc, orthos, partCNV, postNet, psichomics, regutools, REMP, scanMiRApp, scAnnotatR, scmeth, scTensor, shinyDSP, signatureSearch, singleCellTK, SpliceWiz, TENET, tximeta, xCell2, AHLRBaseDbs, AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, CENTREannotation, EPICv2manifest, grasp2db, HPO.db, metaboliteIDmapping, MPO.db, synaptome.db, TENET.AnnotationHub, adductData, BioImageDbs, biscuiteerData, celldex, chipseqDBData, crisprScoreData, curatedMetagenomicData, curatedPCaData, curatedTBData, curatedTCGAData, depmap, DoReMiTra, DropletTestFiles, easierData, EMTscoreData, FieldEffectCrc, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, GenomicDistributionsData, HCAData, HiBED, HiContactsData, HMP16SData, HMP2Data, mcsurvdata, MerfishData, MetaGxPancreas, MouseAgingData, msigdb, orthosData, ProteinGymR, scpdata, scRNAseq, SFEData, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, RNAseqQC suggestsMe: AHMassBank, AlphaMissenseR, autonomics, BgeeCall, Chicago, ChIPpeakAnno, clusterProfiler, CNVRanger, COCOA, crisprViz, DNAshapeR, dupRadar, ELMER, ensembldb, epiNEM, EpiTxDb, epivizrChart, epivizrData, factR, GenomicRanges, Glimma, GOSemSim, HiCool, LRBaseDbi, maser, MIRA, motifTestR, MSnbase, multicrispr, muscat, nullranges, OrganismDbi, peakCombiner, plotgardener, raer, recountmethylation, satuRn, simona, TCGAbiolinks, TCGAutils, tidyCoverage, VariantAnnotation, xcore, AHEnsDbs, CTCF, ENCODExplorerData, excluderanges, gwascatData, ontoProcData, org.Hbacteriophora.eg.db, BioPlex, ChIPDBData, CoSIAdata, HarmonizedTCGAData, easyEWAS, locuszoomr dependencyCount: 62 Package: annotationTools Version: 1.85.0 Imports: Biobase, stats Suggests: BiocStyle License: GPL MD5sum: f1057de780210a2b4653dcb77fa1b25a NeedsCompilation: no Title: Annotate microarrays and perform cross-species gene expression analyses using flat file databases Description: Functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files). biocViews: Microarray, Annotation Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/annotationTools git_branch: devel git_last_commit: 91c80aa git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/annotationTools_1.85.0.tar.gz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R importsMe: CoSIA dependencyCount: 7 Package: annotatr Version: 1.37.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, AnnotationHub, dplyr, GenomicFeatures (>= 1.61.4), GenomicRanges (>= 1.61.1), Seqinfo, ggplot2 (>= 3.5.0), IRanges, methods, readr, regioneR, reshape2, rlang, rtracklayer (>= 1.69.1), S4Vectors (>= 0.23.10), stats, utils Suggests: GenomeInfoDb, BiocStyle, devtools, knitr, org.Dm.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, rmarkdown, roxygen2, testthat, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene License: GPL-3 MD5sum: 36942285618d3e31f08b12f958c90be5 NeedsCompilation: no Title: Annotation of Genomic Regions to Genomic Annotations Description: Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations. biocViews: Software, Annotation, GenomeAnnotation, FunctionalGenomics, Visualization Author: Raymond G. Cavalcante [aut, cre], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://www.github.com/rcavalcante/annotatr/issues git_url: https://git.bioconductor.org/packages/annotatr git_branch: devel git_last_commit: 3e6ad83 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/annotatr_1.37.0.tar.gz vignettes: vignettes/annotatr/inst/doc/annotatr-vignette.html vignetteTitles: annotatr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R importsMe: dmrseq, methodical, scmeth, SOMNiBUS suggestsMe: borealis, ramr dependencyCount: 118 Package: anota Version: 1.59.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: 417eb173e464863c0172108571fa69d3 NeedsCompilation: no Title: ANalysis Of Translational Activity (ANOTA). Description: Genome wide studies of translational control is emerging as a tool to study verious biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the levl of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e. differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallell the library performs a number of tests to assure that the data set is suitable for such analysis. biocViews: GeneExpression, DifferentialExpression, Microarray, Sequencing Author: Ola Larsson , Nahum Sonenberg , Robert Nadon Maintainer: Ola Larsson git_url: https://git.bioconductor.org/packages/anota git_branch: devel git_last_commit: fe5dc6d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/anota_1.59.0.tar.gz vignettes: vignettes/anota/inst/doc/anota.pdf vignetteTitles: ANalysis Of Translational Activity (anota) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota/inst/doc/anota.R dependsOnMe: tRanslatome dependencyCount: 41 Package: anota2seq Version: 1.33.0 Depends: R (>= 3.4.0), methods Imports: multtest,qvalue,limma,DESeq2,edgeR,RColorBrewer, grDevices, graphics, stats, utils, SummarizedExperiment Suggests: BiocStyle,knitr License: GPL-3 MD5sum: 1bc9ca1d621a7b691f2b2d37ceeaa593 NeedsCompilation: no Title: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq Description: anota2seq provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources; translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source (i.e. translated mRNA or total mRNA). Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels (i.e. changes in translated mRNA that are independent of levels of total mRNA) or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels (i.e. changes in total mRNA that are independent of levels of translated mRNA). anota2seq applies analysis of partial variance and the random variance model to fulfill these tasks. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Microarray,GenomeWideAssociation, BatchEffect, Normalization, RNASeq, Sequencing, GeneRegulation, Regression Author: Christian Oertlin , Julie Lorent , Ola Larsson Maintainer: Christian Oertlin , Ola Larsson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/anota2seq git_branch: devel git_last_commit: 8d76555 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/anota2seq_1.33.0.tar.gz vignettes: vignettes/anota2seq/inst/doc/anota2seq.pdf vignetteTitles: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota2seq/inst/doc/anota2seq.R importsMe: postNet dependencyCount: 68 Package: antiProfiles Version: 1.51.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: 859778c2f18960c421f8a06c1cd56f32 NeedsCompilation: no Title: Implementation of gene expression anti-profiles Description: Implements gene expression anti-profiles as described in Corrada Bravo et al., BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272. biocViews: GeneExpression,Classification Author: Hector Corrada Bravo, Rafael A. Irizarry and Jeffrey T. Leek Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/antiProfiles git_url: https://git.bioconductor.org/packages/antiProfiles git_branch: devel git_last_commit: a3184f6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/antiProfiles_1.51.0.tar.gz vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf vignetteTitles: Introduction to antiProfiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R dependencyCount: 9 Package: AnVILAz Version: 1.5.1 Depends: R (>= 4.5.0) Imports: AnVILBase, BiocBaseUtils, curl, httr2, jsonlite, methods, rjsoncons, tibble, utils Suggests: BiocStyle, dplyr, knitr, readr, rmarkdown, tinytest License: Artistic-2.0 MD5sum: bd9c9ef680d1aa62d9a98dcba1a2131c NeedsCompilation: no Title: R / Bioconductor Support for the AnVIL Azure Platform Description: The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVILAz package supports end-users and developers using the AnVIL platform in the Azure cloud. The package provides a programmatic interface to AnVIL resources, including workspaces, notebooks, tables, and workflows. The package also provides utilities for managing resources, including copying files to and from Azure Blob Storage, and creating shared access signatures (SAS) for secure access to Azure resources. biocViews: Software, Infrastructure, ThirdPartyClient Author: Martin Morgan [aut, ctb] (ORCID: ), Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/AnVILAz SystemRequirements: az, azcopy VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnVILAz/issues git_url: https://git.bioconductor.org/packages/AnVILAz git_branch: devel git_last_commit: 07f81c6 git_last_commit_date: 2026-04-03 Date/Publication: 2026-04-20 source.ver: src/contrib/AnVILAz_1.5.1.tar.gz vignettes: vignettes/AnVILAz/inst/doc/AnVILAzWorkspaces.html, vignettes/AnVILAz/inst/doc/IntroductionToAnVILAz.html vignetteTitles: Working with Workspaces on AnVIL Azure, Introduction to the AnVILAz package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILAz/inst/doc/AnVILAzWorkspaces.R, vignettes/AnVILAz/inst/doc/IntroductionToAnVILAz.R suggestsMe: AnVIL, AnVILBase dependencyCount: 33 Package: AnVILBase Version: 1.5.1 Depends: R (>= 4.5.0) Imports: httr, httr2, dplyr, jsonlite, methods, tibble Suggests: AnVIL, AnVILAz, AnVILGCP, BiocStyle, GCPtools, knitr, rmarkdown, testthat (>= 3.0.0), tinytest License: Artistic-2.0 MD5sum: e95be8d70ce3fb0dfe0e6972fe68954f NeedsCompilation: no Title: Generic functions for interacting with the AnVIL ecosystem Description: Provides generic functions for interacting with the AnVIL ecosystem. Packages that use either GCP or Azure in AnVIL are built on top of AnVILBase. Extension packages will provide methods for interacting with other cloud providers. biocViews: Software, Infrastructure Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut, ctb] (ORCID: ), NIH NHGRI U24HG004059 [fnd] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/AnVILBase VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnVILBase/issues git_url: https://git.bioconductor.org/packages/AnVILBase git_branch: devel git_last_commit: fb8f175 git_last_commit_date: 2025-11-10 Date/Publication: 2026-04-20 source.ver: src/contrib/AnVILBase_1.5.1.tar.gz vignettes: vignettes/AnVILBase/inst/doc/AnVILBaseIntroduction.html vignetteTitles: Introduction to AnVILBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILBase/inst/doc/AnVILBaseIntroduction.R dependsOnMe: AnVIL, AnVILWorkflow importsMe: AnVILAz, AnVILGCP, GCPtools suggestsMe: terraTCGAdata dependencyCount: 29 Package: AnVILBilling Version: 1.21.0 Depends: R (>= 4.1) Imports: methods, DT, shiny, bigrquery, shinytoastr, DBI, magrittr, dplyr, lubridate, plotly, ggplot2 Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: f9cf8ff20cf993233dd2a46d2eb8016e NeedsCompilation: no Title: Provide functions to retrieve and report on usage expenses in NHGRI AnVIL (anvilproject.org). Description: AnVILBilling helps monitor AnVIL-related costs in R, using queries to a BigQuery table to which costs are exported daily. Functions are defined to help categorize tasks and associated expenditures, and to visualize and explore expense profiles over time. This package will be expanded to help users estimate costs for specific task sets. biocViews: Infrastructure, Software Author: BJ Stubbs [aut], Vince Carey [aut, cre] Maintainer: Vince Carey VignetteBuilder: knitr BugReports: https://github.com/vjcitn/AnVILBilling/issues git_url: https://git.bioconductor.org/packages/AnVILBilling git_branch: devel git_last_commit: 98d4ced git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AnVILBilling_1.21.0.tar.gz vignettes: vignettes/AnVILBilling/inst/doc/billing.html vignetteTitles: Software for reckoning AnVIL/terra usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILBilling/inst/doc/billing.R dependencyCount: 91 Package: AnVILGCP Version: 1.5.3 Depends: R (>= 4.5.0) Imports: AnVILBase, BiocBaseUtils, dplyr, GCPtools (>= 0.99.4), httr, jsonlite, methods, rlang, stats, tibble, tidyr, utils Suggests: AnVIL, BiocStyle, knitr, rmarkdown, testthat, withr License: Artistic-2.0 MD5sum: 36dd091bb2a3fd16ae5488d5bee946fd NeedsCompilation: no Title: The GCP R Client for the AnVIL Description: The package provides a set of functions to interact with the Google Cloud Platform (GCP) services on the AnVIL platform. The package is designed to use the API calls from the AnVIL package. It coordinates AnVIL workspace functionality with native GCP tools. biocViews: Software, Infrastructure, ThirdPartyClient, DataImport Author: Marcel Ramos [aut, cre] (ORCID: ), Nitesh Turaga [aut], Martin Morgan [aut] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/AnVILGCP VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnVILGCP/issues git_url: https://git.bioconductor.org/packages/AnVILGCP git_branch: devel git_last_commit: a779c08 git_last_commit_date: 2025-11-14 Date/Publication: 2026-04-20 source.ver: src/contrib/AnVILGCP_1.5.3.tar.gz vignettes: vignettes/AnVILGCP/inst/doc/AnVILGCPIntroduction.html vignetteTitles: Working with AnVIL on GCP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILGCP/inst/doc/AnVILGCPIntroduction.R dependsOnMe: AnVILWorkflow, terraTCGAdata importsMe: AnVILPublish suggestsMe: AnVIL, AnVILBase dependencyCount: 38 Package: apComplex Version: 2.77.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: 24ab3ad3599b503fad34ccdc78fbf650 NeedsCompilation: no Title: Estimate protein complex membership using AP-MS protein data Description: Functions to estimate a bipartite graph of protein complex membership using AP-MS data. biocViews: ImmunoOncology, NetworkInference, MassSpectrometry, GraphAndNetwork Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/apComplex git_branch: devel git_last_commit: c10f18a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/apComplex_2.77.0.tar.gz vignettes: vignettes/apComplex/inst/doc/apComplex.pdf vignetteTitles: apComplex hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apComplex/inst/doc/apComplex.R dependencyCount: 48 Package: apeglm Version: 1.33.0 Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats, utils, Rcpp LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: DESeq2, airway, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 0a4b6a00d70e626e44e39d412b47822b NeedsCompilation: yes Title: Approximate posterior estimation for GLM coefficients Description: apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, GeneExpression, Bayesian Author: Anqi Zhu [aut, cre], Joshua Zitovsky [ctb], Joseph Ibrahim [aut], Michael Love [aut] Maintainer: Anqi Zhu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/apeglm git_branch: devel git_last_commit: 3ff43e2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/apeglm_1.33.0.tar.gz vignettes: vignettes/apeglm/inst/doc/apeglm.html vignetteTitles: Effect size estimation with apeglm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apeglm/inst/doc/apeglm.R dependsOnMe: rnaseqGene importsMe: airpart, debrowser, ERSSA, phantasus, Rmmquant, TEKRABber suggestsMe: bambu, dar, DeeDeeExperiment, DESeq2, extraChIPs, fishpond, terapadog, NanoporeRNASeq, RNAseqQC dependencyCount: 36 Package: APL Version: 1.15.0 Depends: R (>= 4.4.0) Imports: Matrix, RSpectra, ggrepel, ggplot2, viridisLite, plotly, SeuratObject, SingleCellExperiment, magrittr, SummarizedExperiment, topGO, methods, stats, utils, org.Hs.eg.db, org.Mm.eg.db, rlang Suggests: BiocStyle, knitr, rmarkdown, scRNAseq, scater, scran, sparseMatrixStats, testthat License: GPL (>= 3) MD5sum: 1531c8defcfa9caf516c07d798df14fd NeedsCompilation: no Title: Association Plots Description: APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest. biocViews: StatisticalMethod, DimensionReduction, SingleCell, Sequencing, RNASeq, GeneExpression Author: Clemens Kohl [cre, aut], Elzbieta Gralinska [aut], Martin Vingron [aut] Maintainer: Clemens Kohl URL: https://vingronlab.github.io/APL/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/APL git_branch: devel git_last_commit: daf6edd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/APL_1.15.0.tar.gz vignettes: vignettes/APL/inst/doc/APL.html vignetteTitles: Analyzing data with APL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/APL/inst/doc/APL.R dependencyCount: 120 Package: aroma.light Version: 3.41.0 Depends: R (>= 2.15.2) Imports: stats, R.methodsS3 (>= 1.7.1), R.oo (>= 1.23.0), R.utils (>= 2.9.0), matrixStats (>= 0.55.0) Suggests: princurve (>= 2.1.4) License: GPL (>= 2) MD5sum: bd6a0e0820a07756aa72bc2c052c6c71 NeedsCompilation: no Title: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types Description: Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. biocViews: Infrastructure, Microarray, OneChannel, TwoChannel, MultiChannel, Visualization, Preprocessing Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb], Aaron Lun [ctb] Maintainer: Henrik Bengtsson URL: https://github.com/HenrikBengtsson/aroma.light, https://www.aroma-project.org BugReports: https://github.com/HenrikBengtsson/aroma.light/issues git_url: https://git.bioconductor.org/packages/aroma.light git_branch: devel git_last_commit: 4e446ad git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/aroma.light_3.41.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq, EventPointer, scone, PSCBS suggestsMe: TIN, aroma.affymetrix, aroma.cn, aroma.core dependencyCount: 8 Package: ArrayExpress Version: 1.71.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: oligo, limma, httr, utils, jsonlite, rlang, tools, methods Suggests: affy License: Artistic-2.0 MD5sum: 3a46e9abd050274b14d56553f8e9d7f7 NeedsCompilation: no Title: Access the ArrayExpress Collection at EMBL-EBI Biostudies and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet Description: Access the ArrayExpress Collection at EMBL-EBI Biostudies and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet. biocViews: Microarray, DataImport, OneChannel, TwoChannel Author: Audrey Kauffmann, Ibrahim Emam, Michael Schubert, Jose Marugan Maintainer: Jose Marugan git_url: https://git.bioconductor.org/packages/ArrayExpress git_branch: devel git_last_commit: 4a25159 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ArrayExpress_1.71.0.tar.gz vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets into R object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R dependsOnMe: DrugVsDisease suggestsMe: bapred dependencyCount: 63 Package: arrayQuality Version: 1.89.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: e5d331db3b2e3d36ec10a3e108a88773 NeedsCompilation: no Title: Assessing array quality on spotted arrays Description: Functions for performing print-run and array level quality assessment. biocViews: Microarray,TwoChannel,QualityControl,Visualization Author: Agnes Paquet and Jean Yee Hwa Yang Maintainer: Agnes Paquet URL: http://arrays.ucsf.edu/ git_url: https://git.bioconductor.org/packages/arrayQuality git_branch: devel git_last_commit: 354cf88 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/arrayQuality_1.89.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: arrayQualityMetrics Version: 3.67.2 Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, genefilter, graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter, jsonlite, lattice, latticeExtra, limma, methods, RColorBrewer, setRNG, stats, utils, vsn (>= 3.23.3), XML, svglite Suggests: ALLMLL, CCl4, BiocStyle, knitr, rmarkdown License: LGPL (>= 2) MD5sum: 60050c0f7ee7525a894ae3c8c75f186b NeedsCompilation: no Title: Quality metrics report for microarray data sets Description: This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported. biocViews: Microarray, QualityControl, OneChannel, TwoChannel, ReportWriting Author: Audrey Kauffmann [aut], Wolfgang Huber [aut], Hugo Gruson [cre] Maintainer: Hugo Gruson VignetteBuilder: knitr BugReports: https://github.com/grimbough/arrayQualityMetrics/issues git_url: https://git.bioconductor.org/packages/arrayQualityMetrics git_branch: devel git_last_commit: 3acb7cb git_last_commit_date: 2026-04-07 Date/Publication: 2026-04-20 source.ver: src/contrib/arrayQualityMetrics_3.67.2.tar.gz vignettes: vignettes/arrayQualityMetrics/inst/doc/aqm.html, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.html vignetteTitles: Advanced topics: Customizing arrayQualityMetrics reports and programmatic processing of the output, Introduction: microarray quality assessment with arrayQualityMetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayQualityMetrics/inst/doc/aqm.R, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.R dependencyCount: 124 Package: ARRmNormalization Version: 1.51.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: aae08810affc3e631810c7d8995d30b7 NeedsCompilation: no Title: Adaptive Robust Regression normalization for Illumina methylation data Description: Perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay. biocViews: DNAMethylation, TwoChannel, Preprocessing, Microarray Author: Jean-Philippe Fortin, Celia M.T. Greenwood, Aurelie Labbe. Maintainer: Jean-Philippe Fortin git_url: https://git.bioconductor.org/packages/ARRmNormalization git_branch: devel git_last_commit: e189ad9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ARRmNormalization_1.51.0.tar.gz vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf vignetteTitles: ARRmNormalization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.R dependencyCount: 1 Package: ASAFE Version: 1.37.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 6f86853cddf168ef8acbb1312e49fc13 NeedsCompilation: no Title: Ancestry Specific Allele Frequency Estimation Description: Given admixed individuals' bi-allelic SNP genotypes and ancestry pairs (where each ancestry can take one of three values) for multiple SNPs, perform an EM algorithm to deal with the fact that SNP genotypes are unphased with respect to ancestry pairs, in order to estimate ancestry-specific allele frequencies for all SNPs. biocViews: SNP, GenomeWideAssociation, LinkageDisequilibrium, BiomedicalInformatics, Genetics, ExperimentalDesign Author: Qian Zhang Maintainer: Qian Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASAFE git_branch: devel git_last_commit: a3553d7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ASAFE_1.37.0.tar.gz vignettes: vignettes/ASAFE/inst/doc/ASAFE.pdf vignetteTitles: ASAFE (Ancestry Specific Allele Frequency Estimation) hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASAFE/inst/doc/ASAFE.R dependencyCount: 0 Package: ASEB Version: 1.55.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) MD5sum: 4b262719ab2595c8f1be88705c49a405 NeedsCompilation: yes Title: Predict Acetylated Lysine Sites Description: ASEB is an R package to predict lysine sites that can be acetylated by a specific KAT-family. biocViews: Proteomics Author: Likun Wang and Tingting Li . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/ASEB git_branch: devel git_last_commit: 193569c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ASEB_1.55.0.tar.gz vignettes: vignettes/ASEB/inst/doc/ASEB.pdf vignetteTitles: ASEB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASEB/inst/doc/ASEB.R dependencyCount: 3 Package: ASGSCA Version: 1.45.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: 10da8338ddef04e425dee9bfe56ea1da NeedsCompilation: no Title: Association Studies for multiple SNPs and multiple traits using Generalized Structured Equation Models Description: The package provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables. The method is based on Generalized Structured Component Analysis (GSCA). biocViews: StructuralEquationModels Author: Hela Romdhani, Stepan Grinek , Heungsun Hwang and Aurelie Labbe. Maintainer: Hela Romdhani git_url: https://git.bioconductor.org/packages/ASGSCA git_branch: devel git_last_commit: d74fe27 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ASGSCA_1.45.0.tar.gz vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf vignetteTitles: Association Studies using Generalized Structured Equation Models. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASGSCA/inst/doc/ASGSCA.R dependencyCount: 9 Package: AssessORF Version: 1.29.0 Depends: R (>= 3.5.0), DECIPHER (>= 2.10.0) Imports: Biostrings, GenomicRanges, IRanges, graphics, grDevices, methods, stats, utils Suggests: AssessORFData, BiocStyle, knitr, rmarkdown, RSQLite (>= 1.1) License: GPL-3 MD5sum: 6f8f72e0856761f6e03cbb5d6a7ab813 NeedsCompilation: no Title: Assess Gene Predictions Using Proteomics and Evolutionary Conservation Description: In order to assess the quality of a set of predicted genes for a genome, evidence must first be mapped to that genome. Next, each gene must be categorized based on how strong the evidence is for or against that gene. The AssessORF package provides the functions and class structures necessary for accomplishing those tasks, using proteomic hits and evolutionarily conserved start codons as the forms of evidence. biocViews: ComparativeGenomics, GenePrediction, GenomeAnnotation, Genetics, Proteomics, QualityControl, Visualization Author: Deepank Korandla [aut, cre], Erik Wright [aut] Maintainer: Deepank Korandla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AssessORF git_branch: devel git_last_commit: 02cf057 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AssessORF_1.29.0.tar.gz vignettes: vignettes/AssessORF/inst/doc/UsingAssessORF.pdf vignetteTitles: Using AssessORF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AssessORF/inst/doc/UsingAssessORF.R suggestsMe: AssessORFData dependencyCount: 18 Package: ASSET Version: 2.29.0 Depends: R (>= 3.5.0), stats, graphics Imports: MASS, msm, rmeta Suggests: RUnit, BiocGenerics, knitr License: GPL-2 + file LICENSE MD5sum: 6efd33f10a206c61b2b67227f295baca NeedsCompilation: no Title: An R package for subset-based association analysis of heterogeneous traits and subtypes Description: An R package for subset-based analysis of heterogeneous traits and disease subtypes. The package allows the user to search through all possible subsets of z-scores to identify the subset of traits giving the best meta-analyzed z-score. Further, it returns a p-value adjusting for the multiple-testing involved in the search. It also allows for searching for the best combination of disease subtypes associated with each variant. biocViews: StatisticalMethod, SNP, GenomeWideAssociation, MultipleComparison Author: Samsiddhi Bhattacharjee [aut, cre], Guanghao Qi [aut], Nilanjan Chatterjee [aut], William Wheeler [aut] Maintainer: Samsiddhi Bhattacharjee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASSET git_branch: devel git_last_commit: 81ccf26 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ASSET_2.29.0.tar.gz vignettes: vignettes/ASSET/inst/doc/vignette.pdf vignetteTitles: ASSET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSET/inst/doc/vignette.R dependsOnMe: REBET dependencyCount: 26 Package: ASSIGN Version: 1.47.0 Depends: R (>= 3.4) Imports: gplots, graphics, grDevices, msm, Rlab, stats, sva, utils, ggplot2, yaml Suggests: testthat, BiocStyle, lintr, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 6675c123dd7910aee6a9850628c03fcc NeedsCompilation: no Title: Adaptive Signature Selection and InteGratioN (ASSIGN) Description: ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literature or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature. biocViews: Software, GeneExpression, Pathways, Bayesian Author: Ying Shen, Andrea H. Bild, W. Evan Johnson, and Mumtehena Rahman Maintainer: Ying Shen , W. Evan Johnson , David Jenkins , Mumtehena Rahman URL: https://compbiomed.github.io/ASSIGN/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/ASSIGN/issues git_url: https://git.bioconductor.org/packages/ASSIGN git_branch: devel git_last_commit: 37395b4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ASSIGN_1.47.0.tar.gz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.html vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R importsMe: TBSignatureProfiler dependencyCount: 93 Package: assorthead Version: 1.5.16 Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 3c9dd7676ed9a517315ffd6edeee51b5 NeedsCompilation: no Title: Assorted Header-Only C++ Libraries Description: Vendors an assortment of useful header-only C++ libraries. Bioconductor packages can use these libraries in their own C++ code by LinkingTo this package without introducing any additional dependencies. The use of a central repository avoids duplicate vendoring of libraries across multiple R packages, and enables better coordination of version updates across cohorts of interdependent C++ libraries. biocViews: SingleCell, QualityControl, Normalization, DataRepresentation, DataImport, DifferentialExpression, Alignment Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun URL: https://github.com/LTLA/assorthead VignetteBuilder: knitr BugReports: https://github.com/LTLA/assorthead/issues git_url: https://git.bioconductor.org/packages/assorthead git_branch: devel git_last_commit: 5e0efa5 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/assorthead_1.5.16.tar.gz vignettes: vignettes/assorthead/inst/doc/userguide.html vignetteTitles: User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/assorthead/inst/doc/userguide.R linksToMe: alabaster.base, beachmat, beachmat.hdf5, beachmat.tiledb, BiocNeighbors, BiocSingular, bluster, bsseq, DropletUtils, glmGamPoi, PCAtools, scrapper, scuttle, SingleR dependencyCount: 0 Package: asuri Version: 0.99.20 Depends: R (>= 4.5.0), stats, methods Imports: SummarizedExperiment, spsUtil, lubridate, survival, glmnet, siggenes, survcomp, scales, ROCR, ggplot2, grDevices, graphics, utils, Rdpack Suggests: BiocStyle, knitr, formatR, rmarkdown, magick, devtools License: LGPL-3 + file LICENSE MD5sum: 6c8db48bdb51728988012fcb589da9a9 NeedsCompilation: no Title: Analysis of SUrvival and RIsk prediction in patients based on gene signatures Description: The ASURI (Analysis of SUrvival and patients RIsk prediction based on gene signatures) package discovers marker genes that are related to risk prediction capabilities and to a clinical variable of interest. It uses two main steps, including subsampling glmnet and unicox. The package implements robust functions to discover survival markers related to a clinical phenotype and to predict a risk score, allowing to study the patient's risk based on the gene signatures. Several plots are provided to visualise the relevance of the genes, the risk score, and patient stratification, as well as a robust version of the Kaplan-Meier curves. biocViews: Software, StatisticalMethod, WorkflowStep, GeneExpression, Microarray, DifferentialExpression, GenePrediction, Regression, Survival, ExonArray, MultipleComparison Author: Alberto Berral-Gonzalez [aut, cre, ctb] (ORCID: ), María Sanchez-Martin [aut, ctb] (ORCID: ), Santiago Bueno-Fortes [aut, ctb] (ORCID: ), Natalia Alonso-Moreda [aut, ctb] (ORCID: ), Jose Manuel Sanchez-Santos [aut, ctb] (ORCID: ), Manuel Martin-Merino Acera [aut, ctb] (ORCID: ), Javier De Las Rivas [aut, ctb] (ORCID: ) Maintainer: Alberto Berral-Gonzalez URL: https://github.com/jdelasrivas-lab/asuri VignetteBuilder: knitr BugReports: https://github.com/jdelasrivas-lab/asuri/issues git_url: https://git.bioconductor.org/packages/asuri git_branch: devel git_last_commit: 47bc30e git_last_commit_date: 2026-03-18 Date/Publication: 2026-04-20 source.ver: src/contrib/asuri_0.99.20.tar.gz vignettes: vignettes/asuri/inst/doc/asuri.html vignetteTitles: Asuri hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/asuri/inst/doc/asuri.R dependencyCount: 101 Package: ATACseqTFEA Version: 1.13.0 Depends: R (>= 4.2) Imports: BiocGenerics, S4Vectors, IRanges, Matrix, GenomicRanges, GenomicAlignments, Seqinfo, SummarizedExperiment, Rsamtools, motifmatchr, TFBSTools, stats, pracma, ggplot2, ggrepel, dplyr, limma, methods, rtracklayer Suggests: BSgenome.Drerio.UCSC.danRer10, knitr, testthat, ATACseqQC, rmarkdown, BiocStyle License: GPL-3 MD5sum: 9d1dd85fd7eb3a6191333198eca6da0b NeedsCompilation: no Title: Transcription Factor Enrichment Analysis for ATAC-seq Description: Assay for Transpose-Accessible Chromatin using sequencing (ATAC-seq) is a technique to assess genome-wide chromatin accessibility by probing open chromatin with hyperactive mutant Tn5 Transposase that inserts sequencing adapters into open regions of the genome. ATACseqTFEA is an improvement of the current computational method that detects differential activity of transcription factors (TFs). ATACseqTFEA not only uses the difference of open region information, but also (or emphasizes) the difference of TFs footprints (cutting sites or insertion sites). ATACseqTFEA provides an easy, rigorous way to broadly assess TF activity changes between two conditions. biocViews: Sequencing, DNASeq, ATACSeq, MNaseSeq, GeneRegulation Author: Jianhong Ou [aut, cre] (ORCID: ) Maintainer: Jianhong Ou URL: https://github.com/jianhong/ATACseqTFEA VignetteBuilder: knitr BugReports: https://github.com/jianhong/ATACseqTFEA/issues git_url: https://git.bioconductor.org/packages/ATACseqTFEA git_branch: devel git_last_commit: e0054ea git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ATACseqTFEA_1.13.0.tar.gz vignettes: vignettes/ATACseqTFEA/inst/doc/ATACseqTFEA.html vignetteTitles: ATACseqTFEA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqTFEA/inst/doc/ATACseqTFEA.R dependencyCount: 102 Package: atena Version: 1.17.0 Depends: R (>= 4.3.0), SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, MatrixGenerics, BiocParallel, S4Vectors, IRanges, Seqinfo, GenomicFeatures, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, SQUAREM, sparseMatrixStats, AnnotationHub, matrixStats, cli Suggests: covr, BiocStyle, knitr, rmarkdown, RUnit, TxDb.Dmelanogaster.UCSC.dm6.ensGene, RColorBrewer License: Artistic-2.0 MD5sum: 8f1050cb5c71d209c6d481261bb828f1 NeedsCompilation: no Title: Analysis of Transposable Elements Description: Quantify expression of transposable elements (TEs) from RNA-seq data through different methods, including ERVmap, TEtranscripts and Telescope. A common interface is provided to use each of these methods, which consists of building a parameter object, calling the quantification function with this object and getting a SummarizedExperiment object as output container of the quantified expression profiles. The implementation allows one to quantify TEs and gene transcripts in an integrated manner. biocViews: Transcription, Transcriptomics, RNASeq, Sequencing, Preprocessing, Software, GeneExpression, Coverage, DifferentialExpression, FunctionalGenomics Author: Beatriz Calvo-Serra [aut], Robert Castelo [aut, cre] Maintainer: Robert Castelo URL: https://github.com/rcastelo/atena VignetteBuilder: knitr BugReports: https://github.com/rcastelo/atena/issues git_url: https://git.bioconductor.org/packages/atena git_branch: devel git_last_commit: 2209be2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/atena_1.17.0.tar.gz vignettes: vignettes/atena/inst/doc/atena.html vignetteTitles: An introduction to the atena package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atena/inst/doc/atena.R dependencyCount: 100 Package: atSNP Version: 1.27.0 Depends: R (>= 3.6) Imports: BSgenome, BiocFileCache, BiocParallel, Rcpp, data.table, ggplot2, grDevices, graphics, grid, motifStack, rappdirs, stats, testthat, utils, lifecycle LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: d14d236565fed57aaac00224757c07b0 NeedsCompilation: yes Title: Affinity test for identifying regulatory SNPs Description: atSNP performs affinity tests of motif matches with the SNP or the reference genomes and SNP-led changes in motif matches. biocViews: Software, ChIPSeq, GenomeAnnotation, MotifAnnotation, Visualization Author: Chandler Zuo [aut], Sunyoung Shin [aut, cre], Sunduz Keles [aut] Maintainer: Sunyoung Shin URL: https://github.com/sunyoungshin/atSNP VignetteBuilder: knitr BugReports: https://github.com/sunyoungshin/atSNP/issues git_url: https://git.bioconductor.org/packages/atSNP git_branch: devel git_last_commit: 0b80e8a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/atSNP_1.27.0.tar.gz vignettes: vignettes/atSNP/inst/doc/atsnp-vignette.html vignetteTitles: atsnp-vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atSNP/inst/doc/atsnp-vignette.R dependencyCount: 139 Package: AUCell Version: 1.33.0 Imports: DelayedArray, DelayedMatrixStats, data.table, graphics, grDevices, GSEABase, Matrix, methods, mixtools, R.utils, stats, SummarizedExperiment, BiocGenerics, utils Suggests: Biobase, BiocStyle, doSNOW, dynamicTreeCut, DT, GEOquery, knitr, NMF, plyr, R2HTML, rmarkdown, reshape2, plotly, Rtsne, testthat, zoo Enhances: doMC, doRNG, doParallel, foreach License: GPL-3 MD5sum: 04a6f172023878b263334b46aa09c315 NeedsCompilation: no Title: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures) Description: AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, Transcription, GeneExpression, WorkflowStep, Normalization Author: Sara Aibar, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium. Maintainer: Gert Hulselmans URL: http://scenic.aertslab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AUCell git_branch: devel git_last_commit: cf45038 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AUCell_1.33.0.tar.gz vignettes: vignettes/AUCell/inst/doc/AUCell.html vignetteTitles: AUCell: Identifying cells with active gene sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AUCell/inst/doc/AUCell.R dependsOnMe: OSCA.basic importsMe: scFeatures, OSTA suggestsMe: decoupleR, escape, GSABenchmark, pathMED, scDiagnostics dependencyCount: 115 Package: AWAggregator Version: 1.1.0 Depends: R (>= 4.5.0) Imports: dplyr, Peptides, progress, purrr, ranger, rlang, stats, stringr, tidyr, toOrdinal, utils Suggests: AWAggregatorData, BiocStyle, ExperimentHub, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: e3f62029c5b974a9bd293115864b186b NeedsCompilation: no Title: Attribute-Weighted Aggregation Description: This package implements an attribute-weighted aggregation algorithm which leverages peptide-spectrum match (PSM) attributes to provide a more accurate estimate of protein abundance compared to conventional aggregation methods. This algorithm employs pre-trained random forest models to predict the quantitative inaccuracy of PSMs based on their attributes. PSMs are then aggregated to the protein level using a weighted average, taking the predicted inaccuracy into account. Additionally, the package allows users to construct their own training sets that are more relevant to their specific experimental conditions if desired. biocViews: Software, MassSpectrometry, Preprocessing, Proteomics, Regression Author: Jiahua Tan [aut, cre] (ORCID: ), Gian L. Negri [aut] (ORCID: ), Gregg B. Morin [aut] (ORCID: ), David D. Y. Chen [aut] (ORCID: ) Maintainer: Jiahua Tan URL: https://github.com/Tan-Jiahua/AWAggregator VignetteBuilder: knitr BugReports: https://github.com/Tan-Jiahua/AWAggregator/issues git_url: https://git.bioconductor.org/packages/AWAggregator git_branch: devel git_last_commit: 5b7083c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AWAggregator_1.1.0.tar.gz vignettes: vignettes/AWAggregator/inst/doc/AWAggregator-vignette.html vignetteTitles: AWAggregator vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AWAggregator/inst/doc/AWAggregator-vignette.R dependencyCount: 41 Package: AWFisher Version: 1.25.0 Depends: R (>= 3.6) Imports: edgeR, limma, stats Suggests: knitr, tightClust License: GPL-3 MD5sum: 70863b0ccb1629919835d775f7d033eb NeedsCompilation: yes Title: An R package for fast computing for adaptively weighted fisher's method Description: Implementation of the adaptively weighted fisher's method, including fast p-value computing, variability index, and meta-pattern. biocViews: StatisticalMethod, Software Author: Zhiguang Huo Maintainer: Zhiguang Huo VignetteBuilder: knitr BugReports: https://github.com/Caleb-Huo/AWFisher/issues git_url: https://git.bioconductor.org/packages/AWFisher git_branch: devel git_last_commit: 4a1aa4a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/AWFisher_1.25.0.tar.gz vignettes: vignettes/AWFisher/inst/doc/AWFisher.html vignetteTitles: AWFisher hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AWFisher/inst/doc/AWFisher.R dependencyCount: 11 Package: awst Version: 1.19.0 Imports: stats, methods, SummarizedExperiment Suggests: airway, ggplot2, testthat, EDASeq, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown License: MIT + file LICENSE MD5sum: c5af4f35e385c0f22ce966311471ddb6 NeedsCompilation: no Title: Asymmetric Within-Sample Transformation Description: We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts. biocViews: Normalization, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph] (ORCID: ), Stefano Pagnotta [aut, cph] (ORCID: ) Maintainer: Davide Risso URL: https://github.com/drisso/awst VignetteBuilder: knitr BugReports: https://github.com/drisso/awst/issues git_url: https://git.bioconductor.org/packages/awst git_branch: devel git_last_commit: 7e33cde git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/awst_1.19.0.tar.gz vignettes: vignettes/awst/inst/doc/awst_intro.html vignetteTitles: Introduction to awst hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/awst/inst/doc/awst_intro.R dependencyCount: 25 Package: BaalChIP Version: 1.37.0 Depends: R (>= 3.3.1), GenomicRanges, IRanges, Rsamtools, Imports: GenomicAlignments, GenomeInfoDb, doParallel, parallel, doBy, reshape2, scales, coda, foreach, ggplot2, methods, utils, graphics, stats Suggests: RUnit, BiocGenerics, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: f99a67d749d8d2bca48e992b68b985de NeedsCompilation: no Title: BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes Description: The package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. Computes allele counts at individual variants (SNPs/SNVs), implements extensive QC steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples. biocViews: Software, ChIPSeq, Bayesian, Sequencing Author: Ines de Santiago, Wei Liu, Ke Yuan, Martin O'Reilly, Chandra SR Chilamakuri, Bruce Ponder, Kerstin Meyer, Florian Markowetz Maintainer: Ines de Santiago VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BaalChIP git_branch: devel git_last_commit: fd6bc03 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BaalChIP_1.37.0.tar.gz vignettes: vignettes/BaalChIP/inst/doc/BaalChIP.html vignetteTitles: Analyzing ChIP-seq and FAIRE-seq data with the BaalChIP package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaalChIP/inst/doc/BaalChIP.R dependencyCount: 104 Package: bacon Version: 1.39.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) MD5sum: 0f236bcdd32c95aa917bc1fb2b05070c NeedsCompilation: yes Title: Controlling bias and inflation in association studies using the empirical null distribution Description: Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores. biocViews: ImmunoOncology, StatisticalMethod, Bayesian, Regression, GenomeWideAssociation, Transcriptomics, RNASeq, MethylationArray, BatchEffect, MultipleComparison Author: Maarten van Iterson [aut, cre], Erik van Zwet [ctb] Maintainer: Maarten van Iterson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bacon git_branch: devel git_last_commit: d5305cf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/bacon_1.39.0.tar.gz vignettes: vignettes/bacon/inst/doc/bacon.html vignetteTitles: Controlling bias and inflation in association studies using the empirical null distribution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bacon/inst/doc/bacon.R dependencyCount: 33 Package: BADER Version: 1.49.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 MD5sum: eb4ba418acb2da7e044cf35a0b2255d1 NeedsCompilation: yes Title: Bayesian Analysis of Differential Expression in RNA Sequencing Data Description: For RNA sequencing count data, BADER fits a Bayesian hierarchical model. The algorithm returns the posterior probability of differential expression for each gene between two groups A and B. The joint posterior distribution of the variables in the model can be returned in the form of posterior samples, which can be used for further down-stream analyses such as gene set enrichment. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, Software, SAGE Author: Andreas Neudecker, Matthias Katzfuss Maintainer: Andreas Neudecker git_url: https://git.bioconductor.org/packages/BADER git_branch: devel git_last_commit: f0f8d03 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BADER_1.49.0.tar.gz vignettes: vignettes/BADER/inst/doc/BADER.pdf vignetteTitles: Analysing RNA-Seq data with the "BADER" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BADER/inst/doc/BADER.R dependencyCount: 0 Package: BadRegionFinder Version: 1.39.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: c17894e80beb9eb291d3910b3612e06f NeedsCompilation: no Title: BadRegionFinder: an R/Bioconductor package for identifying regions with bad coverage Description: BadRegionFinder is a package for identifying regions with a bad, acceptable and good coverage in sequence alignment data available as bam files. The whole genome may be considered as well as a set of target regions. Various visual and textual types of output are available. biocViews: Coverage, Sequencing, Alignment, WholeGenome, Classification Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BadRegionFinder git_branch: devel git_last_commit: d483500 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BadRegionFinder_1.39.0.tar.gz vignettes: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.pdf vignetteTitles: Using BadRegionFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.R dependencyCount: 98 Package: bambu Version: 3.13.1 Depends: R(>= 4.1), SummarizedExperiment(>= 1.1.6), S4Vectors(>= 0.22.1), BSgenome, IRanges Imports: BiocGenerics, BiocParallel, data.table, dplyr, tidyr, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, stats, Rsamtools, methods, Rcpp, xgboost LinkingTo: Rcpp, RcppArmadillo Suggests: AnnotationDbi, Biostrings, rmarkdown, BiocFileCache, ggplot2, ComplexHeatmap, circlize, ggbio, gridExtra, knitr, testthat, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, ExperimentHub (>= 1.15.3), DESeq2, NanoporeRNASeq, purrr, apeglm, utils, DEXSeq Enhances: parallel License: GPL-3 + file LICENSE MD5sum: 1a111d431e08a167b3de53bf5689081d NeedsCompilation: yes Title: Context-Aware Transcript Quantification from Long Read RNA-Seq data Description: bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage. biocViews: Alignment, Coverage, DifferentialExpression, FeatureExtraction, GeneExpression, GenomeAnnotation, GenomeAssembly, ImmunoOncology, LongRead, MultipleComparison, Normalization, RNASeq, Regression, Sequencing, Software, Transcription, Transcriptomics Author: Ying Chen [cre, aut], Andre Sim [aut], Yuk Kei Wan [aut], Jonathan Goeke [aut] Maintainer: Ying Chen URL: https://github.com/GoekeLab/bambu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bambu git_branch: devel git_last_commit: 9a15d51 git_last_commit_date: 2026-01-15 Date/Publication: 2026-04-20 source.ver: src/contrib/bambu_3.13.1.tar.gz vignettes: vignettes/bambu/inst/doc/bambu.html vignetteTitles: bambu hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bambu/inst/doc/bambu.R importsMe: FLAMES suggestsMe: NanoporeRNASeq dependencyCount: 93 Package: BANDITS Version: 1.27.0 Depends: R (>= 4.3.0) Imports: Rcpp, doRNG, MASS, data.table, R.utils, doParallel, parallel, foreach, methods, stats, graphics, ggplot2, DRIMSeq, BiocParallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, tximport, BiocStyle, GenomicFeatures, Biostrings License: GPL (>= 3) MD5sum: e49cda95c6d073b10ad9534bf3ee6ce2 NeedsCompilation: yes Title: BANDITS: Bayesian ANalysis of DIfferenTial Splicing Description: BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via differential transcript usage (DTU), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts. biocViews: DifferentialSplicing, AlternativeSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization Author: Simone Tiberi [aut, cre]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/BANDITS SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/BANDITS/issues git_url: https://git.bioconductor.org/packages/BANDITS git_branch: devel git_last_commit: ed38e4a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BANDITS_1.27.0.tar.gz vignettes: vignettes/BANDITS/inst/doc/BANDITS.html vignetteTitles: BANDITS: Bayesian ANalysis of DIfferenTial Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BANDITS/inst/doc/BANDITS.R importsMe: DifferentialRegulation dependencyCount: 64 Package: Banksy Version: 1.7.0 Depends: R (>= 4.4.0) Imports: aricode, BiocParallel, data.table, dbscan, SpatialExperiment, SingleCellExperiment, SummarizedExperiment, S4Vectors, stats, Matrix, MatrixGenerics, mclust, igraph, irlba, leidenAlg (>= 1.1.0), utils, uwot, RcppHungarian, GenomeInfoDb Suggests: knitr, rmarkdown, pals, scuttle, scater, scran, cowplot, ggplot2, testthat (>= 3.0.0), harmony, Seurat, ExperimentHub, spatialLIBD, BiocStyle License: file LICENSE MD5sum: 4cf71d87707719606feb9151915ff639 NeedsCompilation: no Title: Spatial transcriptomic clustering Description: Banksy is an R package that incorporates spatial information to cluster cells in a feature space (e.g. gene expression). To incorporate spatial information, BANKSY computes the mean neighborhood expression and azimuthal Gabor filters that capture gene expression gradients. These features are combined with the cell's own expression to embed cells in a neighbor-augmented product space which can then be clustered, allowing for accurate and spatially-aware cell typing and tissue domain segmentation. biocViews: Clustering, Spatial, SingleCell, GeneExpression, DimensionReduction Author: Vipul Singhal [aut], Joseph Lee [aut, cre] (ORCID: ) Maintainer: Joseph Lee URL: https://github.com/prabhakarlab/Banksy VignetteBuilder: knitr BugReports: https://github.com/prabhakarlab/Banksy/issues git_url: https://git.bioconductor.org/packages/Banksy git_branch: devel git_last_commit: 4bdc545 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Banksy_1.7.0.tar.gz vignettes: vignettes/Banksy/inst/doc/batch-correction.html, vignettes/Banksy/inst/doc/domain-segment.html, vignettes/Banksy/inst/doc/multi-sample.html, vignettes/Banksy/inst/doc/parameter-selection.html vignetteTitles: Spatial data integration with Harmony (10x Visium Human DLPFC), Domain segmentation (STARmap PLUS mouse brain), Multi-sample analysis (10x Visium Human DLPFC), Parameter selection (VeraFISH Mouse Hippocampus) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Banksy/inst/doc/batch-correction.R, vignettes/Banksy/inst/doc/domain-segment.R, vignettes/Banksy/inst/doc/multi-sample.R, vignettes/Banksy/inst/doc/parameter-selection.R importsMe: OSTA dependencyCount: 110 Package: banocc Version: 1.35.0 Depends: R (>= 3.5.1), rstan (>= 2.17.4) Imports: coda (>= 0.18.1), mvtnorm, stringr Suggests: knitr, rmarkdown, methods, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 410d6b8e1e665108175439273972c65a NeedsCompilation: no Title: Bayesian ANalysis Of Compositional Covariance Description: BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with `rstan`. It provides as output the `stanfit` object as well as posterior median and credible interval estimates for each correlation element. biocViews: ImmunoOncology, Metagenomics, Software, Bayesian Author: Emma Schwager [aut, cre], Curtis Huttenhower [aut] Maintainer: George Weingart , Curtis Huttenhower VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/banocc git_branch: devel git_last_commit: 00fdaad git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/banocc_1.35.0.tar.gz vignettes: vignettes/banocc/inst/doc/banocc-vignette.html vignetteTitles: BAnOCC (Bayesian Analysis of Compositional Covariance) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/banocc/inst/doc/banocc-vignette.R dependencyCount: 60 Package: barbieQ Version: 1.3.0 Depends: R (>= 4.5) Imports: magrittr, tidyr, dplyr, grid, circlize, ComplexHeatmap, ggplot2, logistf, limma, stats, igraph, utils, data.table, S4Vectors, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: GPL-3 MD5sum: 712fd5fb82efbaa37b02d77d0f2eca76 NeedsCompilation: no Title: Analyze Barcode Data from Clonal Tracking Experiments Description: The barbieQ package provides a series of robust statistical tools for analysing barcode count data generated from cell clonal tracking (i.e., lineage tracing) experiments. In these experiments, an initial cell and its offspring collectively form a clone (i.e., lineage). A unique barcode sequence, incorporated into the DNA of the inital cell, is inherited within the clone. This one-to-one mapping of barcodes to clones enables clonal tracking of their behaviors. By counting barcodes, researchers can quantify the population abundance of individual clones under specific experimental perturbations. barbieQ supports barcode count data preprocessing, statistical testing, and visualization. biocViews: Sequencing, Software, Regression, Preprocessing, Visualization Author: Liyang Fei [aut, cre] (ORCID: ) Maintainer: Liyang Fei URL: https://github.com/Oshlack/barbieQ/issues VignetteBuilder: knitr BugReports: https://github.com/Oshlack/barbieQ git_url: https://git.bioconductor.org/packages/barbieQ git_branch: devel git_last_commit: 7a0202b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/barbieQ_1.3.0.tar.gz vignettes: vignettes/barbieQ/inst/doc/barbieQ.html vignetteTitles: Quick start to barbieQ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/barbieQ/inst/doc/barbieQ.R dependencyCount: 114 Package: BaseSpaceR Version: 1.55.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: c972d67535dc05768e845d4be4a67332 NeedsCompilation: no Title: R SDK for BaseSpace RESTful API Description: A rich R interface to Illumina's BaseSpace cloud computing environment, enabling the fast development of data analysis and visualisation tools. biocViews: Infrastructure, DataRepresentation, ConnectTools, Software, DataImport, HighThroughputSequencing, Sequencing, Genetics Author: Adrian Alexa Maintainer: Jared O'Connell git_url: https://git.bioconductor.org/packages/BaseSpaceR git_branch: devel git_last_commit: 04f5512 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BaseSpaceR_1.55.0.tar.gz vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf vignetteTitles: BaseSpaceR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.R dependencyCount: 4 Package: Basic4Cseq Version: 1.47.0 Depends: R (>= 3.5.0), Biostrings, GenomicAlignments, caTools, GenomicRanges, grDevices, graphics, stats, utils Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 773d84be935498d17326e31ffa583add NeedsCompilation: no Title: Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data Description: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile. biocViews: ImmunoOncology, Visualization, QualityControl, Sequencing, Coverage, Alignment, RNASeq, SequenceMatching, DataImport Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Basic4Cseq git_branch: devel git_last_commit: fe75bd1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Basic4Cseq_1.47.0.tar.gz vignettes: vignettes/Basic4Cseq/inst/doc/vignette.pdf vignetteTitles: Basic4Cseq: an R/Bioconductor package for the analysis of 4C-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Basic4Cseq/inst/doc/vignette.R dependencyCount: 61 Package: BASiCS Version: 2.23.2 Depends: R (>= 4.1), SingleCellExperiment Imports: Biobase, BiocGenerics, coda, cowplot, ggExtra, ggplot2, graphics, grDevices, MASS, methods, Rcpp (>= 0.11.3), S4Vectors, scran, scuttle, stats, stats4, SummarizedExperiment, viridis, utils, Matrix (>= 1.5.0), matrixStats, assertthat, reshape2, BiocParallel, posterior, hexbin LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, magick License: GPL-3 MD5sum: c5c679b8cd9889f3eb78abe1d8372697 NeedsCompilation: yes Title: Bayesian Analysis of Single-Cell Sequencing data Description: Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology, ImmunoOncology Author: Catalina Vallejos [aut, cre] (ORCID: ), Nils Eling [aut], Alan O'Callaghan [aut], Sylvia Richardson [ctb], John Marioni [ctb] Maintainer: Catalina Vallejos URL: https://github.com/catavallejos/BASiCS VignetteBuilder: knitr BugReports: https://github.com/catavallejos/BASiCS/issues git_url: https://git.bioconductor.org/packages/BASiCS git_branch: devel git_last_commit: 743d262 git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/BASiCS_2.23.2.tar.gz vignettes: vignettes/BASiCS/inst/doc/BASiCS.html vignetteTitles: Introduction to BASiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BASiCS/inst/doc/BASiCS.R dependsOnMe: BASiCStan suggestsMe: splatter dependencyCount: 129 Package: BasicSTARRseq Version: 1.39.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,Seqinfo,stats Suggests: knitr License: LGPL-3 MD5sum: d1433ff35fac16bc37c5bc3f00e9ee81 NeedsCompilation: no Title: Basic peak calling on STARR-seq data Description: Basic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17. biocViews: PeakDetection, GeneRegulation, FunctionalPrediction, FunctionalGenomics, Coverage Author: Annika Buerger Maintainer: Annika Buerger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BasicSTARRseq git_branch: devel git_last_commit: 9023bc9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BasicSTARRseq_1.39.0.tar.gz vignettes: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.pdf vignetteTitles: BasicSTARRseq.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.R dependencyCount: 42 Package: basilisk.utils Version: 1.23.1 Imports: utils, methods, tools, dir.expiry Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 29a01de34adb12de52a14a78976c7bf7 NeedsCompilation: no Title: Centralized Conda Installation for Bioconductor Packages Description: Provides a centralized conda installation for use by other Bioconductor packages. If conda is not already available on the system, it is downloaded and installed from the Miniforge project; otherwise, no action is performed. Historically, this package was used to provide a Python installation for basilisk, hence the name. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basilisk.utils git_branch: devel git_last_commit: 863ddfb git_last_commit_date: 2025-11-08 Date/Publication: 2026-04-20 source.ver: src/contrib/basilisk.utils_1.23.1.tar.gz vignettes: vignettes/basilisk.utils/inst/doc/purpose.html vignetteTitles: conda for Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basilisk.utils/inst/doc/purpose.R importsMe: HiCool, scifer suggestsMe: Ibex dependencyCount: 5 Package: batchCorr Version: 1.1.0 Depends: R (>= 4.4.0), SummarizedExperiment Imports: reshape, mclust, BiocParallel, methods Suggests: BiocStyle, knitr, testthat License: GPL-2 MD5sum: 64a3c611d7f486a4d88b5bd192c0b654 NeedsCompilation: no Title: Within And Between Batch Correction Of LC-MS Metabolomics Data Description: From the perspective of metabolites as the continuation of the central dogma of biology, metabolomics provides the closest link to many phenotypes of interest. This makes metabolomics research promising in teasing apart the complexities of living systems. However, due to experimental reasons, the data includes non-biological variation which limits quality and reproducibility, especially if the data is obtained from several batches. The batchCorr package reduces unwanted variation by way of between-batch alignment, within-batch drift correction and between-batch normalization using batch-specific quality control samples and long-term reference QC samples. Please see the associated article for more thorough descriptions of algorithms. biocViews: BiomedicalInformatics, Metabolomics, MassSpectrometry, BatchEffect, Normalization, QualityControl Author: Anton Ribbenstedt [cre] (ORCID: ), Carl Brunius [aut] (ORCID: ), Vilhelm Suksi [aut] Maintainer: Anton Ribbenstedt URL: https://github.com/MetaboComp/batchCorr VignetteBuilder: knitr BugReports: https://github.com/MetaboComp/batchCorr/issues git_url: https://git.bioconductor.org/packages/batchCorr git_branch: devel git_last_commit: e89ddb8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/batchCorr_1.1.0.tar.gz vignettes: vignettes/batchCorr/inst/doc/Introduction.html vignetteTitles: Introduction.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/batchCorr/inst/doc/Introduction.R suggestsMe: notameViz dependencyCount: 39 Package: batchelor Version: 1.27.1 Depends: SingleCellExperiment Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats, methods, utils, igraph, BiocNeighbors, BiocSingular, Matrix, SparseArray, DelayedArray (>= 0.31.5), DelayedMatrixStats, BiocParallel, scuttle, ResidualMatrix, ScaledMatrix, beachmat LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, scran, scater, bluster, scRNAseq License: GPL-3 MD5sum: 8115de415c0181e69398a05327200968 NeedsCompilation: yes Title: Single-Cell Batch Correction Methods Description: Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression values. Functions are also provided to perform global rescaling to remove differences in depth between batches, and to perform a principal components analysis that is robust to differences in the numbers of cells across batches. biocViews: Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Normalization Author: Aaron Lun [aut, cre], Laleh Haghverdi [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/batchelor git_branch: devel git_last_commit: a9c93ec git_last_commit_date: 2026-04-09 Date/Publication: 2026-04-20 source.ver: src/contrib/batchelor_1.27.1.tar.gz vignettes: vignettes/batchelor/inst/doc/correction.html, vignettes/batchelor/inst/doc/extension.html vignetteTitles: 1. Correcting batch effects, 2. Extending methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/batchelor/inst/doc/correction.R, vignettes/batchelor/inst/doc/extension.R dependsOnMe: OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: chevreulProcess, ChromSCape, mumosa, scMerge, singleCellTK suggestsMe: anglemania, TSCAN, Canek, RaceID dependencyCount: 56 Package: BatchSVG Version: 1.3.5 Depends: R (>= 4.5.0) Imports: scry, dplyr, stats, rlang, cowplot, ggrepel, ggplot2, RColorBrewer, scales, SummarizedExperiment Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, spatialLIBD License: Artistic-2.0 MD5sum: 933c0a2dcfeeff7cd26b6045641694ad NeedsCompilation: no Title: Identify Batch-Biased Spatially Variable Genes Description: BatchSVG is a method to identify batch-biased spatially variable genes (SVGs) in spatial transcriptomics data. The batch variable can be defined as sample, donor sex, or other batch effects of interest. The BatchSVG method is based on the binomial deviance model (Townes et al, 2019). biocViews: Spatial, Transcriptomics, BatchEffect, QualityControl Author: Christine Hou [aut] (ORCID: ), Kinnary Shah [aut, cre], Jacqueline R. Thompson [aut], Stephanie C. Hicks [aut, fnd] (ORCID: ) Maintainer: Kinnary Shah URL: https://github.com/christinehou11/BatchSVG, https://christinehou11.github.io/BatchSVG VignetteBuilder: knitr BugReports: https://github.com/christinehou11/BatchSVG/issues git_url: https://git.bioconductor.org/packages/BatchSVG git_branch: devel git_last_commit: 58fa855 git_last_commit_date: 2026-03-04 Date/Publication: 2026-04-20 source.ver: src/contrib/BatchSVG_1.3.5.tar.gz vignettes: vignettes/BatchSVG/inst/doc/spe.html vignetteTitles: Tutorial for spe object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BatchSVG/inst/doc/spe.R dependencyCount: 71 Package: Battlefield Version: 0.99.2 Depends: R (>= 4.6) Imports: stats, RANN, dplyr, SummarizedExperiment, methods Suggests: BiocStyle, knitr, markdown, rmarkdown, SpatialExperiment, SpatialExperimentIO, VisiumIO, ggplot2, pheatmap, pals, OSTA.data, tidyr, STexampleData, testthat (>= 3.0.0), codetools, grid, tools License: CeCILL | file LICENSE MD5sum: 197c07e59ed2322d58865ad32a69fb56 NeedsCompilation: no Title: Swiss-army toolkit for selecting niche fronts and invasive margins in spatial transcriptomics data Description: Battlefield is a Swiss-army toolkit originally developed to define and extract spatial spots from specific tissue regions—such as front regions, niche borders, invasive margins, and cluster interfaces—using spatial transcriptomics data or clustered tissue maps. It has since been extended to support trajectory selection and layer inspection, and now provides a collection of low-level utilities for spatial transcriptomics analysis. These utilities are primarily intended to be reused within higher-level analytical packages. It is designed to work with sequencing-based platforms such as Visium at several resolutions and Visium HD(binned). biocViews: Sequencing, Software, Transcriptomics, Spatial Author: Jean-Philippe Villemin [aut, cre] (ORCID: ), European Research Council [fnd] (ERC-2022) Maintainer: Jean-Philippe Villemin URL: https://github.com/ZheFrench/BattleField, https://zhefrench.github.io/Battlefield/ VignetteBuilder: knitr BugReports: https://github.com/ZheFrench/BattleField/issues git_url: https://git.bioconductor.org/packages/Battlefield git_branch: devel git_last_commit: ee2a4c6 git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/Battlefield_0.99.2.tar.gz vignettes: vignettes/Battlefield/inst/doc/Battlefield-Main.html vignetteTitles: Battlefield-Main hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Battlefield/inst/doc/Battlefield-Main.R dependencyCount: 40 Package: BayesKnockdown Version: 1.37.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 MD5sum: 50ec1e6f4d738c599965b673f18fa484 NeedsCompilation: no Title: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data Description: A simple, fast Bayesian method for computing posterior probabilities for relationships between a single predictor variable and multiple potential outcome variables, incorporating prior probabilities of relationships. In the context of knockdown experiments, the predictor variable is the knocked-down gene, while the other genes are potential targets. Can also be used for differential expression/2-class data. biocViews: NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: William Chad Young Maintainer: William Chad Young git_url: https://git.bioconductor.org/packages/BayesKnockdown git_branch: devel git_last_commit: f1134c1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BayesKnockdown_1.37.0.tar.gz vignettes: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.pdf vignetteTitles: BayesKnockdown.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.R dependencyCount: 7 Package: BayesSpace Version: 1.21.2 Depends: R (>= 4.0.0), SingleCellExperiment Imports: Rcpp (>= 1.0.4.6), stats, methods, purrr, scater, scran, SummarizedExperiment, coda, rhdf5, S4Vectors, Matrix, magrittr, assertthat, arrow, mclust, RCurl, DirichletReg, xgboost (>= 3.0.0), utils, dplyr, rlang, ggplot2, tibble, rjson, tidyr, scales, microbenchmark, BiocFileCache, BiocSingular, BiocParallel LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress Suggests: testthat, knitr, rmarkdown, igraph, spatialLIBD, viridis, patchwork, RColorBrewer, Seurat License: MIT + file LICENSE MD5sum: 6b6cf77e054ceaf3b048a49060fa2068 NeedsCompilation: yes Title: Clustering and Resolution Enhancement of Spatial Transcriptomes Description: Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed. biocViews: Software, Clustering, Transcriptomics, GeneExpression, SingleCell, ImmunoOncology, DataImport Author: Edward Zhao [aut], Senbai Kang [aut, cre], Matt Stone [aut], Xing Ren [ctb], Raphael Gottardo [ctb] Maintainer: Senbai Kang URL: edward130603.github.io/BayesSpace SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/edward130603/BayesSpace/issues git_url: https://git.bioconductor.org/packages/BayesSpace git_branch: devel git_last_commit: ba8b422 git_last_commit_date: 2026-01-08 Date/Publication: 2026-04-20 source.ver: src/contrib/BayesSpace_1.21.2.tar.gz vignettes: vignettes/BayesSpace/inst/doc/BayesSpace.html vignetteTitles: BayesSpace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BayesSpace/inst/doc/BayesSpace.R importsMe: RegionalST, OSTA dependencyCount: 148 Package: bayNorm Version: 1.29.0 Depends: R (>= 3.5), Imports: Rcpp (>= 0.12.12), BB, foreach, iterators, doSNOW, Matrix, parallel, MASS, locfit, fitdistrplus, stats, methods, graphics, grDevices, SingleCellExperiment, SummarizedExperiment, BiocParallel, utils LinkingTo: Rcpp, RcppArmadillo,RcppProgress Suggests: knitr, rmarkdown, BiocStyle, devtools, testthat License: GPL (>= 2) MD5sum: 5402998e02832ea93a1b7ce3b35eb8bc NeedsCompilation: yes Title: Single-cell RNA sequencing data normalization Description: bayNorm is used for normalizing single-cell RNA-seq data. biocViews: ImmunoOncology, Normalization, RNASeq, SingleCell,Sequencing Author: Wenhao Tang [aut, cre], Franois Bertaux [aut], Philipp Thomas [aut], Claire Stefanelli [aut], Malika Saint [aut], Samuel Marguerat [aut], Vahid Shahrezaei [aut] Maintainer: Wenhao Tang URL: https://github.com/WT215/bayNorm VignetteBuilder: knitr BugReports: https://github.com/WT215/bayNorm/issues git_url: https://git.bioconductor.org/packages/bayNorm git_branch: devel git_last_commit: afe0e44 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/bayNorm_1.29.0.tar.gz vignettes: vignettes/bayNorm/inst/doc/bayNorm.html vignetteTitles: Introduction to bayNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bayNorm/inst/doc/bayNorm.R dependencyCount: 50 Package: baySeq Version: 2.45.0 Depends: R (>= 2.3.0), methods Imports: edgeR, GenomicRanges, abind, parallel, graphics, stats, utils Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 0d3453e1df10fd6bf0102fbd3132055e NeedsCompilation: no Title: Empirical Bayesian analysis of patterns of differential expression in count data Description: This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE, Bayesian, Coverage Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/baySeq BugReports: https://github.com/samgg/baySeq/issues git_url: https://git.bioconductor.org/packages/baySeq git_branch: devel git_last_commit: 98e102a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/baySeq_2.45.0.tar.gz vignettes: vignettes/baySeq/inst/doc/baySeq_generic.pdf, vignettes/baySeq/inst/doc/baySeq.pdf vignetteTitles: Advanced baySeq analyses, baySeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/baySeq/inst/doc/baySeq_generic.R, vignettes/baySeq/inst/doc/baySeq.R dependsOnMe: clusterSeq, segmentSeq importsMe: riboSeqR dependencyCount: 20 Package: BBCAnalyzer Version: 1.41.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: b1a124cd0a053e003ec0554182b3f828 NeedsCompilation: no Title: BBCAnalyzer: an R/Bioconductor package for visualizing base counts Description: BBCAnalyzer is a package for visualizing the relative or absolute number of bases, deletions and insertions at defined positions in sequence alignment data available as bam files in comparison to the reference bases. Markers for the relative base frequencies, the mean quality of the detected bases, known mutations or polymorphisms and variants called in the data may additionally be included in the plots. biocViews: Sequencing, Alignment, Coverage, GeneticVariability, SNP Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BBCAnalyzer git_branch: devel git_last_commit: 7637b40 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BBCAnalyzer_1.41.0.tar.gz vignettes: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.pdf vignetteTitles: Using BBCAnalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.R dependencyCount: 77 Package: BCRANK Version: 1.73.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 MD5sum: 2fc08d26dc923ab8e89432a7be52bf28 NeedsCompilation: yes Title: Predicting binding site consensus from ranked DNA sequences Description: Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search biocViews: MotifDiscovery, GeneRegulation Author: Adam Ameur Maintainer: Adam Ameur git_url: https://git.bioconductor.org/packages/BCRANK git_branch: devel git_last_commit: 9b14843 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BCRANK_1.73.0.tar.gz vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf vignetteTitles: BCRANK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BCRANK/inst/doc/BCRANK.R dependencyCount: 15 Package: bcSeq Version: 1.33.0 Depends: R (>= 3.4.0) Imports: Rcpp (>= 0.12.12), Matrix, Biostrings LinkingTo: Rcpp, Matrix Suggests: knitr License: GPL (>= 2) MD5sum: 31d7cdf6b828a4ca9549093cebcae919 NeedsCompilation: yes Title: Fast Sequence Mapping in High-Throughput shRNA and CRISPR Screens Description: This Rcpp-based package implements a highly efficient data structure and algorithm for performing alignment of short reads from CRISPR or shRNA screens to reference barcode library. Sequencing error are considered and matching qualities are evaluated based on Phred scores. A Bayes' classifier is employed to predict the originating barcode of a read. The package supports provision of user-defined probability models for evaluating matching qualities. The package also supports multi-threading. biocViews: ImmunoOncology, Alignment, CRISPR, Sequencing, SequenceMatching, MultipleSequenceAlignment, Software, ATACSeq Author: Jiaxing Lin [aut, cre], Jeremy Gresham [aut], Jichun Xie [aut], Kouros Owzar [aut], Tongrong Wang [ctb], So Young Kim [ctb], James Alvarez [ctb], Jeffrey S. Damrauer [ctb], Scott Floyd [ctb], Joshua Granek [ctb], Andrew Allen [ctb], Cliburn Chan [ctb] Maintainer: Jiaxing Lin URL: https://github.com/jl354/bcSeq VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/bcSeq git_branch: devel git_last_commit: 7bf3e3b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/bcSeq_1.33.0.tar.gz vignettes: vignettes/bcSeq/inst/doc/bcSeq.pdf vignetteTitles: bcSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bcSeq/inst/doc/bcSeq.R dependencyCount: 19 Package: beachmat Version: 2.27.5 Imports: methods, DelayedArray (>= 0.27.2), SparseArray, BiocGenerics, Matrix, Rcpp LinkingTo: Rcpp, assorthead (>= 1.5.4) Suggests: testthat, BiocStyle, knitr, rmarkdown, rcmdcheck, BiocParallel, HDF5Array, beachmat.hdf5 License: GPL-3 MD5sum: 3404ff8c9e923d9d96de48b5d57f4906 NeedsCompilation: yes Title: Compiling Bioconductor to Handle Each Matrix Type Description: Provides a consistent C++ class interface for reading from a variety of commonly used matrix types. Ordinary matrices and several sparse/dense Matrix classes are directly supported, along with a subset of the delayed operations implemented in the DelayedArray package. All other matrix-like objects are supported by calling back into R. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre], Hervé Pagès [aut], Mike Smith [aut] Maintainer: Aaron Lun URL: https://github.com/tatami-inc/beachmat SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/tatami-inc/beachmat/issues git_url: https://git.bioconductor.org/packages/beachmat git_branch: devel git_last_commit: 1218c18 git_last_commit_date: 2026-04-09 Date/Publication: 2026-04-20 source.ver: src/contrib/beachmat_2.27.5.tar.gz vignettes: vignettes/beachmat/inst/doc/linking.html vignetteTitles: Developer guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat/inst/doc/linking.R importsMe: batchelor, beachmat.hdf5, beachmat.tiledb, BiocSingular, bsseq, DropletUtils, glmGamPoi, mumosa, omicsGMF, PCAtools, scater, scran, scrapper, scuttle, SingleR suggestsMe: BiocNeighbors, mbkmeans, scCB2, methFuse linksToMe: beachmat.hdf5, beachmat.tiledb, BiocNeighbors, BiocSingular, bsseq, dreamlet, DropletUtils, glmGamPoi, mbkmeans, PCAtools, scran, scrapper, scuttle, SingleR dependencyCount: 23 Package: beachmat.tiledb Version: 1.3.0 Imports: methods, beachmat, tiledb, TileDBArray, DelayedArray, Rcpp LinkingTo: Rcpp, assorthead, beachmat Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix License: GPL-3 MD5sum: 8057c1992aef48365455c848e6f76e73 NeedsCompilation: yes Title: beachmat bindings for TileDB-backed matrices Description: Extends beachmat to initialize tatami matrices from TileDB-backed arrays. This allows C++ code in downstream packages to directly call the TileDB C/C++ library to access array data, without the need for block processing via DelayedArray. Developers only need to import this package to automatically extend the capabilities of beachmat::initializeCpp to TileDBArray instances. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/tatami-inc/beachmat.tiledb SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/tatami-inc/beachmat.tiledb/issues git_url: https://git.bioconductor.org/packages/beachmat.tiledb git_branch: devel git_last_commit: df0fa94 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/beachmat.tiledb_1.3.0.tar.gz vignettes: vignettes/beachmat.tiledb/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat.tiledb/inst/doc/userguide.R dependencyCount: 36 Package: beadarray Version: 2.61.3 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), hexbin Imports: limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, methods, ggplot2, BeadDataPackR Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, knitr License: MIT + file LICENSE MD5sum: d91f3db30ba21b37b2d569cf7cee5bab NeedsCompilation: yes Title: Quality assessment and low-level analysis for Illumina BeadArray data Description: The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided. biocViews: Microarray, OneChannel, QualityControl, Preprocessing Author: Mark Dunning, Mike Smith, Jonathan Cairns, Andy Lynch, Matt Ritchie Maintainer: Mark Dunning VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beadarray git_branch: devel git_last_commit: 7dd13bd git_last_commit_date: 2026-03-30 Date/Publication: 2026-04-20 source.ver: src/contrib/beadarray_2.61.3.tar.gz vignettes: vignettes/beadarray/inst/doc/beadarray.html, vignettes/beadarray/inst/doc/beadlevel.html, vignettes/beadarray/inst/doc/beadsummary.html, vignettes/beadarray/inst/doc/ImageProcessing.html vignetteTitles: Introduction to beadarray, Analysis of Bead-level Data using beadarray, Analysis of bead-summary data, Image Analysis with beadarray hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/beadarray/inst/doc/beadarray.R, vignettes/beadarray/inst/doc/beadlevel.R, vignettes/beadarray/inst/doc/beadsummary.R, vignettes/beadarray/inst/doc/ImageProcessing.R dependsOnMe: beadarrayExampleData importsMe: arrayQualityMetrics, blima, epigenomix, BeadArrayUseCases suggestsMe: lumi, blimaTestingData, maGUI dependencyCount: 65 Package: BeadDataPackR Version: 1.63.0 Imports: stats, utils Suggests: BiocStyle, knitr License: GPL-2 MD5sum: d58470595ce0b46af46130d2a1f691d1 NeedsCompilation: yes Title: Compression of Illumina BeadArray data Description: Provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform. biocViews: Microarray Author: Mike Smith, Andy Lynch Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BeadDataPackR git_branch: devel git_last_commit: e497075 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BeadDataPackR_1.63.0.tar.gz vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf vignetteTitles: BeadDataPackR.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.R importsMe: beadarray dependencyCount: 2 Package: BEAT Version: 1.49.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: 5957be27b767faa040efc183c032fbba NeedsCompilation: no Title: BEAT - BS-Seq Epimutation Analysis Toolkit Description: Model-based analysis of single-cell methylation data biocViews: ImmunoOncology, Genetics, MethylSeq, Software, DNAMethylation, Epigenetics Author: Kemal Akman Maintainer: Kemal Akman git_url: https://git.bioconductor.org/packages/BEAT git_branch: devel git_last_commit: 3a301bb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BEAT_1.49.0.tar.gz vignettes: vignettes/BEAT/inst/doc/BEAT.pdf vignetteTitles: Analysing single-cell BS-Seq data with the "BEAT" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEAT/inst/doc/BEAT.R dependencyCount: 70 Package: BEclear Version: 2.27.1 Depends: BiocParallel (>= 1.14.2) Imports: logger, Rdpack, Matrix, data.table (>= 1.11.8), Rcpp, abind, stats, graphics, utils, methods, dixonTest, ids LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, pander, seewave License: GPL-3 MD5sum: b20e709aa2eb40b31ebe72652de1245f NeedsCompilation: yes Title: Correction of batch effects in DNA methylation data Description: Provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers. biocViews: BatchEffect, DNAMethylation, Software, Preprocessing, StatisticalMethod Author: Livia Rasp [aut, cre] (ORCID: ), Markus Merl [aut], Ruslan Akulenko [aut] Maintainer: Livia Rasp URL: https://github.com/uds-helms/BEclear SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/uds-helms/BEclear/issues git_url: https://git.bioconductor.org/packages/BEclear git_branch: devel git_last_commit: 50d96b6 git_last_commit_date: 2026-01-16 Date/Publication: 2026-04-20 source.ver: src/contrib/BEclear_2.27.1.tar.gz vignettes: vignettes/BEclear/inst/doc/BEclear.html vignetteTitles: BEclear tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BEclear/inst/doc/BEclear.R dependencyCount: 31 Package: BERT Version: 1.7.0 Depends: R (>= 4.3.0) Imports: cluster, comprehenr, foreach (>= 1.5.2), invgamma, iterators (>= 1.0.14), janitor (>= 2.2.0), limma (>= 3.46.0), logging (>= 0.10-108), sva (>= 3.38.0), SummarizedExperiment, methods, BiocParallel Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 0c07e9871cdf40737be76c3e69791ab1 NeedsCompilation: no Title: High Performance Data Integration for Large-Scale Analyses of Incomplete Omic Profiles Using Batch-Effect Reduction Trees (BERT) Description: Provides efficient batch-effect adjustment of data with missing values. BERT orders all batch effect correction to a tree of pairwise computations. BERT allows parallelization over sub-trees. biocViews: BatchEffect, Preprocessing, ExperimentalDesign, QualityControl Author: Yannis Schumann [aut, cre] (ORCID: ), Simon Schlumbohm [aut] (ORCID: ) Maintainer: Yannis Schumann URL: https://github.com/HSU-HPC/BERT/ VignetteBuilder: knitr BugReports: https://github.com/HSU-HPC/BERT/issues git_url: https://git.bioconductor.org/packages/BERT git_branch: devel git_last_commit: 2e49878 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BERT_1.7.0.tar.gz vignettes: vignettes/BERT/inst/doc/BERT-Vignette.html vignetteTitles: BERT-Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BERT/inst/doc/BERT-Vignette.R dependencyCount: 97 Package: betaHMM Version: 1.7.0 Depends: R (>= 4.3.0), SummarizedExperiment, S4Vectors, GenomicRanges Imports: stats, ggplot2, scales, methods, pROC, foreach, doParallel, parallel, cowplot, dplyr, tidyr, tidyselect, stringr, utils Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-3 MD5sum: 902b733f43e79142624b6076bd759643 NeedsCompilation: no Title: A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data Description: A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites. biocViews: DNAMethylation, DifferentialMethylation, ImmunoOncology, BiomedicalInformatics, MethylationArray, Software, MultipleComparison, Sequencing, Spatial, Coverage, GeneTarget, HiddenMarkovModel, Microarray Author: Koyel Majumdar [cre, aut] (ORCID: ), Romina Silva [aut], Antoinette Sabrina Perry [aut], Ronald William Watson [aut], Isobel Claire Gorley [aut] (ORCID: ), Thomas Brendan Murphy [aut] (ORCID: ), Florence Jaffrezic [aut], Andrea Rau [aut] (ORCID: ) Maintainer: Koyel Majumdar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/betaHMM git_branch: devel git_last_commit: 3efe81d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/betaHMM_1.7.0.tar.gz vignettes: vignettes/betaHMM/inst/doc/betaHMM.html vignetteTitles: betaHMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/betaHMM/inst/doc/betaHMM.R dependencyCount: 61 Package: betterChromVAR Version: 0.99.37 Depends: SummarizedExperiment Imports: BiocParallel, Biostrings, GenomicRanges, IRanges, Matrix, matrixStats, methods, S4Vectors, stats Suggests: BiocStyle, knitr, rmarkdown, sessioninfo, testthat License: GPL (>= 3) MD5sum: 5cc712b97082877e69e4152cea463176 NeedsCompilation: no Title: Improved ChromVAR (Chromatin Variation Across Regions) Description: A much faster analytical implementation of chromVAR, with additional features, used to infer TF activity from (bulk or single-cell) ATAC-seq data and motif annotations (or binding probabilities). The package also includes the CVnorm normalization method based on the chromVAR logic. biocViews: Software, ATACSeq, Normalization, Epigenetics, Sequencing Author: Pierre-Luc Germain [aut, cre] (ORCID: ) Maintainer: Pierre-Luc Germain URL: https://github.com/plger/betterChromVAR VignetteBuilder: knitr BugReports: https://github.com/plger/betterChromVAR/issues git_url: https://git.bioconductor.org/packages/betterChromVAR git_branch: devel git_last_commit: d8cbd8e git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/betterChromVAR_0.99.37.tar.gz vignettes: vignettes/betterChromVAR/inst/doc/betterChromVAR.html vignetteTitles: Introduction to betterChromVAR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/betterChromVAR/inst/doc/betterChromVAR.R dependencyCount: 37 Package: bettr Version: 1.7.1 Depends: R (>= 4.4.0) Imports: dplyr (>= 1.0), tidyr, ggplot2 (>= 3.4.1), shiny (>= 1.6), tibble, ComplexHeatmap, bslib, rlang, circlize, stats, grid, methods, cowplot, Hmisc, sortable, shinyjqui, grDevices, scales, DT, SummarizedExperiment, S4Vectors, jsonlite, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: MIT + file LICENSE MD5sum: 23e1a6d6f10004ce96b93854800c8a90 NeedsCompilation: no Title: A Better Way To Explore What Is Best Description: bettr provides a set of interactive visualization methods to explore the results of a benchmarking study, where typically more than a single performance measures are computed. The user can weight the performance measures according to their preferences. Performance measures can also be grouped and aggregated according to additional annotations. biocViews: Visualization, ShinyApps, GUI Author: Federico Marini [aut] (ORCID: ), Charlotte Soneson [aut, cre] (ORCID: ), Daniel Incicau [aut] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/federicomarini/bettr VignetteBuilder: knitr BugReports: https://github.com/federicomarini/bettr/issues git_url: https://git.bioconductor.org/packages/bettr git_branch: devel git_last_commit: 4877911 git_last_commit_date: 2025-11-16 Date/Publication: 2026-04-20 source.ver: src/contrib/bettr_1.7.1.tar.gz vignettes: vignettes/bettr/inst/doc/bettr.html, vignettes/bettr/inst/doc/server-mode.html vignetteTitles: bettr, Server Mode Guide for bettr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bettr/inst/doc/bettr.R, vignettes/bettr/inst/doc/server-mode.R dependencyCount: 123 Package: BG2 Version: 1.11.0 Depends: R (>= 4.2.0) Imports: GA (>= 3.2), caret (>= 6.0-86), memoise (>= 1.1.0), Matrix (>= 1.2-18), MASS (>= 7.3-58.1), stats (>= 4.2.2) Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: 81c5515691ad9666ce06942ba3b392d3 NeedsCompilation: no Title: Performs Bayesian GWAS analysis for non-Gaussian data using BG2 Description: This package is built to perform GWAS analysis for non-Gaussian data using BG2. The BG2 method uses penalized quasi-likelihood along with nonlocal priors in a two step manner to identify SNPs in GWAS analysis. The research related to this package was supported in part by National Science Foundation awards DMS 1853549 and DMS 2054173. biocViews: Bayesian, AssayDomain, SNP, GenomeWideAssociation Author: Jacob Williams [aut, cre] (ORCID: ), Shuangshuang Xu [aut], Marco Ferreira [aut] (ORCID: ) Maintainer: Jacob Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BG2 git_branch: devel git_last_commit: 7d14422 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BG2_1.11.0.tar.gz vignettes: vignettes/BG2/inst/doc/BG2.html vignetteTitles: BG2 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BG2/inst/doc/BG2.R dependencyCount: 89 Package: BicARE Version: 1.69.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase, GO.db Imports: methods Suggests: hgu95av2 License: GPL-2 MD5sum: 9a0324bf35cfd0c5802457c74ebe612e NeedsCompilation: yes Title: Biclustering Analysis and Results Exploration Description: Biclustering Analysis and Results Exploration. biocViews: Microarray, Transcription, Clustering Author: Pierre Gestraud Maintainer: Pierre Gestraud URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/BicARE git_branch: devel git_last_commit: 8dec4bf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BicARE_1.69.0.tar.gz vignettes: vignettes/BicARE/inst/doc/BicARE.pdf vignetteTitles: BicARE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BicARE/inst/doc/BicARE.R importsMe: miRSM dependencyCount: 55 Package: BiFET Version: 1.31.0 Depends: R (>= 3.5.0) Imports: stats, poibin, GenomicRanges Suggests: rmarkdown, testthat, knitr License: GPL-3 MD5sum: ce4233331f72acedfc218e7117f052ca NeedsCompilation: no Title: Bias-free Footprint Enrichment Test Description: BiFET identifies TFs whose footprints are over-represented in target regions compared to background regions after correcting for the bias arising from the imbalance in read counts and GC contents between the target and background regions. For a given TF k, BiFET tests the null hypothesis that the target regions have the same probability of having footprints for the TF k as the background regions while correcting for the read count and GC content bias. For this, we use the number of target regions with footprints for TF k, t_k as a test statistic and calculate the p-value as the probability of observing t_k or more target regions with footprints under the null hypothesis. biocViews: ImmunoOncology, Genetics, Epigenetics, Transcription, GeneRegulation, ATACSeq, DNaseSeq, RIPSeq, Software Author: Ahrim Youn [aut, cre], Eladio Marquez [aut], Nathan Lawlor [aut], Michael Stitzel [aut], Duygu Ucar [aut] Maintainer: Ahrim Youn VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiFET git_branch: devel git_last_commit: 0dec18c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BiFET_1.31.0.tar.gz vignettes: vignettes/BiFET/inst/doc/BiFET.html vignetteTitles: "A Guide to using BiFET" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiFET/inst/doc/BiFET.R dependencyCount: 12 Package: BindingSiteFinder Version: 2.9.0 Depends: GenomicRanges, R (>= 4.2) Imports: tidyr, tibble, plyr, matrixStats, stats, ggplot2, methods, rtracklayer, S4Vectors, ggforce, GenomeInfoDb, ComplexHeatmap, RColorBrewer, lifecycle, rlang, forcats, dplyr, GenomicFeatures, IRanges, kableExtra, ggdist Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicAlignments, scales, Gviz, xlsx, GGally, patchwork, viridis, ggplotify, SummarizedExperiment, DESeq2, ggpointdensity, ggrastr, ashr, txdbmaker, ggrepel, stringr License: Artistic-2.0 MD5sum: fa8d036e6dbe6cd8cc231d590a6ca845 NeedsCompilation: no Title: Binding site defintion based on iCLIP data Description: Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette. biocViews: Sequencing, GeneExpression, GeneRegulation, FunctionalGenomics, Coverage, DataImport Author: Mirko Brüggemann [aut, cre] (ORCID: ), Melina Klostermann [aut] (ORCID: ), Kathi Zarnack [aut] (ORCID: ) Maintainer: Mirko Brüggemann VignetteBuilder: knitr BugReports: https://github.com/ZarnackGroup/BindingSiteFinder/issues git_url: https://git.bioconductor.org/packages/BindingSiteFinder git_branch: devel git_last_commit: 885c4f6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BindingSiteFinder_2.9.0.tar.gz vignettes: vignettes/BindingSiteFinder/inst/doc/vignette.html vignetteTitles: Definition of binding sites from iCLIP signal hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BindingSiteFinder/inst/doc/vignette.R dependencyCount: 140 Package: bioassayR Version: 1.49.0 Depends: R (>= 3.5.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods, Matrix, rjson, BiocGenerics (>= 0.13.8) Imports: XML, ChemmineR Suggests: BiocStyle, RCurl, biomaRt, knitr, knitcitations, knitrBootstrap, testthat, ggplot2, rmarkdown License: Artistic-2.0 MD5sum: 35f1f455387c05a004f3ad0924ff9d51 NeedsCompilation: no Title: Cross-target analysis of small molecule bioactivity Description: bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data. biocViews: ImmunoOncology, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics, Metabolomics Author: Tyler Backman, Ronly Schlenk, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/bioassayR VignetteBuilder: knitr BugReports: https://github.com/girke-lab/bioassayR/issues git_url: https://git.bioconductor.org/packages/bioassayR git_branch: devel git_last_commit: dff449a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/bioassayR_1.49.0.tar.gz vignettes: vignettes/bioassayR/inst/doc/bioassayR.html vignetteTitles: bioassayR Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R dependencyCount: 74 Package: Biobase Version: 2.71.0 Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets, BiocStyle, knitr, limma License: Artistic-2.0 MD5sum: fd2bdbfb2d3c46dd0b241481193916c6 NeedsCompilation: yes Title: Biobase: Base functions for Bioconductor Description: Functions that are needed by many other packages or which replace R functions. biocViews: Infrastructure Author: R. Gentleman [aut], V. Carey [aut], M. Morgan [aut], S. Falcon [aut], Haleema Khan [ctb] ('esApply' and 'BiobaseDevelopment' vignette translation from Sweave to Rmarkdown / HTML), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Biobase VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Biobase/issues git_url: https://git.bioconductor.org/packages/Biobase git_branch: devel git_last_commit: 94f68ab git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Biobase_2.71.0.tar.gz vignettes: vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf, vignettes/Biobase/inst/doc/BiobaseDevelopment.html, vignettes/Biobase/inst/doc/esApply.html vignetteTitles: An introduction to Biobase and ExpressionSets, Notes for eSet developers, esApply Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biobase/inst/doc/BiobaseDevelopment.R, vignettes/Biobase/inst/doc/esApply.R, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.R dependsOnMe: ACME, affy, affycomp, affyContam, affycoretools, affyPLM, AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi, AnnotationForge, ArrayExpress, arrayMvout, BAGS, bandle, beadarray, BicARE, bigmelon, BioMVCClass, BioQC, BLMA, borealis, CAMERA, cancerclass, casper, Category, categoryCompare, CCPROMISE, CGHbase, CGHcall, CGHregions, clippda, clusterStab, CMA, cn.farms, codelink, convert, copa, covEB, covRNA, CytoMDS, DEXSeq, DFP, diggit, doppelgangR, DSS, dyebias, EBarrays, EDASeq, edge, EGSEA, epigenomix, epivizrData, ExiMiR, ExpressionAtlas, fabia, factDesign, fastseg, flowBeads, frma, gaga, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, geNetClassifier, GeoDiff, GeomxTools, GEOquery, GOexpress, goProfiles, GOstats, GSEABase, GSEABenchmarkeR, GSEAlm, GWASTools, hapFabia, HELP, hopach, HTqPCR, HybridMTest, iCheck, IdeoViz, idiogram, INSPEcT, isobar, iterativeBMA, IVAS, lumi, made4, massiR, MEAL, metagenomeSeq, MethPed, methylumi, Mfuzz, MiChip, microbiomeExplorer, mimager, MiRaGE, miRcomp, MLInterfaces, MMDiff2, monocle, MSnbase, Mulcom, MultiDataSet, multtest, NanoStringDiff, NanoStringNCTools, NanoTube, NOISeq, nondetects, normalize450K, NormqPCR, octad, oligo, omicRexposome, OrderedList, OTUbase, pandaR, panp, pcaMethods, pdInfoBuilder, pepStat, phenoTest, PLPE, POWSC, PREDA, pRolocGUI, PROMISE, qpcrNorm, qPLEXanalyzer, R453Plus1Toolbox, RbcBook1, rbsurv, rcellminer, ReadqPCR, rexposome, Rmagpie, Rnits, RTopper, RUVSeq, safe, SCAN.UPC, SeqGSEA, SigCheck, siggenes, singleCellTK, SpeCond, SPEM, spkTools, splineTimeR, SummarizedExperiment, tigre, tilingArray, topGO, TPP, tRanslatome, UNDO, VegaMC, viper, vsn, wateRmelon, webbioc, XDE, yarn, EuPathDB, affycompData, ALL, bcellViper, beadarrayExampleData, bladderbatch, brgedata, cancerdata, CCl4, CLL, colonCA, CRCL18, curatedBreastData, curatedCRCData, curatedOvarianData, davidTiling, diggitdata, DLBCL, dressCheck, etec16s, fabiaData, fibroEset, gaschYHS, golubEsets, GSE103322, GSE13015, GSE62944, GSVAdata, harbChIP, HumanAffyData, humanStemCell, Iyer517, kidpack, leeBamViews, leukemiasEset, lumiBarnes, lungExpression, MAQCsubset, MetaGxBreast, MetaGxOvarian, miRNATarget, msd16s, mvoutData, Neve2006, PREDAsampledata, ProData, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, pumadata, rcellminerData, RUVnormalizeData, SpikeInSubset, TCGAcrcmiRNA, TCGAcrcmRNA, tweeDEseqCountData, yeastCC, coreheat, crmn, eLNNpairedCov, GWASbyCluster, heatmapFlex, lmQCM, MM2Sdata, MMDvariance, propOverlap importsMe: a4Base, a4Classif, a4Core, a4Preproc, ABarray, ACE, aCGH, adSplit, affyILM, AgiMicroRna, ANF, annmap, annotate, AnnotationHubData, annotationTools, arrayQualityMetrics, attract, ballgown, BASiCS, BayesKnockdown, BgeeDB, biobroom, bioCancer, biocViews, BioNet, biosigner, biscuiteer, BiSeq, blima, bnem, BreastSubtypeR, BSgenomeForge, bsseq, CAFE, canceR, Cardinal, CellTrails, cfdnakit, CGHnormaliter, ChIPXpress, ChromHeatMap, cicero, clipper, CluMSID, cn.mops, COCOA, cogena, combi, CompoundDb, ConsensusClusterPlus, consensusOV, coRdon, CoreGx, crlmm, cyanoFilter, cycle, cydar, CytoML, DAPAR, ddCt, DEGreport, DESeq2, destiny, DExMA, discordant, easyRNASeq, EBarrays, ecolitk, EGAD, ensembldb, EpiMix, esetVis, ExiMiR, ffpe, findIPs, flowClust, flowCore, flowFP, flowMatch, flowMeans, flowSpecs, flowStats, flowViz, flowWorkspace, FRASER, frma, frmaTools, gCrisprTools, gcrma, gemma.R, geneClassifiers, GeneExpressionSignature, genefilter, GeneMeta, geneRecommender, GeneRegionScan, GENESIS, GenomicInteractions, GenomicScores, GenomicSuperSignature, GEOsubmission, gep2pep, ggbio, GlobalAncova, globaltest, gmapR, GSRI, GSVA, Gviz, HEM, hermes, HTqPCR, HTSFilter, infinityFlow, IsoformSwitchAnalyzeR, isomiRs, katdetectr, kissDE, LiquidAssociation, LRBaseDbi, makecdfenv, MAPFX, maSigPro, MAST, mastR, mBPCR, MeSHDbi, metaseqR2, MethylAid, methylCC, methylclock, methylumi, MiChip, microbiomeDASim, minfi, MinimumDistance, MiPP, MIRA, miRSM, missMethyl, MLSeq, mogsa, Moonlight2R, MoonlightR, MSnID, MultiAssayExperiment, MultiRNAflow, multiscan, mzR, npGSEA, nucleR, OAtools, oligoClasses, omicade4, omicsViewer, ontoProc, openCyto, oposSOM, oppar, OrganismDbi, panp, phantasus, phantasusLite, PharmacoGx, phyloseq, piano, plgem, plier, podkat, prebs, PrInCE, proBatch, progeny, pRoloc, PROMISE, PRONE, PROPS, Prostar, protGear, ptairMS, puma, PureCN, pvac, pvca, qcmetrics, QDNAseq, QFeatures, qpgraph, quantiseqr, quantro, QuasR, qusage, RadioGx, randPack, ReactomeGSA, RIVER, Rmagpie, RMassBank, RNAseqCovarImpute, roastgsa, rols, ropls, ROTS, rqubic, rScudo, Rtpca, RUVnormalize, scmap, scTGIF, SeqVarTools, shinyMethyl, ShortRead, SigsPack, sigsquared, singscore, sitadela, sketchR, SmartPhos, SomaticSignatures, SpatialDecon, SpatialFeatureExperiment, SpatialOmicsOverlay, spkTools, SplineDV, SPONGE, standR, STATegRa, subSeq, TDbasedUFEadv, TEQC, TFBSTools, tidyFlowCore, timecourse, TMixClust, TnT, topdownr, ToxicoGx, tradeSeq, TTMap, twilight, txdbmaker, uSORT, VanillaICE, variancePartition, VariantAnnotation, VariantFiltering, VariantTools, vidger, wateRmelon, wpm, xcms, Xeva, BloodCancerMultiOmics2017, DeSousa2013, DExMAdata, Fletcher2013a, GSE13015, hgu133plus2CellScore, Hiiragi2013, IHWpaper, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, mcsurvdata, pRolocdata, seqc, signatureSearchData, ExpressionNormalizationWorkflow, GeoMxWorkflows, AnnoProbe, bapred, CIARA, ClassComparison, ClassDiscovery, D4TAlink.light, FMradio, geneExpressionFromGEO, GSEMA, IntegratedJM, maGUI, nlcv, NMF, PCAPAM50, RCPA, RobLox, RPPanalyzer, SCdeconR, scPOEM, ssizeRNA, TailRank suggestsMe: AUCell, autonomics, BiocGenerics, CellMapper, clustComp, ClusterGVis, coseq, cypress, dar, DART, dcanr, dearseq, DeconvoBuddies, DspikeIn, edgeR, EnMCB, EpiDISH, epivizr, epivizrChart, epivizrStandalone, genefu, GENIE3, GenomicPlot, GenomicRanges, GSAR, GSgalgoR, Heatplus, kebabs, les, limma, M3Drop, mCSEA, messina, mitology, MOSim, msa, multiClust, OSAT, pathMED, PCAtools, PLSDAbatch, RFGeneRank, ribosomeProfilingQC, ROC, RTCGA, scater, scmeth, SeqArray, sparrow, spatialHeatmap, stageR, survcomp, TargetScore, TCGAbiolinks, TFutils, tkWidgets, TOP, vbmp, widgetTools, biotmleData, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, dorothea, dyebiasexamples, HMP16SData, HMP2Data, mammaPrintData, mtbls2, RegParallel, rheumaticConditionWOLLBOLD, seventyGeneData, yeastExpData, yeastRNASeq, amap, aroma.affymetrix, BaseSet, CimpleG, clValid, CrossValidate, distrDoc, GenAlgo, ggpicrust2, hexbin, HTSCluster, isatabr, MetabolSSMF, mi4p, Modeler, multiclassPairs, NACHO, omicsTools, ordinalbayes, Patterns, rsconnect, Seurat, sigminer, SomaDataIO, tinyarray dependencyCount: 6 Package: biobtreeR Version: 1.23.0 Imports: httr, httpuv, stringi,jsonlite,methods,utils Suggests: BiocStyle, knitr,testthat,rmarkdown,markdown License: MIT + file LICENSE MD5sum: 906fcf762029f7f16678eeeb0f43db06 NeedsCompilation: no Title: Using biobtree tool from R Description: The biobtreeR package provides an interface to [biobtree](https://github.com/tamerh/biobtree) tool which covers large set of bioinformatics datasets and allows search and chain mappings functionalities. biocViews: Annotation Author: Tamer Gur Maintainer: Tamer Gur URL: https://github.com/tamerh/biobtreeR VignetteBuilder: knitr BugReports: https://github.com/tamerh/biobtreeR/issues git_url: https://git.bioconductor.org/packages/biobtreeR git_branch: devel git_last_commit: 5023269 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/biobtreeR_1.23.0.tar.gz vignettes: vignettes/biobtreeR/inst/doc/biobtreeR.html vignetteTitles: The biobtreeR users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biobtreeR/inst/doc/biobtreeR.R dependencyCount: 23 Package: Bioc.gff Version: 1.1.0 Depends: R (>= 4.5.0) Imports: BiocBaseUtils, BiocGenerics, BiocIO, curl, GenomicRanges, IRanges, methods, Rsamtools, S4Vectors, Seqinfo, stats, utils, XVector LinkingTo: S4Vectors, XVector, IRanges Suggests: BiocFileCache, BiocStyle, GenomicFeatures, GenomeInfoDbData, knitr, httr2, rmarkdown, rvest, tinytest, txdbmaker, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 45d830f555e3b1c51d2c05c3daa3b60b NeedsCompilation: yes Title: Read and write GFF and GTF files Description: Parse GFF and GTF files using C++ classes. The package also provides utilities to read and write GFF3 files. The GFF (General Feature Format) format is a tab-delimited file format for describing genes and other features of DNA, RNA, and protein sequences. GFF files are often used to describe the features of genomes. biocViews: Software, Infrastructure, DataImport Author: Michael Lawrence [aut], Hervé Pagès [aut], Marcel Ramos [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/Bioc.gff VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Bioc.gff/issues git_url: https://git.bioconductor.org/packages/Bioc.gff git_branch: devel git_last_commit: a3d3863 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Bioc.gff_1.1.0.tar.gz vignettes: vignettes/Bioc.gff/inst/doc/Bioc.gff.html vignetteTitles: Bioc.gff: GFF3 File Format Support hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Bioc.gff/inst/doc/Bioc.gff.R suggestsMe: TCGAutils dependencyCount: 32 Package: BioCartaImage Version: 1.9.1 Depends: R (>= 4.3.0) Imports: magick, grid, stats, grDevices, utils Suggests: testthat, knitr, BiocStyle, ragg License: MIT + file LICENSE MD5sum: a88014c3b7d8f85df5a5598e3eb157b6 NeedsCompilation: no Title: BioCarta Pathway Images Description: The core functionality of the package is to provide coordinates of genes on the BioCarta pathway images and to provide methods to add self-defined graphics to the genes of interest. biocViews: Software, Pathways, BioCarta, Visualization Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/BioCartaImage VignetteBuilder: knitr BugReports: https://github.com/jokergoo/BioCartaImage/issues git_url: https://git.bioconductor.org/packages/BioCartaImage git_branch: devel git_last_commit: 19fc97f git_last_commit_date: 2026-01-30 Date/Publication: 2026-04-20 source.ver: src/contrib/BioCartaImage_1.9.1.tar.gz vignettes: vignettes/BioCartaImage/inst/doc/BioCartaImage.html vignetteTitles: Customize BioCarta Pathway Images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioCartaImage/inst/doc/BioCartaImage.R dependencyCount: 9 Package: BiocBaseUtils Version: 1.13.0 Depends: R (>= 4.2.0) Imports: methods, utils Suggests: knitr, rmarkdown, BiocStyle, tinytest License: Artistic-2.0 MD5sum: ab4f851410862d880fd9576c2f293c24 NeedsCompilation: no Title: General utility functions for developing Bioconductor packages Description: The package provides utility functions related to package development. These include functions that replace slots, and selectors for show methods. It aims to coalesce the various helper functions often re-used throughout the Bioconductor ecosystem. biocViews: Software, Infrastructure Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [ctb], Hervé Pagès [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://www.github.com/Bioconductor/BiocBaseUtils/issues git_url: https://git.bioconductor.org/packages/BiocBaseUtils git_branch: devel git_last_commit: d510c94 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocBaseUtils_1.13.0.tar.gz vignettes: vignettes/BiocBaseUtils/inst/doc/BiocBaseUtils.html vignetteTitles: BiocBaseUtils Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocBaseUtils/inst/doc/BiocBaseUtils.R importsMe: AlphaMissenseR, AnnotationHub, AnVIL, AnVILAz, AnVILGCP, AnVILPublish, Bioc.gff, BiocCheck, BiocFHIR, BiocPkgDash, cBioPortalData, DNAfusion, GCPtools, GenomicFiles, HistoImagePlot, imageFeatureTCGA, imageTCGAutils, iSEEfier, looking4clusters, MultiAssayExperiment, RaggedExperiment, scGraphVerse, TCGAutils, TENxIO, UniProt.ws, VisiumIO, visiumStitched, XeniumIO, SingleCellMultiModal suggestsMe: scifer dependencyCount: 2 Package: BiocBook Version: 1.9.0 Depends: R (>= 4.3) Imports: BiocGenerics, pak, cli, glue, gert, gh, gitcreds, httr, usethis, dplyr, purrr, tibble, methods, rprojroot, stringr, yaml, tools, utils, rlang, quarto, renv Suggests: BiocStyle, knitr, testthat (>= 3.0.0), rmarkdown License: MIT + file LICENSE MD5sum: 869589d14c8d776649254aba5a67efee NeedsCompilation: no Title: Write, containerize, publish and version Quarto books with Bioconductor Description: A BiocBook can be created by authors (e.g. R developers, but also scientists, teachers, communicators, ...) who wish to 1) write (compile a body of biological and/or bioinformatics knowledge), 2) containerize (provide Docker images to reproduce the examples illustrated in the compendium), 3) publish (deploy an online book to disseminate the compendium), and 4) version (automatically generate specific online book versions and Docker images for specific Bioconductor releases). biocViews: Infrastructure, ReportWriting, Software Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://bioconductor.org/packages/BiocBook VignetteBuilder: knitr BugReports: https://github.com/js2264/BiocBook/issues git_url: https://git.bioconductor.org/packages/BiocBook git_branch: devel git_last_commit: fed98cf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocBook_1.9.0.tar.gz vignettes: vignettes/BiocBook/inst/doc/BiocBook.html vignetteTitles: BiocBook hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocBook/inst/doc/BiocBook.R dependencyCount: 72 Package: BiocBuildReporter Version: 0.99.2 Depends: R (>= 4.6.0) Imports: arrow, dplyr, BiocFileCache Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown, ggplot2, tidyr, stringr License: Apache License (>= 2) MD5sum: 04c5532e68d27119ca07a2880b8b11d9 NeedsCompilation: no Title: Functions to process a bioconductor build report database Description: This package reads remote parquet files that have processed Bioconductor build report logs. Users may query the tables directly for specific information or use pre-defined helper functions for common queries. The logs processed are from https://bioconductor.org/checkResults/. In the future we will extend this package out to include processing of r-universe logs. biocViews: Software, Infrastructure Author: Sean Davis [aut], Lori Shepherd [aut, cre] (ORCID: ) Maintainer: Lori Shepherd URL: https://github.com/lshep/BiocBuildReporter.git VignetteBuilder: knitr BugReports: https://github.com/lshep/BiocBuildReporter/issues git_url: https://git.bioconductor.org/packages/BiocBuildReporter git_branch: devel git_last_commit: 2a578e4 git_last_commit_date: 2026-03-16 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocBuildReporter_0.99.2.tar.gz vignettes: vignettes/BiocBuildReporter/inst/doc/BiocBuildReporter.html vignetteTitles: BiocBuildReporter Data Use Cases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocBuildReporter/inst/doc/BiocBuildReporter.R dependencyCount: 45 Package: BiocFHIR Version: 1.13.0 Depends: R (>= 4.2) Imports: DT, shiny, jsonlite, graph, tidyr, visNetwork, dplyr, utils, methods, BiocBaseUtils Suggests: knitr, testthat, rjsoncons, igraph, BiocStyle License: Artistic-2.0 MD5sum: 8412fccb76c9a36d1f594ba11632b5a4 NeedsCompilation: no Title: Illustration of FHIR ingestion and transformation using R Description: FHIR R4 bundles in JSON format are derived from https://synthea.mitre.org/downloads. Transformation inspired by a kaggle notebook published by Dr Alexander Scarlat, https://www.kaggle.com/code/drscarlat/fhir-starter-parse-healthcare-bundles-into-tables. This is a very limited illustration of some basic parsing and reorganization processes. Additional tooling will be required to move beyond the Synthea data illustrations. biocViews: Infrastructure, DataImport, DataRepresentation Author: Vincent Carey [aut, cre] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/BiocFHIR VignetteBuilder: knitr BugReports: https://github.com/vjcitn/BiocFHIR/issues git_url: https://git.bioconductor.org/packages/BiocFHIR git_branch: devel git_last_commit: 4c84357 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocFHIR_1.13.0.tar.gz vignettes: vignettes/BiocFHIR/inst/doc/A_upper.html, vignettes/BiocFHIR/inst/doc/B_handling.html, vignettes/BiocFHIR/inst/doc/BiocFHIR.html, vignettes/BiocFHIR/inst/doc/C_tables.html, vignettes/BiocFHIR/inst/doc/D_linking.html vignetteTitles: Upper level FHIR concepts, Handling FHIR documents with BiocFHIR, BiocFHIR -- infrastructure for parsing and analyzing FHIR data, Transforming FHIR documents to tables with BiocFHIR, Linking information between FHIR resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFHIR/inst/doc/A_upper.R, vignettes/BiocFHIR/inst/doc/B_handling.R, vignettes/BiocFHIR/inst/doc/BiocFHIR.R, vignettes/BiocFHIR/inst/doc/C_tables.R, vignettes/BiocFHIR/inst/doc/D_linking.R dependencyCount: 65 Package: BiocFileCache Version: 3.1.0 Depends: R (>= 3.4.0), dbplyr (>= 1.0.0) Imports: methods, stats, utils, dplyr, RSQLite, DBI, filelock, curl, httr2 Suggests: testthat, knitr, BiocStyle, rmarkdown, rtracklayer License: Artistic-2.0 MD5sum: 186a9b7fd208c8ba802040b44fd0006a NeedsCompilation: no Title: Manage Files Across Sessions Description: This package creates a persistent on-disk cache of files that the user can add, update, and retrieve. It is useful for managing resources (such as custom Txdb objects) that are costly or difficult to create, web resources, and data files used across sessions. biocViews: DataImport Author: Lori Shepherd [aut, cre], Martin Morgan [aut] Maintainer: Lori Shepherd VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocFileCache/issues git_url: https://git.bioconductor.org/packages/BiocFileCache git_branch: devel git_last_commit: c4f8ba6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocFileCache_3.1.0.tar.gz vignettes: vignettes/BiocFileCache/inst/doc/BiocFileCache_Troubleshooting.html, vignettes/BiocFileCache/inst/doc/BiocFileCache_UseCases.html, vignettes/BiocFileCache/inst/doc/BiocFileCache.html vignetteTitles: 3. BiocFileCache Troubleshooting, 2. BiocFileCache: Use Cases, 1. BiocFileCache Overview: Managing File Resources Across Sessions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFileCache/inst/doc/BiocFileCache_Troubleshooting.R, vignettes/BiocFileCache/inst/doc/BiocFileCache_UseCases.R, vignettes/BiocFileCache/inst/doc/BiocFileCache.R dependsOnMe: AnnotationHub, easylift, ExperimentHub, RcwlPipelines, JASPAR2022, JASPAR2024, scATAC.Explorer, TMExplorer, csawBook, OSCA.basic, OSCA.intro, OSCA.workflows importsMe: AlphaMissenseR, AMARETTO, atSNP, autonomics, BayesSpace, bedbaser, BiocBuildReporter, BiocCheck, BiocHail, BiocPkgTools, biomaRt, brendaDb, bugsigdbr, BulkSignalR, cbaf, cBioPortalData, CBNplot, CellBench, CTDquerier, customCMPdb, CytoPipeline, DeconvoBuddies, easyRNASeq, enhancerHomologSearch, EnMCB, EnrichmentBrowser, EpiTxDb, fenr, fgga, GeDi, GenomicScores, GenomicSuperSignature, ggkegg, GSEABenchmarkeR, gwascat, imageFeatureTCGA, iSEEindex, MBQN, MIRit, MotifPeeker, MsBackendMetaboLights, msPurity, OmicsMLRepoR, ontoProc, ORFik, OSTA.data, PhIPData, PlinkMatrix, PMScanR, postNet, psichomics, rBLAST, recount3, recountmethylation, regutools, ReUseData, RiboDiPA, rpx, scviR, sesame, signeR, SMTrackR, spacexr, SpatialExperiment, SpatialOmicsOverlay, SpliceImpactR, SpliceWiz, SurfR, tenXplore, terraTCGAdata, TFutils, tidyexposomics, tomoseqr, toppgene, tximeta, UMI4Cats, UniProt.ws, waddR, xenLite, geneplast.data, HPO.db, MPO.db, org.Mxanthus.db, PANTHER.db, BioPlex, bugphyzz, depmap, DNAZooData, fourDNData, HiContactsData, HumanRetinaLRSData, MetaScope, MicrobiomeBenchmarkData, NxtIRFdata, orthosData, SFEData, SingleCellMultiModal, spatialLIBD, OSTA, convertid suggestsMe: anndataR, AnnotationForge, bambu, Bioc.gff, BiocSet, ChIPpeakAnno, CoGAPS, CRISPRseek, dominoSignal, EpiCompare, fastreeR, GRaNIE, HicAggR, HiCDCPlus, HiCExperiment, HiCool, iscream, MethReg, MetMashR, Nebulosa, nipalsMCIA, progeny, qsvaR, seqsetvis, spatialHeatmap, structToolbox, TCGAutils, TREG, visiumStitched, XeniumIO, zellkonverter, emtdata, EMTscoreData, HighlyReplicatedRNASeq, MethylSeqData, msigdb, TENxBrainData, TENxPBMCData, chipseqDB, fluentGenomics, simpleSingleCell, scCustomize dependencyCount: 42 Package: BiocGenerics Version: 0.57.1 Depends: R (>= 4.0.0), methods, utils, graphics, stats, generics Imports: methods, utils, graphics, stats Suggests: Biobase, S4Vectors, IRanges, S4Arrays, SparseArray, DelayedArray, HDF5Array, GenomicRanges, pwalign, Rsamtools, AnnotationDbi, affy, affyPLM, DESeq2, flowClust, MSnbase, annotate, MultiAssayExperiment, RUnit License: Artistic-2.0 MD5sum: c6f83aadac64b5877e678350faed4cb7 NeedsCompilation: no Title: S4 generic functions used in Bioconductor Description: The package defines many S4 generic functions used in Bioconductor. biocViews: Infrastructure Author: The Bioconductor Dev Team [aut], Hervé Pagès [aut, cre] (ORCID: ), Laurent Gatto [ctb] (ORCID: ), Nathaniel Hayden [ctb], James Hester [ctb], Wolfgang Huber [ctb], Michael Lawrence [ctb], Martin Morgan [ctb] (ORCID: ), Valerie Obenchain [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/BiocGenerics BugReports: https://github.com/Bioconductor/BiocGenerics/issues git_url: https://git.bioconductor.org/packages/BiocGenerics git_branch: devel git_last_commit: 48c3a6f git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocGenerics_0.57.1.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, affyPLM, altcdfenvs, amplican, AnnotationDbi, AnnotationForge, AnnotationHub, ATACseqQC, beadarray, bioassayR, Biobase, Biostrings, bnbc, BSgenome, BSgenomeForge, bsseq, Cardinal, Category, categoryCompare, chipseq, ChIPseqR, ChromHeatMap, cigarillo, clusterExperiment, codelink, consensusSeekeR, CoreGx, CRISPRseek, DelayedArray, ensembldb, ExperimentHub, ExperimentHubData, GDSArray, geneplotter, GenomeInfoDb, genomeIntervals, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges, GenomicScores, ggbio, graph, GSEABase, GUIDEseq, h5mread, HelloRanges, IRanges, ISLET, MBASED, MGnifyR, minfi, MLInterfaces, MotifDb, mpra, MSnbase, multtest, NADfinder, ngsReports, oligo, OrganismDbi, plyranges, pwalign, PWMEnrich, QSutils, RareVariantVis, REDseq, RnBeads, RPA, rsbml, S4Arrays, S4Vectors, Seqinfo, ShortRead, SparseArray, spqn, StructuralVariantAnnotation, svaNUMT, svaRetro, TEQC, tigre, topdownr, topGO, txdbmaker, UNDO, updateObject, VanillaICE, VariantAnnotation, VariantFiltering, VCFArray, XVector, yamss, ChAMPdata, liftOver, rsolr importsMe: a4Preproc, affycoretools, affylmGUI, alabaster.bumpy, alabaster.files, alabaster.matrix, alabaster.ranges, alabaster.se, AllelicImbalance, annmap, annoLinker, annotate, AnnotationHubData, ASpli, ATACseqTFEA, atena, AUCell, autonomics, bambu, bamsignals, BASiCS, batchelor, beachmat, bigmelon, Bioc.gff, BiocBook, biocGraph, BiocHail, BiocIO, BiocSingular, biotmle, biovizBase, biscuiteer, BiSeq, blima, breakpointR, BrowserViz, bumphunter, BUSpaRse, CAGEfightR, CAGEr, casper, celaref, CellBench, CellMixS, CellTrails, cfDNAPro, cghMCR, ChemmineOB, ChemmineR, chipenrich, ChIPpeakAnno, ChIPseeker, chipseq, cicero, CircSeqAlignTk, CleanUpRNAseq, clusterSeq, cn.mops, CNEr, CNVPanelizer, CNVRanger, COCOA, cola, compEpiTools, CompoundDb, concordexR, crisprBase, crisprBowtie, crisprBwa, crisprDesign, crisprScore, crisprShiny, crisprViz, crlmm, csaw, CTexploreR, CuratedAtlasQueryR, cydar, dada2, dagLogo, DAMEfinder, dandelionR, ddCt, decompTumor2Sig, deconvR, DegCre, DEGreport, DelayedDataFrame, demuxSNP, derfinder, DEScan2, DESeq2, DESpace, destiny, DEWSeq, DEXSeq, DFplyr, diffcoexp, diffHic, dinoR, DirichletMultinomial, DiscoRhythm, DNAfusion, DOTSeq, dreamlet, DRIMSeq, DropletUtils, DrugVsDisease, easyRNASeq, EBImage, EDASeq, eiR, eisaR, ELViS, enhancerHomologSearch, EnrichDO, epialleleR, EpiCompare, epigenomix, epimutacions, epiSeeker, epistack, EpiTxDb, epivizrChart, epivizrStandalone, esATAC, factR, FamAgg, fastseg, ffpe, FindIT2, FLAMES, flowBin, flowClust, flowCore, flowFP, FlowSOM, flowSpecs, flowStats, flowWorkspace, fmcsR, FRASER, frma, GA4GHclient, GA4GHshiny, gcapc, gDNAx, geneAttribution, geneClassifiers, GENESIS, GenomAutomorphism, GenomicAlignments, GenomicInteractions, GenomicPlot, GenomicTuples, GenVisR, geomeTriD, GeomxTools, glmGamPoi, gmapR, gmoviz, goseq, GOTHiC, GSVA, Gviz, HDF5Array, heatmaps, hermes, HicAggR, HiCDOC, HiCExperiment, HiContacts, HiCParser, HiLDA, hopach, icetea, igvR, igvShiny, IHW, infercnv, INSPEcT, intansv, InteractionSet, IntEREst, IONiseR, iSEE, IsoformSwitchAnalyzeR, isomiRs, IVAS, KCsmart, ldblock, lefser, lemur, lisaClust, LOLA, maaslin3, mariner, maser, MAST, matter, MEAL, meshr, MetaboAnnotation, metaMS, metaseqR2, methInheritSim, MethylAid, methylPipe, methylumi, mia, miaViz, miloR, mimager, MinimumDistance, MIRA, MiRaGE, missMethyl, mist, mobileRNA, Modstrings, mogsa, monaLisa, monocle, Moonlight2R, Motif2Site, msa, MsBackendSql, MsExperiment, MSnID, MultiAssayExperiment, multicrispr, MultiDataSet, multiMiR, MultimodalExperiment, mumosa, MutationalPatterns, mutscan, MutSeqR, mzR, NanoStringNCTools, nearBynding, notame, notameStats, notameViz, npGSEA, nucleR, oligoClasses, openCyto, openPrimeR, ORFik, OUTRIDER, parati, parglms, pcaMethods, PDATK, pdInfoBuilder, PharmacoGx, PhIPData, PhosR, phyloseq, piano, PIPETS, plyinteractions, podkat, pram, primirTSS, proDA, profileScoreDist, pRoloc, pRolocGUI, ProteoDisco, PSMatch, PureCN, QDNAseq, QFeatures, qPLEXanalyzer, qsea, QTLExperiment, QuasR, R3CPET, R453Plus1Toolbox, RadioGx, raer, RaggedExperiment, ramr, ramwas, RCAS, RCy3, RCyjs, recoup, ReducedExperiment, REMP, ReportingTools, RGSEA, RiboCrypt, RiboDiPA, ribosomeProfilingQC, RJMCMCNucleosomes, rnaEditr, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RNAseqCovarImpute, roar, Rqc, rqubic, Rsamtools, rsbml, rScudo, RTCGAToolbox, rtracklayer, SanityR, saseR, SC3, SCArray.sat, scater, scDblFinder, scDotPlot, scECODA, scmap, scmeth, SCnorm, SCOPE, scPipe, scran, scruff, scuttle, SEMPLR, SeqVarTools, sevenC, SGSeq, SharedObject, shinyDSP, shinyMethyl, signatureSearch, signeR, signifinder, simPIC, SingleCellExperiment, SingleMoleculeFootprinting, SingleR, sitadela, Site2Target, SNPhood, snpStats, sparrow, SpatialExperiment, SpatialFeatureExperiment, Spectra, splatter, SpliceWiz, SplicingGraphs, SplineDV, sRACIPE, sscu, StabMap, standR, strandCheckR, Structstrings, SummarizedExperiment, SVP, SynMut, systemPipeR, tadar, TAPseq, target, TCGAutils, TCseq, TENxIO, TFBSTools, tidySpatialExperiment, ToxicoGx, trackViewer, transcriptR, transite, TreeSummarizedExperiment, tRNA, tRNAscanImport, TVTB, txcutr, UMI4Cats, unifiedWMWqPCR, UniProt.ws, universalmotif, uSORT, VariantTools, velociraptor, VisiumIO, visiumStitched, wavClusteR, weitrix, xcms, XDE, XeniumIO, XVector, ZarrArray, zitools, CENTREannotation, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, CENTREprecomputed, chipenrich.data, curatedCRCData, curatedOvarianData, gDNAinRNAseqData, IHWpaper, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, microbiomeDataSets, MouseGastrulationData, MouseThymusAgeing, raerdata, scRNAseq, spatialLIBD, systemPipeRdata, TENxBUSData, VariantToolsData, GeoMxWorkflows, crispRdesignR, DCLEAR, EEMDlstm, geno2proteo, hicream, locuszoomr, revert, RNAseqQC, scPOEM, Signac, TaxaNorm, toxpiR, treediff, TSdeeplearning suggestsMe: acde, adverSCarial, aggregateBioVar, AIMS, AlphaMissenseR, ASSET, ASURAT, BaalChIP, baySeq, bigmelon, BiocParallel, BiocStyle, biocViews, biosigner, BLMA, BloodGen3Module, bnem, borealis, BUScorrect, BUSseq, CAFE, CAMERA, CausalR, ccrepe, CDI, cellmigRation, CexoR, chihaya, ChIPanalyser, ChIPXpress, CHRONOS, cleanUpdTSeq, clipper, ClustAll, clustComp, CNORfeeder, CNORfuzzy, consensus, cosmiq, COSNet, cpvSNP, crumblr, cypress, DEsubs, DExMA, DMRcaller, DMRcate, DNAcycP2, DspikeIn, EnhancedVolcano, ENmix, EpiMix, epiNEM, EventPointer, fCCAC, fcScan, fgga, FGNet, flowCut, flowTime, fmrs, GateFinder, gCrisprTools, gdsfmt, GEM, GeneNetworkBuilder, GeneOverlap, geneplast, geneRxCluster, geNetClassifier, genomation, GEOquery, GeoTcgaData, ginmappeR, GMRP, GOstats, GrafGen, GreyListChIP, GWASTools, h5vc, Harman, HiCDCPlus, hierGWAS, HIREewas, HPiP, hypergraph, iCARE, iClusterPlus, IFAA, illuminaio, immunotation, INPower, IPO, kebabs, KEGGREST, LACE, LRDE, MAGAR, magpie, massiR, MatrixQCvis, MatrixRider, MBttest, mCSEA, Mergeomics, MetaboSignal, metagene2, metagenomeSeq, MetCirc, methylCC, methylInheritance, MetNet, microbiome, miRBaseConverter, miRcomp, mirIntegrator, mnem, mosbi, MOSClip, motifStack, MsQuality, multiClust, MultiMed, MultiRNAflow, MungeSumstats, MWASTools, ncRNAtools, nempi, NetSAM, nondetects, nucleoSim, omicsGMF, OMICsPCA, OncoScore, PAA, panelcn.mops, Path2PPI, pathMED, PathNet, pathview, PCAtools, pepXMLTab, powerTCR, proBAMr, qpgraph, quantro, RBGL, rBiopaxParser, RbowtieCuda, rcellminer, rCGH, REBET, RESOLVE, rfaRm, RFGeneRank, RGraph2js, Rgraphviz, rgsepd, riboSeqR, ROntoTools, ropls, ROSeq, RTN, RTNduals, RTNsurvival, rTRM, SAIGEgds, sangerseqR, SANTA, sarks, SCArray, scDataviz, scLANE, scp, screenCounter, scry, segmentSeq, SeqArray, seqPattern, SICtools, sigFeature, sigsquared, SIMAT, similaRpeak, SIMLR, singleCellTK, slingshot, SNPRelate, SparseSignatures, spatialHeatmap, specL, STATegRa, STRINGdb, SUITOR, systemPipeTools, TCC, TFEA.ChIP, TIN, transcriptogramer, traseR, TreeAndLeaf, tripr, tRNAdbImport, TRONCO, Uniquorn, variancePartition, VERSO, XAItest, xcore, zenith, ENCODExplorerData, geneplast.data, ConnectivityMap, FieldEffectCrc, grndata, HarmanData, healthyControlsPresenceChecker, microRNAome, RegParallel, scMultiome, sesameData, xcoredata, adjclust, aroma.affymetrix, ggpicrust2, gkmSVM, GSEMA, inDAGO, MarZIC, pagoda2, polyRAD, Seurat dependencyCount: 5 Package: biocGraph Version: 1.73.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: 6e4c90d7ac373f4a2008bc6be9b515f8 NeedsCompilation: no Title: Graph examples and use cases in Bioinformatics Description: This package provides examples and code that make use of the different graph related packages produced by Bioconductor. biocViews: Visualization, GraphAndNetwork Author: Li Long , Robert Gentleman , Seth Falcon Florian Hahne Maintainer: Florian Hahne git_url: https://git.bioconductor.org/packages/biocGraph git_branch: devel git_last_commit: 68d8be3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/biocGraph_1.73.0.tar.gz vignettes: vignettes/biocGraph/inst/doc/biocGraph.pdf, vignettes/biocGraph/inst/doc/layingOutPathways.pdf vignetteTitles: Examples of plotting graphs Using Rgraphviz, HOWTO layout pathways hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocGraph/inst/doc/biocGraph.R, vignettes/biocGraph/inst/doc/layingOutPathways.R suggestsMe: EnrichmentBrowser dependencyCount: 51 Package: BiocHubsShiny Version: 1.11.0 Depends: R (>= 4.3.0), shiny Imports: AnnotationHub, ExperimentHub, DT, htmlwidgets, rclipboard, S4Vectors, shinyAce, shinybiocloader, shinyjs, shinythemes, utils Suggests: BiocManager, BiocStyle, curl, glue, knitr, rmarkdown, sessioninfo, shinytest2 License: Artistic-2.0 MD5sum: 0c9a6d43b1217b80617b9d5c1324cc46 NeedsCompilation: no Title: View AnnotationHub and ExperimentHub Resources Interactively Description: A package that allows interactive exploration of AnnotationHub and ExperimentHub resources. It uses DT / DataTable to display resources for multiple organisms. It provides template code for reproducibility and for downloading resources via the indicated Hub package. biocViews: Software, ShinyApps Author: Marcel Ramos [aut, cre] (ORCID: ), Vincent Carey [ctb] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/BiocHubsShiny VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocHubsShiny/issues git_url: https://git.bioconductor.org/packages/BiocHubsShiny git_branch: devel git_last_commit: 8c29714 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocHubsShiny_1.11.0.tar.gz vignettes: vignettes/BiocHubsShiny/inst/doc/BiocHubsShiny.html vignetteTitles: BiocHubsShiny Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocHubsShiny/inst/doc/BiocHubsShiny.R dependencyCount: 96 Package: BiocIO Version: 1.21.0 Depends: R (>= 4.3.0) Imports: BiocGenerics, S4Vectors, methods, tools Suggests: testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 88ffb204d22f28e2592964f9429b8702 NeedsCompilation: no Title: Standard Input and Output for Bioconductor Packages Description: The `BiocIO` package contains high-level abstract classes and generics used by developers to build IO funcionality within the Bioconductor suite of packages. Implements `import()` and `export()` standard generics for importing and exporting biological data formats. `import()` supports whole-file as well as chunk-wise iterative import. The `import()` interface optionally provides a standard mechanism for 'lazy' access via `filter()` (on row or element-like components of the file resource), `select()` (on column-like components of the file resource) and `collect()`. The `import()` interface optionally provides transparent access to remote (e.g. via https) as well as local access. Developers can register a file extension, e.g., `.loom` for dispatch from character-based URIs to specific `import()` / `export()` methods based on classes representing file types, e.g., `LoomFile()`. biocViews: Annotation,DataImport Author: Martin Morgan [aut], Michael Lawrence [aut], Daniel Van Twisk [aut], Marcel Ramos [cre] (ORCID: ) Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocIO/issues git_url: https://git.bioconductor.org/packages/BiocIO git_branch: devel git_last_commit: 884e288 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocIO_1.21.0.tar.gz vignettes: vignettes/BiocIO/inst/doc/BiocIO.html vignetteTitles: BiocIO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocIO/inst/doc/BiocIO.R dependsOnMe: BSgenome, HelloRanges, LoomExperiment importsMe: Bioc.gff, BiocSet, BSgenomeForge, gmapR, HiCExperiment, HiContacts, HiCool, imageFeatureTCGA, rtracklayer, TENxIO, tidyCoverage, txdbmaker, VisiumIO, XeniumIO dependencyCount: 9 Package: BiocMaintainerApp Version: 0.99.0 Depends: R (>= 4.6.0) Imports: shiny, jsonlite, DT, shinyjs, shinythemes Suggests: knitr, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: b1b811e20a3c51cd76c65a1f228a0f80 NeedsCompilation: no Title: View Bioconductor Package Maintainer Information Interactively Description: This package allows interactive viewing of package maintainer information. The Bioconductor Package Maintainer Application sends yearly verification emails to accept Bioconductor policies; this application also depicts maintainer status on opting in and if the email is deemed valid. biocViews: Infrastructure, ShinyApps Author: Lori Shepherd [aut, cre] Maintainer: Lori Shepherd URL: https://github.com/lshep/BiocMaintainerApp VignetteBuilder: knitr BugReports: https://github.com/lshep/BiocMaintainerApp/issues git_url: https://git.bioconductor.org/packages/BiocMaintainerApp git_branch: devel git_last_commit: f33c44d git_last_commit_date: 2026-01-23 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocMaintainerApp_0.99.0.tar.gz vignettes: vignettes/BiocMaintainerApp/inst/doc/BiocMaintainerQueries.html, vignettes/BiocMaintainerApp/inst/doc/BiocMaintainerShiny.html vignetteTitles: BiocMaintainerQueries, BiocMaintainerShiny Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocMaintainerApp/inst/doc/BiocMaintainerQueries.R, vignettes/BiocMaintainerApp/inst/doc/BiocMaintainerShiny.R dependencyCount: 49 Package: biocmake Version: 1.3.1 Imports: utils, tools, dir.expiry Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: 3f53804433b0de8781b64b54b8229ef7 NeedsCompilation: no Title: CMake for Bioconductor Description: Manages the installation of CMake for building Bioconductor packages. This avoids the need for end-users to manually install CMake on their system. No action is performed if a suitable version of CMake is already available. biocViews: Infrastructure Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun URL: https://github.com/LTLA/biocmake VignetteBuilder: knitr BugReports: https://github.com/LTLA/biocmake/issues git_url: https://git.bioconductor.org/packages/biocmake git_branch: devel git_last_commit: db9e091 git_last_commit_date: 2025-11-21 Date/Publication: 2026-04-20 source.ver: src/contrib/biocmake_1.3.1.tar.gz vignettes: vignettes/biocmake/inst/doc/userguide.html vignetteTitles: Cmake for Bioconductor hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biocmake/inst/doc/userguide.R linksToMe: Rhdf5lib, Rigraphlib dependencyCount: 4 Package: BiocNeighbors Version: 2.5.4 Imports: Rcpp, methods LinkingTo: Rcpp, assorthead, beachmat Suggests: Matrix, DelayedArray, beachmat, BiocParallel, testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 24d04f88111d6207cbfd2aa03ef16d9e NeedsCompilation: yes Title: Nearest Neighbor Detection for Bioconductor Packages Description: Implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Exact searches can be performed using the k-means for k-nearest neighbors algorithm, vantage point trees, or an exhaustive search. Approximate searches can be performed using the Annoy or HNSW libraries. Each search can be performed with a variety of different distance metrics, parallelization, and variable numbers of neighbors. Range-based searches (to find all neighbors within a certain distance) are also supported. biocViews: Clustering, Classification Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun SystemRequirements: C++17 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocNeighbors git_branch: devel git_last_commit: 2d1b405 git_last_commit_date: 2026-02-12 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocNeighbors_2.5.4.tar.gz vignettes: vignettes/BiocNeighbors/inst/doc/userguide.html vignetteTitles: Finding neighbors in high-dimensional space hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocNeighbors/inst/doc/userguide.R dependsOnMe: OSCA.workflows, SingleRBook importsMe: batchelor, bluster, CellMixS, clustSIGNAL, concordexR, cydar, GeDi, imcRtools, jvecfor, lemur, miloR, mumosa, poem, scater, scDblFinder, scider, scMerge, scrapper, smoothclust, SpatialFeatureExperiment, SpotSweeper, StabMap, SVP, UCell suggestsMe: ClassifyR, scLANE, TrajectoryUtils, TSCAN linksToMe: scrapper dependencyCount: 24 Package: BioCor Version: 1.35.0 Depends: R (>= 4.4) Imports: BiocParallel, GSEABase, Matrix, methods Suggests: airway, BiocStyle, boot, DESeq2, ggplot2 (>= 3.4.1), GOSemSim, Hmisc, knitr (>= 1.43), org.Hs.eg.db, reactome.db, rmarkdown, spelling, testthat (>= 3.1.5), WGCNA License: MIT + file LICENSE MD5sum: 1e55e97e5f4e9389099e39afc989b2ca NeedsCompilation: no Title: Functional Similarities Description: Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships... biocViews: StatisticalMethod, Clustering, GeneExpression, Network, Pathways, NetworkEnrichment, SystemsBiology Author: Lluís Revilla Sancho [aut, cre] (ORCID: ), Pau Sancho-Bru [ths] (ORCID: ), Juan José Salvatella Lozano [ths] (ORCID: ) Maintainer: Lluís Revilla Sancho URL: https://bioconductor.org/packages/BioCor, https://biocor.llrs.dev VignetteBuilder: knitr BugReports: https://github.com/llrs/BioCor/issues git_url: https://git.bioconductor.org/packages/BioCor git_branch: devel git_last_commit: e26910c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BioCor_1.35.0.tar.gz vignettes: vignettes/BioCor/inst/doc/BioCor_1_basics.html, vignettes/BioCor/inst/doc/BioCor_2_advanced.html vignetteTitles: About BioCor, Advanced usage of BioCor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioCor/inst/doc/BioCor_1_basics.R, vignettes/BioCor/inst/doc/BioCor_2_advanced.R dependencyCount: 59 Package: BiocParallel Version: 1.45.0 Depends: methods, R (>= 4.1.0) Imports: stats, utils, futile.logger, parallel, snow, codetools LinkingTo: BH (>= 1.87.0), cpp11 Suggests: BiocGenerics, tools, foreach, BBmisc, doParallel, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, RUnit, BiocStyle, knitr, batchtools, data.table Enhances: Rmpi License: GPL-2 | GPL-3 | BSL-1.0 MD5sum: 15a631ec65d080537fffe2ab28cc9fdd NeedsCompilation: yes Title: Bioconductor facilities for parallel evaluation Description: This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga [aut], Aaron Lun [ctb], Henrik Bengtsson [ctb], Madelyn Carlson [ctb] (Translated 'Random Numbers' vignette from Sweave to RMarkdown / HTML.), Phylis Atieno [ctb] (Translated 'Introduction to BiocParallel' vignette from Sweave to Rmarkdown / HTML.), Sergio Oller [ctb] (Improved bpmapply() efficiency., ORCID: ) Maintainer: Jiefei Wang URL: https://github.com/Bioconductor/BiocParallel SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocParallel/issues git_url: https://git.bioconductor.org/packages/BiocParallel git_branch: devel git_last_commit: 6d29df0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocParallel_1.45.0.tar.gz vignettes: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.html, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.html, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.html, vignettes/BiocParallel/inst/doc/Random_Numbers.html vignetteTitles: 2. Introduction to BatchtoolsParam, 3. Errors,, Logs and Debugging, 1. Introduction to BiocParallel, 4. Random Numbers in BiocParallel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.R, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.R, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.R, vignettes/BiocParallel/inst/doc/Random_Numbers.R dependsOnMe: bacon, BEclear, Cardinal, CardinalIO, Chromatograms, ClassifyR, clusterSeq, consensusSeekeR, DEWSeq, DEXSeq, DMCFB, DMCHMM, doppelgangR, DSS, extraChIPs, FEAST, FRASER, GenomicFiles, INSPEcT, iPath, ISLET, matter, MBASED, metagene2, metapone, ncGTW, Oscope, OUTRIDER, PCAN, periodicDNA, pRoloc, RedisParam, Rqc, ShortRead, SigCheck, Spectra, sva, variancePartition, xcms, sequencing, OSCA.workflows, SingleRBook importsMe: abseqR, ADImpute, AffiXcan, ALDEx2, AlphaBeta, AlpsNMR, amplican, ASICS, ATACseqQC, atena, atSNP, bambu, BANDITS, bandle, Banksy, BASiCS, batchCorr, batchelor, BayesSpace, bayNorm, beer, benchdamic, BERT, betterChromVAR, BioCor, BiocSingular, BioNERO, biotmle, biscuiteer, blase, bluster, brendaDb, bsseq, CAGEfightR, CAGEr, CARDspa, carnation, CBN2Path, ccImpute, CDI, cellbaseR, CellBench, CelliD, CellMentor, CellMixS, censcyt, Cepo, ChIPexoQual, ChromSCape, ClusterFoldSimilarity, clustSIGNAL, CNVMetrics, CNVRanger, CoGAPS, comapr, coMethDMR, CompoundDb, concordexR, condiments, consensusOV, consICA, Coralysis, CoreGx, coseq, cpvSNP, CrispRVariants, crupR, csaw, CTSV, cydar, cypress, CytoGLMM, cytoKernel, cytomapper, CytoMDS, CytoPipeline, damidBind, dcGSA, DeconvoBuddies, decoupleR, DeepTarget, DegCre, DepInfeR, derfinder, DEScan2, DESeq2, DEsingle, DESpace, Dino, DMRcaller, dmrseq, DNEA, DOTSeq, dreamlet, DRIMSeq, DropletUtils, Dune, easier, easyRNASeq, EMDomics, enhancerHomologSearch, epimutacions, epiregulon, epistasisGA, ERSSA, EWCE, factR, faers, fgsea, findIPs, FindIT2, FLAMES, flowcatchR, flowSpecs, GDCRNATools, gDNAx, gDRcore, gDRutils, GeDi, GENESIS, GenomAutomorphism, GenomicAlignments, GloScope, gmapR, gscreend, GSEABenchmarkeR, GSVA, h5vc, HicAggR, HiCBricks, HiCcompare, HiCDOC, HiCExperiment, HiContacts, HTSFilter, HybridExpress, iasva, icetea, ideal, imcRtools, IntEREst, IONiseR, IPO, IsoformSwitchAnalyzeR, jazzPanda, jvecfor, katdetectr, KinSwingR, lcmsPlot, LimROTS, lisaClust, loci2path, LRcell, Macarron, magpie, magrene, mariner, mbkmeans, MCbiclust, MetaboAnnotation, MetaboCoreUtils, metabomxtr, metaseqR2, methodical, MethylAid, methylGSA, methyLImp2, methylInheritance, methylscaper, MetNet, mia, miaViz, MICSQTL, miloR, minfi, MIRit, mist, mixOmics, MOGAMUN, MoleculeExperiment, monaLisa, MotifPeeker, MPAC, MPRAnalyze, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MSnbase, msqrob2, MsQuality, MSstatsResponse, multiHiCcompare, mumosa, muscat, NBAMSeq, nnSVG, notame, notameStats, NPARC, omicsGMF, ORFik, orthos, OVESEG, PAIRADISE, pairedGSEA, pathMED, PCAtools, PDATK, pengls, PharmacoGx, pipeComp, poem, pram, proActiv, ProteoDisco, PSMatch, qpgraph, QRscore, qsea, QuasR, RadioGx, raer, rawDiag, Rcwl, recount, ReducedExperiment, RegEnrich, REMP, RiboCrypt, RJMCMCNucleosomes, RNAmodR, RNAseqCovarImpute, RNAshapeQC, ROTS, Rsamtools, RUVcorr, SanityR, saseR, satuRn, scanMiR, scanMiRApp, SCArray, SCArray.sat, scater, scBubbletree, scClassify, scConform, scDblFinder, scDD, scDDboost, scde, scDesign3, SCFA, scFeatures, scGraphVerse, scHiCcompare, scHOT, scMerge, scMultiSim, SCnorm, scone, scoreInvHap, scPCA, scran, scRecover, screenCounter, scruff, scShapes, scTHI, scTypeEval, scuttle, SEraster, sesame, SEtools, sigFeature, signatureSearch, SimBu, simpleSeg, singIST, SingleCellAlleleExperiment, singleCellTK, singscore, SmartPhos, SNPhood, spacexr, SpaNorm, spARI, sparrow, SpatialFeatureExperiment, SpectralTAD, spicyR, splatter, SpliceImpactR, SpliceWiz, SplicingGraphs, spoon, SpotSweeper, srnadiff, StabMap, Statial, SUITOR, SuperCellCyto, SVP, syntenet, TAPseq, TBSignatureProfiler, ternarynet, TFBSTools, tidyCoverage, TMixClust, ToxicoGx, TPP2D, tpSVG, tradeSeq, TreeSummarizedExperiment, Trendy, TVTB, txcutr, UCell, UPDhmm, VariantFiltering, VariantTools, VDJdive, velociraptor, vmrseq, Voyager, waddR, weitrix, xCell2, zinbwave, CytoMethIC, IHWpaper, JohnsonKinaseData, seqpac, OSTA, causalBatch, DCLEAR, DTSEA, DysPIA, enviGCMS, GSEMA, Holomics, LDM, minSNPs, oosse, robin, scGate, spatialGE suggestsMe: alabaster.mae, beachmat, BiocNeighbors, cliqueMS, DelayedArray, EpiCompare, escape, GenomicDataCommons, ggsc, glmGamPoi, GRaNIE, h5mread, HDF5Array, imageFeatureTCGA, ISAnalytics, MeLSI, MungeSumstats, netSmooth, omicsPrint, plyinteractions, PureCN, randRotation, rebook, rhdf5, S4Arrays, scGPS, scLANE, SeqArray, SingleR, spatialHeatmap, survClust, TFutils, TileDBArray, TrajectoryUtils, TSCAN, universalmotif, xcore, MethylAidData, Single.mTEC.Transcriptomes, TENxBrainData, TENxPBMCData, CAGEWorkflow, bioLeak, clustermq, conos, easyEWAS, futurize, pagoda2, phase1RMD, RaMS, survBootOutliers, SVG, wrTopDownFrag dependencyCount: 12 Package: BiocPkgDash Version: 0.99.49 Depends: R (>= 4.6.0), shiny Imports: BiocBaseUtils, BiocPkgTools (>= 1.27.6), BiocManager, bsicons, bslib, dplyr, DT, ggplot2, gh, htmlwidgets, plotly, rmarkdown, shinybiocloader, shinyjs, tibble, tidyr, utils, whisker, yaml Suggests: BiocStyle, knitr, sessioninfo, tinytest License: Artistic-2.0 MD5sum: 53d00c99895dca969218f222216a5a87 NeedsCompilation: no Title: An interactive Shiny dashboard for Bioconductor package maintainers Description: This package provides an interactive Shiny dashboard for Bioconductor package maintainers. It visualizes various package statuses, metadata, and development metrics, offering insights into package health and activity. This tool aims to support maintainers of multiple packages by filtering packages via maintainer email. biocViews: Software, Infrastructure, Visualization, GUI Author: Marcel Ramos [aut, cre] (ORCID: ), Vincent Carey [aut] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/BiocPkgDash VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocPkgDash/issues git_url: https://git.bioconductor.org/packages/BiocPkgDash git_branch: devel git_last_commit: 3650cbd git_last_commit_date: 2026-03-23 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocPkgDash_0.99.49.tar.gz vignettes: vignettes/BiocPkgDash/inst/doc/BiocPkgDash.html vignetteTitles: Bioconductor Package Dashboard Intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocPkgDash/inst/doc/BiocPkgDash.R dependencyCount: 123 Package: BiocPkgTools Version: 1.29.4 Depends: htmlwidgets, R (>= 4.1.0) Imports: BiocFileCache, BiocManager, biocViews, tibble, methods, rlang, stringr, stats, rvest, dplyr, xml2, readr, httr, httr2, htmltools, DT, tools, utils, igraph (>= 2.0.0), jsonlite, gh, RBGL, graph, curl, glue, lubridate, purrr, tidyr, yaml Suggests: BiocStyle, knitr, rmarkdown, testthat, tm, networkD3, visNetwork, clipr, blastula, kableExtra, DiagrammeR, SummarizedExperiment License: MIT + file LICENSE MD5sum: 301d41711343d0671c342d2c67053648 NeedsCompilation: no Title: Collection of simple tools for learning about Bioconductor Packages Description: Bioconductor has a rich ecosystem of metadata around packages, usage, and build status. This package is a simple collection of functions to access that metadata from R. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages. biocViews: Software, Infrastructure Author: Shian Su [aut, ctb], Lori Shepherd [ctb], Marcel Ramos [aut, ctb] (ORCID: ), Felix G.M. Ernst [ctb], Jennifer Wokaty [ctb], Charlotte Soneson [ctb], Martin Morgan [ctb], Vince Carey [ctb], Sean Davis [aut, cre] Maintainer: Sean Davis URL: https://github.com/seandavi/BiocPkgTools SystemRequirements: mailsend-go VignetteBuilder: knitr BugReports: https://github.com/seandavi/BiocPkgTools/issues/new git_url: https://git.bioconductor.org/packages/BiocPkgTools git_branch: devel git_last_commit: 84c17b5 git_last_commit_date: 2026-03-13 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocPkgTools_1.29.4.tar.gz vignettes: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.html vignetteTitles: Overview of BiocPkgTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.R importsMe: BiocPkgDash suggestsMe: biocViews, OSTA, rworkflows dependencyCount: 101 Package: biocroxytest Version: 1.7.0 Depends: R (>= 4.4.0) Imports: cli, glue, roxygen2, stringr Suggests: BiocStyle, here, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: 6079d901e08ef4f99d7a095fab26e063 NeedsCompilation: no Title: Handle Long Tests in Bioconductor Packages Description: This package provides a roclet for roxygen2 that identifies and processes code blocks in your documentation marked with `@longtests`. These blocks should contain tests that take a long time to run and thus cannot be included in the regular test suite of the package. When you run `roxygen2::roxygenise` with the `longtests_roclet`, it will extract these long tests from your documentation and save them in a separate directory. This allows you to run these long tests separately from the rest of your tests, for example, on a continuous integration server that is set up to run long tests. biocViews: Software, Infrastructure Author: Francesc Catala-Moll [aut, cre] (ORCID: ) Maintainer: Francesc Catala-Moll URL: https://github.com/xec-cm/biocroxytest VignetteBuilder: knitr BugReports: https://github.com/xec-cm/biocroxytest/issues git_url: https://git.bioconductor.org/packages/biocroxytest git_branch: devel git_last_commit: 10bd227 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/biocroxytest_1.7.0.tar.gz vignettes: vignettes/biocroxytest/inst/doc/biocroxytest.html vignetteTitles: Introduction to biocroxytest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocroxytest/inst/doc/biocroxytest.R dependencyCount: 35 Package: BiocSingular Version: 1.27.1 Imports: BiocGenerics, S4Vectors, Matrix, methods, utils, DelayedArray, BiocParallel, ScaledMatrix, irlba, rsvd, Rcpp, beachmat (>= 2.25.1) LinkingTo: Rcpp, beachmat, assorthead Suggests: testthat, BiocStyle, knitr, rmarkdown, ResidualMatrix License: GPL-3 MD5sum: 2f3c02c81415c45064ff4afc7453448b NeedsCompilation: yes Title: Singular Value Decomposition for Bioconductor Packages Description: Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework. biocViews: Software, DimensionReduction, PrincipalComponent Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/BiocSingular SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/LTLA/BiocSingular/issues git_url: https://git.bioconductor.org/packages/BiocSingular git_branch: devel git_last_commit: 9c87cf6 git_last_commit_date: 2025-11-16 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocSingular_1.27.1.tar.gz vignettes: vignettes/BiocSingular/inst/doc/decomposition.html, vignettes/BiocSingular/inst/doc/representations.html vignetteTitles: 1. SVD and PCA, 2. Matrix classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSingular/inst/doc/decomposition.R, vignettes/BiocSingular/inst/doc/representations.R dependsOnMe: OSCA.basic, OSCA.multisample, OSCA.workflows importsMe: batchelor, BayesSpace, clusterExperiment, COTAN, DelayedTensor, Dino, GSVA, miloR, MPAC, mumosa, NanoMethViz, NewWave, omicsGMF, PCAtools, ReactomeGSA, SCArray, SCArray.sat, scater, scDblFinder, scMerge, scran, scry, Seqtometry, SpaNorm, StabMap, velociraptor suggestsMe: alabaster.matrix, chihaya, ResidualMatrix, ScaledMatrix, spatialHeatmap, splatter, SuperCellCyto, Voyager, HCAData dependencyCount: 37 Package: BiocStyle Version: 2.39.0 Imports: bookdown, knitr (>= 1.30), rmarkdown (>= 1.2), stats, utils, yaml, BiocManager Suggests: BiocGenerics, RUnit, htmltools License: Artistic-2.0 MD5sum: 120310c299deba99712f702cd00f83cc NeedsCompilation: no Title: Standard styles for vignettes and other Bioconductor documents Description: Provides standard formatting styles for Bioconductor PDF and HTML documents. Package vignettes illustrate use and functionality. biocViews: Software Author: Andrzej Oleś [aut] (ORCID: ), Mike Smith [ctb] (ORCID: ), Martin Morgan [ctb], Wolfgang Huber [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocStyle VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocStyle/issues git_url: https://git.bioconductor.org/packages/BiocStyle git_branch: devel git_last_commit: 126f794 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocStyle_2.39.0.tar.gz vignettes: vignettes/BiocStyle/inst/doc/LatexStyle2.pdf, vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.html vignetteTitles: Bioconductor LaTeX Style 2.0, Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.R, vignettes/BiocStyle/inst/doc/LatexStyle2.R dependsOnMe: ExpressionAtlas, 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multiclassPairs, net4pg, openSkies, Rediscover, rjsoncons, rworkflows, SRscore, StepReg, StepRegShiny, TFactSR, tidyGenR dependencyCount: 32 Package: biocthis Version: 1.21.0 Imports: BiocManager, fs, glue, rlang, styler, usethis (>= 2.0.1) Suggests: BiocStyle, covr, devtools, knitr, pkgdown, RefManageR, rmarkdown, sessioninfo, testthat, utils License: Artistic-2.0 MD5sum: c57b30c0fcaba68c1e3aeef3c2f5a128 NeedsCompilation: no Title: Automate package and project setup for Bioconductor packages Description: This package expands the usethis package with the goal of helping automate the process of creating R packages for Bioconductor or making them Bioconductor-friendly. biocViews: Software, ReportWriting Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Marcel Ramos [ctb] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/biocthis VignetteBuilder: knitr BugReports: https://github.com/lcolladotor/biocthis/issues git_url: https://git.bioconductor.org/packages/biocthis git_branch: devel git_last_commit: 87afbda git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/biocthis_1.21.0.tar.gz vignettes: vignettes/biocthis/inst/doc/biocthis_dev_notes.html, vignettes/biocthis/inst/doc/biocthis.html vignetteTitles: biocthis developer notes, Introduction to biocthis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocthis/inst/doc/biocthis_dev_notes.R, vignettes/biocthis/inst/doc/biocthis.R importsMe: HubPub suggestsMe: drugfindR, tripr dependencyCount: 44 Package: BiocVersion Version: 3.23.1 Depends: R (>= 4.6.0) License: Artistic-2.0 MD5sum: fe7c0139d54c8cec98646973d0db527b NeedsCompilation: no Title: Set the appropriate version of Bioconductor packages Description: This package provides repository information for the appropriate version of Bioconductor. biocViews: Infrastructure Author: Martin Morgan [aut], Marcel Ramos [ctb], Bioconductor Package Maintainer [ctb, cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/BiocVersion git_branch: devel git_last_commit: 252d7c8 git_last_commit_date: 2025-10-30 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocVersion_3.23.1.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHub suggestsMe: BiocBookDemo, OSTA, BiocManager dependencyCount: 0 Package: biocViews Version: 1.79.6 Depends: R (>= 3.6.0) Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools, utils, XML, RCurl, RUnit, BiocManager Suggests: BiocGenerics, BiocPkgTools, knitr, commonmark, BiocStyle License: Artistic-2.0 MD5sum: 9f53459a253603ec9aa43c7e39626c58 NeedsCompilation: no Title: Categorized views of R package repositories Description: Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and 'experiment data' packages. biocViews: Infrastructure Author: Vincent Carey [aut], Benjamin Harshfield [aut], Seth Falcon [aut], Sonali Arora [aut], Lori Shepherd [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/biocViews VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/biocViews/issues git_url: https://git.bioconductor.org/packages/biocViews git_branch: devel git_last_commit: 2233375 git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/biocViews_1.79.6.tar.gz vignettes: vignettes/biocViews/inst/doc/createReposHtml.html, vignettes/biocViews/inst/doc/HOWTO-BCV.html vignetteTitles: biocViews-CreateRepositoryHTML, biocViews-HOWTO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocViews/inst/doc/createReposHtml.R, vignettes/biocViews/inst/doc/HOWTO-BCV.R importsMe: AnnotationHubData, BiocCheck, BiocPkgTools, BioGA, monocle, sigFeature, RforProteomics, genetic.algo.optimizeR suggestsMe: packFinder, plasmut, ReducedExperiment, rworkflows dependencyCount: 17 Package: BiocWorkflowTools Version: 1.37.0 Depends: R (>= 3.4) Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown, rstudioapi, stringr, tools, utils, usethis License: MIT + file LICENSE MD5sum: 6938f7788d165fe47fa1f63f0f3c1b64 NeedsCompilation: no Title: Tools to aid the development of Bioconductor Workflow packages Description: Provides functions to ease the transition between Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow. biocViews: Software, ReportWriting Author: Mike Smith [aut, cre], Andrzej Oleś [aut] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocWorkflowTools git_branch: devel git_last_commit: 23b271e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BiocWorkflowTools_1.37.0.tar.gz vignettes: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.html vignetteTitles: Converting Rmarkdown to F1000Research LaTeX Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.R dependsOnMe: RNAseq123 suggestsMe: CAGEWorkflow, recountWorkflow dependencyCount: 61 Package: biodb Version: 1.19.0 Depends: R (>= 4.1.0) Imports: R6, RSQLite, Rcpp, XML, chk, fscache (>= 1.0.2), jsonlite, lgr, lifecycle, methods, openssl, plyr, progress, rappdirs, sched (>= 1.0.1), sqlq, stats, stringr, tools, withr, yaml LinkingTo: Rcpp, testthat Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, xml2 License: AGPL-3 MD5sum: 1754c7a9210063e3131cb552c5a0e7cd NeedsCompilation: yes Title: Biodb, a Library and a Development Framework for Connecting to Chemical and Biological Databases Description: The biodb package provides access to standard remote chemical and biological databases (ChEBI, KEGG, HMDB, ...), as well as to in-house local database files (CSV, SQLite), with easy retrieval of entries, access to web services, search of compounds by mass and/or name, and mass spectra matching for LCMS and MSMS. Its architecture as a development framework facilitates the development of new database connectors for local projects or inside separate published packages. biocViews: Software, Infrastructure, DataImport, KEGG Author: Pierrick Roger [aut, cre] (ORCID: ), Alexis Delabrière [ctb] (ORCID: ) Maintainer: Pierrick Roger URL: https://gitlab.com/rbiodb/biodb VignetteBuilder: knitr BugReports: https://gitlab.com/rbiodb/biodb/-/issues git_url: https://git.bioconductor.org/packages/biodb git_branch: devel git_last_commit: 8a361b3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/biodb_1.19.0.tar.gz vignettes: vignettes/biodb/inst/doc/biodb.html, vignettes/biodb/inst/doc/details.html, vignettes/biodb/inst/doc/entries.html vignetteTitles: Introduction to the biodb package., Details on general *biodb* usage and principles, Manipulating entry objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodb/inst/doc/biodb.R, vignettes/biodb/inst/doc/details.R, vignettes/biodb/inst/doc/entries.R dependencyCount: 63 Package: bioDist Version: 1.83.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: 8c4d3e9d64da8055890571b01c3a8d6a NeedsCompilation: no Title: Different distance measures Description: A collection of software tools for calculating distance measures. biocViews: Clustering, Classification Author: B. Ding, R. Gentleman and Vincent Carey Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/bioDist git_branch: devel git_last_commit: 65ed63e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/bioDist_1.83.0.tar.gz vignettes: vignettes/bioDist/inst/doc/bioDist.pdf vignetteTitles: bioDist Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioDist/inst/doc/bioDist.R importsMe: CHETAH, PhyloProfile dependencyCount: 8 Package: BioGA Version: 1.5.0 Depends: R (>= 4.4) Imports: ggplot2, graphics, Rcpp, SummarizedExperiment, animation, rlang, biocViews, sessioninfo, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 09489ddd81ecafa6df25c6829f66c035 NeedsCompilation: yes Title: Bioinformatics Genetic Algorithm (BioGA) Description: Genetic algorithm are a class of optimization algorithms inspired by the process of natural selection and genetics. This package allows users to analyze and optimize high throughput genomic data using genetic algorithms. The functions provided are implemented in C++ for improved speed and efficiency, with an easy-to-use interface for use within R. biocViews: ExperimentalDesign, Technology Author: Dany Mukesha [aut, cre] (ORCID: ) Maintainer: Dany Mukesha URL: https://danymukesha.github.io/BioGA/ VignetteBuilder: knitr BugReports: https://github.com/danymukesha/BioGA/issues git_url: https://git.bioconductor.org/packages/BioGA git_branch: devel git_last_commit: 7c8e9a3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BioGA_1.5.0.tar.gz vignettes: vignettes/BioGA/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioGA/inst/doc/Introduction.R dependencyCount: 80 Package: biomaRt Version: 2.67.7 Depends: methods, R (>= 4.5.0) Imports: AnnotationDbi, BiocFileCache, curl, httr2, progress, stringr, utils, xml2 Suggests: BiocStyle, httptest2, knitr, mockery, rmarkdown, testthat (>= 3.0.0), withr License: Artistic-2.0 MD5sum: 3a40bc72f7ee9109c5d83997926d1f5a NeedsCompilation: no Title: Interface to BioMart databases (i.e. Ensembl) Description: In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite (). The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. The most prominent examples of BioMart databases are maintained by Ensembl, which provides biomaRt users direct access to a diverse set of data and enables a wide range of powerful online queries from gene annotation to database mining. biocViews: Annotation Author: Steffen Durinck [aut], Wolfgang Huber [aut], Sean Davis [ctb], Francois Pepin [ctb], Vince S Buffalo [ctb], Mike Smith [ctb] (ORCID: ), Hugo Gruson [ctb, cre] (ORCID: ), German Network for Bioinformatics Infrastructure - de.NBI [fnd] Maintainer: Hugo Gruson URL: https://github.com/Huber-group-EMBL/biomaRt, https://huber-group-embl.github.io/biomaRt/ VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/biomaRt/issues git_url: https://git.bioconductor.org/packages/biomaRt git_branch: devel git_last_commit: d8106a4 git_last_commit_date: 2026-04-07 Date/Publication: 2026-04-20 source.ver: src/contrib/biomaRt_2.67.7.tar.gz vignettes: vignettes/biomaRt/inst/doc/accessing_ensembl.html, vignettes/biomaRt/inst/doc/accessing_other_marts.html vignetteTitles: Accessing Ensembl annotation with biomaRt, Using a BioMart other than Ensembl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/accessing_ensembl.R, vignettes/biomaRt/inst/doc/accessing_other_marts.R dependsOnMe: chromPlot, customProDB, DrugVsDisease, genefu, GenomicOZone, NetSAM, PPInfer, RepViz, VegaMC, annotation importsMe: BadRegionFinder, branchpointer, BUSpaRse, ChIPpeakAnno, CHRONOS, CoSIA, dagLogo, DEXSeq, DMRcate, DominoEffect, dominoSignal, easyRNASeq, EDASeq, ELMER, EpiMix, epimutacions, FRASER, GDCRNATools, GenVisR, glmSparseNet, GOexpress, goSTAG, GRaNIE, Gviz, hermes, InterCellar, isobar, LACE, mCSEA, MEDIPS, MetaboSignal, metaseqR2, MGFR, MouseFM, OncoScore, oposSOM, ORFik, pcaExplorer, phenoTest, pRoloc, ProteoMM, R453Plus1Toolbox, ramwas, recoup, ReducedExperiment, rgsepd, scafari, scPipe, scQTLtools, seq2pathway, SeqGSEA, singIST, sitadela, SpliceImpactR, SPLINTER, SPONGE, surfaltr, SurfR, SWATH2stats, TCGAbiolinks, TEKRABber, terapadog, TFEA.ChIP, transcriptogramer, txdbmaker, ViSEAGO, yarn, biomartr, BioVenn, convertid, DiNAMIC.Duo, scGOclust, snplinkage, snplist suggestsMe: AnnotationForge, bioassayR, celda, ClusterJudge, crisprDesign, cTRAP, Damsel, DELocal, DOTSeq, epistack, fedup, FELLA, GeDi, h5vc, martini, massiR, MethReg, MiRaGE, MIRit, MutationalPatterns, netSmooth, oligo, OrganismDbi, pathlinkR, piano, Pigengene, progeny, R3CPET, RnBeads, rTRM, scater, ShortRead, SIM, sincell, tidysbml, trackViewer, wiggleplotr, zinbwave, BioMartGOGeneSets, BloodCancerMultiOmics2017, leeBamViews, RegParallel, RforProteomics, BED, BioInsight, CimpleG, DGEobj, DGEobj.utils, gaawr2, geneviewer, grandR, GRIN2, kangar00, MoBPS, Patterns, ProFAST, scDiffCom, SNPassoc dependencyCount: 62 Package: biomformat Version: 1.39.17 Depends: R (>= 4.1), methods Imports: jsonlite (>= 0.9.16), Matrix (>= 1.7-0) Suggests: testthat (>= 0.10), knitr (>= 1.10), BiocStyle (>= 1.6), rmarkdown (>= 0.7), SummarizedExperiment, S4Vectors, tibble, rhdf5 License: GPL-2 MD5sum: 4b896f467aa1f31f1b294e63a427411d NeedsCompilation: no Title: An interface package for the BIOM file format Description: This is an R package for interfacing with the BIOM file format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods. biocViews: ImmunoOncology, DataImport, Metagenomics, Microbiome Author: Paul J. McMurdie [aut, cre], Joseph N. Paulson [aut] Maintainer: Paul J. McMurdie URL: https://github.com/joey711/biomformat/, http://biom-format.org/ VignetteBuilder: knitr BugReports: https://github.com/joey711/biomformat/issues git_url: https://git.bioconductor.org/packages/biomformat git_branch: devel git_last_commit: 9f77c5e git_last_commit_date: 2026-04-11 Date/Publication: 2026-04-20 source.ver: src/contrib/biomformat_1.39.17.tar.gz vignettes: vignettes/biomformat/inst/doc/biomformat.html vignetteTitles: The biomformat package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomformat/inst/doc/biomformat.R importsMe: microbiomeExplorer, phyloseq suggestsMe: animalcules, iSEEtree, metagenomeSeq, MGnifyR, mia, MicrobiotaProcess, MetaScope, metacoder dependencyCount: 9 Package: BioMVCClass Version: 1.79.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 70455c1e2aeaa3c03bfb176d27655b48 NeedsCompilation: no Title: Model-View-Controller (MVC) Classes That Use Biobase Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/BioMVCClass git_branch: devel git_last_commit: 901e3c4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BioMVCClass_1.79.0.tar.gz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: biomvRCNS Version: 1.51.0 Depends: IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 2047eb30dd9370c58ad4e34c4491c6c9 NeedsCompilation: yes Title: Copy Number study and Segmentation for multivariate biological data Description: In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing. biocViews: aCGH, CopyNumberVariation, Microarray, Sequencing, Visualization, Genetics Author: Yang Du Maintainer: Yang Du git_url: https://git.bioconductor.org/packages/biomvRCNS git_branch: devel git_last_commit: 2ef9790 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/biomvRCNS_1.51.0.tar.gz vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf vignetteTitles: biomvRCNS package introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomvRCNS/inst/doc/biomvRCNS.R dependencyCount: 152 Package: BioNAR Version: 1.13.3 Depends: R (>= 3.5.0), igraph (>= 2.0.1.1), poweRlaw, latex2exp, RSpectra, Rdpack Imports: stringr, viridis, fgsea, grid, methods, AnnotationDbi, dplyr, GO.db, org.Hs.eg.db (>= 3.19.1), rSpectral, WGCNA, ggplot2, ggrepel, minpack.lm, cowplot, data.table, scales, stats, Matrix Suggests: knitr, BiocStyle, magick, rmarkdown, igraphdata, testthat (>= 3.0.0), vdiffr, devtools, pander, plotly, randomcoloR License: Artistic-2.0 MD5sum: bfaa40c84f2cbe7f765daba3140141b8 NeedsCompilation: no Title: Biological Network Analysis in R Description: the R package BioNAR, developed to step by step analysis of PPI network. The aim is to quantify and rank each protein’s simultaneous impact into multiple complexes based on network topology and clustering. Package also enables estimating of co-occurrence of diseases across the network and specific clusters pointing towards shared/common mechanisms. biocViews: Software, GraphAndNetwork, Network Author: Colin Mclean [aut], Anatoly Sorokin [aut, cre], Oksana Sorokina [aut], J. Douglas Armstrong [aut, fnd], T. Ian Simpson [ctb, fnd] Maintainer: Anatoly Sorokin VignetteBuilder: knitr BugReports: https://github.com/lptolik/BioNAR/issues/ git_url: https://git.bioconductor.org/packages/BioNAR git_branch: devel git_last_commit: 5e908e7 git_last_commit_date: 2025-11-21 Date/Publication: 2026-04-20 source.ver: src/contrib/BioNAR_1.13.3.tar.gz vignettes: vignettes/BioNAR/inst/doc/BioNAR_overview.html vignetteTitles: BioNAR: Biological Network Analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNAR/inst/doc/BioNAR_overview.R dependencyCount: 133 Package: BioNERO Version: 1.19.0 Depends: R (>= 4.1) Imports: WGCNA, dynamicTreeCut, ggdendro, matrixStats, sva, RColorBrewer, ComplexHeatmap, ggplot2, rlang, ggrepel, patchwork, reshape2, igraph, ggnetwork, intergraph, NetRep, stats, grDevices, utils, methods, BiocParallel, minet, GENIE3, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle, DESeq2, networkD3, covr License: GPL-3 MD5sum: c75c32b5982a0affe72174d1453c36a3 NeedsCompilation: no Title: Biological Network Reconstruction Omnibus Description: BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks. biocViews: Software, GeneExpression, GeneRegulation, SystemsBiology, GraphAndNetwork, Preprocessing, Network, NetworkInference Author: Fabricio Almeida-Silva [cre, aut] (ORCID: ), Thiago Venancio [aut] (ORCID: ) Maintainer: Fabricio Almeida-Silva URL: https://github.com/almeidasilvaf/BioNERO VignetteBuilder: knitr BugReports: https://github.com/almeidasilvaf/BioNERO/issues git_url: https://git.bioconductor.org/packages/BioNERO git_branch: devel git_last_commit: 632a85b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BioNERO_1.19.0.tar.gz vignettes: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.html, vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.html, vignettes/BioNERO/inst/doc/vignette_03_network_comparison.html vignetteTitles: Gene coexpression network inference, Gene regulatory network inference with BioNERO, Network comparison: consensus modules and module preservation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.R, vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.R, vignettes/BioNERO/inst/doc/vignette_03_network_comparison.R importsMe: cageminer dependencyCount: 158 Package: BioNet Version: 1.71.0 Depends: R (>= 2.10.0), graph, RBGL Imports: igraph (>= 1.0.1), AnnotationDbi, Biobase Suggests: rgl, impute, DLBCL, genefilter, xtable, ALL, limma, hgu95av2.db, XML License: GPL (>= 2) MD5sum: b8fe423c9a64176206c5e1aa172dedd0 NeedsCompilation: no Title: Routines for the functional analysis of biological networks Description: This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork. biocViews: Microarray, DataImport, GraphAndNetwork, Network, NetworkEnrichment, GeneExpression, DifferentialExpression Author: Marcus Dittrich and Daniela Beisser Maintainer: Marcus Dittrich URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/ git_url: https://git.bioconductor.org/packages/BioNet git_branch: devel git_last_commit: 9742714 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BioNet_1.71.0.tar.gz vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf vignetteTitles: BioNet Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNet/inst/doc/Tutorial.R importsMe: gatom, SMITE suggestsMe: SANTA, mwcsr dependencyCount: 50 Package: BioQC Version: 1.39.0 Depends: R (>= 3.5.0), Biobase Imports: edgeR, Rcpp, methods, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, lattice, latticeExtra, rbenchmark, gplots, gridExtra, org.Hs.eg.db, hgu133plus2.db, ggplot2, reshape2, plyr, ineq, covr, limma, RColorBrewer License: GPL (>=3) + file LICENSE MD5sum: 041b2a7c66c9aa9f661c8011ee4eeb2a NeedsCompilation: yes Title: Detect tissue heterogeneity in expression profiles with gene sets Description: BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance. biocViews: GeneExpression,QualityControl,StatisticalMethod, GeneSetEnrichment Author: Jitao David Zhang [cre, aut], Laura Badi [aut], Gregor Sturm [aut], Roland Ambs [aut], Iakov Davydov [aut] Maintainer: Jitao David Zhang URL: https://accio.github.io/BioQC VignetteBuilder: knitr BugReports: https://accio.github.io/BioQC/issues git_url: https://git.bioconductor.org/packages/BioQC git_branch: devel git_last_commit: c2f01e7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BioQC_1.39.0.tar.gz vignettes: vignettes/BioQC/inst/doc/bioqc-efficiency.html, vignettes/BioQC/inst/doc/bioqc-introduction.html, vignettes/BioQC/inst/doc/bioqc-signedGenesets.html, vignettes/BioQC/inst/doc/bioqc-simulation.html, vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.html, vignettes/BioQC/inst/doc/BioQC.html vignetteTitles: BioQC Algorithm: Speeding up the Wilcoxon-Mann-Whitney Test, BioQC: Detect tissue heterogeneity in gene expression data, Using BioQC with signed genesets, BioQC-benchmark: Testing Efficiency,, Sensitivity and Specificity of BioQC on simulated and real-world data, Comparing the Wilcoxon-Mann-Whitney to alternative statistical tests, BioQC-kidney: The kidney expression example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioQC/inst/doc/bioqc-efficiency.R, vignettes/BioQC/inst/doc/bioqc-introduction.R, vignettes/BioQC/inst/doc/bioqc-signedGenesets.R, vignettes/BioQC/inst/doc/bioqc-simulation.R, vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.R, vignettes/BioQC/inst/doc/BioQC.R dependencyCount: 15 Package: Biostrings Version: 2.79.5 Depends: R (>= 4.1.0), BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.31.2), XVector (>= 0.37.1), Seqinfo Imports: methods, utils, grDevices, stats, crayon LinkingTo: S4Vectors, IRanges, XVector Suggests: graphics, pwalign, BSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe, hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy (>= 1.41.3), affydata (>= 1.11.5), RUnit, BiocStyle, knitr, testthat (>= 3.0.0), covr License: Artistic-2.0 MD5sum: a6653c6d030376921ac2e29a3d1e4d68 NeedsCompilation: yes Title: Efficient manipulation of biological strings Description: Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. biocViews: SequenceMatching, Alignment, Sequencing, Genetics, DataImport, DataRepresentation, Infrastructure Author: Hervé Pagès [aut, cre], Patrick Aboyoun [aut], Robert Gentleman [aut], Saikat DebRoy [aut], Vince Carey [ctb], Nicolas Delhomme [ctb], Felix Ernst [ctb], Wolfgang Huber [ctb] ('matchprobes' vignette), Beryl Kanali [ctb] (Converted 'MultipleAlignments' vignette from Sweave to RMarkdown), Haleema Khan [ctb] (Converted 'matchprobes' vignette from Sweave to RMarkdown), Aidan Lakshman [ctb], Kieran O'Neill [ctb], Valerie Obenchain [ctb], Marcel Ramos [ctb], Albert Vill [ctb], Jen Wokaty [ctb] (Converted 'matchprobes' vignette from Sweave to RMarkdown), Erik Wright [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/Biostrings VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Biostrings/issues git_url: https://git.bioconductor.org/packages/Biostrings git_branch: devel git_last_commit: 1006022 git_last_commit_date: 2026-03-06 Date/Publication: 2026-04-20 source.ver: src/contrib/Biostrings_2.79.5.tar.gz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf, vignettes/Biostrings/inst/doc/matchprobes.html, vignettes/Biostrings/inst/doc/MultipleAlignments.html vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Pairwise Sequence Alignments, Handling probe sequence information, Multiple Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R, vignettes/Biostrings/inst/doc/matchprobes.R, vignettes/Biostrings/inst/doc/MultipleAlignments.R dependsOnMe: alabaster.string, altcdfenvs, amplican, Basic4Cseq, BRAIN, BSgenome, BSgenomeForge, chimeraviz, ChIPanalyser, ChIPsim, cigarillo, cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, GenomicAlignments, GOTHiC, HelloRanges, igblastr, kebabs, MethTargetedNGS, minfi, Modstrings, MotifDb, motifTestR, msa, muscle, oligo, ORFhunteR, periodicDNA, pqsfinder, pwalign, PWMEnrich, QSutils, R453Plus1Toolbox, R4RNA, rBLAST, REDseq, Rsamtools, RSVSim, rSWeeP, sangeranalyseR, sangerseqR, SCAN.UPC, SELEX, ShortRead, SICtools, SimFFPE, ssviz, Structstrings, svaNUMT, systemPipeR, topdownr, transmogR, 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GencoDymo2, GenomicSig, iimi, kmeRtone, longreadvqs, metaCluster, MitoHEAR, OpEnCAST, OpEnHiMR, PACVr, piglet, refseqR, revert, seqmagick, SMITIDstruct, SQMtools, SVAlignR, tidyGenR, TmCalculator, vhcub, VIProDesign suggestsMe: alabaster.files, annotate, AnnotationForge, AnnotationHub, autonomics, bambu, BANDITS, CSAR, DNAcycP2, eisaR, GenomicFiles, GenomicRanges, GenomicTuples, ggseqalign, ggtree, GWASTools, HiContacts, HPiP, maftools, methrix, methylumi, MiRaGE, mitoClone2, mutscan, nuCpos, plyinteractions, PTMods, RNAmodR.AlkAnilineSeq, rpx, rTRM, screenCounter, splatter, systemPipeTools, treeio, tripr, XVector, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BeadArrayUseCases, baseq, bbl, bio3d, BOLDconnectR, demulticoder, file2meco, geneviewer, gkmSVM, gwas2crispr, inDAGO, karyotapR, maGUI, MiscMetabar, msaR, NameNeedle, orthGS, phangorn, polyRAD, protr, sigminer, Signac, tidysq linksToMe: DECIPHER, kebabs, MatrixRider, posDemux, pwalign, Rsamtools, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 14 Package: BioTIP Version: 1.25.0 Depends: R (>= 3.6) Imports: igraph, cluster, psych, stringr, GenomicRanges, MASS, scran, methods, stats, utils, grDevices, graphics, foreach, doParallel Suggests: knitr, markdown, base, rmarkdown, ggplot2 License: GPL-2 MD5sum: 93ff7ba3e33f821ad3f20c625e1e4cb3 NeedsCompilation: no Title: BioTIP: An R package for characterization of Biological Tipping-Point Description: Adopting tipping-point theory to transcriptome profiles to unravel disease regulatory trajectory. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Software Author: Zhezhen Wang, Andrew Goldstein, Yuxi Sun, Biniam Feleke, Qier An, Antonio Feliciano, Xinan Yang Maintainer: Felix Yu and X Holly Yang URL: https://github.com/xyang2uchicago/BioTIP VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioTIP git_branch: devel git_last_commit: ba9af28 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BioTIP_1.25.0.tar.gz vignettes: vignettes/BioTIP/inst/doc/BioTIP.html vignetteTitles: BioTIP- an R package for characterization of Biological Tipping-Point hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioTIP/inst/doc/BioTIP.R dependencyCount: 73 Package: biotmle Version: 1.35.0 Depends: R (>= 4.0) Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, drtmle (>= 1.0.4), S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma Suggests: testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger, SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1) License: MIT + file LICENSE MD5sum: 1a9f3426f8b2b6b634cc774ae1f4f620 NeedsCompilation: no Title: Targeted Learning with Moderated Statistics for Biomarker Discovery Description: Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions. biocViews: Regression, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq, ImmunoOncology Author: Nima Hejazi [aut, cre, cph] (ORCID: ), Alan Hubbard [aut, ths] (ORCID: ), Mark van der Laan [aut, ths] (ORCID: ), Weixin Cai [ctb] (ORCID: ), Philippe Boileau [ctb] (ORCID: ) Maintainer: Nima Hejazi URL: https://code.nimahejazi.org/biotmle VignetteBuilder: knitr BugReports: https://github.com/nhejazi/biotmle/issues git_url: https://git.bioconductor.org/packages/biotmle git_branch: devel git_last_commit: 5690334 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/biotmle_1.35.0.tar.gz vignettes: vignettes/biotmle/inst/doc/exposureBiomarkers.html vignetteTitles: Identifying Biomarkers from an Exposure Variable hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biotmle/inst/doc/exposureBiomarkers.R dependencyCount: 96 Package: biovizBase Version: 1.59.0 Depends: R (>= 3.5.0), methods Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.23.19), IRanges (>= 1.99.28), Seqinfo, GenomeInfoDb (>= 1.45.5), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), Biostrings (>= 2.77.2), Rsamtools (>= 2.25.1), GenomicAlignments (>= 1.45.1), GenomicFeatures (>= 1.61.4), AnnotationDbi, VariantAnnotation (>= 1.55.1), ensembldb (>= 2.33.1), AnnotationFilter (>= 0.99.8), rlang Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer, EnsDb.Hsapiens.v75, RUnit License: Artistic-2.0 MD5sum: e0cfa3e809e5e48372989f00165b0544 NeedsCompilation: yes Title: Basic graphic utilities for visualization of genomic data. Description: The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency. biocViews: Infrastructure, Visualization, Preprocessing Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/biovizBase git_branch: devel git_last_commit: 7895cbf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/biovizBase_1.59.0.tar.gz vignettes: vignettes/biovizBase/inst/doc/intro.pdf vignetteTitles: An Introduction to biovizBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biovizBase/inst/doc/intro.R dependsOnMe: CAFE importsMe: ChIPexoQual, ggbio, Gviz, karyoploteR, Pviz, Rqc suggestsMe: Damsel, derfinderPlot, FRASER, NanoStringNCTools, OUTRIDER, R3CPET, regionReport, StructuralVariantAnnotation, Signac dependencyCount: 126 Package: BiSeq Version: 1.51.0 Depends: R (>= 3.5.0), methods, S4Vectors, IRanges (>= 1.17.24), GenomicRanges, SummarizedExperiment (>= 0.2.0), Formula Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, Seqinfo, GenomicRanges, SummarizedExperiment, rtracklayer, parallel, betareg, lokern, Formula, globaltest License: LGPL-3 MD5sum: 5d18a6b5e0e1ecd5c3c85e050ac3ba6c NeedsCompilation: no Title: Processing and analyzing bisulfite sequencing data Description: The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples. biocViews: Genetics, Sequencing, MethylSeq, DNAMethylation Author: Katja Hebestreit, Hans-Ulrich Klein Maintainer: Katja Hebestreit git_url: https://git.bioconductor.org/packages/BiSeq git_branch: devel git_last_commit: 6c33e6b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BiSeq_1.51.0.tar.gz vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf vignetteTitles: An Introduction to BiSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiSeq/inst/doc/BiSeq.R dependsOnMe: RRBSdata suggestsMe: updateObject dependencyCount: 89 Package: blacksheepr Version: 1.25.0 Depends: R (>= 3.6) Imports: grid, stats, grDevices, utils, circlize, viridis, RColorBrewer, ComplexHeatmap, SummarizedExperiment, pasilla Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, curl License: MIT + file LICENSE MD5sum: 3d47fb704098a2ee909f7116dbbb4ece NeedsCompilation: no Title: Outlier Analysis for pairwise differential comparison Description: Blacksheep is a tool designed for outlier analysis in the context of pairwise comparisons in an effort to find distinguishing characteristics from two groups. This tool was designed to be applied for biological applications such as phosphoproteomics or transcriptomics, but it can be used for any data that can be represented by a 2D table, and has two sub populations within the table to compare. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, DifferentialExpression, Transcriptomics Author: MacIntosh Cornwell [aut], RugglesLab [cre] Maintainer: RugglesLab VignetteBuilder: knitr BugReports: https://github.com/ruggleslab/blacksheepr/issues git_url: https://git.bioconductor.org/packages/blacksheepr git_branch: devel git_last_commit: 0a6ceb6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/blacksheepr_1.25.0.tar.gz vignettes: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.html vignetteTitles: Outlier Analysis using blacksheepr - Phosphoprotein hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.R dependencyCount: 125 Package: blase Version: 1.1.2 Depends: R (>= 4.5.0) Imports: SummarizedExperiment, SingleCellExperiment, ggplot2, viridis, patchwork, Matrix, scater, methods, rlang, BiocParallel, boot, dplyr, mgcv, stats, MatrixGenerics, Seurat (>= 4.0.0), lsa Suggests: knitr, rmarkdown, testthat (>= 3.2.3), covr, tradeSeq, scran, slingshot, tools, ami, reshape2, plyr, fs, sparseMatrixStats, ggVennDiagram, uwot, BiocStyle, DelayedMatrixStats, limma License: GPL (>= 3) MD5sum: a179876512e033d9f1d6471358a82ca5 NeedsCompilation: no Title: Bulk Linking Analysis for Single-cell Experiments Description: BLASE is a method for finding where bulk RNA-seq data lies on a single-cell pseudotime trajectory. It uses a fast and understandable approach based on Spearman correlation, with bootstrapping to provide confidence. BLASE can be used to "date" bulk RNA-seq data, annotate cell types in scRNA-seq, and help correct for developmental phenotype differences in bulk RNA-seq experiments. biocViews: Transcriptomics, SingleCell, Sequencing, GeneExpression, Transcription, RNASeq, TimeCourse, CellBiology, Software, CellBasedAssays Author: Andrew McCluskey [aut, cre] (ORCID: ), Toby Kettlewell [aut] (ORCID: ), Adrian M. Smith [aut] (ORCID: ), Rhiannon Kundu [aut] (ORCID: ), David A. Gunn [aut] (ORCID: ), Thomas D. Otto [aut, ths] (ORCID: ) Maintainer: Andrew McCluskey <2117532m@student.gla.ac.uk> URL: https://andrewmccluskey-uog.github.io/blase/ VignetteBuilder: knitr BugReports: https://andrewmccluskey-uog.github.io/blase/issues git_url: https://git.bioconductor.org/packages/blase git_branch: devel git_last_commit: c3e30a8 git_last_commit_date: 2026-02-13 Date/Publication: 2026-04-20 source.ver: src/contrib/blase_1.1.2.tar.gz vignettes: vignettes/blase/inst/doc/assign-bulk-to-pseudotime.html, vignettes/blase/inst/doc/BLASE-for-annotating-scRNA-seq.html, vignettes/blase/inst/doc/BLASE-for-excluding-developmental-genes-from-bulk-RNA-seq.html vignetteTitles: Assigning bulk RNA-seq to pseudotime, BLASE for annotating scRNA-seq, BLASE for excluding developmental genes from bulk RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/blase/inst/doc/assign-bulk-to-pseudotime.R, vignettes/blase/inst/doc/BLASE-for-annotating-scRNA-seq.R, vignettes/blase/inst/doc/BLASE-for-excluding-developmental-genes-from-bulk-RNA-seq.R dependencyCount: 194 Package: blima Version: 1.45.0 Depends: R(>= 3.3) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), Rcpp (>= 0.12.8), BiocGenerics, grDevices, stats, graphics LinkingTo: Rcpp Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi, knitr License: GPL-3 MD5sum: 927d607c0c656eaf9dde08aae33b0a81 NeedsCompilation: yes Title: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level Description: Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing. biocViews: Microarray, Preprocessing, Normalization, DifferentialExpression, GeneRegulation, GeneExpression Author: Vojtěch Kulvait Maintainer: Vojtěch Kulvait URL: https://bitbucket.org/kulvait/blima VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/blima git_branch: devel git_last_commit: 171a351 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/blima_1.45.0.tar.gz vignettes: vignettes/blima/inst/doc/blima.pdf vignetteTitles: blima.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/blima/inst/doc/blima.R suggestsMe: blimaTestingData dependencyCount: 66 Package: BLMA Version: 1.35.0 Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel, Biobase, metafor, methods Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 8cb715935cefb8361d6bc534a6df9ac9 NeedsCompilation: no Title: BLMA: A package for bi-level meta-analysis Description: Suit of tools for bi-level meta-analysis. The package can be used in a wide range of applications, including general hypothesis testings, differential expression analysis, functional analysis, and pathway analysis. biocViews: GeneSetEnrichment, Pathways, DifferentialExpression, Microarray Author: Tin Nguyen , Hung Nguyen , and Sorin Draghici Maintainer: Van-Dung Pham git_url: https://git.bioconductor.org/packages/BLMA git_branch: devel git_last_commit: 2a54068 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BLMA_1.35.0.tar.gz vignettes: vignettes/BLMA/inst/doc/BLMA.pdf vignetteTitles: BLMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BLMA/inst/doc/BLMA.R dependencyCount: 74 Package: BloodGen3Module Version: 1.19.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, ExperimentHub, methods, grid, graphics, stats, grDevices, circlize, testthat, ComplexHeatmap(>= 1.99.8), ggplot2, matrixStats, gtools, reshape2, preprocessCore, randomcoloR, V8, limma Suggests: RUnit, devtools, BiocGenerics, knitr, rmarkdown License: GPL-2 MD5sum: 8665e96f58a3b51c777dea2b4fb140dd NeedsCompilation: no Title: This R package for performing module repertoire analyses and generating fingerprint representations Description: The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.1101/525709v2 and https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of module repertoire analyses can be represented in a fingerprint format, where red and blue spots indicate increases or decreases in module activity. These spots are subsequently represented either on a grid, with each position being assigned to a given module, or in a heatmap where the samples are arranged in columns and the modules in rows. biocViews: Software, Visualization, GeneExpression Author: Darawan Rinchai [aut, cre] (ORCID: ) Maintainer: Darawan Rinchai VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BloodGen3Module git_branch: devel git_last_commit: 825e116 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BloodGen3Module_1.19.0.tar.gz vignettes: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.html vignetteTitles: BloodGen3Module: Modular Repertoire Analysis and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.R dependencyCount: 123 Package: bluster Version: 1.21.1 Imports: stats, methods, utils, cluster, Matrix, Rcpp, igraph, S4Vectors, BiocParallel, BiocNeighbors LinkingTo: Rcpp, assorthead Suggests: knitr, rmarkdown, testthat, BiocStyle, dynamicTreeCut, scRNAseq, scuttle, scater, scran, pheatmap, viridis, mbkmeans, kohonen, apcluster, DirichletMultinomial, vegan, fastcluster License: GPL-3 MD5sum: d307245e3a7c3e15b7a6ba159420a144 NeedsCompilation: yes Title: Clustering Algorithms for Bioconductor Description: Wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results. biocViews: ImmunoOncology, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Aaron Lun [aut, cre], Stephanie Hicks [ctb], Basil Courbayre [ctb], Tuomas Borman [ctb], Leo Lahti [ctb] Maintainer: Aaron Lun SystemRequirements: C++17 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bluster git_branch: devel git_last_commit: a8a23b6 git_last_commit_date: 2026-03-04 Date/Publication: 2026-04-20 source.ver: src/contrib/bluster_1.21.1.tar.gz vignettes: vignettes/bluster/inst/doc/clusterRows.html, vignettes/bluster/inst/doc/diagnostics.html vignetteTitles: 1. Clustering algorithms, 2. Clustering diagnostics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bluster/inst/doc/clusterRows.R, vignettes/bluster/inst/doc/diagnostics.R dependsOnMe: OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, scrapbook, SingleRBook importsMe: chevreulProcess, clustSIGNAL, concordexR, dandelionR, jvecfor, mia, miaDash, MPAC, poem, scDblFinder, scDiagnostics, scran, scTypeEval, Voyager, Canek suggestsMe: anglemania, batchelor, ChromSCape, Coralysis, dittoSeq, GSVA, Ibex, mbkmeans, miaViz, MOSim, mumosa, scLANE, sketchR, SuperCellCyto, SuperCell dependencyCount: 44 Package: bnem Version: 1.19.0 Depends: R (>= 4.1) Imports: CellNOptR, matrixStats, snowfall, Rgraphviz, cluster, flexclust, stats, RColorBrewer, epiNEM, mnem, Biobase, methods, utils, graphics, graph, affy, binom, limma, sva, vsn, rmarkdown Suggests: knitr, BiocGenerics, MatrixGenerics, BiocStyle, RUnit License: GPL-3 MD5sum: 6b60a3d78bfbfe62380a0fe90164e28b NeedsCompilation: no Title: Training of logical models from indirect measurements of perturbation experiments Description: bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate). biocViews: Pathways, SystemsBiology, NetworkInference, Network, GeneExpression, GeneRegulation, Preprocessing Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/MartinFXP/bnem/ VignetteBuilder: knitr BugReports: https://github.com/MartinFXP/bnem/issues git_url: https://git.bioconductor.org/packages/bnem git_branch: devel git_last_commit: c656bcb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/bnem_1.19.0.tar.gz vignettes: vignettes/bnem/inst/doc/bnem.html vignetteTitles: bnem.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnem/inst/doc/bnem.R dependencyCount: 172 Package: BRAIN Version: 1.57.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: f922285827afb6f2ee0c1aa99448d64a NeedsCompilation: no Title: Baffling Recursive Algorithm for Isotope distributioN calculations Description: Package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Piotr Dittwald, with contributions of Dirk Valkenborg and Jurgen Claesen Maintainer: Piotr Dittwald git_url: https://git.bioconductor.org/packages/BRAIN git_branch: devel git_last_commit: 55549e0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BRAIN_1.57.0.tar.gz vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf vignetteTitles: BRAIN Usage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRAIN/inst/doc/BRAIN-vignette.R suggestsMe: cleaver, RforProteomics dependencyCount: 19 Package: branchpointer Version: 1.37.0 Depends: caret, R(>= 3.4) Imports: plyr, kernlab, gbm, stringr, cowplot, ggplot2, biomaRt, Biostrings, parallel, utils, stats, BSgenome.Hsapiens.UCSC.hg38, rtracklayer, GenomicRanges, Seqinfo, IRanges, S4Vectors, data.table Suggests: knitr, BiocStyle License: BSD_3_clause + file LICENSE MD5sum: 064dedd2798696f4059c0a1be489beb0 NeedsCompilation: no Title: Prediction of intronic splicing branchpoints Description: Predicts branchpoint probability for sites in intronic branchpoint windows. Queries can be supplied as intronic regions; or to evaluate the effects of mutations, SNPs. biocViews: Software, GenomeAnnotation, GenomicVariation, MotifAnnotation Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/branchpointer git_branch: devel git_last_commit: a9cef4c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/branchpointer_1.37.0.tar.gz vignettes: vignettes/branchpointer/inst/doc/branchpointer.pdf vignetteTitles: Using Branchpointer for annotation of intronic human splicing branchpoints hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/branchpointer/inst/doc/branchpointer.R dependencyCount: 153 Package: breakpointR Version: 1.29.0 Depends: R (>= 3.5), GenomicRanges, cowplot, breakpointRdata Imports: methods, utils, grDevices, stats, S4Vectors, GenomeInfoDb (>= 1.12.3), IRanges, Rsamtools, GenomicAlignments, ggplot2, BiocGenerics, gtools, doParallel, foreach Suggests: knitr, BiocStyle, testthat License: file LICENSE MD5sum: ea5e4262762f4d05a461c12bb890a12a NeedsCompilation: no Title: Find breakpoints in Strand-seq data Description: This package implements functions for finding breakpoints, plotting and export of Strand-seq data. biocViews: Software, Sequencing, DNASeq, SingleCell, Coverage Author: David Porubsky, Ashley Sanders, Aaron Taudt Maintainer: David Porubsky URL: https://github.com/daewoooo/BreakPointR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/breakpointR git_branch: devel git_last_commit: 5469268 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/breakpointR_1.29.0.tar.gz vignettes: vignettes/breakpointR/inst/doc/breakpointR.pdf vignetteTitles: How to use breakpointR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/breakpointR/inst/doc/breakpointR.R dependencyCount: 73 Package: BreastSubtypeR Version: 1.3.3 Depends: R (>= 4.5.0) Imports: methods, Biobase, tidyselect, dplyr, ggplot2, magrittr, rlang, stringr, withr, edgeR, ComplexHeatmap, impute (>= 1.80.0), data.table (>= 1.16.0), RColorBrewer (>= 1.1-3), circlize (>= 0.4.16), ggrepel (>= 0.9.6), e1071 (>= 1.7-8), SummarizedExperiment, utils Suggests: lifecycle, tidyverse, shiny (>= 1.9.1), bslib (>= 0.8.0), BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 1a7f648c6fdda3c57bb587739545b487 NeedsCompilation: no Title: Cohort-aware methods for intrinsic molecular subtyping of breast cancer Description: BreastSubtypeR provides an assumption-aware, multi-method framework for intrinsic molecular subtyping of breast cancer. The package harmonizes several published nearest-centroid (NC) and single-sample predictor (SSP) classifiers, supplies method-specific preprocessing and robust probe-to-gene mapping, and implements a cohort-aware AUTO mode that selectively enables classifiers compatible with the cohort composition. A local Shiny app (iBreastSubtypeR) is included for interactive analyses and to support users without programming experience. biocViews: RNASeq, Software, GeneExpression, Classification, Preprocessing, Visualization Author: Qiao Yang [aut, cre] (ORCID: ), Emmanouil G. Sifakis [aut] (ORCID: ) Maintainer: Qiao Yang URL: https://doi.org/10.18129/B9.bioc.BreastSubtypeR,https://github.com/yqkiuo/BreastSubtypeR,https://github.com/JohanHartmanGroupBioteam/BreastSubtypeR VignetteBuilder: knitr BugReports: https://github.com/yqkiuo/BreastSubtypeR/issues git_url: https://git.bioconductor.org/packages/BreastSubtypeR git_branch: devel git_last_commit: 7b36c11 git_last_commit_date: 2026-02-20 Date/Publication: 2026-04-20 source.ver: src/contrib/BreastSubtypeR_1.3.3.tar.gz vignettes: vignettes/BreastSubtypeR/inst/doc/BreastSubtypeR.html vignetteTitles: BreastSubtypeR: Introduction and Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BreastSubtypeR/inst/doc/BreastSubtypeR.R dependencyCount: 80 Package: brendaDb Version: 1.25.0 Imports: dplyr, Rcpp, tibble, stringr, magrittr, purrr, BiocParallel, crayon, utils, tidyr, grDevices, rlang, BiocFileCache, rappdirs LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, devtools License: MIT + file LICENSE MD5sum: 463ecb286725e5c5307ea0853f0a6eff NeedsCompilation: yes Title: The BRENDA Enzyme Database Description: R interface for importing and analyzing enzyme information from the BRENDA database. biocViews: ThirdPartyClient, Annotation, DataImport Author: Yi Zhou [aut, cre] (ORCID: ) Maintainer: Yi Zhou URL: https://github.com/y1zhou/brendaDb SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/y1zhou/brendaDb/issues git_url: https://git.bioconductor.org/packages/brendaDb git_branch: devel git_last_commit: 171bc74 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/brendaDb_1.25.0.tar.gz vignettes: vignettes/brendaDb/inst/doc/brendaDb.html vignetteTitles: brendaDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/brendaDb/inst/doc/brendaDb.R dependencyCount: 54 Package: BREW3R.r Version: 1.7.0 Imports: GenomicRanges, methods, rlang, S4Vectors, utils Suggests: testthat (>= 3.0.0), IRanges, knitr, rmarkdown, BiocStyle, rtracklayer License: GPL-3 MD5sum: 707862dd98367c1aab4420e9ff14a72f NeedsCompilation: no Title: R package associated to BREW3R Description: This R package provide functions that are used in the BREW3R workflow. This mainly contains a function that extend a gtf as GRanges using information from another gtf (also as GRanges). The process allows to extend gene annotation without increasing the overlap between gene ids. biocViews: GenomeAnnotation Author: Lucille Lopez-Delisle [aut, cre] (ORCID: ) Maintainer: Lucille Lopez-Delisle URL: https://github.com/lldelisle/BREW3R.r VignetteBuilder: knitr BugReports: https://github.com/lldelisle/BREW3R.r/issues/ git_url: https://git.bioconductor.org/packages/BREW3R.r git_branch: devel git_last_commit: 8f8b303 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BREW3R.r_1.7.0.tar.gz vignettes: vignettes/BREW3R.r/inst/doc/BREW3R.r.html vignetteTitles: BREW3R.r hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BREW3R.r/inst/doc/BREW3R.r.R dependencyCount: 12 Package: BridgeDbR Version: 2.21.0 Depends: R (>= 3.3.0), rJava Imports: curl Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 MD5sum: 3cd53539b5146c4f41eb8763796f4c13 NeedsCompilation: no Title: Code for using BridgeDb identifier mapping framework from within R Description: Use BridgeDb functions and load identifier mapping databases in R. It uses GitHub, Zenodo, and Figshare if you use this package to download identifier mappings files. biocViews: Software, Annotation, Metabolomics, Cheminformatics Author: Christ Leemans , Egon Willighagen , Denise Slenter, Anwesha Bohler , Lars Eijssen , Tooba Abbassi-Daloii Maintainer: Egon Willighagen URL: https://github.com/bridgedb/BridgeDbR VignetteBuilder: knitr BugReports: https://github.com/bridgedb/BridgeDbR/issues git_url: https://git.bioconductor.org/packages/BridgeDbR git_branch: devel git_last_commit: 2db2f82 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BridgeDbR_2.21.0.tar.gz vignettes: vignettes/BridgeDbR/inst/doc/secondary.html, vignettes/BridgeDbR/inst/doc/tutorial.html vignetteTitles: 2. Secondary IDs, 1. Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/secondary.R, vignettes/BridgeDbR/inst/doc/tutorial.R dependencyCount: 3 Package: BrowserViz Version: 2.33.0 Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 4728db60256e81827ec47d240ee2e32b NeedsCompilation: no Title: BrowserViz: interactive R/browser graphics using websockets and JSON Description: Interactvive graphics in a web browser from R, using websockets and JSON. biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Arkadiusz Gladki URL: https://gladkia.github.io/BrowserViz/ VignetteBuilder: knitr BugReports: https://github.com/gladkia/BrowserViz/issues git_url: https://git.bioconductor.org/packages/BrowserViz git_branch: devel git_last_commit: 0964377 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BrowserViz_2.33.0.tar.gz vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.html vignetteTitles: "BrowserViz: support programmatic access to javascript apps running in your web browser" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R dependsOnMe: igvR, RCyjs dependencyCount: 18 Package: BSgenome Version: 1.79.1 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.47.6), IRanges (>= 2.13.16), Seqinfo, GenomicRanges (>= 1.61.1), Biostrings (>= 2.77.2), BiocIO, rtracklayer (>= 1.69.1) Imports: utils, stats, matrixStats, XVector, Rsamtools (>= 2.25.1) Suggests: BiocManager, GenomeInfoDb, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, hgu95av2probe, RUnit, BSgenomeForge License: Artistic-2.0 MD5sum: f5607caf8f415b17b7703974ac18b25e NeedsCompilation: no Title: Software infrastructure for efficient representation of full genomes and their SNPs Description: Infrastructure shared by all the Biostrings-based genome data packages. biocViews: Genetics, Infrastructure, DataRepresentation, SequenceMatching, Annotation, SNP Author: Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/BSgenome BugReports: https://github.com/Bioconductor/BSgenome/issues git_url: https://git.bioconductor.org/packages/BSgenome git_branch: devel git_last_commit: c2fc0db git_last_commit_date: 2025-11-03 Date/Publication: 2026-04-20 source.ver: src/contrib/BSgenome_1.79.1.tar.gz vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf, vignettes/BSgenome/inst/doc/GenomeSearching.pdf vignetteTitles: How to forge a BSgenome data package, Efficient genome searching with Biostrings and the BSgenome data packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenome/inst/doc/GenomeSearching.R dependsOnMe: bambu, BSgenomeForge, ChIPanalyser, GOTHiC, HelloRanges, MEDIPS, periodicDNA, REDseq, VarCon, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4, BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.major, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.minor, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Hsapiens.UCSC.hs1, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, leeBamViews, annotation importsMe: AllelicImbalance, appreci8R, ATACseqQC, atSNP, BEAT, bsseq, BUSpaRse, CAGEr, cleanUpdTSeq, CleanUpRNAseq, cliProfiler, crisprBowtie, crisprBwa, crisprDesign, CRISPRseek, crisprShiny, crisprViz, diffHic, DMRcaller, DOTSeq, enhancerHomologSearch, esATAC, EventPointer, FRASER, gcapc, genomation, GenVisR, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, HiCaptuRe, IsoformSwitchAnalyzeR, katdetectr, m6Aboost, methodical, methrix, MethylSeekR, MMDiff2, monaLisa, Motif2Site, motifmatchr, MotifPeeker, msgbsR, multicrispr, MungeSumstats, musicatk, MutationalPatterns, MutSeqR, ORFik, pipeFrame, podkat, qsea, QuasR, R453Plus1Toolbox, raer, RAIDS, RareVariantVis, RCAS, regioneR, REMP, RESOLVE, ribosomeProfilingQC, RNAmodR, scmeth, SCOPE, signeR, SigsPack, SingleMoleculeFootprinting, SparseSignatures, spiky, SpliceWiz, TAPseq, TFBSTools, transmogR, tRNAscanImport, UMI4Cats, VariantAnnotation, VariantFiltering, VariantTools, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4, BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hs1, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, GenomicDistributionsData, ActiveDriverWGS, GencoDymo2, revert, TmCalculator suggestsMe: Biostrings, biovizBase, ChIPpeakAnno, chipseq, DegCre, easyRNASeq, eisaR, epiSeeker, factR, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, maftools, metaseqR2, MiRaGE, PICB, plotgardener, ProteoDisco, PWMEnrich, QDNAseq, recoup, RiboCrypt, rtracklayer, Seqinfo, sitadela, gkmSVM, polyRAD, sigminer, Signac dependencyCount: 57 Package: BSgenomeForge Version: 1.11.2 Depends: R (>= 4.3.0), methods, BiocGenerics, IRanges, Seqinfo, GenomeInfoDb (>= 1.45.5), Biostrings (>= 2.77.2), BSgenome (>= 1.77.1) Imports: utils, stats, Biobase, S4Vectors (>= 0.47.6), GenomicRanges (>= 1.61.1), BiocIO, rtracklayer (>= 1.69.1) Suggests: GenomicFeatures, Rsamtools, testthat, knitr, rmarkdown, BiocStyle, devtools, BSgenome.Celegans.UCSC.ce2 License: Artistic-2.0 MD5sum: da3b83b58be7850902f0fbaa89f0a6a4 NeedsCompilation: no Title: Forge your own BSgenome data package Description: A set of tools to forge BSgenome data packages. Supersedes the old seed-based tools from the BSgenome software package. This package allows the user to create a BSgenome data package in one function call, simplifying the old seed-based process. biocViews: Infrastructure, DataRepresentation, GenomeAssembly, Annotation, GenomeAnnotation, Sequencing, Alignment, DataImport, SequenceMatching Author: Hervé Pagès [aut, cre], Atuhurira Kirabo Kakopo [aut], Emmanuel Chigozie Elendu [ctb], Prisca Chidimma Maduka [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/BSgenomeForge VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BSgenomeForge/issues git_url: https://git.bioconductor.org/packages/BSgenomeForge git_branch: devel git_last_commit: b2d250d git_last_commit_date: 2025-12-01 Date/Publication: 2026-04-20 source.ver: src/contrib/BSgenomeForge_1.11.2.tar.gz vignettes: vignettes/BSgenomeForge/inst/doc/AdvancedBSgenomeForge.pdf, vignettes/BSgenomeForge/inst/doc/QuickBSgenomeForge.html vignetteTitles: Advanced BSgenomeForge usage, A quick introduction to the BSgenomeForge package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenomeForge/inst/doc/AdvancedBSgenomeForge.R, vignettes/BSgenomeForge/inst/doc/QuickBSgenomeForge.R suggestsMe: BSgenome dependencyCount: 60 Package: BufferedMatrix Version: 1.75.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) MD5sum: ff11605b83a5262a7548bb1ad683c6b5 NeedsCompilation: yes Title: A matrix data storage object held in temporary files Description: A tabular style data object where most data is stored outside main memory. A buffer is used to speed up access to data. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/BufferedMatrix git_url: https://git.bioconductor.org/packages/BufferedMatrix git_branch: devel git_last_commit: ecdbf23 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BufferedMatrix_1.75.0.tar.gz vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf vignetteTitles: BufferedMatrix: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.R dependsOnMe: BufferedMatrixMethods linksToMe: BufferedMatrixMethods dependencyCount: 1 Package: BufferedMatrixMethods Version: 1.75.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) MD5sum: cd008a8d0024e2ea43fbd2e20ef4eab6 NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.bom/bmbolstad/BufferedMatrixMethods git_url: https://git.bioconductor.org/packages/BufferedMatrixMethods git_branch: devel git_last_commit: a8ce008 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BufferedMatrixMethods_1.75.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 2 Package: bugsigdbr Version: 1.17.3 Depends: R (>= 4.1) Imports: BiocFileCache, methods, vroom, utils Suggests: BiocStyle, knitr, ontologyIndex, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 9c3fc17ecfd6db2868c3b29bad3b25ef NeedsCompilation: no Title: R-side access to published microbial signatures from BugSigDB Description: The bugsigdbr package implements convenient access to bugsigdb.org from within R/Bioconductor. The goal of the package is to facilitate import of BugSigDB data into R/Bioconductor, provide utilities for extracting microbe signatures, and enable export of the extracted signatures to plain text files in standard file formats such as GMT. biocViews: DataImport, GeneSetEnrichment, Metagenomics, Microbiome Author: Ludwig Geistlinger [aut, cre], Jennifer Wokaty [aut], Levi Waldron [aut], NCI [fnd] (GrantNo.: R01CA230551) Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/bugsigdbr VignetteBuilder: knitr BugReports: https://github.com/waldronlab/bugsigdbr/issues git_url: https://git.bioconductor.org/packages/bugsigdbr git_branch: devel git_last_commit: 1e45c74 git_last_commit_date: 2026-04-12 Date/Publication: 2026-04-20 source.ver: src/contrib/bugsigdbr_1.17.3.tar.gz vignettes: vignettes/bugsigdbr/inst/doc/bugsigdbr.html vignetteTitles: R-side access to BugSigDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bugsigdbr/inst/doc/bugsigdbr.R suggestsMe: TaxSEA dependencyCount: 49 Package: BulkSignalR Version: 1.3.2 Depends: R (>= 4.5) Imports: BiocFileCache, httr2, RCurl, cli, curl, rlang, jsonlite, matrixStats, methods, doParallel, glmnet, ggalluvial, ggplot2, gridExtra, grid, Rtsne, ggrepel, foreach, multtest, igraph, orthogene, stabledist, circlize (>= 0.4.14), ComplexHeatmap (>= 2.0.0), stats, scales, RANN, SpatialExperiment, SummarizedExperiment, tools Suggests: knitr, markdown, rmarkdown, STexampleData, testthat (>= 3.0.0), codetools, Matrix, lattice, cluster, survival, MASS, nlme License: CeCILL | file LICENSE MD5sum: 6b9fe05ba45633691d2c812e40853b0c NeedsCompilation: no Title: Infer Ligand-Receptor Interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics Description: Inference of ligand-receptor (LR) interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics. BulkSignalR bases its inferences on the LRdb database included in our other package, SingleCellSignalR available from Bioconductor. It relies on a statistical model that is specific to bulk data sets. Different visualization and data summary functions are proposed to help navigating prediction results. biocViews: Network, RNASeq, Software, Proteomics, Transcriptomics, NetworkInference, Spatial Author: Jacques Colinge [aut] (ORCID: ), Jean-Philippe Villemin [cre] (ORCID: ) Maintainer: Jean-Philippe Villemin URL: https://github.com/jcolinge/BulkSignalR VignetteBuilder: knitr BugReports: https://github.com/jcolinge/BulkSignalR/issues git_url: https://git.bioconductor.org/packages/BulkSignalR git_branch: devel git_last_commit: 8559355 git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/BulkSignalR_1.3.2.tar.gz vignettes: vignettes/BulkSignalR/inst/doc/BulkSignalR-Configure.html, vignettes/BulkSignalR/inst/doc/BulkSignalR-Differential.html, vignettes/BulkSignalR/inst/doc/BulkSignalR-Main.html vignetteTitles: BulkSignalR-Configure, BulkSignalR-Differential, BulkSignalR-Main hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BulkSignalR/inst/doc/BulkSignalR-Configure.R, vignettes/BulkSignalR/inst/doc/BulkSignalR-Differential.R, vignettes/BulkSignalR/inst/doc/BulkSignalR-Main.R importsMe: SingleCellSignalR dependencyCount: 202 Package: BUMHMM Version: 1.35.0 Depends: R (>= 3.5.0) Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges Suggests: testthat, knitr, BiocStyle License: GPL-3 MD5sum: e5c9ea8aa551ffd5fdfa37beeb09bf1a NeedsCompilation: no Title: Computational pipeline for computing probability of modification from structure probing experiment data Description: This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment. biocViews: ImmunoOncology, GeneticVariability, Transcription, GeneExpression, GeneRegulation, Coverage, Genetics, StructuralPrediction, Transcriptomics, Bayesian, Classification, FeatureExtraction, HiddenMarkovModel, Regression, RNASeq, Sequencing Author: Alina Selega (alina.selega@gmail.com), Sander Granneman, Guido Sanguinetti Maintainer: Alina Selega VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUMHMM git_branch: devel git_last_commit: 468b3c7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BUMHMM_1.35.0.tar.gz vignettes: vignettes/BUMHMM/inst/doc/BUMHMM.pdf vignetteTitles: An Introduction to the BUMHMM pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUMHMM/inst/doc/BUMHMM.R dependencyCount: 122 Package: bumphunter Version: 1.53.0 Depends: R (>= 3.5), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), Seqinfo, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils, GenomicFeatures, AnnotationDbi, stats Suggests: testthat, RUnit, doParallel, GenomeInfoDb, txdbmaker, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: b950f760d207994b866eedb4f2ad1010 NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison, ImmunoOncology Author: Rafael A. Irizarry [aut], Martin Aryee [aut], Kasper Daniel Hansen [aut], Hector Corrada Bravo [aut], Shan Andrews [ctb], Andrew E. Jaffe [ctb], Harris Jaffee [ctb], Leonardo Collado-Torres [ctb], Tamilselvi Guharaj [cre] Maintainer: Tamilselvi Guharaj URL: https://github.com/rafalab/bumphunter git_url: https://git.bioconductor.org/packages/bumphunter git_branch: devel git_last_commit: 19ed02f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/bumphunter_1.53.0.tar.gz vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf vignetteTitles: The bumphunter user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bumphunter/inst/doc/bumphunter.R dependsOnMe: minfi importsMe: coMethDMR, DAMEfinder, derfinder, dmrseq, epimutacions, epivizr, methylCC, rnaEditr, vmrseq, GenomicState, recountWorkflow suggestsMe: bigmelon, derfinderPlot, epivizrData, regionReport dependencyCount: 83 Package: BumpyMatrix Version: 1.19.0 Imports: utils, methods, Matrix, S4Vectors, IRanges Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 6cf1a3bfaacaea670d1d59836a90c5fe NeedsCompilation: no Title: Bumpy Matrix of Non-Scalar Objects Description: Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects. biocViews: Software, Infrastructure, DataRepresentation Author: Aaron Lun [aut, cre], Genentech, Inc. [cph] Maintainer: Aaron Lun URL: https://bioconductor.org/packages/BumpyMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/BumpyMatrix/issues git_url: https://git.bioconductor.org/packages/BumpyMatrix git_branch: devel git_last_commit: 05bcf92 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BumpyMatrix_1.19.0.tar.gz vignettes: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.html vignetteTitles: The BumpyMatrix class hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.R dependsOnMe: alabaster.bumpy importsMe: CoreGx, gDRcore, gDRimport, gDRutils, imageFeatureTCGA, MerfishData, MouseGastrulationData, TENxXeniumData suggestsMe: escheR, gDR, ggspavis, SpatialExperiment, tpSVG, STexampleData dependencyCount: 13 Package: BUS Version: 1.67.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 MD5sum: b4317bdf2339e0d44933937046aa5c14 NeedsCompilation: yes Title: Gene network reconstruction Description: This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical). biocViews: Preprocessing Author: Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/BUS git_branch: devel git_last_commit: cd1c6ee git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BUS_1.67.0.tar.gz vignettes: vignettes/BUS/inst/doc/bus.pdf vignetteTitles: bus.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUS/inst/doc/bus.R dependencyCount: 3 Package: BUScorrect Version: 1.29.0 Depends: R (>= 3.5.0) Imports: gplots, methods, grDevices, stats, SummarizedExperiment Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 13e31d3c0dad8e2d0aef246e99bf5e2d NeedsCompilation: yes Title: Batch Effects Correction with Unknown Subtypes Description: High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity. biocViews: GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect Author: Xiangyu Luo , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUScorrect git_branch: devel git_last_commit: f70aa1c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BUScorrect_1.29.0.tar.gz vignettes: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.R dependencyCount: 30 Package: BUSseq Version: 1.17.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, gplots, grDevices, methods, stats, utils Suggests: BiocStyle, knitr, BiocGenerics License: Artistic-2.0 MD5sum: 0f48b3f0cd911a684cd6597fd3a8ebde NeedsCompilation: yes Title: Batch Effect Correction with Unknow Subtypes for scRNA-seq data Description: BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. BUSseq can integrate read count matrices obtained from different scRNA-seq platforms and allow cell types to be measured in some but not all of the batches as long as the experimental design fulfills the conditions listed in our manuscript. biocViews: ExperimentalDesign, GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect, SingleCell, Sequencing Author: Fangda Song [aut, cre] (ORCID: ), Ga Ming Chan [aut], Yingying Wei [aut] (ORCID: ) Maintainer: Fangda Song URL: https://github.com/songfd2018/BUSseq VignetteBuilder: knitr BugReports: https://github.com/songfd2018/BUSseq/issues git_url: https://git.bioconductor.org/packages/BUSseq git_branch: devel git_last_commit: effd843 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/BUSseq_1.17.0.tar.gz vignettes: vignettes/BUSseq/inst/doc/BUSseq_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUSseq/inst/doc/BUSseq_user_guide.R dependencyCount: 31 Package: CaDrA Version: 1.9.0 Depends: R (>= 4.4.0) Imports: doParallel, ggplot2, gplots, graphics, grid, gtable, knnmi, MASS, methods, misc3d, plyr, ppcor, R.cache, reshape2, stats, SummarizedExperiment Suggests: BiocManager, devtools, knitr, pheatmap, rmarkdown, testthat (>= 3.1.6) License: GPL-3 + file LICENSE MD5sum: 092ca794c1e4444edd754e4f134e7153 NeedsCompilation: yes Title: Candidate Driver Analysis Description: Performs both stepwise and backward heuristic search for candidate (epi)genetic drivers based on a binary multi-omics dataset. CaDrA's main objective is to identify features which, together, are significantly skewed or enriched pertaining to a given vector of continuous scores (e.g. sample-specific scores representing a phenotypic readout of interest, such as protein expression, pathway activity, etc.), based on the union occurence (i.e. logical OR) of the events. biocViews: Microarray, RNASeq, GeneExpression, Software, FeatureExtraction Author: Reina Chau [aut, cre] (ORCID: ), Katia Bulekova [aut] (ORCID: ), Vinay Kartha [aut], Stefano Monti [aut] (ORCID: ) Maintainer: Reina Chau URL: https://github.com/montilab/CaDrA/ VignetteBuilder: knitr BugReports: https://github.com/montilab/CaDrA/issues git_url: https://git.bioconductor.org/packages/CaDrA git_branch: devel git_last_commit: 210ae5d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CaDrA_1.9.0.tar.gz vignettes: vignettes/CaDrA/inst/doc/docker.html, vignettes/CaDrA/inst/doc/permutation_based_testing.html, vignettes/CaDrA/inst/doc/scoring_functions.html vignetteTitles: How to run CaDrA within a Docker Environment, Permutation-Based Testing, Scoring Functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CaDrA/inst/doc/permutation_based_testing.R, vignettes/CaDrA/inst/doc/scoring_functions.R dependencyCount: 68 Package: CAEN Version: 1.19.0 Depends: R (>= 4.1) Imports: stats,PoiClaClu,SummarizedExperiment,methods Suggests: knitr,rmarkdown,BiocManager,SummarizedExperiment,BiocStyle License: GPL-2 MD5sum: 79fa08cf327d3ec1724ec942bcff13b2 NeedsCompilation: no Title: Category encoding method for selecting feature genes for the classification of single-cell RNA-seq Description: With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples. biocViews: DifferentialExpression, Sequencing, Classification, RNASeq, ATACSeq, SingleCell, GeneExpression, RIPSeq Author: Zhou Yan [aut, cre] Maintainer: Zhou Yan <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAEN git_branch: devel git_last_commit: af0f850 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CAEN_1.19.0.tar.gz vignettes: vignettes/CAEN/inst/doc/CAEN.html vignetteTitles: CAEN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAEN/inst/doc/CAEN.R dependencyCount: 26 Package: CAGEfightR Version: 1.31.1 Depends: R (>= 3.5), GenomicRanges (>= 1.61.1), rtracklayer (>= 1.69.1), SummarizedExperiment (>= 1.39.1) Imports: lobstr(>= 1.1.3), purrr(>= 1.2.0), assertthat(>= 0.2.0), methods(>= 3.6.3), Matrix(>= 1.2-12), BiocGenerics(>= 0.24.0), S4Vectors(>= 0.16.0), IRanges(>= 2.12.0), Seqinfo(>= 1.0.0), GenomicFeatures(>= 1.61.4), GenomicAlignments(>= 1.45.1), BiocParallel(>= 1.12.0), GenomicFiles(>= 1.14.0), Gviz(>= 1.22.2), InteractionSet(>= 1.9.4), GenomicInteractions(>= 1.15.1) Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 + file LICENSE MD5sum: 118d7c536711d23eac5ad690e4b3cd66 NeedsCompilation: no Title: Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor Description: CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization. biocViews: Software, Transcription, Coverage, GeneExpression, GeneRegulation, PeakDetection, DataImport, DataRepresentation, Transcriptomics, Sequencing, Annotation, GenomeBrowsers, Normalization, Preprocessing, Visualization Author: Malte Thodberg Maintainer: Malte Thodberg URL: https://github.com/MalteThodberg/CAGEfightR VignetteBuilder: knitr BugReports: https://github.com/MalteThodberg/CAGEfightR/issues git_url: https://git.bioconductor.org/packages/CAGEfightR git_branch: devel git_last_commit: 26802e1 git_last_commit_date: 2026-03-19 Date/Publication: 2026-04-20 source.ver: src/contrib/CAGEfightR_1.31.1.tar.gz vignettes: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.html vignetteTitles: Introduction to CAGEfightR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.R dependsOnMe: CAGEWorkflow importsMe: CAGEr suggestsMe: nanotubes dependencyCount: 158 Package: CAGEr Version: 2.17.0 Depends: methods, MultiAssayExperiment, R (>= 4.1.0) Imports: BiocGenerics, BiocParallel, Biostrings, BSgenome, CAGEfightR, data.table, formula.tools, Seqinfo, GenomicAlignments (>= 1.45.1), GenomicFeatures (>= 1.61.4), GenomicRanges (>= 1.61.1), ggplot2 (>= 4.0.0), gtools, IRanges (>= 2.18.0), KernSmooth, Matrix, memoise, plyr, rlang, Rsamtools (>= 2.25.1), reshape2, rtracklayer (>= 1.69.1), S4Vectors (>= 0.27.5), scales, som, stringdist, stringi, SummarizedExperiment (>= 1.39.1), utils, vegan, VGAM Suggests: BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Drerio.UCSC.danRer7, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9, DESeq2, FANTOM3and4CAGE, ggseqlogo, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: f42892a0d8b386aa3c1a899d15e64999 NeedsCompilation: no Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining Description: The _CAGEr_ package identifies transcription start sites (TSS) and their usage frequency from CAGE (Cap Analysis Gene Expression) sequencing data. It normalises raw CAGE tag count, clusters TSSs into tag clusters (TC) and aggregates them across multiple CAGE experiments to construct consensus clusters (CC) representing the promoterome. CAGEr provides functions to profile expression levels of these clusters by cumulative expression and rarefaction analysis, and outputs the plots in ggplot2 format for further facetting and customisation. After clustering, CAGEr performs analyses of promoter width and detects differential usage of TSSs (promoter shifting) between samples. CAGEr also exports its data as genome browser tracks, and as R objects for downsteam expression analysis by other Bioconductor packages such as DESeq2, CAGEfightR, or seqArchR. biocViews: Preprocessing, Sequencing, Normalization, FunctionalGenomics, Transcription, GeneExpression, Clustering, Visualization Author: Vanja Haberle [aut], Charles Plessy [cre], Damir Baranasic [ctb], Katalin Ferenc [ctb], Sarvesh Nikumbh [ctb] Maintainer: Charles Plessy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEr git_branch: devel git_last_commit: 0a93a41 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CAGEr_2.17.0.tar.gz vignettes: vignettes/CAGEr/inst/doc/CAGE_Resources.html, vignettes/CAGEr/inst/doc/CAGEexp.html vignetteTitles: Use of CAGE resources with CAGEr, CAGEr: an R package for CAGE data analysis and promoterome mining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGE_Resources.R, vignettes/CAGEr/inst/doc/CAGEexp.R suggestsMe: seqPattern dependencyCount: 174 Package: CalibraCurve Version: 1.1.3 Depends: R (>= 4.5.0) Imports: checkmate, dplyr, ggplot2, magrittr, openxlsx, scales, SummarizedExperiment, tidyr Suggests: BiocStyle, knitr, msqc1, RefManageR, rmarkdown, sessioninfo, testthat, vdiffr License: BSD 3-clause License + file LICENSE MD5sum: 2806651d372af17ccc33bda48f65f5f8 NeedsCompilation: no Title: Calibration curves for targeted proteomics, lipidomics and metabolomics data Description: CalibraCurve is a computational tool designed to generate calibration curves for targeted mass spectrometry-based quantitative data. It is applicable to various omics disciplines, including proteomics, lipidomics, and metabolomics. The package also offers functionalities for data and calibration curve visualization and concentration prediction from new datasets based on the established curves. biocViews: Proteomics, Lipidomics, Metabolomics, Regression, MassSpectrometry, Visualization Author: Karin Schork [aut, cre] (ORCID: ), Robin Grugel [aut], Michael Kohl [aut], Markus Stepath [aut], Martin Eisenacher [aut, fnd] Maintainer: Karin Schork URL: https://github.com/mpc-bioinformatics/CalibraCurve VignetteBuilder: knitr BugReports: https://github.com/mpc-bioinformatics/CalibraCurve/issues git_url: https://git.bioconductor.org/packages/CalibraCurve git_branch: devel git_last_commit: 5fe36d7 git_last_commit_date: 2026-03-11 Date/Publication: 2026-04-20 source.ver: src/contrib/CalibraCurve_1.1.3.tar.gz vignettes: vignettes/CalibraCurve/inst/doc/CalibraCurve_Visualization.html, vignettes/CalibraCurve/inst/doc/CalibraCurve.html vignetteTitles: 2. Customizing the visualizations of CalibraCurve, 1. Introduction to CalibraCurve hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CalibraCurve/inst/doc/CalibraCurve_Visualization.R, vignettes/CalibraCurve/inst/doc/CalibraCurve.R dependencyCount: 58 Package: calm Version: 1.25.0 Imports: mgcv, stats, graphics Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 51213860a5b5422fbfd52924d72a5ced NeedsCompilation: no Title: Covariate Assisted Large-scale Multiple testing Description: Statistical methods for multiple testing with covariate information. Traditional multiple testing methods only consider a list of test statistics, such as p-values. Our methods incorporate the auxiliary information, such as the lengths of gene coding regions or the minor allele frequencies of SNPs, to improve power. biocViews: Bayesian, DifferentialExpression, GeneExpression, Regression, Microarray, Sequencing, RNASeq, MultipleComparison, Genetics, ImmunoOncology, Metabolomics, Proteomics, Transcriptomics Author: Kun Liang [aut, cre] Maintainer: Kun Liang VignetteBuilder: knitr BugReports: https://github.com/k22liang/calm/issues git_url: https://git.bioconductor.org/packages/calm git_branch: devel git_last_commit: cd3e034 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/calm_1.25.0.tar.gz vignettes: vignettes/calm/inst/doc/calm_intro.html vignetteTitles: Userguide for calm package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/calm/inst/doc/calm_intro.R dependencyCount: 11 Package: CaMutQC Version: 1.7.0 Depends: R (>= 4.5.0) Imports: ggplot2, dplyr, org.Hs.eg.db, vcfR, clusterProfiler, stringr, DT, MesKit, maftools, data.table, utils, stats, methods, tidyr Suggests: knitr, rmarkdown, BiocStyle, shiny License: GPL-3 MD5sum: 2786a193cb2b8d3d3b3bce6530905441 NeedsCompilation: no Title: An R Package for Comprehensive Filtration and Selection of Cancer Somatic Mutations Description: CaMutQC is able to filter false positive mutations generated due to technical issues, as well as to select candidate cancer mutations through a series of well-structured functions by labeling mutations with various flags. And a detailed and vivid filter report will be offered after completing a whole filtration or selection section. Also, CaMutQC integrates serveral methods and gene panels for Tumor Mutational Burden (TMB) estimation. biocViews: Software, QualityControl, GeneTarget Author: Xin Wang [aut, cre] (ORCID: ) Maintainer: Xin Wang URL: https://github.com/likelet/CaMutQC VignetteBuilder: knitr BugReports: https://github.com/likelet/CaMutQC/issues git_url: https://git.bioconductor.org/packages/CaMutQC git_branch: devel git_last_commit: 65ebd3c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CaMutQC_1.7.0.tar.gz vignettes: vignettes/CaMutQC/inst/doc/CaMutQC-manual.html vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CaMutQC/inst/doc/CaMutQC-manual.R dependencyCount: 168 Package: cancerclass Version: 1.55.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 MD5sum: 85ebd67557f9f541452b59f8af5cfe25 NeedsCompilation: yes Title: Development and validation of diagnostic tests from high-dimensional molecular data Description: The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements. biocViews: Cancer, Microarray, Classification, Visualization Author: Jan Budczies, Daniel Kosztyla Maintainer: Daniel Kosztyla git_url: https://git.bioconductor.org/packages/cancerclass git_branch: devel git_last_commit: 644997a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cancerclass_1.55.0.tar.gz vignettes: vignettes/cancerclass/inst/doc/vignette_cancerclass.pdf vignetteTitles: Cancerclass: An R package for development and validation of diagnostic tests from high-dimensional molecular data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cancerclass/inst/doc/vignette_cancerclass.R dependencyCount: 8 Package: cardelino Version: 1.13.0 Depends: R (>= 4.2), stats Imports: combinat, GenomeInfoDb, GenomicRanges, ggplot2, ggtree, Matrix, matrixStats, methods, pheatmap, snpStats, S4Vectors, utils, VariantAnnotation, vcfR Suggests: BiocStyle, foreach, knitr, pcaMethods, rmarkdown, testthat, VGAM Enhances: doMC License: GPL-3 MD5sum: 66da3379e31b2c4ae57a8fa02c3e61a4 NeedsCompilation: yes Title: Clone Identification from Single Cell Data Description: Methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stochastic dropout events as well as systematic allelic imbalance is used. biocViews: SingleCell, RNASeq, Visualization, Transcriptomics, GeneExpression, Sequencing, Software, ExomeSeq Author: Jeffrey Pullin [aut], Yuanhua Huang [aut], Davis McCarthy [aut, cre] Maintainer: Davis McCarthy URL: https://github.com/single-cell-genetics/cardelino VignetteBuilder: knitr BugReports: https://github.com/single-cell-genetics/cardelino/issues git_url: https://git.bioconductor.org/packages/cardelino git_branch: devel git_last_commit: d0e349d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cardelino_1.13.0.tar.gz vignettes: vignettes/cardelino/inst/doc/vignette-cloneid.html vignetteTitles: Clone ID with cardelino hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cardelino/inst/doc/vignette-cloneid.R dependencyCount: 147 Package: Cardinal Version: 3.13.0 Depends: R (>= 4.4), BiocParallel, BiocGenerics, ProtGenerics, S4Vectors, methods, stats, stats4 Imports: CardinalIO, Biobase, EBImage, graphics, grDevices, irlba, Matrix, matter (>= 2.7.10), nlme, parallel, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, emmeans, lme4, lmerTest License: Artistic-2.0 | file LICENSE MD5sum: c909bba37888bf6d397940a5f726e8d0 NeedsCompilation: no Title: A mass spectrometry imaging toolbox for statistical analysis Description: Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification. biocViews: Software, Infrastructure, Proteomics, Lipidomics, MassSpectrometry, ImagingMassSpectrometry, ImmunoOncology, Normalization, Clustering, Classification, Regression Author: Kylie Ariel Bemis [aut, cre] Maintainer: Kylie Ariel Bemis URL: http://www.cardinalmsi.org VignetteBuilder: knitr BugReports: https://github.com/kuwisdelu/Cardinal/issues git_url: https://git.bioconductor.org/packages/Cardinal git_branch: devel git_last_commit: c5d37e9 git_last_commit_date: 2026-02-12 Date/Publication: 2026-04-20 source.ver: src/contrib/Cardinal_3.13.0.tar.gz vignettes: vignettes/Cardinal/inst/doc/Cardinal3-guide.html, vignettes/Cardinal/inst/doc/Cardinal3-stats.html vignetteTitles: 1. Cardinal 3: User guide for mass spectrometry imaging analysis, 2. Cardinal 3: Statistical methods for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Cardinal/inst/doc/Cardinal3-guide.R, vignettes/Cardinal/inst/doc/Cardinal3-stats.R dependsOnMe: CardinalWorkflows dependencyCount: 65 Package: CardinalIO Version: 1.9.0 Depends: R (>= 4.4), BiocParallel, matter, ontologyIndex Imports: methods, S4Vectors, stats, utils, tools Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 3702e8a8dc4e52e335900fe736f6d537 NeedsCompilation: yes Title: Read and write mass spectrometry imaging files Description: Fast and efficient reading and writing of mass spectrometry imaging data files. Supports imzML and Analyze 7.5 formats. Provides ontologies for mass spectrometry imaging. biocViews: Software, Infrastructure, DataImport, MassSpectrometry, ImagingMassSpectrometry Author: Kylie Ariel Bemis [aut, cre] Maintainer: Kylie Ariel Bemis URL: http://www.cardinalmsi.org VignetteBuilder: knitr BugReports: https://github.com/kuwisdelu/CardinalIO/issues git_url: https://git.bioconductor.org/packages/CardinalIO git_branch: devel git_last_commit: 59b71c2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CardinalIO_1.9.0.tar.gz vignettes: vignettes/CardinalIO/inst/doc/CardinalIO-guide.html vignetteTitles: Parsing and writing imzML files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CardinalIO/inst/doc/CardinalIO-guide.R importsMe: Cardinal dependencyCount: 28 Package: CARDspa Version: 1.3.1 Depends: R (>= 4.3.0) Imports: Rcpp (>= 1.0.7),RcppArmadillo, SummarizedExperiment, methods, MCMCpack, fields, wrMisc, concaveman, sp, dplyr, sf, Matrix, RANN, ggplot2, reshape2, RColorBrewer, S4Vectors, scatterpie, grDevices,ggcorrplot, stats, nnls, BiocParallel, NMF, spatstat.random, gtools, SingleCellExperiment, SpatialExperiment LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, RcppML, BiocStyle License: GPL-3 + file LICENSE MD5sum: fcefbf5d7d6e346300dcf58da849c9c0 NeedsCompilation: yes Title: Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics Description: CARD is a reference-based deconvolution method that estimates cell type composition in spatial transcriptomics based on cell type specific expression information obtained from a reference scRNA-seq data. A key feature of CARD is its ability to accommodate spatial correlation in the cell type composition across tissue locations, enabling accurate and spatially informed cell type deconvolution as well as refined spatial map construction. CARD relies on an efficient optimization algorithm for constrained maximum likelihood estimation and is scalable to spatial transcriptomics with tens of thousands of spatial locations and tens of thousands of genes. biocViews: Spatial, SingleCell, Transcriptomics, Visualization Author: Ying Ma [aut], Jing Fu [cre] Maintainer: Jing Fu URL: https://github.com/YMa-lab/CARDspa VignetteBuilder: knitr BugReports: https://github.com/YMa-lab/CARDspa/issues git_url: https://git.bioconductor.org/packages/CARDspa git_branch: devel git_last_commit: efbe7a0 git_last_commit_date: 2026-02-16 Date/Publication: 2026-04-20 source.ver: src/contrib/CARDspa_1.3.1.tar.gz vignettes: vignettes/CARDspa/inst/doc/Example_Analysis.html vignetteTitles: Example_Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CARDspa/inst/doc/Example_Analysis.R importsMe: OSTA dependencyCount: 144 Package: CARNIVAL Version: 2.21.0 Depends: R (>= 4.0) Imports: readr, stringr, lpSolve, igraph, dplyr, tibble, tidyr, rjson, rmarkdown Suggests: RefManageR, BiocStyle, covr, knitr, testthat (>= 3.0.0), sessioninfo License: GPL-3 MD5sum: 860aba0f3b6d75cc7bd982bba5892a91 NeedsCompilation: no Title: A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming Description: An upgraded causal reasoning tool from Melas et al in R with updated assignments of TFs' weights from PROGENy scores. Optimization parameters can be freely adjusted and multiple solutions can be obtained and aggregated. biocViews: Transcriptomics, GeneExpression, Network Author: Enio Gjerga [aut] (ORCID: ), Panuwat Trairatphisan [aut], Anika Liu [ctb], Alberto Valdeolivas [ctb], Nikolas Peschke [ctb], Aurelien Dugourd [ctb], Attila Gabor [cre], Olga Ivanova [aut] Maintainer: Attila Gabor URL: https://github.com/saezlab/CARNIVAL VignetteBuilder: knitr BugReports: https://github.com/saezlab/CARNIVAL/issues git_url: https://git.bioconductor.org/packages/CARNIVAL git_branch: devel git_last_commit: 9910af4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CARNIVAL_2.21.0.tar.gz vignettes: vignettes/CARNIVAL/inst/doc/CARNIVAL.html vignetteTitles: Contextualizing large scale signalling networks from expression footprints with CARNIVAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CARNIVAL/inst/doc/CARNIVAL.R importsMe: cosmosR dependencyCount: 63 Package: CATALYST Version: 1.35.2 Depends: R (>= 4.6), SingleCellExperiment Imports: circlize, ComplexHeatmap, ConsensusClusterPlus, cowplot, dplyr, drc, flowCore, FlowSOM, ggplot2, ggrepel, ggridges, graphics, grDevices, grid, gridExtra, Matrix, matrixStats, methods, nnls, RColorBrewer, reshape2, Rtsne, SummarizedExperiment, S4Vectors, scales, scater, stats Suggests: BiocStyle, diffcyt, flowWorkspace, ggcyto, knitr, openCyto, rmarkdown, testthat, uwot License: GPL (>=2) MD5sum: 620780692fd9737730a79b0b8a0095e9 NeedsCompilation: no Title: Cytometry dATa anALYSis Tools Description: CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively. biocViews: Clustering, DataImport, DifferentialExpression, ExperimentalDesign, FlowCytometry, ImmunoOncology, MassSpectrometry,Normalization, Preprocessing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre] (ORCID: ), Vito R.T. Zanotelli [aut] (ORCID: ), Stéphane Chevrier [aut, dtc] (ORCID: ), Mark D. Robinson [aut, fnd] (ORCID: ), Bernd Bodenmiller [fnd] (ORCID: ) Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/CATALYST VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/CATALYST/issues git_url: https://git.bioconductor.org/packages/CATALYST git_branch: devel git_last_commit: b91b3ed git_last_commit_date: 2026-03-09 Date/Publication: 2026-04-20 source.ver: src/contrib/CATALYST_1.35.2.tar.gz vignettes: vignettes/CATALYST/inst/doc/differential.html, vignettes/CATALYST/inst/doc/preprocessing.html vignetteTitles: "2. Differential discovery", "1. Preprocessing" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CATALYST/inst/doc/differential.R, vignettes/CATALYST/inst/doc/preprocessing.R dependsOnMe: spillR, cytofWorkflow importsMe: cytofQC suggestsMe: diffcyt, imcRtools, treekoR dependencyCount: 181 Package: CatsCradle Version: 1.5.2 Depends: R (>= 4.4.0) Imports: Seurat (>= 5.0.1), ggplot2, networkD3, stringr, pracma, reshape2, rdist, igraph, geometry, Rfast, data.table, abind, pheatmap, EBImage, S4Vectors, SeuratObject, SingleCellExperiment, SpatialExperiment, Matrix, methods, SummarizedExperiment, msigdbr Suggests: fossil, interp, knitr, BiocStyle, tictoc License: MIT + file LICENSE MD5sum: b6cd44840e26d3d5f483642d2d35de29 NeedsCompilation: no Title: This package provides methods for analysing spatial transcriptomics data and for discovering gene clusters Description: This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. 'CatsCradle' allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes. biocViews: BiologicalQuestion, StatisticalMethod, GeneExpression, SingleCell, Transcriptomics, Spatial Author: Anna Laddach [aut] (ORCID: ), Michael Shapiro [aut, cre] (ORCID: ) Maintainer: Michael Shapiro URL: https://github.com/AnnaLaddach/CatsCradle VignetteBuilder: knitr BugReports: https://github.com/AnnaLaddach/CatsCradle/issues git_url: https://git.bioconductor.org/packages/CatsCradle git_branch: devel git_last_commit: 0f9d76f git_last_commit_date: 2025-12-22 Date/Publication: 2026-04-20 source.ver: src/contrib/CatsCradle_1.5.2.tar.gz vignettes: vignettes/CatsCradle/inst/doc/CatsCradle.html, vignettes/CatsCradle/inst/doc/CatsCradleExampleData.html, vignettes/CatsCradle/inst/doc/CatsCradleQuickStart.html, vignettes/CatsCradle/inst/doc/CatsCradleSingleCellExperimentQuickStart.html, vignettes/CatsCradle/inst/doc/CatsCradleSpatial.html vignetteTitles: CatsCradle, CatsCradle Example Data, CatsCradle Quick Start, CatsCradle SingleCellExperiment Quick Start, CatsCradle Spatial Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CatsCradle/inst/doc/CatsCradle.R, vignettes/CatsCradle/inst/doc/CatsCradleExampleData.R, vignettes/CatsCradle/inst/doc/CatsCradleQuickStart.R, vignettes/CatsCradle/inst/doc/CatsCradleSingleCellExperimentQuickStart.R, vignettes/CatsCradle/inst/doc/CatsCradleSpatial.R dependencyCount: 198 Package: CausalR Version: 1.43.0 Depends: R (>= 3.2.0) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 7cf11cac0c1176c95e543f9caaab51be NeedsCompilation: no Title: Causal network analysis methods Description: Causal network analysis methods for regulator prediction and network reconstruction from genome scale data. biocViews: ImmunoOncology, SystemsBiology, Network, GraphAndNetwork, Network Inference, Transcriptomics, Proteomics, DifferentialExpression, RNASeq, Microarray Author: Glyn Bradley, Steven Barrett, Chirag Mistry, Mark Pipe, David Wille, David Riley, Bhushan Bonde, Peter Woollard Maintainer: Glyn Bradley , Steven Barrett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CausalR git_branch: devel git_last_commit: 147e5d1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CausalR_1.43.0.tar.gz vignettes: vignettes/CausalR/inst/doc/CausalR.pdf vignetteTitles: CausalR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CausalR/inst/doc/CausalR.R dependencyCount: 17 Package: CBN2Path Version: 1.1.7 Depends: R (>= 4.1.0) Imports: R6, ggraph, tidygraph, ggplot2, patchwork, cowplot, magrittr, igraph, rlang, grDevices, coda, graphics, stats, TCGAbiolinks, BiocParallel Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE OS_type: unix MD5sum: f894630293d4b47100d076bc743c8bc6 NeedsCompilation: yes Title: CBN2Path: an R/Bioconductor package for the analysis of cancer progression pathways using Conjunctive Bayesian Networks Description: CBN2Path package provides a unifying interface to facilitate CBN-based quantification, analysis and visualization of cancer progression pathways. biocViews: Software, StatisticalMethod, GraphAndNetwork, Bayesian, Pathways Author: William Choi-Kim [aut, cre], Sayed-Rzgar Hosseini [aut, cre] Maintainer: William Choi-Kim , Sayed-Rzgar Hosseini URL: https://github.com/rockwillck/CBN2Path, http://dx.doi.org/10.1093/biomet/asp023, http://dx.doi.org/10.1093/bioinformatics/btp505 SystemRequirements: GNU Scientific Library (GSL) VignetteBuilder: knitr BugReports: https://github.com/rockwillck/CBN2Path/issues git_url: https://git.bioconductor.org/packages/CBN2Path git_branch: devel git_last_commit: 491571a git_last_commit_date: 2026-04-18 Date/Publication: 2026-04-20 source.ver: src/contrib/CBN2Path_1.1.7.tar.gz vignettes: vignettes/CBN2Path/inst/doc/CBN2Path.html vignetteTitles: CBN2Path Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/CBN2Path/inst/doc/CBN2Path.R dependencyCount: 131 Package: CBNplot Version: 1.11.0 Depends: R (>= 4.3.0) Imports: ggplot2, magrittr, graphite, ggraph, igraph, bnlearn (>= 4.7), patchwork, org.Hs.eg.db, clusterProfiler, utils, enrichplot, reshape2, ggforce, dplyr, tidyr, stringr, depmap, ExperimentHub, Rmpfr, graphlayouts, BiocFileCache, ggdist, purrr, pvclust, stats, rlang Suggests: knitr, arules, concaveman, ReactomePA, bnviewer, rmarkdown, withr, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 9efa8348c3a13f83f61f202f799c6cb7 NeedsCompilation: no Title: plot bayesian network inferred from gene expression data based on enrichment analysis results Description: This package provides the visualization of bayesian network inferred from gene expression data. The networks are based on enrichment analysis results inferred from packages including clusterProfiler and ReactomePA. The networks between pathways and genes inside the pathways can be inferred and visualized. biocViews: Visualization, Bayesian, GeneExpression, NetworkInference, Pathways, Reactome, Network, NetworkEnrichment, GeneSetEnrichment Author: Noriaki Sato [cre, aut] Maintainer: Noriaki Sato URL: https://github.com/noriakis/CBNplot VignetteBuilder: knitr BugReports: https://github.com/noriakis/CBNplot/issues git_url: https://git.bioconductor.org/packages/CBNplot git_branch: devel git_last_commit: a7bbf54 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CBNplot_1.11.0.tar.gz vignettes: vignettes/CBNplot/inst/doc/CBNplot_basic_usage.html vignetteTitles: CBNplot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CBNplot/inst/doc/CBNplot_basic_usage.R dependencyCount: 156 Package: CCAFE Version: 1.3.0 Depends: R (>= 4.4.0) Imports: dplyr, VariantAnnotation Suggests: testthat (>= 3.0.0), rmarkdown, markdown, knitr, tidyverse, DescTools, cowplot, BiocStyle, GenomicRanges, SummarizedExperiment, S4Vectors, IRanges License: GPL-3 MD5sum: ce985b2be14d4e123f62ec7d72ee4d0c NeedsCompilation: no Title: Case Control Allele Frequency Estimation Description: Functions to reconstruct case and control AFs from summary statistics. One function uses OR, NCase, NControl, and SE(log(OR)). The second function uses OR, NCase, NControl, and AF for the whole sample. biocViews: GenomeWideAssociation, ComparativeGenomics, Genetics, Preprocessing, SNP, Software, WholeGenome Author: Hayley Wolff [cre, aut] Maintainer: Hayley Wolff URL: https://github.com/wolffha/CCAFE/ VignetteBuilder: knitr BugReports: https://github.com/wolffha/CCAFE/issues git_url: https://git.bioconductor.org/packages/CCAFE git_branch: devel git_last_commit: 2736f80 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CCAFE_1.3.0.tar.gz vignettes: vignettes/CCAFE/inst/doc/CCAFE_Extra_Details.html, vignettes/CCAFE/inst/doc/CCAFE.html vignetteTitles: CCAFE Extra Details, CCAFE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CCAFE/inst/doc/CCAFE_Extra_Details.R, vignettes/CCAFE/inst/doc/CCAFE.R dependencyCount: 84 Package: ccfindR Version: 1.31.0 Depends: R (>= 3.6.0) Imports: stats, S4Vectors, utils, methods, Matrix, SummarizedExperiment, SingleCellExperiment, Rtsne, graphics, grDevices, gtools, RColorBrewer, ape, Rmpi, irlba, Rcpp, Rdpack (>= 0.7) LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 9e36d29d8f8614fd33e678cd9e4a1ea9 NeedsCompilation: yes Title: Cancer Clone Finder Description: A collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters. biocViews: Transcriptomics, SingleCell, ImmunoOncology, Bayesian, Clustering Author: Jun Woo [aut, cre], Jinhua Wang [aut] Maintainer: Jun Woo URL: http://dx.doi.org/10.26508/lsa.201900443 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccfindR git_branch: devel git_last_commit: ee2b32c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ccfindR_1.31.0.tar.gz vignettes: vignettes/ccfindR/inst/doc/ccfindR.html vignetteTitles: ccfindR: single-cell RNA-seq analysis using Bayesian non-negative matrix factorization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccfindR/inst/doc/ccfindR.R suggestsMe: MutationalPatterns dependencyCount: 39 Package: ccImpute Version: 1.13.0 Imports: Rcpp, sparseMatrixStats, stats, BiocParallel, irlba, SingleCellExperiment, Matrix, SummarizedExperiment LinkingTo: Rcpp, RcppEigen Suggests: knitr, rmarkdown, BiocStyle, sessioninfo, scRNAseq, scater, mclust, testthat (>= 3.0.0), splatter License: GPL-3 MD5sum: cf68af9357deb8644f5b1ae54fd808c4 NeedsCompilation: yes Title: ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (https://doi.org/10.1186/s12859-022-04814-8) Description: Dropout events make the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult. ccImpute is an imputation algorithm that uses cell similarity established by consensus clustering to impute the most probable dropout events in the scRNA-seq datasets. ccImpute demonstrated performance which exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities. biocViews: SingleCell, Sequencing, PrincipalComponent, DimensionReduction, Clustering, RNASeq, Transcriptomics Author: Marcin Malec [cre, aut] (ORCID: ), Parichit Sharma [aut] (ORCID: ), Hasan Kurban [aut] (ORCID: ), Mehmet Dalkilic [aut] Maintainer: Marcin Malec URL: https://github.com/khazum/ccImpute/ VignetteBuilder: knitr BugReports: https://github.com/khazum/ccImpute/issues git_url: https://git.bioconductor.org/packages/ccImpute git_branch: devel git_last_commit: 076b708 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ccImpute_1.13.0.tar.gz vignettes: vignettes/ccImpute/inst/doc/ccImpute.html vignetteTitles: ccImpute package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccImpute/inst/doc/ccImpute.R dependencyCount: 40 Package: CCPlotR Version: 1.9.1 Imports: plyr, tidyr, dplyr, ggplot2, forcats, ggraph, igraph, scatterpie, circlize, ComplexHeatmap, tibble, grid, stringr, ggtext, ggh4x, patchwork, RColorBrewer, scales, viridis, grDevices, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 04be4e30376346bfeb3bd9b6053cc0ab NeedsCompilation: no Title: Plots For Visualising Cell-Cell Interactions Description: CCPlotR is an R package for visualising results from tools that predict cell-cell interactions from single-cell RNA-seq data. These plots are generic and can be used to visualise results from multiple tools such as Liana, CellPhoneDB, NATMI etc. biocViews: SingleCell, Network, Visualization, CellBiology, SystemsBiology Author: Sarah Ennis [aut, cre] (ORCID: ), Pilib Ó Broin [aut], Eva Szegezdi [aut] Maintainer: Sarah Ennis URL: https://github.com/Sarah145/CCPlotR VignetteBuilder: knitr BugReports: https://github.com/Sarah145/CCPlotR/issues git_url: https://git.bioconductor.org/packages/CCPlotR git_branch: devel git_last_commit: 6e010b7 git_last_commit_date: 2026-03-16 Date/Publication: 2026-04-20 source.ver: src/contrib/CCPlotR_1.9.1.tar.gz vignettes: vignettes/CCPlotR/inst/doc/CCPlotR_visualisations.html vignetteTitles: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CCPlotR/inst/doc/CCPlotR_visualisations.R suggestsMe: OSTA dependencyCount: 97 Package: CCPROMISE Version: 1.37.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: 328de40ef9b79b4c3fd1255addbe6812 NeedsCompilation: no Title: PROMISE analysis with Canonical Correlation for Two Forms of High Dimensional Genetic Data Description: Perform Canonical correlation between two forms of high demensional genetic data, and associate the first compoent of each form of data with a specific biologically interesting pattern of associations with multiple endpoints. A probe level analysis is also implemented. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao git_url: https://git.bioconductor.org/packages/CCPROMISE git_branch: devel git_last_commit: e350525 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CCPROMISE_1.37.0.tar.gz vignettes: vignettes/CCPROMISE/inst/doc/CCPROMISE.pdf vignetteTitles: An introduction to CCPROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CCPROMISE/inst/doc/CCPROMISE.R dependencyCount: 49 Package: CDI Version: 1.9.3 Depends: R(>= 3.6) Imports: matrixStats, SeuratObject, Seurat, stats, BiocParallel, ggplot2, reshape2, grDevices, ggsci, SingleCellExperiment, SummarizedExperiment, methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, magick, BiocStyle License: GPL-3 + file LICENSE MD5sum: 53119bfa0d26f9ec185c17478eeb317f NeedsCompilation: no Title: Clustering Deviation Index (CDI) Description: Single-cell RNA-sequencing (scRNA-seq) is widely used to explore cellular variation. The analysis of scRNA-seq data often starts from clustering cells into subpopulations. This initial step has a high impact on downstream analyses, and hence it is important to be accurate. However, there have not been unsupervised metric designed for scRNA-seq to evaluate clustering performance. Hence, we propose clustering deviation index (CDI), an unsupervised metric based on the modeling of scRNA-seq UMI counts to evaluate clustering of cells. biocViews: SingleCell, Software, Clustering, Visualization, Sequencing, RNASeq, CellBasedAssays Author: Jiyuan Fang [cre, aut] (ORCID: ), Jichun Xie [ctb], Cliburn Chan [ctb], Kouros Owzar [ctb], Liuyang Wang [ctb], Diyuan Qin [ctb], Qi-Jing Li [ctb] Maintainer: Jiyuan Fang URL: https://github.com/jichunxie/CDI VignetteBuilder: knitr BugReports: https://github.com/jichunxie/CDI/issues git_url: https://git.bioconductor.org/packages/CDI git_branch: devel git_last_commit: 7c325d2 git_last_commit_date: 2026-03-15 Date/Publication: 2026-04-20 source.ver: src/contrib/CDI_1.9.3.tar.gz vignettes: vignettes/CDI/inst/doc/CDI.html vignetteTitles: Clustering Deviation Index (CDI) Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CDI/inst/doc/CDI.R dependencyCount: 172 Package: celaref Version: 1.29.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: MAST, ggplot2, Matrix, dplyr, magrittr, stats, utils, rlang, BiocGenerics, S4Vectors, readr, tibble, DelayedArray Suggests: limma, parallel, knitr, rmarkdown, ExperimentHub, testthat License: GPL-3 MD5sum: bc3dbc5403d7ac7dfe479412fc94b376 NeedsCompilation: no Title: Single-cell RNAseq cell cluster labelling by reference Description: After the clustering step of a single-cell RNAseq experiment, this package aims to suggest labels/cell types for the clusters, on the basis of similarity to a reference dataset. It requires a table of read counts per cell per gene, and a list of the cells belonging to each of the clusters, (for both test and reference data). biocViews: SingleCell Author: Sarah Williams [aut, cre] Maintainer: Sarah Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/celaref git_branch: devel git_last_commit: 20dbb3d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/celaref_1.29.0.tar.gz vignettes: vignettes/celaref/inst/doc/celaref_doco.html vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/celaref/inst/doc/celaref_doco.R dependencyCount: 68 Package: celda Version: 1.27.0 Depends: R (>= 4.0), SingleCellExperiment, Matrix Imports: plyr, foreach, ggplot2, RColorBrewer, grid, scales, gtable, grDevices, graphics, matrixStats, doParallel, digest, methods, reshape2, S4Vectors, data.table, Rcpp, RcppEigen, uwot, enrichR, SummarizedExperiment, MCMCprecision, ggrepel, Rtsne, withr, scater (>= 1.14.4), scran, dbscan, DelayedArray, stringr, ComplexHeatmap, gridExtra, circlize, dendextend, ggdendro, pROC LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr, BiocManager, BiocStyle, TENxPBMCData, singleCellTK, M3DExampleData License: MIT + file LICENSE MD5sum: 57e35cf1976faa4863b56633f235a1eb NeedsCompilation: yes Title: CEllular Latent Dirichlet Allocation Description: Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included. biocViews: SingleCell, GeneExpression, Clustering, Sequencing, Bayesian, ImmunoOncology, DataImport Author: Joshua Campbell [aut, cre], Shiyi Yang [aut], Zhe Wang [aut], Sean Corbett [aut], Yusuke Koga [aut] Maintainer: Joshua Campbell VignetteBuilder: knitr BugReports: https://github.com/campbio/celda/issues git_url: https://git.bioconductor.org/packages/celda git_branch: devel git_last_commit: 9bcee6f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/celda_1.27.0.tar.gz vignettes: vignettes/celda/inst/doc/celda.html, vignettes/celda/inst/doc/decontX.html vignetteTitles: Analysis of single-cell genomic data with celda, Estimate and remove cross-contamination from ambient RNA in single-cell data with DecontX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/celda/inst/doc/celda.R, vignettes/celda/inst/doc/decontX.R importsMe: decontX, singleCellTK dependencyCount: 133 Package: CellBarcode Version: 1.17.0 Depends: R (>= 4.1.0) Imports: methods, stats, Rcpp (>= 1.0.5), data.table (>= 1.12.6), plyr, ggplot2, stringr, magrittr, ShortRead (>= 1.48.0), Biostrings (>= 2.58.0), egg, Ckmeans.1d.dp, utils, S4Vectors, seqinr, Rsamtools LinkingTo: Rcpp, BH Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: Artistic-2.0 MD5sum: ec5e6e402569046a7714d5076272844e NeedsCompilation: yes Title: Cellular DNA Barcode Analysis toolkit Description: The package CellBarcode performs Cellular DNA Barcode analysis. It can handle all kinds of DNA barcodes, as long as the barcode is within a single sequencing read and has a pattern that can be matched by a regular expression. \code{CellBarcode} can handle barcodes with flexible lengths, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing, and metagenome data. biocViews: Preprocessing, QualityControl, Sequencing, CRISPR Author: Wenjie Sun [cre, aut] (ORCID: ), Anne-Marie Lyne [aut], Leila Perie [aut] Maintainer: Wenjie Sun URL: https://wenjie1991.github.io/CellBarcode/ VignetteBuilder: knitr BugReports: https://github.com/wenjie1991/CellBarcode/issues git_url: https://git.bioconductor.org/packages/CellBarcode git_branch: devel git_last_commit: bc09e4a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CellBarcode_1.17.0.tar.gz vignettes: vignettes/CellBarcode/inst/doc/Barcode_in_10X_scRNASeq.html, vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.html vignetteTitles: 10X_Barcode, UMI_Barcode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellBarcode/inst/doc/Barcode_in_10X_scRNASeq.R, vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.R dependencyCount: 86 Package: cellbaseR Version: 1.35.0 Depends: R(>= 3.4) Imports: methods, jsonlite, httr, data.table, pbapply, tidyr, R.utils, Rsamtools, BiocParallel, foreach, utils, parallel, doParallel Suggests: BiocStyle, knitr, rmarkdown, Gviz, VariantAnnotation License: Apache License (== 2.0) MD5sum: 51bf39520e75bd29ddfafbaccb15be3d NeedsCompilation: no Title: Querying annotation data from the high performance Cellbase web Description: This R package makes use of the exhaustive RESTful Web service API that has been implemented for the Cellabase database. It enable researchers to query and obtain a wealth of biological information from a single database saving a lot of time. Another benefit is that researchers can easily make queries about different biological topics and link all this information together as all information is integrated. biocViews: Annotation, VariantAnnotation Author: Mohammed OE Abdallah Maintainer: Mohammed OE Abdallah URL: https://github.com/melsiddieg/cellbaseR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellbaseR git_branch: devel git_last_commit: 6f10847 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cellbaseR_1.35.0.tar.gz vignettes: vignettes/cellbaseR/inst/doc/cellbaseR.html vignetteTitles: "Simplifying Genomic Annotations in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellbaseR/inst/doc/cellbaseR.R dependencyCount: 62 Package: CellBench Version: 1.27.0 Depends: R (>= 3.6), SingleCellExperiment, magrittr, methods, stats, tibble, utils Imports: assertthat, BiocGenerics, BiocFileCache, BiocParallel, dplyr, rlang, glue, memoise, purrr (>= 0.3.0), rappdirs, tidyr, tidyselect, lubridate Suggests: BiocStyle, covr, knitr, rmarkdown, testthat, limma, ggplot2 License: GPL-3 MD5sum: d0b501ce2423fb34d7e686c55b663f1f NeedsCompilation: no Title: Construct Benchmarks for Single Cell Analysis Methods Description: This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods. biocViews: Software, Infrastructure, SingleCell Author: Shian Su [cre, aut], Saskia Freytag [aut], Luyi Tian [aut], Xueyi Dong [aut], Matthew Ritchie [aut], Peter Hickey [ctb], Stuart Lee [ctb] Maintainer: Shian Su URL: https://github.com/shians/cellbench VignetteBuilder: knitr BugReports: https://github.com/Shians/CellBench/issues git_url: https://git.bioconductor.org/packages/CellBench git_branch: devel git_last_commit: ecbe643 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CellBench_1.27.0.tar.gz vignettes: vignettes/CellBench/inst/doc/DataManipulation.html, vignettes/CellBench/inst/doc/Introduction.html, vignettes/CellBench/inst/doc/TidyversePatterns.html, vignettes/CellBench/inst/doc/Timing.html, vignettes/CellBench/inst/doc/WritingWrappers.html vignetteTitles: Data Manipulation, Introduction, Tidyverse Patterns, Timing, Writing Wrappers hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellBench/inst/doc/DataManipulation.R, vignettes/CellBench/inst/doc/Introduction.R, vignettes/CellBench/inst/doc/TidyversePatterns.R, vignettes/CellBench/inst/doc/Timing.R, vignettes/CellBench/inst/doc/WritingWrappers.R suggestsMe: corral, speckle dependencyCount: 74 Package: cellity Version: 1.39.0 Depends: R (>= 3.3) Imports: AnnotationDbi, e1071, ggplot2, graphics, grDevices, grid, mvoutlier, org.Hs.eg.db, org.Mm.eg.db, robustbase, stats, topGO, utils Suggests: BiocStyle, caret, knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: 30a4737c8a610d3187b2cbd2912a7bee NeedsCompilation: no Title: Quality Control for Single-Cell RNA-seq Data Description: A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets. biocViews: ImmunoOncology, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, SupportVectorMachine Author: Tomislav Illicic, Davis McCarthy Maintainer: Tomislav Ilicic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellity git_branch: devel git_last_commit: 6d83db0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cellity_1.39.0.tar.gz vignettes: vignettes/cellity/inst/doc/cellity_vignette.html vignetteTitles: An introduction to the cellity package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R dependencyCount: 69 Package: CellMapper Version: 1.37.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 MD5sum: 208f4aeb457883102a3c444c9f6d0a78 NeedsCompilation: no Title: Predict genes expressed selectively in specific cell types Description: Infers cell type-specific expression based on co-expression similarity with known cell type marker genes. Can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before. biocViews: Microarray, Software, GeneExpression Author: Brad Nelms Maintainer: Brad Nelms git_url: https://git.bioconductor.org/packages/CellMapper git_branch: devel git_last_commit: 1372904 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CellMapper_1.37.0.tar.gz vignettes: vignettes/CellMapper/inst/doc/CellMapper.pdf vignetteTitles: CellMapper Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMapper/inst/doc/CellMapper.R dependsOnMe: CellMapperData dependencyCount: 8 Package: CellMentor Version: 0.99.3 Depends: R (>= 4.5.0) Imports: methods, Matrix, BiocParallel, SingleR, Seurat (>= 4.0.0), utils, stats, parallel, progress, ggplot2, data.table, magrittr, graphics, RMTstat, sparsesvd, cluster, skmeans, MLmetrics, tibble, lsa, nnls, SingleCellExperiment, entropy, irlba, aricode Suggests: testthat (>= 3.0.0), covr, withr, rmarkdown, knitr, BiocStyle, scater, scRNAseq License: Apache License (>= 2) MD5sum: 59c3a555652f3df9dd6939b16f8c23ee NeedsCompilation: no Title: Supervised Non-negative Matrix Factorization for Dimensional Reduction in Single-Cell Analysis Description: Implements supervised cell type-aware non-negative matrix factorization (NMF) for dimensional reduction in single-cell RNA sequencing analysis. The package provides methods for incorporating cell type information into the dimensionality reduction process, enabling improved visualization and downstream analysis of single-cell data while preserving biological structure. CellMentor employs a unique loss function that simultaneously minimizes variation within known cell populations while maximizing distinctions between different cell types, enabling effective transfer of learned patterns from labeled reference datasets to new unlabeled data. biocViews: Software, SingleCell, Transcriptomics, DimensionReduction Author: Ekaterina Petrenko [aut, cre] (ORCID: ) Maintainer: Ekaterina Petrenko URL: https://github.com/petrenkokate/CellMentor VignetteBuilder: knitr BugReports: https://github.com/petrenkokate/CellMentor/issues git_url: https://git.bioconductor.org/packages/CellMentor git_branch: devel git_last_commit: 7660309 git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/CellMentor_0.99.3.tar.gz vignettes: vignettes/CellMentor/inst/doc/CellMentor_vignette.html vignetteTitles: Introduction to CellMentor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMentor/inst/doc/CellMentor_vignette.R dependencyCount: 189 Package: cellmig Version: 1.1.8 Depends: R (>= 4.5.0) Imports: base, ggplot2, ggforce, ggtree, patchwork, ape, methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), reshape2, rstan (>= 2.18.1), rstantools (>= 2.4.0), stats, utils, scales LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, knitr, testthat License: GPL-3 + file LICENSE MD5sum: 3d4116a3c40211162015ffe6cc28b534 NeedsCompilation: yes Title: Uncertainty-aware quantitative analysis of high-throughput live cell migration data Description: High-throughput cell imaging facilitates the analysis of cell migration across many wells treated under different biological conditions. These workflows generate considerable technical noise and biological variability, and therefore technical and biological replicates are necessary, leading to large, hierarchically structured datasets, i.e., cells are nested within technical replicates that are nested within biological replicates. Current statistical analyses of such data usually ignore the hierarchical structure of the data and fail to explicitly quantify uncertainty arising from technical or biological variability. To address this gap, we present cellmig, an R package implementing Bayesian hierarchical models for migration analysis. cellmig quantifies condition- specific velocity changes (e.g., drug effects) while modeling nested data structures and technical artifacts. It further enables synthetic data generation for experimental design optimization. biocViews: SingleCell, CellBiology, Bayesian, ExperimentalDesign, Software, BatchEffect, Regression, Clustering Author: Simo Kitanovski [aut, cre] (ORCID: ) Maintainer: Simo Kitanovski URL: https://github.com/snaketron/cellmig SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/snaketron/cellmig/issues git_url: https://git.bioconductor.org/packages/cellmig git_branch: devel git_last_commit: ee2392e git_last_commit_date: 2026-03-30 Date/Publication: 2026-04-20 source.ver: src/contrib/cellmig_1.1.8.tar.gz vignettes: vignettes/cellmig/inst/doc/User_manual_analysis.html, vignettes/cellmig/inst/doc/User_manual_simulation.html vignetteTitles: User Manual: cellmig, User manual: data simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cellmig/inst/doc/User_manual_analysis.R, vignettes/cellmig/inst/doc/User_manual_simulation.R dependencyCount: 110 Package: cellmigRation Version: 1.19.0 Depends: R (>= 4.1), methods, foreach Imports: tiff, graphics, stats, utils, reshape2, parallel, doParallel, grDevices, matrixStats, FME, SpatialTools, sp, vioplot, FactoMineR, Hmisc Suggests: knitr, rmarkdown, dplyr, ggplot2, RUnit, BiocGenerics, BiocManager, kableExtra, rgl License: GPL-2 MD5sum: 8f90196ec53dd75ecdf4d2846ac42ae7 NeedsCompilation: no Title: Track Cells, Analyze Cell Trajectories and Compute Migration Statistics Description: Import TIFF images of fluorescently labeled cells, and track cell movements over time. Parallelization is supported for image processing and for fast computation of cell trajectories. In-depth analysis of cell trajectories is enabled by 15 trajectory analysis functions. biocViews: CellBiology, DataRepresentation, DataImport Author: Salim Ghannoum [aut, cph], Damiano Fantini [aut, cph], Waldir Leoncio [cre, aut], Øystein Sørensen [aut] Maintainer: Waldir Leoncio URL: https://github.com/ocbe-uio/cellmigRation/ VignetteBuilder: knitr BugReports: https://github.com/ocbe-uio/cellmigRation/issues git_url: https://git.bioconductor.org/packages/cellmigRation git_branch: devel git_last_commit: 18cb9ed git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cellmigRation_1.19.0.tar.gz vignettes: vignettes/cellmigRation/inst/doc/cellmigRation.html vignetteTitles: cellmigRation hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellmigRation/inst/doc/cellmigRation.R dependencyCount: 144 Package: CellMixS Version: 1.27.0 Depends: kSamples, R (>= 4.0) Imports: BiocNeighbors, ggplot2, scater, viridis, cowplot, SummarizedExperiment, SingleCellExperiment, tidyr, magrittr, dplyr, ggridges, stats, purrr, methods, BiocParallel, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, testthat, limma, Rtsne License: GPL (>=2) MD5sum: d213cc834baa20828d9d31cd2dcafb9b NeedsCompilation: no Title: Evaluate Cellspecific Mixing Description: CellMixS provides metrics and functions to evaluate batch effects, data integration and batch effect correction in single cell trancriptome data with single cell resolution. Results can be visualized and summarised on different levels, e.g. on cell, celltype or dataset level. biocViews: SingleCell, Transcriptomics, GeneExpression, BatchEffect Author: Almut Lütge [aut, cre] Maintainer: Almut Lütge URL: https://github.com/almutlue/CellMixS VignetteBuilder: knitr BugReports: https://github.com/almutlue/CellMixS/issues git_url: https://git.bioconductor.org/packages/CellMixS git_branch: devel git_last_commit: 4692bc3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CellMixS_1.27.0.tar.gz vignettes: vignettes/CellMixS/inst/doc/CellMixS.html vignetteTitles: Explore data integration and batch effects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMixS/inst/doc/CellMixS.R dependencyCount: 102 Package: CellNOptR Version: 1.57.0 Depends: R (>= 4.0.0), RBGL, graph, methods, RCurl, Rgraphviz, XML, ggplot2, rmarkdown Imports: igraph, stringi, stringr Suggests: data.table, dplyr, tidyr, readr, knitr, RUnit, BiocGenerics, Enhances: doParallel, foreach License: GPL-3 MD5sum: e27f946c2e55161cead57df6d5ceb218 NeedsCompilation: yes Title: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data Description: This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network. biocViews: CellBasedAssays, CellBiology, Proteomics, Pathways, Network, TimeCourse, ImmunoOncology Author: Thomas Cokelaer [aut], Federica Eduati [aut], Aidan MacNamara [aut], S Schrier [ctb], Camille Terfve [aut], Enio Gjerga [ctb], Attila Gabor [cre] Maintainer: Attila Gabor SystemRequirements: Graphviz version >= 2.2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellNOptR git_branch: devel git_last_commit: 1b9d01e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CellNOptR_1.57.0.tar.gz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.html vignetteTitles: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data with CellNOptR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R dependsOnMe: CNORdt, CNORfuzzy, CNORode importsMe: bnem, CNORfeeder suggestsMe: MEIGOR dependencyCount: 62 Package: cellscape Version: 1.35.0 Depends: R (>= 3.3) Imports: dplyr (>= 0.4.3), gtools (>= 3.5.0), htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), reshape2 (>= 1.4.1), stringr (>= 1.0.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: f13f30f53ca9fce3a948e234abbf2257 NeedsCompilation: no Title: Explores single cell copy number profiles in the context of a single cell tree Description: CellScape facilitates interactive browsing of single cell clonal evolution datasets. The tool requires two main inputs: (i) the genomic content of each single cell in the form of either copy number segments or targeted mutation values, and (ii) a single cell phylogeny. Phylogenetic formats can vary from dendrogram-like phylogenies with leaf nodes to evolutionary model-derived phylogenies with observed or latent internal nodes. The CellScape phylogeny is flexibly input as a table of source-target edges to support arbitrary representations, where each node may or may not have associated genomic data. The output of CellScape is an interactive interface displaying a single cell phylogeny and a cell-by-locus genomic heatmap representing the mutation status in each cell for each locus. biocViews: Visualization Author: Shixiang Wang [aut, cre] (ORCID: ), Maia Smith [aut] Maintainer: Shixiang Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellscape git_branch: devel git_last_commit: 2216fc1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cellscape_1.35.0.tar.gz vignettes: vignettes/cellscape/inst/doc/cellscape_vignette.html vignetteTitles: CellScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellscape/inst/doc/cellscape_vignette.R dependencyCount: 49 Package: CellTrails Version: 1.29.0 Depends: R (>= 3.5), SingleCellExperiment Imports: BiocGenerics, Biobase, cba, dendextend, dtw, EnvStats, ggplot2, ggrepel, grDevices, igraph, maptree, methods, mgcv, reshape2, Rtsne, stats, splines, SummarizedExperiment, utils Suggests: AnnotationDbi, destiny, RUnit, scater, scran, knitr, org.Mm.eg.db, rmarkdown License: Artistic-2.0 MD5sum: 212456fa191b5860131311deb809ca4a NeedsCompilation: no Title: Reconstruction, visualization and analysis of branching trajectories Description: CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes. biocViews: ImmunoOncology, Clustering, DataRepresentation, DifferentialExpression, DimensionReduction, GeneExpression, Sequencing, SingleCell, Software, TimeCourse Author: Daniel Ellwanger [aut, cre, cph] Maintainer: Daniel Ellwanger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellTrails git_branch: devel git_last_commit: 75d815a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CellTrails_1.29.0.tar.gz vignettes: vignettes/CellTrails/inst/doc/vignette.pdf vignetteTitles: CellTrails: Reconstruction,, visualization,, and analysis of branching trajectories from single-cell expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellTrails/inst/doc/vignette.R dependencyCount: 69 Package: cellxgenedp Version: 1.15.0 Depends: R (>= 4.1.0), dplyr Imports: httr, curl, utils, tools, cli, shiny, DT, rjsoncons Suggests: zellkonverter, SingleCellExperiment, HDF5Array, tidyr, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), mockery License: Artistic-2.0 MD5sum: 94ac8af3e126fa78ebf1980c64e88925 NeedsCompilation: no Title: Discover and Access Single Cell Data Sets in the CELLxGENE Data Portal Description: The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading. biocViews: SingleCell, DataImport, ThirdPartyClient Author: Martin Morgan [aut, cre] (ORCID: ), Kayla Interdonato [aut] Maintainer: Martin Morgan URL: https://mtmorgan.github.io/cellxgenedp/, https://github.com/mtmorgan/cellxgenedp VignetteBuilder: knitr BugReports: https://github.com/mtmorgan/cellxgenedp/issues git_url: https://git.bioconductor.org/packages/cellxgenedp git_branch: devel git_last_commit: 844e4cf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cellxgenedp_1.15.0.tar.gz vignettes: vignettes/cellxgenedp/inst/doc/a_using_cellxgenedp.html, vignettes/cellxgenedp/inst/doc/b_case_studies.html vignetteTitles: Discovery and retrieval, Case studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellxgenedp/inst/doc/a_using_cellxgenedp.R, vignettes/cellxgenedp/inst/doc/b_case_studies.R dependencyCount: 62 Package: CEMiTool Version: 1.35.0 Depends: R (>= 4.0) Imports: methods, scales, dplyr, data.table (>= 1.9.4), WGCNA, grid, ggplot2, ggpmisc, ggthemes, ggrepel, sna, clusterProfiler, fgsea, stringr, knitr, rmarkdown, igraph, DT, htmltools, pracma, intergraph, grDevices, utils, network, matrixStats, ggdendro, gridExtra, gtable, fastcluster Suggests: testthat, BiocManager License: GPL-3 MD5sum: 05e5f0d620dc5b03ba6d6edb99944446 NeedsCompilation: no Title: Co-expression Modules identification Tool Description: The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology Author: Pedro Russo [aut], Gustavo Ferreira [aut], Matheus Bürger [aut], Lucas Cardozo [aut], Diogenes Lima [aut], Thiago Hirata [aut], Melissa Lever [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CEMiTool git_branch: devel git_last_commit: b427585 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CEMiTool_1.35.0.tar.gz vignettes: vignettes/CEMiTool/inst/doc/CEMiTool.html vignetteTitles: CEMiTool: Co-expression Modules Identification Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CEMiTool/inst/doc/CEMiTool.R dependencyCount: 190 Package: censcyt Version: 1.19.0 Depends: R (>= 4.0), diffcyt Imports: BiocParallel, broom.mixed, dirmult, dplyr, edgeR, fitdistrplus, lme4, magrittr, MASS, methods, mice, multcomp, purrr, rlang, S4Vectors, stats, stringr, SummarizedExperiment, survival, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2 License: MIT + file LICENSE MD5sum: a0ae1451d64328382fc6b069b11da4fb NeedsCompilation: no Title: Differential abundance analysis with a right censored covariate in high-dimensional cytometry Description: Methods for differential abundance analysis in high-dimensional cytometry data when a covariate is subject to right censoring (e.g. survival time) based on multiple imputation and generalized linear mixed models. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software, Survival Author: Reto Gerber [aut, cre] (ORCID: ) Maintainer: Reto Gerber URL: https://github.com/retogerber/censcyt VignetteBuilder: knitr BugReports: https://github.com/retogerber/censcyt/issues git_url: https://git.bioconductor.org/packages/censcyt git_branch: devel git_last_commit: e0a4611 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/censcyt_1.19.0.tar.gz vignettes: vignettes/censcyt/inst/doc/censored_covariate.html vignetteTitles: Censored covariate hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/censcyt/inst/doc/censored_covariate.R dependencyCount: 182 Package: ceRNAnetsim Version: 1.23.0 Depends: R (>= 4.0.0), dplyr, tidygraph Imports: furrr, rlang, tibble, ggplot2, ggraph, igraph, purrr, tidyr, future, stats Suggests: knitr, png, rmarkdown, testthat, covr License: GPL (>= 3.0) MD5sum: 3bb6bf8e048b68820e45041be57cc0e9 NeedsCompilation: no Title: Regulation Simulator of Interaction between miRNA and Competing RNAs (ceRNA) Description: This package simulates regulations of ceRNA (Competing Endogenous) expression levels after a expression level change in one or more miRNA/mRNAs. The methodolgy adopted by the package has potential to incorparate any ceRNA (circRNA, lincRNA, etc.) into miRNA:target interaction network. The package basically distributes miRNA expression over available ceRNAs where each ceRNA attracks miRNAs proportional to its amount. But, the package can utilize multiple parameters that modify miRNA effect on its target (seed type, binding energy, binding location, etc.). The functions handle the given dataset as graph object and the processes progress via edge and node variables. biocViews: NetworkInference, SystemsBiology, Network, GraphAndNetwork, Transcriptomics Author: Selcen Ari Yuka [aut, cre] (ORCID: ), Alper Yilmaz [aut] (ORCID: ) Maintainer: Selcen Ari Yuka URL: https://github.com/selcenari/ceRNAnetsim VignetteBuilder: knitr BugReports: https://github.com/selcenari/ceRNAnetsim/issues git_url: https://git.bioconductor.org/packages/ceRNAnetsim git_branch: devel git_last_commit: 4b53125 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ceRNAnetsim_1.23.0.tar.gz vignettes: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.html, vignettes/ceRNAnetsim/inst/doc/basic_usage.html, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.html, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.html vignetteTitles: auxiliary_commands, basic_usage, A Suggestion: How to Find the Appropriate Iteration for Simulation, An TCGA dataset application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.R, vignettes/ceRNAnetsim/inst/doc/basic_usage.R, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.R, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.R dependencyCount: 65 Package: CeTF Version: 1.23.0 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, clusterProfiler, DESeq2, dplyr, GenomicTools.fileHandler, GGally, ggnetwork, ggplot2, ggpubr, ggrepel, graphics, grid, igraph, Matrix, network, Rcpp, RCy3, stats, SummarizedExperiment, S4Vectors, utils, methods LinkingTo: Rcpp, RcppArmadillo Suggests: airway, kableExtra, knitr, org.Hs.eg.db, rmarkdown, testthat License: GPL-3 MD5sum: 0f269591e4796c60c8a57fbe6a498380 NeedsCompilation: yes Title: Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis Description: This package provides the necessary functions for performing the Partial Correlation coefficient with Information Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network and can be applied to any correlation-based network including but not limited to gene co-expression networks, while the RIF algorithm identify critical Transcription Factors (TF) from gene expression data. These two algorithms when combined provide a very relevant layer of information for gene expression studies (Microarray, RNA-seq and single-cell RNA-seq data). biocViews: Sequencing, RNASeq, Microarray, GeneExpression, Transcription, Normalization, DifferentialExpression, SingleCell, Network, Regression, ChIPSeq, ImmunoOncology, Coverage Author: Carlos Alberto Oliveira de Biagi Junior [aut, cre], Ricardo Perecin Nociti [aut], Breno Osvaldo Funicheli [aut], João Paulo Bianchi Ximenez [ctb], Patrícia de Cássia Ruy [ctb], Marcelo Gomes de Paula [ctb], Rafael dos Santos Bezerra [ctb], Wilson Araújo da Silva Junior [aut, ths] Maintainer: Carlos Alberto Oliveira de Biagi Junior SystemRequirements: libcurl4-openssl-dev, libxml2-dev, libssl-dev, gfortran, build-essential, libz-dev, zlib1g-dev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CeTF git_branch: devel git_last_commit: d8786fc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CeTF_1.23.0.tar.gz vignettes: vignettes/CeTF/inst/doc/CeTF.html vignetteTitles: Analyzing Regulatory Impact Factors and Partial Correlation and Information Theory hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CeTF/inst/doc/CeTF.R dependencyCount: 228 Package: CexoR Version: 1.49.0 Depends: R (>= 4.2.0), S4Vectors, IRanges Imports: Rsamtools, Seqinfo, GenomicRanges, rtracklayer, idr, RColorBrewer, genomation Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | GPL-2 + file LICENSE MD5sum: 747246f7cc1eec9069b76350e20c71b8 NeedsCompilation: no Title: An R package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates Description: Strand specific peak-pair calling in ChIP-exo replicates. The cumulative Skellam distribution function is used to detect significant normalised count differences of opposed sign at each DNA strand (peak-pairs). Then, irreproducible discovery rate for overlapping peak-pairs across biological replicates is computed. biocViews: FunctionalGenomics, Sequencing, Coverage, ChIPSeq, PeakDetection Author: Pedro Madrigal [aut, cre] (ORCID: ) Maintainer: Pedro Madrigal URL: https://github.com/pmb59/CexoR BugReports: https://github.com/pmb59/CexoR/issues git_url: https://git.bioconductor.org/packages/CexoR git_branch: devel git_last_commit: e9655e9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CexoR_1.49.0.tar.gz vignettes: vignettes/CexoR/inst/doc/CexoR.pdf vignetteTitles: CexoR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CexoR/inst/doc/CexoR.R dependencyCount: 101 Package: CFAssay Version: 1.45.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: d5fcc9ec68d96616db6cf6048f591140 NeedsCompilation: no Title: Statistical analysis for the Colony Formation Assay Description: The package provides functions for calculation of linear-quadratic cell survival curves and for ANOVA of experimental 2-way designs along with the colony formation assay. biocViews: CellBasedAssays, CellBiology, ImmunoOncology, Regression, Survival Author: Herbert Braselmann Maintainer: Herbert Braselmann git_url: https://git.bioconductor.org/packages/CFAssay git_branch: devel git_last_commit: f9526bc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CFAssay_1.45.0.tar.gz vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf vignetteTitles: CFAssay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CFAssay/inst/doc/cfassay.R dependencyCount: 0 Package: cfdnakit Version: 1.9.0 Depends: R (>= 4.3) Imports: Biobase, dplyr, GenomicRanges, GenomeInfoDb, ggplot2, IRanges, magrittr, PSCBS, QDNAseq, Rsamtools, utils, S4Vectors, stats, rlang Suggests: rmarkdown, knitr, roxygen2, BiocStyle License: GPL-3 MD5sum: 3ed281ded34fc807fd48f12ca59ca8bc NeedsCompilation: no Title: Fragmen-length analysis package from high-throughput sequencing of cell-free DNA (cfDNA) Description: This package provides basic functions for analyzing shallow whole-genome sequencing (~0.3X or more) of cell-free DNA (cfDNA). The package basically extracts the length of cfDNA fragments and aids the vistualization of fragment-length information. The package also extract fragment-length information per non-overlapping fixed-sized bins and used it for calculating ctDNA estimation score (CES). biocViews: CopyNumberVariation, Sequencing, WholeGenome Author: Pitithat Puranachot [aut, cre] (ORCID: ) Maintainer: Pitithat Puranachot VignetteBuilder: knitr BugReports: https://github.com/Pitithat-pu/cfdnakit/issues git_url: https://git.bioconductor.org/packages/cfdnakit git_branch: devel git_last_commit: 17e7520 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cfdnakit_1.9.0.tar.gz vignettes: vignettes/cfdnakit/inst/doc/cfdnakit-vignette.html vignetteTitles: cfdnakit vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cfdnakit/inst/doc/cfdnakit-vignette.R dependencyCount: 85 Package: cfDNAPro Version: 1.17.1 Depends: R (>= 4.1.0), magrittr (>= 1.5.0) Imports: tibble, GenomicAlignments, IRanges, plyranges, GenomeInfoDb, GenomicRanges, BiocGenerics, stats, utils, dplyr (>= 0.8.3), stringr (>= 1.4.0), quantmod (>= 0.4), ggplot2 (>= 3.2.1), Rsamtools (>= 2.4.0), rlang (>= 0.4.0), BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.NCBI.GRCh38 Suggests: scales, ggpubr, knitr (>= 1.23), rmarkdown (>= 1.14), devtools (>= 2.3.0), BiocStyle, testthat License: GPL-3 MD5sum: be3a92f5d230daddb0167ca7863a5051 NeedsCompilation: no Title: cfDNAPro extracts and Visualises biological features from whole genome sequencing data of cell-free DNA Description: cfDNA fragments carry important features for building cancer sample classification ML models, such as fragment size, and fragment end motif etc. Analyzing and visualizing fragment size metrics, as well as other biological features in a curated, standardized, scalable, well-documented, and reproducible way might be time intensive. This package intends to resolve these problems and simplify the process. It offers two sets of functions for cfDNA feature characterization and visualization. biocViews: Visualization, Sequencing, WholeGenome Author: Haichao Wang [aut, cre], Hui Zhao [ctb], Elkie Chan [ctb], Christopher Smith [ctb], Tomer Kaplan [ctb], Florian Markowetz [ctb], Nitzan Rosenfeld [ctb] Maintainer: Haichao Wang URL: https://github.com/hw538/cfDNAPro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cfDNAPro git_branch: devel git_last_commit: de9427a git_last_commit_date: 2026-02-14 Date/Publication: 2026-04-20 source.ver: src/contrib/cfDNAPro_1.17.1.tar.gz vignettes: vignettes/cfDNAPro/inst/doc/cfDNAPro.html vignetteTitles: cfDNAPro Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cfDNAPro/inst/doc/cfDNAPro.R dependencyCount: 92 Package: CGEN Version: 3.47.0 Depends: R (>= 4.0), survival, mvtnorm Imports: stats, graphics, utils, grDevices Suggests: cluster License: GPL-2 + file LICENSE MD5sum: 8b4afa5140ce43630ce33cb1bf22ac3d NeedsCompilation: yes Title: An R package for analysis of case-control studies in genetic epidemiology Description: This is a package for analysis of case-control data in genetic epidemiology. It provides a set of statistical methods for evaluating gene-environment (or gene-genes) interactions under multiplicative and additive risk models, with or without assuming gene-environment (or gene-gene) independence in the underlying population. biocViews: SNP, MultipleComparison, Clustering Author: Samsiddhi Bhattacharjee [aut], Nilanjan Chatterjee [aut], Summer Han [aut], Minsun Song [aut], William Wheeler [aut], Matthieu de Rochemonteix [aut], Nilotpal Sanyal [aut], Justin Lee [cre] Maintainer: Justin Lee git_url: https://git.bioconductor.org/packages/CGEN git_branch: devel git_last_commit: 1523d57 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CGEN_3.47.0.tar.gz vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf, vignettes/CGEN/inst/doc/vignette.pdf vignetteTitles: CGEN Scan Vignette, CGEN Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CGEN/inst/doc/vignette_GxE.R, vignettes/CGEN/inst/doc/vignette.R dependencyCount: 11 Package: CGHbase Version: 1.71.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: c61f9e07d248e42e74748c1236bd0ac2 NeedsCompilation: no Title: CGHbase: Base functions and classes for arrayCGH data analysis. Description: Contains functions and classes that are needed by arrayCGH packages. biocViews: Infrastructure, Microarray, CopyNumberVariation Author: Sjoerd Vosse, Mark van de Wiel Maintainer: Mark van de Wiel URL: https://github.com/tgac-vumc/CGHbase BugReports: https://github.com/tgac-vumc/CGHbase/issues git_url: https://git.bioconductor.org/packages/CGHbase git_branch: devel git_last_commit: 709264e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CGHbase_1.71.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak importsMe: CGHnormaliter, QDNAseq dependencyCount: 11 Package: CGHcall Version: 2.73.0 Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods, Biobase, CGHbase (>= 1.15.1), snowfall License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: f2f1bd45b0268b77321bc0cab925c2ae NeedsCompilation: no Title: Calling aberrations for array CGH tumor profiles. Description: Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. biocViews: Microarray,Preprocessing,Visualization Author: Mark van de Wiel, Sjoerd Vosse Maintainer: Mark van de Wiel git_url: https://git.bioconductor.org/packages/CGHcall git_branch: devel git_last_commit: 0cacd10 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CGHcall_2.73.0.tar.gz vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHcall/inst/doc/CGHcall.R dependsOnMe: CGHnormaliter, GeneBreak importsMe: CGHnormaliter, QDNAseq dependencyCount: 16 Package: cghMCR Version: 1.69.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: ca01cee3fa2bd81d2ab5949c527a1a08 NeedsCompilation: no Title: Find chromosome regions showing common gains/losses Description: This package provides functions to identify genomic regions of interests based on segmented copy number data from multiple samples. biocViews: Microarray, CopyNumberVariation Author: J. Zhang and B. Feng Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/cghMCR git_branch: devel git_last_commit: 310f992 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cghMCR_1.69.0.tar.gz vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf vignetteTitles: cghMCR findMCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cghMCR/inst/doc/findMCR.R dependencyCount: 57 Package: CGHnormaliter Version: 1.65.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: 69dacfcfe37d45138cbb8c4eceae9129 NeedsCompilation: no Title: Normalization of array CGH data with imbalanced aberrations. Description: Normalization and centralization of array comparative genomic hybridization (aCGH) data. The algorithm uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). biocViews: Microarray, Preprocessing Author: Bart P.P. van Houte, Thomas W. Binsl, Hannes Hettling Maintainer: Bart P.P. van Houte git_url: https://git.bioconductor.org/packages/CGHnormaliter git_branch: devel git_last_commit: 1dc052d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CGHnormaliter_1.65.0.tar.gz vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf vignetteTitles: CGHnormaliter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.R dependencyCount: 17 Package: CGHregions Version: 1.69.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 6bc13286c4f7f42bef27dc3983c2c9ea NeedsCompilation: no Title: Dimension Reduction for Array CGH Data with Minimal Information Loss. Description: Dimension Reduction for Array CGH Data with Minimal Information Loss biocViews: Microarray, CopyNumberVariation, Visualization Author: Sjoerd Vosse & Mark van de Wiel Maintainer: Sjoerd Vosse git_url: https://git.bioconductor.org/packages/CGHregions git_branch: devel git_last_commit: 84741eb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CGHregions_1.69.0.tar.gz vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHregions/inst/doc/CGHregions.R suggestsMe: ADaCGH2 dependencyCount: 12 Package: ChemmineOB Version: 1.49.2 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp Suggests: ChemmineR, BiocStyle, knitr, knitrBootstrap, BiocManager, rmarkdown,RUnit,codetools Enhances: ChemmineR (>= 2.13.0) License: Artistic-2.0 MD5sum: 585fa47048ecd7f33664888be05d2bce NeedsCompilation: yes Title: R interface to a subset of OpenBabel functionalities Description: ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineOB SystemRequirements: OpenBabel (>= 3.0.0) with headers (http://openbabel.org). Eigen3 with headers. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineOB git_branch: devel git_last_commit: 4771ee7 git_last_commit_date: 2026-03-12 Date/Publication: 2026-04-20 source.ver: src/contrib/ChemmineOB_1.49.2.tar.gz vignettes: vignettes/ChemmineOB/inst/doc/ChemmineOB.html vignetteTitles: ChemmineOB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R dependencyCount: 8 Package: ChemmineR Version: 3.63.1 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0), ggplot2,grid,gridExtra, png,base64enc,DT,rsvg,jsonlite,stringi LinkingTo: Rcpp, BH Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL, BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineDrugs, png,rmarkdown, BiocManager,bibtex,codetools Enhances: ChemmineOB License: Artistic-2.0 MD5sum: 0ab1b42b0f2665deb647cf245df9c7fc NeedsCompilation: yes Title: Cheminformatics Toolkit for R Description: ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics,Metabolomics Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineR SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineR git_branch: devel git_last_commit: aea8b48 git_last_commit_date: 2026-03-12 Date/Publication: 2026-04-20 source.ver: src/contrib/ChemmineR_3.63.1.tar.gz vignettes: vignettes/ChemmineR/inst/doc/ChemmineR.html vignetteTitles: ChemmineR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R dependsOnMe: eiR, fmcsR, ChemmineDrugs importsMe: bioassayR, CompoundDb, customCMPdb, eiR, fmcsR, MetID, RMassBank, chemodiv suggestsMe: ChemmineOB, xnet dependencyCount: 65 Package: CHETAH Version: 1.27.0 Depends: R (>= 4.2), ggplot2, SingleCellExperiment Imports: shiny, plotly, pheatmap, bioDist, dendextend, cowplot, corrplot, grDevices, stats, graphics, reshape2, S4Vectors, SummarizedExperiment Suggests: knitr, rmarkdown, Matrix, testthat, vdiffr License: file LICENSE MD5sum: 85bb55dd06ec4c9f3466d786d50c5656 NeedsCompilation: no Title: Fast and accurate scRNA-seq cell type identification Description: CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate, selective and fast scRNA-seq classifier. Classification is guided by a reference dataset, preferentially also a scRNA-seq dataset. By hierarchical clustering of the reference data, CHETAH creates a classification tree that enables a step-wise, top-to-bottom classification. Using a novel stopping rule, CHETAH classifies the input cells to the cell types of the references and to "intermediate types": more general classifications that ended in an intermediate node of the tree. biocViews: Classification, RNASeq, SingleCell, Clustering, GeneExpression, ImmunoOncology Author: Jurrian de Kanter [aut, cre], Philip Lijnzaad [aut] Maintainer: Jurrian de Kanter URL: https://github.com/jdekanter/CHETAH VignetteBuilder: knitr BugReports: https://github.com/jdekanter/CHETAH git_url: https://git.bioconductor.org/packages/CHETAH git_branch: devel git_last_commit: 93f3784 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CHETAH_1.27.0.tar.gz vignettes: vignettes/CHETAH/inst/doc/CHETAH_introduction.html vignetteTitles: Introduction to the CHETAH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CHETAH/inst/doc/CHETAH_introduction.R suggestsMe: adverSCarial dependencyCount: 105 Package: chevreulPlot Version: 1.3.0 Depends: R (>= 4.5.0), SingleCellExperiment, chevreulProcess Imports: base, cluster, clustree, ComplexHeatmap (>= 2.5.4), circlize, dplyr, EnsDb.Hsapiens.v86, forcats, fs, ggplot2, grid, plotly, purrr, S4Vectors, scales, scater, scran, scuttle, stats, stringr, tibble, tidyr, utils, wiggleplotr (>= 1.13.1), tidyselect, patchwork Suggests: BiocStyle, knitr, RefManageR, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: af9a1c7b44657cfbbf6397c69c44fa61 NeedsCompilation: no Title: Plots used in the chevreulPlot package Description: Tools for plotting SingleCellExperiment objects in the chevreulPlot package. Includes functions for analysis and visualization of single-cell data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik. biocViews: Coverage, RNASeq, Sequencing, Visualization, GeneExpression, Transcription, SingleCell, Transcriptomics, Normalization, Preprocessing, QualityControl, DimensionReduction, DataImport Author: Kevin Stachelek [aut, cre] (ORCID: ), Bhavana Bhat [aut] Maintainer: Kevin Stachelek URL: https://github.com/whtns/chevreulPlot, https://whtns.github.io/chevreulPlot/ VignetteBuilder: knitr BugReports: https://github.com/cobriniklab/chevreulPlot/issues git_url: https://git.bioconductor.org/packages/chevreulPlot git_branch: devel git_last_commit: a18fe12 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/chevreulPlot_1.3.0.tar.gz vignettes: vignettes/chevreulPlot/inst/doc/chevreulPlot.html, vignettes/chevreulPlot/inst/doc/visualization.html vignetteTitles: Preprocessing, Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chevreulPlot/inst/doc/chevreulPlot.R, vignettes/chevreulPlot/inst/doc/visualization.R dependsOnMe: chevreulShiny dependencyCount: 232 Package: chevreulProcess Version: 1.3.0 Depends: R (>= 4.5.0), SingleCellExperiment, scater Imports: batchelor, bluster, circlize, cluster, DBI, dplyr, EnsDb.Hsapiens.v86, ensembldb, fs, GenomicFeatures, glue, megadepth, methods, purrr, RSQLite, S4Vectors, scran, scuttle, stringr, tibble, tidyr, tidyselect, utils Suggests: BiocStyle, knitr, RefManageR, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 860c0f6c6af484677b45f8f5d5467d72 NeedsCompilation: no Title: Tools for managing SingleCellExperiment objects as projects Description: Tools for analyzing SingleCellExperiment objects as projects. for input into the chevreulShiny app downstream. Includes functions for analysis of single cell RNA sequencing data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik. biocViews: Coverage, RNASeq, Sequencing, Visualization, GeneExpression, Transcription, SingleCell, Transcriptomics, Normalization, Preprocessing, QualityControl, DimensionReduction, DataImport Author: Kevin Stachelek [aut, cre] (ORCID: ), Bhavana Bhat [aut] Maintainer: Kevin Stachelek URL: https://github.com/whtns/chevreulProcess, https://whtns.github.io/chevreulProcess/ VignetteBuilder: knitr BugReports: https://github.com/cobriniklab/chevreulProcess/issues git_url: https://git.bioconductor.org/packages/chevreulProcess git_branch: devel git_last_commit: c6b3ecf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/chevreulProcess_1.3.0.tar.gz vignettes: vignettes/chevreulProcess/inst/doc/chevreulProcess.html vignetteTitles: Preprocessing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chevreulProcess/inst/doc/chevreulProcess.R dependsOnMe: chevreulPlot, chevreulShiny dependencyCount: 191 Package: chevreulShiny Version: 1.3.0 Depends: R (>= 4.5.0), SingleCellExperiment, shiny (>= 1.6.0), shinydashboard, chevreulProcess, chevreulPlot Imports: alabaster.base, clustree, ComplexHeatmap, DataEditR (>= 0.0.9), DBI, dplyr, DT, EnhancedVolcano, fs, future, ggplot2, ggplotify, grDevices, methods, patchwork, plotly, purrr, rappdirs, readr, RSQLite, S4Vectors, scales, shinyFiles, shinyhelper, shinyjs, shinyWidgets, stats, stringr, tibble, tidyr, tidyselect, utils, waiter, wiggleplotr Suggests: BiocStyle, knitr, RefManageR, rmarkdown, testthat (>= 3.0.0), EnsDb.Mmusculus.v79, EnsDb.Hsapiens.v86 License: MIT + file LICENSE MD5sum: bcd86fe21673ad5240b9c32418609997 NeedsCompilation: no Title: Tools for managing SingleCellExperiment objects as projects Description: Tools for managing SingleCellExperiment objects as projects. Includes functions for analysis and visualization of single-cell data. Also included is a shiny app for visualization of pre-processed scRNA data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik. biocViews: Coverage, RNASeq, Sequencing, Visualization, GeneExpression, Transcription, SingleCell, Transcriptomics, Normalization, Preprocessing, QualityControl, DimensionReduction, DataImport Author: Kevin Stachelek [aut, cre] (ORCID: ), Bhavana Bhat [aut] Maintainer: Kevin Stachelek URL: https://github.com/whtns/chevreulShiny, https://whtns.github.io/chevreulShiny/ VignetteBuilder: knitr BugReports: https://github.com/cobriniklab/chevreulShiny/issues git_url: https://git.bioconductor.org/packages/chevreulShiny git_branch: devel git_last_commit: 7845161 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/chevreulShiny_1.3.0.tar.gz vignettes: vignettes/chevreulShiny/inst/doc/chevreulShiny.html, vignettes/chevreulShiny/inst/doc/shiny_app.html vignetteTitles: Preprocessing, Shiny App hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chevreulShiny/inst/doc/chevreulShiny.R, vignettes/chevreulShiny/inst/doc/shiny_app.R dependencyCount: 269 Package: Chicago Version: 1.39.0 Depends: R (>= 3.3.1), data.table Imports: matrixStats, MASS, Hmisc, Delaporte, methods, grDevices, graphics, stats, utils Suggests: argparser, BiocStyle, knitr, rmarkdown, PCHiCdata, testthat, GenomeInfoDb, Rsamtools, GenomicInteractions, GenomicRanges, IRanges, AnnotationHub License: Artistic-2.0 MD5sum: 2b2d22e729b21ecc792ad564abb0da47 NeedsCompilation: no Title: CHiCAGO: Capture Hi-C Analysis of Genomic Organization Description: A pipeline for analysing Capture Hi-C data. biocViews: Epigenetics, HiC, Sequencing, Software Author: Jonathan Cairns, Paula Freire Pritchett, Steven Wingett, Mikhail Spivakov Maintainer: Mikhail Spivakov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Chicago git_branch: devel git_last_commit: c2054da git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Chicago_1.39.0.tar.gz vignettes: vignettes/Chicago/inst/doc/Chicago.html vignetteTitles: CHiCAGO Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chicago/inst/doc/Chicago.R dependsOnMe: PCHiCdata dependencyCount: 66 Package: chimeraviz Version: 1.37.0 Depends: Biostrings, GenomicRanges, IRanges, Gviz, S4Vectors, ensembldb, AnnotationFilter, data.table Imports: methods, grid, Rsamtools, GenomeInfoDb, GenomicAlignments, RColorBrewer, graphics, AnnotationDbi, RCircos, org.Hs.eg.db, org.Mm.eg.db, rmarkdown, graph, Rgraphviz, DT, plyr, dplyr, BiocStyle, checkmate, gtools, magick Suggests: testthat, roxygen2, devtools, knitr, lintr License: Artistic-2.0 MD5sum: 45e8b0c046ec254566c003b4965d49ce NeedsCompilation: no Title: Visualization tools for gene fusions Description: chimeraviz manages data from fusion gene finders and provides useful visualization tools. biocViews: Infrastructure, Alignment Author: Stian Lågstad [aut, cre], Sen Zhao [ctb], Andreas M. Hoff [ctb], Bjarne Johannessen [ctb], Ole Christian Lingjærde [ctb], Rolf Skotheim [ctb] Maintainer: Stian Lågstad URL: https://github.com/stianlagstad/chimeraviz SystemRequirements: bowtie, samtools, and egrep are required for some functionalities VignetteBuilder: knitr BugReports: https://github.com/stianlagstad/chimeraviz/issues git_url: https://git.bioconductor.org/packages/chimeraviz git_branch: devel git_last_commit: 3490116 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/chimeraviz_1.37.0.tar.gz vignettes: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.html vignetteTitles: chimeraviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R dependencyCount: 167 Package: ChIPanalyser Version: 1.33.0 Depends: R (>= 3.5.0),GenomicRanges, Biostrings, BSgenome, RcppRoll, parallel Imports: methods, IRanges, S4Vectors,grDevices,graphics,stats,utils,rtracklayer,ROCR, BiocManager,GenomeInfoDb,RColorBrewer Suggests: BSgenome.Dmelanogaster.UCSC.dm6,knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: dd4f10fb69d19bfb77e8011cc4c26083 NeedsCompilation: no Title: ChIPanalyser: Predicting Transcription Factor Binding Sites Description: ChIPanalyser is a package to predict and understand TF binding by utilizing a statistical thermodynamic model. The model incorporates 4 main factors thought to drive TF binding: Chromatin State, Binding energy, Number of bound molecules and a scaling factor modulating TF binding affinity. Taken together, ChIPanalyser produces ChIP-like profiles that closely mimic the patterns seens in real ChIP-seq data. biocViews: Software, BiologicalQuestion, WorkflowStep, Transcription, Sequencing, ChipOnChip, Coverage, Alignment, ChIPSeq, SequenceMatching, DataImport ,PeakDetection Author: Patrick C.N.Martin & Nicolae Radu Zabet Maintainer: Patrick C.N. Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPanalyser git_branch: devel git_last_commit: 873269e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ChIPanalyser_1.33.0.tar.gz vignettes: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.pdf, vignettes/ChIPanalyser/inst/doc/GA_ChIPanalyser.pdf vignetteTitles: ChIPanalyser User's Guide, ChIPanalyser User's Guide for Genetic Algorithms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.R, vignettes/ChIPanalyser/inst/doc/GA_ChIPanalyser.R dependencyCount: 69 Package: chipenrich Version: 2.35.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, BiocGenerics, chipenrich.data, Seqinfo, GenomicRanges, grDevices, grid, IRanges, lattice, latticeExtra, MASS, methods, mgcv, org.Dm.eg.db, org.Dr.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, parallel, plyr, rms, rtracklayer, S4Vectors (>= 0.23.10), stats, stringr, utils Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat License: GPL-3 MD5sum: a8b7cfc86cc9a00bbeb37965d6b18b0c NeedsCompilation: no Title: Gene Set Enrichment For ChIP-seq Peak Data Description: ChIP-Enrich and Poly-Enrich perform gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes. biocViews: ImmunoOncology, ChIPSeq, Epigenetics, FunctionalGenomics, GeneSetEnrichment, HistoneModification, Regression Author: Ryan P. Welch [aut, cph], Chee Lee [aut], Raymond G. Cavalcante [aut], Kai Wang [cre], Chris Lee [aut], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Kai Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipenrich git_branch: devel git_last_commit: 93dd65a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/chipenrich_2.35.0.tar.gz vignettes: vignettes/chipenrich/inst/doc/chipenrich-vignette.html vignetteTitles: chipenrich_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich-vignette.R dependencyCount: 157 Package: ChIPexoQual Version: 1.35.0 Depends: R (>= 3.5.0), GenomicAlignments (>= 1.45.1) Imports: methods, utils, Seqinfo, stats, BiocParallel, GenomicRanges (>= 1.61.1), ggplot2 (>= 1.0), data.table (>= 1.9.6), Rsamtools (>= 2.25.1), IRanges (>= 1.6), S4Vectors (>= 0.8), biovizBase (>= 1.18), broom (>= 0.4), RColorBrewer (>= 1.1), dplyr (>= 0.5), scales (>= 0.4.0), viridis (>= 0.3), hexbin (>= 1.27), rmarkdown Suggests: ChIPexoQualExample (>= 0.99.1), knitr (>= 1.10), BiocStyle, gridExtra (>= 2.2), testthat License: GPL (>=2) MD5sum: a851398415d5c8e3572d4b5337e6a6a2 NeedsCompilation: no Title: ChIPexoQual Description: Package with a quality control pipeline for ChIP-exo/nexus data. biocViews: ChIPSeq, Sequencing, Transcription, Visualization, QualityControl, Coverage, Alignment Author: Rene Welch, Dongjun Chung, Sunduz Keles Maintainer: Rene Welch URL: https:github.com/keleslab/ChIPexoQual VignetteBuilder: knitr BugReports: https://github.com/welch16/ChIPexoQual/issues git_url: https://git.bioconductor.org/packages/ChIPexoQual git_branch: devel git_last_commit: c1a42ad git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ChIPexoQual_1.35.0.tar.gz vignettes: vignettes/ChIPexoQual/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPexoQual/inst/doc/vignette.R dependencyCount: 137 Package: ChIPseeker Version: 1.47.1 Depends: R (>= 4.1.0) Imports: AnnotationDbi, aplot, BiocGenerics, boot, dplyr, enrichplot, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, graphics, grDevices, gtools, magrittr, methods, plotrix, parallel, RColorBrewer, rlang, rtracklayer, S4Vectors, scales, stats, tibble, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, yulab.utils (>= 0.2.0) Suggests: clusterProfiler, ggimage, ggplotify, ggupset, ggVennDiagram, knitr, org.Hs.eg.db, prettydoc, ReactomePA, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 MD5sum: 57a56af370968f3313c4bf098091e661 NeedsCompilation: no Title: ChIPseeker for ChIP peak Annotation, Comparison, and Visualization Description: This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. biocViews: Annotation, ChIPSeq, Software, Visualization, MultipleComparison Author: Guangchuang Yu [aut, cre] (ORCID: ), Ming Li [ctb], Qianwen Wang [ctb], Yun Yan [ctb], Hervé Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb], Zhougeng Xu [ctb], Chun-Hui Gao [ctb] (ORCID: ) Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ChIPseeker/issues git_url: https://git.bioconductor.org/packages/ChIPseeker git_branch: devel git_last_commit: be79240 git_last_commit_date: 2025-11-04 Date/Publication: 2026-04-20 source.ver: src/contrib/ChIPseeker_1.47.1.tar.gz vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.html vignetteTitles: ChIPseeker: an R package for ChIP peak Annotation,, Comparison and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R importsMe: EpiCompare, esATAC, segmenter suggestsMe: GRaNIE, curatedAdipoChIP, cinaR dependencyCount: 157 Package: chipseq Version: 1.61.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), ShortRead Imports: methods, stats, lattice, BiocGenerics, IRanges, GenomicRanges, ShortRead Suggests: BSgenome, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, BSgenome.Mmusculus.UCSC.mm9, BiocStyle, knitr License: Artistic-2.0 MD5sum: dfd3c86cdf8059a62bb02a22670b7dfc NeedsCompilation: yes Title: chipseq: A package for analyzing chipseq data Description: Tools for helping process short read data for chipseq experiments. biocViews: ChIPSeq, Sequencing, Coverage, QualityControl, DataImport Author: Deepayan Sarkar [aut], Robert Gentleman [aut], Michael Lawrence [aut], Zizhen Yao [aut], Oluwabukola Bamigbade [ctb] (Converted vignette from Sweave to R Markdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipseq git_branch: devel git_last_commit: f81d061 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/chipseq_1.61.0.tar.gz vignettes: vignettes/chipseq/inst/doc/Workflow.html vignetteTitles: Some Basic Analysis of ChIP-Seq Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseq/inst/doc/Workflow.R importsMe: transcriptR dependencyCount: 54 Package: ChIPseqR Version: 1.65.0 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors (>= 0.9.25) Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.5.14), graphics, grDevices, HilbertVis, ShortRead, stats, timsac, utils License: GPL (>= 2) MD5sum: be816aed24be0e515865ac03db4c0b6a NeedsCompilation: yes Title: Identifying Protein Binding Sites in High-Throughput Sequencing Data Description: ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should flexible enough to handle other types of experiments as well. biocViews: ChIPSeq, Infrastructure Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPseqR git_branch: devel git_last_commit: 7948588 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ChIPseqR_1.65.0.tar.gz vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf vignetteTitles: Introduction to ChIPseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseqR/inst/doc/Introduction.R dependencyCount: 62 Package: ChIPsim Version: 1.65.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: 111aeeb7961f85944b25f66674e37af1 NeedsCompilation: no Title: Simulation of ChIP-seq experiments Description: A general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments. biocViews: Infrastructure, ChIPSeq Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPsim git_branch: devel git_last_commit: 5fed093 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ChIPsim_1.65.0.tar.gz vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf vignetteTitles: Simulating ChIP-seq experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPsim/inst/doc/ChIPsimIntro.R dependencyCount: 54 Package: ChIPXpress Version: 1.55.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 19ef8305286769cc601c7543dba19f91 NeedsCompilation: no Title: ChIPXpress: enhanced transcription factor target gene identification from ChIP-seq and ChIP-chip data using publicly available gene expression profiles Description: ChIPXpress takes as input predicted TF bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target. biocViews: ChIPchip, ChIPSeq Author: George Wu Maintainer: George Wu git_url: https://git.bioconductor.org/packages/ChIPXpress git_branch: devel git_last_commit: 7be5116 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ChIPXpress_1.55.0.tar.gz vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf vignetteTitles: ChIPXpress hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPXpress/inst/doc/ChIPXpress.R dependencyCount: 104 Package: chopsticks Version: 1.77.0 Imports: graphics, stats, utils, methods, survival Suggests: hexbin License: GPL-3 MD5sum: 08b1f38d456ecf107e7cb0dcc724aece NeedsCompilation: yes Title: The 'snp.matrix' and 'X.snp.matrix' Classes Description: Implements classes and methods for large-scale SNP association studies biocViews: Microarray, SNPsAndGeneticVariability, SNP, GeneticVariability Author: Hin-Tak Leung Maintainer: Hin-Tak Leung URL: http://outmodedbonsai.sourceforge.net/ git_url: https://git.bioconductor.org/packages/chopsticks git_branch: devel git_last_commit: 06c2564 git_last_commit_date: 2026-03-25 Date/Publication: 2026-04-20 source.ver: src/contrib/chopsticks_1.77.0.tar.gz vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf vignetteTitles: snpMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chopsticks/inst/doc/chopsticks-vignette.R dependencyCount: 10 Package: Chromatograms Version: 1.1.8 Depends: BiocParallel, ProtGenerics (>= 1.39.2), R (>= 4.5.0) Imports: data.table, methods, S4Vectors, MsCoreUtils (>= 1.7.5), Spectra Suggests: msdata (>= 0.19.3), roxygen2, BiocStyle (>= 2.5.19), testthat, knitr (>= 1.1.0), rmarkdown, mzR (>= 2.41.4), MsBackendMetaboLights (>= 1.3.1), pheatmap, vdiffr, IRanges, RColorBrewer License: Artistic-2.0 MD5sum: 4edcd03fbf4507495184921b92b1d327 NeedsCompilation: no Title: Infrastructure for Chromatographic Mass Spectrometry Data Description: The Chromatograms packages defines an efficient infrastructure for storing and handling of chromatographic mass spectrometry data. It provides different implementations of *backends* to store and represent the data. Such backends can be optimized for small memory footprint or fast data access/processing. A lazy evaluation queue and chunk-wise processing capabilities ensure efficient analysis of also very large data sets. biocViews: Infrastructure, Metabolomics, MassSpectrometry, Proteomics Author: Johannes Rainer [aut] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Philippine Louail [aut, cre] (ORCID: , fnd: European Union HORIZON-MSCA-2021 project Grant No. 101073062: HUMAN – Harmonising and Unifying Blood Metabolic Analysis Networks) Maintainer: Philippine Louail URL: https://github.com/RforMassSpectrometry/Chromatograms VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/Chromatograms/issues git_url: https://git.bioconductor.org/packages/Chromatograms git_branch: devel git_last_commit: 01b62cf git_last_commit_date: 2026-04-15 Date/Publication: 2026-04-20 source.ver: src/contrib/Chromatograms_1.1.8.tar.gz vignettes: vignettes/Chromatograms/inst/doc/creating-backend-classes.html, vignettes/Chromatograms/inst/doc/using-a-chromatograms-object.html vignetteTitles: Creating new `ChromBackend` class for Chromatograms, Using and understanding a Chromatograms object hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chromatograms/inst/doc/creating-backend-classes.R, vignettes/Chromatograms/inst/doc/using-a-chromatograms-object.R importsMe: MsQuality dependencyCount: 31 Package: chromDraw Version: 2.41.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 MD5sum: 60fc713999b3b847823cbed88289cef1 NeedsCompilation: yes Title: chromDraw is a R package for drawing the schemes of karyotypes in the linear and circular fashion. Description: ChromDraw is a R package for drawing the schemes of karyotype(s) in the linear and circular fashion. It is possible to visualized cytogenetic marsk on the chromosomes. This tool has own input data format. Input data can be imported from the GenomicRanges data structure. This package can visualized the data in the BED file format. Here is requirement on to the first nine fields of the BED format. Output files format are *.eps and *.svg. biocViews: Software Author: Jan Janecka, Ing., Mgr. CEITEC Masaryk University Maintainer: Jan Janecka URL: www.plantcytogenomics.org/chromDraw SystemRequirements: Rtools (>= 3.1) git_url: https://git.bioconductor.org/packages/chromDraw git_branch: devel git_last_commit: bf9f6d0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/chromDraw_2.41.0.tar.gz vignettes: vignettes/chromDraw/inst/doc/chromDraw.pdf vignetteTitles: chromDraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromDraw/inst/doc/chromDraw.R dependencyCount: 12 Package: ChromHeatMap Version: 1.65.0 Depends: R (>= 2.9.0), BiocGenerics (>= 0.3.2), annotate (>= 1.20.0), AnnotationDbi (>= 1.4.0) Imports: Biobase (>= 2.17.8), graphics, grDevices, methods, stats, IRanges, rtracklayer, GenomicRanges Suggests: ALL, hgu95av2.db License: Artistic-2.0 MD5sum: c6360168c92bc0af1680a6accc2ca0eb NeedsCompilation: no Title: Heat map plotting by genome coordinate Description: The ChromHeatMap package can be used to plot genome-wide data (e.g. expression, CGH, SNP) along each strand of a given chromosome as a heat map. The generated heat map can be used to interactively identify probes and genes of interest. biocViews: Visualization Author: Tim F. Rayner Maintainer: Tim F. Rayner git_url: https://git.bioconductor.org/packages/ChromHeatMap git_branch: devel git_last_commit: d47e520 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ChromHeatMap_1.65.0.tar.gz vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf vignetteTitles: Plotting expression data with ChromHeatMap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.R dependencyCount: 76 Package: chromPlot Version: 1.39.0 Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt, GenomicRanges, R (>= 3.1.0) Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 481a98c2ee6325a39958c1b27f90efbf NeedsCompilation: no Title: Global visualization tool of genomic data Description: Package designed to visualize genomic data along the chromosomes, where the vertical chromosomes are sorted by number, with sex chromosomes at the end. biocViews: DataRepresentation, FunctionalGenomics, Genetics, Sequencing, Annotation, Visualization Author: Ricardo A. Verdugo and Karen Y. Orostica Maintainer: Karen Y. Orostica git_url: https://git.bioconductor.org/packages/chromPlot git_branch: devel git_last_commit: 022a816 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/chromPlot_1.39.0.tar.gz vignettes: vignettes/chromPlot/inst/doc/chromPlot.pdf vignetteTitles: General Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromPlot/inst/doc/chromPlot.R dependencyCount: 66 Package: ChromSCape Version: 1.21.4 Depends: R (>= 4.5) Imports: shiny, colourpicker, shinyjs, rtracklayer, shinyFiles, shinyhelper, shinyWidgets, shinydashboardPlus, flexdashboard, shinycssloaders, Matrix, plotly, shinydashboard, colorRamps, kableExtra, viridis, batchelor, BiocParallel, parallel, Rsamtools, ggplot2, ggrepel, gggenes, gridExtra, qualV, stringdist, stringr, fs, qs2, DT, scran, scater, ConsensusClusterPlus, Rtsne, dplyr, tidyr, GenomicRanges, IRanges, irlba, rlist, umap, tibble, methods, jsonlite, edgeR, stats, graphics, grDevices, utils, S4Vectors, SingleCellExperiment, SummarizedExperiment, msigdbr, forcats, Rcpp, coop, matrixTests, DelayedArray LinkingTo: Rcpp Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, Signac, future, igraph, bluster, httr License: GPL-3 MD5sum: 3a0145bcc3183a932624bb934ca778a3 NeedsCompilation: yes Title: Analysis of single-cell epigenomics datasets with a Shiny App Description: ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis. biocViews: ShinyApps, Software, SingleCell, ChIPSeq, ATACSeq, MethylSeq, Classification, Clustering, Epigenetics, PrincipalComponent, SingleCell, ATACSeq, ChIPSeq, Annotation, BatchEffect, MultipleComparison, Normalization, Pathways, Preprocessing, QualityControl, ReportWriting, Visualization, GeneSetEnrichment, DifferentialPeakCalling Author: Pacome Prompsy [aut, cre] (ORCID: ), Celine Vallot [aut] (ORCID: ) Maintainer: Pacome Prompsy URL: https://github.com/vallotlab/ChromSCape VignetteBuilder: knitr BugReports: https://github.com/vallotlab/ChromSCape/issues git_url: https://git.bioconductor.org/packages/ChromSCape git_branch: devel git_last_commit: af1b98a git_last_commit_date: 2026-04-20 Date/Publication: 2026-04-20 source.ver: src/contrib/ChromSCape_1.21.4.tar.gz vignettes: vignettes/ChromSCape/inst/doc/vignette.html vignetteTitles: ChromSCape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromSCape/inst/doc/vignette.R dependencyCount: 206 Package: CHRONOS Version: 1.39.0 Depends: R (>= 3.5) Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx, igraph, circlize, graph, stats, utils, grDevices, graphics, methods, biomaRt, rJava Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL-2 MD5sum: 6b5df259dc17a364144cab9e5e59784f NeedsCompilation: no Title: CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis Description: A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG Author: Aristidis G. Vrahatis, Konstantina Dimitrakopoulou, Panos Balomenos Maintainer: Panos Balomenos SystemRequirements: Java version >= 1.7, Pandoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CHRONOS git_branch: devel git_last_commit: 5dec64b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CHRONOS_1.39.0.tar.gz vignettes: vignettes/CHRONOS/inst/doc/CHRONOS.pdf vignetteTitles: CHRONOS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CHRONOS/inst/doc/CHRONOS.R dependencyCount: 86 Package: cicero Version: 1.29.2 Depends: R (>= 3.5.0), monocle, Gviz (>= 1.22.3) Imports: assertthat (>= 0.2.0), Biobase (>= 2.37.2), BiocGenerics (>= 0.23.0), data.table (>= 1.10.4), dplyr (>= 0.7.4), FNN (>= 1.1), GenomicRanges (>= 1.30.3), ggplot2 (>= 2.2.1), glasso (>= 1.8), grDevices, igraph (>= 1.1.0), IRanges (>= 2.10.5), Matrix (>= 1.2-12), methods, parallel, plyr (>= 1.8.4), reshape2 (>= 1.4.3), S4Vectors (>= 0.14.7), stats, stringi, stringr (>= 1.2.0), tibble (>= 1.4.2), tidyr, VGAM (>= 1.0-5), utils Suggests: AnnotationDbi (>= 1.38.2), knitr, markdown, rmarkdown, rtracklayer (>= 1.36.6), testthat, vdiffr (>= 0.2.3), covr License: MIT + file LICENSE MD5sum: 18b431dcdf06966f51ee46b12fc14eff NeedsCompilation: no Title: Predict cis-co-accessibility from single-cell chromatin accessibility data Description: Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data. biocViews: Sequencing, Clustering, CellBasedAssays, ImmunoOncology, GeneRegulation, GeneTarget, Epigenetics, ATACSeq, SingleCell Author: Hannah Pliner [aut, cre], Cole Trapnell [aut] Maintainer: Hannah Pliner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cicero git_branch: devel git_last_commit: 2cc70b9 git_last_commit_date: 2026-03-18 Date/Publication: 2026-04-20 source.ver: src/contrib/cicero_1.29.2.tar.gz vignettes: vignettes/cicero/inst/doc/website.html vignetteTitles: Vignette from Cicero Website hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cicero/inst/doc/website.R importsMe: scPOEM dependencyCount: 179 Package: cigarillo Version: 1.1.0 Depends: methods, BiocGenerics, S4Vectors (>= 0.47.2), IRanges, Biostrings Imports: stats LinkingTo: S4Vectors, IRanges Suggests: Rsamtools, GenomicAlignments, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 00a7fb37ac1e8a4a837222ee08287b22 NeedsCompilation: yes Title: Efficient manipulation of CIGAR strings Description: CIGAR stands for Concise Idiosyncratic Gapped Alignment Report. CIGAR strings are found in the BAM files produced by most aligners and in the AIRR-formatted output produced by IgBLAST. The cigarillo package provides functions to parse and inspect CIGAR strings, trim them, turn them into ranges of positions relative to the "query space" or "reference space", and project positions or sequences from one space to the other. Note that these operations are low-level operations that the user rarely needs to perform directly. More typically, they are performed behind the scene by higher-level functionality implemented in other packages like Bioconductor packages GenomicAlignments and igblastr. biocViews: Infrastructure, Alignment, SequenceMatching, Sequencing Author: Hervé Pagès [aut, cre] (ORCID: ), Valerie Obenchain [aut], Michael Lawrence [aut], Patrick Aboyoun [ctb], Fedor Bezrukov [ctb], Martin Morgan [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/cigarillo VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/cigarillo/issues git_url: https://git.bioconductor.org/packages/cigarillo git_branch: devel git_last_commit: f89db3d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cigarillo_1.1.0.tar.gz vignettes: vignettes/cigarillo/inst/doc/cigarillo.html vignetteTitles: The cigarillo package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cigarillo/inst/doc/cigarillo.R importsMe: GenomicAlignments dependencyCount: 15 Package: CIMICE Version: 1.19.0 Imports: dplyr, ggplot2, glue, tidyr, igraph, networkD3, visNetwork, ggcorrplot, purrr, ggraph, stats, utils, maftools, assertthat, tidygraph, expm, Matrix Suggests: BiocStyle, knitr, rmarkdown, testthat, webshot License: Artistic-2.0 MD5sum: dd8068c25c6d23cdf7a391cbc36b2841 NeedsCompilation: no Title: CIMICE-R: (Markov) Chain Method to Inferr Cancer Evolution Description: CIMICE is a tool in the field of tumor phylogenetics and its goal is to build a Markov Chain (called Cancer Progression Markov Chain, CPMC) in order to model tumor subtypes evolution. The input of CIMICE is a Mutational Matrix, so a boolean matrix representing altered genes in a collection of samples. These samples are assumed to be obtained with single-cell DNA analysis techniques and the tool is specifically written to use the peculiarities of this data for the CMPC construction. biocViews: Software, BiologicalQuestion, NetworkInference, ResearchField, Phylogenetics, StatisticalMethod, GraphAndNetwork, Technology, SingleCell Author: Nicolò Rossi [aut, cre] (Lab. of Computational Biology and Bioinformatics, Department of Mathematics, Computer Science and Physics, University of Udine, ORCID: ) Maintainer: Nicolò Rossi URL: https://github.com/redsnic/CIMICE VignetteBuilder: knitr BugReports: https://github.com/redsnic/CIMICE/issues git_url: https://git.bioconductor.org/packages/CIMICE git_branch: devel git_last_commit: 057a38f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CIMICE_1.19.0.tar.gz vignettes: vignettes/CIMICE/inst/doc/CIMICE_SHORT.html, vignettes/CIMICE/inst/doc/CIMICER.html vignetteTitles: Quick guide, CIMICE-R: (Markov) Chain Method to Infer Cancer Evolution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CIMICE/inst/doc/CIMICE_SHORT.R, vignettes/CIMICE/inst/doc/CIMICER.R dependencyCount: 89 Package: circRNAprofiler Version: 1.25.0 Depends: R(>= 4.5.0) Imports: dplyr, magrittr, readr, rtracklayer, stringr, stringi, DESeq2, edgeR, GenomicRanges, IRanges, seqinr, R.utils, reshape2, ggplot2, utils, rlang, S4Vectors, stats, GenomeInfoDb, universalmotif, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, Biostrings, gwascat, BSgenome, Suggests: testthat, knitr, roxygen2, rmarkdown, devtools, gridExtra, ggpubr, VennDiagram, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BiocManager, License: GPL-3 MD5sum: 79461c164ddd1b03052434895b847cb5 NeedsCompilation: no Title: circRNAprofiler: An R-Based Computational Framework for the Downstream Analysis of Circular RNAs Description: R-based computational framework for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis. biocViews: Annotation, StructuralPrediction, FunctionalPrediction, GenePrediction, GenomeAssembly, DifferentialExpression Author: Simona Aufiero Maintainer: Simona Aufiero URL: https://github.com/Aufiero/circRNAprofiler VignetteBuilder: knitr BugReports: https://github.com/Aufiero/circRNAprofiler/issues git_url: https://git.bioconductor.org/packages/circRNAprofiler git_branch: devel git_last_commit: a9049f9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/circRNAprofiler_1.25.0.tar.gz vignettes: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.html vignetteTitles: circRNAprofiler hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.R dependencyCount: 142 Package: CiteFuse Version: 1.23.0 Depends: R (>= 4.0) Imports: SingleCellExperiment (>= 1.8.0), SummarizedExperiment (>= 1.16.0), Matrix, mixtools, cowplot, ggplot2, gridExtra, grid, dbscan, uwot, Rtsne, S4Vectors (>= 0.24.0), igraph, scales, scran (>= 1.14.6), graphics, methods, stats, utils, reshape2, ggridges, randomForest, pheatmap, ggraph, grDevices, rhdf5, rlang, Rcpp, compositions LinkingTo: Rcpp Suggests: knitr, rmarkdown, DT, mclust, scater, ExPosition, BiocStyle, pkgdown License: GPL-3 MD5sum: 9c8511128a7000c6bca9e0d2c8386d5d NeedsCompilation: yes Title: CiteFuse: multi-modal analysis of CITE-seq data Description: CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses. biocViews: SingleCell, GeneExpression Author: Yingxin Lin [aut, cre], Hani Kim [aut] Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/CiteFuse/issues git_url: https://git.bioconductor.org/packages/CiteFuse git_branch: devel git_last_commit: ef26cba git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CiteFuse_1.23.0.tar.gz vignettes: vignettes/CiteFuse/inst/doc/CiteFuse.html vignetteTitles: CiteFuse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CiteFuse/inst/doc/CiteFuse.R suggestsMe: MuData dependencyCount: 160 Package: ClassifyR Version: 3.15.0 Depends: R (>= 4.1.0), generics, methods, S4Vectors, MultiAssayExperiment, BiocParallel, survival Imports: grid, genefilter, utils, dplyr, tidyr, rlang, ranger, ggplot2 (>= 3.5.0), ggpubr, reshape2, ggupset, broom, dcanr Suggests: limma, edgeR, car, Rmixmod, gridExtra (>= 2.0.0), cowplot, BiocStyle, pamr, PoiClaClu, knitr, htmltools, gtable, scales, e1071, rmarkdown, IRanges, robustbase, glmnet, class, randomForestSRC, MatrixModels, xgboost, data.tree, ggnewscale, TOP, BiocNeighbors License: GPL-3 MD5sum: fd699887e4347b2362420a71177b91ea NeedsCompilation: yes Title: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing Description: The software formalises a framework for classification and survival model evaluation in R. There are four stages; Data transformation, feature selection, model training, and prediction. The requirements of variable types and variable order are fixed, but specialised variables for functions can also be provided. The framework is wrapped in a driver loop that reproducibly carries out a number of cross-validation schemes. Functions for differential mean, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework. biocViews: Classification, Survival Author: Dario Strbenac [aut, cre], Ellis Patrick [aut], Sourish Iyengar [aut], Harry Robertson [aut], Andy Tran [aut], John Ormerod [aut], Graham Mann [aut], Jean Yang [aut] Maintainer: Dario Strbenac URL: https://sydneybiox.github.io/ClassifyR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClassifyR git_branch: devel git_last_commit: 766b130 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ClassifyR_3.15.0.tar.gz vignettes: vignettes/ClassifyR/inst/doc/ClassifyR.html, vignettes/ClassifyR/inst/doc/DevelopersGuide.html vignetteTitles: An Introduction to the ClassifyR Package, Developer's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R, vignettes/ClassifyR/inst/doc/DevelopersGuide.R importsMe: spicyR, TOP suggestsMe: scFeatures, Statial dependencyCount: 148 Package: cleanUpdTSeq Version: 1.49.0 Depends: R (>= 3.5.0), BSgenome.Drerio.UCSC.danRer7, methods Imports: BSgenome, GenomicRanges, seqinr, e1071, Biostrings, Seqinfo, IRanges, utils, stringr, stats, S4Vectors Suggests: BiocStyle, rmarkdown, knitr, RUnit, BiocGenerics (>= 0.1.0) License: GPL-2 MD5sum: 4061734310808407051d0d3c6c1bba07 NeedsCompilation: no Title: cleanUpdTSeq cleans up artifacts from polyadenylation sites from oligo(dT)-mediated 3' end RNA sequending data Description: This package implements a Naive Bayes classifier for accurately differentiating true polyadenylation sites (pA sites) from oligo(dT)-mediated 3' end sequencing such as PAS-Seq, PolyA-Seq and RNA-Seq by filtering out false polyadenylation sites, mainly due to oligo(dT)-mediated internal priming during reverse transcription. The classifer is highly accurate and outperforms other heuristic methods. biocViews: Sequencing, 3' end sequencing, polyadenylation site, internal priming Author: Sarah Sheppard, Haibo Liu, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou ; Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cleanUpdTSeq git_branch: devel git_last_commit: 8cf2661 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cleanUpdTSeq_1.49.0.tar.gz vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html vignetteTitles: cleanUpdTSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R dependencyCount: 79 Package: CleanUpRNAseq Version: 1.5.0 Depends: R (>= 4.4.0) Imports: AnnotationFilter, BiocGenerics, Biostrings, BSgenome, DESeq2, edgeR, ensembldb, Seqinfo, GenomicRanges, ggplot2, ggrepel, graphics, grDevices, KernSmooth, limma, methods, pheatmap, qsmooth, R6, RColorBrewer, Rsamtools, Rsubread, reshape2, SummarizedExperiment, stats, tximport, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86, ggplotify, knitr, patchwork, R.utils, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 95f01b5d33672af69bdd36ac1acc9d03 NeedsCompilation: no Title: Detect and Correct Genomic DNA Contamination in RNA-seq Data Description: RNA-seq data generated by some library preparation methods, such as rRNA-depletion-based method and the SMART-seq method, might be contaminated by genomic DNA (gDNA), if DNase I disgestion is not performed properly during RNA preparation. CleanUpRNAseq is developed to check if RNA-seq data is suffered from gDNA contamination. If so, it can perform correction for gDNA contamination and reduce false discovery rate of differentially expressed genes. biocViews: QualityControl, Sequencing, GeneExpression Author: Haibo Liu [aut, cre] (ORCID: ), Kevin O'Connor [ctb], Michelle Kelliher [ctb], Lihua Julie Zhu [aut], Kai Hu [aut] Maintainer: Haibo Liu VignetteBuilder: knitr BugReports: https://github.com/haibol2016/CleanUpRNAseq/issues git_url: https://git.bioconductor.org/packages/CleanUpRNAseq git_branch: devel git_last_commit: 4f653f6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CleanUpRNAseq_1.5.0.tar.gz vignettes: vignettes/CleanUpRNAseq/inst/doc/CleanUpRNAseq.html vignetteTitles: CleanUpRNAseq: detecting and correcting for DNA contamination\nin RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CleanUpRNAseq/inst/doc/CleanUpRNAseq.R dependencyCount: 147 Package: cleaver Version: 1.49.0 Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8) Imports: S4Vectors, IRanges Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), rmarkdown, BRAIN, UniProt.ws (>= 2.36.5) License: GPL (>= 3) MD5sum: ad8b00fdeba10e1366f64435c4cd436f NeedsCompilation: no Title: Cleavage of Polypeptide Sequences Description: In-silico cleavage of polypeptide sequences. The cleavage rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html biocViews: Proteomics Author: Sebastian Gibb [aut, cre] (ORCID: ) Maintainer: Sebastian Gibb URL: https://codeberg.org/sgibb/cleaver/ VignetteBuilder: knitr BugReports: https://codeberg.org/sgibb/cleaver/issues/ git_url: https://git.bioconductor.org/packages/cleaver git_branch: devel git_last_commit: d68c701 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cleaver_1.49.0.tar.gz vignettes: vignettes/cleaver/inst/doc/cleaver.html vignetteTitles: In-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleaver/inst/doc/cleaver.R importsMe: ProteoDisco suggestsMe: RforProteomics dependencyCount: 15 Package: clevRvis Version: 1.11.0 Imports: shiny, ggraph, igraph, ggiraph, cowplot, htmlwidgets, readxl, dplyr, readr, purrr, tibble, patchwork, R.utils, shinyWidgets, colorspace, shinyhelper, shinycssloaders, ggnewscale, shinydashboard, DT, colourpicker, grDevices, methods, utils, stats, ggplot2, magrittr, tools Suggests: knitr, rmarkdown, BiocStyle License: LGPL-3 MD5sum: 7179351d5aebce492cdad63a28a1aee5 NeedsCompilation: no Title: Visualization Techniques for Clonal Evolution Description: clevRvis provides a set of visualization techniques for clonal evolution. These include shark plots, dolphin plots and plaice plots. Algorithms for time point interpolation as well as therapy effect estimation are provided. Phylogeny-aware color coding is implemented. A shiny-app for generating plots interactively is additionally provided. biocViews: Software, ShinyApps, Visualization Author: Sarah Sandmann [aut, cre] (ORCID: ) Maintainer: Sarah Sandmann URL: https://github.com/sandmanns/clevRvis VignetteBuilder: knitr BugReports: https://github.com/sandmanns/clevRvis/issues git_url: https://git.bioconductor.org/packages/clevRvis git_branch: devel git_last_commit: 02b71a5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/clevRvis_1.11.0.tar.gz vignettes: vignettes/clevRvis/inst/doc/clevRvis.html vignetteTitles: ClEvR Viz vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clevRvis/inst/doc/clevRvis.R dependencyCount: 119 Package: clippda Version: 1.61.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: 05a2ffb3eabdab85dfeccd85c11106ae NeedsCompilation: no Title: A package for the clinical proteomic profiling data analysis Description: Methods for the nalysis of data from clinical proteomic profiling studies. The focus is on the studies of human subjects, which are often observational case-control by design and have technical replicates. A method for sample size determination for planning these studies is proposed. It incorporates routines for adjusting for the expected heterogeneities and imbalances in the data and the within-sample replicate correlations. biocViews: Proteomics, OneChannel, Preprocessing, DifferentialExpression, MultipleComparison Author: Stephen Nyangoma Maintainer: Stephen Nyangoma URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml git_url: https://git.bioconductor.org/packages/clippda git_branch: devel git_last_commit: 28950e5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/clippda_1.61.0.tar.gz vignettes: vignettes/clippda/inst/doc/clippda.pdf vignetteTitles: Sample Size Calculation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clippda/inst/doc/clippda.R dependencyCount: 42 Package: clipper Version: 1.51.0 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor Suggests: RUnit, BiocGenerics, graphite, ALL, hgu95av2.db, MASS, BiocStyle Enhances: RCy3 License: AGPL-3 MD5sum: 3610676bb9e929480e6491ee58a283c6 NeedsCompilation: no Title: Gene Set Analysis Exploiting Pathway Topology Description: Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype. Author: Paolo Martini , Gabriele Sales , Chiara Romualdi Maintainer: Paolo Martini git_url: https://git.bioconductor.org/packages/clipper git_branch: devel git_last_commit: 09e9ef4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/clipper_1.51.0.tar.gz vignettes: vignettes/clipper/inst/doc/clipper.pdf vignetteTitles: clipper hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clipper/inst/doc/clipper.R suggestsMe: graphite dependencyCount: 90 Package: cliProfiler Version: 1.17.0 Depends: S4Vectors, methods, R (>= 4.1) Imports: dplyr, rtracklayer, GenomicRanges, ggplot2, BSgenome, Biostrings, utils Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 MD5sum: e46d327c24268c427e12966cb5119387 NeedsCompilation: no Title: A package for the CLIP data visualization Description: An easy and fast way to visualize and profile the high-throughput IP data. This package generates the meta gene profile and other profiles. These profiles could provide valuable information for understanding the IP experiment results. biocViews: Sequencing, ChIPSeq, Visualization, Epigenetics, Genetics Author: You Zhou [aut, cre] (ORCID: ), Kathi Zarnack [aut] (ORCID: ) Maintainer: You Zhou URL: https://github.com/Codezy99/cliProfiler VignetteBuilder: knitr BugReports: https://github.com/Codezy99/cliProfiler/issues git_url: https://git.bioconductor.org/packages/cliProfiler git_branch: devel git_last_commit: ac5b6e2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cliProfiler_1.17.0.tar.gz vignettes: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.html vignetteTitles: cliProfiler Vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.R dependencyCount: 80 Package: Clomial Version: 1.47.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: e84147300ab8c4f998ecd43be7491322 NeedsCompilation: no Title: Infers clonal composition of a tumor Description: Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor. biocViews: Genetics, GeneticVariability, Sequencing, Clustering, MultipleComparison, Bayesian, DNASeq, ExomeSeq, TargetedResequencing, ImmunoOncology Author: Habil Zare and Alex Hu Maintainer: Habil Zare git_url: https://git.bioconductor.org/packages/Clomial git_branch: devel git_last_commit: 0dfe7f2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Clomial_1.47.0.tar.gz vignettes: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.pdf vignetteTitles: A likelihood maximization approach to infer the clonal structure of a cancer using multiple tumor samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.R dependencyCount: 4 Package: clst Version: 1.59.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: e09ce7f80a73b2a3ec7d358f96affb39 NeedsCompilation: no Title: Classification by local similarity threshold Description: Package for modified nearest-neighbor classification based on calculation of a similarity threshold distinguishing within-group from between-group comparisons. biocViews: Classification Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clst git_branch: devel git_last_commit: 6f26ede git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/clst_1.59.0.tar.gz vignettes: vignettes/clst/inst/doc/clstDemo.pdf vignetteTitles: clst hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clst/inst/doc/clstDemo.R dependsOnMe: clstutils dependencyCount: 14 Package: clstutils Version: 1.59.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit License: GPL-3 MD5sum: 61c98686093758197c1de0d42096d342 NeedsCompilation: no Title: Tools for performing taxonomic assignment Description: Tools for performing taxonomic assignment based on phylogeny using pplacer and clst. biocViews: Sequencing, Classification, Visualization, QualityControl Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clstutils git_branch: devel git_last_commit: c01bd65 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/clstutils_1.59.0.tar.gz vignettes: vignettes/clstutils/inst/doc/pplacerDemo.pdf, vignettes/clstutils/inst/doc/refSet.pdf vignetteTitles: clst, clstutils hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clstutils/inst/doc/pplacerDemo.R, vignettes/clstutils/inst/doc/refSet.R dependencyCount: 36 Package: ClustAll Version: 1.7.0 Depends: R (>= 4.2.0) Imports: FactoMineR, bigstatsr, clValid, doSNOW, parallel, foreach, dplyr, fpc, mice, modeest, flock, networkD3, methods, ComplexHeatmap, cluster, RColorBrewer, circlize, grDevices, ggplot2, grid, stats, utils, pbapply Suggests: RUnit, knitr, BiocGenerics, rmarkdown, BiocStyle, roxygen2 License: GPL-2 MD5sum: d05a456aadecb91b30f0ec04738f6e8f NeedsCompilation: no Title: ClustAll: Data driven strategy to robustly identify stratification of patients within complex diseases Description: Data driven strategy to find hidden groups of patients with complex diseases using clinical data. ClustAll facilitates the unsupervised identification of multiple robust stratifications. ClustAll, is able to overcome the most common limitations found when dealing with clinical data (missing values, correlated data, mixed data types). biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: Asier Ortega-Legarreta [aut, cre] (ORCID: ), Sara Palomino-Echeverria [aut] Maintainer: Asier Ortega-Legarreta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClustAll git_branch: devel git_last_commit: e78dc53 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ClustAll_1.7.0.tar.gz vignettes: vignettes/ClustAll/inst/doc/Vignette_Clustall.html vignetteTitles: ClustALL User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClustAll/inst/doc/Vignette_Clustall.R dependencyCount: 190 Package: clustComp Version: 1.39.0 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 6bcce6fa1e9b5a71d6752953fd247406 NeedsCompilation: no Title: Clustering Comparison Package Description: clustComp is a package that implements several techniques for the comparison and visualisation of relationships between different clustering results, either flat versus flat or hierarchical versus flat. These relationships among clusters are displayed using a weighted bi-graph, in which the nodes represent the clusters and the edges connect pairs of nodes with non-empty intersection; the weight of each edge is the number of elements in that intersection and is displayed through the edge thickness. The best layout of the bi-graph is provided by the barycentre algorithm, which minimises the weighted number of crossings. In the case of comparing a hierarchical and a non-hierarchical clustering, the dendrogram is pruned at different heights, selected by exploring the tree by depth-first search, starting at the root. Branches are decided to be split according to the value of a scoring function, that can be based either on the aesthetics of the bi-graph or on the mutual information between the hierarchical and the flat clusterings. A mapping between groups of clusters from each side is constructed with a greedy algorithm, and can be additionally visualised. biocViews: GeneExpression, Clustering, Visualization Author: Aurora Torrente and Alvis Brazma. Maintainer: Aurora Torrente git_url: https://git.bioconductor.org/packages/clustComp git_branch: devel git_last_commit: 325b7b6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/clustComp_1.39.0.tar.gz vignettes: vignettes/clustComp/inst/doc/clustComp.pdf vignetteTitles: The clustComp Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clustComp/inst/doc/clustComp.R dependencyCount: 4 Package: ClusterFoldSimilarity Version: 1.7.1 Imports: methods, igraph, ggplot2, scales, BiocParallel, graphics, stats, utils, Matrix, cowplot, dplyr, reshape2, Seurat, SeuratObject, SingleCellExperiment, ggdendro Suggests: knitr, rmarkdown, kableExtra, scRNAseq, BiocStyle License: Artistic-2.0 MD5sum: 1a878e16418e3814e2e40b878bf6f32b NeedsCompilation: no Title: Calculate similarity of clusters from different single cell samples using foldchanges Description: This package calculates a similarity coefficient using the fold changes of shared features (e.g. genes) among clusters of different samples/batches/datasets. The similarity coefficient is calculated using the dot-product (Hadamard product) of every pairwise combination of Fold Changes between a source cluster i of sample/dataset n and all the target clusters j in sample/dataset m biocViews: SingleCell, Clustering, FeatureExtraction, GraphAndNetwork, GeneTarget, RNASeq Author: Oscar Gonzalez-Velasco [cre, aut] (ORCID: ) Maintainer: Oscar Gonzalez-Velasco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterFoldSimilarity git_branch: devel git_last_commit: 1666570 git_last_commit_date: 2026-02-02 Date/Publication: 2026-04-20 source.ver: src/contrib/ClusterFoldSimilarity_1.7.1.tar.gz vignettes: vignettes/ClusterFoldSimilarity/inst/doc/ClusterFoldSimilarity.html vignetteTitles: ClusterFoldSimilarity: hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ClusterFoldSimilarity/inst/doc/ClusterFoldSimilarity.R dependencyCount: 172 Package: ClusterGVis Version: 0.99.9 Depends: R (>= 4.5) Imports: colorRamps, dplyr, e1071, factoextra, ggplot2, grDevices, grid, Matrix, methods, purrr, reshape2, scales, stats, tibble, SingleCellExperiment, SummarizedExperiment, igraph, VGAM, scuttle Suggests: Biobase, ComplexHeatmap, clusterProfiler, TCseq, org.Mm.eg.db, circlize, knitr, monocle, pheatmap, rmarkdown, Seurat, WGCNA, utils, BiocManager, S4Vectors, pheatmap, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 7115c181b3de701fbfda0df218f6e47b NeedsCompilation: no Title: One-Step to Cluster and Visualize Gene Expression Data Description: Provides a streamlined workflow for clustering and visualizing gene expression patterns, particularly from time-series RNA-Seq and single-cell experiments. The package is designed to integrate seamlessly within the Bioconductor ecosystem by operating directly on standard data classes such as `SummarizedExperiment` and `SingleCellExperiment`. It implements common clustering algorithms (e.g., k-means, fuzzy c-means) and generates a suite of publication-ready visualizations to explore co-expressed gene modules. Functions are also included to facilitate the visualization of clustering results derived from other popular tools. biocViews: RNASeq,Transcriptomics,Visualization,SingleCell,GeneExpression,Clustering Author: Jun Zhang [aut, cre, cph] (ORCID: ) Maintainer: Jun Zhang <1138976957@qq.com> URL: https://github.com/junjunlab/ClusterGVis/, https://junjunlab.github.io/ClusterGvis-manual/ VignetteBuilder: knitr BugReports: https://github.com/junjunlab/ClusterGVis/issues git_url: https://git.bioconductor.org/packages/ClusterGVis git_branch: devel git_last_commit: 15fc470 git_last_commit_date: 2025-11-12 Date/Publication: 2026-04-20 source.ver: src/contrib/ClusterGVis_0.99.9.tar.gz vignettes: vignettes/ClusterGVis/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ClusterGVis/inst/doc/vignette.R dependencyCount: 162 Package: ClusterJudge Version: 1.33.0 Depends: R (>= 3.6), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt License: Artistic-2.0 MD5sum: 70eddb67d3284517269228af576ab1fb NeedsCompilation: no Title: Judging Quality of Clustering Methods using Mutual Information Description: ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview. biocViews: Software, StatisticalMethod, Clustering, GeneExpression, GO Author: Adrian Pasculescu Maintainer: Adrian Pasculescu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterJudge git_branch: devel git_last_commit: b9ce00a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ClusterJudge_1.33.0.tar.gz vignettes: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.R dependencyCount: 26 Package: clusterProfiler Version: 4.19.7 Depends: R (>= 4.2.0) Imports: aisdk, AnnotationDbi, dplyr, enrichit (>= 0.1.1), enrichplot (>= 1.9.3), ggplot2, GO.db, GOSemSim (>= 2.27.2), gson (>= 0.0.7), httr, igraph, jsonlite, magrittr, methods, plyr, qvalue, rlang, stats, tidyr, utils, yulab.utils (>= 0.2.3) Suggests: AnnotationHub, BiocManager, DOSE, ggtangle, readr, org.Hs.eg.db, quarto, testthat License: Artistic-2.0 MD5sum: 70cc33826628810d0ffc752f3dbcfeee NeedsCompilation: no Title: A Universal Enrichment Tool for Interpreting Omics Data Description: A universal tool for interpreting functional characteristics of omics data. It supports Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) for both coding and non-coding genomics data of thousands of species. It provides a unified and tidy interface to access, manipulate, and visualize enrichment results. A key capability is the simultaneous analysis and comparison of datasets from multiple treatments or time points. Furthermore, it integrates Large Language Model (LLM) capabilities to provide automated and insightful interpretation of enrichment results. biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG, MultipleComparison, Pathways, Reactome, Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Li-Gen Wang [ctb], Xiao Luo [ctb], Meijun Chen [ctb], Giovanni Dall'Olio [ctb], Wanqian Wei [ctb], Chun-Hui Gao [ctb] (ORCID: ) Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: quarto BugReports: https://github.com/YuLab-SMU/clusterProfiler/issues git_url: https://git.bioconductor.org/packages/clusterProfiler git_branch: devel git_last_commit: 34939c3 git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/clusterProfiler_4.19.7.tar.gz vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.html vignetteTitles: clusterProfiler.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: bioCancer, broadSeq, CaMutQC, CBNplot, CEMiTool, CeTF, damidBind, debrowser, EasyCellType, epiregulon.extra, esATAC, famat, GDCRNATools, goatea, goSorensen, MetaPhOR, methylGSA, miRSM, miRspongeR, mitology, Moonlight2R, MoonlightR, mosdef, PanomiR, pathlinkR, Pigengene, postNet, ReducedExperiment, RFLOMICS, VISTA, vsclust, recountWorkflow, DRviaSPCN, genekitr, PathwayVote, SurprisalAnalysis, tinyarray, XYomics suggestsMe: ChIPseeker, ClusterGVis, cola, DAPAR, DeeDeeExperiment, DOSE, enrichplot, EpiCompare, EpiMix, epiSeeker, GeDi, GeneTonic, GenomicSuperSignature, GeoTcgaData, ggkegg, GOSemSim, GRaNIE, GSEAmining, mastR, MesKit, MicrobiomeProfiler, ReactomePA, rrvgo, scFeatures, scGPS, scGraphVerse, SpliceImpactR, TCGAbiolinks, tidybulk, org.Mxanthus.db, easyEWAS, enrichit, ggpicrust2, grandR, ivolcano, OlinkAnalyze, ReporterScore, SRscore dependencyCount: 128 Package: clusterSeq Version: 1.35.0 Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils Imports: BiocGenerics Suggests: BiocStyle License: GPL-3 MD5sum: d8d0805c1d19ba403e880ff940b32b53 NeedsCompilation: no Title: Clustering of high-throughput sequencing data by identifying co-expression patterns Description: Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples. biocViews: Sequencing, DifferentialExpression, MultipleComparison, Clustering, GeneExpression Author: Thomas J. Hardcastle [aut], Irene Papatheodorou [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/clusterSeq BugReports: https://github.com/samgg/clusterSeq/issues git_url: https://git.bioconductor.org/packages/clusterSeq git_branch: devel git_last_commit: de26638 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/clusterSeq_1.35.0.tar.gz vignettes: vignettes/clusterSeq/inst/doc/clusterSeq.pdf vignetteTitles: Advanced baySeq analyses hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterSeq/inst/doc/clusterSeq.R dependencyCount: 30 Package: ClusterSignificance Version: 1.39.0 Depends: R (>= 3.3.0) Imports: methods, pracma, princurve (>= 2.0.5), scatterplot3d, RColorBrewer, grDevices, graphics, utils, stats Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics, covr License: GPL-3 MD5sum: 6d58fd38008c35c7b20805508d26b863 NeedsCompilation: no Title: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data Description: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method. biocViews: Clustering, Classification, PrincipalComponent, StatisticalMethod Author: Jason T. Serviss [aut, cre], Jesper R. Gadin [aut] Maintainer: Jason T Serviss URL: https://github.com/jasonserviss/ClusterSignificance/ VignetteBuilder: knitr BugReports: https://github.com/jasonserviss/ClusterSignificance/issues git_url: https://git.bioconductor.org/packages/ClusterSignificance git_branch: devel git_last_commit: ce3fb62 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ClusterSignificance_1.39.0.tar.gz vignettes: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html vignetteTitles: ClusterSignificance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R dependencyCount: 10 Package: clusterStab Version: 1.83.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: 9f3b49028d6444584b6b032d9917162f NeedsCompilation: no Title: Compute cluster stability scores for microarray data Description: This package can be used to estimate the number of clusters in a set of microarray data, as well as test the stability of these clusters. biocViews: Clustering Author: James W. MacDonald, Debashis Ghosh, Mark Smolkin Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/clusterStab git_branch: devel git_last_commit: bf26eed git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/clusterStab_1.83.0.tar.gz vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf vignetteTitles: clusterStab Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterStab/inst/doc/clusterStab.R dependencyCount: 7 Package: clustifyr Version: 1.23.0 Depends: R (>= 2.10) Imports: cowplot, dplyr, entropy, fgsea, ggplot2, Matrix, rlang, scales, stringr, tibble, tidyr, stats, methods, SingleCellExperiment, SummarizedExperiment, SeuratObject, matrixStats, S4Vectors, proxy, httr, utils Suggests: ComplexHeatmap, covr, knitr, rmarkdown, testthat, ggrepel, BiocStyle, BiocManager, remotes, shiny, gprofiler2, purrr, data.table, R.utils License: MIT + file LICENSE MD5sum: 3c28de09ad65643c69e9bcaa8428ab34 NeedsCompilation: no Title: Classifier for Single-cell RNA-seq Using Cell Clusters Description: Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment. biocViews: SingleCell, Annotation, Sequencing, Microarray, GeneExpression Author: Rui Fu [cre, aut], Kent Riemondy [aut], Austin Gillen [ctb], Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb], Michelle Daya [ctb], Sidhant Puntambekar [ctb], RNA Bioscience Initiative [fnd, cph] (ROR: ) Maintainer: Rui Fu URL: https://github.com/rnabioco/clustifyr, https://rnabioco.github.io/clustifyr/ VignetteBuilder: knitr BugReports: https://github.com/rnabioco/clustifyr/issues git_url: https://git.bioconductor.org/packages/clustifyr git_branch: devel git_last_commit: a7c098e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/clustifyr_1.23.0.tar.gz vignettes: vignettes/clustifyr/inst/doc/clustifyr.html, vignettes/clustifyr/inst/doc/geo-annotations.html vignetteTitles: Introduction to clustifyr, geo-annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clustifyr/inst/doc/clustifyr.R, vignettes/clustifyr/inst/doc/geo-annotations.R suggestsMe: clustifyrdatahub dependencyCount: 89 Package: ClustIRR Version: 1.9.32 Depends: R (>= 4.3.0) Imports: grDevices, igraph, methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), reshape2, rstan (>= 2.18.1), rstantools (>= 2.4.0), stats, stringdist, utils, posterior, visNetwork, dplyr, tidyr, ggplot2, ggforce, scales, msa, Biostrings, RADanalysis, ggseqlogo, rBLAST LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, knitr, testthat, ggrepel, patchwork, htmlwidgets License: GPL-3 + file LICENSE MD5sum: c9c35709643bc94d0630ed0bfb88d4a1 NeedsCompilation: yes Title: Clustering of Immune Receptor Repertoires Description: ClustIRR analyzes repertoires of B- and T-cell receptors. It starts by identifying communities of immune receptors with similar specificities, based on the sequences of their complementarity-determining regions (CDRs). Next, it employs a Bayesian probabilistic models to quantify differential community occupancy (DCO) between repertoires, allowing the identification of expanding or contracting communities in response to e.g. infection or cancer treatment. biocViews: Clustering, ImmunoOncology, SingleCell, Software, Classification, Bayesian, BiomedicalInformatics, ImmunoOncology, MathematicalBiology Author: Simo Kitanovski [aut, cre] (ORCID: ), Kai Wollek [aut] (ORCID: ) Maintainer: Simo Kitanovski URL: https://github.com/snaketron/ClustIRR SystemRequirements: GNU make, ncbi-blast+ VignetteBuilder: knitr BugReports: https://github.com/snaketron/ClustIRR/issues git_url: https://git.bioconductor.org/packages/ClustIRR git_branch: devel git_last_commit: 0e829cd git_last_commit_date: 2026-04-10 Date/Publication: 2026-04-20 source.ver: src/contrib/ClustIRR_1.9.32.tar.gz vignettes: vignettes/ClustIRR/inst/doc/User_manual_groups.html, vignettes/ClustIRR/inst/doc/User_manual_introduction.html vignetteTitles: Finding biological condition-specific changes in T- and B-cell receptor repertoires with ClustIRR, Decoding T- and B-cell receptor repertoires with ClustIRR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ClustIRR/inst/doc/User_manual_groups.R, vignettes/ClustIRR/inst/doc/User_manual_introduction.R dependencyCount: 121 Package: clustSIGNAL Version: 1.3.0 Depends: R (>= 4.4.0), SpatialExperiment Imports: BiocParallel, BiocNeighbors, bluster (>= 1.16.0), scater, harmony, SingleCellExperiment, SummarizedExperiment, methods, Matrix, reshape2 Suggests: knitr, BiocStyle, testthat (>= 3.0.0), aricode, ggplot2, patchwork, dplyr, scattermore License: GPL-2 MD5sum: 14878e65924236d249cc8555af8f4611 NeedsCompilation: no Title: ClustSIGNAL: a spatial clustering method Description: clustSIGNAL: clustering of Spatially Informed Gene expression with Neighbourhood Adapted Learning. A tool for adaptively smoothing and clustering gene expression data. clustSIGNAL uses entropy to measure heterogeneity of cell neighbourhoods and performs a weighted, adaptive smoothing, where homogeneous neighbourhoods are smoothed more and heterogeneous neighbourhoods are smoothed less. This not only overcomes data sparsity but also incorporates spatial context into the gene expression data. The resulting smoothed gene expression data is used for clustering and could be used for other downstream analyses. biocViews: Clustering, Software, GeneExpression, Spatial, Transcriptomics, SingleCell Author: Pratibha Panwar [cre, aut, ctb] (ORCID: ), Boyi Guo [aut], Haowen Zhao [aut], Stephanie Hicks [aut], Shila Ghazanfar [aut, ctb] (ORCID: ) Maintainer: Pratibha Panwar URL: https://sydneybiox.github.io/clustSIGNAL/ VignetteBuilder: knitr BugReports: https://github.com/sydneybiox/clustSIGNAL/issues git_url: https://git.bioconductor.org/packages/clustSIGNAL git_branch: devel git_last_commit: 2c2540c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/clustSIGNAL_1.3.0.tar.gz vignettes: vignettes/clustSIGNAL/inst/doc/clustSIGNAL.html vignetteTitles: ClustSIGNAL tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clustSIGNAL/inst/doc/clustSIGNAL.R dependencyCount: 127 Package: CMA Version: 1.69.0 Depends: R (>= 2.10), methods, stats, Biobase Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics, gbm, mgcv, corpcor, limma, st, mvtnorm License: GPL (>= 2) MD5sum: f96d5967408ccf0a01dbe9266ace58e0 NeedsCompilation: no Title: Synthesis of microarray-based classification Description: This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment. biocViews: Classification, DecisionTree Author: Martin Slawski , Anne-Laure Boulesteix , Christoph Bernau . Maintainer: Roman Hornung git_url: https://git.bioconductor.org/packages/CMA git_branch: devel git_last_commit: fdba720 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CMA_1.69.0.tar.gz vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf vignetteTitles: CMA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CMA/inst/doc/CMA_vignette.R dependencyCount: 7 Package: cmapR Version: 1.23.0 Depends: R (>= 4.0) Imports: methods, rhdf5, data.table, flowCore, SummarizedExperiment, matrixStats Suggests: knitr, testthat, BiocStyle, rmarkdown License: file LICENSE MD5sum: 6f5fa21bcdc4cf227248f13a1c6da4d7 NeedsCompilation: no Title: CMap Tools in R Description: The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets. biocViews: DataImport, DataRepresentation, GeneExpression Author: Ted Natoli [aut, cre] (ORCID: ) Maintainer: Ted Natoli URL: https://github.com/cmap/cmapR VignetteBuilder: knitr BugReports: https://github.com/cmap/cmapR/issues git_url: https://git.bioconductor.org/packages/cmapR git_branch: devel git_last_commit: b3d9762 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cmapR_1.23.0.tar.gz vignettes: vignettes/cmapR/inst/doc/tutorial.html vignetteTitles: cmapR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cmapR/inst/doc/tutorial.R dependencyCount: 38 Package: cn.farms Version: 1.59.0 Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice License: LGPL (>= 2.0) MD5sum: 520d6e6eca55747482edac71e63dc962 NeedsCompilation: yes Title: cn.FARMS - factor analysis for copy number estimation Description: This package implements the cn.FARMS algorithm for copy number variation (CNV) analysis. cn.FARMS allows to analyze the most common Affymetrix (250K-SNP6.0) array types, supports high-performance computing using snow and ff. biocViews: Microarray, CopyNumberVariation Author: Andreas Mitterecker, Djork-Arne Clevert Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/cnfarms/cnfarms.html git_url: https://git.bioconductor.org/packages/cn.farms git_branch: devel git_last_commit: cd33296 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cn.farms_1.59.0.tar.gz vignettes: vignettes/cn.farms/inst/doc/cn.farms.pdf vignetteTitles: cn.farms: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.farms/inst/doc/cn.farms.R dependencyCount: 55 Package: cn.mops Version: 1.57.0 Depends: R (>= 3.5.0), methods, utils, stats, graphics, parallel, GenomicRanges Imports: BiocGenerics, Biobase, IRanges, Rsamtools, Seqinfo, S4Vectors Suggests: DNAcopy License: LGPL (>= 2.0) MD5sum: cca5c29ce4d0f606778642102cbab897 NeedsCompilation: yes Title: cn.mops - Mixture of Poissons for CNV detection in NGS data Description: cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++. biocViews: Sequencing, CopyNumberVariation, Homo_sapiens, CellBiology, HapMap, Genetics Author: Guenter Klambauer [aut], Gundula Povysil [cre] Maintainer: Gundula Povysil URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html git_url: https://git.bioconductor.org/packages/cn.mops git_branch: devel git_last_commit: 79a0901 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cn.mops_1.57.0.tar.gz vignettes: vignettes/cn.mops/inst/doc/cn.mops.pdf vignetteTitles: cn.mops: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.mops/inst/doc/cn.mops.R dependsOnMe: panelcn.mops importsMe: CopyNumberPlots dependencyCount: 30 Package: CNAnorm Version: 1.57.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 MD5sum: 1a105767eec13d1422fb8c45837291e8 NeedsCompilation: yes Title: A normalization method for Copy Number Aberration in cancer samples Description: Performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found. biocViews: CopyNumberVariation, Sequencing, Coverage, Normalization, WholeGenome, DNASeq, GenomicVariation Author: Stefano Berri , Henry M. Wood , Arief Gusnanto Maintainer: Stefano Berri URL: http://www.r-project.org, git_url: https://git.bioconductor.org/packages/CNAnorm git_branch: devel git_last_commit: e51424e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CNAnorm_1.57.0.tar.gz vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf vignetteTitles: CNAnorm.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNAnorm/inst/doc/CNAnorm.R dependencyCount: 2 Package: CNEr Version: 1.47.0 Depends: R (>= 3.5.0) Imports: Biostrings (>= 2.33.4), pwalign, DBI (>= 0.7), RSQLite (>= 0.11.4), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.23.16), Seqinfo (>= 0.99.2), rtracklayer (>= 1.25.5), XVector (>= 0.5.4), GenomicAlignments (>= 1.1.9), methods, S4Vectors (>= 0.13.13), IRanges (>= 2.5.27), readr (>= 0.2.2), BiocGenerics, tools, parallel, reshape2 (>= 1.4.1), ggplot2 (>= 2.1.0), poweRlaw (>= 0.60.3), annotate (>= 1.50.0), GO.db (>= 3.3.0), R.utils (>= 2.3.0), KEGGREST (>= 1.14.0) LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz (>= 1.7.4), BiocStyle, knitr, rmarkdown, testthat, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, TxDb.Drerio.UCSC.danRer10.refGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Ggallus.UCSC.galGal3 License: GPL-2 | file LICENSE License_restricts_use: yes MD5sum: a69fefaf7cadcc76c3c8857e8614cd5a NeedsCompilation: yes Title: CNE Detection and Visualization Description: Large-scale identification and advanced visualization of sets of conserved noncoding elements. biocViews: GeneRegulation, Visualization, DataImport Author: Ge Tan Maintainer: Boris Lenhard Damir Baranasic URL: https://github.com/ComputationalRegulatoryGenomicsICL/CNEr VignetteBuilder: knitr BugReports: https://github.com/ge11232002/CNEr/issues git_url: https://git.bioconductor.org/packages/CNEr git_branch: devel git_last_commit: 4d7f8ce git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CNEr_1.47.0.tar.gz vignettes: vignettes/CNEr/inst/doc/CNEr.html, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html vignetteTitles: CNE identification and visualisation, Pairwise whole genome alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R dependencyCount: 112 Package: CNORdt Version: 1.53.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 MD5sum: 333b22cd51429d3839f33b8d88e8174e NeedsCompilation: yes Title: Add-on to CellNOptR: Discretized time treatments Description: This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: A. MacNamara Maintainer: A. MacNamara git_url: https://git.bioconductor.org/packages/CNORdt git_branch: devel git_last_commit: 1e21485 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CNORdt_1.53.0.tar.gz vignettes: vignettes/CNORdt/inst/doc/CNORdt-vignette.pdf vignetteTitles: Using multiple time points to train logic models to data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORdt/inst/doc/CNORdt-vignette-example.R, vignettes/CNORdt/inst/doc/CNORdt-vignette.R dependencyCount: 64 Package: CNORfeeder Version: 1.51.0 Depends: R (>= 4.0.0), graph Imports: CellNOptR (>= 1.4.0) Suggests: minet, Rgraphviz, RUnit, BiocGenerics, igraph Enhances: MEIGOR License: GPL-3 MD5sum: a928e47dd56f8e176588e4d1a14099eb NeedsCompilation: no Title: Integration of CellNOptR to add missing links Description: This package integrates literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. It permits to extends a given network with links derived from the data via various inference methods and uses information on physical interactions of proteins to guide and validate the integration of links. biocViews: CellBasedAssays, CellBiology, Proteomics, NetworkInference Author: Federica Eduati [aut, cre] Maintainer: Attila Gabor git_url: https://git.bioconductor.org/packages/CNORfeeder git_branch: devel git_last_commit: ec6ad0e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CNORfeeder_1.51.0.tar.gz vignettes: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfeeder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.R dependencyCount: 63 Package: CNORfuzzy Version: 1.53.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 MD5sum: 1e7b292a732b3d43fd52c64323b70c5b NeedsCompilation: yes Title: Addon to CellNOptR: Fuzzy Logic Description: This package is an extension to CellNOptR. It contains additional functionality needed to simulate and train a prior knowledge network to experimental data using constrained fuzzy logic (cFL, rather than Boolean logic as is the case in CellNOptR). Additionally, this package will contain functions to use for the compilation of multiple optimization results (either Boolean or cFL). biocViews: Network Author: M. Morris, T. Cokelaer Maintainer: T. Cokelaer git_url: https://git.bioconductor.org/packages/CNORfuzzy git_branch: devel git_last_commit: 486e43d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CNORfuzzy_1.53.0.tar.gz vignettes: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfuzzyl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.R dependencyCount: 64 Package: CNORode Version: 1.53.1 Depends: CellNOptR, genalg Suggests: knitr, rmarkdown Enhances: doParallel, foreach License: GPL-2 MD5sum: 62717914b09ed9c6d16e9b0e3005986b NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: Logic based ordinary differential equation (ODE) add-on to CellNOptR. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer, Attila Gabor, Federica Eduati, Enio Gjerga Maintainer: Aurelien Dugourd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNORode git_branch: devel git_last_commit: d912837 git_last_commit_date: 2025-12-10 Date/Publication: 2026-04-20 source.ver: src/contrib/CNORode_1.53.1.tar.gz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR dependencyCount: 64 Package: CNTools Version: 1.67.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL MD5sum: 8553af5214b6f705160eedf34a1e8fd8 NeedsCompilation: yes Title: Convert segment data into a region by sample matrix to allow for other high level computational analyses. Description: This package provides tools to convert the output of segmentation analysis using DNAcopy to a matrix structure with overlapping segments as rows and samples as columns so that other computational analyses can be applied to segmented data biocViews: Microarray, CopyNumberVariation Author: Jianhua Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/CNTools git_branch: devel git_last_commit: ed16efd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CNTools_1.67.0.tar.gz vignettes: vignettes/CNTools/inst/doc/HowTo.pdf vignetteTitles: NCTools HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNTools/inst/doc/HowTo.R dependsOnMe: cghMCR dependencyCount: 53 Package: cnvGSA Version: 1.55.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: edc0da78a106f46e2dfaa3560df13746 NeedsCompilation: no Title: Gene Set Analysis of (Rare) Copy Number Variants Description: This package is intended to facilitate gene-set association with rare CNVs in case-control studies. biocViews: MultipleComparison Author: Daniele Merico , Robert Ziman ; packaged by Joseph Lugo Maintainer: Joseph Lugo git_url: https://git.bioconductor.org/packages/cnvGSA git_branch: devel git_last_commit: ef4d543 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cnvGSA_1.55.0.tar.gz vignettes: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf, vignettes/cnvGSA/inst/doc/cnvGSAUsersGuide.pdf vignetteTitles: cnvGSA - Gene-Set Analysis of Rare Copy Number Variants, cnvGSAUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cnvGSAdata dependencyCount: 20 Package: CNVMetrics Version: 1.15.2 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, S4Vectors, BiocParallel, methods, magrittr, stats, pheatmap, gridExtra, grDevices, rBeta2009 Suggests: BiocStyle, knitr, rmarkdown, testthat, XVector License: Artistic-2.0 MD5sum: cf2953877850575a004ec9cc03190c19 NeedsCompilation: no Title: Copy Number Variant Metrics Description: The CNVMetrics package calculates similarity metrics to facilitate copy number variant comparison among samples and/or methods. Similarity metrics can be employed to compare CNV profiles of genetically unrelated samples as well as those with a common genetic background. Some metrics are based on the shared amplified/deleted regions while other metrics rely on the level of amplification/deletion. The data type used as input is a plain text file containing the genomic position of the copy number variations, as well as the status and/or the log2 ratio values. Finally, a visualization tool is provided to explore resulting metrics. biocViews: BiologicalQuestion, Software, CopyNumberVariation Author: Astrid Deschênes [aut, cre] (ORCID: ), Pascal Belleau [aut] (ORCID: ), David A. Tuveson [aut] (ORCID: ), Alexander Krasnitz [aut] Maintainer: Astrid Deschênes URL: https://github.com/krasnitzlab/CNVMetrics, https://krasnitzlab.github.io/CNVMetrics/ VignetteBuilder: knitr BugReports: https://github.com/krasnitzlab/CNVMetrics/issues git_url: https://git.bioconductor.org/packages/CNVMetrics git_branch: devel git_last_commit: f9a36a2 git_last_commit_date: 2025-12-31 Date/Publication: 2026-04-20 source.ver: src/contrib/CNVMetrics_1.15.2.tar.gz vignettes: vignettes/CNVMetrics/inst/doc/CNVMetrics.html vignetteTitles: Copy number variant metrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVMetrics/inst/doc/CNVMetrics.R dependencyCount: 38 Package: CNVPanelizer Version: 1.43.0 Depends: R (>= 3.2.0), GenomicRanges Imports: BiocGenerics, S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, foreach, ggplot2, plyr, GenomeInfoDb, gplots, reshape2, stringr, testthat, graphics, methods, shiny, shinyFiles, shinyjs, grid, openxlsx Suggests: knitr, RUnit License: GPL-3 MD5sum: d1be337e6da172e5a929c04654934687 NeedsCompilation: no Title: Reliable CNV detection in targeted sequencing applications Description: A method that allows for the use of a collection of non-matched normal tissue samples. Our approach uses a non-parametric bootstrap subsampling of the available reference samples to estimate the distribution of read counts from targeted sequencing. As inspired by random forest, this is combined with a procedure that subsamples the amplicons associated with each of the targeted genes. The obtained information allows us to reliably classify the copy number aberrations on the gene level. biocViews: Classification, Sequencing, Normalization, CopyNumberVariation, Coverage Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb], Benedikt Brors [ths], Wilko Weichert [ths] Maintainer: Thomas Wolf VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVPanelizer git_branch: devel git_last_commit: 6ed963e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CNVPanelizer_1.43.0.tar.gz vignettes: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.pdf vignetteTitles: CNVPanelizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.R dependencyCount: 113 Package: CNVRanger Version: 1.27.0 Depends: GenomicRanges, RaggedExperiment Imports: BiocGenerics, BiocParallel, GDSArray, GenomeInfoDb, IRanges, S4Vectors, SNPRelate, SummarizedExperiment, data.table, edgeR, gdsfmt, grDevices, lattice, limma, methods, plyr, qqman, rappdirs, reshape2, stats, utils Suggests: AnnotationHub, BSgenome.Btaurus.UCSC.bosTau6.masked, BiocStyle, ComplexHeatmap, Gviz, MultiAssayExperiment, TCGAutils, TxDb.Hsapiens.UCSC.hg19.knownGene, curatedTCGAData, ensembldb, grid, knitr, org.Hs.eg.db, regioneR, rmarkdown, statmod License: Artistic-2.0 MD5sum: e69bfeb32cd40c548e799a5f890a601e NeedsCompilation: no Title: Summarization and expression/phenotype association of CNV ranges Description: The CNVRanger package implements a comprehensive tool suite for CNV analysis. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and association analysis with gene expression and quantitative phenotypes. biocViews: CopyNumberVariation, DifferentialExpression, GeneExpression, GenomeWideAssociation, GenomicVariation, Microarray, RNASeq, SNP Author: Ludwig Geistlinger [aut, cre] (ORCID: ), Vinicius Henrique da Silva [aut], Marcel Ramos [ctb] (ORCID: ), Levi Waldron [ctb] (ORCID: ) Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/waldronlab/CNVRanger/issues git_url: https://git.bioconductor.org/packages/CNVRanger git_branch: devel git_last_commit: 16600ca git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CNVRanger_1.27.0.tar.gz vignettes: vignettes/CNVRanger/inst/doc/CNVRanger.html vignetteTitles: Summarization and quantitative trait analysis of CNV ranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVRanger/inst/doc/CNVRanger.R dependencyCount: 75 Package: CNVrd2 Version: 1.49.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: c900cb690776812beb7a152e7c28eb3b NeedsCompilation: no Title: CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data. Description: CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions. biocViews: CopyNumberVariation, SNP, Sequencing, Software, Coverage, LinkageDisequilibrium, Clustering. Author: Hoang Tan Nguyen, Tony R Merriman and Mik Black Maintainer: Hoang Tan Nguyen URL: https://github.com/hoangtn/CNVrd2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVrd2 git_branch: devel git_last_commit: 1ed26d8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CNVrd2_1.49.0.tar.gz vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf vignetteTitles: A Markdown Vignette with knitr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVrd2/inst/doc/CNVrd2.R dependencyCount: 91 Package: CoCiteStats Version: 1.83.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 7390259280f4c9c0e42116dd627d6dd9 NeedsCompilation: no Title: Different test statistics based on co-citation. Description: A collection of software tools for dealing with co-citation data. biocViews: Software Author: B. Ding and R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/CoCiteStats git_branch: devel git_last_commit: 405e2c3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CoCiteStats_1.83.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 43 Package: codelink Version: 1.79.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: 5c02cc81ca4b281c529e0c8b35037049 NeedsCompilation: no Title: Manipulation of Codelink microarray data Description: This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software. biocViews: Microarray, OneChannel, DataImport, Preprocessing Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/codelink VignetteBuilder: knitr BugReports: https://github.com/ddiez/codelink/issues git_url: https://git.bioconductor.org/packages/codelink git_branch: devel git_last_commit: 28ca5f2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/codelink_1.79.0.tar.gz vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf, vignettes/codelink/inst/doc/Codelink_Legacy.pdf vignetteTitles: Codelink Intruction, Codelink Legacy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/codelink/inst/doc/Codelink_Introduction.R, vignettes/codelink/inst/doc/Codelink_Legacy.R suggestsMe: MAQCsubset dependencyCount: 47 Package: CODEX Version: 1.43.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: cfd1b1f43329fdcce4b2a32e645c1fcb NeedsCompilation: no Title: A Normalization and Copy Number Variation Detection Method for Whole Exome Sequencing Description: A normalization and copy number variation calling procedure for whole exome DNA sequencing data. CODEX relies on the availability of multiple samples processed using the same sequencing pipeline for normalization, and does not require matched controls. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and targeting and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. biocViews: ImmunoOncology, ExomeSeq, Normalization, QualityControl, CopyNumberVariation Author: Yuchao Jiang, Nancy R. Zhang Maintainer: Yuchao Jiang git_url: https://git.bioconductor.org/packages/CODEX git_branch: devel git_last_commit: 6fce599 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CODEX_1.43.0.tar.gz vignettes: vignettes/CODEX/inst/doc/CODEX_vignettes.pdf vignetteTitles: Using CODEX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CODEX/inst/doc/CODEX_vignettes.R dependsOnMe: iCNV dependencyCount: 61 Package: CoGAPS Version: 3.31.0 Depends: R (>= 3.5.0) Imports: BiocParallel, cluster, methods, gplots, graphics, grDevices, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tools, utils, rhdf5, dplyr, fgsea, forcats, ggplot2 LinkingTo: Rcpp, BH, testthat Suggests: testthat, knitr, rmarkdown, BiocStyle, SeuratObject, BiocFileCache, xml2 License: BSD_3_clause + file LICENSE MD5sum: 94c268cd092de6f50333d73395f54a8d NeedsCompilation: yes Title: Coordinated Gene Activity in Pattern Sets Description: Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis. biocViews: GeneExpression, Transcription, GeneSetEnrichment, DifferentialExpression, Bayesian, Clustering, TimeCourse, RNASeq, Microarray, MultipleComparison, DimensionReduction, ImmunoOncology Author: Jeanette Johnson, Ashley Tsang, Jacob Mitchell, Thomas Sherman, Wai-shing Lee, Conor Kelton, Ondrej Maxian, Jacob Carey, Genevieve Stein-O'Brien, Michael Considine, Maggie Wodicka, John Stansfield, Shawn Sivy, Carlo Colantuoni, Alexander Favorov, Mike Ochs, Elana Fertig Maintainer: Elana J. Fertig , Thomas D. Sherman , Jeanette Johnson , Dmitrijs Lvovs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoGAPS git_branch: devel git_last_commit: 46dd3c5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CoGAPS_3.31.0.tar.gz vignettes: vignettes/CoGAPS/inst/doc/CoGAPS.html vignetteTitles: CoGAPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPS.R suggestsMe: projectR, SpaceMarkers dependencyCount: 93 Package: cogena Version: 1.45.0 Depends: R (>= 3.6), cluster, ggplot2, kohonen Imports: methods, class, gplots, mclust, amap, apcluster, foreach, parallel, doParallel, fastcluster, corrplot, biwt, Biobase, reshape2, stringr, tibble, tidyr, dplyr, devtools Suggests: knitr, rmarkdown (>= 2.1) License: LGPL-3 MD5sum: dd4cc7c1f93c6776e3d8606b310f1b2a NeedsCompilation: no Title: co-expressed gene-set enrichment analysis Description: cogena is a workflow for co-expressed gene-set enrichment analysis. It aims to discovery smaller scale, but highly correlated cellular events that may be of great biological relevance. A novel pipeline for drug discovery and drug repositioning based on the cogena workflow is proposed. Particularly, candidate drugs can be predicted based on the gene expression of disease-related data, or other similar drugs can be identified based on the gene expression of drug-related data. Moreover, the drug mode of action can be disclosed by the associated pathway analysis. In summary, cogena is a flexible workflow for various gene set enrichment analysis for co-expressed genes, with a focus on pathway/GO analysis and drug repositioning. biocViews: Clustering, GeneSetEnrichment, GeneExpression, Visualization, Pathways, KEGG, GO, Microarray, Sequencing, SystemsBiology, DataRepresentation, DataImport Author: Zhilong Jia [aut, cre], Michael Barnes [aut] Maintainer: Zhilong Jia URL: https://github.com/zhilongjia/cogena VignetteBuilder: knitr BugReports: https://github.com/zhilongjia/cogena/issues git_url: https://git.bioconductor.org/packages/cogena git_branch: devel git_last_commit: db4e1a0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cogena_1.45.0.tar.gz vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf, vignettes/cogena/inst/doc/cogena-vignette_html.html vignetteTitles: a workflow of cogena, cogena,, a workflow for gene set enrichment analysis of co-expressed genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogena/inst/doc/cogena-vignette_html.R, vignettes/cogena/inst/doc/cogena-vignette_pdf.R dependencyCount: 143 Package: cogeqc Version: 1.15.1 Depends: R (>= 4.2.0) Imports: utils, graphics, stats, methods, reshape2, ggplot2, scales, ggtree, patchwork, igraph, rlang, ggbeeswarm, jsonlite, Biostrings Suggests: testthat (>= 3.0.0), sessioninfo, knitr, BiocStyle, rmarkdown, covr License: GPL-3 MD5sum: c69735db81fa76ab7ec35686154a3bbe NeedsCompilation: no Title: Systematic quality checks on comparative genomics analyses Description: cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualization functions to explore QC summary statistics. biocViews: Software, GenomeAssembly, ComparativeGenomics, FunctionalGenomics, Phylogenetics, QualityControl, Network Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/cogeqc SystemRequirements: BUSCO (>= 5.1.3) VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/cogeqc git_url: https://git.bioconductor.org/packages/cogeqc git_branch: devel git_last_commit: fe27862 git_last_commit_date: 2026-03-05 Date/Publication: 2026-04-20 source.ver: src/contrib/cogeqc_1.15.1.tar.gz vignettes: vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.html, vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.html, vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.html vignetteTitles: Assessing genome assembly and annotation quality, Assessing orthogroup inference, Assessing synteny identification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.R, vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.R, vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.R dependencyCount: 95 Package: Cogito Version: 1.17.0 Depends: R (>= 4.1), GenomicRanges, jsonlite, GenomicFeatures, entropy Imports: BiocManager, rmarkdown, Seqinfo, S4Vectors, AnnotationDbi, graphics, stats, utils, methods, magrittr, ggplot2, TxDb.Mmusculus.UCSC.mm9.knownGene Suggests: BiocStyle, knitr, markdown, testthat (>= 3.0.0) License: LGPL-3 MD5sum: 71d88f8adbb609bce9cfd09ed9759875 NeedsCompilation: no Title: Compare genomic intervals tool - Automated, complete, reproducible and clear report about genomic and epigenomic data sets Description: Biological studies often consist of multiple conditions which are examined with different laboratory set ups like RNA-sequencing or ChIP-sequencing. To get an overview about the whole resulting data set, Cogito provides an automated, complete, reproducible and clear report about all samples and basic comparisons between all different samples. This report can be used as documentation about the data set or as starting point for further custom analysis. biocViews: FunctionalGenomics, GeneRegulation, Software, Sequencing Author: Annika Bürger [cre, aut] Maintainer: Annika Bürger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cogito git_branch: devel git_last_commit: 6bd2b9d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Cogito_1.17.0.tar.gz vignettes: vignettes/Cogito/inst/doc/Cogito.html vignetteTitles: Cogito: Compare annotated genomic intervals tool hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cogito/inst/doc/Cogito.R dependencyCount: 104 Package: coGPS Version: 1.55.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: 476caf0dc78e96096f84d51609a6a7cb NeedsCompilation: no Title: cancer outlier Gene Profile Sets Description: Gene Set Enrichment Analysis of P-value based statistics for outlier gene detection in dataset merged from multiple studies biocViews: Microarray, DifferentialExpression Author: Yingying Wei, Michael Ochs Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/coGPS git_branch: devel git_last_commit: b866584 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/coGPS_1.55.0.tar.gz vignettes: vignettes/coGPS/inst/doc/coGPS.pdf vignetteTitles: coGPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coGPS/inst/doc/coGPS.R dependencyCount: 2 Package: cola Version: 2.17.1 Depends: R (>= 4.0.0) Imports: grDevices, graphics, grid, stats, utils, ComplexHeatmap (>= 2.5.4), matrixStats (>= 1.2.0), GetoptLong, circlize (>= 0.4.7), GlobalOptions (>= 0.1.0), clue, parallel, RColorBrewer, cluster, skmeans, png, mclust, crayon, methods, xml2, microbenchmark, httr, knitr (>= 1.4.0), markdown (>= 1.6), digest, impute, brew, Rcpp (>= 0.11.0), BiocGenerics, eulerr, foreach, doParallel, doRNG, irlba LinkingTo: Rcpp Suggests: genefilter, mvtnorm, testthat (>= 0.3), samr, pamr, kohonen, NMF, WGCNA, Rtsne, umap, clusterProfiler, ReactomePA, DOSE, AnnotationDbi, gplots, hu6800.db, BiocManager, data.tree, dendextend, Polychrome, rmarkdown, simplifyEnrichment, cowplot, flexclust, randomForest, e1071 License: MIT + file LICENSE MD5sum: 20843ce9380b77be116f41076ed7e7d1 NeedsCompilation: yes Title: A Framework for Consensus Partitioning Description: Subgroup classification is a basic task in genomic data analysis, especially for gene expression and DNA methylation data analysis. It can also be used to test the agreement to known clinical annotations, or to test whether there exist significant batch effects. The cola package provides a general framework for subgroup classification by consensus partitioning. It has the following features: 1. It modularizes the consensus partitioning processes that various methods can be easily integrated. 2. It provides rich visualizations for interpreting the results. 3. It allows running multiple methods at the same time and provides functionalities to straightforward compare results. 4. It provides a new method to extract features which are more efficient to separate subgroups. 5. It automatically generates detailed reports for the complete analysis. 6. It allows applying consensus partitioning in a hierarchical manner. biocViews: Clustering, GeneExpression, Classification, Software Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/cola, https://jokergoo.github.io/cola_collection/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cola git_branch: devel git_last_commit: 0cbc999 git_last_commit_date: 2026-01-30 Date/Publication: 2026-04-20 source.ver: src/contrib/cola_2.17.1.tar.gz vignettes: vignettes/cola/inst/doc/cola.html vignetteTitles: The cola package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: InteractiveComplexHeatmap, simplifyEnrichment dependencyCount: 67 Package: comapr Version: 1.15.1 Depends: R (>= 4.1.0) Imports: methods, ggplot2, reshape2, dplyr, gridExtra, plotly, circlize, rlang, GenomicRanges, IRanges, foreach, BiocParallel, GenomeInfoDb, scales, RColorBrewer, tidyr, S4Vectors, utils, Matrix, grid, stats, SummarizedExperiment, plyr, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), statmod License: MIT + file LICENSE MD5sum: fd04e0b377557fba57aab145ef6f9e56 NeedsCompilation: no Title: Crossover analysis and genetic map construction Description: comapr detects crossover intervals for single gametes from their haplotype states sequences and stores the crossovers in GRanges object. The genetic distances can then be calculated via the mapping functions using estimated crossover rates for maker intervals. Visualisation functions for plotting interval-based genetic map or cumulative genetic distances are implemented, which help reveal the variation of crossovers landscapes across the genome and across individuals. biocViews: Software, SingleCell, Visualization, Genetics Author: Ruqian Lyu [aut, cre] (ORCID: ) Maintainer: Ruqian Lyu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/comapr git_branch: devel git_last_commit: 7df6a18 git_last_commit_date: 2026-01-26 Date/Publication: 2026-04-20 source.ver: src/contrib/comapr_1.15.1.tar.gz vignettes: vignettes/comapr/inst/doc/getStarted.html, vignettes/comapr/inst/doc/single-sperm-co-analysis.html vignetteTitles: Get-Started-With-comapr, single-sperm-co-analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/comapr/inst/doc/getStarted.R, vignettes/comapr/inst/doc/single-sperm-co-analysis.R dependencyCount: 163 Package: combi Version: 1.23.0 Depends: R (>= 4.0), DBI Imports: ggplot2, nleqslv, phyloseq, tensor, stats, limma, Matrix (>= 1.6.0), BB, reshape2, alabama, cobs, Biobase, vegan, grDevices, graphics, methods, SummarizedExperiment Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 99d03b09d9992c0c821326f0b67f9a8a NeedsCompilation: no Title: Compositional omics model based visual integration Description: This explorative ordination method combines quasi-likelihood estimation, compositional regression models and latent variable models for integrative visualization of several omics datasets. Both unconstrained and constrained integration are available. The results are shown as interpretable, compositional multiplots. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization, Metabolomics Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/combi/issues git_url: https://git.bioconductor.org/packages/combi git_branch: devel git_last_commit: 29ef8d9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/combi_1.23.0.tar.gz vignettes: vignettes/combi/inst/doc/combi.html vignetteTitles: Manual for the combi pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/combi/inst/doc/combi.R dependencyCount: 88 Package: coMethDMR Version: 1.15.0 Depends: R (>= 4.1) Imports: AnnotationHub, BiocParallel, bumphunter, ExperimentHub, GenomicRanges, IRanges, lmerTest, methods, stats, utils Suggests: BiocStyle, corrplot, knitr, rmarkdown, testthat, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 License: GPL-3 MD5sum: 48ac7f7203966886d1b75ef8f5862c92 NeedsCompilation: no Title: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies Description: coMethDMR identifies genomic regions associated with continuous phenotypes by optimally leverages covariations among CpGs within predefined genomic regions. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first without using any outcome information. Next, coMethDMR tests association between methylation within the sub-region and continuous phenotype using a random coefficient mixed effects model, which models both variations between CpG sites within the region and differential methylation simultaneously. biocViews: DNAMethylation, Epigenetics, MethylationArray, DifferentialMethylation, GenomeWideAssociation Author: Fernanda Veitzman [cre], Lissette Gomez [aut], Tiago Silva [aut], Ning Lijiao [ctb], Boissel Mathilde [ctb], Lily Wang [aut], Gabriel Odom [aut] Maintainer: Fernanda Veitzman URL: https://github.com/TransBioInfoLab/coMethDMR VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/coMethDMR/issues git_url: https://git.bioconductor.org/packages/coMethDMR git_branch: devel git_last_commit: f721129 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/coMethDMR_1.15.0.tar.gz vignettes: vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.html, vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.html vignetteTitles: "Introduction to coMethDMR", "coMethDMR with Parallel Computing" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.R, vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.R dependencyCount: 128 Package: compcodeR Version: 1.47.0 Depends: R (>= 4.0), sm Imports: knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods, stats, utils, ape, phylolm, matrixStats, grDevices, graphics, rmarkdown, shiny, shinydashboard Suggests: BiocStyle, EBSeq, DESeq2 (>= 1.1.31), genefilter, NOISeq, TCC, NBPSeq (>= 0.3.0), phytools, phangorn, testthat, ggtree, tidytree, statmod, covr, sva, tcltk Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: b9da9b0933ad8e9e2f4c7b80b84cacb3 NeedsCompilation: no Title: RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods Description: This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data. Finally, it provides convenient interfaces to several packages for performing the differential expression analysis. These can also be used as templates for setting up and running a user-defined differential analysis workflow within the framework of the package. biocViews: ImmunoOncology, RNASeq, DifferentialExpression Author: Charlotte Soneson [aut, cre] (ORCID: ), Paul Bastide [aut] (ORCID: ), Mélina Gallopin [aut] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/compcodeR VignetteBuilder: knitr BugReports: https://github.com/csoneson/compcodeR/issues git_url: https://git.bioconductor.org/packages/compcodeR git_branch: devel git_last_commit: 7aac3d1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/compcodeR_1.47.0.tar.gz vignettes: vignettes/compcodeR/inst/doc/compcodeR.html, vignettes/compcodeR/inst/doc/phylocompcodeR.html vignetteTitles: compcodeR, phylocompcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R, vignettes/compcodeR/inst/doc/phylocompcodeR.R dependencyCount: 99 Package: CompensAID Version: 0.99.6 Depends: R (>= 4.1.0) Imports: checkmate, dplyr, flowCore, flowDensity, ggcyto, ggplot2 (>= 3.5.2), methods, ParallelLogger, reshape2, rlang, stats, tibble, tidyr, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: GPL (>= 3) MD5sum: 449635871a716fc7785d01bff6f11806 NeedsCompilation: no Title: Automated detection tool for spillover errors Description: The CompensAID is an automated quality control tool, which determines for each marker combination in the FCS file, whether there a potential presence of reference errors. Such reference errors, which represent themselves in the form of skewed populations, are detected by integrating the Secondary Stain Index (SSI) score. Marker combinations with an SSI < 1 are flagged by CompensAID. biocViews: FlowCytometry, QualityControl, Preprocessing Author: Rosan Olsman [aut, cre] (ORCID: ), Sarah Bonte [aut] (ORCID: ), Mattias Hofmans [aut] (ORCID: ), Malicorne Buysse [aut], Katrien Van der Borght [aut] (ORCID: ), Yvan Saeys [aut] (ORCID: ), Vincent van der Velden [aut] (ORCID: ), Sofie Van Gassen [aut] (ORCID: ) Maintainer: Rosan Olsman URL: https://github.com/Olsman/CompensAID VignetteBuilder: knitr BugReports: https://github.com/Olsman/CompensAID/issues git_url: https://git.bioconductor.org/packages/CompensAID git_branch: devel git_last_commit: 68496b8 git_last_commit_date: 2026-01-12 Date/Publication: 2026-04-20 source.ver: src/contrib/CompensAID_0.99.6.tar.gz vignettes: vignettes/CompensAID/inst/doc/CompensAID.html vignetteTitles: Overview_CompensAID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompensAID/inst/doc/CompensAID.R dependencyCount: 130 Package: compEpiTools Version: 1.45.0 Depends: R (>= 3.5.0), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, Seqinfo Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: a973647ba83b2e8d8211879b6639161e NeedsCompilation: no Title: Tools for computational epigenomics Description: Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples. biocViews: GeneExpression, Sequencing, Visualization, GenomeAnnotation, Coverage Author: Mattia Pelizzola [aut], Kamal Kishore [aut], Mattia Furlan [ctb, cre] Maintainer: Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/compEpiTools git_branch: devel git_last_commit: 949eb04 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/compEpiTools_1.45.0.tar.gz vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf vignetteTitles: compEpiTools.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compEpiTools/inst/doc/compEpiTools.R dependencyCount: 163 Package: ComplexHeatmap Version: 2.27.1 Depends: R (>= 4.0.0), methods, grid, graphics, stats, grDevices Imports: circlize (>= 0.4.14), GetoptLong, colorspace, clue, RColorBrewer, GlobalOptions (>= 0.1.0), png, digest, IRanges, matrixStats, foreach, doParallel, codetools Suggests: testthat (>= 1.0.0), knitr, markdown, dendsort, jpeg, tiff, fastcluster, EnrichedHeatmap, dendextend (>= 1.0.1), grImport, grImport2, glue, GenomicRanges, gridtext, pheatmap (>= 1.0.12), gridGraphics, gplots, rmarkdown, Cairo, magick License: MIT + file LICENSE MD5sum: 37112c1255e516da455fd551eee83ced NeedsCompilation: no Title: Make Complex Heatmaps Description: Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/ComplexHeatmap, https://jokergoo.github.io/ComplexHeatmap-reference/book/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComplexHeatmap git_branch: devel git_last_commit: 28b5474 git_last_commit_date: 2026-01-30 Date/Publication: 2026-04-20 source.ver: src/contrib/ComplexHeatmap_2.27.1.tar.gz vignettes: vignettes/ComplexHeatmap/inst/doc/complex_heatmap.html, vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.html vignetteTitles: complex_heatmap.html, Most probably asked questions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.R dependsOnMe: AMARETTO, EnrichedHeatmap, InteractiveComplexHeatmap, multistateQTL, recoup, sechm, countToFPKM importsMe: airpart, ASURAT, barbieQ, bettr, BindingSiteFinder, BioNERO, blacksheepr, BloodGen3Module, BreastSubtypeR, BulkSignalR, CATALYST, CCPlotR, celda, CeTF, chevreulPlot, chevreulShiny, ClustAll, COCOA, cola, COTAN, CRISPRball, CTexploreR, cytoKernel, damidBind, Damsel, dar, DEGreport, diffcyt, diffUTR, dinoR, dominoSignal, ELMER, ELViS, epiregulon.extra, fCCAC, FLAMES, gCrisprTools, GeDi, GeneTonic, GenomicPlot, GenomicSuperSignature, geyser, gmoviz, goatea, GRaNIE, gVenn, hermes, hoodscanR, HybridExpress, iModMix, InterCellar, iSEE, MAPFX, markeR, MatrixQCvis, MesKit, mitology, MOMA, monaLisa, Moonlight2R, MOSClip, MPAC, MultiRNAflow, muscat, musicatk, MWASTools, nipalsMCIA, pathlinkR, PathoStat, PeacoQC, pipeComp, POMA, PRONE, RFLOMICS, RiboCrypt, RNAshapeQC, RUCova, scafari, scRNAseqApp, segmenter, shinyDSP, signifinder, simona, simplifyEnrichment, SingleCellSignalR, singleCellTK, sparrow, SPONGE, StatescopeR, TBSignatureProfiler, TMSig, ViSEAGO, Xeva, YAPSA, spatialLIBD, autoGO, cellGeometry, coda4microbiome, conos, DeSciDe, DiscreteGapStatistic, dtGAP, GAPR, GSSTDA, karyotapR, mineSweepR, missoNet, MitoHEAR, MKomics, ogrdbstats, Path.Analysis, PCAPAM50, pkgndep, rCISSVAE, RepeatedHighDim, rKOMICS, RNAseqQC, scITD, SingleCellComplexHeatMap, spatialGE, spiralize, tidyHeatmap, TransProR, visxhclust, wilson suggestsMe: artMS, bambu, ClusterGVis, clustifyr, CNVRanger, Coralysis, demuxSNP, dittoSeq, EnrichmentBrowser, FlowSOM, gtrellis, HilbertCurve, mastR, miaViz, msImpute, msqrob2, plotgardener, projectR, QFeatures, raer, scDblFinder, scDiagnostics, scLANE, SpaceMarkers, SPIAT, TCGAbiolinks, TCGAutils, VISTA, weitrix, curatedPCaData, LegATo, NanoporeRNASeq, ProteinGymR, BeeBDC, CIARA, circlize, circlizePlus, ClustAssess, ConsensusOPLS, ggpicrust2, grandR, inferCSN, metasnf, multipanelfigure, pepdiff, piglet, plotthis, rliger, scCustomize, SCpubr, SeuratExplorer, sfcurve, singleCellHaystack, SRscore, thisplot, tinyarray dependencyCount: 29 Package: CompoundDb Version: 1.15.4 Depends: R (>= 4.1), methods, AnnotationFilter, S4Vectors Imports: BiocGenerics, ChemmineR, tibble, jsonlite, dplyr, DBI, dbplyr, RSQLite, Biobase, ProtGenerics (>= 1.35.3), xml2, IRanges, Spectra (>= 1.15.10), MsCoreUtils, MetaboCoreUtils, BiocParallel, stringi, data.table Suggests: knitr, rmarkdown, testthat, BiocStyle (>= 2.5.19), MsBackendMgf License: Artistic-2.0 MD5sum: e584459f3c75480f5477056c38c9dbc5 NeedsCompilation: no Title: Creating and Using (Chemical) Compound Annotation Databases Description: CompoundDb provides functionality to create and use (chemical) compound annotation databases from a variety of different sources such as LipidMaps, HMDB, ChEBI or MassBank. The database format allows to store in addition MS/MS spectra along with compound information. The package provides also a backend for Bioconductor's Spectra package and allows thus to match experimetal MS/MS spectra against MS/MS spectra in the database. Databases can be stored in SQLite format and are thus portable. biocViews: MassSpectrometry, Metabolomics, Annotation Author: Jan Stanstrup [aut] (ORCID: ), Johannes Rainer [aut, cre] (ORCID: ), Josep M. Badia [ctb] (ORCID: ), Roger Gine [aut] (ORCID: ), Andrea Vicini [aut] (ORCID: ), Prateek Arora [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/CompoundDb VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/CompoundDb/issues git_url: https://git.bioconductor.org/packages/CompoundDb git_branch: devel git_last_commit: 7daa575 git_last_commit_date: 2026-03-19 Date/Publication: 2026-04-20 source.ver: src/contrib/CompoundDb_1.15.4.tar.gz vignettes: vignettes/CompoundDb/inst/doc/CompoundDb-usage.html, vignettes/CompoundDb/inst/doc/create-compounddb.html vignetteTitles: Usage of Annotation Resources with the CompoundDb Package, Creating CompoundDb annotation resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompoundDb/inst/doc/CompoundDb-usage.R, vignettes/CompoundDb/inst/doc/create-compounddb.R importsMe: MetaboAnnotation, pubchem.bio suggestsMe: AHMassBank, AnnotationHub, MetMashR dependencyCount: 104 Package: ComPrAn Version: 1.19.0 Imports: data.table, dplyr, forcats, ggplot2, magrittr, purrr, tidyr, rlang, stringr, shiny, DT, RColorBrewer, VennDiagram, rio, scales, shinydashboard, shinyjs, stats, tibble, grid Suggests: testthat (>= 2.1.0), knitr, rmarkdown License: MIT + file LICENSE MD5sum: c4d8c701b36d272bd563f96778ea2932 NeedsCompilation: no Title: Complexome Profiling Analysis package Description: This package is for analysis of SILAC labeled complexome profiling data. It uses peptide table in tab-delimited format as an input and produces ready-to-use tables and plots. biocViews: MassSpectrometry, Proteomics, Visualization Author: Rick Scavetta [aut], Petra Palenikova [aut, cre] (ORCID: ) Maintainer: Petra Palenikova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComPrAn git_branch: devel git_last_commit: 7a1221b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ComPrAn_1.19.0.tar.gz vignettes: vignettes/ComPrAn/inst/doc/fileFormats.html, vignettes/ComPrAn/inst/doc/proteinWorkflow.html, vignettes/ComPrAn/inst/doc/SILACcomplexomics.html vignetteTitles: fileFormats.html, Protein workflow, SILAC complexomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComPrAn/inst/doc/fileFormats.R, vignettes/ComPrAn/inst/doc/proteinWorkflow.R, vignettes/ComPrAn/inst/doc/SILACcomplexomics.R dependencyCount: 100 Package: compSPOT Version: 1.9.0 Depends: R (>= 4.3.0) Imports: stats, base, ggplot2, plotly, magrittr, ggpubr, gridExtra, utils, data.table Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: f85c93e2e0a9151706d564908155e242 NeedsCompilation: no Title: compSPOT: Tool for identifying and comparing significantly mutated genomic hotspots Description: Clonal cell groups share common mutations within cancer, precancer, and even clinically normal appearing tissues. The frequency and location of these mutations may predict prognosis and cancer risk. It has also been well established that certain genomic regions have increased sensitivity to acquiring mutations. Mutation-sensitive genomic regions may therefore serve as markers for predicting cancer risk. This package contains multiple functions to establish significantly mutated hotspots, compare hotspot mutation burden between samples, and perform exploratory data analysis of the correlation between hotspot mutation burden and personal risk factors for cancer, such as age, gender, and history of carcinogen exposure. This package allows users to identify robust genomic markers to help establish cancer risk. biocViews: Software, Technology, Sequencing, DNASeq, WholeGenome, Classification, SingleCell, Survival, MultipleComparison Author: Sydney Grant [aut, cre] (ORCID: ), Ella Sampson [aut], Rhea Rodrigues [aut] (ORCID: ), Gyorgy Paragh [aut] (ORCID: ) Maintainer: Sydney Grant URL: https://github.com/sydney-grant/compSPOT VignetteBuilder: knitr BugReports: https://github.com/sydney-grant/compSPOT/issues git_url: https://git.bioconductor.org/packages/compSPOT git_branch: devel git_last_commit: fa7146f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/compSPOT_1.9.0.tar.gz vignettes: vignettes/compSPOT/inst/doc/compSPOT-vignette.html vignetteTitles: compSPOT-Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compSPOT/inst/doc/compSPOT-vignette.R dependencyCount: 121 Package: concordexR Version: 1.11.0 Depends: R (>= 4.5.0) Imports: BiocGenerics, BiocNeighbors, BiocParallel, bluster, cli, DelayedArray, Matrix, methods, purrr, rlang, SingleCellExperiment, sparseMatrixStats, SpatialExperiment, SummarizedExperiment Suggests: BiocManager, BiocStyle, ggplot2, glue, knitr, mbkmeans, patchwork, rmarkdown, scater, SFEData, SpatialFeatureExperiment, TENxPBMCData, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 6446bab5130455f4e6e103a80105dc95 NeedsCompilation: no Title: Identify Spatial Homogeneous Regions with concordex Description: Spatial homogeneous regions (SHRs) in tissues are domains that are homogenous with respect to cell type composition. We present a method for identifying SHRs using spatial transcriptomics data, and demonstrate that it is efficient and effective at finding SHRs for a wide variety of tissue types. concordex relies on analysis of k-nearest-neighbor (kNN) graphs. The tool is also useful for analysis of non-spatial transcriptomics data, and can elucidate the extent of concordance between partitions of cells derived from clustering algorithms, and transcriptomic similarity as represented in kNN graphs. biocViews: SingleCell, Clustering, Spatial, Transcriptomics Author: Kayla Jackson [aut, cre] (ORCID: ), A. Sina Booeshaghi [aut] (ORCID: ), Angel Galvez-Merchan [aut] (ORCID: ), Lambda Moses [aut] (ORCID: ), Alexandra Kim [ctb], Laura Luebbert [ctb] (ORCID: ), Lior Pachter [aut, rev, ths] (ORCID: ) Maintainer: Kayla Jackson URL: https://github.com/pachterlab/concordexR, https://pachterlab.github.io/concordexR/ VignetteBuilder: knitr BugReports: https://github.com/pachterlab/concordexR/issues git_url: https://git.bioconductor.org/packages/concordexR git_branch: devel git_last_commit: 7b24dc8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/concordexR_1.11.0.tar.gz vignettes: vignettes/concordexR/inst/doc/concordex-nonspatial.html, vignettes/concordexR/inst/doc/overview.html vignetteTitles: concordex-nonspatial, overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/concordexR/inst/doc/concordex-nonspatial.R, vignettes/concordexR/inst/doc/overview.R dependencyCount: 82 Package: condiments Version: 1.19.0 Depends: R (>= 4.0) Imports: slingshot (>= 1.9), mgcv, RANN, stats, SingleCellExperiment, SummarizedExperiment, utils, magrittr, dplyr (>= 1.0), Ecume (>= 0.9.1), methods, pbapply, matrixStats, BiocParallel, TrajectoryUtils, igraph, distinct Suggests: knitr, testthat, rmarkdown, covr, viridis, ggplot2, RColorBrewer, randomForest, tidyr, TSCAN, DelayedMatrixStats License: MIT + file LICENSE MD5sum: be65ff4c74ac5c08989bb6ea0d1c6a9a NeedsCompilation: no Title: Differential Topology, Progression and Differentiation Description: This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an 'omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format. biocViews: RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Hector Roux de Bezieux [aut, cre] (ORCID: ), Koen Van den Berge [aut, ctb], Kelly Street [aut, ctb] Maintainer: Hector Roux de Bezieux URL: https://hectorrdb.github.io/condiments/index.html VignetteBuilder: knitr BugReports: https://github.com/HectorRDB/condiments/issues git_url: https://git.bioconductor.org/packages/condiments git_branch: devel git_last_commit: 68a222c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/condiments_1.19.0.tar.gz vignettes: vignettes/condiments/inst/doc/condiments.html, vignettes/condiments/inst/doc/controls.html, vignettes/condiments/inst/doc/examples.html vignetteTitles: The condiments workflow, Using condiments, Generating more examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/condiments/inst/doc/condiments.R, vignettes/condiments/inst/doc/controls.R, vignettes/condiments/inst/doc/examples.R dependencyCount: 158 Package: CONFESS Version: 1.39.0 Depends: R (>= 3.3),grDevices,utils,stats,graphics Imports: methods,changepoint,cluster,contrast,data.table(>= 1.9.7),ecp,EBImage,flexmix,flowCore,flowClust,flowMeans,flowMerge,flowPeaks,foreach,ggplot2,grid,limma,MASS,moments,outliers,parallel,plotrix,raster,readbitmap,reshape2,SamSPECTRAL,waveslim,wavethresh,zoo Suggests: BiocStyle, knitr, rmarkdown, CONFESSdata License: GPL-2 MD5sum: 09ca681c33d0d5e2b049acfc98d6ffbe NeedsCompilation: no Title: Cell OrderiNg by FluorEScence Signal Description: Single Cell Fluidigm Spot Detector. biocViews: ImmunoOncology, GeneExpression,DataImport,CellBiology,Clustering,RNASeq,QualityControl,Visualization,TimeCourse,Regression,Classification Author: Diana LOW and Efthimios MOTAKIS Maintainer: Diana LOW VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CONFESS git_branch: devel git_last_commit: b97e3ac git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CONFESS_1.39.0.tar.gz vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf, vignettes/CONFESS/inst/doc/vignette.html vignetteTitles: CONFESS, CONFESS Walkthrough hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R, vignettes/CONFESS/inst/doc/vignette.R dependencyCount: 150 Package: consensus Version: 1.29.0 Depends: R (>= 3.5), RColorBrewer Imports: matrixStats, gplots, grDevices, methods, graphics, stats, utils Suggests: knitr, RUnit, rmarkdown, BiocGenerics License: BSD_3_clause + file LICENSE MD5sum: f76d31370ed1700e3cf9fcac296a0082 NeedsCompilation: no Title: Cross-platform consensus analysis of genomic measurements via interlaboratory testing method Description: An implementation of the American Society for Testing and Materials (ASTM) Standard E691 for interlaboratory testing procedures, designed for cross-platform genomic measurements. Given three (3) or more genomic platforms or laboratory protocols, this package provides interlaboratory testing procedures giving per-locus comparisons for sensitivity and precision between platforms. biocViews: QualityControl, Regression, DataRepresentation, GeneExpression, Microarray, RNASeq Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensus git_branch: devel git_last_commit: 5af026c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/consensus_1.29.0.tar.gz vignettes: vignettes/consensus/inst/doc/consensus.pdf vignetteTitles: Fitting and visualising row-linear models with \texttt{consensus} hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/consensus/inst/doc/consensus.R dependencyCount: 12 Package: ConsensusClusterPlus Version: 1.75.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: 6abd0de974f25a9a9e48bb310ebea2bc NeedsCompilation: no Title: ConsensusClusterPlus Description: algorithm for determining cluster count and membership by stability evidence in unsupervised analysis biocViews: Software, Clustering Author: Matt Wilkerson , Peter Waltman Maintainer: Matt Wilkerson git_url: https://git.bioconductor.org/packages/ConsensusClusterPlus git_branch: devel git_last_commit: 2632db7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ConsensusClusterPlus_1.75.0.tar.gz vignettes: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf vignetteTitles: ConsensusClusterPlus Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.R importsMe: CATALYST, ChromSCape, DEGreport, FlowSOM, PDATK, DeSousa2013, iSubGen, longmixr, neatmaps suggestsMe: RNAshapeQC, TCGAbiolinks dependencyCount: 10 Package: consensusSeekeR Version: 1.39.0 Depends: R (>= 3.5.0), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: Seqinfo, rtracklayer, stringr, S4Vectors, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: c991761cbc67eda73994084352281218 NeedsCompilation: no Title: Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges Description: This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. In genomic analysis where feature identification generates a position value surrounded by a genomic range, such as ChIP-Seq peaks and nucleosome positions, the replication of an experiment may result in slight differences between predicted values. This package enables the conciliation of the results into consensus regions. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, Transcription, PeakDetection, Sequencing, Coverage Author: Astrid Deschênes [cre, aut] (ORCID: ), Fabien Claude Lamaze [ctb], Pascal Belleau [aut] (ORCID: ), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/consensusSeekeR VignetteBuilder: knitr BugReports: https://github.com/adeschen/consensusSeekeR/issues git_url: https://git.bioconductor.org/packages/consensusSeekeR git_branch: devel git_last_commit: 35fd991 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/consensusSeekeR_1.39.0.tar.gz vignettes: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html vignetteTitles: Detection of consensus regions inside a group of experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R importsMe: RJMCMCNucleosomes suggestsMe: EpiCompare dependencyCount: 65 Package: consICA Version: 2.9.0 Depends: R (>= 4.2.0) Imports: fastICA (>= 1.2.1), sm, org.Hs.eg.db, GO.db, stats, SummarizedExperiment, BiocParallel, graph, ggplot2, methods, Rfast, pheatmap, survival, topGO, graphics, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat, Seurat License: MIT + file LICENSE MD5sum: e2e06684779525b8d653354f9073ddb8 NeedsCompilation: no Title: consensus Independent Component Analysis Description: consICA implements a data-driven deconvolution method – consensus independent component analysis (ICA) to decompose heterogeneous omics data and extract features suitable for patient diagnostics and prognostics. The method separates biologically relevant transcriptional signals from technical effects and provides information about the cellular composition and biological processes. The implementation of parallel computing in the package ensures efficient analysis of modern multicore systems. biocViews: Technology, StatisticalMethod, Sequencing, RNASeq, Transcriptomics, Classification, FeatureExtraction Author: Petr V. Nazarov [aut, cre] (ORCID: ), Tony Kaoma [aut] (ORCID: ), Maryna Chepeleva [aut] (ORCID: ) Maintainer: Petr V. Nazarov VignetteBuilder: knitr BugReports: https://github.com/biomod-lih/consICA/issues git_url: https://git.bioconductor.org/packages/consICA git_branch: devel git_last_commit: 093c307 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/consICA_2.9.0.tar.gz vignettes: vignettes/consICA/inst/doc/ConsICA.html vignetteTitles: The consICA package: User’s manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/consICA/inst/doc/ConsICA.R dependencyCount: 87 Package: CONSTANd Version: 1.19.0 Depends: R (>= 4.1) Suggests: BiocStyle, knitr, rmarkdown, tidyr, ggplot2, gridExtra, magick, Cairo, limma License: file LICENSE MD5sum: c3f58a73ce87c5e0fd65727568702f44 NeedsCompilation: no Title: Data normalization by matrix raking Description: Normalizes a data matrix `data` by raking (using the RAS method by Bacharach, see references) the Nrows by Ncols matrix such that the row means and column means equal 1. The result is a normalized data matrix `K=RAS`, a product of row mulipliers `R` and column multipliers `S` with the original matrix `A`. Missing information needs to be presented as `NA` values and not as zero values, because CONSTANd is able to ignore missing values when calculating the mean. Using CONSTANd normalization allows for the direct comparison of values between samples within the same and even across different CONSTANd-normalized data matrices. biocViews: MassSpectrometry, Cheminformatics, Normalization, Preprocessing, DifferentialExpression, Genetics, Transcriptomics, Proteomics Author: Joris Van Houtven [aut, trl], Geert Jan Bex [trl], Dirk Valkenborg [aut, cre] Maintainer: Dirk Valkenborg URL: qcquan.net/constand VignetteBuilder: knitr BugReports: https://github.com/PDiracDelta/CONSTANd/issues git_url: https://git.bioconductor.org/packages/CONSTANd git_branch: devel git_last_commit: 5d9a80c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CONSTANd_1.19.0.tar.gz vignettes: vignettes/CONSTANd/inst/doc/CONSTANd.html vignetteTitles: CONSTANd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CONSTANd/inst/doc/CONSTANd.R dependencyCount: 0 Package: convert Version: 1.87.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: d2c0f3213291ad55d6eb3d6c24135bdb NeedsCompilation: no Title: Convert Microarray Data Objects Description: Define coerce methods for microarray data objects. biocViews: Infrastructure, Microarray, TwoChannel Author: Gordon Smyth , James Wettenhall , Yee Hwa (Jean Yang) , Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html git_url: https://git.bioconductor.org/packages/convert git_branch: devel git_last_commit: 3e7aa0d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/convert_1.87.0.tar.gz vignettes: vignettes/convert/inst/doc/convert.pdf vignetteTitles: Converting Between Microarray Data Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: TurboNorm suggestsMe: dyebias, OLIN, dyebiasexamples dependencyCount: 11 Package: copa Version: 1.79.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 MD5sum: 68a725918996a194957c74e8b408db64 NeedsCompilation: yes Title: Functions to perform cancer outlier profile analysis. Description: COPA is a method to find genes that undergo recurrent fusion in a given cancer type by finding pairs of genes that have mutually exclusive outlier profiles. biocViews: OneChannel, TwoChannel, DifferentialExpression, Visualization Author: James W. MacDonald Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/copa git_branch: devel git_last_commit: f78854e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/copa_1.79.0.tar.gz vignettes: vignettes/copa/inst/doc/copa.pdf vignetteTitles: copa Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copa/inst/doc/copa.R dependencyCount: 7 Package: Coralysis Version: 1.1.0 Depends: R (>= 4.2.0) Imports: Matrix, aricode, LiblineaR, SparseM, ggplot2, umap, Rtsne, pheatmap, reshape2, dplyr, SingleCellExperiment, SummarizedExperiment, S4Vectors, methods, stats, utils, RANN, sparseMatrixStats, irlba, flexclust, scran, class, matrixStats, tidyr, cowplot, uwot, scatterpie, RColorBrewer, ggrastr, ggrepel, RSpectra, BiocParallel, withr Suggests: knitr, rmarkdown, bluster, ComplexHeatmap, circlize, scater, viridis, scRNAseq, SingleR, MouseGastrulationData, testthat (>= 3.0.0), BiocStyle, scrapper License: GPL-3 MD5sum: eb16b30bc5fd968e4bf5d5485cd1d26f NeedsCompilation: no Title: Coralysis sensitive identification of imbalanced cell types and states in single-cell data via multi-level integration Description: Coralysis is an R package featuring a multi-level integration algorithm for sensitive integration, reference-mapping, and cell-state identification in single-cell data. The multi-level integration algorithm is inspired by the process of assembling a puzzle - where one begins by grouping pieces based on low-to high-level features, such as color and shading, before looking into shape and patterns. This approach progressively blends the batch effects and separates cell types across multiple rounds of divisive clustering. biocViews: SingleCell, RNASeq, Proteomics, Transcriptomics, GeneExpression, BatchEffect, Clustering, Annotation, Classification, DifferentialExpression, DimensionReduction, Software Author: António Sousa [cre, aut] (ORCID: ), Johannes Smolander [ctb, aut] (ORCID: ), Sini Junttila [aut] (ORCID: ), Laura L Elo [aut] (ORCID: ) Maintainer: António Sousa URL: https://github.com/elolab/Coralysis, https://elolab.github.io/Coralysis/ VignetteBuilder: knitr BugReports: https://github.com/elolab/Coralysis/issues git_url: https://git.bioconductor.org/packages/Coralysis git_branch: devel git_last_commit: 94d3ee0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Coralysis_1.1.0.tar.gz vignettes: vignettes/Coralysis/inst/doc/CellState.html, vignettes/Coralysis/inst/doc/Coralysis.html, vignettes/Coralysis/inst/doc/Integration.html, vignettes/Coralysis/inst/doc/RefMap.html vignetteTitles: Cell States, Get started, Integration, Reference-mapping hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Coralysis/inst/doc/CellState.R, vignettes/Coralysis/inst/doc/Coralysis.R, vignettes/Coralysis/inst/doc/Integration.R, vignettes/Coralysis/inst/doc/RefMap.R dependencyCount: 132 Package: coRdon Version: 1.29.0 Depends: R (>= 3.5) Imports: methods, stats, utils, Biostrings, Biobase, dplyr, stringr, purrr, ggplot2, data.table Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: bc4e18e539da49a94c041db0cfeb72ff NeedsCompilation: no Title: Codon Usage Analysis and Prediction of Gene Expressivity Description: Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results. biocViews: Software, Metagenomics, GeneExpression, GeneSetEnrichment, GenePrediction, Visualization, KEGG, Pathways, Genetics CellBiology, BiomedicalInformatics, ImmunoOncology Author: Anamaria Elek [cre, aut], Maja Kuzman [aut], Kristian Vlahovicek [aut] Maintainer: Anamaria Elek URL: https://github.com/BioinfoHR/coRdon VignetteBuilder: knitr BugReports: https://github.com/BioinfoHR/coRdon/issues git_url: https://git.bioconductor.org/packages/coRdon git_branch: devel git_last_commit: f418d09 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/coRdon_1.29.0.tar.gz vignettes: vignettes/coRdon/inst/doc/coRdon.html vignetteTitles: coRdon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coRdon/inst/doc/coRdon.R importsMe: vhcub dependencyCount: 45 Package: CoreGx Version: 2.15.0 Depends: R (>= 4.1), BiocGenerics, SummarizedExperiment Imports: Biobase, S4Vectors, MultiAssayExperiment, MatrixGenerics, piano, BiocParallel, parallel, BumpyMatrix, checkmate, methods, stats, utils, graphics, grDevices, lsa, data.table, crayon, glue, rlang, bench Suggests: pander, markdown, BiocStyle, rmarkdown, knitr, formatR, testthat License: GPL (>= 3) MD5sum: 5159b8b3a6f85daf9995c77d06a77a74 NeedsCompilation: no Title: Classes and Functions to Serve as the Basis for Other 'Gx' Packages Description: A collection of functions and classes which serve as the foundation for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This package was created to abstract shared functionality from other lab package releases to increase ease of maintainability and reduce code repetition in current and future 'Gx' suite programs. Major features include a 'CoreSet' class, from which 'RadioSet' and 'PharmacoSet' are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, as well as: Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., Lupien, M., Goldenberg, A. (2015) . Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) . biocViews: Software, Pharmacogenomics, Classification, Survival Author: Jermiah Joseph [aut], Petr Smirnov [aut], Ian Smith [aut], Christopher Eeles [aut], Feifei Li [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoreGx git_branch: devel git_last_commit: e18fa83 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CoreGx_2.15.0.tar.gz vignettes: vignettes/CoreGx/inst/doc/coreGx.html, vignettes/CoreGx/inst/doc/TreatmentResponseExperiment.html vignetteTitles: CoreGx: Class and Function Abstractions, The TreatmentResponseExperiment Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoreGx/inst/doc/coreGx.R, vignettes/CoreGx/inst/doc/TreatmentResponseExperiment.R dependsOnMe: PharmacoGx, RadioGx, ToxicoGx importsMe: gDRimport, PDATK dependencyCount: 127 Package: Cormotif Version: 1.57.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: b95edea7199ca8fcaefab593f96ba70e NeedsCompilation: no Title: Correlation Motif Fit Description: It fits correlation motif model to multiple studies to detect study specific differential expression patterns. biocViews: Microarray, DifferentialExpression Author: Hongkai Ji, Yingying Wei Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/Cormotif git_branch: devel git_last_commit: 3b9844b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Cormotif_1.57.0.tar.gz vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf vignetteTitles: Cormotif Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cormotif/inst/doc/CormotifVignette.R dependencyCount: 14 Package: corral Version: 1.21.0 Imports: ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix, methods, MultiAssayExperiment, pals, reshape2, SingleCellExperiment, SummarizedExperiment, transport Suggests: ade4, BiocStyle, CellBench, DuoClustering2018, knitr, rmarkdown, scater, testthat License: GPL-2 MD5sum: a8de33dc93c082f8344c161cb355977d NeedsCompilation: no Title: Correspondence Analysis for Single Cell Data Description: Correspondence analysis (CA) is a matrix factorization method, and is similar to principal components analysis (PCA). Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale. The corral package implements CA for dimensionality reduction of a single matrix of single-cell data, as well as a multi-table adaptation of CA that leverages data-optimized scaling to align data generated from different sequencing platforms by projecting into a shared latent space. corral utilizes sparse matrices and a fast implementation of SVD, and can be called directly on Bioconductor objects (e.g., SingleCellExperiment) for easy pipeline integration. The package also includes additional options, including variations of CA to address overdispersion in count data (e.g., Freeman-Tukey chi-squared residual), as well as the option to apply CA-style processing to continuous data (e.g., proteomic TOF intensities) with the Hellinger distance adaptation of CA. biocViews: BatchEffect, DimensionReduction, GeneExpression, Preprocessing, PrincipalComponent, Sequencing, SingleCell, Software, Visualization Author: Lauren Hsu [aut, cre] (ORCID: ), Aedin Culhane [aut] (ORCID: ) Maintainer: Lauren Hsu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/corral git_branch: devel git_last_commit: 87340f9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/corral_1.21.0.tar.gz vignettes: vignettes/corral/inst/doc/corral_dimred.html, vignettes/corral/inst/doc/corralm_alignment.html vignetteTitles: dim reduction with corral, alignment with corralm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/corral/inst/doc/corral_dimred.R, vignettes/corral/inst/doc/corralm_alignment.R dependencyCount: 70 Package: coseq Version: 1.35.0 Depends: R (>= 4.0.0), SummarizedExperiment, S4Vectors Imports: edgeR, DESeq2, capushe, Rmixmod, e1071, BiocParallel, ggplot2, scales, HTSFilter, corrplot, HTSCluster, grDevices, graphics, stats, methods, compositions, mvtnorm Suggests: Biobase, knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: ddc948820be991baca31842739b6b60a NeedsCompilation: no Title: Co-Expression Analysis of Sequencing Data Description: Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided. biocViews: GeneExpression, RNASeq, Sequencing, Software, ImmunoOncology Author: Andrea Rau [cre, aut] (ORCID: ), Cathy Maugis-Rabusseau [ctb], Antoine Godichon-Baggioni [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coseq git_branch: devel git_last_commit: 9d19790 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/coseq_1.35.0.tar.gz vignettes: vignettes/coseq/inst/doc/coseq.html vignetteTitles: coseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coseq/inst/doc/coseq.R dependsOnMe: RFLOMICS dependencyCount: 75 Package: CoSIA Version: 1.11.1 Depends: R (>= 4.3.0), methods (>= 4.3.0), ExperimentHub (>= 2.7.0) Imports: dplyr (>= 1.0.7), magrittr (>= 2.0.1), RColorBrewer (>= 1.1-2), tidyr (>= 1.2.0), plotly (>= 4.10.0), stringr (>= 1.4.0), ggplot2 (>= 3.3.5), tibble (>= 3.1.7), org.Hs.eg.db (>= 3.12.0), org.Mm.eg.db (>= 3.12.0), org.Dr.eg.db (>= 3.12.0), org.Ce.eg.db (>= 3.12.0), org.Dm.eg.db (>= 3.12.0), org.Rn.eg.db (>= 3.12.0), AnnotationDbi (>= 1.52.0), biomaRt (>= 2.46.3), homologene (>= 1.4.68.19), annotationTools (>= 1.64.0), readr (>= 2.1.1), tidyselect (>= 1.1.2), stats (>= 4.1.2) Suggests: BiocStyle (>= 2.22.0), tidyverse (>= 1.3.1), knitr (>= 1.42), rmarkdown (>= 2.20), testthat (>= 3.1.6), qpdf (>= 1.3.0) License: MIT + file LICENSE MD5sum: 146b8ff9c0e07411e3bd2b4d0e07bf58 NeedsCompilation: no Title: An Investigation Across Different Species and Tissues Description: Cross-Species Investigation and Analysis (CoSIA) is a package that provides researchers with an alternative methodology for comparing across species and tissues using normal wild-type RNA-Seq Gene Expression data from Bgee. Using RNA-Seq Gene Expression data, CoSIA provides multiple visualization tools to explore the transcriptome diversity and variation across genes, tissues, and species. CoSIA uses the Coefficient of Variation and Shannon Entropy and Specificity to calculate transcriptome diversity and variation. CoSIA also provides additional conversion tools and utilities to provide a streamlined methodology for cross-species comparison. biocViews: Software, BiologicalQuestion, GeneExpression, MultipleComparison, ThirdPartyClient, DataImport, GUI Author: Anisha Haldar [aut] (ORCID: ), Vishal H. Oza [aut] (ORCID: ), Amanda D. Clark [aut] (ORCID: ), Anthony B. Crumley [cre, aut] (ORCID: ), Nathaniel S. DeVoss [aut] (ORCID: ), Brittany N. Lasseigne [aut] (ORCID: ) Maintainer: Anthony B. Crumley URL: https://www.lasseigne.org/ VignetteBuilder: knitr BugReports: https://github.com/lasseignelab/CoSIA/issues git_url: https://git.bioconductor.org/packages/CoSIA git_branch: devel git_last_commit: 3d2338a git_last_commit_date: 2025-11-05 Date/Publication: 2026-04-20 source.ver: src/contrib/CoSIA_1.11.1.tar.gz vignettes: vignettes/CoSIA/inst/doc/CoSIA_Intro.html vignetteTitles: CoSIA_Intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CoSIA/inst/doc/CoSIA_Intro.R dependencyCount: 117 Package: COSNet Version: 1.45.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 30796449f466bd2caa93bfaf34b7c94d NeedsCompilation: yes Title: Cost Sensitive Network for node label prediction on graphs with highly unbalanced labelings Description: Package that implements the COSNet classification algorithm. The algorithm predicts node labels in partially labeled graphs where few positives are available for the class being predicted. biocViews: GraphAndNetwork, Classification,Network, NeuralNetwork Author: Marco Frasca and Giorgio Valentini -- Universita' degli Studi di Milano Maintainer: Marco Frasca URL: https://github.com/m1frasca/COSNet_GitHub git_url: https://git.bioconductor.org/packages/COSNet git_branch: devel git_last_commit: 639a0fb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/COSNet_1.45.0.tar.gz vignettes: vignettes/COSNet/inst/doc/COSNet_v.pdf vignetteTitles: An R Package for Predicting Binary Labels in Partially-Labeled Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COSNet/inst/doc/COSNet_v.R dependencyCount: 0 Package: COTAN Version: 2.11.4 Depends: R (>= 4.3) Imports: stats, methods, grDevices, Matrix, ggplot2, ggrepel, ggdist, ggthemes, graphics, parallel, parallelly, tibble, tidyr, dplyr, BiocSingular, parallelDist, ComplexHeatmap, circlize, grid, scales, RColorBrewer, utils, rlang, Rfast, stringr, Seurat, dendextend, zeallot, assertthat, withr, SummarizedExperiment, SingleCellExperiment, proxy, RSpectra Suggests: testthat (>= 3.2.0), proto, spelling, knitr, ragg, Cairo, conflicted, data.table, gsubfn, R.utils, tidyverse, rmarkdown, htmlwidgets, MASS, Rtsne, plotly, BiocStyle, cowplot, qpdf, GEOquery, sf, torch, S4Vectors License: GPL-3 MD5sum: 8db75f08c70991b51143dbd6d9452ad0 NeedsCompilation: no Title: COexpression Tables ANalysis Description: Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes. biocViews: SystemsBiology, Transcriptomics, GeneExpression, SingleCell, DifferentialExpression, Clustering, GPU Author: Galfrè Silvia Giulia [aut, cre] (ORCID: ), Morandin Francesco [aut] (ORCID: ), Fantozzi Marco [aut] (ORCID: ), Pietrosanto Marco [aut] (ORCID: ), Puttini Daniel [aut] (ORCID: ), Priami Corrado [aut] (ORCID: ), Cremisi Federico [aut] (ORCID: ), Helmer-Citterich Manuela [aut] (ORCID: ) Maintainer: Galfrè Silvia Giulia URL: https://github.com/seriph78/COTAN VignetteBuilder: knitr BugReports: https://github.com/seriph78/COTAN/issues git_url: https://git.bioconductor.org/packages/COTAN git_branch: devel git_last_commit: 3b79cc3 git_last_commit_date: 2026-03-19 Date/Publication: 2026-04-20 source.ver: src/contrib/COTAN_2.11.4.tar.gz vignettes: vignettes/COTAN/inst/doc/Cleaning_GuidedTutorial.html, vignettes/COTAN/inst/doc/DiffExprAnalysis_GuidedTutorial.html, vignettes/COTAN/inst/doc/GenesClustering_GuidedTutorial.html, vignettes/COTAN/inst/doc/UniformClustering_GuidedTutorial.html vignetteTitles: Cleaning tutorial using COTAN, DEA using COTAN, Genes' clustering using COTAN, Uniform clustering using COTAN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COTAN/inst/doc/Cleaning_GuidedTutorial.R, vignettes/COTAN/inst/doc/DiffExprAnalysis_GuidedTutorial.R, vignettes/COTAN/inst/doc/GenesClustering_GuidedTutorial.R, vignettes/COTAN/inst/doc/UniformClustering_GuidedTutorial.R dependencyCount: 203 Package: countsimQC Version: 1.29.1 Depends: R (>= 3.5) Imports: rmarkdown (>= 2.5), edgeR, DESeq2 (>= 1.16.0), dplyr, tidyr, ggplot2, grDevices, tools, SummarizedExperiment, genefilter, DT, GenomeInfoDbData, caTools, randtests, stats, utils, methods, ragg, rlang Suggests: knitr, testthat License: GPL (>=2) MD5sum: 7cf014de24bec4e6d46892ce0ae31713 NeedsCompilation: no Title: Compare Characteristic Features of Count Data Sets Description: countsimQC provides functionality to create a comprehensive report comparing a broad range of characteristics across a collection of count matrices. One important use case is the comparison of one or more synthetic count matrices to a real count matrix, possibly the one underlying the simulations. However, any collection of count matrices can be compared. biocViews: Microbiome, RNASeq, SingleCell, ExperimentalDesign, QualityControl, ReportWriting, Visualization, ImmunoOncology Author: Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/countsimQC VignetteBuilder: knitr BugReports: https://github.com/csoneson/countsimQC/issues git_url: https://git.bioconductor.org/packages/countsimQC git_branch: devel git_last_commit: 7a888aa git_last_commit_date: 2026-02-06 Date/Publication: 2026-04-20 source.ver: src/contrib/countsimQC_1.29.1.tar.gz vignettes: vignettes/countsimQC/inst/doc/countsimQC.html vignetteTitles: countsimQC User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/countsimQC/inst/doc/countsimQC.R suggestsMe: muscat dependencyCount: 125 Package: covEB Version: 1.37.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats, LaplacesDemon, Matrix Suggests: curatedBladderData License: GPL-3 MD5sum: 5bfdb05f820ebd623ac0db126df50bd3 NeedsCompilation: no Title: Empirical Bayes estimate of block diagonal covariance matrices Description: Using bayesian methods to estimate correlation matrices assuming that they can be written and estimated as block diagonal matrices. These block diagonal matrices are determined using shrinkage parameters that values below this parameter to zero. biocViews: ImmunoOncology, Bayesian, Microarray, RNASeq, Preprocessing, Software, GeneExpression, StatisticalMethod Author: C. Pacini Maintainer: C. Pacini git_url: https://git.bioconductor.org/packages/covEB git_branch: devel git_last_commit: 68edce5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/covEB_1.37.0.tar.gz vignettes: vignettes/covEB/inst/doc/covEB.pdf vignetteTitles: covEB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covEB/inst/doc/covEB.R dependencyCount: 24 Package: CoverageView Version: 1.49.0 Depends: R (>= 2.10), methods, Rsamtools (>= 1.19.17), rtracklayer Imports: S4Vectors (>= 0.7.21), IRanges(>= 2.3.23), GenomicRanges, GenomicAlignments, parallel, tools License: Artistic-2.0 MD5sum: dd89ce4822f64442503e32599126712c NeedsCompilation: no Title: Coverage visualization package for R Description: This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome biocViews: ImmunoOncology, Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software Author: Ernesto Lowy Maintainer: Ernesto Lowy git_url: https://git.bioconductor.org/packages/CoverageView git_branch: devel git_last_commit: b07c17a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CoverageView_1.49.0.tar.gz vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf vignetteTitles: Easy visualization of the read coverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoverageView/inst/doc/CoverageView.R dependencyCount: 57 Package: covRNA Version: 1.37.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 30683bb00d2e91358bbd57c0cd7ba959 NeedsCompilation: no Title: Multivariate Analysis of Transcriptomic Data Description: This package provides the analysis methods fourthcorner and RLQ analysis for large-scale transcriptomic data. biocViews: GeneExpression, Transcription Author: Lara Urban Maintainer: Lara Urban VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/covRNA git_branch: devel git_last_commit: 041af1b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/covRNA_1.37.0.tar.gz vignettes: vignettes/covRNA/inst/doc/covRNA.html vignetteTitles: An Introduction to covRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covRNA/inst/doc/covRNA.R dependencyCount: 60 Package: CPSM Version: 1.3.1 Depends: R (>= 4.5) Imports: SummarizedExperiment, grDevices, reshape2 , survival , survminer , ggplot2 , MTLR , glmnet , rms , preprocessCore , Matrix , stats, Hmisc, ggfortify, randomForestSRC, caret, SurvMetrics, MASS, Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: GPL-3 | file LICENSE MD5sum: dadd36d981f10ecfe47484ec0ed56ad1 NeedsCompilation: no Title: CPSM: Cancer patient survival model Description: CPSM provides a comprehensive computational pipeline for predicting survival probability and risk groups in cancer patients. The package includes steps for data preprocessing, training/test split, and normalization. It enables feature selection using univariate survival analysis and computes a LASSO-based prognostic index (PI) score. CPSM supports the development of predictive models using various feature sets and offers a suite of visualization tools, including survival curves based on predicted probabilities, barplots for predicted mean and median survival times, KM plots overlaid with individual survival predictions, and nomograms for estimating 1-, 3-, 5-, and 10-year survival probabilities. This makes CPSM a versatile tool for survival analysis in cancer research. biocViews: Normalization, Survival, GeneExpression, Preprocessing,FeatureExtraction, Software, Visualization Author: Harpreet Kaur [aut, cre] (ORCID: ), Pijush Das [aut], Kevin Camphausen [aut], Uma Shankavaram [aut, ctb] Maintainer: Harpreet Kaur URL: https://github.com/hks5august/CPSM/ VignetteBuilder: knitr BugReports: https://github.com/hks5august/CPSM/issues git_url: https://git.bioconductor.org/packages/CPSM git_branch: devel git_last_commit: 0ae9f9e git_last_commit_date: 2026-02-02 Date/Publication: 2026-04-20 source.ver: src/contrib/CPSM_1.3.1.tar.gz vignettes: vignettes/CPSM/inst/doc/CPSM.html vignetteTitles: CPSM: Cancer patient survival model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CPSM/inst/doc/CPSM.R dependencyCount: 208 Package: cpvSNP Version: 1.43.0 Depends: R (>= 3.5.0), GenomicFeatures, GSEABase (>= 1.24.0) Imports: methods, corpcor, BiocParallel, ggplot2, plyr Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 4684396f6afc0afe62ab8221dc1cbee5 NeedsCompilation: no Title: Gene set analysis methods for SNP association p-values that lie in genes in given gene sets Description: Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results. biocViews: Genetics, StatisticalMethod, Pathways, GeneSetEnrichment, GenomicVariation Author: Caitlin McHugh, Jessica Larson, and Jason Hackney Maintainer: Caitlin McHugh git_url: https://git.bioconductor.org/packages/cpvSNP git_branch: devel git_last_commit: f6e351b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cpvSNP_1.43.0.tar.gz vignettes: vignettes/cpvSNP/inst/doc/cpvSNP.pdf vignetteTitles: Running gene set analyses with the "cpvSNP" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cpvSNP/inst/doc/cpvSNP.R dependencyCount: 92 Package: cqn Version: 1.57.0 Depends: R (>= 2.10.0), mclust Imports: splines, graphics, nor1mix, stats, quantreg Suggests: scales, edgeR License: Artistic-2.0 MD5sum: 2c36ced0dcfb07968676aee5151a6765 NeedsCompilation: no Title: Conditional quantile normalization Description: A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. biocViews: ImmunoOncology, RNASeq, Preprocessing, DifferentialExpression Author: Jean (Zhijin) Wu, Kasper Daniel Hansen Maintainer: Kasper Daniel Hansen git_url: https://git.bioconductor.org/packages/cqn git_branch: devel git_last_commit: c3dc1bb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cqn_1.57.0.tar.gz vignettes: vignettes/cqn/inst/doc/cqn.pdf vignetteTitles: CQN (Conditional Quantile Normalization) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cqn/inst/doc/cqn.R dependsOnMe: KnowSeq importsMe: GeoTcgaData, tweeDEseq dependencyCount: 16 Package: CrcBiomeScreen Version: 0.99.15 Depends: R (>= 4.3.0) Imports: rlang, methods, dplyr, doFuture, doParallel, foreach, future, future.apply, pROC, progress, progressr, stats, tibble, tidyr, TreeSummarizedExperiment, ggplot2, GUniFrac, magrittr, parallel, withr, SummarizedExperiment, caret, ranger, utils, graphics, grDevices Suggests: rstatix, MASS, mgcv, ggplotify, ggpubr, ggrepel, ggtree, glmnet, Matrix, microbiome, phyloseq, vegan, gt, testthat (>= 3.0.0), BiocManager, devtools, knitr, rmarkdown, BiocStyle, curatedMetagenomicData, xgboost License: MIT + file LICENSE MD5sum: 9efe399d428ebec868a4f579740393b0 NeedsCompilation: no Title: An R package for colorectal cancer screening and microbiome analysis Description: A developed and benchmarked reproducible machine learning framework for microbiome-based colorectal cancer (CRC) screening. By systematically evaluating normalization strategies, taxonomic resolutions, and class imbalance handling. This R package allows users to apply the full pipeline or selectively run specific components depending on their analytical needs. It establishes a scalable foundation for developing interpretable microbiome-based screening tools to support early CRC detection. This approach could be easily implemented in a national screening programme, to improve early detection rates for this disease. biocViews: Software, Microbiome, Metagenomics, Classification, Normalization, Visualization Author: Chengxin Li [cre, aut] (ORCID: ), Rishabh Bezbaruah [aut], Henry Wood [aut], Arief Gusnanto [aut] Maintainer: Chengxin Li URL: https://github.com/omicsForestry/CrcBiomeScreen VignetteBuilder: knitr BugReports: https://github.com/omicsForestry/CrcBiomeScreen/issues git_url: https://git.bioconductor.org/packages/CrcBiomeScreen git_branch: devel git_last_commit: 6dc7ff6 git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/CrcBiomeScreen_0.99.15.tar.gz vignettes: vignettes/CrcBiomeScreen/inst/doc/CrcBiomeScreen.html vignetteTitles: CrcBiomeScreen hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CrcBiomeScreen/inst/doc/CrcBiomeScreen.R dependencyCount: 142 Package: CRImage Version: 1.59.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 399299ef92c09443806c74af82928591 NeedsCompilation: no Title: CRImage a package to classify cells and calculate tumour cellularity Description: CRImage provides functionality to process and analyze images, in particular to classify cells in biological images. Furthermore, in the context of tumor images, it provides functionality to calculate tumour cellularity. biocViews: CellBiology, Classification Author: Henrik Failmezger , Yinyin Yuan , Oscar Rueda , Florian Markowetz Maintainer: Henrik Failmezger , Yinyin Yuan git_url: https://git.bioconductor.org/packages/CRImage git_branch: devel git_last_commit: 850c6ef git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CRImage_1.59.0.tar.gz vignettes: vignettes/CRImage/inst/doc/CRImage.pdf vignetteTitles: CRImage Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRImage/inst/doc/CRImage.R dependencyCount: 62 Package: CRISPRball Version: 1.7.0 Depends: R (>= 4.4.0), shinyBS Imports: DT, shiny, grid, ComplexHeatmap, InteractiveComplexHeatmap, graphics, stats, ggplot2, plotly, shinyWidgets, shinycssloaders, shinyjqui, dittoSeq, matrixStats, colourpicker, shinyjs, circlize, PCAtools, utils, grDevices, htmlwidgets, methods Suggests: BiocStyle, msigdbr, depmap, pool, RSQLite, mygene, testthat (>= 3.0.0), knitr, rmarkdown License: MIT + file LICENSE MD5sum: 547c431a4e6ce86ff2b55f9c080a629e NeedsCompilation: no Title: Shiny Application for Interactive CRISPR Screen Visualization, Exploration, Comparison, and Filtering Description: A Shiny application for visualization, exploration, comparison, and filtering of CRISPR screens analyzed with MAGeCK RRA or MLE. Features include interactive plots with on-click labeling, full customization of plot aesthetics, data upload and/or download, and much more. Quickly and easily explore your CRISPR screen results and generate publication-quality figures in seconds. biocViews: Software, ShinyApps, CRISPR, QualityControl, Visualization, GUI Author: Jared Andrews [aut, cre] (ORCID: ), Jacob Steele [ctb] (ORCID: ) Maintainer: Jared Andrews URL: https://github.com/j-andrews7/CRISPRball VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/CRISPRball git_branch: devel git_last_commit: 43680fe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CRISPRball_1.7.0.tar.gz vignettes: vignettes/CRISPRball/inst/doc/CRISPRball.html vignetteTitles: CRISPRball Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CRISPRball/inst/doc/CRISPRball.R dependencyCount: 152 Package: crisprBase Version: 1.15.0 Depends: utils, methods, R (>= 4.1) Imports: BiocGenerics, Biostrings, GenomicRanges, graphics, IRanges, S4Vectors, stringr Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: d7fd3a62d116b43345a3468752b00754 NeedsCompilation: no Title: Base functions and classes for CRISPR gRNA design Description: Provides S4 classes for general nucleases, CRISPR nucleases, CRISPR nickases, and base editors.Several CRISPR-specific genome arithmetic functions are implemented to help extract genomic coordinates of spacer and protospacer sequences. Commonly-used CRISPR nuclease objects are provided that can be readily used in other packages. Both DNA- and RNA-targeting nucleases are supported. biocViews: CRISPR, FunctionalGenomics Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBase VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBase/issues git_url: https://git.bioconductor.org/packages/crisprBase git_branch: devel git_last_commit: 8c55499 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/crisprBase_1.15.0.tar.gz vignettes: vignettes/crisprBase/inst/doc/crisprBase.html vignetteTitles: Introduction to crisprBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBase/inst/doc/crisprBase.R dependsOnMe: crisprDesign, crisprViz importsMe: crisprBowtie, crisprBwa, crisprShiny, crisprVerse dependencyCount: 24 Package: crisprBowtie Version: 1.15.0 Depends: methods Imports: BiocGenerics, Biostrings, BSgenome, crisprBase (>= 0.99.15), Seqinfo, GenomicRanges, IRanges, Rbowtie, readr, stats, stringr, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 49654eab9016658eeb5167e7e6374ecf NeedsCompilation: no Title: Bowtie-based alignment of CRISPR gRNA spacer sequences Description: Provides a user-friendly interface to map on-targets and off-targets of CRISPR gRNA spacer sequences using bowtie. The alignment is fast, and can be performed using either commonly-used or custom CRISPR nucleases. The alignment can work with any reference or custom genomes. Both DNA- and RNA-targeting nucleases are supported. biocViews: CRISPR, FunctionalGenomics, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBowtie VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBowtie/issues git_url: https://git.bioconductor.org/packages/crisprBowtie git_branch: devel git_last_commit: 5838b33 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/crisprBowtie_1.15.0.tar.gz vignettes: vignettes/crisprBowtie/inst/doc/crisprBowtie.html vignetteTitles: Introduction to crisprBowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBowtie/inst/doc/crisprBowtie.R importsMe: crisprDesign, crisprVerse dependencyCount: 83 Package: crisprBwa Version: 1.15.1 Depends: methods Imports: BiocGenerics, BSgenome, crisprBase (>= 0.99.15), Seqinfo, Rbwa, readr, stats, stringr, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, testthat License: MIT + file LICENSE OS_type: unix MD5sum: 6637092a552e184a5ff70a787fce6ac5 NeedsCompilation: no Title: BWA-based alignment of CRISPR gRNA spacer sequences Description: Provides a user-friendly interface to map on-targets and off-targets of CRISPR gRNA spacer sequences using bwa. The alignment is fast, and can be performed using either commonly-used or custom CRISPR nucleases. The alignment can work with any reference or custom genomes. Currently not supported on Windows machines. biocViews: CRISPR, FunctionalGenomics, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBwa VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBwa/issues git_url: https://git.bioconductor.org/packages/crisprBwa git_branch: devel git_last_commit: 97195ce git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/crisprBwa_1.15.1.tar.gz vignettes: vignettes/crisprBwa/inst/doc/crisprBwa.html vignetteTitles: Introduction to crisprBwa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBwa/inst/doc/crisprBwa.R suggestsMe: crisprDesign dependencyCount: 83 Package: crisprScore Version: 1.15.3 Depends: R (>= 4.1), crisprScoreData (>= 1.1.3) Imports: BiocGenerics, Biostrings, IRanges, methods, randomForest, reticulate, stringr, utils, XVector Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 367c22fb0683b66dcc0258ce8a8682a4 NeedsCompilation: no Title: On-Target and Off-Target Scoring Algorithms for CRISPR gRNAs Description: Provides R wrappers of several on-target and off-target scoring methods for CRISPR guide RNAs (gRNAs). The following nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The available on-target cutting efficiency scoring methods are RuleSet1, RuleSet3, DeepHF, enPAM+GB, and CRISPRscan. Both the CFD and MIT scoring methods are available for off-target specificity prediction. The package also provides a Lindel-derived score to predict the probability of a gRNA to produce indels inducing a frameshift for the Cas9 nuclease. Note that DeepHF and enPAM+GB are not available on Windows machines. biocViews: CRISPR, FunctionalGenomics, FunctionalPrediction Author: Jean-Philippe Fortin [aut, cre, cph], Aaron Lun [aut], Luke Hoberecht [ctb], Pirunthan Perampalam [ctb] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprScore/issues VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprScore git_url: https://git.bioconductor.org/packages/crisprScore git_branch: devel git_last_commit: cbd6f9f git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/crisprScore_1.15.3.tar.gz vignettes: vignettes/crisprScore/inst/doc/crisprScore.html vignetteTitles: crisprScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprScore/inst/doc/crisprScore.R importsMe: crisprDesign, crisprShiny, crisprVerse dependencyCount: 74 Package: CRISPRseek Version: 1.51.0 Depends: R (>= 3.5.0), BiocGenerics, Biostrings, GenomicFeatures Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges, BSgenome, hash, methods,reticulate,rhdf5,XVector, DelayedArray, Seqinfo, GenomicRanges, dplyr, keras, mltools, gtools, openxlsx, rio, rlang, stringr Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, lattice, MASS, tensorflow, BSgenome.Hsapiens.UCSC.hg38, BiocFileCache, TxDb.Hsapiens.UCSC.hg38.knownGene, testthat, knitr License: file LICENSE MD5sum: fdb609576d212bc2d8d10022798cf7e7 NeedsCompilation: no Title: Design of guide RNAs in CRISPR genome-editing systems Description: The package encompasses functions to find potential guide RNAs for the CRISPR-based genome-editing systems including the Base Editors and the Prime Editors when supplied with target sequences as input. Users have the flexibility to filter resulting guide RNAs based on parameters such as the absence of restriction enzyme cut sites or the lack of paired guide RNAs. The package also facilitates genome-wide exploration for off-targets, offering features to score and rank off-targets, retrieve flanking sequences, and indicate whether the hits are located within exon regions. All detected guide RNAs are annotated with the cumulative scores of the top5 and topN off-targets together with the detailed information such as mismatch sites and restrictuion enzyme cut sites. The package also outputs INDELs and their frequencies for Cas9 targeted sites. biocViews: ImmunoOncology, GeneRegulation, SequenceMatching, CRISPR Author: Lihua Julie Zhu Paul Scemama Benjamin R. Holmes Hervé Pagès Kai Hu Hui Mao Michael Lawrence Isana Veksler-Lublinsky Victor Ambros Neil Aronin Michael Brodsky Devin M Burris Maintainer: Lihua Julie Zhu Kai Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CRISPRseek git_branch: devel git_last_commit: 0934262 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CRISPRseek_1.51.0.tar.gz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.html vignetteTitles: CRISPRseek: guide RNA design and off-target analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R importsMe: GUIDEseq, multicrispr dependencyCount: 141 Package: CrispRVariants Version: 1.39.0 Depends: R (>= 4.3.0), ggplot2 (>= 2.2.0) Imports: AnnotationDbi, BiocParallel, Biostrings, methods, GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices, grid, gridExtra, IRanges, reshape2, Rsamtools, S4Vectors (>= 0.9.38), utils Suggests: BiocStyle, GenomicFeatures, knitr, rmarkdown, readxl, rtracklayer, sangerseqR, testthat, VariantAnnotation License: GPL-2 MD5sum: 8bfd31956c71f27616b306f5b5936d98 NeedsCompilation: no Title: Tools for counting and visualising mutations in a target location Description: CrispRVariants provides tools for analysing the results of a CRISPR-Cas9 mutagenesis sequencing experiment, or other sequencing experiments where variants within a given region are of interest. These tools allow users to localize variant allele combinations with respect to any genomic location (e.g. the Cas9 cut site), plot allele combinations and calculate mutation rates with flexible filtering of unrelated variants. biocViews: ImmunoOncology, CRISPR, GenomicVariation, VariantDetection, GeneticVariability, DataRepresentation, Visualization, Sequencing Author: Helen Lindsay [aut, cre] Maintainer: Helen Lindsay VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CrispRVariants git_branch: devel git_last_commit: 4dcc360 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CrispRVariants_1.39.0.tar.gz vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf, vignettes/CrispRVariants/inst/doc/user_guide.html vignetteTitles: CrispRVariants, CrispRVariants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R dependencyCount: 86 Package: crlmm Version: 1.69.0 Depends: R (>= 2.14.0), oligoClasses (>= 1.21.12), preprocessCore (>= 1.17.7) Imports: methods, Biobase (>= 2.15.4), BiocGenerics, affyio (>= 1.23.2), illuminaio, ellipse, mvtnorm, splines, stats, utils, lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats, VGAM, parallel, graphics, limma, beanplot LinkingTo: preprocessCore (>= 1.17.7) Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), snpStats, RUnit License: Artistic-2.0 MD5sum: 132fad5ba089fc669cb3a549183394da NeedsCompilation: yes Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms. biocViews: Microarray, Preprocessing, SNP, CopyNumberVariation Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry Maintainer: Benilton S Carvalho , Robert Scharpf , Matt Ritchie git_url: https://git.bioconductor.org/packages/crlmm git_branch: devel git_last_commit: 51993cf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/crlmm_1.69.0.tar.gz vignettes: vignettes/crlmm/inst/doc/AffyGW.pdf, vignettes/crlmm/inst/doc/CopyNumberOverview.pdf, vignettes/crlmm/inst/doc/genotyping.pdf, vignettes/crlmm/inst/doc/gtypeDownstream.pdf, vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf, vignettes/crlmm/inst/doc/Infrastructure.pdf vignetteTitles: Copy number estimation, Overview of copy number vignettes, crlmm Vignette - Genotyping, crlmm Vignette - Downstream Analysis, Preprocessing and genotyping Illumina arrays for copy number analysis, Infrastructure for copy number analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/crlmm/inst/doc/genotyping.R dependsOnMe: MAGAR importsMe: VanillaICE suggestsMe: oligoClasses, hapmap370k dependencyCount: 65 Package: CSAR Version: 1.63.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, Seqinfo, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 MD5sum: 6450e57dcd08752166ee21d2e78f4d99 NeedsCompilation: yes Title: Statistical tools for the analysis of ChIP-seq data Description: Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation. biocViews: ChIPSeq, Transcription, Genetics Author: Jose M Muino Maintainer: Jose M Muino git_url: https://git.bioconductor.org/packages/CSAR git_branch: devel git_last_commit: 3494fdf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CSAR_1.63.0.tar.gz vignettes: vignettes/CSAR/inst/doc/CSAR.pdf vignetteTitles: CSAR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSAR/inst/doc/CSAR.R dependencyCount: 11 Package: csaw Version: 1.45.0 Depends: R (>= 3.5.0), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1) Imports: Rcpp, Matrix, BiocGenerics, Rsamtools, edgeR, limma, methods, S4Vectors, IRanges, Seqinfo, stats, BiocParallel, metapod, utils LinkingTo: Rhtslib, Rcpp Suggests: AnnotationDbi, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat, GenomicFeatures, GenomicAlignments, knitr, BiocStyle, rmarkdown, BiocManager License: GPL-3 MD5sum: bac078bef640a9d1f141ac2f833a3159 NeedsCompilation: yes Title: ChIP-Seq Analysis with Windows Description: Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. biocViews: MultipleComparison, ChIPSeq, Normalization, Sequencing, Coverage, Genetics, Annotation, DifferentialPeakCalling Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/csaw git_branch: devel git_last_commit: 39b0f81 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/csaw_1.45.0.tar.gz vignettes: vignettes/csaw/inst/doc/csaw.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csaw/inst/doc/csaw.R dependsOnMe: csawBook importsMe: diffHic, epigraHMM, extraChIPs, icetea, mutscan, NADfinder, hicream, treediff suggestsMe: GRaNIE, chipseqDB dependencyCount: 46 Package: csdR Version: 1.17.0 Depends: R (>= 4.1.0) Imports: WGCNA, glue, RhpcBLASctl, matrixStats, Rcpp LinkingTo: Rcpp Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle, magrittr, igraph, dplyr License: GPL-3 MD5sum: 801615fa0004cc4e6bbdb70298c37751 NeedsCompilation: yes Title: Differential gene co-expression Description: This package contains functionality to run differential gene co-expression across two different conditions. The algorithm is inspired by Voigt et al. 2017 and finds Conserved, Specific and Differentiated genes (hence the name CSD). This package include efficient and variance calculation by bootstrapping and Welford's algorithm. biocViews: DifferentialExpression, GraphAndNetwork, GeneExpression, Network Author: Jakob Peder Pettersen [aut, cre] (ORCID: ) Maintainer: Jakob Peder Pettersen URL: https://almaaslab.github.io/csdR, https://github.com/AlmaasLab/csdR VignetteBuilder: knitr BugReports: https://github.com/AlmaasLab/csdR/issues git_url: https://git.bioconductor.org/packages/csdR git_branch: devel git_last_commit: 3f28c20 git_last_commit_date: 2026-01-16 Date/Publication: 2026-04-20 source.ver: src/contrib/csdR_1.17.0.tar.gz vignettes: vignettes/csdR/inst/doc/csdR.html vignetteTitles: csdR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csdR/inst/doc/csdR.R dependencyCount: 79 Package: CSOA Version: 1.1.7 Imports: dplyr, ggplot2, henna, kerntools, methods, paletteer, qs2, reshape2, rlang, Seurat, SeuratObject, SummarizedExperiment, spatstat.utils, stats, textshape Suggests: BiocStyle, knitr, patchwork, rmarkdown, scRNAseq, scuttle, stringr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: d049f9d9f9fdf88aa748120041dca3f2 NeedsCompilation: no Title: Calculate per-cell gene signature scores in scRNA-seq data using cell set overlaps Description: Cell Set Overlap Analysis (CSOA) is a tool for calculating per-cell gene signature scores in an scRNA-seq dataset. CSOA constructs a set for each gene in the signature, consisting of the cells that highly express the gene. Next, all overlaps of pairs of cell sets are computed, ranked, filtered and scored. The CSOA per-cell score is calculated by summing up all products of the overlap scores and the min-max-normalized expression of the two involved genes. CSOA can run on a Seurat object, a SingleCellExperiment object, a matrix and a dgCMatrix. biocViews: Software, SingleCell, GeneSetEnrichment, GeneExpression Author: Andrei-Florian Stoica [aut, cre] (ORCID: ) Maintainer: Andrei-Florian Stoica URL: https://github.com/andrei-stoica26/CSOA VignetteBuilder: knitr BugReports: https://github.com/andrei-stoica26/CSOA/issues git_url: https://git.bioconductor.org/packages/CSOA git_branch: devel git_last_commit: a309ed1 git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/CSOA_1.1.7.tar.gz vignettes: vignettes/CSOA/inst/doc/Advanced-CSOA.html, vignettes/CSOA/inst/doc/CSOA.html, vignettes/CSOA/inst/doc/The-CSOA-algorithm.html vignetteTitles: Advanced CSOA, Getting started with CSOA, The CSOA algorithm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CSOA/inst/doc/Advanced-CSOA.R, vignettes/CSOA/inst/doc/CSOA.R, vignettes/CSOA/inst/doc/The-CSOA-algorithm.R importsMe: GSABenchmark dependencyCount: 190 Package: CSSQ Version: 1.23.0 Depends: SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, rtracklayer Imports: GenomicAlignments, GenomicFeatures, Rsamtools, ggplot2, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, markdown License: Artistic-2.0 MD5sum: 31a54b1a625caf0d38bacb5efccce492 NeedsCompilation: no Title: Chip-seq Signal Quantifier Pipeline Description: This package is desgined to perform statistical analysis to identify statistically significant differentially bound regions between multiple groups of ChIP-seq dataset. biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Normalization Author: Ashwath Kumar [aut], Michael Y Hu [aut], Yajun Mei [aut], Yuhong Fan [aut] Maintainer: Fan Lab at Georgia Institute of Technology VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CSSQ git_branch: devel git_last_commit: b5e08ec git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CSSQ_1.23.0.tar.gz vignettes: vignettes/CSSQ/inst/doc/CSSQ.html vignetteTitles: Introduction to CSSQ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSQ/inst/doc/CSSQ.R dependencyCount: 85 Package: ctc Version: 1.85.0 Depends: amap License: GPL-2 MD5sum: 271f47faaa7fc1b46aedd583767a195c NeedsCompilation: no Title: Cluster and Tree Conversion. Description: Tools for export and import classification trees and clusters to other programs biocViews: Microarray, Clustering, Classification, DataImport, Visualization Author: Antoine Lucas , Laurent Gautier Maintainer: Antoine Lucas URL: http://antoinelucas.free.fr/ctc git_url: https://git.bioconductor.org/packages/ctc git_branch: devel git_last_commit: 75b3c60 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ctc_1.85.0.tar.gz vignettes: vignettes/ctc/inst/doc/ctc.pdf vignetteTitles: Introduction to ctc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctc/inst/doc/ctc.R importsMe: miRLAB, multiClust dependencyCount: 1 Package: CTdata Version: 1.11.0 Depends: R (>= 4.2) Imports: ExperimentHub, utils Suggests: testthat (>= 3.0.0), DT, BiocStyle, knitr, rmarkdown, SummarizedExperiment, SingleCellExperiment License: Artistic-2.0 MD5sum: 2ab91f4592781310a726e6dadf365488 NeedsCompilation: no Title: Data companion to CTexploreR Description: Data from publicly available databases (GTEx, CCLE, TCGA and ENCODE) that go with CTexploreR in order to re-define a comprehensive and thoroughly curated list of CT genes and their main characteristics. biocViews: Transcriptomics, Epigenetics, GeneExpression, DataImport, ExperimentHubSoftware Author: Axelle Loriot [aut] (ORCID: ), Julie Devis [aut] (ORCID: ), Anna Diacofotaki [ctb], Charles De Smet [ths], Laurent Gatto [aut, ths, cre] (ORCID: ) Maintainer: Laurent Gatto VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CTdata/issues git_url: https://git.bioconductor.org/packages/CTdata git_branch: devel git_last_commit: 28e1af6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CTdata_1.11.0.tar.gz vignettes: vignettes/CTdata/inst/doc/CTdata.html vignetteTitles: Cancer Testis Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CTdata/inst/doc/CTdata.R dependsOnMe: CTexploreR dependencyCount: 64 Package: CTDquerier Version: 2.19.0 Depends: R (>= 4.1) Imports: RCurl, stringr, S4Vectors, stringdist, ggplot2, igraph, utils, grid, gridExtra, methods, stats, BiocFileCache Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 09c4dd0f35bf5372010de03d6f072d80 NeedsCompilation: no Title: Package for CTDbase data query, visualization and downstream analysis Description: Package to retrieve and visualize data from the Comparative Toxicogenomics Database (http://ctdbase.org/). The downloaded data is formated as DataFrames for further downstream analyses. biocViews: Software, BiomedicalInformatics, Infrastructure, DataImport, DataRepresentation, GeneSetEnrichment, NetworkEnrichment, Pathways, Network, GO, KEGG Author: Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut], Xavier Escribà-Montagut [cre] Maintainer: Xavier Escribà-Montagut VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/CTDquerier git_branch: devel git_last_commit: 7edaaa1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CTDquerier_2.19.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 64 Package: CTexploreR Version: 1.7.0 Depends: R (>= 4.3), CTdata (>= 1.5.3) Imports: BiocGenerics, ComplexHeatmap, grid, SummarizedExperiment, GenomicRanges, IRanges, dplyr, tidyr, tibble, ggplot2, rlang, grDevices, stats, circlize, ggrepel, SingleCellExperiment, MatrixGenerics Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), InteractiveComplexHeatmap License: Artistic-2.0 MD5sum: 231f1ba759e2562bc0fc79470f91a299 NeedsCompilation: no Title: Explores Cancer Testis Genes Description: The CTexploreR package re-defines the list of Cancer Testis/Germline (CT) genes. It is based on publicly available RNAseq databases (GTEx, CCLE and TCGA) and summarises CT genes' main characteristics. Several visualisation functions allow to explore their expression in different types of tissues and cancer cells, or to inspect the methylation status of their promoters in normal tissues. biocViews: Transcriptomics, Epigenetics, DifferentialExpression, GeneExpression, DNAMethylation, ExperimentHubSoftware, DataImport Author: Axelle Loriot [aut, cre] (ORCID: ), Julie Devis [aut] (ORCID: ), Anna Diacofotaki [ctb], Charles De Smet [ths], Laurent Gatto [aut, ths] (ORCID: ) Maintainer: Axelle Loriot URL: https://github.com/UCLouvain-CBIO/CTexploreR VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CTexploreR/issues git_url: https://git.bioconductor.org/packages/CTexploreR git_branch: devel git_last_commit: db48290 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CTexploreR_1.7.0.tar.gz vignettes: vignettes/CTexploreR/inst/doc/CTexploreR.html vignetteTitles: Cancer Testis Explorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CTexploreR/inst/doc/CTexploreR.R dependencyCount: 103 Package: ctsGE Version: 1.37.0 Depends: R (>= 3.2) Imports: ccaPP, ggplot2, limma, reshape2, shiny, stats, stringr, utils Suggests: BiocStyle, dplyr, DT, GEOquery, knitr, pander, rmarkdown, testthat License: GPL-2 MD5sum: 3d571f36284e118438bda98699149ef5 NeedsCompilation: no Title: Clustering of Time Series Gene Expression data Description: Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Bayesian, Clustering, TimeCourse, Sequencing, RNASeq Author: Michal Sharabi-Schwager [aut, cre], Ron Ophir [aut] Maintainer: Michal Sharabi-Schwager URL: https://github.com/michalsharabi/ctsGE VignetteBuilder: knitr BugReports: https://github.com/michalsharabi/ctsGE/issues git_url: https://git.bioconductor.org/packages/ctsGE git_branch: devel git_last_commit: 87924de git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ctsGE_1.37.0.tar.gz vignettes: vignettes/ctsGE/inst/doc/ctsGE.html vignetteTitles: ctsGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctsGE/inst/doc/ctsGE.R dependencyCount: 61 Package: CTSV Version: 1.13.0 Depends: R (>= 4.2), Imports: stats, pscl, qvalue, BiocParallel, methods, knitr, SpatialExperiment, SummarizedExperiment Suggests: testthat, BiocStyle License: GPL-3 MD5sum: 62113c62491f834358f6c1cc7b6163ef NeedsCompilation: yes Title: Identification of cell-type-specific spatially variable genes accounting for excess zeros Description: The R package CTSV implements the CTSV approach developed by Jinge Yu and Xiangyu Luo that detects cell-type-specific spatially variable genes accounting for excess zeros. CTSV directly models sparse raw count data through a zero-inflated negative binomial regression model, incorporates cell-type proportions, and performs hypothesis testing based on R package pscl. The package outputs p-values and q-values for genes in each cell type, and CTSV is scalable to datasets with tens of thousands of genes measured on hundreds of spots. CTSV can be installed in Windows, Linux, and Mac OS. biocViews: GeneExpression, StatisticalMethod, Regression, Spatial, Genetics Author: Jinge Yu Developer [aut, cre], Xiangyu Luo Developer [aut] Maintainer: Jinge Yu Developer URL: https://github.com/jingeyu/CTSV VignetteBuilder: knitr BugReports: https://github.com/jingeyu/CTSV/issues git_url: https://git.bioconductor.org/packages/CTSV git_branch: devel git_last_commit: 2a4282f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CTSV_1.13.0.tar.gz vignettes: vignettes/CTSV/inst/doc/CTSV.html vignetteTitles: Basic Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CTSV/inst/doc/CTSV.R dependencyCount: 96 Package: customCMPdb Version: 1.21.0 Depends: R (>= 4.0) Imports: AnnotationHub, RSQLite, XML, utils, ChemmineR, methods, stats, rappdirs, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: 4e16f112409844c08d79c47a19015b36 NeedsCompilation: no Title: Customize and Query Compound Annotation Database Description: This package serves as a query interface for important community collections of small molecules, while also allowing users to include custom compound collections. biocViews: Software, Cheminformatics,AnnotationHubSoftware Author: Yuzhu Duan [aut, cre], Thomas Girke [aut] Maintainer: Yuzhu Duan URL: https://github.com/yduan004/customCMPdb/ VignetteBuilder: knitr BugReports: https://github.com/yduan004/customCMPdb/issues git_url: https://git.bioconductor.org/packages/customCMPdb git_branch: devel git_last_commit: 5a0a0a6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/customCMPdb_1.21.0.tar.gz vignettes: vignettes/customCMPdb/inst/doc/customCMPdb.html vignetteTitles: customCMPdb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customCMPdb/inst/doc/customCMPdb.R dependencyCount: 103 Package: cyanoFilter Version: 1.19.0 Depends: R(>= 4.1.0) Imports: Biobase, flowCore, flowDensity, flowClust, cytometree, ggplot2, GGally, graphics, grDevices, methods, mrfDepth, stats, utils Suggests: magrittr, dplyr, purrr, knitr, stringr, rmarkdown, tidyr License: MIT + file LICENSE MD5sum: fb5025d2e72b8b30a040d006c7ca8d70 NeedsCompilation: no Title: Phytoplankton Population Identification using Cell Pigmentation and/or Complexity Description: An approach to filter out and/or identify phytoplankton cells from all particles measured via flow cytometry pigment and cell complexity information. It does this using a sequence of one-dimensional gates on pre-defined channels measuring certain pigmentation and complexity. The package is especially tuned for cyanobacteria, but will work fine for phytoplankton communities where there is at least one cell characteristic that differentiates every phytoplankton in the community. biocViews: FlowCytometry, Clustering, OneChannel Author: Oluwafemi Olusoji [cre, aut], Aerts Marc [ctb], Delaender Frederik [ctb], Neyens Thomas [ctb], Spaak jurg [aut] Maintainer: Oluwafemi Olusoji URL: https://github.com/fomotis/cyanoFilter VignetteBuilder: knitr BugReports: https://github.com/fomotis/cyanoFilter/issues git_url: https://git.bioconductor.org/packages/cyanoFilter git_branch: devel git_last_commit: 8ca42f1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cyanoFilter_1.19.0.tar.gz vignettes: vignettes/cyanoFilter/inst/doc/cyanoFilter.html vignetteTitles: cyanoFilter: A Semi-Automated Framework for Identifying Phytplanktons and Cyanobacteria Population in Flow Cytometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cyanoFilter/inst/doc/cyanoFilter.R dependencyCount: 128 Package: cycle Version: 1.65.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: ae8822203d464b250af601722f5eb726 NeedsCompilation: no Title: Significance of periodic expression pattern in time-series data Description: Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models biocViews: Microarray, TimeCourse Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://cycle.sysbiolab.eu git_url: https://git.bioconductor.org/packages/cycle git_branch: devel git_last_commit: cf91b5c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cycle_1.65.0.tar.gz vignettes: vignettes/cycle/inst/doc/cycle.pdf vignetteTitles: Introduction to cycle hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cycle/inst/doc/cycle.R dependencyCount: 18 Package: cydar Version: 1.35.0 Depends: SingleCellExperiment Imports: viridis, methods, shiny, graphics, stats, grDevices, utils, BiocGenerics, S4Vectors, BiocParallel, SummarizedExperiment, flowCore, Biobase, Rcpp, BiocNeighbors LinkingTo: Rcpp Suggests: ncdfFlow, testthat, rmarkdown, knitr, edgeR, limma, glmnet, BiocStyle, flowStats License: GPL-3 MD5sum: 21c7209ec9de03736948ec680d82afd3 NeedsCompilation: yes Title: Using Mass Cytometry for Differential Abundance Analyses Description: Identifies differentially abundant populations between samples and groups in mass cytometry data. Provides methods for counting cells into hyperspheres, controlling the spatial false discovery rate, and visualizing changes in abundance in the high-dimensional marker space. biocViews: ImmunoOncology, FlowCytometry, MultipleComparison, Proteomics, SingleCell Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cydar git_branch: devel git_last_commit: d8b7be4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cydar_1.35.0.tar.gz vignettes: vignettes/cydar/inst/doc/cydar.html vignetteTitles: Detecting differential abundance hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cydar/inst/doc/cydar.R dependencyCount: 88 Package: cypress Version: 1.7.0 Depends: R(>= 4.4.0) Imports: stats, abind, sirt, MASS,TOAST, tibble, parallel, preprocessCore, SummarizedExperiment, TCA, PROPER, methods,dplyr, utils, RColorBrewer, graphics, edgeR, BiocParallel, checkmate, mvtnorm, DESeq2, rlang, e1071 Suggests: knitr, rmarkdown, MatrixGenerics, htmltools, RUnit, BiocGenerics, BiocManager, BiocStyle, Biobase License: GPL-2 | GPL-3 MD5sum: 9ae6dd0a5824068e9a407bb7d1fea6ba NeedsCompilation: no Title: Cell-Type-Specific Power Assessment Description: CYPRESS is a cell-type-specific power tool. This package aims to perform power analysis for the cell-type-specific data. It calculates FDR, FDC, and power, under various study design parameters, including but not limited to sample size, and effect size. It takes the input of a SummarizeExperimental(SE) object with observed mixture data (feature by sample matrix), and the cell-type mixture proportions (sample by cell-type matrix). It can solve the cell-type mixture proportions from the reference free panel from TOAST and conduct tests to identify cell-type-specific differential expression (csDE) genes. biocViews: Software, GeneExpression, DataImport, RNASeq, Sequencing Author: Shilin Yu [aut, cre] (ORCID: ), Guanqun Meng [aut], Wen Tang [aut] Maintainer: Shilin Yu URL: https://github.com/renlyly/cypress VignetteBuilder: knitr BugReports: https://github.com/renlyly/cypress/issues git_url: https://git.bioconductor.org/packages/cypress git_branch: devel git_last_commit: 40f21f4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cypress_1.7.0.tar.gz vignettes: vignettes/cypress/inst/doc/cypress.html vignetteTitles: cypress Package User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cypress/inst/doc/cypress.R dependencyCount: 138 Package: CytoDx Version: 1.31.0 Depends: R (>= 3.5) Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats, flowCore,grDevices, graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 0d65b85fba35ccefb6cf6af57447ac77 NeedsCompilation: no Title: Robust prediction of clinical outcomes using cytometry data without cell gating Description: This package provides functions that predict clinical outcomes using single cell data (such as flow cytometry data, RNA single cell sequencing data) without the requirement of cell gating or clustering. biocViews: ImmunoOncology, CellBiology, FlowCytometry, StatisticalMethod, Software, CellBasedAssays, Regression, Classification, Survival Author: Zicheng Hu Maintainer: Zicheng Hu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/CytoDx git_branch: devel git_last_commit: 541a9d4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CytoDx_1.31.0.tar.gz vignettes: vignettes/CytoDx/inst/doc/CytoDx_Vignette.pdf vignetteTitles: Introduction to CytoDx hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoDx/inst/doc/CytoDx_Vignette.R dependencyCount: 51 Package: cytofQC Version: 1.99.1 Imports: CATALYST, flowCore, e1071, EZtune, gbm, ggplot2, matrixStats, randomForest, rmarkdown, SingleCellExperiment, stats, SummarizedExperiment, ssc, S4Vectors, graphics, methods, mixtools Suggests: gridExtra, knitr, RColorBrewer, testthat, uwot License: Artistic-2.0 MD5sum: 536efb780d117897131b14a737fe8ca4 NeedsCompilation: no Title: Labels normalized cells for CyTOF data and assigns probabilities for each label Description: cytofQC is a package for initial cleaning of CyTOF data. It uses a semi-supervised approach for labeling cells with their most likely data type (bead, doublet, debris, dead) and the probability that they belong to each label type. This package does not remove data from the dataset, but provides labels and information to aid the data user in cleaning their data. Our algorithm is able to distinguish between doublets and large cells. biocViews: Software, SingleCell, Annotation Author: Jill Lundell [aut, cre] (ORCID: ), Kelly Street [aut] (ORCID: ) Maintainer: Jill Lundell URL: https://github.com/jillbo1000/cytofQC VignetteBuilder: knitr BugReports: https://github.com/jillbo1000/cytofQC/issues git_url: https://git.bioconductor.org/packages/cytofQC git_branch: devel git_last_commit: d6548db git_last_commit_date: 2026-03-30 Date/Publication: 2026-04-20 source.ver: src/contrib/cytofQC_1.99.1.tar.gz vignettes: vignettes/cytofQC/inst/doc/cytofQC.html vignetteTitles: Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytofQC/inst/doc/cytofQC.R dependencyCount: 237 Package: CytoGLMM Version: 1.19.0 Imports: stats, methods, BiocParallel, RColorBrewer, cowplot, doParallel, dplyr, factoextra, flexmix, ggplot2, magrittr, mbest, pheatmap, stringr, strucchange, tibble, ggrepel, MASS, logging, Matrix, tidyr, caret, rlang, grDevices Suggests: knitr, rmarkdown, testthat, BiocStyle License: LGPL-3 MD5sum: efca608e97fd8b643c6d29decd078f3a NeedsCompilation: no Title: Conditional Differential Analysis for Flow and Mass Cytometry Experiments Description: The CytoGLMM R package implements two multiple regression strategies: A bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM). Most current data analysis tools compare expressions across many computationally discovered cell types. CytoGLMM focuses on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. As a result, CytoGLMM finds differential proteins in flow and mass cytometry data while reducing biases arising from marker correlations and safeguarding against false discoveries induced by patient heterogeneity. biocViews: FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, ImmunoOncology, Regression, StatisticalMethod, Software Author: Christof Seiler [aut, cre] (ORCID: ) Maintainer: Christof Seiler URL: https://christofseiler.github.io/CytoGLMM, https://github.com/ChristofSeiler/CytoGLMM VignetteBuilder: knitr BugReports: https://github.com/ChristofSeiler/CytoGLMM/issues git_url: https://git.bioconductor.org/packages/CytoGLMM git_branch: devel git_last_commit: 9682447 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/CytoGLMM_1.19.0.tar.gz vignettes: vignettes/CytoGLMM/inst/doc/CytoGLMM.html vignetteTitles: CytoGLMM Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoGLMM/inst/doc/CytoGLMM.R importsMe: CyTOFpower dependencyCount: 181 Package: cytoKernel Version: 1.17.0 Depends: R (>= 4.1) Imports: Rcpp, SummarizedExperiment, utils, methods, ComplexHeatmap, circlize, ashr, data.table, BiocParallel, dplyr, stats, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 8aa7fddccb6c9ce8826b8cfe453d1b36 NeedsCompilation: yes Title: Differential expression using kernel-based score test Description: cytoKernel implements a kernel-based score test to identify differentially expressed features in high-dimensional biological experiments. This approach can be applied across many different high-dimensional biological data including gene expression data and dimensionally reduced cytometry-based marker expression data. In this R package, we implement functions that compute the feature-wise p values and their corresponding adjusted p values. Additionally, it also computes the feature-wise shrunk effect sizes and their corresponding shrunken effect size. Further, it calculates the percent of differentially expressed features and plots user-friendly heatmap of the top differentially expressed features on the rows and samples on the columns. biocViews: ImmunoOncology, Proteomics, SingleCell, Software, OneChannel, FlowCytometry, DifferentialExpression, GeneExpression, Clustering Author: Tusharkanti Ghosh [aut, cre], Victor Lui [aut], Pratyaydipta Rudra [aut], Souvik Seal [aut], Thao Vu [aut], Elena Hsieh [aut], Debashis Ghosh [aut, cph] Maintainer: Tusharkanti Ghosh VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/cytoKernel/issues git_url: https://git.bioconductor.org/packages/cytoKernel git_branch: devel git_last_commit: 25ec3e0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cytoKernel_1.17.0.tar.gz vignettes: vignettes/cytoKernel/inst/doc/cytoKernel.html vignetteTitles: The CytoK user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoKernel/inst/doc/cytoKernel.R dependencyCount: 75 Package: cytolib Version: 2.23.0 Depends: R (>= 3.4) Imports: RProtoBufLib LinkingTo: BH(>= 1.84.0.0), RProtoBufLib(>= 2.13.1),Rhdf5lib Suggests: knitr, rmarkdown License: AGPL-3.0-only License_restricts_use: no MD5sum: c3a7d3ee47cf4fdce009a06178c185f7 NeedsCompilation: yes Title: C++ infrastructure for representing and interacting with the gated cytometry data Description: This package provides the core data structure and API to represent and interact with the gated cytometry data. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytolib git_branch: devel git_last_commit: 7752793 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cytolib_2.23.0.tar.gz vignettes: vignettes/cytolib/inst/doc/cytolib.html vignetteTitles: Using cytolib hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cytolib/inst/doc/cytolib.R importsMe: CytoML, flowCore, flowWorkspace linksToMe: CytoML, flowCore, flowWorkspace dependencyCount: 8 Package: CytoMDS Version: 1.7.2 Depends: R (>= 4.4), Biobase Imports: methods, stats, rlang, pracma, withr, flowCore, reshape2, ggplot2, ggrepel, ggforce, patchwork, transport, smacof, BiocParallel, CytoPipeline Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown, BiocStyle, HDCytoData License: GPL-3 MD5sum: da563eb1d1724eb25f8dc74f4c691d78 NeedsCompilation: no Title: Low Dimensions projection of cytometry samples Description: This package implements a low dimensional visualization of a set of cytometry samples, in order to visually assess the 'distances' between them. This, in turn, can greatly help the user to identify quality issues like batch effects or outlier samples, and/or check the presence of potential sample clusters that might align with the exeprimental design. The CytoMDS algorithm combines, on the one hand, the concept of Earth Mover's Distance (EMD), a.k.a. Wasserstein metric and, on the other hand, the Multi Dimensional Scaling (MDS) algorithm for the low dimensional projection. Also, the package provides some diagnostic tools for both checking the quality of the MDS projection, as well as tools to help with the interpretation of the axes of the projection. biocViews: FlowCytometry, QualityControl, DimensionReduction, MultidimensionalScaling, Software, Visualization Author: Philippe Hauchamps [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Dan Lin [ctb] Maintainer: Philippe Hauchamps URL: https://uclouvain-cbio.github.io/CytoMDS VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CytoMDS/issues git_url: https://git.bioconductor.org/packages/CytoMDS git_branch: devel git_last_commit: 0b893cc git_last_commit_date: 2026-01-18 Date/Publication: 2026-04-20 source.ver: src/contrib/CytoMDS_1.7.2.tar.gz vignettes: vignettes/CytoMDS/inst/doc/CytoMDS.html vignetteTitles: Low Dimensional Projection of Cytometry Samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoMDS/inst/doc/CytoMDS.R importsMe: MDSvis dependencyCount: 192 Package: cytoMEM Version: 1.15.0 Depends: R (>= 4.2.0) Imports: gplots, tools, flowCore, grDevices, stats, utils, matrixStats, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: df682111f9a2f7b5953e9a24edf8ce0f NeedsCompilation: no Title: Marker Enrichment Modeling (MEM) Description: MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry, mass cytometry, single cell RNA-seq, and spectral flow cytometry using RMSD. biocViews: Proteomics, SystemsBiology, Classification, FlowCytometry, DataRepresentation, DataImport, CellBiology, SingleCell, Clustering Author: Sierra Lima [aut] (ORCID: ), Kirsten Diggins [aut] (ORCID: ), Jonathan Irish [aut, cre] (ORCID: ) Maintainer: Jonathan Irish URL: https://github.com/cytolab/cytoMEM VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytoMEM git_branch: devel git_last_commit: 64c426a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/cytoMEM_1.15.0.tar.gz vignettes: vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.html vignetteTitles: Intro_to_Marker_Enrichment_Modeling_Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.R dependencyCount: 27 Package: CytoML Version: 2.23.2 Depends: R (>= 3.5.0) Imports: cytolib(>= 2.3.10), flowCore (>= 1.99.10), flowWorkspace (>= 4.1.8), openCyto (>= 1.99.2), XML, data.table, jsonlite, RBGL, Rgraphviz, Biobase, methods, graph, graphics, utils, jsonlite, dplyr, grDevices, methods, ggcyto (>= 1.11.4), yaml, stats, tibble LinkingTo: cpp11, BH(>= 1.62.0-1), RProtoBufLib, cytolib, Rhdf5lib, flowWorkspace Suggests: testthat, flowWorkspaceData , knitr, rmarkdown, parallel License: AGPL-3.0-only License_restricts_use: no MD5sum: ce3de82e6b3908d69163acf1711f38b7 NeedsCompilation: yes Title: A GatingML Interface for Cross Platform Cytometry Data Sharing Description: Uses platform-specific implemenations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms. biocViews: ImmunoOncology, FlowCytometry, DataImport, DataRepresentation Author: Mike Jiang, Jake Wagner Maintainer: Mike Jiang URL: https://github.com/RGLab/CytoML SystemRequirements: xml2, GNU make, C++17 VignetteBuilder: knitr BugReports: https://github.com/RGLab/CytoML/issues git_url: https://git.bioconductor.org/packages/CytoML git_branch: devel git_last_commit: 9494b90 git_last_commit_date: 2026-02-18 Date/Publication: 2026-04-20 source.ver: src/contrib/CytoML_2.23.2.tar.gz vignettes: vignettes/CytoML/inst/doc/cytobank2GatingSet.html, vignettes/CytoML/inst/doc/flowjo_to_gatingset.html, vignettes/CytoML/inst/doc/HowToExportGatingSet.html vignetteTitles: How to import Cytobank into a GatingSet, flowJo parser, How to export a GatingSet to GatingML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoML/inst/doc/cytobank2GatingSet.R, vignettes/CytoML/inst/doc/flowjo_to_gatingset.R, vignettes/CytoML/inst/doc/HowToExportGatingSet.R suggestsMe: FlowSOM, flowWorkspace, openCyto dependencyCount: 81 Package: CytoPipeline Version: 1.11.1 Depends: R (>= 4.4) Imports: methods, stats, utils, withr, rlang, ggplot2 (>= 3.4.1), ggcyto, BiocFileCache, BiocParallel, flowCore, PeacoQC, flowAI, diagram, jsonlite, scales Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown, BiocStyle, reshape2, dplyr, CytoPipelineGUI License: GPL-3 MD5sum: 5e11353acac1f7738983e6d46e667d2e NeedsCompilation: no Title: Automation and visualization of flow cytometry data analysis pipelines Description: This package provides support for automation and visualization of flow cytometry data analysis pipelines. In the current state, the package focuses on the preprocessing and quality control part. The framework is based on two main S4 classes, i.e. CytoPipeline and CytoProcessingStep. The pipeline steps are linked to corresponding R functions - that are either provided in the CytoPipeline package itself, or exported from a third party package, or coded by the user her/himself. The processing steps need to be specified centrally and explicitly using either a json input file or through step by step creation of a CytoPipeline object with dedicated methods. After having run the pipeline, obtained results at all steps can be retrieved and visualized thanks to file caching (the running facility uses a BiocFileCache implementation). The package provides also specific visualization tools like pipeline workflow summary display, and 1D/2D comparison plots of obtained flowFrames at various steps of the pipeline. biocViews: FlowCytometry, Preprocessing, QualityControl, WorkflowStep, ImmunoOncology, Software, Visualization Author: Philippe Hauchamps [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Dan Lin [ctb] Maintainer: Philippe Hauchamps URL: https://uclouvain-cbio.github.io/CytoPipeline VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CytoPipeline/issues git_url: https://git.bioconductor.org/packages/CytoPipeline git_branch: devel git_last_commit: d2d9d72 git_last_commit_date: 2026-01-14 Date/Publication: 2026-04-20 source.ver: src/contrib/CytoPipeline_1.11.1.tar.gz vignettes: vignettes/CytoPipeline/inst/doc/CytoPipeline.html, vignettes/CytoPipeline/inst/doc/Demo.html vignetteTitles: Automation and Visualization of Flow Cytometry Data Analysis Pipelines, Demonstration of the CytoPipeline R package suite functionalities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoPipeline/inst/doc/CytoPipeline.R, vignettes/CytoPipeline/inst/doc/Demo.R dependsOnMe: CytoPipelineGUI importsMe: CytoMDS dependencyCount: 131 Package: CytoPipelineGUI Version: 1.9.1 Depends: R (>= 4.4), CytoPipeline (>= 1.9.3) Imports: shiny, plotly, ggplot2, flowCore Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown, BiocStyle, patchwork License: GPL-3 MD5sum: 29991f8be49daa6357d74d204ae19698 NeedsCompilation: no Title: GUI's for visualization of flow cytometry data analysis pipelines Description: This package is the companion of the `CytoPipeline` package. It provides GUI's (shiny apps) for the visualization of flow cytometry data analysis pipelines that are run with `CytoPipeline`. Two shiny applications are provided, i.e. an interactive flow frame assessment and comparison tool and an interactive scale transformations visualization and adjustment tool. biocViews: FlowCytometry, Preprocessing, QualityControl, WorkflowStep, ImmunoOncology, Software, Visualization, GUI, ShinyApps Author: Philippe Hauchamps [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Dan Lin [ctb] Maintainer: Philippe Hauchamps URL: https://uclouvain-cbio.github.io/CytoPipelineGUI VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CytoPipelineGUI/issues git_url: https://git.bioconductor.org/packages/CytoPipelineGUI git_branch: devel git_last_commit: 72ada89 git_last_commit_date: 2026-01-14 Date/Publication: 2026-04-20 source.ver: src/contrib/CytoPipelineGUI_1.9.1.tar.gz vignettes: vignettes/CytoPipelineGUI/inst/doc/CytoPipelineGUI.html, vignettes/CytoPipelineGUI/inst/doc/Demo.html vignetteTitles: CytoPipelineGUI : visualization of Flow Cytometry Data Analysis Pipelines run with CytoPipeline, Demonstration of the CytoPipeline R package suite functionalities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoPipelineGUI/inst/doc/CytoPipelineGUI.R, vignettes/CytoPipelineGUI/inst/doc/Demo.R suggestsMe: CytoPipeline dependencyCount: 145 Package: dada2 Version: 1.39.0 Depends: R (>= 4.1.0), Rcpp (>= 0.12.0), methods (>= 3.4.0) Imports: Biostrings (>= 2.42.1), ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), ShortRead (>= 1.32.0), RcppParallel (>= 4.3.0), parallel (>= 3.2.0), IRanges (>= 2.6.0), XVector (>= 0.16.0), BiocGenerics (>= 0.22.0) LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, knitr, rmarkdown License: LGPL-2 MD5sum: 01b3be41c0551b9ea407b2300395eded NeedsCompilation: yes Title: Accurate, high-resolution sample inference from amplicon sequencing data Description: The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Paul McMurdie, Susan Holmes Maintainer: Benjamin Callahan URL: http://benjjneb.github.io/dada2/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/benjjneb/dada2/issues git_url: https://git.bioconductor.org/packages/dada2 git_branch: devel git_last_commit: 29f93c2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dada2_1.39.0.tar.gz vignettes: vignettes/dada2/inst/doc/dada2-intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dada2/inst/doc/dada2-intro.R dependsOnMe: MiscMetabar importsMe: Rbec, DBTC, tidyGenR suggestsMe: mia, demulticoder dependencyCount: 75 Package: dagLogo Version: 1.49.0 Depends: R (>= 3.0.1), methods, grid Imports: pheatmap, Biostrings, UniProt.ws, BiocGenerics, utils, biomaRt, motifStack, httr Suggests: XML, grImport, grImport2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: 17b1b30142425e679980441e28496035 NeedsCompilation: no Title: dagLogo: a Bioconductor package for visualizing conserved amino acid sequence pattern in groups based on probability theory Description: Visualize significant conserved amino acid sequence pattern in groups based on probability theory. biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Haibo Liu, Alexey Stukalov, Niraj Nirala, Usha Acharya, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dagLogo git_branch: devel git_last_commit: 73a5187 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dagLogo_1.49.0.tar.gz vignettes: vignettes/dagLogo/inst/doc/dagLogo.html vignetteTitles: dagLogo Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R dependencyCount: 138 Package: daMA Version: 1.83.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: f1feb024aff57f59a05400377201d9b5 NeedsCompilation: no Title: Efficient design and analysis of factorial two-colour microarray data Description: This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data. Statistical details are described in Bretz et al. (2003, submitted) biocViews: Microarray, TwoChannel, DifferentialExpression Author: Jobst Landgrebe and Frank Bretz Maintainer: Jobst Landgrebe URL: http://www.microarrays.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/daMA git_branch: devel git_last_commit: 77fe168 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/daMA_1.83.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: DAMEfinder Version: 1.23.0 Depends: R (>= 4.0) Imports: stats, Seqinfo, GenomicRanges, IRanges, S4Vectors, readr, SummarizedExperiment, GenomicAlignments, stringr, plyr, VariantAnnotation, parallel, ggplot2, Rsamtools, BiocGenerics, methods, limma, bumphunter, Biostrings, reshape2, cowplot, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: 1a3230da765989d0f9bbec39322c6e97 NeedsCompilation: no Title: Finds DAMEs - Differential Allelicly MEthylated regions Description: 'DAMEfinder' offers functionality for taking methtuple or bismark outputs to calculate ASM scores and compute DAMEs. It also offers nice visualization of methyl-circle plots. biocViews: DNAMethylation, DifferentialMethylation, Coverage Author: Stephany Orjuela [aut, cre] (ORCID: ), Dania Machlab [aut], Mark Robinson [aut] Maintainer: Stephany Orjuela VignetteBuilder: knitr BugReports: https://github.com/markrobinsonuzh/DAMEfinder/issues git_url: https://git.bioconductor.org/packages/DAMEfinder git_branch: devel git_last_commit: cb8c833 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DAMEfinder_1.23.0.tar.gz vignettes: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.html vignetteTitles: DAMEfinder Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.R dependencyCount: 114 Package: damidBind Version: 0.99.14 Depends: R (>= 4.4.0) Imports: ggplot2, ggrepel, dplyr, tibble, stringr, tools, fs, rlang, BiocParallel, AnnotationHub, DBI, ensembldb, GenomeInfoDb, IRanges, GenomicRanges, S4Vectors, rtracklayer, limma, NOISeq, BioVenn, clusterProfiler, enrichplot, forcats, scales, colorspace, ggnewscale, methods, stats, igvShiny, shiny, DT, dbscan, circlize, ComplexHeatmap, patchwork, splines Suggests: testthat, curl, knitr, htmltools, rmarkdown, BiocStyle, bookdown, org.Dm.eg.db License: GPL-3 MD5sum: 218e30518d97425296a903281f8e455e NeedsCompilation: no Title: Differential Binding and Expression Analysis for DamID-seq Data Description: The damidBind package provides a straightforward formal analysis pipeline to analyse and explore differential DamID binding, gene transcription or chromatin accessibility between two conditions. The package imports processed data from DamID-seq experiments, either as external raw files in the form of binding bedGraphs and GFF/BED peak calls, or as internal lists of GRanges objects. After optionally normalising data, combining peaks across replicates and determining per-replicate peak occupancy, the package links bound loci to nearby genes. For RNA Polymerase DamID data, the package calculates occupancy over genes, and optionally calcualates the FDR of significantly-enriched gene occupancy. damidBind then uses either limma (for conventional log2 ratio DamID binding data) or NOIseq (for counts-based CATaDa chromatin accessibility data) to identify differentially-enriched regions, or differentially epxressed genes, between two conditions. The package provides a number of visualisation tools (volcano plots, Gene Ontology enrichment plots via ClusterProfiler and proportional Venn diagrams via BioVenn for downstream data exploration and analysis. An powerful, interactive IGV genome browser interface (powered by Shiny and igvShiny) allows users to rapidly and intuitively assess significant differentially-bound regions in their genomic context. biocViews: DifferentialExpression, GeneExpression, Transcription, Epigenetics, Visualization, Sequencing, Software, GeneRegulation Author: Owen Marshall [aut, cre] (ORCID: ) Maintainer: Owen Marshall URL: https://marshall-lab.org/damidBind VignetteBuilder: knitr BugReports: https://github.com/marshall-lab/damidBind/issues git_url: https://git.bioconductor.org/packages/damidBind git_branch: devel git_last_commit: b2a04c8 git_last_commit_date: 2026-03-04 Date/Publication: 2026-04-20 source.ver: src/contrib/damidBind_0.99.14.tar.gz vignettes: vignettes/damidBind/inst/doc/damidBind_vignette.html vignetteTitles: damidBind hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/damidBind/inst/doc/damidBind_vignette.R dependencyCount: 210 Package: dandelionR Version: 1.3.0 Depends: R (>= 4.4.0) Imports: BiocGenerics, bluster, destiny, igraph, MASS, Matrix, methods, miloR, purrr, rlang, S4Vectors, SingleCellExperiment, spam, stats, SummarizedExperiment, uwot, RANN Suggests: BiocStyle, fields, knitr, rmarkdown, RColorBrewer, scater, scRepertoire, DelayedMatrixStats, slingshot, testthat License: MIT + file LICENSE MD5sum: 9a5b84c670b8de4649f6a173d561b5db NeedsCompilation: no Title: Single-cell Immune Repertoire Trajectory Analysis in R Description: dandelionR is an R package for performing single-cell immune repertoire trajectory analysis, based on the original python implementation. It provides the necessary functions to interface with scRepertoire and a custom implementation of an absorbing Markov chain for pseudotime inference, inspired by the Palantir Python package. biocViews: Software, ImmunoOncology, SingleCell Author: Jiawei Yu [aut] (ORCID: ), Nicholas Borcherding [aut] (ORCID: ), Kelvin Tuong [aut, cre] (ORCID: ) Maintainer: Kelvin Tuong URL: https://www.github.com/tuonglab/dandelionR/ VignetteBuilder: knitr BugReports: https://www.github.com/tuonglab/dandelionR/issues git_url: https://git.bioconductor.org/packages/dandelionR git_branch: devel git_last_commit: 29dd237 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dandelionR_1.3.0.tar.gz vignettes: vignettes/dandelionR/inst/doc/dandelionR_with_slingshot.html, vignettes/dandelionR/inst/doc/dandelionR.html, vignettes/dandelionR/inst/doc/vignette_reproduce_original.html vignetteTitles: Single-cell immune repertoire trajectory analysis with dandelionR and slingshot, Single-cell immune repertoire trajectory analysis with dandelionR, vignette_reproduce_original.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dandelionR/inst/doc/dandelionR_with_slingshot.R, vignettes/dandelionR/inst/doc/dandelionR.R, vignettes/dandelionR/inst/doc/vignette_reproduce_original.R dependencyCount: 191 Package: DART Version: 1.59.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: 0d793e31a795cee5e41f681581a2797c NeedsCompilation: no Title: Denoising Algorithm based on Relevance network Topology Description: Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples. biocViews: GeneExpression, DifferentialExpression, GraphAndNetwork, Pathways Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff, Charles Shijie Zheng Maintainer: Charles Shijie Zheng git_url: https://git.bioconductor.org/packages/DART git_branch: devel git_last_commit: 9908860 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DART_1.59.0.tar.gz vignettes: vignettes/DART/inst/doc/DART.pdf vignetteTitles: DART Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DART/inst/doc/DART.R dependencyCount: 17 Package: dcanr Version: 1.27.0 Depends: R (>= 3.6.0) Imports: igraph, foreach, plyr, stringr, reshape2, methods, Matrix, graphics, stats, RColorBrewer, circlize, doRNG Suggests: EBcoexpress, testthat, EBarrays, GeneNet, mclust, minqa, SummarizedExperiment, Biobase, knitr, rmarkdown, BiocStyle, edgeR Enhances: parallel, doSNOW, doParallel License: GPL-3 MD5sum: 6b5a873053ab2fc8bbc340733b849046 NeedsCompilation: no Title: Differential co-expression/association network analysis Description: This package implements methods and an evaluation framework to infer differential co-expression/association networks. Various methods are implemented and can be evaluated using simulated datasets. Inference of differential co-expression networks can allow identification of networks that are altered between two conditions (e.g., health and disease). biocViews: NetworkInference, GraphAndNetwork, DifferentialExpression, Network Author: Dharmesh D. Bhuva [aut, cre] (ORCID: ) Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/dcanr/, https://github.com/DavisLaboratory/dcanr VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/dcanr/issues git_url: https://git.bioconductor.org/packages/dcanr git_branch: devel git_last_commit: d3a5b3c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dcanr_1.27.0.tar.gz vignettes: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.html, vignettes/dcanr/inst/doc/dcanr_vignette.html vignetteTitles: 2. DC method evaluation, 1. Differential co-expression analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.R, vignettes/dcanr/inst/doc/dcanr_vignette.R importsMe: ClassifyR, multiWGCNA dependencyCount: 35 Package: DCATS Version: 1.9.0 Depends: R (>= 4.1.0), stats Imports: MCMCpack, matrixStats, robustbase, aod, e1071 Suggests: testthat (>= 3.0.0), knitr, Seurat, SeuratObject, tidyverse, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 38b26e09276c32f859016c86d646253b NeedsCompilation: no Title: Differential Composition Analysis Transformed by a Similarity matrix Description: Methods to detect the differential composition abundances between conditions in singel-cell RNA-seq experiments, with or without replicates. It aims to correct bias introduced by missclaisification and enable controlling of confounding covariates. To avoid the influence of proportion change from big cell types, DCATS can use either total cell number or specific reference group as normalization term. biocViews: SingleCell, Normalization Author: Xinyi Lin [aut, cre] (ORCID: ), Chuen Chau [aut], Yuanhua Huang [aut], Joshua W.K. Ho [aut] Maintainer: Xinyi Lin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DCATS git_branch: devel git_last_commit: 8e6ca39 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DCATS_1.9.0.tar.gz vignettes: vignettes/DCATS/inst/doc/Intro_to_DCATS.html vignetteTitles: Differential Composition Analysis with DCATS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DCATS/inst/doc/Intro_to_DCATS.R dependencyCount: 24 Package: dcGSA Version: 1.39.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: af18584ec85d34f409d536234164443e NeedsCompilation: no Title: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles Description: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes. biocViews: ImmunoOncology, GeneSetEnrichment,Microarray, StatisticalMethod, Sequencing, RNASeq, GeneExpression Author: Jiehuan Sun [aut, cre], Jose Herazo-Maya [aut], Xiu Huang [aut], Naftali Kaminski [aut], and Hongyu Zhao [aut] Maintainer: Jiehuan sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dcGSA git_branch: devel git_last_commit: 7a6df68 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dcGSA_1.39.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 18 Package: ddCt Version: 1.67.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: testthat (>= 3.0.0), RUnit License: LGPL-3 MD5sum: 01c1d560b0cce90d5baae5786c29df3f NeedsCompilation: no Title: The ddCt Algorithm for the Analysis of Quantitative Real-Time PCR (qRT-PCR) Description: The Delta-Delta-Ct (ddCt) Algorithm is an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. Compared to other approaches, it requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The ddCt package implements a pipeline to collect, analyse and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions. biocViews: GeneExpression, DifferentialExpression, MicrotitrePlateAssay, qPCR Author: Jitao David Zhang, Rudolf Biczok, and Markus Ruschhaupt Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/ddCt git_branch: devel git_last_commit: cd8a4fe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ddCt_1.67.0.tar.gz vignettes: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf, vignettes/ddCt/inst/doc/rtPCR-usage.pdf, vignettes/ddCt/inst/doc/rtPCR.pdf vignetteTitles: How to apply the ddCt method, Analyse RT-PCR data with the end-to-end script in ddCt package, Introduction to the ddCt method for qRT-PCR data analysis: background,, algorithm and example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R, vignettes/ddCt/inst/doc/rtPCR-usage.R, vignettes/ddCt/inst/doc/rtPCR.R dependencyCount: 12 Package: ddPCRclust Version: 1.31.0 Depends: R (>= 3.5) Imports: plotrix, clue, parallel, ggplot2, openxlsx, R.utils, flowCore, flowDensity (>= 1.13.3), SamSPECTRAL, flowPeaks Suggests: BiocStyle License: Artistic-2.0 MD5sum: 63771409ce6d64db98ed2ef08e09f65e NeedsCompilation: no Title: Clustering algorithm for ddPCR data Description: The ddPCRclust algorithm can automatically quantify the CPDs of non-orthogonal ddPCR reactions with up to four targets. In order to determine the correct droplet count for each target, it is crucial to both identify all clusters and label them correctly based on their position. For more information on what data can be analyzed and how a template needs to be formatted, please check the vignette. biocViews: ddPCR, Clustering Author: Benedikt G. Brink [aut, cre], Justin Meskas [ctb], Ryan R. Brinkman [ctb] Maintainer: Benedikt G. Brink URL: https://github.com/bgbrink/ddPCRclust BugReports: https://github.com/bgbrink/ddPCRclust/issues git_url: https://git.bioconductor.org/packages/ddPCRclust git_branch: devel git_last_commit: 3ada4f8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ddPCRclust_1.31.0.tar.gz vignettes: vignettes/ddPCRclust/inst/doc/ddPCRclust.pdf vignetteTitles: Bioconductor LaTeX Style hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddPCRclust/inst/doc/ddPCRclust.R suggestsMe: Polytect dependencyCount: 116 Package: dearseq Version: 1.23.0 Depends: R (>= 3.6.0) Imports: CompQuadForm, dplyr, ggplot2, KernSmooth, magrittr, matrixStats, methods, patchwork, parallel, pbapply, reshape2, rlang, scattermore, stats, statmod, survey, tibble, viridisLite Suggests: Biobase, BiocManager, BiocSet, edgeR, DESeq2, GEOquery, GSA, knitr, limma, readxl, rmarkdown, S4Vectors, SummarizedExperiment, testthat, covr License: GPL-2 | file LICENSE MD5sum: 27c56751bfee02af1b1086266bd12d56 NeedsCompilation: no Title: Differential Expression Analysis for RNA-seq data through a robust variance component test Description: Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093. biocViews: BiomedicalInformatics, CellBiology, DifferentialExpression, DNASeq, GeneExpression, Genetics, GeneSetEnrichment, ImmunoOncology, KEGG, Regression, RNASeq, Sequencing, SystemsBiology, TimeCourse, Transcription, Transcriptomics Author: Denis Agniel [aut], Boris P. Hejblum [aut, cre] (ORCID: ), Marine Gauthier [aut], Mélanie Huchon [ctb] Maintainer: Boris P. Hejblum VignetteBuilder: knitr BugReports: https://github.com/borishejblum/dearseq/issues git_url: https://git.bioconductor.org/packages/dearseq git_branch: devel git_last_commit: cab35ff git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dearseq_1.23.0.tar.gz vignettes: vignettes/dearseq/inst/doc/dearseqUserguide.html vignetteTitles: dearseqUserguide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dearseq/inst/doc/dearseqUserguide.R importsMe: benchdamic suggestsMe: GeoTcgaData, TcGSA dependencyCount: 55 Package: debrowser Version: 1.39.0 Depends: R (>= 3.5.0), Imports: shiny, jsonlite, shinyjs, shinydashboard, shinyBS, gplots, DT, ggplot2, RColorBrewer, annotate, AnnotationDbi, DESeq2, DOSE, igraph, grDevices, graphics, stats, utils, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, stringi, reshape2, org.Hs.eg.db, org.Mm.eg.db, limma, edgeR, clusterProfiler, methods, sva, RCurl, enrichplot, colourpicker, plotly, heatmaply, Harman, pathview, apeglm, ashr Suggests: testthat, rmarkdown, knitr License: GPL-3 + file LICENSE MD5sum: 8d08d90c899cf57776c26143de104e17 NeedsCompilation: no Title: Interactive Differential Expresion Analysis Browser Description: Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps. biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Clustering, ImmunoOncology Author: Alper Kucukural , Onur Yukselen , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/debrowser VignetteBuilder: knitr, rmarkdown BugReports: https://github.com/UMMS-Biocore/debrowser/issues/new git_url: https://git.bioconductor.org/packages/debrowser git_branch: devel git_last_commit: 17795bc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/debrowser_1.39.0.tar.gz vignettes: vignettes/debrowser/inst/doc/DEBrowser.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R dependencyCount: 225 Package: decemedip Version: 0.99.8 Depends: R (>= 4.5.0) Imports: bayesplot, cowplot, dplyr, GenomicRanges, ggplot2, IRanges, magrittr, Matrix, matrixStats, MEDIPS, methods, purrr, R.utils, Rcpp, RcppParallel, rlang, rstan, rstantools, S4Vectors, SummarizedExperiment LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: knitr, rmarkdown, BiocStyle, devtools, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: cdd72395cfc5d9501abea3da4ffe63cf NeedsCompilation: yes Title: hierarchical Bayesian modeling for cell type deconvolution of immunoprecipitation-based DNA methylome Description: The R package decemedip is a novel computational paradigm developed for inferring the relative abundances of cell types and tissues measure by methylated DNA immunoprecipitation sequencing (MeDIP-Seq). This paradigm allows using reference data from other technologies such as microarray or WGBS. biocViews: Software, ImmunoOncology, DNAMethylation, Epigenetics, Sequencing, WholeGenome Author: Ning Shen [aut, cre] (ORCID: ) Maintainer: Ning Shen URL: https://github.com/nshen7/decemedip SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/nshen7/decemedip/issues git_url: https://git.bioconductor.org/packages/decemedip git_branch: devel git_last_commit: 0615c99 git_last_commit_date: 2025-10-06 Date/Publication: 2026-04-20 source.ver: src/contrib/decemedip_0.99.8.tar.gz vignettes: vignettes/decemedip/inst/doc/how-to-use-decemedip.html vignetteTitles: Cell type deconvolutiond of (cf)MeDIP-seq data with decemedip hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/decemedip/inst/doc/how-to-use-decemedip.R dependencyCount: 142 Package: DECIPHER Version: 3.7.1 Depends: R (>= 3.5.0), Biostrings (>= 2.59.1), stats Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, S4Vectors, IRanges, XVector Suggests: RSQLite (>= 1.1) License: GPL-3 MD5sum: 418cc673b0ae99f8d2f608f334f0fb82 NeedsCompilation: yes Title: Tools for curating, analyzing, and manipulating biological sequences Description: A toolset for deciphering and managing biological sequences. biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization, Microarray, QualityControl, qPCR, Alignment, WholeGenome, Microbiome, ImmunoOncology, GenePrediction Author: Erik Wright Maintainer: Erik Wright URL: http://DECIPHER.codes git_url: https://git.bioconductor.org/packages/DECIPHER git_branch: devel git_last_commit: bedfd21 git_last_commit_date: 2026-03-22 Date/Publication: 2026-04-20 source.ver: src/contrib/DECIPHER_3.7.1.tar.gz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf, vignettes/DECIPHER/inst/doc/ClassifySequences.pdf, vignettes/DECIPHER/inst/doc/ClusteringSequences.pdf, vignettes/DECIPHER/inst/doc/DECIPHERing.pdf, vignettes/DECIPHER/inst/doc/DesignMicroarray.pdf, vignettes/DECIPHER/inst/doc/DesignPrimers.pdf, vignettes/DECIPHER/inst/doc/DesignProbes.pdf, vignettes/DECIPHER/inst/doc/DesignSignatures.pdf, vignettes/DECIPHER/inst/doc/FindChimeras.pdf, vignettes/DECIPHER/inst/doc/FindingGenes.pdf, vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.pdf, vignettes/DECIPHER/inst/doc/GrowingTrees.pdf, vignettes/DECIPHER/inst/doc/PopulationGenetics.pdf, vignettes/DECIPHER/inst/doc/RepeatRepeat.pdf, vignettes/DECIPHER/inst/doc/SearchForResearch.pdf vignetteTitles: The Art of Multiple Sequence Alignment in R, Classify Sequences in R, Upsize Your Clustering with Clusterize, Getting Started DECIPHERing, Design Microarray Probes in R, Design Group-Specific Primers in R, Design Group-Specific FISH Probes in R, Design Primers that Yield Group-Specific Signatures, Finding Chimeric Sequences in R, The Magic of Gene Finding, The Double Life of RNA: Uncovering Non-Coding RNAs, Growing Phylogenetic Trees in R with Treeline, Population Genetics Inference in R, Detecting Obscure Tandem Repeats in Sequences, Searching Biological Sequences for Research hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.R, vignettes/DECIPHER/inst/doc/ClassifySequences.R, vignettes/DECIPHER/inst/doc/ClusteringSequences.R, vignettes/DECIPHER/inst/doc/DECIPHERing.R, vignettes/DECIPHER/inst/doc/DesignMicroarray.R, vignettes/DECIPHER/inst/doc/DesignPrimers.R, vignettes/DECIPHER/inst/doc/DesignProbes.R, vignettes/DECIPHER/inst/doc/DesignSignatures.R, vignettes/DECIPHER/inst/doc/FindChimeras.R, vignettes/DECIPHER/inst/doc/FindingGenes.R, vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.R, vignettes/DECIPHER/inst/doc/GrowingTrees.R, vignettes/DECIPHER/inst/doc/PopulationGenetics.R, vignettes/DECIPHER/inst/doc/RepeatRepeat.R, vignettes/DECIPHER/inst/doc/SearchForResearch.R dependsOnMe: AssessORF, sangeranalyseR, SynExtend importsMe: DspikeIn, mia, openPrimeR, scifer, AssessORFData, copyseparator, ensembleTax, piglet, tidyGenR, VIProDesign suggestsMe: MicrobiotaProcess, microbial, MiscMetabar dependencyCount: 16 Package: decompTumor2Sig Version: 2.27.0 Depends: R(>= 4.0), ggplot2 Imports: methods, Matrix, quadprog(>= 1.5-5), GenomicRanges, stats, GenomicFeatures, Biostrings, BiocGenerics, S4Vectors, plyr, utils, graphics, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, SummarizedExperiment, ggseqlogo, gridExtra, data.table, Seqinfo, readxl Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: f22bf1455496110d40ca9625e029b8a6 NeedsCompilation: no Title: Decomposition of individual tumors into mutational signatures by signature refitting Description: Uses quadratic programming for signature refitting, i.e., to decompose the mutation catalog from an individual tumor sample into a set of given mutational signatures (either Alexandrov-model signatures or Shiraishi-model signatures), computing weights that reflect the contributions of the signatures to the mutation load of the tumor. biocViews: Software, SNP, Sequencing, DNASeq, GenomicVariation, SomaticMutation, BiomedicalInformatics, Genetics, BiologicalQuestion, StatisticalMethod Author: Rosario M. Piro [aut, cre], Sandra Krueger [ctb] Maintainer: Rosario M. Piro URL: http://rmpiro.net/decompTumor2Sig/, https://github.com/rmpiro/decompTumor2Sig VignetteBuilder: knitr BugReports: https://github.com/rmpiro/decompTumor2Sig/issues git_url: https://git.bioconductor.org/packages/decompTumor2Sig git_branch: devel git_last_commit: d30b743 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/decompTumor2Sig_2.27.0.tar.gz vignettes: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.html vignetteTitles: A brief introduction to decompTumor2Sig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.R importsMe: musicatk dependencyCount: 105 Package: decontam Version: 1.31.0 Depends: R (>= 3.4.1), methods (>= 3.4.1) Imports: ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), stats Suggests: BiocStyle, knitr, rmarkdown, phyloseq License: Artistic-2.0 MD5sum: c25cd864f04f2da997f1f7a634ca01e0 NeedsCompilation: no Title: Identify Contaminants in Marker-gene and Metagenomics Sequencing Data Description: Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan [aut, cre], Nicole Marie Davis [aut], Felix G.M. Ernst [ctb] (ORCID: ) Maintainer: Benjamin Callahan URL: https://github.com/benjjneb/decontam VignetteBuilder: knitr BugReports: https://github.com/benjjneb/decontam/issues git_url: https://git.bioconductor.org/packages/decontam git_branch: devel git_last_commit: 9e5a9f4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/decontam_1.31.0.tar.gz vignettes: vignettes/decontam/inst/doc/decontam_intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decontam/inst/doc/decontam_intro.R importsMe: mia dependencyCount: 30 Package: decontX Version: 1.9.0 Depends: R (>= 4.3.0) Imports: celda, dbscan, DelayedArray, ggplot2, Matrix (>= 1.5.3), MCMCprecision, methods, patchwork, plyr, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), reshape2, rstan (>= 2.18.1), rstantools (>= 2.2.0), S4Vectors, scater, Seurat, SingleCellExperiment, SummarizedExperiment, withr LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, dplyr, knitr, rmarkdown, scran, SingleCellMultiModal, TENxPBMCData, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 612d5361737b7c52176f6f6a0260e5f5 NeedsCompilation: yes Title: Decontamination of single cell genomics data Description: This package contains implementation of DecontX (Yang et al. 2020), a decontamination algorithm for single-cell RNA-seq, and DecontPro (Yin et al. 2023), a decontamination algorithm for single cell protein expression data. DecontX is a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. DecontPro is a Bayesian method that estimates the level of contamination from ambient and background sources in CITE-seq ADT dataset and decontaminate the dataset. biocViews: SingleCell, Bayesian Author: Yuan Yin [aut] (ORCID: ), Masanao Yajima [aut] (ORCID: ), Joshua Campbell [aut, cre] (ORCID: ) Maintainer: Joshua Campbell SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/decontX git_branch: devel git_last_commit: 0f7be1c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/decontX_1.9.0.tar.gz vignettes: vignettes/decontX/inst/doc/decontPro.html, vignettes/decontX/inst/doc/decontX.html vignetteTitles: decontPro, Estimate and remove cross-contamination from ambient RNA in single-cell data with DecontX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/decontX/inst/doc/decontPro.R, vignettes/decontX/inst/doc/decontX.R dependencyCount: 237 Package: DeconvoBuddies Version: 1.3.3 Depends: R (>= 4.4.0) Imports: AnnotationHub, BiocFileCache, BiocParallel, DelayedMatrixStats, dplyr, ExperimentHub, ggplot2, ggrepel, graphics, grDevices, MatrixGenerics, methods, purrr, rafalib, reshape2, S4Vectors, scran, SingleCellExperiment, spatialLIBD, stats, stringr, SummarizedExperiment, tibble, utils Suggests: Biobase, BiocStyle, covr, HDF5Array, knitr, RColorBrewer, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), tidyr, tidyverse License: Artistic-2.0 MD5sum: 0164bbeba0315e5dcdcbda7987b474c3 NeedsCompilation: no Title: Helper Functions for LIBD Deconvolution Description: Functions helpful for LIBD deconvolution project. Includes tools for marker finding with mean ratio, expression plotting, and plotting deconvolution results. Working to include DLPFC datasets. biocViews: Software, SingleCell, RNASeq, GeneExpression, Transcriptomics, ExperimentHubSoftware Author: Louise Huuki-Myers [aut, cre] (ORCID: ), Leonardo Collado-Torres [ctb] (ORCID: ), Nicholas J. Eagles [ctb] (ORCID: ) Maintainer: Louise Huuki-Myers URL: https://github.com/LieberInstitute/DeconvoBuddies VignetteBuilder: knitr BugReports: https://github.com/LieberInstitute/DeconvoBuddies/issues git_url: https://git.bioconductor.org/packages/DeconvoBuddies git_branch: devel git_last_commit: 2ec3f7e git_last_commit_date: 2026-04-15 Date/Publication: 2026-04-20 source.ver: src/contrib/DeconvoBuddies_1.3.3.tar.gz vignettes: vignettes/DeconvoBuddies/inst/doc/DeconvoBuddies.html, vignettes/DeconvoBuddies/inst/doc/Deconvolution_Benchmark_DLPFC.html, vignettes/DeconvoBuddies/inst/doc/Marker_Finding.html vignetteTitles: Get Started with DeconvoBuddies, Deconvolution Benchmark in Human DLPFC, Finding Marker Genes with DeconvoBuddies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeconvoBuddies/inst/doc/DeconvoBuddies.R, vignettes/DeconvoBuddies/inst/doc/Deconvolution_Benchmark_DLPFC.R, vignettes/DeconvoBuddies/inst/doc/Marker_Finding.R dependencyCount: 207 Package: DeeDeeExperiment Version: 1.1.5 Depends: R (>= 4.5.0), SingleCellExperiment Imports: SummarizedExperiment, methods, S4Vectors, utils, DESeq2, edgeR, limma, writexl, cli Suggests: macrophage, knitr, BiocStyle, apeglm, mosdef, org.Hs.eg.db, topGO, clusterProfiler, DEFormats, ExperimentHub, scater, muscat, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 54a20208cb5d6d5f99cd7b69155fa7b0 NeedsCompilation: no Title: DeeDeeExperiment: An S4 Class for managing and exploring omics analysis results Description: DeeDeeExperiment is an S4 class extending the SingleCellExperiment class, designed to integrate and manage omics analysis results. It introduces two dedicated slots to store Differential Expression Analysis (DEA) results and Functional Enrichment Analysis (FEA) results, providing a structured approach for downstream analysis. biocViews: Software, Infrastructure, DataRepresentation, GeneExpression, Transcription, Transcriptomics, DifferentialExpression, Pathways, GO Author: Najla Abassi [aut, cre] (ORCID: ), Lea Schwarz [aut] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Najla Abassi URL: https://github.com/imbeimainz/DeeDeeExperiment VignetteBuilder: knitr BugReports: https://github.com/imbeimainz/DeeDeeExperiment/issues git_url: https://git.bioconductor.org/packages/DeeDeeExperiment git_branch: devel git_last_commit: 714b2f8 git_last_commit_date: 2026-04-15 Date/Publication: 2026-04-20 source.ver: src/contrib/DeeDeeExperiment_1.1.5.tar.gz vignettes: vignettes/DeeDeeExperiment/inst/doc/dde_with_single_cell.html, vignettes/DeeDeeExperiment/inst/doc/DeeDeeExperiment_manual.html vignetteTitles: 2. How to use DeeDeeExperiment with single-cell data, 1. The DeeDeeExperiment User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DeeDeeExperiment/inst/doc/dde_with_single_cell.R, vignettes/DeeDeeExperiment/inst/doc/DeeDeeExperiment_manual.R dependencyCount: 60 Package: DeepPINCS Version: 1.19.0 Depends: keras, R (>= 4.1) Imports: tensorflow, CatEncoders, matlab, rcdk, stringdist, tokenizers, webchem, purrr, ttgsea, PRROC, reticulate, stats Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: cdc1bcfc5e612ac71c275ae6491abdc3 NeedsCompilation: no Title: Protein Interactions and Networks with Compounds based on Sequences using Deep Learning Description: The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences. biocViews: Software, Network, GraphAndNetwork, NeuralNetwork Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepPINCS git_branch: devel git_last_commit: bdbf3fc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DeepPINCS_1.19.0.tar.gz vignettes: vignettes/DeepPINCS/inst/doc/DeepPINCS.html vignetteTitles: DeepPINCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepPINCS/inst/doc/DeepPINCS.R importsMe: GenProSeq, VAExprs dependencyCount: 142 Package: deepSNV Version: 1.57.0 Depends: R (>= 2.13.0), methods, graphics, parallel, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.27.6), Imports: Rhtslib LinkingTo: Rhtslib (>= 1.13.1) Suggests: RColorBrewer, knitr, rmarkdown License: GPL-3 MD5sum: 8ac4d90149b94d2ad1fbd7d683b20ad0 NeedsCompilation: yes Title: Detection of subclonal SNVs in deep sequencing data. Description: This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC. biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport Author: Niko Beerenwinkel [ths], Raul Alcantara [ctb], David Jones [ctb], John Marshall [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre] Maintainer: Moritz Gerstung SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deepSNV git_branch: devel git_last_commit: 445cbf8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/deepSNV_1.57.0.tar.gz vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf, vignettes/deepSNV/inst/doc/shearwaterML.html vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater, Shearwater ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R, vignettes/deepSNV/inst/doc/shearwater.R, vignettes/deepSNV/inst/doc/shearwaterML.R importsMe: mitoClone2 suggestsMe: GenomicFiles dependencyCount: 79 Package: DeepTarget Version: 1.5.0 Depends: R (>= 4.2.0) Imports: fgsea, ggplot2, stringr, ggpubr, BiocParallel, pROC, stats, grDevices, graphics, depmap, readr, dplyr Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 2ff2f8060cb1ad95eb1d1511d5f748a4 NeedsCompilation: no Title: Deep characterization of cancer drugs Description: This package predicts a drug’s primary target(s) or secondary target(s) by integrating large-scale genetic and drug screens from the Cancer Dependency Map project run by the Broad Institute. It further investigates whether the drug specifically targets the wild-type or mutated target forms. To show how to use this package in practice, we provided sample data along with step-by-step example. biocViews: GeneTarget, GenePrediction,Pathways, GeneExpression, RNASeq, ImmunoOncology,DifferentialExpression, GeneSetEnrichment, ReportWriting,CRISPR Author: Sanju Sinha [aut], Trinh Nguyen [aut, cre] (ORCID: ), Ying Hu [aut] Maintainer: Trinh Nguyen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepTarget git_branch: devel git_last_commit: fa9f844 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DeepTarget_1.5.0.tar.gz vignettes: vignettes/DeepTarget/inst/doc/DeepTarget_Vignette.html vignetteTitles: Workflow Demonstration for Deep characterization of cancer drugs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepTarget/inst/doc/DeepTarget_Vignette.R dependencyCount: 144 Package: DEFormats Version: 1.39.0 Imports: checkmate, data.table, DESeq2, edgeR (>= 3.13.4), GenomicRanges, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat License: GPL-3 MD5sum: 1a3a18bc5ce5ca1f3d8df8408d807c92 NeedsCompilation: no Title: Differential gene expression data formats converter Description: Convert between different data formats used by differential gene expression analysis tools. biocViews: ImmunoOncology, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Transcription Author: Andrzej Oleś Maintainer: Andrzej Oleś URL: https://github.com/aoles/DEFormats VignetteBuilder: knitr BugReports: https://github.com/aoles/DEFormats/issues git_url: https://git.bioconductor.org/packages/DEFormats git_branch: devel git_last_commit: 5034890 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DEFormats_1.39.0.tar.gz vignettes: vignettes/DEFormats/inst/doc/DEFormats.html vignetteTitles: Differential gene expression data formats converter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R importsMe: regionReport suggestsMe: DeeDeeExperiment, ideal dependencyCount: 61 Package: DegCre Version: 1.7.0 Depends: R (>= 4.4) Imports: GenomicRanges, InteractionSet, plotgardener, S4Vectors, stats, graphics, grDevices, BiocGenerics, Seqinfo, IRanges, BiocParallel, qvalue, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, utils Suggests: BSgenome, BSgenome.Hsapiens.UCSC.hg38, BiocStyle, magick, knitr, rmarkdown, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 429378c0363ff77c284384a207fbc5c8 NeedsCompilation: no Title: Probabilistic association of DEGs to CREs from differential data Description: DegCre generates associations between differentially expressed genes (DEGs) and cis-regulatory elements (CREs) based on non-parametric concordance between differential data. The user provides GRanges of DEG TSS and CRE regions with differential p-value and optionally log-fold changes and DegCre returns an annotated Hits object with associations and their calculated probabilities. Additionally, the package provides functionality for visualization and conversion to other formats. biocViews: GeneExpression, GeneRegulation, ATACSeq, ChIPSeq, DNaseSeq, RNASeq Author: Brian S. Roberts [aut, cre] (ORCID: ) Maintainer: Brian S. Roberts URL: https://github.com/brianSroberts/DegCre VignetteBuilder: knitr BugReports: https://github.com/brianSroberts/DegCre/issues git_url: https://git.bioconductor.org/packages/DegCre git_branch: devel git_last_commit: 97a3d4d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DegCre_1.7.0.tar.gz vignettes: vignettes/DegCre/inst/doc/degcre_introduction_and_examples.html vignetteTitles: DegCre Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DegCre/inst/doc/degcre_introduction_and_examples.R dependencyCount: 120 Package: DEGraph Version: 1.63.0 Depends: R (>= 2.10.0), R.utils Imports: graph, KEGGgraph, lattice, mvtnorm, R.methodsS3, RBGL, Rgraphviz, rrcov, NCIgraph Suggests: corpcor, fields, graph, KEGGgraph, lattice, marray, RBGL, rrcov, Rgraphviz, NCIgraph License: GPL-3 MD5sum: 0fa0e0914a2944bcbe49fef6e0df5d5e NeedsCompilation: no Title: Two-sample tests on a graph Description: DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results. biocViews: Microarray, DifferentialExpression, GraphAndNetwork, Network, NetworkEnrichment, DecisionTree Author: Laurent Jacob, Pierre Neuvial and Sandrine Dudoit Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/DEGraph git_branch: devel git_last_commit: 71c129b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DEGraph_1.63.0.tar.gz vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf vignetteTitles: DEGraph: differential expression testing for gene networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGraph/inst/doc/DEGraph.R dependencyCount: 65 Package: DEGreport Version: 1.47.0 Depends: R (>= 4.0.0) Imports: utils, methods, Biobase, BiocGenerics, broom, circlize, ComplexHeatmap, cowplot, ConsensusClusterPlus, cluster, dendextend, DESeq2, dplyr, edgeR, ggplot2, ggdendro, grid, ggrepel, grDevices, knitr, logging, magrittr, psych, RColorBrewer, reshape, rlang, scales, stats, stringr, stringi, S4Vectors, SummarizedExperiment, tidyr, tibble Suggests: BiocStyle, AnnotationDbi, limma, pheatmap, rmarkdown, statmod, testthat License: MIT + file LICENSE MD5sum: 5eec23ef236234e63447b45255ef150f NeedsCompilation: no Title: Report of DEG analysis Description: Creation of ready-to-share figures of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. biocViews: DifferentialExpression, Visualization, RNASeq, ReportWriting, GeneExpression, ImmunoOncology Author: Lorena Pantano [aut, cre], John Hutchinson [ctb], Victor Barrera [ctb], Mary Piper [ctb], Radhika Khetani [ctb], Kenneth Daily [ctb], Thanneer Malai Perumal [ctb], Rory Kirchner [ctb], Michael Steinbaugh [ctb], Ivo Zeller [ctb] Maintainer: Lorena Pantano URL: http://lpantano.github.io/DEGreport/ VignetteBuilder: knitr BugReports: https://github.com/lpantano/DEGreport/issues git_url: https://git.bioconductor.org/packages/DEGreport git_branch: devel git_last_commit: 792957e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DEGreport_1.47.0.tar.gz vignettes: vignettes/DEGreport/inst/doc/DEGreport.html vignetteTitles: QC and downstream analysis for differential expression RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R importsMe: isomiRs suggestsMe: carnation dependencyCount: 108 Package: DEGseq Version: 1.65.0 Depends: R (>= 2.8.0), qvalue, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) MD5sum: 027b9775449aaec08e00fbc86856dd04 NeedsCompilation: yes Title: Identify Differentially Expressed Genes from RNA-seq data Description: DEGseq is an R package to identify differentially expressed genes from RNA-Seq data. biocViews: RNASeq, Preprocessing, GeneExpression, DifferentialExpression, ImmunoOncology Author: Likun Wang , Xiaowo Wang and Xuegong Zhang . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/DEGseq git_branch: devel git_last_commit: 2c62565 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DEGseq_1.65.0.tar.gz vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf vignetteTitles: DEGseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGseq/inst/doc/DEGseq.R dependencyCount: 32 Package: DelayedArray Version: 0.37.1 Depends: R (>= 4.0.0), methods, stats4, Matrix, BiocGenerics (>= 0.53.3), MatrixGenerics (>= 1.1.3), S4Vectors (>= 0.47.6), IRanges (>= 2.17.3), S4Arrays (>= 1.9.3), SparseArray (>= 1.7.5) Imports: stats Suggests: BiocParallel, HDF5Array (>= 1.17.12), genefilter, SummarizedExperiment, airway, lobstr, DelayedMatrixStats, knitr, rmarkdown, BiocStyle, RUnit License: Artistic-2.0 MD5sum: ca2ed0a89fa864c5cd40d9d365889968 NeedsCompilation: no Title: A unified framework for working transparently with on-disk and in-memory array-like datasets Description: Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. biocViews: Infrastructure, DataRepresentation, Annotation, GenomeAnnotation Author: Hervé Pagès [aut, cre] (ORCID: ), Aaron Lun [ctb], Peter Hickey [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/DelayedArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedArray/issues git_url: https://git.bioconductor.org/packages/DelayedArray git_branch: devel git_last_commit: f0d27f4 git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/DelayedArray_0.37.1.tar.gz vignettes: vignettes/DelayedArray/inst/doc/A-Working_with_large_arrays.pdf, vignettes/DelayedArray/inst/doc/C-DelayedArray_HDF5Array_update.pdf, vignettes/DelayedArray/inst/doc/B-Implementing_a_backend.html vignetteTitles: 1. Working with large arrays in R (slides from July 2017), 3. A DelayedArray / HDF5Array update (slides from April 2021), 2. Implementing A DelayedArray Backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedArray/inst/doc/A-Working_with_large_arrays.R, vignettes/DelayedArray/inst/doc/C-DelayedArray_HDF5Array_update.R dependsOnMe: chihaya, DelayedDataFrame, DelayedMatrixStats, DelayedRandomArray, GDSArray, HDF5Array, PlinkMatrix, rhdf5client, SCArray, singleCellTK, TileDBArray, VCFArray, ZarrArray importsMe: adverSCarial, alabaster.matrix, AUCell, batchelor, beachmat, beachmat.hdf5, beachmat.tiledb, BiocSingular, bsseq, celaref, celda, Cepo, ChromSCape, clusterExperiment, concordexR, CRISPRseek, cytomapper, decontX, DelayedTensor, DEScan2, dreamlet, DropletUtils, ELMER, EWCE, flowWorkspace, FRASER, GenomicScores, glmGamPoi, GSVA, hipathia, LoomExperiment, Macarron, mariner, mbkmeans, methodical, MethReg, methrix, methylSig, mia, miaViz, minfi, MOFA2, MuData, MultiAssayExperiment, mumosa, mutscan, NetActivity, netSmooth, NewWave, omicsGMF, orthogene, orthos, PCAtools, RBedMethyl, ResidualMatrix, RTCGAToolbox, ScaledMatrix, SCArray.sat, scater, scDblFinder, scFeatures, scMerge, scmeth, scPCA, scran, scrapper, scry, scuttle, signatureSearch, SingleCellAlleleExperiment, SingleCellExperiment, SingleR, sketchR, SpliceWiz, SummarizedExperiment, transformGamPoi, TSCAN, VariantExperiment, velociraptor, vmrseq, Voyager, weitrix, xcore, zellkonverter, ZygosityPredictor, celldex, imcdatasets, scRNAseq, cellGeometry, ebvcube, rliger, scDiffCom, spatialGE suggestsMe: BiocGenerics, BiocNeighbors, ChIPpeakAnno, gwascat, hermes, iSEE, MAST, MatrixGenerics, ProteoDisco, S4Arrays, S4Vectors, satuRn, scone, SPOTlight, TrajectoryUtils, methFuse, Seurat, SeuratObject dependencyCount: 20 Package: DelayedDataFrame Version: 1.27.0 Depends: R (>= 3.6), S4Vectors (>= 0.23.19), DelayedArray (>= 0.7.5) Imports: methods, stats, BiocGenerics Suggests: testthat, knitr, rmarkdown, BiocStyle, SeqArray, GDSArray License: GPL-3 MD5sum: 0cc5debef407c4d225981457dec8b12e NeedsCompilation: no Title: Delayed operation on DataFrame using standard DataFrame metaphor Description: Based on the standard DataFrame metaphor, we are trying to implement the feature of delayed operation on the DelayedDataFrame, with a slot of lazyIndex, which saves the mapping indexes for each column of DelayedDataFrame. Methods like show, validity check, [/[[ subsetting, rbind/cbind are implemented for DelayedDataFrame to be operated around lazyIndex. The listData slot stays untouched until a realization call e.g., DataFrame constructor OR as.list() is invoked. biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/DelayedDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedDataFrame/issues git_url: https://git.bioconductor.org/packages/DelayedDataFrame git_branch: devel git_last_commit: 7dcd028 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DelayedDataFrame_1.27.0.tar.gz vignettes: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.html vignetteTitles: DelayedDataFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.R importsMe: VariantExperiment dependencyCount: 21 Package: DelayedMatrixStats Version: 1.33.0 Depends: MatrixGenerics (>= 1.15.1), DelayedArray (>= 0.31.7) Imports: methods, sparseMatrixStats (>= 1.13.2), Matrix (>= 1.5-0), S4Vectors (>= 0.17.5), IRanges (>= 2.25.10), SparseArray (>= 1.5.19) Suggests: testthat, knitr, rmarkdown, BiocStyle, microbenchmark, profmem, HDF5Array, matrixStats (>= 1.0.0) License: MIT + file LICENSE MD5sum: 32b4056983e0c5e32ead9fd3dcf1a4aa NeedsCompilation: no Title: Functions that Apply to Rows and Columns of 'DelayedMatrix' Objects Description: A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. biocViews: Infrastructure, DataRepresentation, Software Author: Peter Hickey [aut, cre] (ORCID: ), Hervé Pagès [ctb], Aaron Lun [ctb] Maintainer: Peter Hickey URL: https://github.com/PeteHaitch/DelayedMatrixStats VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/DelayedMatrixStats/issues git_url: https://git.bioconductor.org/packages/DelayedMatrixStats git_branch: devel git_last_commit: ec9ae22 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DelayedMatrixStats_1.33.0.tar.gz vignettes: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.html vignetteTitles: Overview of DelayedMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.R importsMe: AUCell, batchelor, biscuiteer, bsseq, Cepo, DeconvoBuddies, dmrseq, dreamlet, DropletUtils, FRASER, glmGamPoi, GSVA, lemur, methrix, methylSig, mia, minfi, mumosa, NetActivity, PCAtools, RBedMethyl, recountmethylation, SCArray, scFeatures, scMerge, scone, singleCellTK, sparrow, SpliceWiz, SVP, weitrix, celldex, spatialGE suggestsMe: blase, condiments, dandelionR, DelayedArray, escape, EWCE, HDF5Array, MatrixGenerics, mbkmeans, ScaledMatrix, scater, scPCA, scran, scuttle, slingshot, SplineDV, tradeSeq, TrajectoryUtils, Voyager, ClustAssess dependencyCount: 23 Package: DelayedRandomArray Version: 1.19.0 Depends: SparseArray (>= 1.5.15), DelayedArray (>= 0.31.6) Imports: methods, dqrng, Rcpp LinkingTo: dqrng, BH, Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, Matrix License: GPL-3 MD5sum: d8595c0e005572f2cae65890e090bbd9 NeedsCompilation: yes Title: Delayed Arrays of Random Values Description: Implements a DelayedArray of random values where the realization of the sampled values is delayed until they are needed. Reproducible sampling within any subarray is achieved by chunking where each chunk is initialized with a different random seed and stream. The usual distributions in the stats package are supported, along with scalar, vector and arrays for the parameters. biocViews: DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/LTLA/DelayedRandomArray SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/LTLA/DelayedRandomArray/issues git_url: https://git.bioconductor.org/packages/DelayedRandomArray git_branch: devel git_last_commit: 206ccc1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DelayedRandomArray_1.19.0.tar.gz vignettes: vignettes/DelayedRandomArray/inst/doc/userguide.html vignetteTitles: User's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedRandomArray/inst/doc/userguide.R importsMe: DelayedTensor dependencyCount: 25 Package: DELocal Version: 1.11.2 Imports: DESeq2, dplyr, reshape2, limma, SummarizedExperiment, ggplot2, matrixStats, stats Suggests: biomaRt, knitr, rmarkdown, stringr, BiocStyle License: MIT + file LICENSE MD5sum: c5d794019110a188e037d9d697d7509c NeedsCompilation: no Title: Identifies differentially expressed genes with respect to other local genes Description: The goal of DELocal is to identify DE genes compared to their neighboring genes from the same chromosomal location. It has been shown that genes of related functions are generally very far from each other in the chromosome. DELocal utilzes this information to identify DE genes comparing with their neighbouring genes. biocViews: GeneExpression, DifferentialExpression, RNASeq, Transcriptomics Author: Rishi Das Roy [aut, cre] (ORCID: ) Maintainer: Rishi Das Roy URL: https://github.com/dasroy/DELocal VignetteBuilder: knitr BugReports: https://github.com/dasroy/DELocal/issues git_url: https://git.bioconductor.org/packages/DELocal git_branch: devel git_last_commit: eb7eed9 git_last_commit_date: 2026-03-16 Date/Publication: 2026-04-20 source.ver: src/contrib/DELocal_1.11.2.tar.gz vignettes: vignettes/DELocal/inst/doc/DELocal.html vignetteTitles: DELocal hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DELocal/inst/doc/DELocal.R importsMe: broadSeq dependencyCount: 68 Package: deltaCaptureC Version: 1.25.0 Depends: R (>= 3.6) Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2, tictoc Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 6d9ae08b07e4e1da962f222caeed396a NeedsCompilation: no Title: This Package Discovers Meso-scale Chromatin Remodeling from 3C Data Description: This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred 'viewpoint' region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments. biocViews: BiologicalQuestion, StatisticalMethod Author: Michael Shapiro [aut, cre] (ORCID: ) Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaCaptureC git_branch: devel git_last_commit: 012ccf3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/deltaCaptureC_1.25.0.tar.gz vignettes: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.html vignetteTitles: Delta Capture-C hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.R dependencyCount: 56 Package: deltaGseg Version: 1.51.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: 8138120a6e7726d287a1810baddbb68c NeedsCompilation: no Title: deltaGseg Description: Identifying distinct subpopulations through multiscale time series analysis biocViews: Proteomics, TimeCourse, Visualization, Clustering Author: Diana Low, Efthymios Motakis Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaGseg git_branch: devel git_last_commit: 7aa6b79 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/deltaGseg_1.51.0.tar.gz vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf vignetteTitles: deltaGseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deltaGseg/inst/doc/deltaGseg.R dependencyCount: 45 Package: DeMAND Version: 1.41.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: 0a588f3c6ad594fd34faa92fde33a181 NeedsCompilation: no Title: DeMAND Description: DEMAND predicts Drug MoA by interrogating a cell context specific regulatory network with a small number (N >= 6) of compound-induced gene expression signatures, to elucidate specific proteins whose interactions in the network is dysregulated by the compound. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, StatisticalMethod, Network Author: Jung Hoon Woo , Yishai Shimoni Maintainer: Jung Hoon Woo , Mariano Alvarez git_url: https://git.bioconductor.org/packages/DeMAND git_branch: devel git_last_commit: 96e41c4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DeMAND_1.41.0.tar.gz vignettes: vignettes/DeMAND/inst/doc/DeMAND.pdf vignetteTitles: Using DeMAND hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DeMAND/inst/doc/DeMAND.R dependencyCount: 3 Package: DeMixT Version: 1.99.0 Depends: R (>= 4.0.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment Imports: matrixStats, stats, truncdist, base64enc, ggplot2, KernSmooth, matrixcalc, sva, dendextend, fitdistrplus, pbapply, psych, magrittr, graphics, grDevices, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, calibrate, BiocStyle License: GPL-3 MD5sum: 7c585161e565b22aa79d8a00f0c5dd42 NeedsCompilation: yes Title: Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms Description: DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components. biocViews: Software, StatisticalMethod, Classification, GeneExpression, Sequencing, Microarray, TissueMicroarray, Coverage Author: Zeya Wang [aut], Shaolong Cao [aut], Liyang Xie [aut], Ruonan Li [cre], Wenyi Wang [aut] Maintainer: Ruonan Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeMixT git_branch: devel git_last_commit: d5587c3 git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/DeMixT_1.99.0.tar.gz vignettes: vignettes/DeMixT/inst/doc/DeMixNB_tutorial.html, vignettes/DeMixT/inst/doc/DeMixT_tutorial.html vignetteTitles: 2. DeMixNB: Deconvolution for Sparse Count Data, 1. DeMixT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeMixT/inst/doc/DeMixNB_tutorial.R, vignettes/DeMixT/inst/doc/DeMixT_tutorial.R dependencyCount: 102 Package: demuxmix Version: 1.13.0 Depends: R (>= 4.0.0) Imports: stats, MASS, Matrix, ggplot2, gridExtra, methods Suggests: BiocStyle, cowplot, DropletUtils, knitr, reshape2, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 3daf7491c2adf930d5771c7dcd6a7201 NeedsCompilation: no Title: Demultiplexing oligo-barcoded scRNA-seq data using regression mixture models Description: A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided. biocViews: SingleCell, Sequencing, Preprocessing, Classification, Regression Author: Hans-Ulrich Klein [aut, cre] (ORCID: ) Maintainer: Hans-Ulrich Klein URL: https://github.com/huklein/demuxmix VignetteBuilder: knitr BugReports: https://github.com/huklein/demuxmix/issues git_url: https://git.bioconductor.org/packages/demuxmix git_branch: devel git_last_commit: a6af3ec git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/demuxmix_1.13.0.tar.gz vignettes: vignettes/demuxmix/inst/doc/demuxmix.html vignetteTitles: Demultiplexing cells with demuxmix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/demuxmix/inst/doc/demuxmix.R importsMe: demuxSNP dependencyCount: 27 Package: demuxSNP Version: 1.9.0 Depends: R (>= 4.3.0), SingleCellExperiment, VariantAnnotation, ensembldb Imports: MatrixGenerics, BiocGenerics, class, Seqinfo, IRanges, Matrix, SummarizedExperiment, demuxmix, methods, KernelKnn, dplyr Suggests: knitr, rmarkdown, ComplexHeatmap, viridisLite, ggpubr, dittoSeq, EnsDb.Hsapiens.v86, BiocStyle, RefManageR, testthat (>= 3.0.0), Seurat License: GPL-3 MD5sum: fea6380ba8c5ab04169db11dc1b436c6 NeedsCompilation: no Title: scRNAseq demultiplexing using cell hashing and SNPs Description: This package assists in demultiplexing scRNAseq data using both cell hashing and SNPs data. The SNP profile of each group os learned using high confidence assignments from the cell hashing data. Cells which cannot be assigned with high confidence from the cell hashing data are assigned to their most similar group based on their SNPs. We also provide some helper function to optimise SNP selection, create training data and merge SNP data into the SingleCellExperiment framework. biocViews: Classification, SingleCell Author: Michael Lynch [aut, cre] (ORCID: ), Aedin Culhane [aut] (ORCID: ) Maintainer: Michael Lynch URL: https://github.com/michaelplynch/demuxSNP VignetteBuilder: knitr BugReports: https://github.com/michaelplynch/demuxSNP/issues git_url: https://git.bioconductor.org/packages/demuxSNP git_branch: devel git_last_commit: e594034 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/demuxSNP_1.9.0.tar.gz vignettes: vignettes/demuxSNP/inst/doc/supervised_demultiplexing.html vignetteTitles: Supervised Demultiplexing using Cell Hashing and SNPs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/demuxSNP/inst/doc/supervised_demultiplexing.R dependencyCount: 107 Package: DenoIST Version: 0.99.4 Depends: R (>= 3.5.0) Imports: flexmix, hexbin, pbapply, sparseMatrixStats, SpatialExperiment, stats, SummarizedExperiment, parallel, Matrix, dbscan, methods Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2, patchwork License: MIT + file LICENSE MD5sum: 9da331702a47444a5e17063e3af61ab5 NeedsCompilation: no Title: DenoIST: Denoising Image-based Spatial Transcriptomics data Description: DenoIST identifies and removes contamination in Image-based Spatial Transcriptomics data, using a transposed poisson mixture model with local neighbourhood offsets to infer genes that are likely to be due to neighbourhood contamination rather than endogenous expression. biocViews: Software, Preprocessing, Spatial, GeneExpression, SingleCell, Transcriptomics Author: Aaron Kwok [aut, cre] (ORCID: ), Heejung Shim [aut], Davis McCarthy [aut] Maintainer: Aaron Kwok URL: https://github.com/aaronkwc/DenoIST VignetteBuilder: knitr BugReports: https://github.com/aaronkwc/DenoIST/issues git_url: https://git.bioconductor.org/packages/DenoIST git_branch: devel git_last_commit: 5a33eb8 git_last_commit_date: 2026-03-17 Date/Publication: 2026-04-20 source.ver: src/contrib/DenoIST_0.99.4.tar.gz vignettes: vignettes/DenoIST/inst/doc/denoist_spe.html vignetteTitles: denoist_spe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DenoIST/inst/doc/denoist_spe.R dependencyCount: 74 Package: DepecheR Version: 1.27.0 Depends: R (>= 4.0) Imports: ggplot2 (>= 3.1.0), MASS (>= 7.3.51), Rcpp (>= 1.0.0), dplyr (>= 0.7.8), gplots (>= 3.0.1), viridis (>= 0.5.1), foreach (>= 1.4.4), doSNOW (>= 1.0.16), matrixStats (>= 0.54.0), mixOmics (>= 6.6.1), moments (>= 0.14), grDevices (>= 3.5.2), graphics (>= 3.5.2), stats (>= 3.5.2), utils (>= 3.5), methods (>= 3.5), parallel (>= 3.5.2), reshape2 (>= 1.4.3), beanplot (>= 1.2), FNN (>= 1.1.3), robustbase (>= 0.93.5), gmodels (>= 2.18.1), collapse (>= 1.9.2), ClusterR (>= 1.3.2) LinkingTo: Rcpp, RcppEigen Suggests: uwot, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: eee41614344cff60e0e7f7815dcd5d62 NeedsCompilation: yes Title: Determination of essential phenotypic elements of clusters in high-dimensional entities Description: The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters. biocViews: Software,CellBasedAssays,Transcription,DifferentialExpression, DataRepresentation,ImmunoOncology,Transcriptomics,Classification,Clustering, DimensionReduction,FeatureExtraction,FlowCytometry,RNASeq,SingleCell, Visualization Author: Jakob Theorell [aut, cre] (ORCID: ), Axel Theorell [aut] Maintainer: Jakob Theorell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DepecheR git_branch: devel git_last_commit: eb6e467 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DepecheR_1.27.0.tar.gz vignettes: vignettes/DepecheR/inst/doc/DepecheR_test.html, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.html vignetteTitles: Example of a cytometry data analysis with DepecheR, Using the groupProbPlot plot function for single-cell probability display hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DepecheR/inst/doc/DepecheR_test.R, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.R suggestsMe: flowSpecs dependencyCount: 106 Package: DepInfeR Version: 1.15.0 Depends: R (>= 4.2.0) Imports: matrixStats, glmnet, stats, BiocParallel Suggests: testthat (>= 3.0.0), knitr, rmarkdown, dplyr, tidyr, tibble, ggplot2, missForest, pheatmap, RColorBrewer, ggrepel, BiocStyle, ggbeeswarm License: GPL-3 MD5sum: e6e79507adc31d0ab1a2c90155bc8c8f NeedsCompilation: no Title: Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling Description: DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864. biocViews: Software, Regression, Pharmacogenetics, Pharmacogenomics, FunctionalGenomics Author: Junyan Lu [aut, cre] (ORCID: ), Alina Batzilla [aut] Maintainer: Junyan Lu VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/DepInfeR/issues git_url: https://git.bioconductor.org/packages/DepInfeR git_branch: devel git_last_commit: b403931 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DepInfeR_1.15.0.tar.gz vignettes: vignettes/DepInfeR/inst/doc/vignette.html vignetteTitles: DepInfeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DepInfeR/inst/doc/vignette.R dependencyCount: 27 Package: DEqMS Version: 1.29.0 Depends: R(>= 3.5),graphics,stats,ggplot2,matrixStats,dplyr,limma(>= 3.34) Suggests: BiocStyle,knitr,rmarkdown,markdown,plyr,reshape2,utils,ggrepel,ExperimentHub,LSD License: LGPL MD5sum: a91c74376fff6a293f3fbf0304c3c67d NeedsCompilation: no Title: a tool to perform statistical analysis of differential protein expression for quantitative proteomics data. Description: DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Preprocessing, DifferentialExpression, MultipleComparison,Normalization,Bayesian,ExperimentHubSoftware Author: Yafeng Zhu Maintainer: Yafeng Zhu VignetteBuilder: knitr BugReports: https://github.com/yafeng/DEqMS/issues git_url: https://git.bioconductor.org/packages/DEqMS git_branch: devel git_last_commit: 4ec4f19 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DEqMS_1.29.0.tar.gz vignettes: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.html vignetteTitles: DEqMS R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.R importsMe: PRONE dependencyCount: 34 Package: derfinder Version: 1.45.0 Depends: R (>= 3.5.0) Imports: BiocGenerics (>= 0.25.1), AnnotationDbi (>= 1.27.9), BiocParallel (>= 1.15.15), bumphunter (>= 1.9.2), derfinderHelper (>= 1.1.0), Seqinfo (>= 0.99.2), GenomeInfoDb (>= 1.45.9), GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges (>= 1.61.1), Hmisc, IRanges (>= 2.3.23), methods, qvalue (>= 1.99.0), Rsamtools (>= 2.25.1), rtracklayer, S4Vectors (>= 0.23.19), stats, utils Suggests: BiocStyle (>= 2.5.19), sessioninfo, derfinderData (>= 0.99.0), derfinderPlot, DESeq2, ggplot2, knitr (>= 1.6), limma, RefManageR, rmarkdown (>= 0.3.3), testthat (>= 2.1.0), TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 MD5sum: 331f50ffac6b2b2ba003005554ab921a NeedsCompilation: no Title: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach Description: This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks. biocViews: DifferentialExpression, Sequencing, RNASeq, ChIPSeq, DifferentialPeakCalling, Software, ImmunoOncology, Coverage Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Alyssa C. Frazee [ctb], Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinder VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinder/ git_url: https://git.bioconductor.org/packages/derfinder git_branch: devel git_last_commit: 38d7a03 git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/derfinder_1.45.0.tar.gz vignettes: vignettes/derfinder/inst/doc/derfinder-quickstart.html, vignettes/derfinder/inst/doc/derfinder-users-guide.html vignetteTitles: derfinder quick start guide, derfinder users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R, vignettes/derfinder/inst/doc/derfinder-users-guide.R importsMe: derfinderPlot, recount, regionReport, GenomicState, recountWorkflow suggestsMe: megadepth dependencyCount: 137 Package: derfinderHelper Version: 1.45.0 Depends: R(>= 3.2.2) Imports: IRanges (>= 1.99.27), Matrix, methods, S4Vectors (>= 0.2.2) Suggests: sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), RefManageR, rmarkdown (>= 0.3.3), testthat, covr License: Artistic-2.0 MD5sum: 3de5d91dbc9eb28b444408695e088b61 NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. This package is particularly useful when using BiocParallel and it helps reduce the time spent loading the full derfinder package when running the F-statistics calculation in parallel. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderHelper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderHelper git_url: https://git.bioconductor.org/packages/derfinderHelper git_branch: devel git_last_commit: 9c62cf1 git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/derfinderHelper_1.45.0.tar.gz vignettes: vignettes/derfinderHelper/inst/doc/derfinderHelper.html vignetteTitles: Introduction to derfinderHelper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R importsMe: derfinder dependencyCount: 13 Package: DESeq2 Version: 1.51.7 Depends: S4Vectors (>= 0.23.18), IRanges, GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, matrixStats, methods, stats4, locfit, ggplot2 (>= 3.4.0), Rcpp (>= 0.11.0), MatrixGenerics LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, vsn, pheatmap, RColorBrewer, apeglm, ashr, tximport, tximeta, tximportData, readr, pbapply, airway, glmGamPoi, BiocManager License: LGPL (>= 3) MD5sum: b4bcde8969c0d14fc5cbf8775de671fc NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. biocViews: Sequencing, RNASeq, ChIPSeq, GeneExpression, Transcription, Normalization, DifferentialExpression, Bayesian, Regression, PrincipalComponent, Clustering, ImmunoOncology Author: Michael Love [aut, cre], Constantin Ahlmann-Eltze [ctb], Anqi Zhu [ctb], Nikolaos Ignatiadis [ctb], Raphael Rossellini [ctb], Kwame Forbes [ctb], Simon Anders [aut, ctb], Wolfgang Huber [aut, ctb], RADIANT EU FP7 [fnd], NIH NHGRI [fnd], CZI [fnd] Maintainer: Michael Love URL: https://github.com/thelovelab/DESeq2 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/DESeq2 git_branch: devel git_last_commit: 15f2ec9 git_last_commit_date: 2026-03-12 Date/Publication: 2026-04-20 source.ver: src/contrib/DESeq2_1.51.7.tar.gz vignettes: vignettes/DESeq2/inst/doc/DESeq2.html vignetteTitles: Analyzing RNA-seq data with DESeq2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: DEWSeq, DEXSeq, metaseqR2, octad, rgsepd, SeqGSEA, TCC, tRanslatome, rnaseqDTU, rnaseqGene, Anaconda, DRomics, ordinalbayes importsMe: Anaquin, animalcules, APAlyzer, BatchQC, benchdamic, broadSeq, carnation, CeTF, circRNAprofiler, CleanUpRNAseq, coseq, countsimQC, cypress, DaMiRseq, debrowser, DeeDeeExperiment, DEFormats, DEGreport, DELocal, deltaCaptureC, DEsubs, DOtools, DOTSeq, DspikeIn, easier, EBSEA, ERSSA, fourSynergy, GDCRNATools, GeneTonic, gg4way, Glimma, GRaNIE, hermes, HTSFilter, HybridExpress, icetea, ideal, INSPEcT, IntEREst, iSEEde, isomiRs, kissDE, magpie, microbiomeExplorer, MIRit, MLSeq, mobileRNA, mosdef, MultiRNAflow, NBAMSeq, NetActivity, ORFik, OUTRIDER, pairedGSEA, PathoStat, pcaExplorer, phantasus, POMA, proActiv, RegEnrich, regionReport, ReportingTools, RiboDiPA, Rmmquant, saseR, scBFA, scECODA, scGPS, scQTLtools, SEtools, singleCellTK, SNPhood, srnadiff, SurfR, systemPipeTools, TBSignatureProfiler, TEKRABber, terapadog, UMI4Cats, vidger, VISTA, zitools, BloodCancerMultiOmics2017, FieldEffectCrc, IHWpaper, recountWorkflow, autoGO, cinaR, ExpGenetic, HEssRNA, limorhyde2, microbial, RCPA, RNAseqQC, sRNAGenetic, TransProR, wilson suggestsMe: aggregateBioVar, apeglm, bambu, BindingSiteFinder, biobroom, BiocGenerics, BioCor, BiocSet, BioNERO, CAGEr, compcodeR, dar, dearseq, derfinder, dittoSeq, EDASeq, EnhancedVolcano, EnrichmentBrowser, EWCE, extraChIPs, fishpond, gage, GeDi, GenomicAlignments, GenomicRanges, GeoTcgaData, geyser, glmGamPoi, HiCDCPlus, IHW, InteractiveComplexHeatmap, methodical, muscat, OPWeight, pathlinkR, PCAtools, phyloseq, progeny, QRscore, raer, recount, RFGeneRank, ribosomeProfilingQC, roastgsa, RUVSeq, Rvisdiff, scran, scToppR, sparrow, spatialHeatmap, SpliceWiz, subSeq, systemPipeR, systemPipeShiny, TFEA.ChIP, tidybulk, topconfects, tximeta, tximport, variancePartition, Wrench, zinbwave, ChIPDBData, curatedAdipoChIP, curatedAdipoRNA, GSE62944, RegParallel, Single.mTEC.Transcriptomes, CAGEWorkflow, fluentGenomics, seqpac, bakR, conos, dependentsimr, FateID, futurize, ggpicrust2, GiANT, glmmSeq, grandR, lfc, LorMe, metacoder, metaRNASeq, MiscMetabar, myTAI, pctax, pmartR, RaceID, rliger, SCdeconR, seqgendiff, Seurat, SeuratExplorer, volcano3D dependencyCount: 54 Package: DEsingle Version: 1.31.0 Depends: R (>= 3.4.0) Imports: stats, Matrix (>= 1.2-14), MASS (>= 7.3-45), VGAM (>= 1.0-2), bbmle (>= 1.0.18), gamlss (>= 4.4-0), maxLik (>= 1.3-4), pscl (>= 1.4.9), BiocParallel (>= 1.12.0), Suggests: knitr, rmarkdown, SingleCellExperiment License: GPL-2 MD5sum: 61804d799cc35c935203ea960b5caff0 NeedsCompilation: no Title: DEsingle for detecting three types of differential expression in single-cell RNA-seq data Description: DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions. biocViews: DifferentialExpression, GeneExpression, SingleCell, ImmunoOncology, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao Maintainer: Zhun Miao URL: https://miaozhun.github.io/DEsingle/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsingle git_branch: devel git_last_commit: 22a31b8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DEsingle_1.31.0.tar.gz vignettes: vignettes/DEsingle/inst/doc/DEsingle.html vignetteTitles: DEsingle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsingle/inst/doc/DEsingle.R dependencyCount: 39 Package: DESpace Version: 2.3.2 Depends: R (>= 4.5.0) Imports: edgeR, limma, dplyr, stats, Matrix, SpatialExperiment, ggplot2, SummarizedExperiment, S4Vectors, BiocGenerics, data.table, assertthat, terra, sf, spatstat.explore, spatstat.geom, ggforce, ggnewscale, patchwork, BiocParallel, methods, scales, scuttle Suggests: knitr, rmarkdown, testthat, BiocStyle, muSpaData, ExperimentHub, spatialLIBD, purrr, reshape2, tidyverse, concaveman License: GPL-3 MD5sum: b45cd01572727e198625382353db72bb NeedsCompilation: no Title: DESpace: a framework to discover spatially variable genes and differential spatial patterns across conditions Description: Intuitive framework for identifying spatially variable genes (SVGs) and differential spatial variable pattern (DSP) between conditions via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. For multi-sample, multi-condition datasets, we again fit a NB model via edgeR, incorporating spatial clusters, conditions and their interactions as covariates. DSP genes-representing differences in spatial gene expression patterns across experimental conditions-are identified by testing the interaction between spatial clusters and conditions. biocViews: Spatial, SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, DifferentialExpression,StatisticalMethod, Visualization Author: Peiying Cai [aut, cre] (ORCID: ), Simone Tiberi [aut] (ORCID: ) Maintainer: Peiying Cai URL: https://github.com/peicai/DESpace, https://peicai.github.io/DESpace/ VignetteBuilder: knitr BugReports: https://github.com/peicai/DESpace/issues git_url: https://git.bioconductor.org/packages/DESpace git_branch: devel git_last_commit: 855bfba git_last_commit_date: 2025-11-28 Date/Publication: 2026-04-20 source.ver: src/contrib/DESpace_2.3.2.tar.gz vignettes: vignettes/DESpace/inst/doc/DSP.html, vignettes/DESpace/inst/doc/SVG.html vignetteTitles: Differential Spatial Pattern between conditions, A framework to discover spatially variable genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESpace/inst/doc/DSP.R, vignettes/DESpace/inst/doc/SVG.R importsMe: OSTA dependencyCount: 123 Package: destiny Version: 3.25.0 Depends: R (>= 3.4.0) Imports: methods, graphics, grDevices, grid, utils, stats, Matrix, Rcpp (>= 0.10.3), RcppEigen, RSpectra (>= 0.14-0), irlba, pcaMethods, Biobase, BiocGenerics, SummarizedExperiment, SingleCellExperiment, ggplot2, ggplot.multistats, rlang, tidyr, tidyselect, ggthemes, VIM, knn.covertree, proxy, RcppHNSW, scales, scatterplot3d LinkingTo: Rcpp, RcppEigen, grDevices Suggests: knitr, rmarkdown, igraph, testthat, FNN, tidyverse, gridExtra, cowplot, conflicted, viridis, rgl, scRNAseq, org.Mm.eg.db, scran, repr Enhances: rgl, SingleCellExperiment License: GPL-3 MD5sum: dee83ff7d382cf5ff3b77268a39d6ada NeedsCompilation: yes Title: Creates diffusion maps Description: Create and plot diffusion maps. biocViews: CellBiology, CellBasedAssays, Clustering, Software, Visualization Author: Philipp Angerer [cre, aut] (ORCID: ), Laleh Haghverdi [ctb], Maren Büttner [ctb] (ORCID: ), Fabian Theis [ctb] (ORCID: ), Carsten Marr [ctb] (ORCID: ), Florian Büttner [ctb] (ORCID: ) Maintainer: Philipp Angerer URL: https://github.com/theislab/destiny/, https://bioconductor.org/packages/destiny, https://doi.org/10.1093/bioinformatics/btv715 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/theislab/destiny/issues git_url: https://git.bioconductor.org/packages/destiny git_branch: devel git_last_commit: a20a812 git_last_commit_date: 2026-04-15 Date/Publication: 2026-04-20 source.ver: src/contrib/destiny_3.25.0.tar.gz vignettes: vignettes/destiny/inst/doc/Diffusion-Map-recap.html, vignettes/destiny/inst/doc/Diffusion-Maps.html, vignettes/destiny/inst/doc/DPT.html, vignettes/destiny/inst/doc/Gene-Relevance.html, vignettes/destiny/inst/doc/Global-Sigma.html, vignettes/destiny/inst/doc/tidyverse.html vignetteTitles: Reproduce the Diffusion Map vignette with the supplied data(), destiny main vignette: Start here!, destiny 2.0 brought the Diffusion Pseudo Time (DPT) class, detecting relevant genes with destiny 3, The effects of a global vs. local kernel, tidyverse and ggplot integration with destiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/destiny/inst/doc/Diffusion-Map-recap.R, vignettes/destiny/inst/doc/Diffusion-Maps.R, vignettes/destiny/inst/doc/DPT.R, vignettes/destiny/inst/doc/Gene-Relevance.R, vignettes/destiny/inst/doc/Global-Sigma.R, vignettes/destiny/inst/doc/tidyverse.R importsMe: dandelionR suggestsMe: CelliD, CellTrails, monocle dependencyCount: 140 Package: DEWSeq Version: 1.25.0 Depends: R(>= 4.0.0), R.utils, DESeq2, BiocParallel Imports: BiocGenerics, data.table(>= 1.11.8), Seqinfo, GenomicRanges, methods, S4Vectors, SummarizedExperiment, stats, utils Suggests: knitr, tidyverse, rmarkdown, testthat, BiocStyle, IHW License: LGPL (>= 3) MD5sum: 4a5b3e4aaa59520ad4ac9b7326554b61 NeedsCompilation: no Title: Differential Expressed Windows Based on Negative Binomial Distribution Description: DEWSeq is a sliding window approach for the analysis of differentially enriched binding regions eCLIP or iCLIP next generation sequencing data. biocViews: Sequencing, GeneRegulation, FunctionalGenomics, DifferentialExpression Author: Sudeep Sahadevan [aut], Thomas Schwarzl [aut], bioinformatics team Hentze [aut, cre] Maintainer: bioinformatics team Hentze URL: https://github.com/EMBL-Hentze-group/DEWSeq/ VignetteBuilder: knitr BugReports: https://github.com/EMBL-Hentze-group/DEWSeq/issues git_url: https://git.bioconductor.org/packages/DEWSeq git_branch: devel git_last_commit: 065d2b7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DEWSeq_1.25.0.tar.gz vignettes: vignettes/DEWSeq/inst/doc/DEWSeq.html vignetteTitles: Analyzing eCLIP/iCLIP data with DEWSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEWSeq/inst/doc/DEWSeq.R dependencyCount: 59 Package: DExMA Version: 1.19.0 Depends: R (>= 4.1), DExMAdata Imports: Biobase, GEOquery, impute, limma, pheatmap, plyr, scales, snpStats, sva, swamp, stats, methods, utils, bnstruct, RColorBrewer, grDevices Suggests: BiocStyle, qpdf, BiocGenerics, RUnit License: GPL-2 MD5sum: 656e723fe5e38d4347c8aded3d8c830d NeedsCompilation: no Title: Differential Expression Meta-Analysis Description: performing all the steps of gene expression meta-analysis considering the possible existence of missing genes. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO datasets and show graphical representations of the results. biocViews: DifferentialExpression, GeneExpression, StatisticalMethod, QualityControl Author: Juan Antonio Villatoro-García [aut, cre], Pedro Carmona-Sáez [aut] Maintainer: Juan Antonio Villatoro-García git_url: https://git.bioconductor.org/packages/DExMA git_branch: devel git_last_commit: f2e6f45 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DExMA_1.19.0.tar.gz vignettes: vignettes/DExMA/inst/doc/DExMA.pdf vignetteTitles: Differential Expression Meta-Analysis with DExMA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DExMA/inst/doc/DExMA.R dependencyCount: 126 Package: DEXSeq Version: 1.57.2 Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.39.6), AnnotationDbi, S4Vectors (>= 0.23.18) Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter Suggests: GenomeInfoDb, GenomicFeatures, txdbmaker, pasilla (>= 0.2.22), BiocStyle, knitr, rmarkdown, testthat, pasillaBamSubset, GenomicAlignments, roxygen2, glmGamPoi License: GPL (>= 3) MD5sum: 1d6435b09c3c25cfebaeb3614cc31806 NeedsCompilation: no Title: Inference of differential exon usage in RNA-Seq Description: The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, AlternativeSplicing, DifferentialSplicing, GeneExpression, Visualization Author: Simon Anders [aut], Alejandro Reyes [aut, ccp] (Maintainer until 2026.), Hugo Gruson [cre] Maintainer: Hugo Gruson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEXSeq git_branch: devel git_last_commit: c7c7949 git_last_commit_date: 2026-04-12 Date/Publication: 2026-04-20 source.ver: src/contrib/DEXSeq_1.57.2.tar.gz vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.html vignetteTitles: Inferring differential exon usage in RNA-Seq data with the DEXSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R dependsOnMe: IsoformSwitchAnalyzeR, pasilla, rnaseqDTU importsMe: diffUTR, IntEREst, pairedGSEA suggestsMe: bambu, GenomicRanges, satuRn, stageR, subSeq, BioPlex dependencyCount: 108 Package: DFP Version: 1.69.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: 7a159a05a52ab2f7f2cb653ce325f7ee NeedsCompilation: no Title: Gene Selection Description: This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values. biocViews: Microarray, DifferentialExpression Author: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola Maintainer: Rodrigo Alvarez-Glez git_url: https://git.bioconductor.org/packages/DFP git_branch: devel git_last_commit: 5d2534a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DFP_1.69.0.tar.gz vignettes: vignettes/DFP/inst/doc/DFP.pdf vignetteTitles: Howto: Discriminat Fuzzy Pattern hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFP/inst/doc/DFP.R dependencyCount: 7 Package: DFplyr Version: 1.5.2 Depends: R (>= 4.1.0), dplyr Imports: BiocGenerics, methods, rlang, S4Vectors, tidyselect Suggests: BiocStyle, GenomeInfoDb, GenomicRanges, IRanges, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0), tibble License: GPL-3 MD5sum: 6f0e1eaac984c7014d05a07e1cb42bd3 NeedsCompilation: no Title: A `DataFrame` (`S4Vectors`) backend for `dplyr` Description: Provides `dplyr` verbs (`mutate`, `select`, `filter`, etc...) supporting `S4Vectors::DataFrame` objects. Importantly, this is achieved without conversion to an intermediate `tibble`. Adds grouping infrastructure to `DataFrame` which is respected by the transformation verbs. biocViews: DataRepresentation, Infrastructure, Software Author: Jonathan Carroll [aut, cre] (ORCID: ), Pierre-Paul Axisa [ctb] Maintainer: Jonathan Carroll URL: https://github.com/jonocarroll/DFplyr VignetteBuilder: knitr BugReports: https://github.com/jonocarroll/DFplyr/issues git_url: https://git.bioconductor.org/packages/DFplyr git_branch: devel git_last_commit: bc6a1bd git_last_commit_date: 2026-02-15 Date/Publication: 2026-04-20 source.ver: src/contrib/DFplyr_1.5.2.tar.gz vignettes: vignettes/DFplyr/inst/doc/example_usage.html vignetteTitles: Example Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFplyr/inst/doc/example_usage.R importsMe: drugfindR suggestsMe: toppgene dependencyCount: 23 Package: diffcoexp Version: 1.31.0 Depends: R (>= 3.5), WGCNA, SummarizedExperiment Imports: stats, DiffCorr, psych, igraph, BiocGenerics Suggests: GEOquery, RUnit License: GPL (>2) MD5sum: d8676f588c4be5e5dcc9dbfa5a3de8b8 NeedsCompilation: no Title: Differential Co-expression Analysis Description: A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance. biocViews: GeneExpression, DifferentialExpression, Transcription, Microarray, OneChannel, TwoChannel, RNASeq, Sequencing, Coverage, ImmunoOncology Author: Wenbin Wei, Sandeep Amberkar, Winston Hide Maintainer: Wenbin Wei URL: https://github.com/hidelab/diffcoexp git_url: https://git.bioconductor.org/packages/diffcoexp git_branch: devel git_last_commit: 8943d77 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/diffcoexp_1.31.0.tar.gz vignettes: vignettes/diffcoexp/inst/doc/diffcoexp.pdf vignetteTitles: About diffcoexp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffcoexp/inst/doc/diffcoexp.R dependencyCount: 104 Package: diffcyt Version: 1.31.0 Depends: R (>= 3.4.0) Imports: flowCore, FlowSOM, SummarizedExperiment, S4Vectors, limma, edgeR, lme4, multcomp, dplyr, tidyr, reshape2, magrittr, stats, methods, utils, grDevices, graphics, ComplexHeatmap, circlize, grid Suggests: BiocStyle, knitr, rmarkdown, testthat, HDCytoData, CATALYST License: MIT + file LICENSE MD5sum: e2afd0db788d996eeddf26873468ecaf NeedsCompilation: no Title: Differential discovery in high-dimensional cytometry via high-resolution clustering Description: Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software Author: Lukas M. Weber [aut, cre] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/diffcyt VignetteBuilder: knitr BugReports: https://github.com/lmweber/diffcyt/issues git_url: https://git.bioconductor.org/packages/diffcyt git_branch: devel git_last_commit: 5b52099 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/diffcyt_1.31.0.tar.gz vignettes: vignettes/diffcyt/inst/doc/diffcyt_workflow.html vignetteTitles: diffcyt workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffcyt/inst/doc/diffcyt_workflow.R dependsOnMe: censcyt, cytofWorkflow importsMe: CyTOFpower, treeclimbR, treekoR suggestsMe: CATALYST dependencyCount: 147 Package: DifferentialRegulation Version: 2.9.0 Depends: R (>= 4.3.0) Imports: methods, Rcpp, doRNG, MASS, data.table, doParallel, parallel, foreach, stats, BANDITS, Matrix, SingleCellExperiment, SummarizedExperiment, ggplot2, tximport, gridExtra LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 8c36b002ff32c9e28a783388085ae2af NeedsCompilation: yes Title: Differentially regulated genes from scRNA-seq data Description: DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) 'ambiguous' reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs). biocViews: DifferentialSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, GeneTarget Author: Simone Tiberi [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ) Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/DifferentialRegulation SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/DifferentialRegulation/issues git_url: https://git.bioconductor.org/packages/DifferentialRegulation git_branch: devel git_last_commit: e4187ce git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DifferentialRegulation_2.9.0.tar.gz vignettes: vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.html vignetteTitles: DifferentialRegulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.R dependencyCount: 78 Package: diffGeneAnalysis Version: 1.93.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: 8f5999c24ce0922652870d9aec37b00d NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi git_url: https://git.bioconductor.org/packages/diffGeneAnalysis git_branch: devel git_last_commit: 9a23ca1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/diffGeneAnalysis_1.93.0.tar.gz vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf vignetteTitles: Documentation on diffGeneAnalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.R dependencyCount: 5 Package: diffHic Version: 1.43.0 Depends: R (>= 3.5), GenomicRanges, InteractionSet, SummarizedExperiment Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics, grDevices, graphics, stats, utils, Rcpp, rtracklayer LinkingTo: Rhtslib (>= 1.13.1), Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix, testthat License: GPL-3 MD5sum: 331d9d903aae5736c09c8f1a541788f6 NeedsCompilation: yes Title: Differential Analysis of Hi-C Data Description: Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available. biocViews: MultipleComparison, Preprocessing, Sequencing, Coverage, Alignment, Normalization, Clustering, HiC Author: Aaron Lun, Gordon Smyth Maintainer: Aaron Lun , Gordon Smyth , Hannah Coughlin SystemRequirements: C++, GNU make git_url: https://git.bioconductor.org/packages/diffHic git_branch: devel git_last_commit: c909cdb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/diffHic_1.43.0.tar.gz vignettes: vignettes/diffHic/inst/doc/diffHic.pdf, vignettes/diffHic/inst/doc/diffHicUsersGuide.pdf vignetteTitles: diffHic Vignette, diffHicUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: OHCA, hicream dependencyCount: 74 Package: DiffLogo Version: 2.35.0 Depends: R (>= 3.4), stats, cba Imports: grDevices, graphics, utils, tools Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: a9bffb5b5d43dc64330674e124f23125 NeedsCompilation: no Title: DiffLogo: A comparative visualisation of biooligomer motifs Description: DiffLogo is an easy-to-use tool to visualize motif differences. biocViews: Software, SequenceMatching, MultipleComparison, MotifAnnotation, Visualization, Alignment Author: c( person("Martin", "Nettling", role = c("aut", "cre"), email = "martin.nettling@informatik.uni-halle.de"), person("Hendrik", "Treutler", role = c("aut", "cre"), email = "hendrik.treutler@ipb-halle.de"), person("Jan", "Grau", role = c("aut", "ctb"), email = "grau@informatik.uni-halle.de"), person("Andrey", "Lando", role = c("aut", "ctb"), email = "dronte@autosome.ru"), person("Jens", "Keilwagen", role = c("aut", "ctb"), email = "jens.keilwagen@julius-kuehn.de"), person("Stefan", "Posch", role = "aut", email = "posch@informatik.uni-halle.de"), person("Ivo", "Grosse", role = "aut", email = "grosse@informatik.uni-halle.de")) Maintainer: Hendrik Treutler URL: https://github.com/mgledi/DiffLogo/ BugReports: https://github.com/mgledi/DiffLogo/issues git_url: https://git.bioconductor.org/packages/DiffLogo git_branch: devel git_last_commit: 933892e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DiffLogo_2.35.0.tar.gz vignettes: vignettes/DiffLogo/inst/doc/DiffLogoBasics.pdf vignetteTitles: Basics of the DiffLogo package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffLogo/inst/doc/DiffLogoBasics.R dependencyCount: 9 Package: diffuStats Version: 1.31.0 Depends: R (>= 3.4) Imports: grDevices, stats, methods, Matrix, MASS, checkmate, expm, igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata, BiocStyle, reshape2, utils License: GPL-3 MD5sum: 6e0f6143c96f0f71af7d539c03721688 NeedsCompilation: yes Title: Diffusion scores on biological networks Description: Label propagation approaches are a widely used procedure in computational biology for giving context to molecular entities using network data. Node labels, which can derive from gene expression, genome-wide association studies, protein domains or metabolomics profiling, are propagated to their neighbours in the network, effectively smoothing the scores through prior annotated knowledge and prioritising novel candidates. The R package diffuStats contains a collection of diffusion kernels and scoring approaches that facilitates their computation, characterisation and benchmarking. biocViews: Network, GeneExpression, GraphAndNetwork, Metabolomics, Transcriptomics, Proteomics, Genetics, GenomeWideAssociation, Normalization Author: Sergio Picart-Armada [aut, cre], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/diffuStats git_branch: devel git_last_commit: 5cda747 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/diffuStats_1.31.0.tar.gz vignettes: vignettes/diffuStats/inst/doc/diffuStats.pdf, vignettes/diffuStats/inst/doc/intro.html vignetteTitles: Case study: predicting protein function, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffuStats/inst/doc/diffuStats.R, vignettes/diffuStats/inst/doc/intro.R dependencyCount: 41 Package: diffUTR Version: 1.19.0 Depends: R (>= 4.0) Imports: S4Vectors, SummarizedExperiment, limma, edgeR, DEXSeq, GenomicRanges, Rsubread, ggplot2, rtracklayer, ComplexHeatmap, ggrepel, stringi, methods, stats, GenomeInfoDb, dplyr, matrixStats, IRanges, ensembldb, viridisLite Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 0538760fe407d72ffe70682ebc18a0e4 NeedsCompilation: no Title: diffUTR: Streamlining differential exon and 3' UTR usage Description: The diffUTR package provides a uniform interface and plotting functions for limma/edgeR/DEXSeq -powered differential bin/exon usage. It includes in addition an improved version of the limma::diffSplice method. Most importantly, diffUTR further extends the application of these frameworks to differential UTR usage analysis using poly-A site databases. biocViews: GeneExpression Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Stefan Gerber [aut] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/ETHZ-INS/diffUTR git_url: https://git.bioconductor.org/packages/diffUTR git_branch: devel git_last_commit: 2f86b30 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/diffUTR_1.19.0.tar.gz vignettes: vignettes/diffUTR/inst/doc/diffSplice2.html, vignettes/diffUTR/inst/doc/diffUTR.html vignetteTitles: diffUTR_diffSplice2, 1_diffUTR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffUTR/inst/doc/diffSplice2.R, vignettes/diffUTR/inst/doc/diffUTR.R dependencyCount: 140 Package: diggit Version: 1.43.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE MD5sum: e5dda1dcb6aad4c15f4525a749b3d56f NeedsCompilation: no Title: Inference of Genetic Variants Driving Cellular Phenotypes Description: Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/diggit git_branch: devel git_last_commit: ea7ead6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/diggit_1.43.0.tar.gz vignettes: vignettes/diggit/inst/doc/diggit.pdf vignetteTitles: Using DIGGIT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diggit/inst/doc/diggit.R dependencyCount: 95 Package: Dino Version: 1.17.0 Depends: R (>= 4.0.0) Imports: BiocParallel, BiocSingular, SummarizedExperiment, SingleCellExperiment, S4Vectors, Matrix, Seurat, matrixStats, parallel, scran, grDevices, stats, methods Suggests: testthat (>= 2.1.0), knitr, rmarkdown, BiocStyle, devtools, ggplot2, gridExtra, ggpubr, grid, magick, hexbin License: GPL-3 MD5sum: aae0cfc71e048fa32a3ae45e0943c61f NeedsCompilation: no Title: Normalization of Single-Cell mRNA Sequencing Data Description: Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge. biocViews: Software, Normalization, RNASeq, SingleCell, Sequencing, GeneExpression, Transcriptomics, Regression, CellBasedAssays Author: Jared Brown [aut, cre] (ORCID: ), Christina Kendziorski [ctb] Maintainer: Jared Brown URL: https://github.com/JBrownBiostat/Dino VignetteBuilder: knitr BugReports: https://github.com/JBrownBiostat/Dino/issues git_url: https://git.bioconductor.org/packages/Dino git_branch: devel git_last_commit: 89317b9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Dino_1.17.0.tar.gz vignettes: vignettes/Dino/inst/doc/Dino.html vignetteTitles: Normalization by distributional resampling of high throughput single-cell RNA-sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Dino/inst/doc/Dino.R dependencyCount: 185 Package: dinoR Version: 1.7.0 Depends: R (>= 4.3.0), SummarizedExperiment Imports: BiocGenerics, circlize, ComplexHeatmap, cowplot, dplyr, edgeR, GenomicRanges, ggplot2, Matrix, methods, rlang, stats, stringr, tibble, tidyr, tidyselect Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: c33b8fcf642701ebe3500d6044f671a3 NeedsCompilation: no Title: Differential NOMe-seq analysis Description: dinoR tests for significant differences in NOMe-seq footprints between two conditions, using genomic regions of interest (ROI) centered around a landmark, for example a transcription factor (TF) motif. This package takes NOMe-seq data (GCH methylation/protection) in the form of a Ranged Summarized Experiment as input. dinoR can be used to group sequencing fragments into 3 or 5 categories representing characteristic footprints (TF bound, nculeosome bound, open chromatin), plot the percentage of fragments in each category in a heatmap, or averaged across different ROI groups, for example, containing a common TF motif. It is designed to compare footprints between two sample groups, using edgeR's quasi-likelihood methods on the total fragment counts per ROI, sample, and footprint category. biocViews: NucleosomePositioning, Epigenetics, MethylSeq, DifferentialMethylation, Coverage, Transcription, Sequencing, Software Author: Michaela Schwaiger [aut, cre] (ORCID: ) Maintainer: Michaela Schwaiger URL: https://github.com/xxxmichixxx/dinoR VignetteBuilder: knitr BugReports: https://github.com/xxxmichixxx/dinoR/issues git_url: https://git.bioconductor.org/packages/dinoR git_branch: devel git_last_commit: ad3d7ac git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dinoR_1.7.0.tar.gz vignettes: vignettes/dinoR/inst/doc/dinoR-vignette.html vignetteTitles: dinoR-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dinoR/inst/doc/dinoR-vignette.R dependencyCount: 75 Package: dir.expiry Version: 1.19.0 Imports: utils, filelock Suggests: rmarkdown, knitr, testthat, BiocStyle License: GPL-3 MD5sum: c08b2c958f0e2adf9265f1aa07c08e66 NeedsCompilation: no Title: Managing Expiration for Cache Directories Description: Implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages. biocViews: Software, Infrastructure Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dir.expiry git_branch: devel git_last_commit: 818ef34 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dir.expiry_1.19.0.tar.gz vignettes: vignettes/dir.expiry/inst/doc/userguide.html vignetteTitles: Managing directory expiration hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dir.expiry/inst/doc/userguide.R importsMe: basilisk, basilisk.utils, biocmake, graphite, rebook dependencyCount: 2 Package: DirichletMultinomial Version: 1.53.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, DT, knitr, rmarkdown, BiocStyle License: LGPL-3 MD5sum: 52fac04002272ecdb6efa38727046531 NeedsCompilation: yes Title: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data Description: Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. biocViews: ImmunoOncology, Microbiome, Sequencing, Clustering, Classification, Metagenomics Author: Martin Morgan [aut, cre] (ORCID: ) Maintainer: Martin Morgan URL: https://mtmorgan.github.io/DirichletMultinomial/ SystemRequirements: gsl VignetteBuilder: knitr BugReports: https://github.com/mtmorgan/DirichletMultinomial/issues git_url: https://git.bioconductor.org/packages/DirichletMultinomial git_branch: devel git_last_commit: 673d30b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DirichletMultinomial_1.53.0.tar.gz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.html vignetteTitles: DirichletMultinomial for Clustering and Classification of Microbiome Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.R importsMe: mia, miaViz, TFBSTools suggestsMe: bluster, MicrobiotaProcess dependencyCount: 9 Package: discordant Version: 1.35.0 Depends: R (>= 4.1.0) Imports: Rcpp, Biobase, stats, biwt, gtools, MASS, tools, dplyr, methods, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 88a3618131b6a8178e9deeaa9b69f52d NeedsCompilation: yes Title: The Discordant Method: A Novel Approach for Differential Correlation Description: Discordant is an R package that identifies pairs of features that correlate differently between phenotypic groups, with application to -omics data sets. Discordant uses a mixture model that “bins” molecular feature pairs based on their type of coexpression or coabbundance. Algorithm is explained further in "Differential Correlation for Sequencing Data"" (Siska et al. 2016). biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod, mRNAMicroarray, Microarray, Genetics, RNASeq Author: Charlotte Siska [aut], McGrath Max [aut, cre], Katerina Kechris [aut, cph, ths] Maintainer: McGrath Max URL: https://github.com/siskac/discordant VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/discordant git_branch: devel git_last_commit: 3702a42 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/discordant_1.35.0.tar.gz vignettes: vignettes/discordant/inst/doc/Using_discordant.html vignetteTitles: The discordant R Package: A Novel Approach to Differential Correlation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/discordant/inst/doc/Using_discordant.R dependencyCount: 29 Package: DiscoRhythm Version: 1.27.0 Depends: R (>= 3.6.0) Imports: matrixTests, matrixStats, MetaCycle (>= 1.2.0), data.table, ggplot2, ggExtra, dplyr, broom, shiny, shinyBS, shinycssloaders, shinydashboard, shinyjs, BiocStyle, rmarkdown, knitr, kableExtra, magick, VennDiagram, UpSetR, heatmaply, viridis, plotly, DT, gridExtra, methods, stats, SummarizedExperiment, BiocGenerics, S4Vectors, zip, reshape2 Suggests: testthat License: GPL-3 MD5sum: 42e06e29f0380d8e859f89133c53bb5a NeedsCompilation: no Title: Interactive Workflow for Discovering Rhythmicity in Biological Data Description: Set of functions for estimation of cyclical characteristics, such as period, phase, amplitude, and statistical significance in large temporal datasets. Supporting functions are available for quality control, dimensionality reduction, spectral analysis, and analysis of experimental replicates. Contains a R Shiny web interface to execute all workflow steps. biocViews: Software, TimeCourse, QualityControl, Visualization, GUI, PrincipalComponent Author: Matthew Carlucci [aut, cre], Algimantas Kriščiūnas [aut], Haohan Li [aut], Povilas Gibas [aut], Karolis Koncevičius [aut], Art Petronis [aut], Gabriel Oh [aut] Maintainer: Matthew Carlucci URL: https://github.com/matthewcarlucci/DiscoRhythm SystemRequirements: To generate html reports pandoc (http://pandoc.org/installing.html) is required. VignetteBuilder: knitr BugReports: https://github.com/matthewcarlucci/DiscoRhythm/issues git_url: https://git.bioconductor.org/packages/DiscoRhythm git_branch: devel git_last_commit: 15d7b60 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DiscoRhythm_1.27.0.tar.gz vignettes: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.html vignetteTitles: Introduction to DiscoRhythm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.R dependencyCount: 156 Package: distinct Version: 1.23.0 Depends: R (>= 4.3) Imports: Rcpp, stats, SummarizedExperiment, SingleCellExperiment, methods, Matrix, foreach, parallel, doParallel, doRNG, ggplot2, limma, scater LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, UpSetR, BiocStyle License: GPL (>= 3) MD5sum: 0a9d04461e5eb1472eda2bf121553d13 NeedsCompilation: yes Title: distinct: a method for differential analyses via hierarchical permutation tests Description: distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group. biocViews: Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, FlowCytometry, GeneTarget Author: Simone Tiberi [aut, cre]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/distinct SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/distinct/issues git_url: https://git.bioconductor.org/packages/distinct git_branch: devel git_last_commit: fecbaac git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/distinct_1.23.0.tar.gz vignettes: vignettes/distinct/inst/doc/distinct.html vignetteTitles: distinct: a method for differential analyses via hierarchical permutation tests hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/distinct/inst/doc/distinct.R importsMe: condiments dependencyCount: 97 Package: dittoSeq Version: 1.23.0 Depends: ggplot2 Imports: methods, colorspace (>= 1.4), gridExtra, cowplot, reshape2, pheatmap, grDevices, ggrepel, ggridges, stats, utils, SummarizedExperiment, SingleCellExperiment, S4Vectors Suggests: plotly, testthat, Seurat (>= 2.2), DESeq2, edgeR, ggplot.multistats, knitr, rmarkdown, BiocStyle, scRNAseq, ggrastr (>= 0.2.0), ComplexHeatmap, bluster, scater, scran, MASS License: MIT + file LICENSE MD5sum: c9219a429bedd9dee6ad99c7079a4801 NeedsCompilation: no Title: User Friendly Single-Cell and Bulk RNA Sequencing Visualization Description: A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors(). biocViews: Software, Visualization, RNASeq, SingleCell, GeneExpression, Transcriptomics, DataImport Author: Daniel Bunis [aut, cre], Jared Andrews [aut, ctb] Maintainer: Daniel Bunis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dittoSeq git_branch: devel git_last_commit: 9ee30ee git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dittoSeq_1.23.0.tar.gz vignettes: vignettes/dittoSeq/inst/doc/dittoSeq.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dittoSeq/inst/doc/dittoSeq.R importsMe: CRISPRball, SPIAT suggestsMe: demuxSNP, tidySingleCellExperiment, magmaR, scCustomize dependencyCount: 55 Package: divergence Version: 1.27.0 Depends: R (>= 3.6), SummarizedExperiment Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 66f1e3e045e7fda82ce6065722d051f9 NeedsCompilation: no Title: Divergence: Functionality for assessing omics data by divergence with respect to a baseline Description: This package provides functionality for performing divergence analysis as presented in Dinalankara et al, "Digitizing omics profiles by divergence from a baseline", PANS 2018. This allows the user to simplify high dimensional omics data into a binary or ternary format which encapsulates how the data is divergent from a specified baseline group with the same univariate or multivariate features. biocViews: Software, StatisticalMethod Author: Wikum Dinalankara , Luigi Marchionni , Qian Ke Maintainer: Wikum Dinalankara , Luigi Marchionni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/divergence git_branch: devel git_last_commit: 3e893d2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/divergence_1.27.0.tar.gz vignettes: vignettes/divergence/inst/doc/divergence.html vignetteTitles: Performing Divergence Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/divergence/inst/doc/divergence.R dependencyCount: 25 Package: dks Version: 1.57.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: fbfb69fc30479311cd46d64e7b017f57 NeedsCompilation: no Title: The double Kolmogorov-Smirnov package for evaluating multiple testing procedures. Description: The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated. biocViews: MultipleComparison, QualityControl Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/dks git_branch: devel git_last_commit: 427fe72 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dks_1.57.0.tar.gz vignettes: vignettes/dks/inst/doc/dks.pdf vignetteTitles: dksTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dks/inst/doc/dks.R dependencyCount: 4 Package: DMCFB Version: 1.25.0 Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges Imports: utils, stats, speedglm, MASS, data.table, splines, arm, rtracklayer, benchmarkme, tibble, matrixStats, fastDummies, graphics Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: a1f80879ba435905543f209f42077996 NeedsCompilation: no Title: Differentially Methylated Cytosines via a Bayesian Functional Approach Description: DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method. biocViews: DifferentialMethylation, Sequencing, Coverage, Bayesian, Regression Author: Farhad Shokoohi [aut, cre] (ORCID: ) Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCFB/issues git_url: https://git.bioconductor.org/packages/DMCFB git_branch: devel git_last_commit: 528814e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DMCFB_1.25.0.tar.gz vignettes: vignettes/DMCFB/inst/doc/DMCFB.html vignetteTitles: Identifying DMCs using Bayesian functional regressions in BS-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCFB/inst/doc/DMCFB.R dependencyCount: 97 Package: DMCHMM Version: 1.33.0 Depends: R (>= 4.1.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool Imports: utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 2c1249d2128e1e501115c4ea29c775aa NeedsCompilation: no Title: Differentially Methylated CpG using Hidden Markov Model Description: A pipeline for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. biocViews: DifferentialMethylation, Sequencing, HiddenMarkovModel, Coverage Author: Farhad Shokoohi Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCHMM/issues git_url: https://git.bioconductor.org/packages/DMCHMM git_branch: devel git_last_commit: 338f80e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DMCHMM_1.33.0.tar.gz vignettes: vignettes/DMCHMM/inst/doc/DMCHMM.html vignetteTitles: DMCHMM: Differentially Methylated CpG using Hidden Markov Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCHMM/inst/doc/DMCHMM.R dependencyCount: 67 Package: dmGsea Version: 1.1.2 Depends: utils,stats,parallel,Matrix,SummarizedExperiment,methods,R(>= 3.5.0) Imports: dqrng,AnnotationDbi,poolr,BiasedUrn,Seqinfo Suggests: msigdbr, org.Hs.eg.db, org.Mm.eg.db, minfi, knitr, rmarkdown, GO.db, KEGGREST, testthat, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylation450kanno.ilmn12.hg19, BiocStyle, RUnit License: Artistic-2.0 MD5sum: 5589916dc6a214ac6e2fb171262029b8 NeedsCompilation: no Title: Efficient Gene Set Enrichment Analysis for DNA Methylation Data Description: The R package dmGsea provides efficient gene set enrichment analysis specifically for DNA methylation data. It addresses key biases, including probe dependency and varying probe numbers per gene. The package supports Illumina 450K, EPIC, and mouse methylation arrays. Users can also apply it to other omics data by supplying custom probe-to-gene mapping annotations. dmGsea is flexible, fast, and well-suited for large-scale epigenomic studies. biocViews: GeneSetEnrichment, Pathways,DNAMethylation,Proteomics,Sequencing, CopyNumberVariation, GeneExpression, GenomicVariation, Coverage Author: Zongli Xu [cre, aut] (ORCID: ), Alison Motsinger-Reif [aut], Liang Niu [aut] Maintainer: Zongli Xu URL: https://github.com/Bioconductor/dmGsea VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/dmGsea/issues git_url: https://git.bioconductor.org/packages/dmGsea git_branch: devel git_last_commit: 104635f git_last_commit_date: 2026-01-12 Date/Publication: 2026-04-20 source.ver: src/contrib/dmGsea_1.1.2.tar.gz vignettes: vignettes/dmGsea/inst/doc/dmGsea.html vignetteTitles: dmGsea User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dmGsea/inst/doc/dmGsea.R dependencyCount: 61 Package: DMRcaller Version: 1.43.1 Depends: R (>= 3.5), GenomicRanges, IRanges, S4Vectors Imports: parallel, Rcpp, RcppRoll, betareg, grDevices, graphics, methods, stats, utils, Rsamtools, GenomicRanges, GenomicAlignments, Biostrings, BSgenome, BiocManager, S4Vectors, IRanges, InteractionSet, stringr, inflection, BiocParallel, Seqinfo, GenomeInfoDb Suggests: knitr, RUnit, BiocGenerics, rmarkdown, bookdown, BiocStyle, betareg, rtracklayer, BSgenome.Hsapiens.UCSC.hg38 License: GPL-3 MD5sum: d1619b7575eefa34ef33b4e4df11b503 NeedsCompilation: no Title: Differentially Methylated Regions Caller Description: Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects. biocViews: DifferentialMethylation, DNAMethylation, Software, Sequencing, Coverage Author: Nicolae Radu Zabet , Jonathan Michael Foonlan Tsang , Alessandro Pio Greco , Ryan Merritt and Young Jun Kim Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcaller git_branch: devel git_last_commit: 67d4000 git_last_commit_date: 2025-11-12 Date/Publication: 2026-04-20 source.ver: src/contrib/DMRcaller_1.43.1.tar.gz vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.html vignetteTitles: Overview of the DMRcaller package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRcaller/inst/doc/DMRcaller.R dependencyCount: 81 Package: DMRScan Version: 1.33.0 Depends: R (>= 3.6.0) Imports: Matrix, MASS, RcppRoll,GenomicRanges, IRanges, Seqinfo, methods, mvtnorm, stats, parallel Suggests: knitr, rmarkdown, BiocStyle, BiocManager License: GPL-3 MD5sum: 24ce0a54347b2c0f6397950ed2e1da6b NeedsCompilation: no Title: Detection of Differentially Methylated Regions Description: This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG. biocViews: Software, Technology, Sequencing, WholeGenome Author: Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge [ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut] Maintainer: Christian M Page URL: https://github.com/christpa/DMRScan VignetteBuilder: knitr BugReports: https://github.com/christpa/DMRScan/issues PackageStatus: Active git_url: https://git.bioconductor.org/packages/DMRScan git_branch: devel git_last_commit: 66ea55e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DMRScan_1.33.0.tar.gz vignettes: vignettes/DMRScan/inst/doc/DMRScan_vignette.html vignetteTitles: DMR Scan Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRScan/inst/doc/DMRScan_vignette.R dependencyCount: 20 Package: DNABarcodeCompatibility Version: 1.27.0 Depends: R (>= 3.6.0) Imports: dplyr, tidyr, numbers, purrr, stringr, stats, utils, methods, Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: file LICENSE MD5sum: 5f299934970e73b884df8e85196bc541 NeedsCompilation: yes Title: A Tool for Optimizing Combinations of DNA Barcodes Used in Multiplexed Experiments on Next Generation Sequencing Platforms Description: The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage. biocViews: Preprocessing, Sequencing Author: Céline Trébeau [cre] (ORCID: ), Jacques Boutet de Monvel [aut] (ORCID: ), Fabienne Wong Jun Tai [ctb], Raphaël Etournay [aut] (ORCID: ) Maintainer: Céline Trébeau URL: https://dnabarcodecompatibility.pasteur.fr/ VignetteBuilder: knitr BugReports: https://gitlab.pasteur.fr/ida-public/dnabarcodecompatibility/-/issues git_url: https://git.bioconductor.org/packages/DNABarcodeCompatibility git_branch: devel git_last_commit: 0fc748e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DNABarcodeCompatibility_1.27.0.tar.gz vignettes: vignettes/DNABarcodeCompatibility/inst/doc/introduction.html vignetteTitles: Introduction to DNABarcodeCompatibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DNABarcodeCompatibility/inst/doc/introduction.R dependencyCount: 29 Package: DNABarcodes Version: 1.41.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 MD5sum: d947d733fe2b8ac0e0d7ec9543443cd4 NeedsCompilation: yes Title: A tool for creating and analysing DNA barcodes used in Next Generation Sequencing multiplexing experiments Description: The package offers a function to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analysed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e., assigned to their original reference barcode. biocViews: Preprocessing, Sequencing Author: Tilo Buschmann Maintainer: Tilo Buschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNABarcodes git_branch: devel git_last_commit: 0458254 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DNABarcodes_1.41.0.tar.gz vignettes: vignettes/DNABarcodes/inst/doc/DNABarcodes.html vignetteTitles: DNABarcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R suggestsMe: posDemux dependencyCount: 11 Package: DNAcopy Version: 1.85.0 License: GPL (>= 2) MD5sum: 1c9826981861c7498613c0d4b48a840d NeedsCompilation: yes Title: DNA Copy Number Data Analysis Description: Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number. biocViews: Microarray, CopyNumberVariation Author: Venkatraman E. Seshan, Adam Olshen Maintainer: Venkatraman E. Seshan git_url: https://git.bioconductor.org/packages/DNAcopy git_branch: devel git_last_commit: a82e7ed git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DNAcopy_1.85.0.tar.gz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R dependsOnMe: CGHcall, cghMCR, CRImage, PureCN, CSclone, ParDNAcopy, saasCNV importsMe: ADaCGH2, ChAMP, cn.farms, CNAnorm, CNVrd2, conumee, GWASTools, maftools, MDTS, MEDIPS, MinimumDistance, QDNAseq, SCOPE, jointseg, PSCBS suggestsMe: cn.mops, CopyNumberPlots, fastseg, nullranges, sesame, ACNE, aroma.cn, aroma.core, calmate dependencyCount: 0 Package: DNAfusion Version: 1.13.0 Depends: R (>= 4.4.0) Imports: GenomicRanges, IRanges, Rsamtools, GenomicAlignments, BiocBaseUtils, S4Vectors, GenomicFeatures, TxDb.Hsapiens.UCSC.hg38.knownGene, BiocGenerics Suggests: knitr, rmarkdown, testthat, sessioninfo, BiocStyle License: GPL-3 MD5sum: b333562fe5e3da350cbce0ddd8e321ea NeedsCompilation: no Title: Identification of gene fusions using paired-end sequencing Description: DNAfusion can identify gene fusions such as EML4-ALK based on paired-end sequencing results. This package was developed using position deduplicated BAM files generated with the AVENIO Oncology Analysis Software. These files are made using the AVENIO ctDNA surveillance kit and Illumina Nextseq 500 sequencing. This is a targeted hybridization NGS approach and includes ALK-specific but not EML4-specific probes. biocViews: TargetedResequencing, Genetics, GeneFusionDetection, Sequencing Author: Christoffer Trier Maansson [aut, cre] (ORCID: ), Emma Roger Andersen [ctb, rev], Maiken Parm Ulhoi [dtc], Peter Meldgaard [dtc], Boe Sandahl Sorensen [rev, fnd] Maintainer: Christoffer Trier Maansson URL: https://github.com/CTrierMaansson/DNAfusion VignetteBuilder: knitr BugReports: https://github.com/CTrierMaansson/DNAfusion/issues git_url: https://git.bioconductor.org/packages/DNAfusion git_branch: devel git_last_commit: a0e0c9e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DNAfusion_1.13.0.tar.gz vignettes: vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.html vignetteTitles: Introduction to DNAfusion hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.R dependencyCount: 77 Package: DNAshapeR Version: 1.39.0 Depends: R (>= 3.4), GenomicRanges Imports: Rcpp (>= 0.12.1), Biostrings, fields LinkingTo: Rcpp Suggests: AnnotationHub, knitr, rmarkdown, testthat, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Hsapiens.UCSC.hg19, caret License: GPL-2 MD5sum: f3a0cec7bf00d9dab292a4fe6d7247ff NeedsCompilation: yes Title: High-throughput prediction of DNA shape features Description: DNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. The package allows to predict, visualize and encode DNA shape features for statistical learning. biocViews: StructuralPrediction, DNA3DStructure, Software Author: Tsu-Pei Chiu and Federico Comoglio Maintainer: Tsu-Pei Chiu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNAshapeR git_branch: devel git_last_commit: 6276ca2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DNAshapeR_1.39.0.tar.gz vignettes: vignettes/DNAshapeR/inst/doc/DNAshapeR.html vignetteTitles: DNAshapeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R dependencyCount: 24 Package: DNEA Version: 1.1.0 Depends: R (>= 4.2) Imports: BiocParallel, dplyr, gdata, glasso, igraph (>= 2.0.3), janitor, Matrix, methods, netgsa, stats, stringr, utils, SummarizedExperiment Suggests: BiocStyle, ggplot2, Hmisc, kableExtra, knitr, pheatmap, rmarkdown, testthat (>= 3.0.0), withr, airway Enhances: massdataset License: MIT + file LICENSE MD5sum: 237efde6b4556a8ab69abd7ae81aac4d NeedsCompilation: no Title: Differential Network Enrichment Analysis for Biological Data Description: The DNEA R package is the latest implementation of the Differential Network Enrichment Analysis algorithm and is the successor to the Filigree Java-application described in Iyer et al. (2020). The package is designed to take as input an m x n expression matrix for some -omics modality (ie. metabolomics, lipidomics, proteomics, etc.) and jointly estimate the biological network associations of each condition using the DNEA algorithm described in Ma et al. (2019). This approach provides a framework for data-driven enrichment analysis across two experimental conditions that utilizes the underlying correlation structure of the data to determine feature-feature interactions. biocViews: Metabolomics, Proteomics, Lipidomics, DifferentialExpression, NetworkEnrichment, Network, Clustering, DataImport Author: Christopher Patsalis [cre, aut] (ORCID: ), Gayatri Iyer [aut], Alla Karnovsky [fnd] (NIH_GRANT: 1U01CA235487), George Michailidis [fnd] (NIH_GRANT: 1U01CA235487) Maintainer: Christopher Patsalis URL: https://github.com/Karnovsky-Lab/DNEA VignetteBuilder: knitr BugReports: https://github.com/Karnovsky-Lab/DNEA/issues git_url: https://git.bioconductor.org/packages/DNEA git_branch: devel git_last_commit: 0ff4d1c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DNEA_1.1.0.tar.gz vignettes: vignettes/DNEA/inst/doc/DNEA.html vignetteTitles: DNEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DNEA/inst/doc/DNEA.R dependencyCount: 129 Package: dominatR Version: 0.99.5 Depends: R (>= 4.5.0) Imports: scales, ggnewscale, SummarizedExperiment, dplyr, rlang, ggforce, geomtextpath, ggplot2 Suggests: BiocStyle, airway, tidyverse, knitr, rmarkdown, testthat (>= 3.0.0), dominatRData License: MIT + file LICENSE MD5sum: d666485e2dc4fc4f5e054d0e375ade5d NeedsCompilation: no Title: Feature Dominance-based R Package for Genomic Data Description: dominatR is an R package for quantifying and visualizing feature dominance in datasets. dominatR applies concepts drawn from physics such as center of mass and shannon's entropy to effectively visualize features (e.g. genes) that are present within a specific context or condition. The package integrates, dataframes, matrices and SummerizedExperiment objects and is able to perform common genomic normalization methods. The key aspect is the generation of plots that serve to highlight context-relevant feature dominance. biocViews: Visualization, Normalization, Classification, GeneExpression Author: Simon Lizarazo [aut, cre] (ORCID: ), Ethan Chen [aut], Rajendra K C [aut], Kevin Van Bortle [aut, cph] Maintainer: Simon Lizarazo URL: https://github.com/VanBortleLab/dominatR, https://vanbortlelab.github.io/dominatR/ VignetteBuilder: knitr BugReports: https://github.com/VanBortleLab/dominatR/issues git_url: https://git.bioconductor.org/packages/dominatR git_branch: devel git_last_commit: 2c155d0 git_last_commit_date: 2025-11-18 Date/Publication: 2026-04-20 source.ver: src/contrib/dominatR_0.99.5.tar.gz vignettes: vignettes/dominatR/inst/doc/dominatR.html vignetteTitles: Introduction to dominatR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dominatR/inst/doc/dominatR.R dependencyCount: 60 Package: DominoEffect Version: 1.31.0 Depends: R(>= 3.5) Imports: biomaRt, data.table, utils, stats, Biostrings, pwalign, SummarizedExperiment, VariantAnnotation, AnnotationDbi, Seqinfo, IRanges, GenomicRanges, methods Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 0c1fe6d0d0ec5e99feee3f90213250a4 NeedsCompilation: no Title: Identification and Annotation of Protein Hotspot Residues Description: The functions support identification and annotation of hotspot residues in proteins. These are individual amino acids that accumulate mutations at a much higher rate than their surrounding regions. biocViews: Software, SomaticMutation, Proteomics, SequenceMatching, Alignment Author: Marija Buljan and Peter Blattmann Maintainer: Marija Buljan , Peter Blattmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DominoEffect git_branch: devel git_last_commit: 2cae082 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DominoEffect_1.31.0.tar.gz vignettes: vignettes/DominoEffect/inst/doc/Vignette.html vignetteTitles: Vignette for DominoEffect package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DominoEffect/inst/doc/Vignette.R dependencyCount: 100 Package: dominoSignal Version: 1.5.0 Depends: R(>= 4.2.0), Imports: biomaRt, ComplexHeatmap, circlize, ggpubr, grDevices, grid, igraph, Matrix, methods, plyr, stats, utils, magrittr, purrr, dplyr Suggests: knitr, patchwork, rmarkdown, Seurat, testthat, formatR, BiocFileCache, SingleCellExperiment License: GPL-3 | file LICENSE MD5sum: 40ab80891275f3823856d3a8fb57812b NeedsCompilation: no Title: Cell Communication Analysis for Single Cell RNA Sequencing Description: dominoSignal is a package developed to analyze cell signaling through ligand - receptor - transcription factor networks in scRNAseq data. It takes as input information transcriptomic data, requiring counts, z-scored counts, and cluster labels, as well as information on transcription factor activation (such as from SCENIC) and a database of ligand and receptor pairings (such as from CellPhoneDB). This package creates an object storing ligand - receptor - transcription factor linkages by cluster and provides several methods for exploring, summarizing, and visualizing the analysis. biocViews: SystemsBiology, SingleCell, Transcriptomics, Network Author: Christopher Cherry [aut] (ORCID: ), Jacob T Mitchell [aut, cre] (ORCID: ), Sushma Nagaraj [aut] (ORCID: ), Kavita Krishnan [aut] (ORCID: ), Dmitrijs Lvovs [aut], Elana Fertig [ctb] (ORCID: ), Jennifer Elisseeff [ctb] (ORCID: ) Maintainer: Jacob T Mitchell URL: https://fertiglab.github.io/dominoSignal/ VignetteBuilder: knitr BugReports: https://github.com/FertigLab/dominoSignal/issues git_url: https://git.bioconductor.org/packages/dominoSignal git_branch: devel git_last_commit: a76eebf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dominoSignal_1.5.0.tar.gz vignettes: vignettes/dominoSignal/inst/doc/domino_object_vignette.html, vignettes/dominoSignal/inst/doc/dominoSignal.html, vignettes/dominoSignal/inst/doc/plotting_vignette.html vignetteTitles: Interacting with domino Objects, Get Started with dominoSignal, Plotting Functions and Options hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dominoSignal/inst/doc/domino_object_vignette.R, vignettes/dominoSignal/inst/doc/dominoSignal.R, vignettes/dominoSignal/inst/doc/plotting_vignette.R dependencyCount: 139 Package: doppelgangR Version: 1.39.1 Depends: R (>= 3.5.0), Biobase, BiocParallel Imports: sva, impute, digest, mnormt, methods, grDevices, graphics, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, curatedOvarianData, testthat License: GPL (>=2.0) MD5sum: a6b43fad3692fa7623cda23d5be0f358 NeedsCompilation: no Title: Identify likely duplicate samples from genomic or meta-data Description: The main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset). biocViews: ImmunoOncology, RNASeq, Microarray, GeneExpression, QualityControl Author: Levi Waldron [aut, cre], Markus Reister [aut, ctb], Marcel Ramos [ctb] Maintainer: Levi Waldron URL: https://github.com/lwaldron/doppelgangR, https://waldronlab.github.io/doppelgangR VignetteBuilder: knitr BugReports: https://github.com/lwaldron/doppelgangR/issues git_url: https://git.bioconductor.org/packages/doppelgangR git_branch: devel git_last_commit: ba4ccba git_last_commit_date: 2025-11-17 Date/Publication: 2026-04-20 source.ver: src/contrib/doppelgangR_1.39.1.tar.gz vignettes: vignettes/doppelgangR/inst/doc/doppelgangR.html vignetteTitles: doppelgangR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R dependencyCount: 78 Package: Doscheda Version: 1.33.0 Depends: R (>= 3.4) Imports: methods, drc, stats, httr, jsonlite, reshape2 , vsn, affy, limma, stringr, ggplot2, graphics, grDevices, calibrate, corrgram, gridExtra, DT, shiny, shinydashboard, readxl, prodlim, matrixStats Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: b4a1a5c80a29745fc966b5fb68bec7ca NeedsCompilation: no Title: A DownStream Chemo-Proteomics Analysis Pipeline Description: Doscheda focuses on quantitative chemoproteomics used to determine protein interaction profiles of small molecules from whole cell or tissue lysates using Mass Spectrometry data. The package provides a shiny application to run the pipeline, several visualisations and a downloadable report of an experiment. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, DataImport, Regression Author: Bruno Contrino, Piero Ricchiuto Maintainer: Bruno Contrino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Doscheda git_branch: devel git_last_commit: 3ce1c92 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Doscheda_1.33.0.tar.gz vignettes: vignettes/Doscheda/inst/doc/Doscheda.html vignetteTitles: Doscheda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Doscheda/inst/doc/Doscheda.R dependencyCount: 160 Package: DOSE Version: 4.5.1 Depends: R (>= 3.5.0) Imports: AnnotationDbi, enrichit (>= 0.0.4), ggplot2, GOSemSim (>= 2.37.1), methods, reshape2, utils, yulab.utils (> 0.2.2) Suggests: prettydoc, clusterProfiler, gson (>= 0.0.5), knitr, memoise, org.Hs.eg.db, rmarkdown, testthat License: Artistic-2.0 MD5sum: c362647b05067ee655df03648726bb0f NeedsCompilation: no Title: Disease Ontology Semantic and Enrichment analysis Description: This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. biocViews: Annotation, Visualization, MultipleComparison, GeneSetEnrichment, Pathways, Software Author: Guangchuang Yu [aut, cre], Li-Gen Wang [ctb], Vladislav Petyuk [ctb], Giovanni Dall'Olio [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues git_url: https://git.bioconductor.org/packages/DOSE git_branch: devel git_last_commit: 8491162 git_last_commit_date: 2026-02-01 Date/Publication: 2026-04-20 source.ver: src/contrib/DOSE_4.5.1.tar.gz vignettes: vignettes/DOSE/inst/doc/DOSE.html vignetteTitles: DOSE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOSE/inst/doc/DOSE.R importsMe: bioCancer, debrowser, enrichplot, GDCRNATools, goatea, miRSM, miRspongeR, Moonlight2R, MoonlightR, Pigengene, RegEnrich, scTensor, signatureSearch, SVMDO, TDbasedUFEadv, vsclust suggestsMe: clusterProfiler, cola, GOSemSim, GRaNIE, rrvgo, scFeatures, scGPS, scGraphVerse, enrichit, ggpicrust2 dependencyCount: 66 Package: doseR Version: 1.27.0 Depends: R (>= 3.6) Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4, RUnit, SummarizedExperiment, digest, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: daf4ba17e5a5e7a00d1f556bbff164dc NeedsCompilation: no Title: doseR Description: doseR package is a next generation sequencing package for sex chromosome dosage compensation which can be applied broadly to detect shifts in gene expression among an arbitrary number of pre-defined groups of loci. doseR is a differential gene expression package for count data, that detects directional shifts in expression for multiple, specific subsets of genes, broad utility in systems biology research. doseR has been prepared to manage the nature of the data and the desired set of inferences. doseR uses S4 classes to store count data from sequencing experiment. It contains functions to normalize and filter count data, as well as to plot and calculate statistics of count data. It contains a framework for linear modeling of count data. The package has been tested using real and simulated data. biocViews: Infrastructure, Software, DataRepresentation, Sequencing, GeneExpression, SystemsBiology, DifferentialExpression Author: AJ Vaestermark, JR Walters. Maintainer: ake.vastermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/doseR git_branch: devel git_last_commit: 8901528 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/doseR_1.27.0.tar.gz vignettes: vignettes/doseR/inst/doc/doseR.html vignetteTitles: "doseR" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doseR/inst/doc/doseR.R dependencyCount: 46 Package: doubletrouble Version: 1.11.0 Depends: R (>= 4.2.0) Imports: syntenet, GenomicRanges, Biostrings, mclust, MSA2dist (>= 1.1.5), ggplot2, rlang, stats, utils, AnnotationDbi, GenomicFeatures Suggests: txdbmaker, testthat (>= 3.0.0), knitr, feature, patchwork, BiocStyle, rmarkdown, covr, sessioninfo License: GPL-3 MD5sum: 358fa3f705ed2b883ae4ff4380d1e114 NeedsCompilation: no Title: Identification and classification of duplicated genes Description: doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a simpler classification scheme, duplicates can also be classified into SD- and SSD-derived (small-scale duplication) gene pairs. Besides classifying gene pairs, users can also classify genes, so that each gene is assigned a unique mode of duplication. Users can also calculate substitution rates per substitution site (i.e., Ka and Ks) from duplicate pairs, find peaks in Ks distributions with Gaussian Mixture Models (GMMs), and classify gene pairs into age groups based on Ks peaks. biocViews: Software, WholeGenome, ComparativeGenomics, FunctionalGenomics, Phylogenetics, Network, Classification Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/doubletrouble VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/doubletrouble git_url: https://git.bioconductor.org/packages/doubletrouble git_branch: devel git_last_commit: 60c6759 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/doubletrouble_1.11.0.tar.gz vignettes: vignettes/doubletrouble/inst/doc/doubletrouble_vignette.html vignetteTitles: Identification and classification of duplicated genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doubletrouble/inst/doc/doubletrouble_vignette.R dependencyCount: 137 Package: drawProteins Version: 1.31.0 Depends: R (>= 4.0) Imports: ggplot2, httr, dplyr, readr, tidyr Suggests: covr, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: a0d698f1803cb328e84f491ba67876e3 NeedsCompilation: no Title: Package to Draw Protein Schematics from Uniprot API output Description: This package draws protein schematics from Uniprot API output. From the JSON returned by the GET command, it creates a dataframe from the Uniprot Features API. This dataframe can then be used by geoms based on ggplot2 and base R to draw protein schematics. biocViews: Visualization, FunctionalPrediction, Proteomics Author: Paul Brennan [aut, cre] Maintainer: Paul Brennan URL: https://github.com/brennanpincardiff/drawProteins VignetteBuilder: knitr BugReports: https://github.com/brennanpincardiff/drawProteins/issues/new git_url: https://git.bioconductor.org/packages/drawProteins git_branch: devel git_last_commit: 488a48b git_last_commit_date: 2026-02-02 Date/Publication: 2026-04-20 source.ver: src/contrib/drawProteins_1.31.0.tar.gz vignettes: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.html, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.html vignetteTitles: Using drawProteins, Using extract_transcripts in drawProteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.R, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.R importsMe: factR dependencyCount: 53 Package: DRIMSeq Version: 1.39.0 Depends: R (>= 3.4.0) Imports: utils, stats, MASS, GenomicRanges, IRanges, S4Vectors, BiocGenerics, methods, BiocParallel, limma, edgeR, ggplot2, reshape2 Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid, BiocStyle, knitr, testthat License: GPL (>= 3) MD5sum: 453c27dd53e0a9a67454cb8c2bff4c23 NeedsCompilation: no Title: Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq Description: The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. biocViews: ImmunoOncology, SNP, AlternativeSplicing, DifferentialSplicing, Genetics, RNASeq, Sequencing, WorkflowStep, MultipleComparison, GeneExpression, DifferentialExpression Author: Malgorzata Nowicka [aut, cre] Maintainer: Malgorzata Nowicka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DRIMSeq git_branch: devel git_last_commit: 9076908 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DRIMSeq_1.39.0.tar.gz vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf vignetteTitles: Differential transcript usage and transcript usage QTL analyses in RNA-seq with the DRIMSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R dependsOnMe: rnaseqDTU importsMe: BANDITS dependencyCount: 52 Package: DriverNet Version: 1.51.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: 02e35c8a3781f1168c8929b58876875a NeedsCompilation: no Title: Drivernet: uncovering somatic driver mutations modulating transcriptional networks in cancer Description: DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values. biocViews: Network Author: Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah Maintainer: Jiarui Ding git_url: https://git.bioconductor.org/packages/DriverNet git_branch: devel git_last_commit: 9b4f20e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DriverNet_1.51.0.tar.gz vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf vignetteTitles: An introduction to DriverNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DriverNet/inst/doc/DriverNet-Overview.R dependencyCount: 1 Package: drugfindR Version: 0.99.2516 Depends: R (>= 4.5.0) Imports: tibble, rlang, dplyr, purrr, readr, stringr, stats, lifecycle, S4Vectors, httr2, curl, DFplyr Suggests: AnnotationDbi, BiocStyle, biocthis, codemetar, devtools, here, httptest2, jsonlite, knitr, rmarkdown, testthat (>= 3.0.0), tidyverse, usethis License: GPL-3 + file LICENSE MD5sum: dc3d1c52f88501491480a3ff32f137ad NeedsCompilation: no Title: Investigate iLINCS for candidate repurposable drugs Description: This package provides a convenient way to access the LINCS Signatures available in the iLINCS database. These signatures include Consensus Gene Knockdown Signatures, Gene Overexpression signatures and Chemical Perturbagen Signatures. It also provides a way to enter your own transcriptomic signatures and identify concordant and discordant signatures in the LINCS database. biocViews: FunctionalPrediction, DifferentialExpression, GeneSetEnrichment, SingleCell, Network Author: Ali Sajid Imami [aut, cre] (ORCID: ), Smita Sahay [aut] (ORCID: ), Justin Fortune Creeden [aut] (ORCID: ), Robert Erne McCullumsmith [ctb, fnd] (ORCID: ) Maintainer: Ali Sajid Imami URL: https://github.com/CogDisResLab/drugfindR, https://cogdisreslab.github.io/drugfindR/ VignetteBuilder: knitr BugReports: https://github.com/CogDisResLab/drugfindR/issues git_url: https://git.bioconductor.org/packages/drugfindR git_branch: devel git_last_commit: 6d429bf git_last_commit_date: 2026-03-27 Date/Publication: 2026-04-20 source.ver: src/contrib/drugfindR_0.99.2516.tar.gz vignettes: vignettes/drugfindR/inst/doc/drugfindR.html vignetteTitles: drugfindR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/drugfindR/inst/doc/drugfindR.R dependencyCount: 45 Package: drugTargetInteractions Version: 1.19.0 Depends: methods, R (>= 4.1) Imports: utils, RSQLite, UniProt.ws, biomaRt,ensembldb, BiocFileCache,dplyr,rappdirs, AnnotationFilter, S4Vectors Suggests: RUnit, BiocStyle, knitr, rmarkdown, ggplot2, reshape2, DT, EnsDb.Hsapiens.v86 License: Artistic-2.0 MD5sum: df4fbdc673eaf3ed15a5c58d51143d28 NeedsCompilation: no Title: Drug-Target Interactions Description: Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, Proteomics, Metabolomics Author: Thomas Girke [cre, aut] Maintainer: Thomas Girke URL: https://github.com/girke-lab/drugTargetInteractions VignetteBuilder: knitr BugReports: https://github.com/girke-lab/drugTargetInteractions git_url: https://git.bioconductor.org/packages/drugTargetInteractions git_branch: devel git_last_commit: a35b336 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/drugTargetInteractions_1.19.0.tar.gz vignettes: vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.html vignetteTitles: Drug-Target Interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.R dependencyCount: 105 Package: DrugVsDisease Version: 2.53.0 Depends: R (>= 2.10), affy, limma, biomaRt, ArrayExpress, GEOquery, DrugVsDiseasedata, cMap2data, qvalue Imports: annotate, hgu133a.db, hgu133a2.db, hgu133plus2.db, RUnit, BiocGenerics, xtable License: GPL-3 MD5sum: b5c4b27d744b06a1a7b6d61901030b34 NeedsCompilation: no Title: Comparison of disease and drug profiles using Gene set Enrichment Analysis Description: This package generates ranked lists of differential gene expression for either disease or drug profiles. Input data can be downloaded from Array Express or GEO, or from local CEL files. Ranked lists of differential expression and associated p-values are calculated using Limma. Enrichment scores (Subramanian et al. PNAS 2005) are calculated to a reference set of default drug or disease profiles, or a set of custom data supplied by the user. Network visualisation of significant scores are output in Cytoscape format. biocViews: Microarray, GeneExpression, Clustering Author: C. Pacini Maintainer: j. Saez-Rodriguez git_url: https://git.bioconductor.org/packages/DrugVsDisease git_branch: devel git_last_commit: 6483099 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DrugVsDisease_2.53.0.tar.gz vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf vignetteTitles: DrugVsDisease hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.R dependencyCount: 124 Package: DspikeIn Version: 1.1.0 Depends: R (>= 4.1.0) Imports: ape, Biostrings, data.table, DECIPHER, DESeq2, dplyr, edgeR, flextable, ggalluvial, ggnewscale, ggplot2, ggpubr, ggraph, ggrepel, ggridges, ggtree, ggtreeExtra, graphics, grDevices, igraph, limma, matrixStats, methods, microbiome, officer, grid, reshape2, patchwork, phangorn, phyloseq, randomForest, RColorBrewer, rlang, S4Vectors, scales, stats, tibble, tidyr, SummarizedExperiment, TreeSummarizedExperiment, utils, msa, xml2, ggstar Suggests: Biobase, mia, BiocGenerics, magrittr, BiocManager, cluster, devtools, DT, e1071, foreach, ggtext, intergraph, knitr, optparse, plyr, preprocessCore, qpdf, remotes, rmarkdown, ShortRead, testthat (>= 3.0.0), vegan, viridis License: MIT + file LICENSE MD5sum: 4308feca2df18edb0fafbe838aae5f01 NeedsCompilation: no Title: Estimating Absolute Abundance from Microbial Spike-in Controls Description: Provides a reproducible and modular workflow for absolute microbial quantification using spike-in controls. Supports both single spike-in taxa and synthetic microbial communities with user-defined spike-in volumes and genome copy numbers. Compatible with 'phyloseq' and 'TreeSummarizedExperiment' (TSE) data structures. The package implements methods for spike-in validation, preprocessing, scaling factor estimation, absolute abundance conversion, bias correction, and normalization. Facilitates downstream statistical analyses with 'DESeq2', 'edgeR', and other Bioconductor-compatible methods. Visualization tools are provided via 'ggplot2', 'ggtree', and related packages. Includes detailed vignettes, case studies, and function-level documentation to guide users through experimental design, quantification, and interpretation. biocViews: Microbiome, Preprocessing, QualityControl, DifferentialExpression, Normalization, Sequencing, Visualization, Phylogenetics, ExperimentalDesign, DataImport, Software Author: Mitra Ghotbi [aut, cre] (ORCID: ), Marjan Ghotbi [ctb] (ORCID: ) Maintainer: Mitra Ghotbi URL: https://github.com/mghotbi/DspikeIn VignetteBuilder: knitr BugReports: https://github.com/mghotbi/DspikeIn/issues git_url: https://git.bioconductor.org/packages/DspikeIn git_branch: devel git_last_commit: 18fc5b8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DspikeIn_1.1.0.tar.gz vignettes: vignettes/DspikeIn/inst/doc/DspikeIn-with-TSE.html vignetteTitles: DspikeIn with TSE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DspikeIn/inst/doc/DspikeIn-with-TSE.R dependencyCount: 207 Package: dStruct Version: 1.17.0 Depends: R (>= 4.1) Imports: zoo, ggplot2, purrr, reshape2, parallel, IRanges, S4Vectors, rlang, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, tidyverse, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: 39e6054f24d72ed6b18e912d7970fcfb NeedsCompilation: no Title: Identifying differentially reactive regions from RNA structurome profiling data Description: dStruct identifies differentially reactive regions from RNA structurome profiling data. dStruct is compatible with a broad range of structurome profiling technologies, e.g., SHAPE-MaP, DMS-MaPseq, Structure-Seq, SHAPE-Seq, etc. See Choudhary et al., Genome Biology, 2019 for the underlying method. biocViews: StatisticalMethod, StructuralPrediction, Sequencing, Software Author: Krishna Choudhary [aut, cre] (ORCID: ), Sharon Aviran [aut] (ORCID: ) Maintainer: Krishna Choudhary URL: https://github.com/dataMaster-Kris/dStruct VignetteBuilder: knitr BugReports: https://github.com/dataMaster-Kris/dStruct/issues git_url: https://git.bioconductor.org/packages/dStruct git_branch: devel git_last_commit: 705c1e3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dStruct_1.17.0.tar.gz vignettes: vignettes/dStruct/inst/doc/dStruct.html vignetteTitles: Differential RNA structurome analysis using `dStruct` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dStruct/inst/doc/dStruct.R dependencyCount: 39 Package: DTA Version: 2.57.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: 605302278d28e230a78091c0ac09a5ce NeedsCompilation: no Title: Dynamic Transcriptome Analysis Description: Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements. biocViews: Microarray, DifferentialExpression, GeneExpression, Transcription Author: Bjoern Schwalb, Benedikt Zacher, Sebastian Duemcke, Achim Tresch Maintainer: Bjoern Schwalb git_url: https://git.bioconductor.org/packages/DTA git_branch: devel git_last_commit: a34de2b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DTA_2.57.0.tar.gz vignettes: vignettes/DTA/inst/doc/DTA.pdf vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DTA/inst/doc/DTA.R importsMe: rifiComparative dependencyCount: 5 Package: Dune Version: 1.23.0 Depends: R (>= 3.6) Imports: BiocParallel, SummarizedExperiment, utils, ggplot2, dplyr, tidyr, RColorBrewer, magrittr, gganimate, purrr, aricode Suggests: knitr, rmarkdown, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: e0b0587d7a686a9c79455e2fcb1b304a NeedsCompilation: no Title: Improving replicability in single-cell RNA-Seq cell type discovery Description: Given a set of clustering labels, Dune merges pairs of clusters to increase mean ARI between labels, improving replicability. biocViews: Clustering, GeneExpression, RNASeq, Software, SingleCell, Transcriptomics, Visualization Author: Hector Roux de Bezieux [aut, cre] (ORCID: ), Kelly Street [aut] Maintainer: Hector Roux de Bezieux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Dune git_branch: devel git_last_commit: 9318153 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Dune_1.23.0.tar.gz vignettes: vignettes/Dune/inst/doc/Dune.html vignetteTitles: Dune Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Dune/inst/doc/Dune.R dependencyCount: 83 Package: DuplexDiscovereR Version: 1.5.0 Depends: R (>= 4.5), InteractionSet Imports: Gviz, Biostrings, rtracklayer, GenomicAlignments, GenomicRanges, ggsci, igraph, rlang, scales, stringr, dplyr, tibble, tidyr, purrr, methods, grDevices, stats, utils, vctrs Suggests: knitr, UpSetR, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: af6880282c13b13933f0422bcf612859 NeedsCompilation: no Title: Analysis of the data from RNA duplex probing experiments Description: DuplexDiscovereR is a package designed for analyzing data from RNA cross-linking and proximity ligation protocols such as SPLASH, PARIS, LIGR-seq, and others. DuplexDiscovereR accepts input in the form of chimerically or split-aligned reads. It includes procedures for alignment classification, filtering, and efficient clustering of individual chimeric reads into duplex groups (DGs). Once DGs are identified, the package predicts RNA duplex formation and their hybridization energies. Additional metrics, such as p-values for random ligation hypothesis or mean DG alignment scores, can be calculated to rank final set of RNA duplexes. Data from multiple experiments or replicates can be processed separately and further compared to check the reproducibility of the experimental method. biocViews: Sequencing, Transcriptomics, StructuralPrediction, Clustering, SplicedAlignment Author: Egor Semenchenko [aut, cre, cph] (ORCID: ), Volodymyr Tsybulskyi [ctb] (ORCID: ), Irmtraud M. Meyer [aut, cph] (ORCID: ) Maintainer: Egor Semenchenko URL: https://github.com/Egors01/DuplexDiscovereR/ VignetteBuilder: knitr BugReports: https://github.com/Egors01/DuplexDiscovereR/issues/ git_url: https://git.bioconductor.org/packages/DuplexDiscovereR git_branch: devel git_last_commit: 4cdae86 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DuplexDiscovereR_1.5.0.tar.gz vignettes: vignettes/DuplexDiscovereR/inst/doc/DuplexDiscovereR.html vignetteTitles: DuplexDiscovereR tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DuplexDiscovereR/inst/doc/DuplexDiscovereR.R dependencyCount: 154 Package: dupRadar Version: 1.41.1 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1), KernSmooth Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: 4882011239598bc81388c2931c3bf481 NeedsCompilation: no Title: Assessment of duplication rates in RNA-Seq datasets Description: Duplication rate quality control for RNA-Seq datasets. biocViews: Technology, Sequencing, RNASeq, QualityControl, ImmunoOncology Author: Sergi Sayols , Holger Klein Maintainer: Sergi Sayols , Holger Klein URL: https://www.bioconductor.org/packages/dupRadar, https://ssayols.github.io/dupRadar/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dupRadar git_branch: devel git_last_commit: c166e5e git_last_commit_date: 2026-04-18 Date/Publication: 2026-04-20 source.ver: src/contrib/dupRadar_1.41.1.tar.gz vignettes: vignettes/dupRadar/inst/doc/dupRadar.html vignetteTitles: Using dupRadar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R dependencyCount: 10 Package: dyebias Version: 1.71.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: a1736fbd29834c4b4d7a824b88d4be35 NeedsCompilation: no Title: The GASSCO method for correcting for slide-dependent gene-specific dye bias Description: Many two-colour hybridizations suffer from a dye bias that is both gene-specific and slide-specific. The former depends on the content of the nucleotide used for labeling; the latter depends on the labeling percentage. The slide-dependency was hitherto not recognized, and made addressing the artefact impossible. Given a reasonable number of dye-swapped pairs of hybridizations, or of same vs. same hybridizations, both the gene- and slide-biases can be estimated and corrected using the GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009), doi:10.1038/msb.2009.21) biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Philip Lijnzaad and Thanasis Margaritis Maintainer: Philip Lijnzaad URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad git_url: https://git.bioconductor.org/packages/dyebias git_branch: devel git_last_commit: a92f3f9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/dyebias_1.71.0.tar.gz vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf vignetteTitles: dye bias correction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dyebias/inst/doc/dyebias-vignette.R suggestsMe: dyebiasexamples dependencyCount: 11 Package: DynDoc Version: 1.89.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: 0637c5a38edeb056095880c19430b9c6 NeedsCompilation: no Title: Dynamic document tools Description: A set of functions to create and interact with dynamic documents and vignettes. biocViews: ReportWriting, Infrastructure Author: R. Gentleman, Jeff Gentry Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/DynDoc git_branch: devel git_last_commit: 5b40889 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/DynDoc_1.89.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets dependencyCount: 2 Package: easylift Version: 1.9.0 Depends: R (>= 4.1.0), GenomicRanges, BiocFileCache Imports: rtracklayer, GenomeInfoDb, R.utils, tools, methods Suggests: testthat (>= 3.0.0), IRanges, knitr, BiocStyle, rmarkdown License: MIT + file LICENSE MD5sum: e8cf69770e6fde8f5dc195ffe1884e76 NeedsCompilation: no Title: An R package to perform genomic liftover Description: The easylift package provides a convenient tool for genomic liftover operations between different genome assemblies. It seamlessly works with Bioconductor's GRanges objects and chain files from the UCSC Genome Browser, allowing for straightforward handling of genomic ranges across various genome versions. One noteworthy feature of easylift is its integration with the BiocFileCache package. This integration automates the management and caching of chain files necessary for liftover operations. Users no longer need to manually specify chain file paths in their function calls, reducing the complexity of the liftover process. biocViews: Software, WorkflowStep, Sequencing, Coverage, GenomeAssembly, DataImport Author: Abdullah Al Nahid [aut, cre] (ORCID: ), Hervé Pagès [aut, rev], Michael Love [aut, rev] (ORCID: ) Maintainer: Abdullah Al Nahid URL: https://github.com/nahid18/easylift, https://nahid18.github.io/easylift VignetteBuilder: knitr BugReports: https://github.com/nahid18/easylift/issues git_url: https://git.bioconductor.org/packages/easylift git_branch: devel git_last_commit: d7927b5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/easylift_1.9.0.tar.gz vignettes: vignettes/easylift/inst/doc/easylift.html vignetteTitles: Perform Genomic Liftover hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/easylift/inst/doc/easylift.R dependencyCount: 92 Package: easyreporting Version: 1.23.0 Depends: R (>= 3.5.0) Imports: rmarkdown, methods, tools, shiny, rlang Suggests: distill, BiocStyle, knitr, readxl, edgeR, limma, EDASeq, statmod License: Artistic-2.0 MD5sum: dd787c37259fb4a17a51d9238a31ea1f NeedsCompilation: no Title: Helps creating report for improving Reproducible Computational Research Description: An S4 class for facilitating the automated creation of rmarkdown files inside other packages/software even without knowing rmarkdown language. Best if implemented in functions as "recursive" style programming. biocViews: ReportWriting Author: Dario Righelli [cre, aut] Maintainer: Dario Righelli VignetteBuilder: knitr BugReports: https://github.com/drighelli/easyreporting/issues git_url: https://git.bioconductor.org/packages/easyreporting git_branch: devel git_last_commit: b544232 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/easyreporting_1.23.0.tar.gz vignettes: vignettes/easyreporting/inst/doc/bio_usage.html, vignettes/easyreporting/inst/doc/standard_usage.html vignetteTitles: bio_usage.html, standard_usage.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyreporting/inst/doc/bio_usage.R, vignettes/easyreporting/inst/doc/standard_usage.R dependencyCount: 43 Package: easyRNASeq Version: 2.47.0 Imports: Biobase (>= 2.64.0), BiocFileCache (>= 2.12.0), BiocGenerics (>= 0.50.0), BiocParallel (>= 1.38.0), biomaRt (>= 2.60.1), Biostrings (>= 2.77.2), edgeR (>= 4.2.1), Seqinfo, genomeIntervals (>= 1.60.0), GenomicAlignments (>= 1.45.1), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), graphics, IRanges (>= 2.38.1), LSD (>= 4.1-0), methods, parallel, rappdirs (>= 0.3.3), Rsamtools (>= 2.25.1), S4Vectors (>= 0.42.1), ShortRead (>= 1.62.0), utils Suggests: BiocStyle (>= 2.32.1), BSgenome (>= 1.72.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, knitr, rmarkdown, RUnit (>= 0.4.33) License: Artistic-2.0 MD5sum: 7f8e99238091900c60feac4b7e378b9f NeedsCompilation: no Title: Count summarization and normalization for RNA-Seq data Description: Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package. biocViews: GeneExpression, RNASeq, Genetics, Preprocessing, ImmunoOncology Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler, Niklas Maehler Maintainer: Nicolas Delhomme VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easyRNASeq git_branch: devel git_last_commit: 1cb9a73 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/easyRNASeq_2.47.0.tar.gz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf, vignettes/easyRNASeq/inst/doc/simpleRNASeq.html vignetteTitles: R / Bioconductor for High Throughput Sequence Analysis, geneNetworkR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R, vignettes/easyRNASeq/inst/doc/simpleRNASeq.R importsMe: msgbsR dependencyCount: 106 Package: EBarrays Version: 2.75.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) MD5sum: cdec9915bf6b965ff498f6c035c00ec5 NeedsCompilation: yes Title: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification Description: EBarrays provides tools for the analysis of replicated/unreplicated microarray data. biocViews: Clustering, DifferentialExpression Author: Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski Maintainer: Ming Yuan git_url: https://git.bioconductor.org/packages/EBarrays git_branch: devel git_last_commit: 98d9bf3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/EBarrays_2.75.0.tar.gz vignettes: vignettes/EBarrays/inst/doc/vignette.pdf vignetteTitles: Introduction to EBarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBarrays/inst/doc/vignette.R dependsOnMe: EBcoexpress, gaga, geNetClassifier importsMe: casper suggestsMe: Category, dcanr dependencyCount: 11 Package: EBcoexpress Version: 1.55.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) MD5sum: 6507173a72ff2077a614f161880a4a70 NeedsCompilation: yes Title: EBcoexpress for Differential Co-Expression Analysis Description: An Empirical Bayesian Approach to Differential Co-Expression Analysis at the Gene-Pair Level biocViews: Bayesian Author: John A. Dawson Maintainer: John A. Dawson git_url: https://git.bioconductor.org/packages/EBcoexpress git_branch: devel git_last_commit: d048059 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/EBcoexpress_1.55.0.tar.gz vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf vignetteTitles: EBcoexpress Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R suggestsMe: dcanr dependencyCount: 15 Package: EBImage Version: 4.53.0 Depends: methods Imports: BiocGenerics (>= 0.7.1), graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils, htmltools, htmlwidgets, RCurl Suggests: BiocStyle, digest, knitr, rmarkdown, shiny License: LGPL MD5sum: f022c63c7fc13ba6451f932bd924ed19 NeedsCompilation: yes Title: Image processing and analysis toolbox for R Description: EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data. biocViews: Visualization Author: Andrzej Oleś, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang Huber, with contributions from Joseph Barry and Philip A. Marais Maintainer: Andrzej Oleś URL: https://github.com/aoles/EBImage VignetteBuilder: knitr BugReports: https://github.com/aoles/EBImage/issues git_url: https://git.bioconductor.org/packages/EBImage git_branch: devel git_last_commit: ee2451f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/EBImage_4.53.0.tar.gz vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.html vignetteTitles: Introduction to EBImage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R dependsOnMe: CRImage, cytomapper, flowcatchR, DonaPLLP2013, furrowSeg, MerfishData, nucim importsMe: alabaster.sfe, bnbc, Cardinal, CatsCradle, cytoviewer, flowCHIC, heatmaps, imcRtools, MoleculeExperiment, RBioFormats, simpleSeg, sosta, SpatialFeatureExperiment, SpatialOmicsOverlay, synapsis, xenLite, yamss, BioImageDbs, OSTA, AiES, bioimagetools, BioThermR, GoogleImage2Array, LFApp, LOMAR, ProxReg, RockFab, SAFARI, spatialGE suggestsMe: HilbertVis, Voyager, aroma.core, cooltools, crownsegmentr, glow, ijtiff, juicr, lidR, metagear, pliman, rcaiman dependencyCount: 44 Package: EBSEA Version: 1.39.0 Depends: R (>= 4.0.0) Imports: DESeq2, graphics, stats, EmpiricalBrownsMethod Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 67ce283df21f83e77e67aa9915d5c4cd NeedsCompilation: no Title: Exon Based Strategy for Expression Analysis of genes Description: Calculates differential expression of genes based on exon counts of genes obtained from RNA-seq sequencing data. biocViews: Software, DifferentialExpression, GeneExpression, Sequencing Author: Arfa Mehmood, Asta Laiho, Laura L. Elo Maintainer: Arfa Mehmood VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EBSEA git_branch: devel git_last_commit: 0db6298 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/EBSEA_1.39.0.tar.gz vignettes: vignettes/EBSEA/inst/doc/EBSEA.html vignetteTitles: EBSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSEA/inst/doc/EBSEA.R dependencyCount: 56 Package: ecolitk Version: 1.83.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: 2abfbb3590061e9c6731d347d312ff11 NeedsCompilation: no Title: Meta-data and tools for E. coli Description: Meta-data and tools to work with E. coli. The tools are mostly plotting functions to work with circular genomes. They can used with other genomes/plasmids. biocViews: Annotation, Visualization Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/ecolitk git_branch: devel git_last_commit: 5b8e10e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ecolitk_1.83.0.tar.gz vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf vignetteTitles: ecolitk hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ecolitk/inst/doc/ecolitk.R dependencyCount: 7 Package: EDASeq Version: 2.45.0 Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42) Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9), aroma.light, Rsamtools (>= 1.5.75), biomaRt, Biostrings, AnnotationDbi, GenomicFeatures, GenomicRanges, BiocManager Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR, KernSmooth, testthat, DESeq2, rmarkdown License: Artistic-2.0 MD5sum: ff3bb4ccd64a04793ce968f7d46dedd2 NeedsCompilation: no Title: Exploratory Data Analysis and Normalization for RNA-Seq Description: Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). biocViews: ImmunoOncology, Sequencing, RNASeq, Preprocessing, QualityControl, DifferentialExpression Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Ludwig Geistlinger [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/EDASeq VignetteBuilder: knitr BugReports: https://github.com/drisso/EDASeq/issues git_url: https://git.bioconductor.org/packages/EDASeq git_branch: devel git_last_commit: 1f147bc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/EDASeq_2.45.0.tar.gz vignettes: vignettes/EDASeq/inst/doc/EDASeq.html vignetteTitles: EDASeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R dependsOnMe: RUVSeq importsMe: DaMiRseq, metaseqR2, octad, ribosomeProfilingQC suggestsMe: awst, DEScan2, easyreporting, GRaNIE, HTSFilter, MOSClip, TCGAbiolinks dependencyCount: 112 Package: edge Version: 2.43.0 Depends: R(>= 3.1.0), Biobase Imports: methods, splines, sva, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE MD5sum: ae3df9461a94c2035269673b84ca3fbd NeedsCompilation: yes Title: Extraction of Differential Gene Expression Description: The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as sva and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis. biocViews: MultipleComparison, DifferentialExpression, TimeCourse, Regression, GeneExpression, DataImport Author: John D. Storey, Jeffrey T. Leek and Andrew J. Bass Maintainer: John D. Storey , Andrew J. Bass URL: https://github.com/jdstorey/edge VignetteBuilder: knitr BugReports: https://github.com/jdstorey/edge/issues git_url: https://git.bioconductor.org/packages/edge git_branch: devel git_last_commit: a6a6076 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/edge_2.43.0.tar.gz vignettes: vignettes/edge/inst/doc/edge.pdf vignetteTitles: edge Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/edge/inst/doc/edge.R dependencyCount: 87 Package: edgeR Version: 4.9.8 Depends: R (>= 3.6.0), limma (>= 3.63.6) Imports: methods, graphics, stats, utils, locfit Suggests: jsonlite, knitr, Matrix, nanoparquet, readr, rhdf5, SeuratObject, splines, AnnotationDbi, Biobase, BiocStyle, org.Hs.eg.db, S4Vectors, SummarizedExperiment License: GPL (>=2) MD5sum: 14f049facc248d936acbbb252bf94854 NeedsCompilation: yes Title: Empirical Analysis of Digital Gene Expression Data in R Description: Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage. biocViews: AlternativeSplicing, BatchEffect, Bayesian, BiomedicalInformatics, CellBiology, ChIPSeq, Clustering, Coverage, DifferentialExpression, DifferentialMethylation, DifferentialSplicing, DNAMethylation, Epigenetics, FunctionalGenomics, GeneExpression, GeneSetEnrichment, Genetics, Genetics, ImmunoOncology, MultipleComparison, Normalization, Pathways, Proteomics, QualityControl, Regression, RNASeq, SAGE, Sequencing, SingleCell, SystemsBiology, TimeCourse, Transcription, Transcriptomics Author: Yunshun Chen, Lizhong Chen, Aaron TL Lun, Davis J McCarthy, Pedro Baldoni, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth Maintainer: Yunshun Chen , Gordon Smyth , Aaron Lun , Mark Robinson URL: https://bioinf.wehi.edu.au/edgeR/, https://bioconductor.org/packages/edgeR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/edgeR git_branch: devel git_last_commit: 0965f45 git_last_commit_date: 2026-04-18 Date/Publication: 2026-04-20 source.ver: src/contrib/edgeR_4.9.8.tar.gz vignettes: vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf, vignettes/edgeR/inst/doc/intro.html vignetteTitles: edgeR User's Guide, A brief introduction to edgeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/edgeR/inst/doc/intro.R dependsOnMe: ASpli, IntEREst, methylMnM, miloR, octad, RUVSeq, TCC, tRanslatome, ReactomeGSA.data, EGSEA123, RNAseq123, rnaseqDTU, RnaSeqGeneEdgeRQL, csawBook, OSCA.multisample, OSCA.workflows, scrapbook, babel, BALLI, BioInsight, SCdeconR importsMe: affycoretools, ATACseqQC, autonomics, AWFisher, BatchQC, baySeq, beer, benchdamic, BioQC, BreastSubtypeR, broadSeq, censcyt, ChromSCape, circRNAprofiler, CleanUpRNAseq, clusterExperiment, CNVRanger, compcodeR, coseq, countsimQC, csaw, cypress, DaMiRseq, Damsel, debrowser, DeeDeeExperiment, DEFormats, DEGreport, DESpace, DEsubs, diffcyt, diffHic, diffUTR, dinoR, DMRcate, doseR, dreamlet, DRIMSeq, DropletUtils, DspikeIn, easyRNASeq, EGSEA, eisaR, EnrichmentBrowser, erccdashboard, ERSSA, extraChIPs, GDCRNATools, GenomicPlot, gg4way, Glimma, GSEABenchmarkeR, hermes, HTSFilter, icetea, infercnv, iSEEde, IsoformSwitchAnalyzeR, KnowSeq, Maaslin2, markeR, mastR, MEB, MEDIPS, MetaDICT, metaseqR2, MIRit, MLSeq, moanin, mobileRNA, MOSim, Motif2Site, msgbsR, msmsTests, multiHiCcompare, muscat, mutscan, PathoStat, phantasus, PhIPData, ppcseq, PRONE, PROPER, psichomics, RCM, regsplice, RFLOMICS, RNAseqCovarImpute, ROSeq, Rvisdiff, saseR, scCB2, scde, scone, scran, ScreenR, SEtools, shinyDSP, SIMD, simPIC, singscore, SpaNorm, sparrow, spatialHeatmap, speckle, splatter, SPsimSeq, srnadiff, sSNAPPY, standR, STATegRa, Statial, SurfR, sva, TBSignatureProfiler, TCseq, tradeSeq, treeclimbR, treekoR, tweeDEseq, vidger, VISTA, xcore, yarn, zinbwave, emtdata, spatialLIBD, recountWorkflow, aIc, cinaR, CoreMicrobiomeR, cpam, hicream, HTSCluster, idiffomix, microbial, RCPA, scITD, ssizeRNA, TransProR, TSGS, XYomics suggestsMe: ABSSeq, biobroom, ClassifyR, cqn, cydar, dcanr, dearseq, DEScan2, dittoSeq, DSS, easyreporting, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, GeoTcgaData, glmGamPoi, goseq, groHMM, GSAR, GSVA, ideal, iSEEpathways, iSEEu, lemur, missMethyl, MoonlightR, multiMiR, raer, recount, regionReport, RFGeneRank, ribosomeProfilingQC, satuRn, scider, SeqGate, signifinder, SpliceWiz, stageR, subSeq, systemPipeR, TCGAbiolinks, TFEA.ChIP, tidybulk, tidyexposomics, topconfects, transmogR, tximeta, tximport, variancePartition, weitrix, Wrench, zenith, zFPKM, leeBamViews, CAGEWorkflow, chipseqDB, DGEobj, DGEobj.utils, DiPALM, easybio, ggpicrust2, glmmSeq, inDAGO, MiscMetabar, palasso, pctax, pmartR, seqgendiff, SIBERG, volcano3D dependencyCount: 10 Package: EDIRquery Version: 1.11.0 Depends: R (>= 4.2.0) Imports: tibble (>= 3.1.6), tictoc (>= 1.0.1), utils (>= 4.1.3), stats (>= 4.1.3), readr (>= 2.1.2), InteractionSet (>= 1.22.0), GenomicRanges (>= 1.46.1) Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 6c46fc7929e820900e289dce7aa46ed2 NeedsCompilation: no Title: Query the EDIR Database For Specific Gene Description: EDIRquery provides a tool to search for genes of interest within the Exome Database of Interspersed Repeats (EDIR). A gene name is a required input, and users can additionally specify repeat sequence lengths, minimum and maximum distance between sequences, and whether to allow a 1-bp mismatch. Outputs include a summary of results by repeat length, as well as a dataframe of query results. Example data provided includes a subset of the data for the gene GAA (ENSG00000171298). To query the full database requires providing a path to the downloaded database files as a parameter. biocViews: Genetics, SequenceMatching Author: Laura D.T. Vo Ngoc [aut, cre] (ORCID: ) Maintainer: Laura D.T. Vo Ngoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EDIRquery git_branch: devel git_last_commit: be464df git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/EDIRquery_1.11.0.tar.gz vignettes: vignettes/EDIRquery/inst/doc/EDIRquery.pdf vignetteTitles: EDIRquery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDIRquery/inst/doc/EDIRquery.R dependencyCount: 52 Package: eds Version: 1.13.0 Depends: Matrix Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, tximportData, testthat (>= 3.0.0) License: GPL-2 MD5sum: 13a1b262619b134a6c33fcbd572e81ec NeedsCompilation: yes Title: eds: Low-level reader for Alevin EDS format Description: This packages provides a single function, readEDS. This is a low-level utility for reading in Alevin EDS format into R. This function is not designed for end-users but instead the package is predominantly for simplifying package dependency graph for other Bioconductor packages. biocViews: Sequencing, RNASeq, GeneExpression, SingleCell Author: Avi Srivastava [aut, cre], Michael Love [aut, ctb] Maintainer: Avi Srivastava URL: https://github.com/mikelove/eds SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eds git_branch: devel git_last_commit: 5f1fd2e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/eds_1.13.0.tar.gz vignettes: vignettes/eds/inst/doc/eds.html vignetteTitles: eds: Low-level reader function for Alevin EDS format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eds/inst/doc/eds.R importsMe: singleCellTK suggestsMe: tximport dependencyCount: 9 Package: EGAD Version: 1.39.0 Depends: R(>= 3.5) Imports: gplots, Biobase, GEOquery, limma, impute, RColorBrewer, zoo, igraph, plyr, MASS, RCurl, methods Suggests: knitr, testthat, rmarkdown, markdown License: GPL-2 MD5sum: c88a3d3752ebaf79193f13191e648498 NeedsCompilation: no Title: Extending guilt by association by degree Description: The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods. biocViews: Software, FunctionalGenomics, SystemsBiology, GenePrediction, FunctionalPrediction, NetworkEnrichment, GraphAndNetwork, Network Author: Sara Ballouz [aut, cre], Melanie Weber [aut, ctb], Paul Pavlidis [aut], Jesse Gillis [aut, ctb] Maintainer: Sara Ballouz VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/EGAD git_branch: devel git_last_commit: 7ee1d10 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/EGAD_1.39.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 88 Package: eiR Version: 1.51.0 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl, digest, BiocGenerics, RcppAnnoy (>= 0.0.9) Suggests: BiocStyle, knitcitations, knitr, knitrBootstrap,rmarkdown,RSQLite,codetools License: Artistic-2.0 MD5sum: 87b706dff895f2a78c5a14d389173a05 NeedsCompilation: yes Title: Accelerated similarity searching of small molecules Description: The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Yiqun Cao and Tyler Backman Maintainer: Thomas Girke URL: https://github.com/girke-lab/eiR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eiR git_branch: devel git_last_commit: f6360f0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/eiR_1.51.0.tar.gz vignettes: vignettes/eiR/inst/doc/eiR.html vignetteTitles: eiR: Accelerated Similarity Searching of Small Molecules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R dependencyCount: 70 Package: eisaR Version: 1.23.1 Depends: R (>= 4.1) Imports: graphics, stats, GenomicRanges, S4Vectors, IRanges, limma, edgeR (>= 4.0), methods, SummarizedExperiment, BiocGenerics, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, QuasR, Rbowtie, Rhisat2, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg38, ensembldb, AnnotationDbi, GenomicFeatures, txdbmaker, rtracklayer, withr License: GPL-3 MD5sum: 5f6b794da420ee059b7835e8df6023d1 NeedsCompilation: no Title: Exon-Intron Split Analysis (EISA) in R Description: Exon-intron split analysis (EISA) uses ordinary RNA-seq data to measure changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. For details see Gaidatzis et al., Nat Biotechnol 2015. doi: 10.1038/nbt.3269. eisaR implements the major steps of EISA in R. biocViews: Transcription, GeneExpression, GeneRegulation, FunctionalGenomics, Transcriptomics, Regression, RNASeq Author: Michael Stadler [aut, cre], Dimos Gaidatzis [aut], Lukas Burger [aut], Charlotte Soneson [aut] Maintainer: Michael Stadler URL: https://github.com/fmicompbio/eisaR VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/eisaR/issues git_url: https://git.bioconductor.org/packages/eisaR git_branch: devel git_last_commit: 7a06cdb git_last_commit_date: 2025-11-03 Date/Publication: 2026-04-20 source.ver: src/contrib/eisaR_1.23.1.tar.gz vignettes: vignettes/eisaR/inst/doc/eisaR.html, vignettes/eisaR/inst/doc/rna-velocity.html vignetteTitles: Using eisaR for Exon-Intron Split Analysis (EISA), Generating reference files for spliced and unspliced abundance estimation with alignment-free methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eisaR/inst/doc/eisaR.R, vignettes/eisaR/inst/doc/rna-velocity.R dependencyCount: 29 Package: EMDomics Version: 2.41.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: 536027dfbc076ff4706457722b9d1199 NeedsCompilation: no Title: Earth Mover's Distance for Differential Analysis of Genomics Data Description: The EMDomics algorithm is used to perform a supervised multi-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between groups. Usually the data will be gene expression values from array-based or sequence-based experiments, but data from other types of experiments can also be analyzed (e.g. copy number variation). Traditional methods like Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA) use significance tests based on summary statistics (mean and standard deviation) of the distributions. This approach lacks power to identify expression differences between groups that show high levels of intra-group heterogeneity. The Earth Mover's Distance (EMD) algorithm instead computes the "work" needed to transform one distribution into another, thus providing a metric of the overall difference in shape between two distributions. Permutation of sample labels is used to generate q-values for the observed EMD scores. This package also incorporates the Komolgorov-Smirnov (K-S) test and the Cramer von Mises test (CVM), which are both common distribution comparison tests. biocViews: Software, DifferentialExpression, GeneExpression, Microarray Author: Sadhika Malladi [aut, cre], Daniel Schmolze [aut, cre], Andrew Beck [aut], Sheida Nabavi [aut] Maintainer: Sadhika Malladi and Daniel Schmolze VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EMDomics git_branch: devel git_last_commit: a487db4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/EMDomics_2.41.0.tar.gz vignettes: vignettes/EMDomics/inst/doc/EMDomics.html vignetteTitles: EMDomics Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R dependencyCount: 36 Package: EmpiricalBrownsMethod Version: 1.39.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: ef63e45dfe5af51443a3139285fe4990 NeedsCompilation: no Title: Uses Brown's method to combine p-values from dependent tests Description: Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments. biocViews: StatisticalMethod, GeneExpression, Pathways Author: William Poole Maintainer: David Gibbs URL: https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EmpiricalBrownsMethod git_branch: devel git_last_commit: e53115d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/EmpiricalBrownsMethod_1.39.0.tar.gz vignettes: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html vignetteTitles: Empirical Browns Method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R dependsOnMe: poolVIM importsMe: EBSEA dependencyCount: 0 Package: EnhancedVolcano Version: 1.29.1 Depends: ggplot2, ggrepel Imports: methods, scales, grid, grDevices Suggests: RUnit, ggrastr, BiocGenerics, knitr, DESeq2, pasilla, airway, org.Hs.eg.db, gridExtra, magrittr, rmarkdown License: GPL-3 MD5sum: d755e87aa9fdcb84fec08166ab2ea5d2 NeedsCompilation: no Title: Publication-ready volcano plots with enhanced colouring and labeling Description: Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via colour, shape, size, and shade parameter configurations. biocViews: RNASeq, GeneExpression, Transcription, DifferentialExpression, ImmunoOncology Author: Kevin Blighe [aut], Jared Andrews [cre, ctb] (ORCID: ), Sharmila Rana [aut], Emir Turkes [ctb], Benjamin Ostendorf [ctb], Andrea Grioni [ctb], Myles Lewis [aut] Maintainer: Jared Andrews URL: https://github.com/kevinblighe/EnhancedVolcano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnhancedVolcano git_branch: devel git_last_commit: b1f351b git_last_commit_date: 2025-12-03 Date/Publication: 2026-04-20 source.ver: src/contrib/EnhancedVolcano_1.29.1.tar.gz vignettes: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html vignetteTitles: Publication-ready volcano plots with enhanced colouring and labeling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.R importsMe: chevreulShiny, goatea, MetaProViz suggestsMe: VISTA, xCell2, henna, rliger dependencyCount: 25 Package: enhancerHomologSearch Version: 1.17.0 Depends: R (>= 4.1.0), methods Imports: BiocGenerics, Biostrings, BSgenome, BiocParallel, BiocFileCache, Seqinfo, GenomicRanges, httr, IRanges, jsonlite, motifmatchr, Matrix, pwalign, rtracklayer, Rcpp, S4Vectors, stats, utils LinkingTo: Rcpp Suggests: GenomeInfoDb, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, MotifDb, testthat, TFBSTools License: GPL (>= 2) MD5sum: 567ce9af990f92f5403295be9f5eed05 NeedsCompilation: yes Title: Identification of putative mammalian orthologs to given enhancer Description: Get ENCODE data of enhancer region via H3K4me1 peaks and search homolog regions for given sequences. The candidates of enhancer homolog regions can be filtered by distance to target TSS. The top candidates from human and mouse will be aligned to each other and then exported as multiple alignments with given enhancer. biocViews: Sequencing, GeneRegulation, Alignment Author: Jianhong Ou [aut, cre] (ORCID: ), Valentina Cigliola [dtc], Kenneth Poss [fnd] Maintainer: Jianhong Ou URL: https://jianhong.github.io/enhancerHomologSearch VignetteBuilder: knitr BugReports: https://github.com/jianhong/enhancerHomologSearch/issues git_url: https://git.bioconductor.org/packages/enhancerHomologSearch git_branch: devel git_last_commit: aec9810 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/enhancerHomologSearch_1.17.0.tar.gz vignettes: vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.html vignetteTitles: enhancerHomologSearch Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.R dependencyCount: 98 Package: EnMCB Version: 1.23.0 Depends: R (>= 4.0) Imports: survivalROC, glmnet, rms, mboost, Matrix, igraph, methods, survivalsvm, ggplot2, boot, e1071, survival, BiocFileCache Suggests: SummarizedExperiment, testthat, Biobase, survminer, affycoretools, knitr, plotROC, limma, rmarkdown License: GPL-2 MD5sum: 8211c9388b7e434bda87e73fbb81d94f NeedsCompilation: no Title: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models Description: Creation of the correlated blocks using DNA methylation profiles. Machine learning models can be constructed to predict differentially methylated blocks and disease progression. biocViews: Normalization, DNAMethylation, MethylationArray, SupportVectorMachine Author: Xin Yu Maintainer: Xin Yu VignetteBuilder: knitr BugReports: https://github.com/whirlsyu/EnMCB/issues git_url: https://git.bioconductor.org/packages/EnMCB git_branch: devel git_last_commit: 8ef58ef git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/EnMCB_1.23.0.tar.gz vignettes: vignettes/EnMCB/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnMCB/inst/doc/vignette.R dependencyCount: 124 Package: EnrichDO Version: 1.5.1 Depends: R (>= 4.0.0) Imports: BiocGenerics, Rgraphviz, hash, S4Vectors, dplyr, ggplot2, graph, magrittr, methods, pheatmap, graphics, utils, purrr, tidyr, stats Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: MIT + file LICENSE MD5sum: dcf91b9953e8f9897bada957d6f2a605 NeedsCompilation: no Title: a Global Weighted Model for Disease Ontology Enrichment Analysis Description: To implement disease ontology (DO) enrichment analysis, this package is designed and presents a double weighted model based on the latest annotations of the human genome with DO terms, by integrating the DO graph topology on a global scale. This package exhibits high accuracy that it can identify more specific DO terms, which alleviates the over enriched problem. The package includes various statistical models and visualization schemes for discovering the associations between genes and diseases from biological big data. biocViews: Annotation, Visualization, GeneSetEnrichment, Software Author: Liang Cheng [aut], Haixiu Yang [aut], Hongyu Fu [aut, cre] Maintainer: Hongyu Fu <2287531995@qq.com> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichDO git_branch: devel git_last_commit: ebc1cbf git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/EnrichDO_1.5.1.tar.gz vignettes: vignettes/EnrichDO/inst/doc/EnrichDO.html vignetteTitles: EnrichDO: Disease Ontology Enrichment Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EnrichDO/inst/doc/EnrichDO.R dependencyCount: 43 Package: EnrichedHeatmap Version: 1.41.1 Depends: R (>= 4.0.0), methods, grid, ComplexHeatmap (>= 2.11.0), GenomicRanges Imports: matrixStats, stats, GetoptLong, Rcpp, utils, locfit, circlize (>= 0.4.5), IRanges LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, markdown, rmarkdown, genefilter, RColorBrewer License: MIT + file LICENSE MD5sum: 753bc9de7ddeb0bd468a044b09f93e76 NeedsCompilation: yes Title: Making Enriched Heatmaps Description: Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. Here we implement enriched heatmap by ComplexHeatmap package. Since this type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources. biocViews: Software, Visualization, Sequencing, GenomeAnnotation, Coverage Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/EnrichedHeatmap VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichedHeatmap git_branch: devel git_last_commit: 986bf0c git_last_commit_date: 2026-01-30 Date/Publication: 2026-04-20 source.ver: src/contrib/EnrichedHeatmap_1.41.1.tar.gz vignettes: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html vignetteTitles: The EnrichedHeatmap package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: ComplexHeatmap, epistack, extraChIPs, InteractiveComplexHeatmap dependencyCount: 35 Package: enrichplot Version: 1.31.5 Depends: R (>= 4.2.0) Imports: aplot (>= 0.2.1), DOSE, dplyr, enrichit, ggfun (>= 0.1.7), ggnewscale, ggplot2 (>= 3.5.0), ggrepel (>= 0.9.0), ggtangle (>= 0.0.9), ggtree, GOSemSim (>= 2.37.2), graphics, grid, igraph, methods, purrr, RColorBrewer, reshape2, rlang, scatterpie, stats, tidydr, utils, yulab.utils (>= 0.2.2) Suggests: AnnotationDbi, clusterProfiler, europepmc, ggarchery, ggforce, ggHoriPlot, ggplotify, ggridges, ggstar, ggtreeExtra, ggupset, glue, grDevices, gridExtra, gson, org.Hs.eg.db, quarto, scales, tibble, tidyr License: Artistic-2.0 MD5sum: 887b08310feefacc1862721a2a8cd46b NeedsCompilation: no Title: Visualization of Functional Enrichment Result Description: The 'enrichplot' package provides visualization methods for interpreting functional enrichment results from ORA or GSEA analyses. It is designed to work with the 'clusterProfiler' ecosystem and builds on 'ggplot2' for flexible and extensible graphics. biocViews: Annotation, GeneSetEnrichment, GO, KEGG, Pathways, Software, Visualization Author: Guangchuang Yu [aut, cre] (ORCID: ), Chun-Hui Gao [ctb] (ORCID: ) Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: quarto BugReports: https://github.com/GuangchuangYu/enrichplot/issues git_url: https://git.bioconductor.org/packages/enrichplot git_branch: devel git_last_commit: f344fd6 git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/enrichplot_1.31.5.tar.gz vignettes: vignettes/enrichplot/inst/doc/enrichplot.html vignetteTitles: enrichplot.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: carnation, CBNplot, ChIPseeker, clusterProfiler, damidBind, debrowser, enrichViewNet, epiSeeker, famat, goatea, MicrobiomeProfiler, ReactomePA, RFLOMICS, TDbasedUFEadv suggestsMe: GeoTcgaData, mastR, methylGSA, scFeatures, scGraphVerse, tidybulk, VISTA, easyEWAS, ggpicrust2, ivolcano, ReporterScore, SCpubr dependencyCount: 119 Package: enrichViewNet Version: 1.9.1 Depends: R (>= 4.2.0) Imports: gprofiler2, strex, RCy3, jsonlite, stringr, enrichplot, igraph, reshape2, methods Suggests: BiocStyle, knitr, rmarkdown, ggplot2, scatterpie, ggtangle, ggrepel, testthat, ggnetwork, magick License: Artistic-2.0 MD5sum: c61fc924a171867997829b3f4074b0ba NeedsCompilation: no Title: From functional enrichment results to biological networks Description: This package enables the visualization of functional enrichment results as network graphs. First the package enables the visualization of enrichment results, in a format corresponding to the one generated by gprofiler2, as a customizable Cytoscape network. In those networks, both gene datasets (GO terms/pathways/protein complexes) and genes associated to the datasets are represented as nodes. While the edges connect each gene to its dataset(s). The package also provides the option to create enrichment maps from functional enrichment results. Enrichment maps enable the visualization of enriched terms into a network with edges connecting overlapping genes. biocViews: BiologicalQuestion, Software, Network, NetworkEnrichment, GO Author: Astrid Deschênes [aut, cre] (ORCID: ), Pascal Belleau [aut] (ORCID: ), Robert L. Faure [aut] (ORCID: ), Maria J. Fernandes [aut] (ORCID: ), Alexander Krasnitz [aut], David A. Tuveson [aut] (ORCID: ) Maintainer: Astrid Deschênes URL: https://github.com/adeschen/enrichViewNet, https://adeschen.github.io/enrichViewNet/ VignetteBuilder: knitr BugReports: https://github.com/adeschen/enrichViewNet/issues git_url: https://git.bioconductor.org/packages/enrichViewNet git_branch: devel git_last_commit: 9d4d7b1 git_last_commit_date: 2025-12-31 Date/Publication: 2026-04-20 source.ver: src/contrib/enrichViewNet_1.9.1.tar.gz vignettes: vignettes/enrichViewNet/inst/doc/enrichViewNet.html vignetteTitles: From functional enrichment results to biological networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enrichViewNet/inst/doc/enrichViewNet.R dependencyCount: 147 Package: ensembldb Version: 2.35.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.61.1), GenomicFeatures (>= 1.61.4), AnnotationFilter (>= 1.5.2) Imports: methods, RSQLite (>= 1.1), DBI, Biobase, Seqinfo, GenomeInfoDb (>= 1.45.5), AnnotationDbi (>= 1.31.19), rtracklayer (>= 1.69.1), S4Vectors (>= 0.23.10), Rsamtools, IRanges (>= 2.13.24), ProtGenerics, Biostrings (>= 2.77.2), curl Suggests: BiocStyle, knitr, EnsDb.Hsapiens.v86 (>= 0.99.8), testthat, BSgenome.Hsapiens.NCBI.GRCh38, ggbio (>= 1.24.0), Gviz (>= 1.20.0), rmarkdown, AnnotationHub Enhances: RMariaDB, shiny License: LGPL MD5sum: 9d3c166f0284cf2107dece362bb367fb NeedsCompilation: no Title: Utilities to create and use Ensembl-based annotation databases Description: The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates. biocViews: Genetics, AnnotationData, Sequencing, Coverage Author: Johannes Rainer with contributions from Tim Triche, Sebastian Gibb, Laurent Gatto Christian Weichenberger and Boyu Yu. Maintainer: Johannes Rainer URL: https://github.com/jorainer/ensembldb VignetteBuilder: knitr BugReports: https://github.com/jorainer/ensembldb/issues git_url: https://git.bioconductor.org/packages/ensembldb git_branch: devel git_last_commit: a74ca18 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ensembldb_2.35.0.tar.gz vignettes: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.html, vignettes/ensembldb/inst/doc/coordinate-mapping.html, vignettes/ensembldb/inst/doc/ensembldb.html, vignettes/ensembldb/inst/doc/MySQL-backend.html, vignettes/ensembldb/inst/doc/proteins.html vignetteTitles: Use cases for coordinate mapping with ensembldb, Mapping between genome,, transcript and protein coordinates, Generating an using Ensembl based annotation packages, Using a MariaDB/MySQL server backend, Querying protein features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.R, vignettes/ensembldb/inst/doc/coordinate-mapping.R, vignettes/ensembldb/inst/doc/ensembldb.R, vignettes/ensembldb/inst/doc/MySQL-backend.R, vignettes/ensembldb/inst/doc/proteins.R dependsOnMe: chimeraviz, demuxSNP, AHEnsDbs, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v75, EnsDb.Mmusculus.v79, EnsDb.Rnorvegicus.v75, EnsDb.Rnorvegicus.v79 importsMe: biovizBase, BUSpaRse, chevreulProcess, ChIPpeakAnno, CleanUpRNAseq, damidBind, diffUTR, epimutacions, epivizrData, ggbio, GRaNIE, Gviz, RAIDS, RITAN, scanMiRApp, scFeatures, signifinder, singleCellTK, TVTB, tximeta, GenomicDistributionsData, scRNAseq, cellGeometry, locuszoomr, revert, RNAseqQC suggestsMe: AlphaMissenseR, AnnotationHub, autonomics, CNVRanger, eisaR, EpiTxDb, fishpond, GenomicFeatures, ldblock, multicrispr, nullranges, satuRn, txdbmaker, wiggleplotr, celldex, gaawr2, GRIN2, pQTLdata dependencyCount: 80 Package: epialleleR Version: 1.19.3 Depends: R (>= 4.1) Imports: stats, methods, utils, data.table, BiocGenerics, GenomicRanges, Rcpp LinkingTo: Rcpp, BH, Rhtslib Suggests: GenomeInfoDb, SummarizedExperiment, VariantAnnotation, RUnit, knitr, rmarkdown, ggplot2 License: Artistic-2.0 MD5sum: 1394f0eb628a73465f098b725883978e NeedsCompilation: yes Title: Fast, Accurate, Epiallele-Aware Methylation Caller and Reporter Description: Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls and reports cytosine methylation as well as frequencies of hypermethylated epialleles at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Among other things, this package can also extract and visualise methylation patterns and assess allele specificity of methylation. biocViews: DNAMethylation, Epigenetics, MethylSeq, LongRead Author: Oleksii Nikolaienko [aut, cre] (ORCID: ) Maintainer: Oleksii Nikolaienko URL: https://github.com/BBCG/epialleleR SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/BBCG/epialleleR/issues git_url: https://git.bioconductor.org/packages/epialleleR git_branch: devel git_last_commit: 79e4fec git_last_commit_date: 2026-01-25 Date/Publication: 2026-04-20 source.ver: src/contrib/epialleleR_1.19.3.tar.gz vignettes: vignettes/epialleleR/inst/doc/epialleleR.html, vignettes/epialleleR/inst/doc/values.html vignetteTitles: epialleleR, values hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epialleleR/inst/doc/epialleleR.R, vignettes/epialleleR/inst/doc/values.R dependencyCount: 16 Package: EpiCompare Version: 1.15.2 Depends: R (>= 4.2.0) Imports: AnnotationHub, ChIPseeker, data.table, genomation, GenomicRanges, IRanges (>= 2.41.3), Seqinfo (>= 0.99.2), GenomeInfoDb (>= 1.45.7), ggplot2 (>= 3.5.0), htmltools, methods, plotly, reshape2, rmarkdown, rtracklayer, stats, stringr, utils, BiocGenerics, downloadthis, parallel Suggests: rworkflows, BiocFileCache, BiocParallel, BiocStyle, clusterProfiler, GenomicAlignments, grDevices, knitr, org.Hs.eg.db, testthat (>= 3.0.0), tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, ComplexUpset, plyranges, scales, Matrix, consensusSeekeR, heatmaply, viridis License: GPL-3 MD5sum: b16d7a25eee26fa5713ddd107f930be3 NeedsCompilation: no Title: Comparison, Benchmarking & QC of Epigenomic Datasets Description: EpiCompare is used to compare and analyse epigenetic datasets for quality control and benchmarking purposes. The package outputs an HTML report consisting of three sections: (1. General metrics) Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples, (2. Peak overlap) Percentage and statistical significance of overlapping and non-overlapping peaks. Also includes upset plot and (3. Functional annotation) functional annotation (ChromHMM, ChIPseeker and enrichment analysis) of peaks. Also includes peak enrichment around TSS. biocViews: Epigenetics, Genetics, QualityControl, ChIPSeq, MultipleComparison, FunctionalGenomics, ATACSeq, DNaseSeq Author: Sera Choi [aut] (ORCID: ), Brian Schilder [aut] (ORCID: ), Leyla Abbasova [aut], Alan Murphy [aut] (ORCID: ), Nathan Skene [aut] (ORCID: ), Thomas Roberts [ctb], Hiranyamaya Dash [cre] (ORCID: ) Maintainer: Hiranyamaya Dash URL: https://github.com/neurogenomics/EpiCompare VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/EpiCompare/issues git_url: https://git.bioconductor.org/packages/EpiCompare git_branch: devel git_last_commit: 7ecd95b git_last_commit_date: 2025-12-01 Date/Publication: 2026-04-20 source.ver: src/contrib/EpiCompare_1.15.2.tar.gz vignettes: vignettes/EpiCompare/inst/doc/docker.html, vignettes/EpiCompare/inst/doc/EpiCompare.html, vignettes/EpiCompare/inst/doc/example_report.html vignetteTitles: docker, EpiCompare, example_report hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiCompare/inst/doc/docker.R, vignettes/EpiCompare/inst/doc/EpiCompare.R, vignettes/EpiCompare/inst/doc/example_report.R dependencyCount: 190 Package: epidecodeR Version: 1.19.0 Depends: R (>= 3.1.0) Imports: EnvStats, ggplot2, rtracklayer, GenomicRanges, IRanges, rstatix, ggpubr, methods, stats, utils, dplyr Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 752bbdc0bc5b63b9fb46a1828bda07d4 NeedsCompilation: no Title: epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation Description: epidecodeR is a package capable of analysing impact of degree of DNA/RNA epigenetic chemical modifications on dysregulation of genes or proteins. This package integrates chemical modification data generated from a host of epigenomic or epitranscriptomic techniques such as ChIP-seq, ATAC-seq, m6A-seq, etc. and dysregulated gene lists in the form of differential gene expression, ribosome occupancy or differential protein translation and identify impact of dysregulation of genes caused due to varying degrees of chemical modifications associated with the genes. epidecodeR generates cumulative distribution function (CDF) plots showing shifts in trend of overall log2FC between genes divided into groups based on the degree of modification associated with the genes. The tool also tests for significance of difference in log2FC between groups of genes. biocViews: DifferentialExpression, GeneRegulation, HistoneModification, FunctionalPrediction, Transcription, GeneExpression, Epitranscriptomics, Epigenetics, FunctionalGenomics, SystemsBiology, Transcriptomics, ChipOnChip Author: Kandarp Joshi [aut, cre], Dan Ohtan Wang [aut] Maintainer: Kandarp Joshi URL: https://github.com/kandarpRJ/epidecodeR, https://epidecoder.shinyapps.io/shinyapp VignetteBuilder: knitr BugReports: https://github.com/kandarpRJ/epidecodeR/issues git_url: https://git.bioconductor.org/packages/epidecodeR git_branch: devel git_last_commit: 15cacf7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/epidecodeR_1.19.0.tar.gz vignettes: vignettes/epidecodeR/inst/doc/epidecodeR.html vignetteTitles: epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epidecodeR/inst/doc/epidecodeR.R dependencyCount: 131 Package: EpiDISH Version: 2.27.2 Depends: R (>= 4.1) Imports: MASS, e1071, quadprog, parallel, stats, matrixStats, stringr, locfdr, Matrix, genefilter Suggests: roxygen2, GEOquery, BiocStyle, knitr, rmarkdown, Biobase, testthat License: GPL-2 MD5sum: fcab084233a89b538eac254d1b18ff19 NeedsCompilation: no Title: Epigenetic Dissection of Intra-Sample-Heterogeneity Description: EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of blood-tissue of any age, from birth to old-age, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies. biocViews: DNAMethylation, MethylationArray, Epigenetics, DifferentialMethylation, ImmunoOncology Author: Andrew E. Teschendorff [aut], Shijie C. Zheng [aut, cre] Maintainer: Shijie C. Zheng URL: https://github.com/sjczheng/EpiDISH VignetteBuilder: knitr BugReports: https://github.com/sjczheng/EpiDISH/issues git_url: https://git.bioconductor.org/packages/EpiDISH git_branch: devel git_last_commit: 816f01d git_last_commit_date: 2026-03-12 Date/Publication: 2026-04-20 source.ver: src/contrib/EpiDISH_2.27.2.tar.gz vignettes: vignettes/EpiDISH/inst/doc/EpiDISH.html vignetteTitles: Epigenetic Dissection of Intra-Sample-Heterogeneity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiDISH/inst/doc/EpiDISH.R dependsOnMe: TOAST suggestsMe: planet, CimpleG dependencyCount: 63 Package: epigenomix Version: 1.51.0 Depends: R (>= 3.5.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: 22b665b2727f7f4129e794cde394ae17 NeedsCompilation: no Title: Epigenetic and gene transcription data normalization and integration with mixture models Description: A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types. biocViews: ChIPSeq, GeneExpression, DifferentialExpression, Classification Author: Hans-Ulrich Klein, Martin Schaefer Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/epigenomix git_branch: devel git_last_commit: 5563ca4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/epigenomix_1.51.0.tar.gz vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf vignetteTitles: epigenomix package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epigenomix/inst/doc/epigenomix.R dependencyCount: 97 Package: epiNEM Version: 1.35.0 Depends: R (>= 4.1) Imports: BoutrosLab.plotting.general, BoolNet, e1071, gtools, stats, igraph, utils, lattice, latticeExtra, RColorBrewer, pcalg, minet, grDevices, graph, mnem, latex2exp Suggests: knitr, RUnit, BiocGenerics, STRINGdb, devtools, rmarkdown, GOSemSim, AnnotationHub, org.Sc.sgd.db, BiocStyle License: GPL-3 MD5sum: 03b0bb8037581a8f802bc552cb858f63 NeedsCompilation: no Title: epiNEM Description: epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. It is tailored towards large scale double knock-out screens. biocViews: Pathways, SystemsBiology, NetworkInference, Network Author: Madeline Diekmann & Martin Pirkl Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/epiNEM/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/epiNEM/issues git_url: https://git.bioconductor.org/packages/epiNEM git_branch: devel git_last_commit: d7bc809 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/epiNEM_1.35.0.tar.gz vignettes: vignettes/epiNEM/inst/doc/epiNEM.html vignetteTitles: epiNEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epiNEM/inst/doc/epiNEM.R importsMe: bnem, nempi suggestsMe: mnem dependencyCount: 107 Package: EpipwR Version: 1.5.0 Depends: R (>= 4.4.0) Imports: EpipwR.data, ExperimentHub (>= 2.10.0), ggplot2 Suggests: knitr, rmarkdown, testthat (>= 3.0.0), sessioninfo License: Artistic-2.0 MD5sum: 55e65a9e55f254995752a719952cf762 NeedsCompilation: no Title: Efficient Power Analysis for EWAS with Continuous or Binary Outcomes Description: A quasi-simulation based approach to performing power analysis for EWAS (Epigenome-wide association studies) with continuous or binary outcomes. 'EpipwR' relies on empirical EWAS datasets to determine power at specific sample sizes while keeping computational cost low. EpipwR can be run with a variety of standard statistical tests, controlling for either a false discovery rate or a family-wise type I error rate. biocViews: Epigenetics, ExperimentalDesign Author: Jackson Barth [aut, cre] (ORCID: ), Austin Reynolds [aut], Mary Lauren Benton [ctb], Carissa Fong [ctb] Maintainer: Jackson Barth URL: https://github.com/jbarth216/EpipwR VignetteBuilder: knitr BugReports: https://github.com/jbarth216/EpipwR git_url: https://git.bioconductor.org/packages/EpipwR git_branch: devel git_last_commit: 409b2c6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/EpipwR_1.5.0.tar.gz vignettes: vignettes/EpipwR/inst/doc/EpipwR.html vignetteTitles: EpipwR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpipwR/inst/doc/EpipwR.R dependencyCount: 75 Package: epiregulon Version: 2.1.3 Depends: R (>= 4.5.0), SingleCellExperiment Imports: AnnotationHub, BiocParallel, ExperimentHub, Matrix, Rcpp, S4Vectors, SummarizedExperiment, checkmate, entropy, lifecycle, methods, scran, scuttle, stats, utils, AnnotationHub, GenomeInfoDb, GenomicRanges, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, motifmatchr, IRanges, scrapper LinkingTo: Rcpp Suggests: knitr, rmarkdown, parallel, BiocStyle, testthat (>= 3.0.0), coin, scater, scMultiome License: MIT + file LICENSE MD5sum: a79a00d7df3ed0af3d06dab48121a9fa NeedsCompilation: yes Title: Gene regulatory network inference from single cell epigenomic data Description: Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions. biocViews: SingleCell, GeneRegulation,NetworkInference,Network, GeneExpression, Transcription, GeneTarget Author: Xiaosai Yao [aut, cre] (ORCID: ), Tomasz Włodarczyk [aut] (ORCID: ), Aaron Lun [aut], Shang-Yang Chen [aut] Maintainer: Xiaosai Yao URL: https://github.com/xiaosaiyao/epiregulon/ VignetteBuilder: knitr BugReports: https://github.com/xiaosaiyao/epiregulon/issues git_url: https://git.bioconductor.org/packages/epiregulon git_branch: devel git_last_commit: 3a105e9 git_last_commit_date: 2025-12-08 Date/Publication: 2026-04-20 source.ver: src/contrib/epiregulon_2.1.3.tar.gz vignettes: vignettes/epiregulon/inst/doc/multiome.mae.html vignetteTitles: Epiregulon tutorial with MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epiregulon/inst/doc/multiome.mae.R suggestsMe: epiregulon.extra dependencyCount: 138 Package: epiregulon.extra Version: 1.7.0 Depends: R (>= 4.4), SingleCellExperiment Imports: scran, ComplexHeatmap, Matrix, SummarizedExperiment, checkmate, circlize, clusterProfiler, ggplot2, ggraph, igraph, patchwork, reshape2, scales, scater Suggests: epiregulon, knitr, rmarkdown, parallel, BiocStyle, testthat (>= 3.0.0), msigdb, GSEABase, dorothea, scMultiome, S4Vectors, scuttle, vdiffr, ggrastr, ggrepel License: MIT + file LICENSE MD5sum: 388a833a4651f5acbee4711d0bdd2130 NeedsCompilation: no Title: Companion package to epiregulon with additional plotting, differential and graph functions Description: Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions. biocViews: GeneRegulation, Network, GeneExpression, Transcription, ChipOnChip, DifferentialExpression, GeneTarget, Normalization, GraphAndNetwork Author: Xiaosai Yao [aut, cre] (ORCID: ), Tomasz Włodarczyk [aut] (ORCID: ), Timothy Keyes [aut], Shang-Yang Chen [aut] Maintainer: Xiaosai Yao URL: https://github.com/xiaosaiyao/epiregulon.extra/ VignetteBuilder: knitr BugReports: https://github.com/xiaosaiyao/epiregulon.extra/issues git_url: https://git.bioconductor.org/packages/epiregulon.extra git_branch: devel git_last_commit: a48846b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/epiregulon.extra_1.7.0.tar.gz vignettes: vignettes/epiregulon.extra/inst/doc/Data_visualization.html vignetteTitles: Data visualization with epiregulon.extra hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epiregulon.extra/inst/doc/Data_visualization.R dependencyCount: 199 Package: epistack Version: 1.17.0 Depends: R (>= 4.1) Imports: GenomicRanges, SummarizedExperiment, BiocGenerics, S4Vectors, IRanges, graphics, plotrix, grDevices, stats, methods Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, EnrichedHeatmap, biomaRt, rtracklayer, covr, vdiffr, magick License: MIT + file LICENSE MD5sum: 3c52ce1e628738389a5e33c13da12a46 NeedsCompilation: no Title: Heatmaps of Stack Profiles from Epigenetic Signals Description: The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. epistack needs three different inputs: 1) a genomic score objects, such as ChIP-seq coverage or DNA methylation values, provided as a `GRanges` (easily obtained from `bigwig` or `bam` files). 2) a list of feature of interest, such as peaks or transcription start sites, provided as a `GRanges` (easily obtained from `gtf` or `bed` files). 3) a score to sort the features, such as peak height or gene expression value. biocViews: RNASeq, Preprocessing, ChIPSeq, GeneExpression, Coverage Author: SACI Safia [aut], DEVAILLY Guillaume [cre, aut] Maintainer: DEVAILLY Guillaume URL: https://github.com/GenEpi-GenPhySE/epistack VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epistack git_branch: devel git_last_commit: d86c320 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/epistack_1.17.0.tar.gz vignettes: vignettes/epistack/inst/doc/using_epistack.html vignetteTitles: Using epistack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epistack/inst/doc/using_epistack.R dependencyCount: 26 Package: epistasisGA Version: 1.13.1 Depends: R (>= 4.2) Imports: BiocParallel, data.table, matrixStats, stats, survival, igraph, batchtools, qgraph, grDevices, parallel, ggplot2, grid, bigmemory, graphics, utils LinkingTo: Rcpp, RcppArmadillo, BH, bigmemory Suggests: BiocStyle, knitr, rmarkdown, magrittr, kableExtra, testthat (>= 3.0.0) License: GPL-3 MD5sum: 0e175c6e8bf6cd256a1cdcbf298bc078 NeedsCompilation: yes Title: An R package to identify multi-snp effects in nuclear family studies using the GADGETS method Description: This package runs the GADGETS method to identify epistatic effects in nuclear family studies. It also provides functions for permutation-based inference and graphical visualization of the results. biocViews: Genetics, SNP, GeneticVariability Author: Michael Nodzenski [aut, cre], Juno Krahn [ctb] Maintainer: Michael Nodzenski URL: https://github.com/mnodzenski/epistasisGA VignetteBuilder: knitr BugReports: https://github.com/mnodzenski/epistasisGA/issues git_url: https://git.bioconductor.org/packages/epistasisGA git_branch: devel git_last_commit: 3f4c547 git_last_commit_date: 2026-01-23 Date/Publication: 2026-04-20 source.ver: src/contrib/epistasisGA_1.13.1.tar.gz vignettes: vignettes/epistasisGA/inst/doc/E_GADGETS.html, vignettes/epistasisGA/inst/doc/GADGETS.html, vignettes/epistasisGA/inst/doc/Including_Maternal_SNPs.html vignetteTitles: E-GADGETS, GADGETS, Detecting Maternal-SNP Interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epistasisGA/inst/doc/E_GADGETS.R, vignettes/epistasisGA/inst/doc/GADGETS.R, vignettes/epistasisGA/inst/doc/Including_Maternal_SNPs.R dependencyCount: 111 Package: epivizr Version: 2.41.0 Depends: R (>= 3.5.0), methods Imports: epivizrServer (>= 1.1.1), epivizrData (>= 1.3.4), GenomicRanges, S4Vectors, IRanges, bumphunter, GenomeInfoDb Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, minfi, rmarkdown License: Artistic-2.0 MD5sum: 534035860f08bdc3d831edd4f5065be8 NeedsCompilation: no Title: R Interface to epiviz web app Description: This package provides connections to the epiviz web app (http://epiviz.cbcb.umd.edu) for interactive visualization of genomic data. Objects in R/bioc interactive sessions can be displayed in genome browser tracks or plots to be explored by navigation through genomic regions. Fundamental Bioconductor data structures are supported (e.g., GenomicRanges and RangedSummarizedExperiment objects), while providing an easy mechanism to support other data structures (through package epivizrData). Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Florin Chelaru, Llewellyn Smith, Naomi Goldstein, Jayaram Kancherla, Morgan Walter, Brian Gottfried Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA git_url: https://git.bioconductor.org/packages/epivizr git_branch: devel git_last_commit: ba79c0b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/epivizr_2.41.0.tar.gz vignettes: vignettes/epivizr/inst/doc/IntroToEpivizr.html vignetteTitles: Introduction to epivizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R dependsOnMe: epivizrStandalone, scTreeViz dependencyCount: 102 Package: epivizrChart Version: 1.33.0 Depends: R (>= 3.5.0) Imports: epivizrData (>= 1.5.1), epivizrServer, htmltools, rjson, methods, BiocGenerics Suggests: testthat, roxygen2, knitr, Biobase, GenomicRanges, S4Vectors, IRanges, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, Homo.sapiens, shiny, minfi, Rsamtools, rtracklayer, RColorBrewer, magrittr, AnnotationHub License: Artistic-2.0 MD5sum: 393decc4b60337ab0b5eaf51d4b81204 NeedsCompilation: no Title: R interface to epiviz web components Description: This package provides an API for interactive visualization of genomic data using epiviz web components. Objects in R/BioConductor can be used to generate interactive R markdown/notebook documents or can be visualized in the R Studio's default viewer. biocViews: Visualization, GUI Author: Brian Gottfried [aut], Jayaram Kancherla [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrChart git_branch: devel git_last_commit: 2ed0205 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/epivizrChart_1.33.0.tar.gz vignettes: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.html, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.html, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.html, vignettes/epivizrChart/inst/doc/VisualizeSumExp.html vignetteTitles: Visualizing Files with epivizrChart, Visualizing genomic data in Shiny Apps using epivizrChart, Introduction to epivizrChart, Visualizing `RangeSummarizedExperiment` objects Shiny Apps using epivizrChart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.R, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.R, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.R, vignettes/epivizrChart/inst/doc/VisualizeSumExp.R dependencyCount: 96 Package: epivizrData Version: 1.39.0 Depends: R (>= 3.4), methods, epivizrServer (>= 1.1.1), Biobase Imports: S4Vectors, GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), OrganismDbi, GenomicFeatures (>= 1.61.4), Seqinfo, IRanges, ensembldb (>= 2.33.1) Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus, TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown, BiocStyle, EnsDb.Mmusculus.v79, AnnotationHub, rtracklayer, utils, RMySQL, DBI, matrixStats License: MIT + file LICENSE MD5sum: 34317668e92d0e8edb84cdb0919d353b NeedsCompilation: no Title: Data Management API for epiviz interactive visualization app Description: Serve data from Bioconductor Objects through a WebSocket connection. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre], Florin Chelaru [aut] Maintainer: Hector Corrada Bravo URL: http://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrData/issues git_url: https://git.bioconductor.org/packages/epivizrData git_branch: devel git_last_commit: ec95aa6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/epivizrData_1.39.0.tar.gz vignettes: vignettes/epivizrData/inst/doc/epivizrData.html vignetteTitles: epivizrData Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R importsMe: epivizr, epivizrChart, scTreeViz dependencyCount: 92 Package: epivizrServer Version: 1.39.0 Depends: R (>= 3.2.3), methods Imports: httpuv (>= 1.3.0), R6 (>= 2.0.0), rjson, mime (>= 0.2) Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 26c0278236e322e9063aaf24cc8357d1 NeedsCompilation: no Title: WebSocket server infrastructure for epivizr apps and packages Description: This package provides objects to manage WebSocket connections to epiviz apps. Other epivizr package use this infrastructure. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrServer git_url: https://git.bioconductor.org/packages/epivizrServer git_branch: devel git_last_commit: a4d2e93 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/epivizrServer_1.39.0.tar.gz vignettes: vignettes/epivizrServer/inst/doc/epivizrServer.html vignetteTitles: epivizrServer Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: epivizrData importsMe: epivizr, epivizrChart, epivizrStandalone, scTreeViz dependencyCount: 16 Package: epivizrStandalone Version: 1.39.0 Depends: R (>= 3.2.3), epivizr (>= 2.3.6), methods Imports: git2r, epivizrServer, Seqinfo, BiocGenerics, GenomicFeatures, S4Vectors Suggests: testthat, knitr, rmarkdown, OrganismDbi (>= 1.13.9), Mus.musculus, Biobase, BiocStyle License: MIT + file LICENSE MD5sum: f1857a9a11c2aed6d9dbb04b1907d2b5 NeedsCompilation: no Title: Run Epiviz Interactive Genomic Data Visualization App within R Description: This package imports the epiviz visualization JavaScript app for genomic data interactive visualization. The 'epivizrServer' package is used to provide a web server running completely within R. This standalone version allows to browse arbitrary genomes through genome annotations provided by Bioconductor packages. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Jayaram Kancherla Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrStandalone git_branch: devel git_last_commit: 8cbf848 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/epivizrStandalone_1.39.0.tar.gz vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html vignetteTitles: Introduction to epivizrStandalone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: scTreeViz dependencyCount: 104 Package: erccdashboard Version: 1.45.0 Depends: R (>= 4.0), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0) Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, qvalue, reshape2, ROCR, scales, stringr, knitr Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 7aa070e8a0054b2d4c16957aa614cea1 NeedsCompilation: no Title: Assess Differential Gene Expression Experiments with ERCC Controls Description: Technical performance metrics for differential gene expression experiments using External RNA Controls Consortium (ERCC) spike-in ratio mixtures. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Genetics, Microarray, mRNAMicroarray, RNASeq, BatchEffect, MultipleComparison, QualityControl Author: Sarah Munro, Steve Lund Maintainer: Sarah Munro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/erccdashboard git_branch: devel git_last_commit: 44713e8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/erccdashboard_1.45.0.tar.gz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.html vignetteTitles: erccdashboard introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R dependencyCount: 50 Package: ERSSA Version: 1.29.0 Depends: R (>= 4.0.0) Imports: edgeR (>= 3.23.3), DESeq2 (>= 1.21.16), ggplot2 (>= 3.0.0), RColorBrewer (>= 1.1-2), plyr (>= 1.8.4), BiocParallel (>= 1.15.8), apeglm (>= 1.4.2), grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: e3fa2d90f5b484b9b320095d26786634 NeedsCompilation: no Title: Empirical RNA-seq Sample Size Analysis Description: The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate levels. This allows the user to determine, without relying on any a priori assumptions, whether sufficient differential detection has been acheived with their RNA-seq dataset. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, RNASeq, MultipleComparison, QualityControl Author: Zixuan Shao [aut, cre] Maintainer: Zixuan Shao URL: https://github.com/zshao1/ERSSA VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ERSSA git_branch: devel git_last_commit: 0abfd23 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ERSSA_1.29.0.tar.gz vignettes: vignettes/ERSSA/inst/doc/ERSSA.html vignetteTitles: ERSSA Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ERSSA/inst/doc/ERSSA.R dependencyCount: 69 Package: esATAC Version: 1.33.1 Depends: R (>= 4.0.0), Rsamtools, GenomicRanges, ShortRead, pipeFrame Imports: Rcpp (>= 0.12.11), methods, knitr, Rbowtie2, rtracklayer, ggplot2, Biostrings, ChIPseeker, clusterProfiler, igraph, rJava, magrittr, digest, BSgenome, AnnotationDbi, GenomicAlignments, GenomicFeatures, R.utils, Seqinfo, BiocGenerics, S4Vectors, IRanges, rmarkdown, tools, VennDiagram, grid, JASPAR2018, TFBSTools, grDevices, graphics, stats, utils, parallel, corrplot, BiocManager, motifmatchr LinkingTo: Rcpp Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat, webshot, prettydoc License: GPL-3 | file LICENSE Archs: x64 MD5sum: 9b8b3b270f63b26b84df4dd5ba94337c NeedsCompilation: yes Title: An Easy-to-use Systematic pipeline for ATACseq data analysis Description: This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly. biocViews: ImmunoOncology, Sequencing, DNASeq, QualityControl, Alignment, Preprocessing, Coverage, ATACSeq, DNaseSeq Author: Zheng Wei, Wei Zhang Maintainer: Zheng Wei URL: https://github.com/wzthu/esATAC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/wzthu/esATAC/issues git_url: https://git.bioconductor.org/packages/esATAC git_branch: devel git_last_commit: f884d0f git_last_commit_date: 2026-03-22 Date/Publication: 2026-04-20 source.ver: src/contrib/esATAC_1.33.1.tar.gz vignettes: vignettes/esATAC/inst/doc/esATAC-Introduction.html vignetteTitles: esATAC: an Easy-to-use Systematic pipeline for ATAC-seq data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/esATAC/inst/doc/esATAC-Introduction.R dependencyCount: 193 Package: escape Version: 2.7.3 Depends: R (>= 4.1) Imports: ggdist, ggplot2 (>= 3.5.0), grDevices, Matrix, MatrixGenerics, methods, stats, SummarizedExperiment, utils Suggests: AUCell, BiocParallel, BiocStyle, DelayedMatrixStats, dplyr, fgsea, GSEABase, ggraph, ggridges, ggpointdensity, GSVA, hexbin, igraph, irlba, knitr, msigdb, patchwork, rmarkdown, rlang, scran, SeuratObject, Seurat, SingleCellExperiment, spelling, stringr, testthat (>= 3.0.0), UCell License: MIT + file LICENSE MD5sum: a17c1f02818908515178938be18ad517 NeedsCompilation: no Title: Easy single cell analysis platform for enrichment Description: A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells. Alternatively, escape supports use of rank-based GSEA, such as the use of differential gene expression via fgsea. biocViews: Software, SingleCell, Classification, Annotation, GeneSetEnrichment, Sequencing, GeneSignaling, Pathways Author: Nick Borcherding [aut, cre], Jared Andrews [aut], Tobias Hoch [ctb], Alexei Martsinkovskiy [ctb] Maintainer: Nick Borcherding VignetteBuilder: knitr BugReports: https://github.com/BorchLab/escape/issues git_url: https://git.bioconductor.org/packages/escape git_branch: devel git_last_commit: 38f2be3 git_last_commit_date: 2026-04-03 Date/Publication: 2026-04-20 source.ver: src/contrib/escape_2.7.3.tar.gz vignettes: vignettes/escape/inst/doc/escape.html vignetteTitles: Escape-ingToWork hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/escape/inst/doc/escape.R importsMe: GSABenchmark suggestsMe: Cepo dependencyCount: 52 Package: escheR Version: 1.11.0 Depends: ggplot2, R (>= 4.3) Imports: SpatialExperiment (>= 1.6.1), SingleCellExperiment, rlang, SummarizedExperiment Suggests: STexampleData, BumpyMatrix, knitr, rmarkdown, BiocStyle, ggpubr, scran, scater, scuttle, Seurat, hexbin License: MIT + file LICENSE MD5sum: e8478a269985ff115f682ccc737b79d6 NeedsCompilation: no Title: Unified multi-dimensional visualizations with Gestalt principles Description: The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide this open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows. biocViews: Spatial, SingleCell, Transcriptomics, Visualization, Software Author: Boyi Guo [aut, cre] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ), Erik D. Nelson [ctb] (ORCID: ) Maintainer: Boyi Guo URL: https://github.com/boyiguo1/escheR VignetteBuilder: knitr BugReports: https://github.com/boyiguo1/escheR/issues git_url: https://git.bioconductor.org/packages/escheR git_branch: devel git_last_commit: f2a303a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/escheR_1.11.0.tar.gz vignettes: vignettes/escheR/inst/doc/more_than_visium.html, vignettes/escheR/inst/doc/SRT_eg.html vignetteTitles: beyond_visium, Getting Start with `escheR` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/escheR/inst/doc/more_than_visium.R, vignettes/escheR/inst/doc/SRT_eg.R importsMe: SpotSweeper suggestsMe: tpSVG dependencyCount: 75 Package: esetVis Version: 1.37.0 Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils, grDevices, methods Suggests: ggplot2, ggvis, plotly, ggrepel, knitr, rmarkdown, ALL, hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment, GO.db License: GPL-3 MD5sum: 204b1b27f2cd26757371b2d727da8a48 NeedsCompilation: no Title: Visualizations of expressionSet Bioconductor object Description: Utility functions for visualization of expressionSet (or SummarizedExperiment) Bioconductor object, including spectral map, tsne and linear discriminant analysis. Static plot via the ggplot2 package or interactive via the ggvis or rbokeh packages are available. biocViews: Visualization, DataRepresentation, DimensionReduction, PrincipalComponent, Pathways Author: Laure Cougnaud Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/esetVis git_branch: devel git_last_commit: 966da28 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/esetVis_1.37.0.tar.gz vignettes: vignettes/esetVis/inst/doc/esetVis-vignette.html vignetteTitles: esetVis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/esetVis/inst/doc/esetVis-vignette.R dependencyCount: 55 Package: eudysbiome Version: 1.41.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: b3938b9e017bb224448c27864f099bf5 NeedsCompilation: no Title: Cartesian plot and contingency test on 16S Microbial data Description: eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition. Author: Xiaoyuan Zhou, Christine Nardini Maintainer: Xiaoyuan Zhou git_url: https://git.bioconductor.org/packages/eudysbiome git_branch: devel git_last_commit: 112407c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/eudysbiome_1.41.0.tar.gz vignettes: vignettes/eudysbiome/inst/doc/eudysbiome.pdf vignetteTitles: eudysbiome User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eudysbiome/inst/doc/eudysbiome.R dependencyCount: 34 Package: evaluomeR Version: 1.27.0 Depends: R (>= 3.6), SummarizedExperiment, MultiAssayExperiment, cluster (>= 2.0.9), fpc (>= 2.2-3), randomForest (>= 4.6.14), flexmix (>= 2.3.15), RSKC (>= 2.4.2), sparcl (>= 1.0.4) Imports: corrplot (>= 0.84), grDevices, graphics, reshape2, ggplot2, ggdendro, plotrix, stats, matrixStats, Rdpack, MASS, class, prabclus, mclust, kableExtra, dplyr, dendextend (>= 1.16.0) Suggests: BiocStyle, knitr, rmarkdown, magrittr License: GPL-3 MD5sum: 29cf8c765a41ef96f985590981340c8f NeedsCompilation: no Title: Evaluation of Bioinformatics Metrics Description: Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics. biocViews: Clustering, Classification, FeatureExtraction Author: José Antonio Bernabé-Díaz [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut], Manuel Quesada-Martínez [aut], Astrid Duque-Ramos [aut], Jesualdo Tomás Fernández-Breis [aut] Maintainer: José Antonio Bernabé-Díaz URL: https://github.com/neobernad/evaluomeR VignetteBuilder: knitr BugReports: https://github.com/neobernad/evaluomeR/issues git_url: https://git.bioconductor.org/packages/evaluomeR git_branch: devel git_last_commit: 71a13fd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/evaluomeR_1.27.0.tar.gz vignettes: vignettes/evaluomeR/inst/doc/manual.html vignetteTitles: Evaluation of Bioinformatics Metrics with evaluomeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/evaluomeR/inst/doc/manual.R dependencyCount: 112 Package: EWCE Version: 1.19.1 Depends: R (>= 4.2), RNOmni (>= 1.0) Imports: stats, utils, methods, ewceData (>= 1.7.1), dplyr, ggplot2, reshape2, limma, stringr, HGNChelper, Matrix, parallel, SingleCellExperiment, SummarizedExperiment, DelayedArray, BiocParallel, orthogene (>= 0.99.8), data.table Suggests: rworkflows, remotes, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), readxl, memoise, markdown, sctransform, DESeq2, MAST, DelayedMatrixStats, ggdendro, scales, patchwork License: GPL-3 MD5sum: 49ad40b3aacc1daa91313f63f6780ac8 NeedsCompilation: no Title: Expression Weighted Celltype Enrichment Description: Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses. biocViews: GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Microarray, mRNAMicroarray, OneChannel, RNASeq, BiomedicalInformatics, Proteomics, Visualization, FunctionalGenomics, SingleCell Author: Alan Murphy [aut] (ORCID: ), Brian Schilder [aut] (ORCID: ), Hiranyamaya Dash [cre] (ORCID: ), Nathan Skene [aut] (ORCID: ) Maintainer: Hiranyamaya Dash URL: https://github.com/NathanSkene/EWCE VignetteBuilder: knitr BugReports: https://github.com/NathanSkene/EWCE/issues git_url: https://git.bioconductor.org/packages/EWCE git_branch: devel git_last_commit: 4d035da git_last_commit_date: 2026-03-18 Date/Publication: 2026-04-20 source.ver: src/contrib/EWCE_1.19.1.tar.gz vignettes: vignettes/EWCE/inst/doc/EWCE.html, vignettes/EWCE/inst/doc/extended.html vignetteTitles: Getting started, Extended examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EWCE/inst/doc/EWCE.R, vignettes/EWCE/inst/doc/extended.R dependencyCount: 204 Package: ExCluster Version: 1.29.0 Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges Imports: stats, methods, grDevices, graphics, utils License: GPL-3 MD5sum: 8578cd9290f65b5cf2808ba59fe2ad3e NeedsCompilation: no Title: ExCluster robustly detects differentially expressed exons between two conditions of RNA-seq data, requiring at least two independent biological replicates per condition Description: ExCluster flattens Ensembl and GENCODE GTF files into GFF files, which are used to count reads per non-overlapping exon bin from BAM files. This read counting is done using the function featureCounts from the package Rsubread. Library sizes are normalized across all biological replicates, and ExCluster then compares two different conditions to detect signifcantly differentially spliced genes. This process requires at least two independent biological repliates per condition, and ExCluster accepts only exactly two conditions at a time. ExCluster ultimately produces false discovery rates (FDRs) per gene, which are used to detect significance. Exon log2 fold change (log2FC) means and variances may be plotted for each significantly differentially spliced gene, which helps scientists develop hypothesis and target differential splicing events for RT-qPCR validation in the wet lab. biocViews: ImmunoOncology, DifferentialSplicing, RNASeq, Software Author: R. Matthew Tanner, William L. Stanford, and Theodore J. Perkins Maintainer: R. Matthew Tanner git_url: https://git.bioconductor.org/packages/ExCluster git_branch: devel git_last_commit: 9571f20 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ExCluster_1.29.0.tar.gz vignettes: vignettes/ExCluster/inst/doc/ExCluster.pdf vignetteTitles: ExCluster Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExCluster/inst/doc/ExCluster.R dependencyCount: 58 Package: ExiMiR Version: 2.53.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), affy (>= 1.26.1), limma Imports: affyio(>= 1.13.3), Biobase(>= 2.5.5), preprocessCore(>= 1.10.0) Suggests: mirna10cdf License: GPL-2 MD5sum: 24986e251ff1edfa1fca9f2e14dc099e NeedsCompilation: no Title: R functions for the normalization of Exiqon miRNA array data Description: This package contains functions for reading raw data in ImaGene TXT format obtained from Exiqon miRCURY LNA arrays, annotating them with appropriate GAL files, and normalizing them using a spike-in probe-based method. Other platforms and data formats are also supported. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, GeneExpression, Transcription Author: Sylvain Gubian , Alain Sewer , PMP SA Maintainer: Sylvain Gubian git_url: https://git.bioconductor.org/packages/ExiMiR git_branch: devel git_last_commit: 6d3b680 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ExiMiR_2.53.0.tar.gz vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf vignetteTitles: Description of ExiMiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.R dependencyCount: 14 Package: ExperimentHub Version: 3.1.0 Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 3.99.3), BiocFileCache (>= 2.99.3) Imports: utils, S4Vectors, BiocManager, rappdirs Suggests: knitr, BiocStyle, rmarkdown, HubPub, GenomicAlignments Enhances: ExperimentHubData License: Artistic-2.0 MD5sum: 233481d7258ef577955011d22aa5d0ef NeedsCompilation: no Title: Client to access ExperimentHub resources Description: This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/ExperimentHub VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ExperimentHub/issues git_url: https://git.bioconductor.org/packages/ExperimentHub git_branch: devel git_last_commit: 9d5eefe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ExperimentHub_3.1.0.tar.gz vignettes: vignettes/ExperimentHub/inst/doc/ExperimentHub.html vignetteTitles: ExperimentHub: Access the ExperimentHub Web Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHub/inst/doc/ExperimentHub.R dependsOnMe: adductomicsR, CoSIA, iSEEhub, LRcell, octad, SeqSQC, AWAggregatorData, BeadSorted.Saliva.EPIC, biscuiteerData, bodymapRat, CellMapperData, clustifyrdatahub, CoSIAdata, crisprScoreData, curatedAdipoChIP, CytoMethIC, DMRcatedata, DMRsegaldata, eoPredData, EpiMix.data, ewceData, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, HDCytoData, HiContactsData, HighlyReplicatedRNASeq, HumanAffyData, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, multiWGCNAdata, muscData, muSpaData, NanoporeRNASeq, NestLink, nullrangesData, ObMiTi, octad.db, RNAmodR.Data, scMultiome, scpdata, sesameData, SimBenchData, SpatialDatasets, spatialDmelxsim, STexampleData, tartare, TENxVisiumData, TENxXeniumData, VectraPolarisData, WeberDivechaLCdata importsMe: BiocHubsShiny, BloodGen3Module, CBNplot, coMethDMR, CTdata, DeconvoBuddies, DMRcate, EpiMix, epimutacions, EpipwR, epiregulon, ExperimentHubData, GSEABenchmarkeR, hpar, iModMix, knowYourCG, m6Aboost, MACSr, MatrixQCvis, methodical, MethReg, methylclock, Moonlight2R, MsDataHub, orthos, postNet, shinyDSP, signatureSearch, singleCellTK, spatialFDA, TENET, TFEA.ChIP, adductData, BioImageDbs, celldex, CENTREprecomputed, cfToolsData, ChIPDBData, chipseqDBData, CLLmethylation, curatedMetagenomicData, curatedPCaData, curatedTBData, curatedTCGAData, depmap, DoReMiTra, DropletTestFiles, DuoClustering2018, easierData, emtdata, EMTscoreData, EpipwR.data, FieldEffectCrc, gDNAinRNAseqData, GenomicDistributionsData, HarmonizedTCGAData, HCAData, HCATonsilData, HMP16SData, HMP2Data, humanHippocampus2024, imcdatasets, iModMixData, JohnsonKinaseData, LRcellTypeMarkers, marinerData, MerfishData, methylclockData, MethylSeqData, microbiomeDataSets, MouseAgingData, MouseGastrulationData, MouseThymusAgeing, msigdb, NxtIRFdata, orthosData, PhyloProfileData, preciseTADhub, ProteinGymR, raerdata, scaeData, scRNAseq, SFEData, signatureSearchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialLIBD, TabulaMurisData, TabulaMurisSenisData, TENET.ExperimentHub, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, TumourMethData, xcoredata, OSTA suggestsMe: AlphaMissenseR, ANF, AnnotationHub, AWAggregator, bambu, Banksy, celaref, CellMapper, crumblr, DeeDeeExperiment, DESpace, dreamlet, ELMER, genomicInstability, h5mread, HDF5Array, HVP, jazzPanda, mariner, missMethyl, MsBackendRawFileReader, multiWGCNA, muscat, MutSeqR, nullranges, planet, quantiseqr, rawDiag, rawrr, recountmethylation, SingleMoleculeFootprinting, sosta, SparseArray, SPOTlight, standR, TCGAbiolinks, TENxIO, Voyager, xcore, BioPlex, celarefData, curatedAdipoArray, epimutacionsData, GSE103322, GSE13015, GSE159526, GSE62944, muleaData, smokingMouse, SubcellularSpatialData, tissueTreg, TransOmicsData, easyEWAS dependencyCount: 63 Package: ExperimentSubset Version: 1.21.0 Depends: R (>= 4.0.0), SummarizedExperiment, SingleCellExperiment, SpatialExperiment, TreeSummarizedExperiment Imports: methods, Matrix, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, stats, scran, scater, scds, TENxPBMCData, airway License: MIT + file LICENSE MD5sum: 41825894743266ac955d378317ca524b NeedsCompilation: no Title: Manages subsets of data with Bioconductor Experiment objects Description: Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for one or more matrix-like assays along with the associated row and column data. Often only a subset of the original data is needed for down-stream analysis. For example, filtering out poor quality samples will require excluding some columns before analysis. The ExperimentSubset object is a container to efficiently manage different subsets of the same data without having to make separate objects for each new subset. biocViews: Infrastructure, Software, DataImport, DataRepresentation Author: Irzam Sarfraz [aut, cre] (ORCID: ), Muhammad Asif [aut, ths] (ORCID: ), Joshua D. Campbell [aut] (ORCID: ) Maintainer: Irzam Sarfraz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentSubset git_branch: devel git_last_commit: a61ebd5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ExperimentSubset_1.21.0.tar.gz vignettes: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.html vignetteTitles: An introduction to ExperimentSubset class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.R dependencyCount: 87 Package: ExploreModelMatrix Version: 1.23.1 Imports: shiny (>= 1.5.0), shinydashboard, DT, cowplot, utils, dplyr, magrittr, tidyr, ggplot2, stats, methods, rintrojs, scales, tibble, MASS, limma, S4Vectors, shinyjs, rlang Suggests: testthat (>= 2.1.0), knitr, rmarkdown, htmltools, BiocStyle License: MIT + file LICENSE MD5sum: 506b058f27e90b456eec9a5944ff7596 NeedsCompilation: no Title: Graphical Exploration of Design Matrices Description: Given a sample data table and a design formula, ExploreModelMatrix generates an interactive application for exploration of the resulting design matrix. This can be helpful for interpreting model coefficients and constructing appropriate contrasts in (generalized) linear models. Static visualizations can also be generated. biocViews: ExperimentalDesign, Regression, DifferentialExpression, ShinyApps Author: Charlotte Soneson [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ), Michael Love [aut] (ORCID: ), Florian Geier [aut] (ORCID: ), Michael Stadler [aut] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/ExploreModelMatrix VignetteBuilder: knitr BugReports: https://github.com/csoneson/ExploreModelMatrix/issues git_url: https://git.bioconductor.org/packages/ExploreModelMatrix git_branch: devel git_last_commit: d22b2d9 git_last_commit_date: 2026-03-16 Date/Publication: 2026-04-20 source.ver: src/contrib/ExploreModelMatrix_1.23.1.tar.gz vignettes: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.html, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.html vignetteTitles: ExploreModelMatrix-deploy, ExploreModelMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.R, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.R suggestsMe: msqrob2 dependencyCount: 80 Package: ExpoRiskR Version: 0.99.4 Imports: stats, ggplot2, igraph, SummarizedExperiment, S4Vectors, utils Suggests: BiocStyle, BiocCheck, knitr, rmarkdown, withr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 7f11f1f8fe3835e02cd8e38c9f2a1f3b NeedsCompilation: no Title: Exposure-Aware Multi-Omics Risk Modeling Description: ExpoRiskR provides tools for exposure-aware multi-omics risk modeling in translational and environmental health studies. The package aligns sample identifiers across exposure and multi-omics blocks, performs lightweight preprocessing, and fits exposure-adjusted association models to build interpretable microbe–metabolite networks. It also computes simple exposure perturbation summaries and generates publication-ready visualizations. Workflows support both matrix-based inputs and SummarizedExperiment objects. biocViews: Software, Network, SystemsBiology, Metabolomics, Microbiome, Regression Author: Prem Prashant Chaudhary [aut, cre] (ORCID: ) Maintainer: Prem Prashant Chaudhary URL: https://github.com/ppchaudhary/ExpoRiskR VignetteBuilder: knitr BugReports: https://github.com/ppchaudhary/ExpoRiskR/issues git_url: https://git.bioconductor.org/packages/ExpoRiskR git_branch: devel git_last_commit: 7152b0d git_last_commit_date: 2026-03-11 Date/Publication: 2026-04-20 source.ver: src/contrib/ExpoRiskR_0.99.4.tar.gz vignettes: vignettes/ExpoRiskR/inst/doc/ExpoRiskR.html vignetteTitles: ExpoRiskR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExpoRiskR/inst/doc/ExpoRiskR.R dependencyCount: 45 Package: ExpressionAtlas Version: 2.3.0 Depends: R (>= 4.2.0), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2, RCurl, jsonlite, BiocStyle Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: d741e00ba78a9b8f819a95bb1a225f5b NeedsCompilation: no Title: Download datasets from EMBL-EBI Expression Atlas Description: This package is for searching for datasets in EMBL-EBI Expression Atlas, and downloading them into R for further analysis. Each Expression Atlas dataset is represented as a SimpleList object with one element per platform. Sequencing data is contained in a SummarizedExperiment object, while microarray data is contained in an ExpressionSet or MAList object. biocViews: ExpressionData, ExperimentData, SequencingData, MicroarrayData, ArrayExpress Author: Maria Keays [aut] (ORCID: ), Pedro Madrigal [aut] (ORCID: ), Anil Thanki [cre] (ORCID: ) Maintainer: Anil Thanki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExpressionAtlas git_branch: devel git_last_commit: a94288f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ExpressionAtlas_2.3.0.tar.gz vignettes: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html vignetteTitles: ExpressionAtlas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R suggestsMe: spatialHeatmap dependencyCount: 64 Package: extraChIPs Version: 1.15.2 Depends: BiocParallel, R (>= 4.2.0), GenomicRanges, ggplot2 (>= 4.0.0), ggside (>= 0.4.0), Seqinfo, SummarizedExperiment (>= 1.39.1), tibble Imports: csaw, dplyr (>= 1.1.1), edgeR (>= 4.0), forcats, GenomeInfoDb, glue, ggrepel, InteractionSet, IRanges, matrixStats, methods, patchwork, RColorBrewer, rlang, Rsamtools, rtracklayer, S4Vectors, scales, stats, stringr, tidyr, tidyselect, vctrs Suggests: apeglm, BiocStyle, SimpleUpset, covr, DESeq2, EnrichedHeatmap, GenomicAlignments, GenomicInteractions, Gviz, ggforce, harmonicmeanp, here, knitr, limma, magrittr, plyranges, quantro, rmarkdown, testthat (>= 3.0.0), tidyverse, VennDiagram License: GPL-3 MD5sum: 2327ac12fb7421dc1ed62552bb64e8d4 NeedsCompilation: yes Title: Additional functions for working with ChIP-Seq data Description: This package builds on existing tools and adds some simple but extremely useful capabilities for working wth ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented. biocViews: ChIPSeq, HiC, Sequencing, Coverage Author: Stevie Pederson [aut, cre] (ORCID: ) Maintainer: Stevie Pederson URL: https://github.com/smped/extraChIPs VignetteBuilder: knitr BugReports: https://github.com/smped/extraChIPs/issues git_url: https://git.bioconductor.org/packages/extraChIPs git_branch: devel git_last_commit: 980cc78 git_last_commit_date: 2025-11-18 Date/Publication: 2026-04-20 source.ver: src/contrib/extraChIPs_1.15.2.tar.gz vignettes: vignettes/extraChIPs/inst/doc/differential_signal_fixed.html, vignettes/extraChIPs/inst/doc/differential_signal_sliding.html, vignettes/extraChIPs/inst/doc/range_based_functions.html vignetteTitles: Differential Signal Analysis (Fixed-Width Windows), Differential Signal Analysis (Sliding Windows), Range-Based Operations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/extraChIPs/inst/doc/differential_signal_fixed.R, vignettes/extraChIPs/inst/doc/differential_signal_sliding.R, vignettes/extraChIPs/inst/doc/range_based_functions.R suggestsMe: motifTestR, transmogR dependencyCount: 97 Package: fabia Version: 2.57.0 Depends: R (>= 3.6.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: 0dc8c87040ee36b42c8bfd7f8153713d NeedsCompilation: yes Title: FABIA: Factor Analysis for Bicluster Acquisition Description: Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/fabia/fabia.html git_url: https://git.bioconductor.org/packages/fabia git_branch: devel git_last_commit: 2f36a5b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fabia_2.57.0.tar.gz vignettes: vignettes/fabia/inst/doc/fabia.pdf vignetteTitles: FABIA: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fabia/inst/doc/fabia.R dependsOnMe: hapFabia importsMe: miRSM, mosbi suggestsMe: fabiaData, SUMO dependencyCount: 8 Package: factDesign Version: 1.87.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: 5001321cac4fc1f040ee5ab5752dd186 NeedsCompilation: no Title: Factorial designed microarray experiment analysis Description: This package provides a set of tools for analyzing data from a factorial designed microarray experiment, or any microarray experiment for which a linear model is appropriate. The functions can be used to evaluate tests of contrast of biological interest and perform single outlier detection. biocViews: Microarray, DifferentialExpression Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/factDesign git_branch: devel git_last_commit: dc551b8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/factDesign_1.87.0.tar.gz vignettes: vignettes/factDesign/inst/doc/factDesign.pdf vignetteTitles: factDesign hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/factDesign/inst/doc/factDesign.R dependencyCount: 7 Package: factR Version: 1.13.0 Depends: R (>= 4.2) Imports: BiocGenerics (>= 0.46), Biostrings (>= 2.68), GenomeInfoDb (>= 1.36), dplyr (>= 1.1), GenomicFeatures (>= 1.52), GenomicRanges (>= 1.52), IRanges (>= 2.34), purrr (>= 1.0), rtracklayer (>= 1.60), tidyr (>= 1.3), methods (>= 4.3), BiocParallel (>= 1.34), S4Vectors (>= 0.38), data.table (>= 1.14), rlang (>= 1.1), tibble (>= 3.2), wiggleplotr (>= 1.24), RCurl (>= 1.98), XML (>= 3.99), drawProteins (>= 1.20), ggplot2 (>= 3.4), stringr (>= 1.5), pbapply (>= 1.7), stats (>= 4.3), utils (>= 4.3), graphics (>= 4.3), crayon (>= 1.5) Suggests: AnnotationHub (>= 2.22), BSgenome (>= 1.58), BSgenome.Mmusculus.UCSC.mm10, testthat, knitr, rmarkdown, markdown, zeallot, rmdformats, bio3d (>= 2.4), signalHsmm (>= 1.5), tidyverse (>= 1.3), covr, patchwork License: file LICENSE MD5sum: 87f3f4b6285c3e44d414230f51c2ff45 NeedsCompilation: no Title: Functional Annotation of Custom Transcriptomes Description: factR contain tools to process and interact with custom-assembled transcriptomes (GTF). At its core, factR constructs CDS information on custom transcripts and subsequently predicts its functional output. In addition, factR has tools capable of plotting transcripts, correcting chromosome and gene information and shortlisting new transcripts. biocViews: AlternativeSplicing, FunctionalPrediction, GenePrediction Author: Fursham Hamid [aut, cre] Maintainer: Fursham Hamid URL: https://fursham-h.github.io/factR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/factR git_branch: devel git_last_commit: 20ced47 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/factR_1.13.0.tar.gz vignettes: vignettes/factR/inst/doc/factR.html vignetteTitles: factR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/factR/inst/doc/factR.R dependencyCount: 110 Package: faers Version: 1.7.0 Depends: R (>= 3.5.0) Imports: BiocParallel, brio, cli, curl (>= 6.0.0), data.table, httr2 (>= 1.0.0), MCMCpack, methods, openEBGM, rlang, rvest, tools, utils, vroom, xml2 Suggests: BiocStyle, countrycode, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: c8311a030ca69f3f9180ffc935b93756 NeedsCompilation: no Title: R interface for FDA Adverse Event Reporting System Description: The FDA Adverse Event Reporting System (FAERS) is a database used for the spontaneous reporting of adverse events and medication errors related to human drugs and therapeutic biological products. faers pacakge serves as the interface between the FAERS database and R. Furthermore, faers pacakge offers a standardized approach for performing pharmacovigilance analysis. biocViews: Software, DataImport, BiomedicalInformatics, Pharmacogenomics, Pharmacogenomics Author: Yun Peng [aut, cre] (ORCID: ), YuXuan Song [aut], Caipeng Qin [aut], JiaXing Lin [aut] Maintainer: Yun Peng VignetteBuilder: knitr BugReports: https://github.com/WangLabCSU/faers git_url: https://git.bioconductor.org/packages/faers git_branch: devel git_last_commit: 5bce2e7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/faers_1.7.0.tar.gz vignettes: vignettes/faers/inst/doc/FAERS-Pharmacovigilance.html vignetteTitles: FAERS-Pharmacovigilance hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/faers/inst/doc/FAERS-Pharmacovigilance.R dependencyCount: 75 Package: FamAgg Version: 1.39.0 Depends: methods, kinship2, igraph Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: 6c29d43afe4fa16ffab91a7beebf8676 NeedsCompilation: no Title: Pedigree Analysis and Familial Aggregation Description: Framework providing basic pedigree analysis and plotting utilities as well as a variety of methods to evaluate familial aggregation of traits in large pedigrees. biocViews: Genetics Author: J. Rainer, D. Taliun, C.X. Weichenberger Maintainer: Johannes Rainer URL: https://github.com/EuracBiomedicalResearch/FamAgg VignetteBuilder: knitr BugReports: https://github.com/EuracBiomedicalResearch/FamAgg/issues git_url: https://git.bioconductor.org/packages/FamAgg git_branch: devel git_last_commit: e1e4503 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FamAgg_1.39.0.tar.gz vignettes: vignettes/FamAgg/inst/doc/FamAgg.html vignetteTitles: Pedigree Analysis and Familial Aggregation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R dependencyCount: 89 Package: famat Version: 1.21.4 Depends: R (>= 4.3) Imports: KEGGREST, mgcv, stats, BiasedUrn, dplyr, gprofiler2, rWikiPathways, reactome.db, stringr, GO.db, ontologyIndex, tidyr, shiny, shinydashboard, shinyBS, plotly, magrittr, DT, clusterProfiler, org.Hs.eg.db, ReactomePA, enrichplot Suggests: BiocStyle, knitr, rmarkdown, testthat, BiocManager License: GPL-3 MD5sum: d0b26179f3129993f2b4243e226839e9 NeedsCompilation: no Title: Functional analysis of metabolic and transcriptomic data Description: Famat is made to collect data about lists of genes and metabolites provided by user, and to visualize it through a Shiny app. Information collected is: - Pathways containing some of the user's genes and metabolites (obtained using a pathway enrichment analysis). - Direct interactions between user's elements inside pathways. - Information about elements (their identifiers and descriptions). - Go terms enrichment analysis performed on user's genes. The Shiny app is composed of: - information about genes, metabolites, and direct interactions between them inside pathways. - an heatmap showing which elements from the list are in pathways (pathways are structured in hierarchies). - hierarchies of enriched go terms using Molecular Function and Biological Process. biocViews: FunctionalPrediction, GeneSetEnrichment, Pathways, GO, Reactome, KEGG Author: Mathieu Charles [aut, cre] (ORCID: ) Maintainer: Mathieu Charles URL: https://github.com/emiliesecherre/famat VignetteBuilder: knitr BugReports: https://github.com/emiliesecherre/famat/issues git_url: https://git.bioconductor.org/packages/famat git_branch: devel git_last_commit: 449de54 git_last_commit_date: 2026-03-27 Date/Publication: 2026-04-20 source.ver: src/contrib/famat_1.21.4.tar.gz vignettes: vignettes/famat/inst/doc/famat.html vignetteTitles: famat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/famat/inst/doc/famat.R dependencyCount: 174 Package: fastLiquidAssociation Version: 1.47.0 Depends: methods, LiquidAssociation, parallel, doParallel, stats, Hmisc, utils Imports: WGCNA, impute, preprocessCore Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: a0c6865bfdbf026e5ae2f83b36edbd3a NeedsCompilation: no Title: functions for genome-wide application of Liquid Association Description: This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses. biocViews: Software, GeneExpression, Genetics, Pathways, CellBiology Author: Tina Gunderson Maintainer: Tina Gunderson git_url: https://git.bioconductor.org/packages/fastLiquidAssociation git_branch: devel git_last_commit: bde2bd2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fastLiquidAssociation_1.47.0.tar.gz vignettes: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf vignetteTitles: fastLiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.R dependencyCount: 115 Package: FastqCleaner Version: 1.29.0 Imports: methods, shiny, stats, IRanges, Biostrings, ShortRead, DT, S4Vectors, graphics, htmltools, shinyBS, Rcpp (>= 0.12.12) LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 5976271f38c7bdb6f5f8353b57a738fe NeedsCompilation: yes Title: A Shiny Application for Quality Control, Filtering and Trimming of FASTQ Files Description: An interactive web application for quality control, filtering and trimming of FASTQ files. This user-friendly tool combines a pipeline for data processing based on Biostrings and ShortRead infrastructure, with a cutting-edge visual environment. Single-Read and Paired-End files can be locally processed. Diagnostic interactive plots (CG content, per-base sequence quality, etc.) are provided for both the input and output files. biocViews: QualityControl,Sequencing,Software,SangerSeq,SequenceMatching Author: Leandro Roser [aut, cre], Fernán Agüero [aut], Daniel Sánchez [aut] Maintainer: Leandro Roser VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FastqCleaner git_branch: devel git_last_commit: 464323c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FastqCleaner_1.29.0.tar.gz vignettes: vignettes/FastqCleaner/inst/doc/Overview.html vignetteTitles: An Introduction to FastqCleaner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FastqCleaner/inst/doc/Overview.R dependencyCount: 95 Package: fastRanges Version: 0.99.2 Depends: R (>= 4.5.0) Imports: methods, S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, Rcpp LinkingTo: Rcpp Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown, pkgdown, XVector, ggplot2, dplyr, tidyr, scales License: Artistic-2.0 MD5sum: 482cec0f0f5a0365e56fc69e777a3d10 NeedsCompilation: yes Title: Deterministic Multithreaded Genomic Interval Operations Description: High-performance interval overlap and join operations for 'IRanges' and 'GenomicRanges'. The package provides deterministic multithreaded overlap computation, reusable subject indexes for repeated queries, and join helpers that keep range metadata in a consistent output grammar. biocViews: Software, Infrastructure, Sequencing Author: Chirag Parsania [aut, cre] (github: cparsania) Maintainer: Chirag Parsania URL: https://github.com/cparsania/fastRanges, https://cparsania.github.io/fastRanges/ SystemRequirements: quarto VignetteBuilder: knitr BugReports: https://github.com/cparsania/fastRanges/issues git_url: https://git.bioconductor.org/packages/fastRanges git_branch: devel git_last_commit: 8e74a30 git_last_commit_date: 2026-03-27 Date/Publication: 2026-04-20 source.ver: src/contrib/fastRanges_0.99.2.tar.gz vignettes: vignettes/fastRanges/inst/doc/fastRanges.html vignetteTitles: fastRanges: A Practical Introduction to Genomic Interval Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastRanges/inst/doc/fastRanges.R dependencyCount: 23 Package: fastreeR Version: 2.1.3 Depends: R (>= 4.4) Imports: ape, data.table, dynamicTreeCut, methods, R.utils, rJava, stats, stringr, utils Suggests: BiocFileCache, BiocStyle, ggtree, graphics, knitr, memuse, rmarkdown, spelling, testthat (>= 3.0.0) License: GPL-3 MD5sum: f08c1c44931610561f08a4f36f38206e NeedsCompilation: no Title: Phylogenetic, Distance and Other Calculations on VCF and Fasta Files Description: Calculate distances, build phylogenetic trees or perform hierarchical clustering between the samples of a VCF or FASTA file. Functions are implemented in Java-17 and called via rJava. Parallel implementation that operates directly on the VCF or FASTA file for fast execution. biocViews: Phylogenetics, Metagenomics, Clustering Author: Anestis Gkanogiannis [aut, cre] (ORCID: ) Maintainer: Anestis Gkanogiannis URL: https://github.com/gkanogiannis/fastreeR, https://github.com/gkanogiannis/BioInfoJava-Utils SystemRequirements: Java (>= 17) VignetteBuilder: knitr BugReports: https://github.com/gkanogiannis/fastreeR/issues git_url: https://git.bioconductor.org/packages/fastreeR git_branch: devel git_last_commit: e88bc87 git_last_commit_date: 2026-02-02 Date/Publication: 2026-04-20 source.ver: src/contrib/fastreeR_2.1.3.tar.gz vignettes: vignettes/fastreeR/inst/doc/fastreeR_vignette.html vignetteTitles: fastreeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastreeR/inst/doc/fastreeR_vignette.R dependencyCount: 27 Package: fastseg Version: 1.57.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, grDevices, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, BiocStyle, knitr License: LGPL (>= 2.0) MD5sum: 82d7903f6a2f8be184c57229bef7b1b3 NeedsCompilation: yes Title: fastseg - a fast segmentation algorithm Description: fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. biocViews: Classification, CopyNumberVariation Author: Guenter Klambauer [aut], Sonali Kumari [ctb], Alexander Blume [cre] Maintainer: Alexander Blume URL: http://www.bioinf.jku.at/software/fastseg/index.html VignetteBuilder: knitr BugReports: https://github.com/alexg9010/fastseg/issues git_url: https://git.bioconductor.org/packages/fastseg git_branch: devel git_last_commit: 15a937a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fastseg_1.57.0.tar.gz vignettes: vignettes/fastseg/inst/doc/fastseg.html vignetteTitles: An R Package for fast segmentation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastseg/inst/doc/fastseg.R importsMe: methylKit dependencyCount: 13 Package: fCCAC Version: 1.37.0 Depends: R (>= 4.2.0), S4Vectors, IRanges, GenomicRanges, grid Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 8f8ddcb4b280da85656294b0cee3f5fb NeedsCompilation: no Title: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets Description: Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomics, as it allows both to evaluate reproducibility of replicates, and to compare different datasets to identify potential correlations. fCCAC applies functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. fCCAC represents a more sophisticated approach that complements Pearson correlation of genomic coverage. biocViews: Epigenetics, Transcription, Sequencing, Coverage, ChIPSeq, FunctionalGenomics, RNASeq, ATACSeq, MNaseSeq Author: Pedro Madrigal [aut, cre] (ORCID: ) Maintainer: Pedro Madrigal URL: https://github.com/pmb59/fCCAC BugReports: https://github.com/pmb59/fCCAC/issues git_url: https://git.bioconductor.org/packages/fCCAC git_branch: devel git_last_commit: 5500c70 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fCCAC_1.37.0.tar.gz vignettes: vignettes/fCCAC/inst/doc/fCCAC.pdf vignetteTitles: fCCAC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCCAC/inst/doc/fCCAC.R dependencyCount: 132 Package: fCI Version: 1.41.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: fb6570d08f35677281557411cb97cfa5 NeedsCompilation: no Title: f-divergence Cutoff Index for Differential Expression Analysis in Transcriptomics and Proteomics Description: (f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods. biocViews: Proteomics Author: Shaojun Tang Maintainer: Shaojun Tang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fCI git_branch: devel git_last_commit: 489f36c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fCI_1.41.0.tar.gz vignettes: vignettes/fCI/inst/doc/fCI.html vignetteTitles: fCI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCI/inst/doc/fCI.R dependencyCount: 49 Package: fcScan Version: 1.25.0 Imports: stats, plyr, VariantAnnotation, SummarizedExperiment, rtracklayer, GenomicRanges, methods, IRanges, foreach, doParallel, parallel Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 2474a826f4a0933689be8f8ad5ad2693 NeedsCompilation: no Title: fcScan for detecting clusters of coordinates with user defined options Description: This package is used to detect combination of genomic coordinates falling within a user defined window size along with user defined overlap between identified neighboring clusters. It can be used for genomic data where the clusters are built on a specific chromosome or specific strand. Clustering can be performed with a "greedy" option allowing thus the presence of additional sites within the allowed window size. biocViews: GenomeAnnotation, Clustering Author: Abdullah El-Kurdi [aut], Ghiwa khalil [aut], Georges Khazen [ctb], Pierre Khoueiry [aut, cre] Maintainer: Pierre Khoueiry Abdullah El-Kurdi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcScan git_branch: devel git_last_commit: 5da32a8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fcScan_1.25.0.tar.gz vignettes: vignettes/fcScan/inst/doc/fcScan_vignette.html vignetteTitles: fcScan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcScan/inst/doc/fcScan_vignette.R dependencyCount: 82 Package: fdrame Version: 1.83.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) MD5sum: ea381d1f38db3b46d1f2bdb1f3c7d864 NeedsCompilation: yes Title: FDR adjustments of Microarray Experiments (FDR-AME) Description: This package contains two main functions. The first is fdr.ma which takes normalized expression data array, experimental design and computes adjusted p-values It returns the fdr adjusted p-values and plots, according to the methods described in (Reiner, Yekutieli and Benjamini 2002). The second, is fdr.gui() which creates a simple graphic user interface to access fdr.ma biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli Maintainer: Effi Kenigsberg git_url: https://git.bioconductor.org/packages/fdrame git_branch: devel git_last_commit: 6649122 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fdrame_1.83.0.tar.gz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 5 Package: FEAST Version: 1.19.0 Depends: R (>= 4.1), mclust, BiocParallel, SummarizedExperiment Imports: SingleCellExperiment, methods, stats, utils, irlba, TSCAN, SC3, matrixStats Suggests: rmarkdown, Seurat, ggpubr, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-2 MD5sum: e14d28437c30d3084f72857bbcf5070a NeedsCompilation: yes Title: FEAture SelcTion (FEAST) for Single-cell clustering Description: Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as “features”), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have significant impact on the clustering accuracy. FEAST is an R library for selecting most representative features before performing the core of scRNA-seq clustering. It can be used as a plug-in for the etablished clustering algorithms such as SC3, TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm includes three steps: 1. consensus clustering; 2. gene-level significance inference; 3. validation of an optimized feature set. biocViews: Sequencing, SingleCell, Clustering, FeatureExtraction Author: Kenong Su [aut, cre], Hao Wu [aut] Maintainer: Kenong Su VignetteBuilder: knitr BugReports: https://github.com/suke18/FEAST/issues git_url: https://git.bioconductor.org/packages/FEAST git_branch: devel git_last_commit: 56aecc5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FEAST_1.19.0.tar.gz vignettes: vignettes/FEAST/inst/doc/FEAST.html vignetteTitles: The FEAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FEAST/inst/doc/FEAST.R dependencyCount: 113 Package: FeatSeekR Version: 1.11.0 Imports: pheatmap, MASS, pracma, stats, SummarizedExperiment, methods Suggests: rmarkdown, knitr, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: 0fdc5a2126cc254d7abcd9b833233278 NeedsCompilation: no Title: FeatSeekR an R package for unsupervised feature selection Description: FeatSeekR performs unsupervised feature selection using replicated measurements. It iteratively selects features with the highest reproducibility across replicates, after projecting out those dimensions from the data that are spanned by the previously selected features. The selected a set of features has a high replicate reproducibility and a high degree of uniqueness. biocViews: Software, StatisticalMethod, FeatureExtraction, MassSpectrometry Author: Tuemay Capraz [cre, aut] (ORCID: ) Maintainer: Tuemay Capraz URL: https://github.com/tcapraz/FeatSeekR VignetteBuilder: knitr BugReports: https://github.com/tcapraz/FeatSeekR/issues git_url: https://git.bioconductor.org/packages/FeatSeekR git_branch: devel git_last_commit: a6a78bf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FeatSeekR_1.11.0.tar.gz vignettes: vignettes/FeatSeekR/inst/doc/FeatSeekR-vignette.html vignetteTitles: `FeatSeekR` user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FeatSeekR/inst/doc/FeatSeekR-vignette.R dependencyCount: 39 Package: FELLA Version: 1.31.0 Depends: R (>= 3.5.0) Imports: methods, igraph, Matrix, KEGGREST, plyr, stats, graphics, utils Suggests: shiny, DT, magrittr, visNetwork, knitr, BiocStyle, rmarkdown, testthat, biomaRt, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, GOSemSim License: GPL-3 MD5sum: 1c36a0b07fea1fcc4a7a9a3755edefac NeedsCompilation: no Title: Interpretation and enrichment for metabolomics data Description: Enrichment of metabolomics data using KEGG entries. Given a set of affected compounds, FELLA suggests affected reactions, enzymes, modules and pathways using label propagation in a knowledge model network. The resulting subnetwork can be visualised and exported. biocViews: Software, Metabolomics, GraphAndNetwork, KEGG, GO, Pathways, Network, NetworkEnrichment Author: Sergio Picart-Armada [aut, cre], Francesc Fernandez-Albert [aut], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FELLA git_branch: devel git_last_commit: fa92721 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FELLA_1.31.0.tar.gz vignettes: vignettes/FELLA/inst/doc/FELLA.pdf, vignettes/FELLA/inst/doc/musmusculus.pdf, vignettes/FELLA/inst/doc/zebrafish.pdf, vignettes/FELLA/inst/doc/quickstart.html vignetteTitles: FELLA, Example: a fatty liver study on Mus musculus, Example: oxybenzone exposition in gilt-head bream, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FELLA/inst/doc/FELLA.R, vignettes/FELLA/inst/doc/musmusculus.R, vignettes/FELLA/inst/doc/quickstart.R, vignettes/FELLA/inst/doc/zebrafish.R dependencyCount: 39 Package: fenr Version: 1.9.2 Depends: R (>= 4.1.0) Imports: tools, methods, assertthat, rlang, dplyr, tidyr, tidyselect, tibble, purrr, readr, stringr, httr2, rvest, progress, BiocFileCache, shiny, ggplot2 Suggests: BiocStyle, testthat, knitr, rmarkdown, topGO License: MIT + file LICENSE MD5sum: 52498038c8675372123d77e303d61ae3 NeedsCompilation: no Title: Fast functional enrichment for interactive applications Description: Perform fast functional enrichment on feature lists (like genes or proteins) using the hypergeometric distribution. Tailored for speed, this package is ideal for interactive platforms such as Shiny. It supports the retrieval of functional data from sources like GO, KEGG, Reactome, Bioplanet and WikiPathways. By downloading and preparing data first, it allows for rapid successive tests on various feature selections without the need for repetitive, time-consuming preparatory steps typical of other packages. biocViews: FunctionalPrediction, DifferentialExpression, GeneSetEnrichment, GO, KEGG, Reactome, Proteomics Author: Marek Gierlinski [aut, cre] (ORCID: ) Maintainer: Marek Gierlinski URL: https://github.com/bartongroup/fenr VignetteBuilder: knitr BugReports: https://github.com/bartongroup/fenr/issues git_url: https://git.bioconductor.org/packages/fenr git_branch: devel git_last_commit: 5c4b018 git_last_commit_date: 2026-03-26 Date/Publication: 2026-04-20 source.ver: src/contrib/fenr_1.9.2.tar.gz vignettes: vignettes/fenr/inst/doc/fenr.html vignetteTitles: Fast functional enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/fenr/inst/doc/fenr.R importsMe: tidyexposomics dependencyCount: 85 Package: fgga Version: 1.19.0 Depends: R (>= 4.3), RBGL Imports: graph, stats, e1071, methods, gRbase, jsonlite, BiocFileCache, curl, igraph Suggests: knitr, rmarkdown, GOstats, GO.db, BiocGenerics, pROC, RUnit, BiocStyle License: GPL-3 MD5sum: 9a9a3608f50e6e74a96c9ff98872fa20 NeedsCompilation: no Title: Hierarchical ensemble method based on factor graph Description: Package that implements the FGGA algorithm. This package provides a hierarchical ensemble method based ob factor graphs for the consistent cross-ontology annotation of protein coding genes. FGGA embodies elements of predicate logic, communication theory, supervised learning and inference in graphical models. biocViews: Software, StatisticalMethod, Classification, Network, NetworkInference, SupportVectorMachine, GraphAndNetwork, GO Author: Flavio Spetale [aut, cre] Maintainer: Flavio Spetale URL: https://github.com/fspetale/fgga VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fgga git_branch: devel git_last_commit: dbdfaea git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fgga_1.19.0.tar.gz vignettes: vignettes/fgga/inst/doc/fgga.html vignetteTitles: FGGA: Factor Graph GO Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgga/inst/doc/fgga.R dependencyCount: 61 Package: FGNet Version: 3.45.0 Depends: R (>= 4.2.0) Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png, methods, stats, utils, graphics, grDevices Suggests: RCurl, gage, topGO, GO.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi, BiocManager License: GPL (>= 2) MD5sum: 5f6193faa377284126b5be8fa8720426 NeedsCompilation: no Title: Functional Gene Networks derived from biological enrichment analyses Description: Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO. biocViews: Annotation, GO, Pathways, GeneSetEnrichment, Network, Visualization, FunctionalGenomics, NetworkEnrichment, Clustering Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las Rivas. Maintainer: Sara Aibar URL: http://www.cicancer.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FGNet git_branch: devel git_last_commit: 40fad2f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FGNet_3.45.0.tar.gz vignettes: vignettes/FGNet/inst/doc/FGNet.html vignetteTitles: FGNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet.R importsMe: IntramiRExploreR dependencyCount: 31 Package: fgsea Version: 1.37.4 Depends: R (>= 4.1) Imports: Rcpp, data.table, BiocParallel, stats, ggplot2 (>= 2.2.0), cowplot, grid, fastmatch, Matrix, scales, utils LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi, parallel, org.Mm.eg.db, limma, GEOquery, msigdbr, aggregation, Seurat License: MIT + file LICENCE MD5sum: 57abb6d8bbd1ce3394f2440884063a75 NeedsCompilation: yes Title: Fast Gene Set Enrichment Analysis Description: The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Pathways Author: Gennady Korotkevich [aut], Vladimir Sukhov [aut], Nikita Golikov [aut], Nikolay Budin [ctb], Nikita Gusak [ctb], Zieman Mark [ctb], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/alserglab/fgsea/ VignetteBuilder: knitr BugReports: https://github.com/alserglab/fgsea/issues git_url: https://git.bioconductor.org/packages/fgsea git_branch: devel git_last_commit: 1a82392 git_last_commit_date: 2026-01-01 Date/Publication: 2026-04-20 source.ver: src/contrib/fgsea_1.37.4.tar.gz vignettes: vignettes/fgsea/inst/doc/fgsea-tutorial.html, vignettes/fgsea/inst/doc/geseca-tutorial.html vignetteTitles: Using fgsea package, Gene set co-regulation analysis tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R, vignettes/fgsea/inst/doc/geseca-tutorial.R dependsOnMe: gsean, metapone, PPInfer importsMe: BioNAR, CelliD, CEMiTool, clustifyr, CoGAPS, cTRAP, DeepTarget, EventPointer, fobitools, lipidr, markeR, mCSEA, MIRit, MPAC, multiGSEA, NanoTube, nipalsMCIA, omicsViewer, pairedGSEA, pathlinkR, phantasus, piano, plaid, POMA, postNet, projectR, RegEnrich, RegionalST, signatureSearch, ViSEAGO, cinaR, DTSEA, mulea, scITD suggestsMe: Cepo, decoupleR, escape, gatom, gCrisprTools, iSEEpathways, mdp, pathMED, sparrow, SpliceWiz, TaxSEA, ttgsea, easybio, futurize, genekitr, GeneNMF, ggpicrust2, goat, grandR, RCPA, rliger, Signac dependencyCount: 38 Package: FilterFFPE Version: 1.21.0 Imports: foreach, doParallel, GenomicRanges, IRanges, Rsamtools, parallel, S4Vectors Suggests: BiocStyle License: LGPL-3 MD5sum: 17993f42f8ff6c46d36748e2dd6a5089 NeedsCompilation: no Title: FFPE Artificial Chimeric Read Filter for NGS data Description: This package finds and filters artificial chimeric reads specifically generated in next-generation sequencing (NGS) process of formalin-fixed paraffin-embedded (FFPE) tissues. These artificial chimeric reads can lead to a large number of false positive structural variation (SV) calls. The required input is an indexed BAM file of a FFPE sample. biocViews: StructuralVariation, Sequencing, Alignment, QualityControl, Preprocessing Author: Lanying Wei [aut, cre] (ORCID: ) Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/FilterFFPE git_branch: devel git_last_commit: 8eee577 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FilterFFPE_1.21.0.tar.gz vignettes: vignettes/FilterFFPE/inst/doc/FilterFFPE.pdf vignetteTitles: An introduction to FilterFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FilterFFPE/inst/doc/FilterFFPE.R dependencyCount: 32 Package: findIPs Version: 1.7.0 Depends: graphics, R (>= 4.4.0) Imports: Biobase, BiocParallel, parallel, stats, SummarizedExperiment, survival, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 82597e94b542013bc394879c631b5b01 NeedsCompilation: no Title: Influential Points Detection for Feature Rankings Description: Feature rankings can be distorted by a single case in the context of high-dimensional data. The cases exerts abnormal influence on feature rankings are called influential points (IPs). The package aims at detecting IPs based on case deletion and quantifies their effects by measuring the rank changes (DOI:10.48550/arXiv.2303.10516). The package applies a novel rank comparing measure using the adaptive weights that stress the top-ranked important features and adjust the weights to ranking properties. biocViews: GeneExpression, DifferentialExpression, Regression, Survival Author: Shuo Wang [aut, cre] (ORCID: ), Junyan Lu [aut] Maintainer: Shuo Wang URL: https://github.com/ShuoStat/findIPs VignetteBuilder: knitr BugReports: https://github.com/ShuoStat/findIPs git_url: https://git.bioconductor.org/packages/findIPs git_branch: devel git_last_commit: e70b87c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/findIPs_1.7.0.tar.gz vignettes: vignettes/findIPs/inst/doc/findIPs.html vignetteTitles: Introduction to package findIPs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/findIPs/inst/doc/findIPs.R dependencyCount: 37 Package: FindIT2 Version: 1.17.0 Depends: GenomicRanges, R (>= 3.5.0) Imports: withr, BiocGenerics, Seqinfo, rtracklayer, S4Vectors, GenomicFeatures, dplyr, rlang, patchwork, ggplot2, BiocParallel, qvalue, stringr, utils, stats, ggrepel, tibble, tidyr, SummarizedExperiment, MultiAssayExperiment, IRanges, progress, purrr, glmnet, methods Suggests: BiocStyle, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0), TxDb.Athaliana.BioMart.plantsmart28 License: Artistic-2.0 MD5sum: 40b5e75262e0c776bc8b5bf4e0cb340d NeedsCompilation: no Title: find influential TF and Target based on multi-omics data Description: This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function like integrate different source information, calculate jaccard similarity for your TF. biocViews: Software, Annotation, ChIPSeq, ATACSeq, GeneRegulation, MultipleComparison, GeneTarget Author: Guandong Shang [aut, cre] (ORCID: ) Maintainer: Guandong Shang URL: https://github.com/shangguandong1996/FindIT2 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/FindIT2 git_url: https://git.bioconductor.org/packages/FindIT2 git_branch: devel git_last_commit: 6e3f32a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FindIT2_1.17.0.tar.gz vignettes: vignettes/FindIT2/inst/doc/FindIT2.html vignetteTitles: Introduction to FindIT2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FindIT2/inst/doc/FindIT2.R dependencyCount: 113 Package: FinfoMDS Version: 1.1.0 Depends: R (>= 4.4.0) Imports: phyloseq Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: baa56f771d9a4becf9d117d5321649cd NeedsCompilation: no Title: Multidimensional Scaling with F-ratio for microbiome visualization Description: F-informed MDS is a new multidimensional scaling-based ordination method that configures data distribution based on the F-statistic (i.e., the ratio of dispersion between groups with shared or differing labels). biocViews: DimensionReduction, MultidimensionalScaling, Visualization, Microbiome Author: Soobin Kim [aut, cre], Hyungseok Kim [aut] Maintainer: Soobin Kim URL: https://github.com/soob-kim/FinfoMDS VignetteBuilder: knitr BugReports: https://github.com/soob-kim/FinfoMDS/issues git_url: https://git.bioconductor.org/packages/FinfoMDS git_branch: devel git_last_commit: a94fee5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FinfoMDS_1.1.0.tar.gz vignettes: vignettes/FinfoMDS/inst/doc/FinfoMDS.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FinfoMDS/inst/doc/FinfoMDS.R dependencyCount: 67 Package: FISHalyseR Version: 1.45.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: fe678fb66cf35e6e95b5e4c21cef7251 NeedsCompilation: no Title: FISHalyseR a package for automated FISH quantification Description: FISHalyseR provides functionality to process and analyse digital cell culture images, in particular to quantify FISH probes within nuclei. Furthermore, it extract the spatial location of each nucleus as well as each probe enabling spatial co-localisation analysis. biocViews: CellBiology Author: Karesh Arunakirinathan , Andreas Heindl Maintainer: Karesh Arunakirinathan , Andreas Heindl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FISHalyseR git_branch: devel git_last_commit: 6b40aa5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FISHalyseR_1.45.0.tar.gz vignettes: vignettes/FISHalyseR/inst/doc/FISHalyseR.pdf vignetteTitles: FISHAlyseR Automated fluorescence in situ hybridisation quantification in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FISHalyseR/inst/doc/FISHalyseR.R dependencyCount: 45 Package: fishpond Version: 2.17.0 Imports: graphics, stats, utils, methods, abind, gtools, qvalue, S4Vectors, IRanges, SummarizedExperiment, GenomicRanges, matrixStats, svMisc, Matrix, SingleCellExperiment, jsonlite Suggests: testthat, knitr, rmarkdown, macrophage, tximeta, org.Hs.eg.db, samr, DESeq2, apeglm, tximportData, limma, ensembldb, EnsDb.Hsapiens.v86, GenomicFeatures, AnnotationDbi, pheatmap, Gviz, GenomeInfoDb, data.table License: GPL-2 MD5sum: af8a061a0ac91634e9d992abb42d4a85 NeedsCompilation: no Title: Fishpond: downstream methods and tools for expression data Description: Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains a number of utilities for working with Salmon and Alevin quantification files. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Normalization, Regression, MultipleComparison, BatchEffect, Visualization, DifferentialExpression, DifferentialSplicing, AlternativeSplicing, SingleCell Author: Anqi Zhu [aut, ctb], Michael Love [aut, cre], Avi Srivastava [aut, ctb], Rob Patro [aut, ctb], Joseph Ibrahim [aut, ctb], Hirak Sarkar [ctb], Euphy Wu [ctb], Noor Pratap Singh [ctb], Scott Van Buren [ctb], Dongze He [ctb], Steve Lianoglou [ctb], Wes Wilson [ctb], Jeroen Gilis [ctb] Maintainer: Michael Love URL: https://thelovelab.github.io/fishpond, https://thelovelab.com/mikelove/fishpond VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/fishpond git_url: https://git.bioconductor.org/packages/fishpond git_branch: devel git_last_commit: 2293072 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fishpond_2.17.0.tar.gz vignettes: vignettes/fishpond/inst/doc/allelic.html, vignettes/fishpond/inst/doc/swish.html vignetteTitles: 2. SEESAW - Allelic expression analysis with Salmon and Swish, 1. Swish: DE analysis accounting for inferential uncertainty hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fishpond/inst/doc/allelic.R, vignettes/fishpond/inst/doc/swish.R dependencyCount: 54 Package: FitHiC Version: 1.37.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: 9fae12e3840a3c339a977ca8c3568c29 NeedsCompilation: yes Title: Confidence estimation for intra-chromosomal contact maps Description: Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C. biocViews: DNA3DStructure, Software Author: Ferhat Ay [aut] (Python original, https://noble.gs.washington.edu/proj/fit-hi-c/), Timothy L. Bailey [aut], William S. Noble [aut], Ruyu Tan [aut, cre, trl] (R port) Maintainer: Ruyu Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FitHiC git_branch: devel git_last_commit: 4a22ef6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FitHiC_1.37.0.tar.gz vignettes: vignettes/FitHiC/inst/doc/fithic.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FitHiC/inst/doc/fithic.R dependencyCount: 8 Package: flowAI Version: 1.41.0 Depends: R (>= 4.3.0) Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2, RColorBrewer, scales, methods, graphics, stats, utils, rmarkdown Suggests: testthat, shiny, BiocStyle License: GPL (>= 2) MD5sum: e032b50ba79e8ee0f2cdca5ea7c605dc NeedsCompilation: no Title: Automatic and interactive quality control for flow cytometry data Description: The package is able to perform an automatic or interactive quality control on FCS data acquired using flow cytometry instruments. By evaluating three different properties: 1) flow rate, 2) signal acquisition, 3) dynamic range, the quality control enables the detection and removal of anomalies. biocViews: FlowCytometry, QualityControl, BiomedicalInformatics, ImmunoOncology Author: Gianni Monaco [aut], Chen Hao [ctb] Maintainer: Gianni Monaco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowAI git_branch: devel git_last_commit: 2f7054f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowAI_1.41.0.tar.gz vignettes: vignettes/flowAI/inst/doc/flowAI.html vignetteTitles: Automatic and GUI methods to do quality control on Flow cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowAI/inst/doc/flowAI.R importsMe: CytoPipeline suggestsMe: staRgate dependencyCount: 68 Package: flowBeads Version: 1.49.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: 8faa7b4116934bb0453ba4065bf6b958 NeedsCompilation: no Title: flowBeads: Analysis of flow bead data Description: This package extends flowCore to provide functionality specific to bead data. One of the goals of this package is to automate analysis of bead data for the purpose of normalisation. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: Nikolas Pontikos Maintainer: Nikolas Pontikos git_url: https://git.bioconductor.org/packages/flowBeads git_branch: devel git_last_commit: 01f6eb8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowBeads_1.49.0.tar.gz vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf vignetteTitles: Analysis of Flow Cytometry Bead Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBeads/inst/doc/HowTo-flowBeads.R dependencyCount: 35 Package: flowBin Version: 1.47.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: e968ddb97b078ff3d5116deba4a615bb NeedsCompilation: no Title: Combining multitube flow cytometry data by binning Description: Software to combine flow cytometry data that has been multiplexed into multiple tubes with common markers between them, by establishing common bins across tubes in terms of the common markers, then determining expression within each tube for each bin in terms of the tube-specific markers. biocViews: ImmunoOncology, CellBasedAssays, FlowCytometry Author: Kieran O'Neill Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/flowBin git_branch: devel git_last_commit: aafb320 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowBin_1.47.0.tar.gz vignettes: vignettes/flowBin/inst/doc/flowBin.pdf vignetteTitles: flowBin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBin/inst/doc/flowBin.R dependencyCount: 41 Package: flowcatchR Version: 1.45.0 Depends: R (>= 2.10), methods, EBImage Imports: colorRamps, abind, BiocParallel, graphics, stats, utils, plotly, shiny Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 80d44d362f7add0e4ddddc4b684576ae NeedsCompilation: no Title: Tools to analyze in vivo microscopy imaging data focused on tracking flowing blood cells Description: flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of utilities to help checking the quality of the performed operations (e.g. how good the segmentation was). It allows investigating the issue of tracking flowing cells such as in blood vessels, to categorize the particles in flowing, rolling and adherent. This classification is applied in the study of phenomena such as hemostasis and study of thrombosis development. Moreover, flowcatchR presents an integrated workflow solution, based on the integration with a Shiny App and Jupyter notebooks, which is delivered alongside the package, and can enable fully reproducible bioimage analysis in the R environment. biocViews: Software, Visualization, CellBiology, Classification, Infrastructure, GUI, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/flowcatchR, https://federicomarini.github.io/flowcatchR/ SystemRequirements: ImageMagick VignetteBuilder: knitr BugReports: https://github.com/federicomarini/flowcatchR/issues git_url: https://git.bioconductor.org/packages/flowcatchR git_branch: devel git_last_commit: 03614bd git_last_commit_date: 2025-12-01 Date/Publication: 2026-04-20 source.ver: src/contrib/flowcatchR_1.45.0.tar.gz vignettes: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.html vignetteTitles: flowcatchR: tracking and analyzing cells in time lapse microscopy images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.R dependencyCount: 97 Package: flowCHIC Version: 1.45.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: bdb2a29991578fc2be2821889bb29fae NeedsCompilation: no Title: Analyze flow cytometric data using histogram information Description: A package to analyze flow cytometric data of complex microbial communities based on histogram images biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Ingo Fetzer , Susann Müller Maintainer: Author: Joachim Schumann URL: http://www.ufz.de/index.php?en=16773 git_url: https://git.bioconductor.org/packages/flowCHIC git_branch: devel git_last_commit: c56715d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowCHIC_1.45.0.tar.gz vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf vignetteTitles: Analyze flow cytometric data using histogram information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCHIC/inst/doc/flowCHICmanual.R dependencyCount: 80 Package: flowClean Version: 1.49.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: c989c13818d515c68fb35654031e683d NeedsCompilation: no Title: flowClean Description: A quality control tool for flow cytometry data based on compositional data analysis. biocViews: FlowCytometry, QualityControl, ImmunoOncology Author: Kipper Fletez-Brant Maintainer: Kipper Fletez-Brant git_url: https://git.bioconductor.org/packages/flowClean git_branch: devel git_last_commit: a14fb52 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowClean_1.49.0.tar.gz vignettes: vignettes/flowClean/inst/doc/flowClean.pdf vignetteTitles: flowClean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClean/inst/doc/flowClean.R dependencyCount: 28 Package: flowClust Version: 3.49.0 Depends: R(>= 2.5.0) Imports: BiocGenerics, methods, Biobase, graph, flowCore, parallel Suggests: testthat, flowWorkspace, flowWorkspaceData, knitr, rmarkdown, openCyto, flowStats(>= 4.7.1) License: MIT MD5sum: 501109d875598f128e86bd01ffe6e4e9 NeedsCompilation: yes Title: Clustering for Flow Cytometry Description: Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. biocViews: ImmunoOncology, Clustering, Visualization, FlowCytometry Author: Raphael Gottardo, Kenneth Lo , Greg Finak Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowClust git_branch: devel git_last_commit: 24bd31c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowClust_3.49.0.tar.gz vignettes: vignettes/flowClust/inst/doc/flowClust.html vignetteTitles: Robust Model-based Clustering of Flow Cytometry Data\\ The flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClust/inst/doc/flowClust.R importsMe: cyanoFilter, flowTrans, openCyto suggestsMe: BiocGenerics, flowTime, segmenTier dependencyCount: 24 Package: flowCore Version: 2.23.4 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics (>= 0.29.2), grDevices, graphics, methods, stats, utils, stats4, Rcpp, matrixStats, cytolib (>= 2.13.1), S4Vectors LinkingTo: cpp11, BH(>= 1.81.0.0), cytolib, RProtoBufLib Suggests: Rgraphviz, flowViz, flowStats (>= 3.43.4), testthat, flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto, gridExtra License: Artistic-2.0 MD5sum: 3608826d7c5dcdc0216090fe805b857b NeedsCompilation: yes Title: flowCore: Basic structures for flow cytometry data Description: Provides S4 data structures and basic functions to deal with flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: B Ellis [aut], Perry Haaland [aut], Florian Hahne [aut], Nolwenn Le Meur [aut], Nishant Gopalakrishnan [aut], Josef Spidlen [aut], Mike Jiang [aut, cre], Greg Finak [aut], Samuel Granjeaud [ctb] Maintainer: Mike Jiang SystemRequirements: GNU make, C++17 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCore git_branch: devel git_last_commit: e898b05 git_last_commit_date: 2026-04-18 Date/Publication: 2026-04-20 source.ver: src/contrib/flowCore_2.23.4.tar.gz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf, vignettes/flowCore/inst/doc/fcs3.html, vignettes/flowCore/inst/doc/hyperlog.notice.html vignetteTitles: Basic Functions for Flow Cytometry Data, fcs3.html, hyperlog.notice.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowCut, flowFP, flowMatch, flowTime, flowTrans, flowViz, flowVS, ggcyto, immunoClust, infinityFlow, ncdfFlow, HDCytoData, healthyFlowData, highthroughputassays importsMe: CATALYST, cmapR, CompensAID, cyanoFilter, cydar, cytofQC, CytoMDS, cytoMEM, CytoML, CytoPipeline, CytoPipelineGUI, ddPCRclust, diffcyt, flowAI, flowBeads, flowCHIC, flowClust, flowDensity, flowGate, flowMeans, flowPloidy, FlowSOM, flowSpecs, flowStats, flowTrans, flowViz, flowWorkspace, GateFinder, MAPFX, MetaCyto, openCyto, PeacoQC, scDataviz, scifer, Sconify, staRgate, tidyFlowCore suggestsMe: COMPASS, flowPeaks, MDSvis, SuperCellCyto, flowPloidyData, hypergate, MuPETFlow, segmenTier dependencyCount: 21 Package: flowCut Version: 1.21.0 Depends: R (>= 3.4), flowCore Imports: flowDensity (>= 1.13.1), Cairo, e1071, grDevices, graphics, stats,methods Suggests: RUnit, BiocGenerics, knitr, markdown, rmarkdown License: Artistic-2.0 MD5sum: ea5d8fac9050bd2b0b3b6417b4ad489e NeedsCompilation: no Title: Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis Description: Common techinical complications such as clogging can result in spurious events and fluorescence intensity shifting, flowCut is designed to detect and remove technical artifacts from your data by removing segments that show statistical differences from other segments. biocViews: FlowCytometry, Preprocessing, QualityControl, CellBasedAssays Author: Justin Meskas [cre, aut], Sherrie Wang [aut] Maintainer: Justin Meskas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCut git_branch: devel git_last_commit: fc21b66 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowCut_1.21.0.tar.gz vignettes: vignettes/flowCut/inst/doc/flowCut.html vignetteTitles: _**flowCut**_: Precise and Accurate Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCut/inst/doc/flowCut.R dependencyCount: 110 Package: flowCyBar Version: 1.47.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 802f54d8644d7e460ed0a7479a953524 NeedsCompilation: no Title: Analyze flow cytometric data using gate information Description: A package to analyze flow cytometric data using gate information to follow population/community dynamics biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Susanne Günther , Ingo Fetzer , Susann Müller Maintainer: Joachim Schumann URL: http://www.ufz.de/index.php?de=16773 git_url: https://git.bioconductor.org/packages/flowCyBar git_branch: devel git_last_commit: 8c80d76 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowCyBar_1.47.0.tar.gz vignettes: vignettes/flowCyBar/inst/doc/flowCyBar-manual.pdf vignetteTitles: Analyze flow cytometric data using gate information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCyBar/inst/doc/flowCyBar-manual.R dependencyCount: 20 Package: flowDensity Version: 1.45.0 Imports: flowCore, graphics, flowViz (>= 1.42), car, polyclip, gplots, methods, stats, grDevices Suggests: knitr,rmarkdown License: Artistic-2.0 MD5sum: d4c20910f2e234e4933ac1a32d7f6b5f NeedsCompilation: no Title: Sequential Flow Cytometry Data Gating Description: This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data. biocViews: Bioinformatics, FlowCytometry, CellBiology, Clustering, Cancer, FlowCytData, DataRepresentation, StemCell, DensityGating Author: Mehrnoush Malek,M. Jafar Taghiyar Maintainer: Mehrnoush Malek SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowDensity git_branch: devel git_last_commit: a180b49 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowDensity_1.45.0.tar.gz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R importsMe: CompensAID, cyanoFilter, ddPCRclust, flowCut dependencyCount: 105 Package: flowFP Version: 1.69.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 MD5sum: e13fb8918781f0f7a3ec7df98544a903 NeedsCompilation: yes Title: Fingerprinting for Flow Cytometry Description: Fingerprint generation of flow cytometry data, used to facilitate the application of machine learning and datamining tools for flow cytometry. biocViews: FlowCytometry, CellBasedAssays, Clustering, Visualization Author: Herb Holyst , Wade Rogers Maintainer: Herb Holyst , Wade Rogers git_url: https://git.bioconductor.org/packages/flowFP git_branch: devel git_last_commit: ff6ba06 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowFP_1.69.0.tar.gz vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf vignetteTitles: Fingerprinting for Flow Cytometry hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFP/inst/doc/flowFP_HowTo.R dependsOnMe: flowBin importsMe: GateFinder dependencyCount: 36 Package: flowGate Version: 1.11.0 Depends: flowWorkspace (>= 4.0.6), ggcyto (>= 1.16.0), R (>= 4.2) Imports: shiny (>= 1.5.0), BiocManager (>= 1.30.10), flowCore (>= 2.0.1), dplyr (>= 1.0.0), ggplot2 (>= 3.3.2), rlang (>= 0.4.7), purrr, tibble, methods Suggests: knitr, rmarkdown, stringr, tidyverse, testthat License: MIT + file LICENSE MD5sum: 247adc180c6386f374a45489a7c90aa2 NeedsCompilation: no Title: Interactive Cytometry Gating in R Description: flowGate adds an interactive Shiny app to allow manual GUI-based gating of flow cytometry data in R. Using flowGate, you can draw 1D and 2D span/rectangle gates, quadrant gates, and polygon gates on flow cytometry data by interactively drawing the gates on a plot of your data, rather than by specifying gate coordinates. This package is especially geared toward wet-lab cytometerists looking to take advantage of R for cytometry analysis, without necessarily having a lot of R experience. biocViews: Software, WorkflowStep, FlowCytometry, Preprocessing, ImmunoOncology, DataImport Author: Andrew Wight [aut, cre], Harvey Cantor [aut, ldr] Maintainer: Andrew Wight VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowGate git_branch: devel git_last_commit: 8396c4e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowGate_1.11.0.tar.gz vignettes: vignettes/flowGate/inst/doc/flowGate.html vignetteTitles: flowGate hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowGate/inst/doc/flowGate.R dependencyCount: 90 Package: flowGraph Version: 1.19.0 Depends: R (>= 4.1) Imports: effsize, furrr, future, purrr, ggiraph, ggrepel, ggplot2, igraph, Matrix, matrixStats, stats, utils, visNetwork, htmlwidgets, grDevices, methods, stringr, stringi, Rdpack, data.table (>= 1.9.5), gridExtra, Suggests: BiocStyle, dplyr, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: 6faa85035a1de505fdcef2ff79bb46af NeedsCompilation: no Title: Identifying differential cell populations in flow cytometry data accounting for marker frequency Description: Identifies maximal differential cell populations in flow cytometry data taking into account dependencies between cell populations; flowGraph calculates and plots SpecEnr abundance scores given cell population cell counts. biocViews: FlowCytometry, StatisticalMethod, ImmunoOncology, Software, CellBasedAssays, Visualization Author: Alice Yue [aut, cre] Maintainer: Alice Yue URL: https://github.com/aya49/flowGraph VignetteBuilder: knitr BugReports: https://github.com/aya49/flowGraph/issues git_url: https://git.bioconductor.org/packages/flowGraph git_branch: devel git_last_commit: 7de3bf9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowGraph_1.19.0.tar.gz vignettes: vignettes/flowGraph/inst/doc/flowGraph.html vignetteTitles: flowGraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowGraph/inst/doc/flowGraph.R dependencyCount: 83 Package: flowMatch Version: 1.47.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 MD5sum: a7eb4066c262eb001413136468bf8ced NeedsCompilation: yes Title: Matching and meta-clustering in flow cytometry Description: Matching cell populations and building meta-clusters and templates from a collection of FC samples. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Ariful Azad Maintainer: Ariful Azad git_url: https://git.bioconductor.org/packages/flowMatch git_branch: devel git_last_commit: 58dd401 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowMatch_1.47.0.tar.gz vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf vignetteTitles: flowMatch: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R dependencyCount: 22 Package: flowMeans Version: 1.71.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 MD5sum: 66a97dc2e4ecd2c933a4890f26d85c48 NeedsCompilation: no Title: Non-parametric Flow Cytometry Data Gating Description: Identifies cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection. Note: R 2.11.0 or newer is required. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/flowMeans git_branch: devel git_last_commit: b1307fd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowMeans_1.71.0.tar.gz vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R importsMe: optimalFlow dependencyCount: 44 Package: flowMerge Version: 2.59.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Suggests: knitr, rmarkdown Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 42532205d8f416f63b836b69fbf7e922 NeedsCompilation: no Title: Cluster Merging for Flow Cytometry Data Description: Merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework. Note: users should have a working copy of flowClust 2.0 installed. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Greg Finak , Raphael Gottardo Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMerge git_branch: devel git_last_commit: 07705b4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowMerge_2.59.0.tar.gz vignettes: vignettes/flowMerge/inst/doc/flowmerge.html vignetteTitles: Merging Mixture Components for Cell Population Identification in Flow Cytometry Data The flowMerge Package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMerge/inst/doc/flowmerge.R suggestsMe: segmenTier dependencyCount: 52 Package: flowPeaks Version: 1.57.0 Depends: R (>= 2.12.0) Suggests: flowCore License: Artistic-1.0 MD5sum: 256b0089f257542c8b4de788a95aa38c NeedsCompilation: yes Title: An R package for flow data clustering Description: A fast and automatic clustering to classify the cells into subpopulations based on finding the peaks from the overall density function generated by K-means. biocViews: ImmunoOncology, FlowCytometry, Clustering, Gating Author: Yongchao Ge Maintainer: Yongchao Ge SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/flowPeaks git_branch: devel git_last_commit: 7ccd7be git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowPeaks_1.57.0.tar.gz vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf vignetteTitles: Tutorial of flowPeaks package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPeaks/inst/doc/flowPeaks-guide.R importsMe: ddPCRclust, Polytect dependencyCount: 0 Package: flowPloidy Version: 1.37.2 Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData, testthat License: GPL-3 MD5sum: 35dd380b55153747599925e26415903f NeedsCompilation: no Title: Analyze flow cytometer data to determine sample ploidy Description: Determine sample ploidy via flow cytometry histogram analysis. Reads Flow Cytometry Standard (FCS) files via the flowCore bioconductor package, and provides functions for determining the DNA ploidy of samples based on internal standards. biocViews: FlowCytometry, GUI, Regression, Visualization Author: Tyler Smith Maintainer: Tyler Smith URL: https://github.com/plantarum/flowPloidy VignetteBuilder: knitr BugReports: https://github.com/plantarum/flowPloidy/issues git_url: https://git.bioconductor.org/packages/flowPloidy git_branch: devel git_last_commit: 2d140b4 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/flowPloidy_1.37.2.tar.gz vignettes: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.pdf, vignettes/flowPloidy/inst/doc/histogram-tour.pdf vignetteTitles: flowPloidy: Getting Started, flowPloidy: FCM Histograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.R, vignettes/flowPloidy/inst/doc/histogram-tour.R dependencyCount: 123 Package: flowPlots Version: 1.59.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: b582d11b2ecfa29c78648713b9ef2114 NeedsCompilation: no Title: flowPlots: analysis plots and data class for gated flow cytometry data Description: Graphical displays with embedded statistical tests for gated ICS flow cytometry data, and a data class which stores "stacked" data and has methods for computing summary measures on stacked data, such as marginal and polyfunctional degree data. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Visualization, DataRepresentation Author: N. Hawkins, S. Self Maintainer: N. Hawkins git_url: https://git.bioconductor.org/packages/flowPlots git_branch: devel git_last_commit: bf4f747 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowPlots_1.59.0.tar.gz vignettes: vignettes/flowPlots/inst/doc/flowPlots.pdf vignetteTitles: Plots with Embedded Tests for Gated Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPlots/inst/doc/flowPlots.R dependencyCount: 1 Package: FlowSOM Version: 2.19.4 Depends: R (>= 4.5), igraph Imports: stats, utils, colorRamps, ConsensusClusterPlus, dplyr, flowCore, ggforce, ggnewscale, ggplot2, ggpubr, grDevices, magrittr, methods, rlang, Rtsne, tidyr, BiocGenerics, XML Suggests: BiocStyle, testthat, CytoML, flowWorkspace, ggrepel, scattermore, pheatmap, ggpointdensity, ComplexHeatmap License: GPL (>= 2) MD5sum: 6c383cf792e7268dd707f81780459b60 NeedsCompilation: yes Title: Using self-organizing maps for visualization and interpretation of cytometry data Description: FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. biocViews: CellBiology, FlowCytometry, Clustering, Visualization, Software, CellBasedAssays Author: Sofie Van Gassen [aut, cre], Artuur Couckuyt [aut], Katrien Quintelier [aut], Annelies Emmaneel [aut], Sarah Bonte [aut], Robbe Fonteyn [aut], Britt Callebaut [aut], Yvan Saeys [aut] Maintainer: Sofie Van Gassen URL: http://www.r-project.org, http://dambi.ugent.be git_url: https://git.bioconductor.org/packages/FlowSOM git_branch: devel git_last_commit: b2d43e0 git_last_commit_date: 2026-03-17 Date/Publication: 2026-04-20 source.ver: src/contrib/FlowSOM_2.19.4.tar.gz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: FlowSOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R importsMe: CATALYST, diffcyt suggestsMe: HDCytoData dependencyCount: 113 Package: flowSpecs Version: 1.25.2 Depends: R (>= 4.3) Imports: ggplot2 (>= 3.1.0), BiocGenerics (>= 0.30.0), BiocParallel (>= 1.18.1), Biobase (>= 2.48.0), reshape2 (>= 1.4.3), flowCore (>= 1.50.0), zoo (>= 1.8.6), stats (>= 3.6.0), methods (>= 3.6.0) Suggests: testthat, knitr, rmarkdown, BiocStyle, DepecheR License: MIT + file LICENSE MD5sum: 782736f9c56d3f8adf92c86a1a4bc84a NeedsCompilation: no Title: Tools for processing of high-dimensional cytometry data Description: This package is intended to fill the role of conventional cytometry pre-processing software, for spectral decomposition, transformation, visualization and cleanup, and to aid further downstream analyses, such as with DepecheR, by enabling transformation of flowFrames and flowSets to dataframes. Functions for flowCore-compliant automatic 1D-gating/filtering are in the pipe line. The package name has been chosen both as it will deal with spectral cytometry and as it will hopefully give the user a nice pair of spectacles through which to view their data. biocViews: Software,CellBasedAssays,DataRepresentation,ImmunoOncology, FlowCytometry,SingleCell,Visualization,Normalization,DataImport Author: Jakob Theorell [aut, cre] Maintainer: Jakob Theorell VignetteBuilder: knitr BugReports: https://github.com/jtheorell/flowSpecs/issues git_url: https://git.bioconductor.org/packages/flowSpecs git_branch: devel git_last_commit: 5bcbe70 git_last_commit_date: 2026-03-17 Date/Publication: 2026-04-20 source.ver: src/contrib/flowSpecs_1.25.2.tar.gz vignettes: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.html vignetteTitles: Example workflow for processing of raw spectral cytometry files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.R dependencyCount: 54 Package: flowStats Version: 4.23.0 Depends: R (>= 3.0.2) Imports: BiocGenerics, MASS, flowCore (>= 1.99.6), flowWorkspace, ncdfFlow(>= 2.19.5), flowViz, fda (>= 2.2.6), Biobase, methods, grDevices, graphics, stats, cluster, utils, KernSmooth, lattice, ks, RColorBrewer, rrcov, corpcor, mnormt, clue Suggests: xtable, testthat, openCyto, ggcyto, ggridges Enhances: RBGL,graph License: Artistic-2.0 MD5sum: 158617b9ba2b23854e8d60bc251f813e NeedsCompilation: no Title: Statistical methods for the analysis of flow cytometry data Description: Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi, Chao-Jen Wong, Kyongryun Lee Maintainer: Greg Finak , Mike Jiang URL: http://www.github.com/RGLab/flowStats BugReports: http://www.github.com/RGLab/flowStats/issues git_url: https://git.bioconductor.org/packages/flowStats git_branch: devel git_last_commit: cc57dce git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowStats_4.23.0.tar.gz vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf vignetteTitles: flowStats Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.R dependsOnMe: flowVS, highthroughputassays suggestsMe: cydar, flowClust, flowCore, flowTime, flowViz, ggcyto, openCyto, idendr0 dependencyCount: 101 Package: flowTime Version: 1.35.0 Depends: R (>= 3.4), flowCore Imports: utils, dplyr (>= 1.0.0), tibble, magrittr, plyr, rlang Suggests: knitr, rmarkdown, flowViz, ggplot2, BiocGenerics, stats, flowClust, openCyto, flowStats, ggcyto License: Artistic-2.0 MD5sum: 02d4eb9bbe668c8851409de0a50a6202 NeedsCompilation: no Title: Annotation and analysis of biological dynamical systems using flow cytometry Description: This package facilitates analysis of both timecourse and steady state flow cytometry experiments. This package was originially developed for quantifying the function of gene regulatory networks in yeast (strain W303) expressing fluorescent reporter proteins using BD Accuri C6 and SORP cytometers. However, the functions are for the most part general and may be adapted for analysis of other organisms using other flow cytometers. Functions in this package facilitate the annotation of flow cytometry data with experimental metadata, as often required for publication and general ease-of-reuse. Functions for creating, saving and loading gate sets are also included. In the past, we have typically generated summary statistics for each flowset for each timepoint and then annotated and analyzed these summary statistics. This method loses a great deal of the power that comes from the large amounts of individual cell data generated in flow cytometry, by essentially collapsing this data into a bulk measurement after subsetting. In addition to these summary functions, this package also contains functions to facilitate annotation and analysis of steady-state or time-lapse data utilizing all of the data collected from the thousands of individual cells in each sample. biocViews: FlowCytometry, TimeCourse, Visualization, DataImport, CellBasedAssays, ImmunoOncology Author: R. Clay Wright [aut, cre], Nick Bolten [aut], Edith Pierre-Jerome [aut] Maintainer: R. Clay Wright VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowTime git_branch: devel git_last_commit: dafd153 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowTime_1.35.0.tar.gz vignettes: vignettes/flowTime/inst/doc/gating-vignette.html, vignettes/flowTime/inst/doc/steady-state-vignette.html, vignettes/flowTime/inst/doc/time-course-vignette.html vignetteTitles: Yeast gating, Steady-state analysis of flow cytometry data, Time course analysis of flow cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTime/inst/doc/gating-vignette.R, vignettes/flowTime/inst/doc/steady-state-vignette.R, vignettes/flowTime/inst/doc/time-course-vignette.R dependencyCount: 37 Package: flowTrans Version: 1.63.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: c66be55dbdc33a84dee97ab50340f9ef NeedsCompilation: no Title: Parameter Optimization for Flow Cytometry Data Transformation Description: Profile maximum likelihood estimation of parameters for flow cytometry data transformations. biocViews: ImmunoOncology, FlowCytometry Author: Greg Finak , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak git_url: https://git.bioconductor.org/packages/flowTrans git_branch: devel git_last_commit: 6e8f2c2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowTrans_1.63.0.tar.gz vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf vignetteTitles: flowTrans package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTrans/inst/doc/flowTrans.R dependencyCount: 39 Package: flowViz Version: 1.75.0 Depends: R (>= 2.7.0), flowCore(>= 1.41.9), lattice Imports: stats4, Biobase, flowCore, graphics, grDevices, grid, KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer, stats, utils, hexbin,IDPmisc Suggests: colorspace, flowStats, knitr, rmarkdown, markdown, testthat License: Artistic-2.0 MD5sum: f9872aa00d120084d3a9cf75b6cdee01 NeedsCompilation: no Title: Visualization for flow cytometry Description: Provides visualization tools for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, Visualization Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar, M. Jiang Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowViz git_branch: devel git_last_commit: 0ebf1eb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowViz_1.75.0.tar.gz vignettes: vignettes/flowViz/inst/doc/filters.html vignetteTitles: Visualizing Gates with Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R dependsOnMe: flowFP, flowVS importsMe: flowDensity, flowStats, flowTrans, openCyto suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto dependencyCount: 35 Package: flowVS Version: 1.43.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: 9bd21e0e5c1b346b4ac259579ab6f42e NeedsCompilation: no Title: Variance stabilization in flow cytometry (and microarrays) Description: Per-channel variance stabilization from a collection of flow cytometry samples by Bertlett test for homogeneity of variances. The approach is applicable to microarrays data as well. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Microarray Author: Ariful Azad Maintainer: Ariful Azad VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowVS git_branch: devel git_last_commit: ed38ef9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/flowVS_1.43.0.tar.gz vignettes: vignettes/flowVS/inst/doc/flowVS.pdf vignetteTitles: flowVS: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowVS/inst/doc/flowVS.R dependencyCount: 102 Package: flowWorkspace Version: 4.23.1 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics, cytolib (>= 2.13.1), XML, ggplot2, graph, graphics, grDevices, methods, stats, stats4, utils, RBGL, tools, Rgraphviz, data.table, dplyr, scales(>= 1.3.0), matrixStats, RProtoBufLib, flowCore(>= 2.1.1), ncdfFlow(>= 2.25.4), DelayedArray, S4Vectors LinkingTo: cpp11, BH(>= 1.62.0-1), RProtoBufLib(>= 1.99.4), cytolib (>= 2.3.7),Rhdf5lib Suggests: testthat, flowWorkspaceData (>= 2.23.2), knitr, rmarkdown, ggcyto, parallel, CytoML, openCyto License: AGPL-3.0-only License_restricts_use: no MD5sum: b5acbe455d7f7869cf39800bddb4a372 NeedsCompilation: yes Title: Infrastructure for representing and interacting with gated and ungated cytometry data sets. Description: This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Greg Finak, Mike Jiang Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make, C++17 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowWorkspace git_branch: devel git_last_commit: 2783e5c git_last_commit_date: 2026-01-01 Date/Publication: 2026-04-20 source.ver: src/contrib/flowWorkspace_4.23.1.tar.gz vignettes: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.html, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html vignetteTitles: flowWorkspace Introduction: A Package to store and maninpulate gated flow data, How to merge GatingSets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.R, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R dependsOnMe: flowGate, ggcyto, highthroughputassays importsMe: CytoML, flowStats, openCyto, PeacoQC, staRgate suggestsMe: CATALYST, COMPASS, flowClust, flowCore, FlowSOM linksToMe: CytoML dependencyCount: 61 Package: fmcsR Version: 1.53.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap,rmarkdown, codetools License: Artistic-2.0 MD5sum: e8562f81c3c8963ba9133d64b831a3d8 NeedsCompilation: yes Title: Mismatch Tolerant Maximum Common Substructure Searching Description: The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/fmcsR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fmcsR git_branch: devel git_last_commit: 6151e9b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fmcsR_1.53.0.tar.gz vignettes: vignettes/fmcsR/inst/doc/fmcsR.html vignetteTitles: fmcsR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R importsMe: chemodiv suggestsMe: ChemmineR, xnet dependencyCount: 68 Package: fmrs Version: 1.21.0 Depends: R (>= 4.3.0) Imports: methods, survival, stats Suggests: BiocGenerics, testthat, knitr, utils License: GPL-3 MD5sum: d808b8ca70f47edfe89cda623e2fcd13 NeedsCompilation: yes Title: Variable Selection in Finite Mixture of AFT Regression and FMR Models Description: The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net. biocViews: Survival, Regression, DimensionReduction Author: Farhad Shokoohi [aut, cre] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/fmrs/issues git_url: https://git.bioconductor.org/packages/fmrs git_branch: devel git_last_commit: dec6e5c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fmrs_1.21.0.tar.gz vignettes: vignettes/fmrs/inst/doc/usingfmrs.html vignetteTitles: Using fmrs package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmrs/inst/doc/usingfmrs.R importsMe: HhP dependencyCount: 10 Package: fobitools Version: 1.19.0 Depends: R (>= 4.1) Imports: clisymbols, crayon, dplyr, fgsea, ggplot2, ggraph, magrittr, ontologyIndex, purrr, RecordLinkage, stringr, textclean, tictoc, tidygraph, tidyr, vroom Suggests: BiocStyle, covr, ggrepel, kableExtra, knitr, metabolomicsWorkbenchR, POMA, rmarkdown, rvest, SummarizedExperiment, testthat (>= 2.3.2), tidyverse License: GPL-3 MD5sum: ef0378ed29b461ab7d23000b15f19a88 NeedsCompilation: no Title: Tools for Manipulating the FOBI Ontology Description: A set of tools for interacting with the Food-Biomarker Ontology (FOBI). A collection of basic manipulation tools for biological significance analysis, graphs, and text mining strategies for annotating nutritional data. biocViews: MassSpectrometry, Metabolomics, Software, Visualization, BiomedicalInformatics, GraphAndNetwork, Annotation, Cheminformatics, Pathways, GeneSetEnrichment Author: Pol Castellano-Escuder [aut, cre] (ORCID: ), Alex Sánchez-Pla [aut] (ORCID: ) Maintainer: Pol Castellano-Escuder URL: https://github.com/pcastellanoescuder/fobitools/ VignetteBuilder: knitr BugReports: https://github.com/pcastellanoescuder/fobitools/issues git_url: https://git.bioconductor.org/packages/fobitools git_branch: devel git_last_commit: f026ba6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/fobitools_1.19.0.tar.gz vignettes: vignettes/fobitools/inst/doc/Dietary_data_annotation.html, vignettes/fobitools/inst/doc/food_enrichment_analysis.html, vignettes/fobitools/inst/doc/MW_ST000291_enrichment.html, vignettes/fobitools/inst/doc/MW_ST000629_enrichment.html vignetteTitles: Dietary text annotation, Simple food ORA, Use case ST000291, Use case ST000629 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fobitools/inst/doc/Dietary_data_annotation.R, vignettes/fobitools/inst/doc/food_enrichment_analysis.R, vignettes/fobitools/inst/doc/MW_ST000291_enrichment.R, vignettes/fobitools/inst/doc/MW_ST000629_enrichment.R dependencyCount: 121 Package: fRagmentomics Version: 0.99.12 Depends: R (>= 4.1.0) Imports: Biostrings, data.table, dplyr, future, future.apply, GenomeInfoDb, GenomicRanges, ggh4x, ggplot2, ggseqlogo, IRanges, purrr, RColorBrewer, readr, rlang, Rsamtools (>= 2.4.0), S4Vectors, VariantAnnotation, scales, stringr, tibble, tidyr Suggests: ragg, covr, testthat (>= 3.0.0), knitr, rmarkdown (>= 1.14), BiocStyle License: GPL (>= 3) MD5sum: 4f4903bd65b2aeb0bb4f45949b4c1430 NeedsCompilation: no Title: Extract Fragmentomics Features and Mutational Status Description: A user-friendly R package that enables the characterization of each cfDNA fragment overlapping one or multiple mutations of interest, starting from a sequencing file containing aligned reads (BAM file). fRagmentomics supports multiple mutation input formats (e.g., VCF, TSV, or string "chr:pos:ref:alt" representation), accommodates one-based and zero-based genomic conventions, handles mutation representation ambiguities, and accepts any reference file and species in FASTA format. For each cfDNA fragment, fRagmentomics outputs its size, its 3' and 5' sequences, and its mutational status. Optionally, when users set apply_bcftools_norm = TRUE, fRagmentomics invokes the external command-line tool bcftools norm to left-align and normalize variants. If bcftools is not found on the system PATH while this option is enabled, the function errors. The package does not install external software; see the INSTALL file for per-OS instructions. biocViews: Software, Genetics, VariantDetection, IndelDetection, Sequencing, DNASeq, Alignment, MultipleSequenceAlignment Author: Killian Maudet [aut, cre] (ORCID: ), Juliette Samaniego [aut] (ORCID: ), Yoann Pradat [aut] (ORCID: ), Elsa Bernard [aut] (ORCID: ) Maintainer: Killian Maudet URL: https://github.com/ElsaB-Lab/fRagmentomics SystemRequirements: (optional) bcftools (>= 1.21) for VCF left-alignment/normalization via 'bcftools norm' VignetteBuilder: knitr BugReports: https://github.com/ElsaB-Lab/fRagmentomics/issues git_url: https://git.bioconductor.org/packages/fRagmentomics git_branch: devel git_last_commit: 70ba26a git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/fRagmentomics_0.99.12.tar.gz vignettes: vignettes/fRagmentomics/inst/doc/fRagmentomics.html vignetteTitles: A Per-Fragment Analysis Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/fRagmentomics/inst/doc/fRagmentomics.R dependencyCount: 115 Package: fraq Version: 0.99.2 Depends: R (>= 4.5.0) Imports: Rcpp, ShortRead, Biostrings, RcppParallel, edlibR, stringfish LinkingTo: Rcpp, RcppParallel, edlibR Suggests: knitr, rmarkdown, BiocStyle, processx License: GPL-3 MD5sum: 63663ef255f1495ab3c2f24a23db321c NeedsCompilation: yes Title: A High-Throughput and Extensible Toolkit for Processing FASTQ Data Description: High-throughput extensible toolkit for processing FASTQ data. The goal of this package is to empower users to quickly build out small programmatic 'kernels' to define any FASTQ processing task they may need. Builds on Intel TBB’s flow graph to orchestrate concurrent I/O and data processing; throughput can be as fast as compression and disk speed allows. The package also ships with a suite of predefined kernels for common FASTQ tasks. biocViews: Software, Infrastructure, Sequencing, DNASeq, QualityControl, Alignment Author: Travers Ching [aut, cre, cph] (ORCID: ), Yann Collet [ctb, cph] (Author of the bundled zstd library), Facebook, Inc. [cph] (Copyright holder of the bundled zstd code), Reichardt Tino [ctb, cph] (Contributor/copyright holder of bundled zstd code), Skibinski Przemyslaw [ctb, cph] (Contributor/copyright holder of bundled zstd code), Mori Yuta [ctb, cph] (Contributor/copyright holder of bundled zstd code) Maintainer: Travers Ching URL: https://github.com/traversc/fraq SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/traversc/fraq/issues git_url: https://git.bioconductor.org/packages/fraq git_branch: devel git_last_commit: d2f1ab3 git_last_commit_date: 2026-02-12 Date/Publication: 2026-04-20 source.ver: src/contrib/fraq_0.99.2.tar.gz vignettes: vignettes/fraq/inst/doc/fraq_getting_started.html vignetteTitles: fraq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fraq/inst/doc/fraq_getting_started.R dependencyCount: 65 Package: frenchFISH Version: 1.23.0 Imports: utils, MCMCpack, NHPoisson Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: d15d0f1cec6f99eff7f98fcbca1089c2 NeedsCompilation: no Title: Poisson Models for Quantifying DNA Copy-number from FISH Images of Tissue Sections Description: FrenchFISH comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting. biocViews: Software, BiomedicalInformatics, CellBiology, Genetics, HiddenMarkovModel, Preprocessing Author: Adam Berman, Geoff Macintyre Maintainer: Adam Berman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/frenchFISH git_branch: devel git_last_commit: 91c0f87 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/frenchFISH_1.23.0.tar.gz vignettes: vignettes/frenchFISH/inst/doc/frenchFISH.html vignetteTitles: Correcting FISH probe counts with frenchFISH hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frenchFISH/inst/doc/frenchFISH.R dependencyCount: 83 Package: FRGEpistasis Version: 1.47.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: 0c36fef495e9baf7cb3c34eb3505439e NeedsCompilation: no Title: Epistasis Analysis for Quantitative Traits by Functional Regression Model Description: A Tool for Epistasis Analysis Based on Functional Regression Model biocViews: Genetics, NetworkInference, GeneticVariability, Software Author: Futao Zhang Maintainer: Futao Zhang git_url: https://git.bioconductor.org/packages/FRGEpistasis git_branch: devel git_last_commit: 8b09c94 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FRGEpistasis_1.47.0.tar.gz vignettes: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.pdf vignetteTitles: FRGEpistasis: A Tool for Epistasis Analysis Based on Functional Regression Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.R dependencyCount: 50 Package: frma Version: 1.63.0 Depends: R (>= 2.10.0), Biobase (>= 2.6.0) Imports: Biobase, MASS, DBI, affy, methods, oligo, oligoClasses, preprocessCore, utils, BiocGenerics Suggests: hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: 64532f620e67f88e4b4ce2d928603f75 NeedsCompilation: no Title: Frozen RMA and Barcode Description: Preprocessing and analysis for single microarrays and microarray batches. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry , with contributions from Terry Therneau Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frma git_branch: devel git_last_commit: 06e0f3f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/frma_1.63.0.tar.gz vignettes: vignettes/frma/inst/doc/frma.pdf vignetteTitles: frma: Preprocessing for single arrays and array batches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frma/inst/doc/frma.R importsMe: ChIPXpress, rat2302frmavecs, DeSousa2013 suggestsMe: frmaTools, ath1121501frmavecs, antiProfilesData dependencyCount: 55 Package: frmaTools Version: 1.63.0 Depends: R (>= 2.10.0), affy Imports: Biobase, DBI, methods, preprocessCore, stats, utils Suggests: oligo, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, frma, affyPLM, hgu133aprobe, hgu133atagprobe, hgu133plus2probe, hgu133acdf, hgu133atagcdf, hgu133plus2cdf, hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: c8cc334189694851218f60c863c2b1ff NeedsCompilation: no Title: Frozen RMA Tools Description: Tools for advanced use of the frma package. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frmaTools git_branch: devel git_last_commit: c003f6b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/frmaTools_1.63.0.tar.gz vignettes: vignettes/frmaTools/inst/doc/frmaTools.pdf vignetteTitles: frmaTools: Create packages containing the vectors used by frma. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frmaTools/inst/doc/frmaTools.R importsMe: DeSousa2013 dependencyCount: 13 Package: funOmics Version: 1.5.0 Depends: R (>= 4.4.0), NMF Imports: NMF, pathifier, stats, KEGGREST, AnnotationDbi, org.Hs.eg.db, dplyr, stringr Suggests: knitr, rmarkdown, testthat (>= 3.0.0), MultiAssayExperiment, SummarizedExperiment, airway License: MIT + file LICENSE MD5sum: 0bdd5f6aeeef51269f95f7a4978dbc25 NeedsCompilation: no Title: Aggregating Omics Data into Higher-Level Functional Representations Description: The 'funOmics' package ggregates or summarizes omics data into higher level functional representations such as GO terms gene sets or KEGG metabolic pathways. The aggregated data matrix represents functional activity scores that facilitate the analysis of functional molecular sets while allowing to reduce dimensionality and provide easier and faster biological interpretations. Coordinated functional activity scores can be as informative as single molecules! biocViews: Software, Transcriptomics, Metabolomics, Proteomics, Pathways, GO, KEGG Author: Elisa Gomez de Lope [aut, cre] (ORCID: ), Enrico Glaab [ctb] (ORCID: ) Maintainer: Elisa Gomez de Lope URL: https://github.com/elisagdelope/funomics VignetteBuilder: knitr BugReports: https://github.com/elisagdelope/funomics/issues git_url: https://git.bioconductor.org/packages/funOmics git_branch: devel git_last_commit: ccd72f2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/funOmics_1.5.0.tar.gz vignettes: vignettes/funOmics/inst/doc/funomics_vignette.html vignetteTitles: funOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/funOmics/inst/doc/funomics_vignette.R dependencyCount: 82 Package: FuseSOM Version: 1.13.0 Depends: R (>= 4.2.0) Imports: psych, FCPS, analogue, coop, pheatmap, ggplotify, fastcluster, fpc, ggplot2, stringr, ggpubr, proxy, cluster, diptest, methods, SummarizedExperiment, stats, S4Vectors LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, SingleCellExperiment License: GPL-2 MD5sum: df82d0b4a0b9c874f175512f27792c35 NeedsCompilation: yes Title: A Correlation Based Multiview Self Organizing Maps Clustering For IMC Datasets Description: A correlation-based multiview self-organizing map for the characterization of cell types in highly multiplexed in situ imaging cytometry assays (`FuseSOM`) is a tool for unsupervised clustering. `FuseSOM` is robust and achieves high accuracy by combining a `Self Organizing Map` architecture and a `Multiview` integration of correlation based metrics. This allows FuseSOM to cluster highly multiplexed in situ imaging cytometry assays. biocViews: SingleCell, CellBasedAssays, Clustering, Spatial Author: Elijah Willie [aut, cre] Maintainer: Elijah Willie VignetteBuilder: knitr BugReports: https://github.com/ecool50/FuseSOM/issues git_url: https://git.bioconductor.org/packages/FuseSOM git_branch: devel git_last_commit: e01d218 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/FuseSOM_1.13.0.tar.gz vignettes: vignettes/FuseSOM/inst/doc/Introduction.html vignetteTitles: FuseSOM package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FuseSOM/inst/doc/Introduction.R dependencyCount: 137 Package: G4SNVHunter Version: 1.3.0 Depends: R (>= 4.3.0) Imports: Biostrings, stats, GenomicRanges, IRanges, Rcpp, RcppRoll, data.table, Seqinfo, S4Vectors, ggplot2, cowplot, progress, ggseqlogo, viridis, ggpointdensity, tools, SummarizedExperiment, VariantAnnotation, dplyr, openxlsx, tidyr, magrittr, ggdensity LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, BiocManager, BSgenome.Hsapiens.UCSC.hg19, DT, rtracklayer, testthat (>= 3.0.0), RBGL License: MIT + file LICENSE MD5sum: da0cd090bbc17149737f31afda613b46 NeedsCompilation: yes Title: Evaluating SNV-Induced Disruption of G-Quadruplex Structures Description: G-quadruplexes (G4s) are unique nucleic acid secondary structures predominantly found in guanine-rich regions and have been shown to be involved in various biological regulatory processes. G4SNVHunter is an R package designed to rapidly identify genomic sequences with G4-forming propensity and to accurately screen user-provided single nucleotide variants—as well as other small-scale variants such as indels and MNVs—for their potential to destabilize these structures. This allows researchers to then screen these critical variants for deeper study, digging into how they might influence biological functions—think gene regulation, for instance—by impairing G4 formation propensity. biocViews: Epigenetics, SNP Author: Rongxin Zhang [cre, aut] (ORCID: ) Maintainer: Rongxin Zhang URL: https://github.com/rongxinzh/G4SNVHunter VignetteBuilder: knitr BugReports: https://github.com/rongxinzh/G4SNVHunter/issues git_url: https://git.bioconductor.org/packages/G4SNVHunter git_branch: devel git_last_commit: ec8c408 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/G4SNVHunter_1.3.0.tar.gz vignettes: vignettes/G4SNVHunter/inst/doc/G4SNVHunter.html vignetteTitles: Introduction to G4SNVHunter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/G4SNVHunter/inst/doc/G4SNVHunter.R dependencyCount: 112 Package: GA4GHclient Version: 1.35.0 Depends: R (>= 3.5.0), S4Vectors Imports: BiocGenerics, Biostrings, dplyr, Seqinfo, GenomicRanges, httr, IRanges, jsonlite, methods, VariantAnnotation Suggests: GenomeInfoDb, AnnotationDbi, BiocStyle, DT, knitr, org.Hs.eg.db, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 132813193dcaf4f48e89ec79d9920a36 NeedsCompilation: no Title: A Bioconductor package for accessing GA4GH API data servers Description: GA4GHclient provides an easy way to access public data servers through Global Alliance for Genomics and Health (GA4GH) genomics API. It provides low-level access to GA4GH API and translates response data into Bioconductor-based class objects. biocViews: DataRepresentation, ThirdPartyClient Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHclient VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHclient/issues git_url: https://git.bioconductor.org/packages/GA4GHclient git_branch: devel git_last_commit: 22fbbfa git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GA4GHclient_1.35.0.tar.gz vignettes: vignettes/GA4GHclient/inst/doc/GA4GHclient.html vignetteTitles: GA4GHclient hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHclient/inst/doc/GA4GHclient.R dependsOnMe: GA4GHshiny dependencyCount: 84 Package: GA4GHshiny Version: 1.33.0 Depends: GA4GHclient Imports: AnnotationDbi, BiocGenerics, dplyr, DT, Seqinfo, GenomeInfoDb, openxlsx, GenomicFeatures, methods, purrr, S4Vectors, shiny, shinyjs, tidyr, shinythemes Suggests: BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 437c79cd239c7736687a131175936e3c NeedsCompilation: no Title: Shiny application for interacting with GA4GH-based data servers Description: GA4GHshiny package provides an easy way to interact with data servers based on Global Alliance for Genomics and Health (GA4GH) genomics API through a Shiny application. It also integrates with Beacon Network. biocViews: GUI Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb], Elizabeth Borgognoni [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHshiny VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHshiny/issues git_url: https://git.bioconductor.org/packages/GA4GHshiny git_branch: devel git_last_commit: ae5f20b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GA4GHshiny_1.33.0.tar.gz vignettes: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.html vignetteTitles: GA4GHshiny hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.R dependencyCount: 123 Package: gaga Version: 2.57.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) MD5sum: 47d379575e2194d0c5a8a21002f052f6 NeedsCompilation: yes Title: GaGa hierarchical model for high-throughput data analysis Description: Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package). biocViews: ImmunoOncology, OneChannel, MassSpectrometry, MultipleComparison, DifferentialExpression, Classification Author: David Rossell . Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/gaga git_branch: devel git_last_commit: f057de3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gaga_2.57.0.tar.gz vignettes: vignettes/gaga/inst/doc/gagamanual.pdf vignetteTitles: Manual for the gaga library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaga/inst/doc/gagamanual.R importsMe: casper dependencyCount: 17 Package: gage Version: 2.61.0 Depends: R (>= 3.5.0) Imports: graph, KEGGREST, AnnotationDbi, GO.db Suggests: pathview, gageData, org.Hs.eg.db, hgu133a.db, GSEABase, Rsamtools, GenomicAlignments, TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq2, edgeR, limma License: GPL (>=2.0) MD5sum: aa1f5c641e02bce3f09523aef8f7f346 NeedsCompilation: no Title: Generally Applicable Gene-set Enrichment for Pathway Analysis Description: GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods. biocViews: Pathways, GO, DifferentialExpression, Microarray, OneChannel, TwoChannel, RNASeq, Genetics, MultipleComparison, GeneSetEnrichment, GeneExpression, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/gage, http://www.biomedcentral.com/1471-2105/10/161 git_url: https://git.bioconductor.org/packages/gage git_branch: devel git_last_commit: e6e1013 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gage_2.61.0.tar.gz vignettes: vignettes/gage/inst/doc/dataPrep.pdf, vignettes/gage/inst/doc/gage.pdf, vignettes/gage/inst/doc/RNA-seqWorkflow.pdf vignetteTitles: Gene set and data preparation, Generally Applicable Gene-set/Pathway Analysis, RNA-Seq Data Pathway and Gene-set Analysis Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gage/inst/doc/dataPrep.R, vignettes/gage/inst/doc/gage.R, vignettes/gage/inst/doc/RNA-seqWorkflow.R dependsOnMe: EGSEA importsMe: postNet suggestsMe: FGNet, pathview, SBGNview, gageData dependencyCount: 44 Package: GAprediction Version: 1.37.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: ef8d0399ca9b1f7427532c7962a8cf21 NeedsCompilation: no Title: Prediction of gestational age with Illumina HumanMethylation450 data Description: [GAprediction] predicts gestational age using Illumina HumanMethylation450 CpG data. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, Regression, BiomedicalInformatics Author: Jon Bohlin Maintainer: Jon Bohlin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GAprediction git_branch: devel git_last_commit: d14bebe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GAprediction_1.37.0.tar.gz vignettes: vignettes/GAprediction/inst/doc/GAprediction.html vignetteTitles: GAprediction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GAprediction/inst/doc/GAprediction.R dependencyCount: 17 Package: garfield Version: 1.39.0 Suggests: knitr License: GPL-3 MD5sum: 0a0d357a4c82ba900295d260db3fd45d NeedsCompilation: yes Title: GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction Description: GARFIELD is a non-parametric functional enrichment analysis approach described in the paper GARFIELD: GWAS analysis of regulatory or functional information enrichment with LD correction. Briefly, it is a method that leverages GWAS findings with regulatory or functional annotations (primarily from ENCODE and Roadmap epigenomics data) to find features relevant to a phenotype of interest. It performs greedy pruning of GWAS SNPs (LD r2 > 0.1) and then annotates them based on functional information overlap. Next, it quantifies Fold Enrichment (FE) at various GWAS significance cutoffs and assesses them by permutation testing, while matching for minor allele frequency, distance to nearest transcription start site and number of LD proxies (r2 > 0.8). biocViews: Software, StatisticalMethod, Annotation, FunctionalPrediction, GenomeAnnotation Author: Sandro Morganella Maintainer: Valentina Iotchkova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/garfield git_branch: devel git_last_commit: 4463b90 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/garfield_1.39.0.tar.gz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 0 Package: GateFinder Version: 1.31.0 Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 30f24ed7d023fb2066fee7a6df47023e NeedsCompilation: no Title: Projection-based Gating Strategy Optimization for Flow and Mass Cytometry Description: Given a vector of cluster memberships for a cell population, identifies a sequence of gates (polygon filters on 2D scatter plots) for isolation of that cell type. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour , Erin F. Simonds Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/GateFinder git_branch: devel git_last_commit: d71fb75 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GateFinder_1.31.0.tar.gz vignettes: vignettes/GateFinder/inst/doc/GateFinder.pdf vignetteTitles: GateFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GateFinder/inst/doc/GateFinder.R dependencyCount: 44 Package: gatom Version: 1.9.4 Depends: R (>= 4.3.0) Imports: data.table, igraph, BioNet, plyr, methods, XML, sna, intergraph, network, ggnetwork, scales, grid, ggplot2, mwcsr, htmlwidgets, htmltools, shinyCyJS (>= 1.0.0) Suggests: testthat, knitr, rmarkdown, KEGGREST, AnnotationDbi, org.Mm.eg.db, reactome.db, fgsea, readr, BiocStyle, R.utils License: MIT + file LICENCE MD5sum: 1e4cc252b4cb26940bf785cd1201ab93 NeedsCompilation: no Title: Finding an Active Metabolic Module in Atom Transition Network Description: This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization. biocViews: GeneExpression, DifferentialExpression, Pathways, Network Author: Anastasiia Gainullina [aut], Mariia Emelianova [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/gatom/ VignetteBuilder: knitr BugReports: https://github.com/ctlab/gatom/issues git_url: https://git.bioconductor.org/packages/gatom git_branch: devel git_last_commit: fa3c8c8 git_last_commit_date: 2025-12-02 Date/Publication: 2026-04-20 source.ver: src/contrib/gatom_1.9.4.tar.gz vignettes: vignettes/gatom/inst/doc/gatom-tutorial.html vignetteTitles: Using gatom package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gatom/inst/doc/gatom-tutorial.R dependencyCount: 95 Package: GBScleanR Version: 2.5.10 Depends: SeqArray Imports: stats, utils, methods, ggplot2, tidyr, expm, Rcpp, RcppParallel, gdsfmt LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 2662387974879865529e8fdffc3423b4 NeedsCompilation: yes Title: Error correction tool for noisy genotyping by sequencing (GBS) data Description: GBScleanR is a package for quality check, filtering, and error correction of genotype data derived from next generation sequcener (NGS) based genotyping platforms. GBScleanR takes Variant Call Format (VCF) file as input. The main function of this package is `estGeno()` which estimates the true genotypes of samples from given read counts for genotype markers using a hidden Markov model with incorporating uneven observation ratio of allelic reads. This implementation gives robust genotype estimation even in noisy genotype data usually observed in Genotyping-By-Sequnencing (GBS) and similar methods, e.g. RADseq. The current implementation accepts genotype data of a diploid population at any generation of multi-parental cross, e.g. biparental F2 from inbred parents, biparental F2 from outbred parents, and 8-way recombinant inbred lines (8-way RILs) which can be refered to as MAGIC population. biocViews: GeneticVariability, SNP, Genetics, HiddenMarkovModel, Sequencing, QualityControl Author: Tomoyuki Furuta [aut, cre] (ORCID: ) Maintainer: Tomoyuki Furuta URL: https://github.com/tomoyukif/GBScleanR SystemRequirements: GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/tomoyukif/GBScleanR/issues git_url: https://git.bioconductor.org/packages/GBScleanR git_branch: devel git_last_commit: afbbc07 git_last_commit_date: 2026-03-30 Date/Publication: 2026-04-20 source.ver: src/contrib/GBScleanR_2.5.10.tar.gz vignettes: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.html vignetteTitles: BasicUsageOfGBScleanR.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.R dependencyCount: 54 Package: gcapc Version: 1.35.0 Depends: R (>= 3.4) Imports: BiocGenerics, Seqinfo, S4Vectors, IRanges, Biostrings, BSgenome, GenomicRanges, Rsamtools, GenomicAlignments, matrixStats, MASS, splines, grDevices, graphics, stats, methods Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: GPL-3 MD5sum: 91e9790c53560b87955b90361c7197f8 NeedsCompilation: no Title: GC Aware Peak Caller Description: Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation. biocViews: Sequencing, ChIPSeq, BatchEffect, PeakDetection Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/gcapc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gcapc git_branch: devel git_last_commit: ffa3e8b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gcapc_1.35.0.tar.gz vignettes: vignettes/gcapc/inst/doc/gcapc.html vignetteTitles: The gcapc user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcapc/inst/doc/gcapc.R suggestsMe: epigraHMM dependencyCount: 60 Package: gcatest Version: 2.11.1 Depends: R (>= 4.0) Imports: methods, lfa Suggests: knitr, rmarkdown, ggplot2, testthat, BEDMatrix, genio License: GPL (>= 3) MD5sum: aceaa2e6aa4b2d3fa55f7e0028f6ce2f NeedsCompilation: no Title: Genotype Conditional Association TEST Description: GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait models. This test conditions on the trait, which makes it immune to confounding by unmodeled environmental factors. Population structure is modeled via logistic factors, which are estimated using the `lfa` package. biocViews: SNP, DimensionReduction, PrincipalComponent, GenomeWideAssociation Author: Wei Hao [aut], Minsun Song [aut], Alejandro Ochoa [aut, cre] (ORCID: ), John D. Storey [aut] (ORCID: ) Maintainer: Alejandro Ochoa URL: https://github.com/StoreyLab/gcatest VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/gcatest/issues git_url: https://git.bioconductor.org/packages/gcatest git_branch: devel git_last_commit: 24663f3 git_last_commit_date: 2026-01-28 Date/Publication: 2026-04-20 source.ver: src/contrib/gcatest_2.11.1.tar.gz vignettes: vignettes/gcatest/inst/doc/gcatest.html vignetteTitles: gcatest Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcatest/inst/doc/gcatest.R dependencyCount: 42 Package: GCPtools Version: 1.1.0 Depends: R (>= 4.5.0) Imports: AnVILBase, BiocBaseUtils, dplyr, httr, rlang, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 1f84bf3573b1de8bb2564f314130b42a NeedsCompilation: no Title: Tools for working with gcloud and gsutil Description: Lower-level functionality to interface with Google Cloud Platform tools. 'gcloud' and 'gsutil' are both supported. The functionality provided centers around utilities for the AnVIL platform. biocViews: Software, Infrastructure, ThirdPartyClient, DataImport Author: Marcel Ramos [aut, cre] (ORCID: ), Nitesh Turaga [aut], Martin Morgan [aut] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/GCPtools SystemRequirements: gsutil, gcloud VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GCPtools/issues git_url: https://git.bioconductor.org/packages/GCPtools git_branch: devel git_last_commit: 0bae1dc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GCPtools_1.1.0.tar.gz vignettes: vignettes/GCPtools/inst/doc/GCPtools.html vignetteTitles: GCPtools Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GCPtools/inst/doc/GCPtools.R importsMe: AnVIL, AnVILGCP suggestsMe: AnVILBase, AnVILPublish, terraTCGAdata dependencyCount: 37 Package: gCrisprTools Version: 2.17.2 Depends: R (>= 4.1) Imports: Biobase, limma, ggplot2, SummarizedExperiment, grid, rmarkdown, grDevices, graphics, methods, ComplexHeatmap, stats, utils, parallel, MatrixGenerics, methods Suggests: edgeR, knitr, AnnotationDbi, org.Mm.eg.db, org.Hs.eg.db, BiocGenerics, markdown, RUnit, sparrow, msigdbr, fgsea License: Artistic-2.0 MD5sum: d7ab035d73c8ff210d7b8e67dd9558cc NeedsCompilation: no Title: Suite of Functions for Pooled Crispr Screen QC and Analysis Description: Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. Version 2.0 extends these applications to include a variety of tools for contextualizing and integrating signals across many experiments, incorporates extended signal enrichment methodologies via the "sparrow" package, and streamlines many formal requirements to aid in interpretablity. biocViews: ImmunoOncology, CRISPR, PooledScreens, ExperimentalDesign, BiomedicalInformatics, CellBiology, FunctionalGenomics, Pharmacogenomics, Pharmacogenetics, SystemsBiology, DifferentialExpression, GeneSetEnrichment, Genetics, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Software, Visualization Author: Russell Bainer, Dariusz Ratman, Steve Lianoglou, Peter Haverty Maintainer: Russell Bainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gCrisprTools git_branch: devel git_last_commit: cc6b147 git_last_commit_date: 2026-03-24 Date/Publication: 2026-04-20 source.ver: src/contrib/gCrisprTools_2.17.2.tar.gz vignettes: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.html, vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html vignetteTitles: Contrast_Comparisons_gCrisprTools, Example_Workflow_gCrisprTools, gCrisprTools_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.R, vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R dependencyCount: 81 Package: gcrma Version: 2.83.0 Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats, utils Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector, Biostrings (>= 2.11.32), splines, BiocManager Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL MD5sum: bcbf903147a1e86c825f22415e9fc2e9 NeedsCompilation: yes Title: Background Adjustment Using Sequence Information Description: Background adjustment using sequence information biocViews: Microarray, OneChannel, Preprocessing Author: Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald Jeff Gentry Maintainer: Z. Wu git_url: https://git.bioconductor.org/packages/gcrma git_branch: devel git_last_commit: 2a7d479 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gcrma_2.83.0.tar.gz vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf vignetteTitles: gcrma1.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyILM, affyPLM, maskBAD, webbioc importsMe: affycoretools, affylmGUI suggestsMe: panp, aroma.affymetrix dependencyCount: 21 Package: GDCRNATools Version: 1.31.0 Depends: R (>= 3.5.0) Imports: shiny, jsonlite, rjson, XML, limma, edgeR, DESeq2, clusterProfiler, DOSE, org.Hs.eg.db, biomaRt, survival, survminer, pathview, ggplot2, gplots, DT, GenomicDataCommons, BiocParallel Suggests: knitr, testthat, prettydoc, rmarkdown License: Artistic-2.0 MD5sum: c83b827eea59891ae09e5989d7d823a1 NeedsCompilation: no Title: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC Description: This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, GeneRegulation, GeneTarget, NetworkInference, Survival, Visualization, GeneSetEnrichment, NetworkEnrichment, Network, RNASeq, GO, KEGG Author: Ruidong Li, Han Qu, Shibo Wang, Julong Wei, Le Zhang, Renyuan Ma, Jianming Lu, Jianguo Zhu, Wei-De Zhong, Zhenyu Jia Maintainer: Ruidong Li , Han Qu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GDCRNATools git_branch: devel git_last_commit: 0524945 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GDCRNATools_1.31.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 236 Package: gDNAx Version: 1.9.2 Depends: R (>= 4.3) Imports: methods, BiocGenerics, BiocParallel, matrixStats, Biostrings, S4Vectors, IRanges, Seqinfo, GenomeInfoDb, GenomicRanges, GenomicFiles, GenomicAlignments, GenomicFeatures, Rsamtools, AnnotationHub, RColorBrewer, AnnotationDbi, bitops, plotrix, SummarizedExperiment, grDevices, graphics, stats, utils, cli Suggests: BiocStyle, knitr, rmarkdown, RUnit, TxDb.Hsapiens.UCSC.hg38.knownGene, gDNAinRNAseqData License: Artistic-2.0 MD5sum: 13f6379d025434c57f685453d09c9009 NeedsCompilation: no Title: Diagnostics for assessing genomic DNA contamination in RNA-seq data Description: Provides diagnostics for assessing genomic DNA contamination in RNA-seq data, as well as plots representing these diagnostics. Moreover, the package can be used to get an insight into the strand library protocol used and, in case of strand-specific libraries, the strandedness of the data. Furthermore, it provides functionality to filter out reads of potential gDNA origin. biocViews: Transcription, Transcriptomics, RNASeq, Sequencing, Preprocessing, Software, GeneExpression, Coverage, DifferentialExpression, FunctionalGenomics, SplicedAlignment, Alignment Author: Beatriz Calvo-Serra [aut], Robert Castelo [aut, cre] Maintainer: Robert Castelo URL: https://github.com/functionalgenomics/gDNAx VignetteBuilder: knitr BugReports: https://github.com/functionalgenomics/gDNAx/issues git_url: https://git.bioconductor.org/packages/gDNAx git_branch: devel git_last_commit: 01155c5 git_last_commit_date: 2026-04-13 Date/Publication: 2026-04-20 source.ver: src/contrib/gDNAx_1.9.2.tar.gz vignettes: vignettes/gDNAx/inst/doc/gDNAx.html vignetteTitles: The gDNAx package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gDNAx/inst/doc/gDNAx.R dependencyCount: 102 Package: GDSArray Version: 1.31.0 Depends: R (>= 3.5), gdsfmt, methods, BiocGenerics, DelayedArray (>= 0.5.32) Imports: tools, S4Vectors (>= 0.17.34), SNPRelate, SeqArray Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, BiocManager License: GPL-3 MD5sum: 33cbcc00713cc7617e8d8b798c7f2238 NeedsCompilation: no Title: Representing GDS files as array-like objects Description: GDS files are widely used to represent genotyping or sequence data. The GDSArray package implements the `GDSArray` class to represent nodes in GDS files in a matrix-like representation that allows easy manipulation (e.g., subsetting, mathematical transformation) in _R_. The data remains on disk until needed, so that very large files can be processed. biocViews: Infrastructure, DataRepresentation, Sequencing, GenotypingArray Author: Qian Liu [aut], Martin Morgan [aut], Hervé Pagès [aut], Xiuwen Zheng [aut, cre] Maintainer: Xiuwen Zheng URL: https://github.com/Bioconductor/GDSArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GDSArray/issues git_url: https://git.bioconductor.org/packages/GDSArray git_branch: devel git_last_commit: 4ed315e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GDSArray_1.31.0.tar.gz vignettes: vignettes/GDSArray/inst/doc/GDSArray.html vignetteTitles: GDSArray: Representing GDS files as array-like objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GDSArray/inst/doc/GDSArray.R importsMe: CNVRanger, VariantExperiment suggestsMe: DelayedDataFrame dependencyCount: 31 Package: gdsfmt Version: 1.47.0 Depends: R (>= 2.15.0), methods Suggests: parallel, digest, Matrix, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics License: LGPL-3 MD5sum: 6588c4a8762203da5084ad43b9be3c9b NeedsCompilation: yes Title: R Interface to CoreArray Genomic Data Structure (GDS) Files Description: Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. biocViews: Infrastructure, DataImport Author: Xiuwen Zheng [aut, cre] (), Stephanie Gogarten [ctb], Jean-loup Gailly and Mark Adler [ctb] (for the included zlib sources), Yann Collet [ctb] (for the included LZ4 sources), xz contributors [ctb] (for the included liblzma sources) Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/gdsfmt VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/gdsfmt/issues git_url: https://git.bioconductor.org/packages/gdsfmt git_branch: devel git_last_commit: 650aa25 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gdsfmt_1.47.0.tar.gz vignettes: vignettes/gdsfmt/inst/doc/gdsfmt.html vignetteTitles: Introduction to GDS Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt.R dependsOnMe: bigmelon, GDSArray, RAIDS, SAIGEgds, SCArray, SeqArray, SNPRelate importsMe: CNVRanger, GBScleanR, GENESIS, ggmanh, GWASTools, SCArray.sat, SeqSQC, SeqVarTools, VariantExperiment, CoxMK, EthSEQ, gwid, simplePHENOTYPES, snplinkage suggestsMe: AnnotationHub, HIBAG linksToMe: SeqArray, SNPRelate dependencyCount: 1 Package: GEM Version: 1.37.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics, rmarkdown, markdown License: Artistic-2.0 MD5sum: f8c984798d2aad5dea53e3f64e7ad95c NeedsCompilation: no Title: GEM: fast association study for the interplay of Gene, Environment and Methylation Description: Tools for analyzing EWAS, methQTL and GxE genome widely. biocViews: MethylSeq, MethylationArray, GenomeWideAssociation, Regression, DNAMethylation, SNP, GeneExpression, GUI Author: Hong Pan, Joanna D Holbrook, Neerja Karnani, Chee-Keong Kwoh Maintainer: Hong Pan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEM git_branch: devel git_last_commit: 711157b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GEM_1.37.0.tar.gz vignettes: vignettes/GEM/inst/doc/user_guide.html vignetteTitles: The GEM User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEM/inst/doc/user_guide.R dependencyCount: 24 Package: gemini Version: 1.25.0 Depends: R (>= 4.1.0) Imports: dplyr, grDevices, ggplot2, magrittr, mixtools, scales, pbmcapply, parallel, stats, utils Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: 9633cecfae39d822c0dae420605a1737 NeedsCompilation: no Title: GEMINI: Variational inference approach to infer genetic interactions from pairwise CRISPR screens Description: GEMINI uses log-fold changes to model sample-dependent and independent effects, and uses a variational Bayes approach to infer these effects. The inferred effects are used to score and identify genetic interactions, such as lethality and recovery. More details can be found in Zamanighomi et al. 2019 (in press). biocViews: Software, CRISPR, Bayesian, DataImport Author: Mahdi Zamanighomi [aut], Sidharth Jain [aut, cre] Maintainer: Sidharth Jain VignetteBuilder: knitr BugReports: https://github.com/sellerslab/gemini/issues git_url: https://git.bioconductor.org/packages/gemini git_branch: devel git_last_commit: 37133b1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gemini_1.25.0.tar.gz vignettes: vignettes/gemini/inst/doc/gemini-quickstart.html vignetteTitles: QuickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gemini/inst/doc/gemini-quickstart.R dependencyCount: 82 Package: gemma.R Version: 3.7.1 Imports: magrittr, glue, memoise, jsonlite, data.table, rlang, lubridate, utils, stringr, SummarizedExperiment, Biobase, tibble, tidyr, S4Vectors, httr, rappdirs, bit64, assertthat, digest, R.utils, kableExtra, base64enc Suggests: testthat (>= 2.0.0), rmarkdown, knitr, dplyr, covr, ggplot2, ggrepel, BiocStyle, microbenchmark, magick, purrr, pheatmap, viridis, poolr, listviewer, shiny License: Apache License (>= 2) MD5sum: a1ded30fcc331edec209e2c403b36f51 NeedsCompilation: no Title: A wrapper for Gemma's Restful API to access curated gene expression data and differential expression analyses Description: Low- and high-level wrappers for Gemma's RESTful API. They enable access to curated expression and differential expression data from over 10,000 published studies. Gemma is a web site, database and a set of tools for the meta-analysis, re-use and sharing of genomics data, currently primarily targeted at the analysis of gene expression profiles. biocViews: Software, DataImport, Microarray, SingleCell, ThirdPartyClient, DifferentialExpression, GeneExpression, Bayesian, Annotation, ExperimentalDesign, Normalization, BatchEffect, Preprocessing Author: Javier Castillo-Arnemann [aut] (ORCID: ), Jordan Sicherman [aut] (ORCID: ), Ogan Mancarci [aut] (ORCID: ), Guillaume Poirier-Morency [aut] (ORCID: ), Paul Pavlidis [aut, cre] (ORCID: ) Maintainer: Paul Pavlidis URL: https://pavlidislab.github.io/gemma.R/, https://github.com/PavlidisLab/gemma.R VignetteBuilder: knitr BugReports: https://github.com/PavlidisLab/gemma.R/issues git_url: https://git.bioconductor.org/packages/gemma.R git_branch: devel git_last_commit: ee7b6e9 git_last_commit_date: 2026-01-13 Date/Publication: 2026-04-20 source.ver: src/contrib/gemma.R_3.7.1.tar.gz vignettes: vignettes/gemma.R/inst/doc/gemma.R.html, vignettes/gemma.R/inst/doc/metadata.html, vignettes/gemma.R/inst/doc/metanalysis.html vignetteTitles: Accessing curated gene expression data with gemma.R, A guide to metadata for samples and differential expression analyses, A meta analysis on effects of Parkinson's Disease using Gemma.R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gemma.R/inst/doc/gemma.R.R, vignettes/gemma.R/inst/doc/metadata.R, vignettes/gemma.R/inst/doc/metanalysis.R dependencyCount: 90 Package: genArise Version: 1.87.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 99bb258110dd7ad1db519b2f3dfe4434 NeedsCompilation: no Title: Microarray Analysis tool Description: genArise is an easy to use tool for dual color microarray data. Its GUI-Tk based environment let any non-experienced user performs a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer. biocViews: Microarray, TwoChannel, Preprocessing Author: Ana Patricia Gomez Mayen ,\\ Gustavo Corral Guille , \\ Lina Riego Ruiz ,\\ Gerardo Coello Coutino Maintainer: IFC Development Team URL: http://www.ifc.unam.mx/genarise git_url: https://git.bioconductor.org/packages/genArise git_branch: devel git_last_commit: 5f03e86 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/genArise_1.87.0.tar.gz vignettes: vignettes/genArise/inst/doc/genArise.pdf vignetteTitles: genAriseGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genArise/inst/doc/genArise.R dependencyCount: 11 Package: geneAttribution Version: 1.37.0 Imports: utils, GenomicRanges, org.Hs.eg.db, BiocGenerics, Seqinfo, GenomicFeatures, IRanges, rtracklayer Suggests: TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: aa33a78f30ca307d48cbf5de2a225fd8 NeedsCompilation: no Title: Identification of candidate genes associated with genetic variation Description: Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format. biocViews: SNP, GenePrediction, GenomeWideAssociation, VariantAnnotation, GenomicVariation Author: Arthur Wuster Maintainer: Arthur Wuster VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneAttribution git_branch: devel git_last_commit: 0aa99f2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/geneAttribution_1.37.0.tar.gz vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 76 Package: GeneBreak Version: 1.41.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: 7c605fc2f1cb4f17bc6bfe975d0e13d2 NeedsCompilation: no Title: Gene Break Detection Description: Recurrent breakpoint gene detection on copy number aberration profiles. biocViews: aCGH, CopyNumberVariation, DNASeq, Genetics, Sequencing, WholeGenome, Visualization Author: Evert van den Broek, Stef van Lieshout Maintainer: Evert van den Broek URL: https://github.com/stefvanlieshout/GeneBreak git_url: https://git.bioconductor.org/packages/GeneBreak git_branch: devel git_last_commit: 127da45 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeneBreak_1.41.0.tar.gz vignettes: vignettes/GeneBreak/inst/doc/GeneBreak.pdf vignetteTitles: GeneBreak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneBreak/inst/doc/GeneBreak.R dependencyCount: 49 Package: geneClassifiers Version: 1.35.0 Depends: R (>= 3.6.0) Imports: utils, methods, stats, Biobase, BiocGenerics Suggests: testthat License: GPL-2 MD5sum: 714a1452ddc0bad23d6a1954fbf487b2 NeedsCompilation: no Title: Application of gene classifiers Description: This packages aims for easy accessible application of classifiers which have been published in literature using an ExpressionSet as input. biocViews: GeneExpression, BiomedicalInformatics, Classification, Survival, Microarray Author: R Kuiper [cre, aut] (ORCID: ) Maintainer: R Kuiper URL: https://doi.org/doi:10.18129/B9.bioc.geneClassifiers BugReports: https://github.com/rkuiper/geneClassifiers/issues git_url: https://git.bioconductor.org/packages/geneClassifiers git_branch: devel git_last_commit: c28693f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/geneClassifiers_1.35.0.tar.gz vignettes: vignettes/geneClassifiers/inst/doc/geneClassifiers.pdf, vignettes/geneClassifiers/inst/doc/MissingCovariates.pdf vignetteTitles: geneClassifiers introduction, geneClassifiers and missing probesets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneClassifiers/inst/doc/geneClassifiers.R dependencyCount: 7 Package: GeneExpressionSignature Version: 1.57.0 Depends: R (>= 4.0) Imports: Biobase, stats, methods Suggests: apcluster, GEOquery, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 28c70f0a397421532d093bae6ecdc2d3 NeedsCompilation: no Title: Gene Expression Signature based Similarity Metric Description: This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest. biocViews: GeneExpression Author: Yang Cao [aut, cre], Fei Li [ctb], Lu Han [ctb] Maintainer: Yang Cao URL: https://github.com/yiluheihei/GeneExpressionSignature VignetteBuilder: knitr BugReports: https://github.com/yiluheihei/GeneExpressionSignature/issues/ git_url: https://git.bioconductor.org/packages/GeneExpressionSignature git_branch: devel git_last_commit: 92f29f1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeneExpressionSignature_1.57.0.tar.gz vignettes: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.html vignetteTitles: GeneExpressionSignature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.R dependencyCount: 7 Package: genefilter Version: 1.93.0 Imports: MatrixGenerics (>= 1.11.1), AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival, grDevices Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, RColorBrewer, BiocStyle, knitr License: Artistic-2.0 MD5sum: e9d2eb55c7457e73626f6325405e75c0 NeedsCompilation: yes Title: genefilter: methods for filtering genes from high-throughput experiments Description: Some basic functions for filtering genes. biocViews: Microarray Author: Robert Gentleman [aut], Vincent J. Carey [aut], Wolfgang Huber [aut], Florian Hahne [aut], Emmanuel Taiwo [ctb] ('howtogenefinder' vignette translation from Sweave to RMarkdown / HTML.), Khadijah Amusat [ctb] (Converted genefilter vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefilter git_branch: devel git_last_commit: 434deb9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/genefilter_1.93.0.tar.gz vignettes: vignettes/genefilter/inst/doc/independent_filtering_plots.pdf, vignettes/genefilter/inst/doc/howtogenefilter.html, vignettes/genefilter/inst/doc/howtogenefinder.html vignetteTitles: 03 - Additional plots for: Independent filtering increases power for detecting differentially expressed genes,, Bourgon et al.,, PNAS (2010), Using the genefilter function to filter genes from a microarray, howtogenefinder.knit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefilter/inst/doc/howtogenefilter.R, vignettes/genefilter/inst/doc/howtogenefinder.R, vignettes/genefilter/inst/doc/independent_filtering_plots.R dependsOnMe: CNTools, GeneMeta, sva, Hiiragi2013, rnaseqGene, lmQCM importsMe: a4Base, annmap, arrayQualityMetrics, broadSeq, Category, cbaf, ClassifyR, countsimQC, covRNA, DEXSeq, EpiDISH, GSRI, metaseqR2, methylCC, methylumi, minfi, MLInterfaces, mogsa, NBAMSeq, pcaExplorer, PECA, phenoTest, protGear, spatialHeatmap, SpliceWiz, tilingArray, XDE, zinbwave, FlowSorted.Blood.EPIC, IHWpaper, causalBatch, CoNI, netgsa suggestsMe: annotate, BioNet, categoryCompare, clusterStab, codelink, cola, compcodeR, DelayedArray, EnrichedHeatmap, factDesign, ffpe, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, HDF5Array, logicFS, lumi, MMUPHin, npGSEA, oligo, phyloseq, pvac, qpgraph, rtracklayer, siggenes, simplifyEnrichment, TCGAbiolinks, topGO, BloodCancerMultiOmics2017, curatedBladderData, curatedCRCData, curatedOvarianData, estrogen, ffpeExampleData, gageData, MAQCsubset, RforProteomics, rheumaticConditionWOLLBOLD, Single.mTEC.Transcriptomes, maGUI, SRscore, SuperLearner dependencyCount: 52 Package: genefu Version: 2.43.0 Depends: R (>= 4.1), survcomp, biomaRt, iC10, AIMS Imports: amap, impute, mclust, limma, graphics, stats, utils, iC10TrainingData Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable, knitr, caret, survival, BiocStyle, magick, rmarkdown License: Artistic-2.0 MD5sum: 6fdcf4e2c266954136d30012cc827731 NeedsCompilation: no Title: Computation of Gene Expression-Based Signatures in Breast Cancer Description: This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis. biocViews: DifferentialExpression, GeneExpression, Visualization, Clustering, Classification Author: Deena M.A. Gendoo [aut], Natchar Ratanasirigulchai [aut], Markus S. Schroeder [aut], Laia Pare [aut], Joel S Parker [aut], Aleix Prat [aut], Nikta Feizi [ctb], Christopher Eeles [ctb], Jermiah Joseph [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/genefu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefu git_branch: devel git_last_commit: d2e64e7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/genefu_2.43.0.tar.gz vignettes: vignettes/genefu/inst/doc/genefu.html vignetteTitles: genefu: A Package For Breast Cancer Gene Expression Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefu/inst/doc/genefu.R importsMe: consensusOV, PDATK suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 117 Package: GeneGA Version: 1.61.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: 8053f4cbb150732550ca686ba8a43173 NeedsCompilation: no Title: Design gene based on both mRNA secondary structure and codon usage bias using Genetic algorithm Description: R based Genetic algorithm for gene expression optimization by considering both mRNA secondary structure and codon usage bias, GeneGA includes the information of highly expressed genes of almost 200 genomes. Meanwhile, Vienna RNA Package is needed to ensure GeneGA to function properly. biocViews: GeneExpression Author: Zhenpeng Li and Haixiu Huang Maintainer: Zhenpeng Li URL: http://www.tbi.univie.ac.at/~ivo/RNA/ git_url: https://git.bioconductor.org/packages/GeneGA git_branch: devel git_last_commit: 9fcdeba git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeneGA_1.61.0.tar.gz vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf vignetteTitles: GeneGA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGA/inst/doc/GeneGA.R dependencyCount: 17 Package: GeneMeta Version: 1.83.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: a7053b2c0ee4b5bea1adff420c408548 NeedsCompilation: no Title: MetaAnalysis for High Throughput Experiments Description: A collection of meta-analysis tools for analysing high throughput experimental data biocViews: Sequencing, GeneExpression, Microarray Author: Lara Lusa , R. Gentleman, M. Ruschhaupt Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GeneMeta git_branch: devel git_last_commit: 810d001 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeneMeta_1.83.0.tar.gz vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf vignetteTitles: GeneMeta Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneMeta/inst/doc/GeneMeta.R importsMe: XDE suggestsMe: genefu dependencyCount: 53 Package: GeneNetworkBuilder Version: 1.53.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13) Imports: plyr, graph, htmlwidgets, Rgraphviz, RCy3, rjson, XML, methods, grDevices, stats, graphics LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, RBGL, knitr, shiny, STRINGdb, BiocStyle, magick, rmarkdown, org.Hs.eg.db License: GPL (>= 2) MD5sum: a94f3b1ecfb877c2d0e0cfb67f00b3cc NeedsCompilation: yes Title: GeneNetworkBuilder: a bioconductor package for building regulatory network using ChIP-chip/ChIP-seq data and Gene Expression Data Description: Appliation for discovering direct or indirect targets of transcription factors using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, GeneNetworkBuilder generates a regulatory network of the TF. biocViews: Sequencing, Microarray, GraphAndNetwork Author: Jianhong Ou, Haibo Liu, Heidi A Tissenbaum and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneNetworkBuilder git_branch: devel git_last_commit: 98b73cc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeneNetworkBuilder_1.53.0.tar.gz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.html vignetteTitles: GeneNetworkBuilder Vignette, Generate Network from a list of gene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.R, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.R dependencyCount: 69 Package: GeneOverlap Version: 1.47.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 5426ea20a686a4984c8389047ba4e28c NeedsCompilation: no Title: Test and visualize gene overlaps Description: Test two sets of gene lists and visualize the results. biocViews: MultipleComparison, Visualization Author: Li Shen, Icahn School of Medicine at Mount Sinai Maintainer: Antnio Miguel de Jesus Domingues, Max-Planck Institute for Cell Biology and Genetics URL: http://shenlab-sinai.github.io/shenlab-sinai/ git_url: https://git.bioconductor.org/packages/GeneOverlap git_branch: devel git_last_commit: 901d224 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeneOverlap_1.47.0.tar.gz vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf vignetteTitles: Testing and visualizing gene overlaps with the "GeneOverlap" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R dependencyCount: 9 Package: geneplast Version: 1.37.0 Depends: R (>= 4.0), methods Imports: igraph, snow, ape, grDevices, graphics, stats, utils, data.table Suggests: RTN, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown, Fletcher2013b, geneplast.data, geneplast.data.string.v91, ggplot2, ggpubr, plyr License: GPL (>= 2) MD5sum: 9d8170e1053529a36c2083c93339ae32 NeedsCompilation: no Title: Evolutionary and plasticity analysis of orthologous groups Description: Geneplast is designed for evolutionary and plasticity analysis based on orthologous groups distribution in a given species tree. It uses Shannon information theory and orthologs abundance to estimate the Evolutionary Plasticity Index. Additionally, it implements the Bridge algorithm to determine the evolutionary root of a given gene based on its orthologs distribution. biocViews: Genetics, GeneRegulation, SystemsBiology Author: Rodrigo Dalmolin, Mauro Castro Maintainer: Mauro Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplast git_branch: devel git_last_commit: 14a7214 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/geneplast_1.37.0.tar.gz vignettes: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.html, vignettes/geneplast/inst/doc/geneplast.html vignetteTitles: "Supporting Material for Trefflich2019.", "Geneplast: evolutionary analysis of orthologous groups." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.R, vignettes/geneplast/inst/doc/geneplast.R importsMe: geneplast.data suggestsMe: geneplast.data dependencyCount: 24 Package: geneplotter Version: 1.89.0 Depends: R (>= 2.10), methods, Biobase, BiocGenerics, lattice, annotate Imports: AnnotationDbi, graphics, grDevices, grid, RColorBrewer, stats, utils Suggests: Rgraphviz, fibroEset, hgu95av2.db, hu6800.db, hgu133a.db, BiocStyle, knitr License: Artistic-2.0 MD5sum: 759c5a037472b2202c21ddc5323822aa NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: Robert Gentleman [aut], Rohit Satyam [ctb] (Converted geneplotter vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplotter git_branch: devel git_last_commit: 0a5a7d6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/geneplotter_1.89.0.tar.gz vignettes: vignettes/geneplotter/inst/doc/visualize.pdf, vignettes/geneplotter/inst/doc/byChroms.html vignetteTitles: Visualization of Microarray Data, How to Assemble a chromLocation Object hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplotter/inst/doc/byChroms.R, vignettes/geneplotter/inst/doc/visualize.R dependsOnMe: HD2013SGI, Hiiragi2013 importsMe: biocGraph, DEXSeq, MethylSeekR suggestsMe: biocGraph, Category, EnrichmentBrowser, GOstats, Single.mTEC.Transcriptomes dependencyCount: 48 Package: geneRecommender Version: 1.83.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: 4fb4422da385e1ae2a775c296158e216 NeedsCompilation: no Title: A gene recommender algorithm to identify genes coexpressed with a query set of genes Description: This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions. biocViews: Microarray, Clustering Author: Gregory J. Hather , with contributions from Art B. Owen and Terence P. Speed Maintainer: Greg Hather git_url: https://git.bioconductor.org/packages/geneRecommender git_branch: devel git_last_commit: 3e25083 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/geneRecommender_1.83.0.tar.gz vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf vignetteTitles: Using the geneRecommender Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRecommender/inst/doc/geneRecommender.R dependencyCount: 7 Package: GeneRegionScan Version: 1.67.0 Depends: methods, Biobase (>= 2.5.5), Biostrings Imports: S4Vectors (>= 0.9.25), Biobase (>= 2.5.5), affxparser, RColorBrewer, Biostrings Suggests: BSgenome, affy, AnnotationDbi License: GPL (>= 2) MD5sum: ce6133a63eeb5c5ecda6dcb9bc75528b NeedsCompilation: no Title: GeneRegionScan Description: A package with focus on analysis of discrete regions of the genome. This package is useful for investigation of one or a few genes using Affymetrix data, since it will extract probe level data using the Affymetrix Power Tools application and wrap these data into a ProbeLevelSet. A ProbeLevelSet directly extends the expressionSet, but includes additional information about the sequence of each probe and the probe set it is derived from. The package includes a number of functions used for plotting these probe level data as a function of location along sequences of mRNA-strands. This can be used for analysis of variable splicing, and is especially well suited for use with exon-array data. biocViews: Microarray, DataImport, SNP, OneChannel, Visualization Author: Lasse Folkersen, Diego Diez Maintainer: Lasse Folkersen git_url: https://git.bioconductor.org/packages/GeneRegionScan git_branch: devel git_last_commit: e7590a3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeneRegionScan_1.67.0.tar.gz vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf vignetteTitles: GeneRegionScan hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.R dependencyCount: 18 Package: geneRxCluster Version: 1.47.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 639a13e027653498ed62fc4781bca6ae NeedsCompilation: yes Title: gRx Differential Clustering Description: Detect Differential Clustering of Genomic Sites such as gene therapy integrations. The package provides some functions for exploring genomic insertion sites originating from two different sources. Possibly, the two sources are two different gene therapy vectors. Vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of insertions and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of False Discovery Rates (FDRs) providing statistical methods for comparing retroviral vectors. A scan statistic for comparing two vectors using multiple window widths to detect clustering differentials and compute FDRs is implemented here. biocViews: Sequencing, Clustering, Genetics Author: Charles Berry Maintainer: Charles Berry git_url: https://git.bioconductor.org/packages/geneRxCluster git_branch: devel git_last_commit: e8d24da git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/geneRxCluster_1.47.0.tar.gz vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf vignetteTitles: Using geneRxCluster hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRxCluster/inst/doc/tutorial.R dependencyCount: 11 Package: GeneSelectMMD Version: 2.55.0 Depends: R (>= 2.13.2), Biobase Imports: MASS, graphics, stats, limma Suggests: ALL License: GPL (>= 2) MD5sum: b446d25177e2f54899e364cd74f1a171 NeedsCompilation: yes Title: Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions Description: Gene selection based on a mixture of marginal distributions. biocViews: DifferentialExpression Author: Jarrett Morrow , Weiliang Qiu , Wenqing He , Xiaogang Wang , Ross Lazarus . Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/GeneSelectMMD git_branch: devel git_last_commit: 6c8a3d6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeneSelectMMD_2.55.0.tar.gz vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf vignetteTitles: Gene Selection based on a mixture of marginal distributions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R importsMe: iCheck dependencyCount: 11 Package: GENESIS Version: 2.41.0 Imports: Biobase, BiocGenerics, BiocParallel, GWASTools, gdsfmt, GenomicRanges, IRanges, S4Vectors, SeqArray, SeqVarTools, SNPRelate, data.table, graphics, grDevices, igraph, Matrix, methods, reshape2, stats, utils Suggests: CompQuadForm, COMPoissonReg, poibin, SPAtest, survey, testthat, BiocStyle, knitr, rmarkdown, GWASdata, dplyr, ggplot2, GGally, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, GenomeInfoDb License: GPL-3 MD5sum: 0f240ee1a66cfc1335297a2787868fb0 NeedsCompilation: yes Title: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness Description: The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes. biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod, DimensionReduction, PrincipalComponent, GenomeWideAssociation, QualityControl, BiocViews Author: Matthew P. Conomos [aut], Stephanie M. Gogarten [aut, cre], Lisa Brown [aut], Han Chen [aut], Thomas Lumley [aut], Kenneth Rice [aut], Tamar Sofer [aut], Adrienne Stilp [aut], Timothy Thornton [aut], Chaoyu Yu [aut] Maintainer: Stephanie M. Gogarten URL: https://github.com/UW-GAC/GENESIS VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENESIS git_branch: devel git_last_commit: dd48038 git_last_commit_date: 2026-03-03 Date/Publication: 2026-04-20 source.ver: src/contrib/GENESIS_2.41.0.tar.gz vignettes: vignettes/GENESIS/inst/doc/assoc_test_seq.html, vignettes/GENESIS/inst/doc/assoc_test.html, vignettes/GENESIS/inst/doc/pcair.html vignetteTitles: Analyzing Sequence Data using the GENESIS Package, Genetic Association Testing using the GENESIS Package, Population Structure and Relatedness Inference using the GENESIS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENESIS/inst/doc/assoc_test_seq.R, vignettes/GENESIS/inst/doc/assoc_test.R, vignettes/GENESIS/inst/doc/pcair.R dependsOnMe: RAIDS dependencyCount: 117 Package: GeneStructureTools Version: 1.31.0 Imports: Biostrings,GenomicRanges,IRanges,data.table,plyr,stringdist,stringr,S4Vectors,BSgenome.Mmusculus.UCSC.mm10,stats,utils,Gviz,rtracklayer,methods Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 20ceba69374ef400399403fdef0756de NeedsCompilation: no Title: Tools for spliced gene structure manipulation and analysis Description: GeneStructureTools can be used to create in silico alternative splicing events, and analyse potential effects this has on functional gene products. biocViews: ImmunoOncology, Software, DifferentialSplicing, FunctionalPrediction, Transcriptomics, AlternativeSplicing, RNASeq Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneStructureTools git_branch: devel git_last_commit: 995a387 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeneStructureTools_1.31.0.tar.gz vignettes: vignettes/GeneStructureTools/inst/doc/Vignette.html vignetteTitles: Introduction to GeneStructureTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneStructureTools/inst/doc/Vignette.R dependencyCount: 154 Package: geNetClassifier Version: 1.51.0 Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods Imports: e1071, graphics, grDevices Suggests: leukemiasEset, RUnit, BiocGenerics Enhances: RColorBrewer, igraph, infotheo License: GPL (>= 2) MD5sum: 8f7442cf3c646f89878b33758843c5a3 NeedsCompilation: no Title: Classify diseases and build associated gene networks using gene expression profiles Description: Comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier. biocViews: Classification, DifferentialExpression, Microarray Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Sara Aibar URL: http://www.cicancer.org git_url: https://git.bioconductor.org/packages/geNetClassifier git_branch: devel git_last_commit: dd74241 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/geNetClassifier_1.51.0.tar.gz vignettes: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf vignetteTitles: geNetClassifier-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.R importsMe: bioCancer, canceR dependencyCount: 18 Package: GeneticsPed Version: 1.73.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE MD5sum: 0369045502e96404cc9e6dc646884bbe NeedsCompilation: yes Title: Pedigree and genetic relationship functions Description: Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care! biocViews: Genetics Author: Gregor Gorjanc and David A. Henderson , with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING) Maintainer: David Henderson URL: http://rgenetics.org git_url: https://git.bioconductor.org/packages/GeneticsPed git_branch: devel git_last_commit: 41bd03e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeneticsPed_1.73.0.tar.gz vignettes: vignettes/GeneticsPed/inst/doc/geneticRelatedness.pdf, vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf vignetteTitles: Calculation of genetic relatedness/relationship between individuals in the pedigree, Pedigree handling, Quantitative genetic (animal) model example in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneticsPed/inst/doc/geneticRelatedness.R, vignettes/GeneticsPed/inst/doc/pedigreeHandling.R, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.R dependencyCount: 11 Package: GENIE3 Version: 1.33.0 Imports: stats, reshape2, dplyr Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase, SummarizedExperiment, testthat, methods, BiocStyle License: GPL (>= 2) MD5sum: 5bd3bffb0b9a9c2738e0b4dfdad333a5 NeedsCompilation: yes Title: GEne Network Inference with Ensemble of trees Description: This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data. biocViews: NetworkInference, SystemsBiology, DecisionTree, Regression, Network, GraphAndNetwork, GeneExpression Author: Van Anh Huynh-Thu, Sara Aibar, Pierre Geurts Maintainer: Van Anh Huynh-Thu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENIE3 git_branch: devel git_last_commit: a1fea06 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GENIE3_1.33.0.tar.gz vignettes: vignettes/GENIE3/inst/doc/GENIE3.html vignetteTitles: GENIE3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENIE3/inst/doc/GENIE3.R importsMe: BioNERO, MetNet, scGraphVerse dependencyCount: 26 Package: genomation Version: 1.43.0 Depends: R (>= 3.5.0), grid Imports: Biostrings (>= 2.47.6), BSgenome (>= 1.47.3), data.table, Seqinfo, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), S4Vectors (>= 0.17.25), ggplot2, gridBase, impute, IRanges (>= 2.13.12), matrixStats, methods, parallel, plotrix, plyr, readr, reshape2, Rsamtools (>= 1.31.2), seqPattern, rtracklayer (>= 1.39.7), Rcpp (>= 0.12.14) LinkingTo: Rcpp Suggests: BiocGenerics, genomationData, knitr, RColorBrewer, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 6e278648d2d98bd2e1637d1f6ffc26b5 NeedsCompilation: yes Title: Summary, annotation and visualization of genomic data Description: A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input. biocViews: Annotation, Sequencing, Visualization, CpGIsland Author: Altuna Akalin [aut, cre], Vedran Franke [aut, cre], Katarzyna Wreczycka [aut], Alexander Gosdschan [ctb], Liz Ing-Simmons [ctb], Bozena Mika-Gospodorz [ctb] Maintainer: Altuna Akalin , Vedran Franke , Katarzyna Wreczycka URL: http://bioinformatics.mdc-berlin.de/genomation/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/genomation/issues git_url: https://git.bioconductor.org/packages/genomation git_branch: devel git_last_commit: c618839 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/genomation_1.43.0.tar.gz vignettes: vignettes/genomation/inst/doc/GenomationManual.html vignetteTitles: genomation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomation/inst/doc/GenomationManual.R importsMe: CexoR, EpiCompare, fCCAC, GenomicPlot, RCAS suggestsMe: methylKit dependencyCount: 99 Package: GenomAutomorphism Version: 1.13.0 Depends: R (>= 4.4.0), Imports: Biostrings, BiocGenerics, BiocParallel, Seqinfo, GenomicRanges, IRanges, matrixStats, XVector, dplyr, data.table, parallel, doParallel, foreach, methods, S4Vectors, stats, numbers, utils Suggests: spelling, rmarkdown, BiocStyle, testthat (>= 3.0.0), knitr License: Artistic-2.0 MD5sum: e51643e2215459cf1177fdb09a823033 NeedsCompilation: no Title: Compute the automorphisms between DNA's Abelian group representations Description: This is a R package to compute the automorphisms between pairwise aligned DNA sequences represented as elements from a Genomic Abelian group. In a general scenario, from genomic regions till the whole genomes from a given population (from any species or close related species) can be algebraically represented as a direct sum of cyclic groups or more specifically Abelian p-groups. Basically, we propose the representation of multiple sequence alignments of length N bp as element of a finite Abelian group created by the direct sum of homocyclic Abelian group of prime-power order. biocViews: MathematicalBiology, ComparativeGenomics, FunctionalGenomics, MultipleSequenceAlignment, WholeGenome Author: Robersy Sanchez [aut, cre] (ORCID: ) Maintainer: Robersy Sanchez URL: https://github.com/genomaths/GenomAutomorphism VignetteBuilder: knitr BugReports: https://github.com/genomaths/GenomAutomorphism/issues git_url: https://git.bioconductor.org/packages/GenomAutomorphism git_branch: devel git_last_commit: 7283b3b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomAutomorphism_1.13.0.tar.gz vignettes: vignettes/GenomAutomorphism/inst/doc/GenomAutomorphism.html vignetteTitles: Get started-with GenomAutomorphism hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomAutomorphism/inst/doc/GenomAutomorphism.R dependencyCount: 46 Package: GenomeInfoDb Version: 1.47.2 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.53.2), S4Vectors (>= 0.47.6), IRanges (>= 2.41.1), Seqinfo (>= 0.99.2) Imports: stats, utils, UCSC.utils Suggests: GenomeInfoDbData, R.utils, data.table, GenomicRanges, Rsamtools, GenomicAlignments, BSgenome, GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 56254f4e77ce0ed3b6443e6bf756129d NeedsCompilation: no Title: Utilities for manipulating chromosome names, including modifying them to follow a particular naming style Description: Contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order. biocViews: Genetics, DataRepresentation, Annotation, GenomeAnnotation Author: Sonali Arora [aut], Martin Morgan [aut], Marc Carlson [aut], Hervé Pagès [aut, cre], Prisca Chidimma Maduka [ctb], Atuhurira Kirabo Kakopo [ctb], Haleema Khan [ctb] (vignette translation from Sweave to Rmarkdown / HTML), Emmanuel Chigozie Elendu [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomeInfoDb VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w BugReports: https://github.com/Bioconductor/GenomeInfoDb/issues git_url: https://git.bioconductor.org/packages/GenomeInfoDb git_branch: devel git_last_commit: becc410 git_last_commit_date: 2025-12-03 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomeInfoDb_1.47.2.tar.gz vignettes: vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf, vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.html vignetteTitles: GenomeInfoDb: Introduction to GenomeInfoDb, Submitting your organism to GenomeInfoDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.R, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R dependsOnMe: BSgenomeForge, CODEX, IdeoViz, SCOPE, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, UCSCRepeatMasker, annotation, liftOver, variants importsMe: AllelicImbalance, annoLinker, AnnotationHubData, ATACseqQC, atena, BaalChIP, bambu, Banksy, bedbaser, BindingSiteFinder, biovizBase, biscuiteer, breakpointR, BUSpaRse, cageminer, cardelino, cfdnakit, cfDNAPro, chimeraviz, ChIPpeakAnno, ChIPseeker, circRNAprofiler, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, comapr, CopyNumberPlots, crisprDesign, CrispRVariants, customProDB, damidBind, Damsel, derfinder, derfinderPlot, DEScan2, diffHic, diffUTR, DMRcaller, DOTSeq, easylift, ensembldb, EpiCompare, epigenomix, epimutacions, epiregulon, epiSeeker, epivizr, EventPointer, extraChIPs, factR, fastRanges, fourSynergy, fRagmentomics, FRASER, funtooNorm, GA4GHshiny, gDNAx, GenomicDistributions, GenomicFiles, GenomicPlot, GenomicScores, ggbio, GRaNIE, GUIDEseq, Gviz, gwascat, h5vc, HiCaptuRe, HiContacts, idr2d, igblastr, karyoploteR, katdetectr, mariner, metagene2, metaseqR2, methimpute, methodical, MethylSeekR, methylumi, missMethyl, mobileRNA, Motif2Site, multiHiCcompare, MungeSumstats, musicatk, MutationalPatterns, myvariant, NADfinder, normr, OGRE, ORFik, parati, plotgardener, proActiv, ProteoDisco, PureCN, R3CPET, raer, RareVariantVis, RCAS, recount, regioneR, regionReport, RESOLVE, rGREAT, ribosomeProfilingQC, roar, scanMiRApp, scDblFinder, scmeth, scRNAseqApp, scruff, SEMPLR, seqCAT, SGSeq, signeR, SigsPack, Site2Target, SNPhood, SOMNiBUS, SparseSignatures, SPICEY, spiky, SpliceWiz, STADyUM, svaNUMT, svaRetro, TAPseq, TCGAutils, tidyCoverage, TnT, trackViewer, transcriptR, txdbmaker, UMI4Cats, UPDhmm, VanillaICE, VariantFiltering, VariantTools, VaSP, VplotR, wiggleplotr, fitCons.UCSC.hg19, grasp2db, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, GenomicDistributionsData, MethylSeqData, OSTA, ActiveDriverWGS, crispRdesignR, DESNP, driveR, hicream, karyotapR, locuszoomr, ocrRBBR, revert, Signac, tepr, TmCalculator suggestsMe: AlphaMissenseR, AnnotationForge, AnnotationHub, annotatr, BgeeCall, BSgenome, bumphunter, Chicago, crupR, dar, DEXSeq, DFplyr, DMRcate, enhancerHomologSearch, epialleleR, epigraHMM, ExperimentHubData, fishpond, GA4GHclient, GENESIS, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, gmoviz, HelloRanges, HicAggR, icetea, jazzPanda, LACHESIS, ldblock, megadepth, methrix, multicrispr, nullranges, OUTRIDER, parglms, peakCombiner, PICB, PlinkMatrix, plyinteractions, QDNAseq, RaggedExperiment, recoup, regioneReloaded, rtracklayer, scGraphVerse, scLANE, scTreeViz, Seqinfo, seqsetvis, sesame, sitadela, SomaticSignatures, splatter, SummarizedExperiment, systemPipeR, TEKRABber, treeclimbR, UCSC.utils, VariantAnnotation, BioMartGOGeneSets, CTCF, excluderanges, sesameData, xcoredata, seqpac, gkmSVM, GRIN2, polyRAD, RapidoPGS, Seurat dependencyCount: 20 Package: genomeIntervals Version: 1.67.0 Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics (>= 0.15.2) Imports: Seqinfo, GenomicRanges (>= 1.21.16), IRanges(>= 2.3.14), S4Vectors (>= 0.7.10) License: Artistic-2.0 MD5sum: 22f078bdafd497bcee9c2f0f96098989 NeedsCompilation: no Title: Operations on genomic intervals Description: This package defines classes for representing genomic intervals and provides functions and methods for working with these. Note: The package provides the basic infrastructure for and is enhanced by the package 'girafe'. biocViews: DataImport, Infrastructure, Genetics Author: Julien Gagneur , Joern Toedling, Richard Bourgon, Nicolas Delhomme Maintainer: Julien Gagneur git_url: https://git.bioconductor.org/packages/genomeIntervals git_branch: devel git_last_commit: 8406dc4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/genomeIntervals_1.67.0.tar.gz vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf vignetteTitles: Overview of the genomeIntervals package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomeIntervals/inst/doc/genomeIntervals.R importsMe: easyRNASeq dependencyCount: 12 Package: genomes Version: 3.41.0 Depends: readr, curl License: GPL-3 MD5sum: 74b3d52fdd9c1d94898479021b04c21a NeedsCompilation: no Title: Genome sequencing project metadata Description: Download genome and assembly reports from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben git_url: https://git.bioconductor.org/packages/genomes git_branch: devel git_last_commit: 58e46b8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/genomes_3.41.0.tar.gz vignettes: vignettes/genomes/inst/doc/genomes.pdf vignetteTitles: Genome metadata hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomes/inst/doc/genomes.R dependencyCount: 30 Package: GenomicAlignments Version: 1.47.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.47.6), IRanges (>= 2.23.9), Seqinfo, GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), Biostrings (>= 2.77.2), Rsamtools (>= 2.25.1) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel, cigarillo (>= 0.99.2) LinkingTo: S4Vectors, IRanges Suggests: ShortRead, rtracklayer, BSgenome, GenomicFeatures, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19, DESeq2, edgeR, RUnit, knitr, BiocStyle License: Artistic-2.0 MD5sum: 3d2c900384c04c0fb401c6b97fdb54f3 NeedsCompilation: yes Title: Representation and manipulation of short genomic alignments Description: Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. biocViews: Infrastructure, DataImport, Genetics, Sequencing, RNASeq, SNP, Coverage, Alignment, ImmunoOncology Author: Hervé Pagès [aut, cre], Valerie Obenchain [aut], Martin Morgan [aut], Fedor Bezrukov [ctb], Robert Castelo [ctb], Halimat C. Atanda [ctb] (Translated 'WorkingWithAlignedNucleotides' vignette from Sweave to RMarkdown / HTML.) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomicAlignments VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs BugReports: https://github.com/Bioconductor/GenomicAlignments/issues git_url: https://git.bioconductor.org/packages/GenomicAlignments git_branch: devel git_last_commit: 085e9df git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomicAlignments_1.47.0.tar.gz vignettes: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.pdf, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.html vignetteTitles: An Introduction to the GenomicAlignments Package, Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides (WORK-IN-PROGRESS!) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.R, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R dependsOnMe: AllelicImbalance, Basic4Cseq, ChIPexoQual, groHMM, HelloRanges, igvR, ORFik, prebs, recoup, RiboDiPA, ShortRead, SplicingGraphs, sequencing importsMe: APAlyzer, ASpli, ATACseqQC, ATACseqTFEA, atena, BaalChIP, bambu, biovizBase, breakpointR, CAGEfightR, CAGEr, cfDNAPro, chimeraviz, ChIPpeakAnno, CNEr, CoverageView, CrispRVariants, crupR, CSSQ, customProDB, DAMEfinder, DegNorm, derfinder, DEScan2, DMRcaller, DNAfusion, DOTSeq, DuplexDiscovereR, easyRNASeq, esATAC, EventPointer, FLAMES, FRASER, gcapc, gDNAx, genomation, GenomicFiles, GenomicPlot, ggbio, gmapR, gmoviz, GreyListChIP, GUIDEseq, Gviz, icetea, INSPEcT, IntEREst, MDTS, metagene2, metaseqR2, methylPipe, mosaics, Motif2Site, MotifPeeker, msgbsR, NADfinder, PICB, plyranges, pram, proActiv, raer, ramwas, ribosomeProfilingQC, RNAmodR, roar, Rqc, rtracklayer, saseR, scPipe, scruff, seqsetvis, SGSeq, spiky, SPLINTER, srnadiff, strandCheckR, TAPseq, TCseq, trackViewer, transcriptR, UMI4Cats, VaSP, VplotR, ZygosityPredictor, leeBamViews, alakazam, iimi, PACVr, VALERIE suggestsMe: amplican, BindingSiteFinder, BiocParallel, cigarillo, csaw, DEXSeq, EpiCompare, ExperimentHub, extraChIPs, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, GenomicTuples, igblastr, igvShiny, IRanges, QuasR, Rsamtools, similaRpeak, systemPipeR, NanoporeRNASeq, RNAseqData.HNRNPC.bam.chr14, futurize, seqmagick dependencyCount: 41 Package: GenomicCoordinates Version: 0.99.3 Depends: R (>= 4.5), GenomicRanges, IRanges Imports: S4Vectors, Seqinfo, InteractionSet, methods, plyranges, plyinteractions Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: b06b17f3b6d12d30ce873cdc7909f3ee NeedsCompilation: no Title: Enhanced string parsing for genomic coordinates Description: Extends string parsing capabilities for genomic coordinates, supporting various formats including comma-separated numbers, space-delimited coordinates, and automatic detection of GRanges, GPos, and GInteractions objects. biocViews: Infrastructure, DataRepresentation, GenomeAnnotation Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/GenomicCoordinates VignetteBuilder: knitr BugReports: https://github.com/js2264/GenomicCoordinates/issues git_url: https://git.bioconductor.org/packages/GenomicCoordinates git_branch: devel git_last_commit: f4f7d9b git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomicCoordinates_0.99.3.tar.gz vignettes: vignettes/GenomicCoordinates/inst/doc/GenomicCoordinates.html vignetteTitles: Introduction to GenomicCoordinates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicCoordinates/inst/doc/GenomicCoordinates.R dependencyCount: 74 Package: GenomicDataCommons Version: 1.35.1 Depends: R (>= 4.1.0) Imports: stats, httr, xml2, jsonlite, utils, rlang, readr, GenomicRanges, IRanges, dplyr, rappdirs, tibble, tidyr Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer, ggplot2, GenomicAlignments, Rsamtools, BiocParallel, TxDb.Hsapiens.UCSC.hg38.knownGene, VariantAnnotation, maftools, R.utils, data.table License: Artistic-2.0 MD5sum: ee05183e0ab26c686dd4f6ad6efc1f52 NeedsCompilation: no Title: NIH / NCI Genomic Data Commons Access Description: Programmatically access the NIH / NCI Genomic Data Commons RESTful service. biocViews: DataImport, Sequencing Author: Martin Morgan [aut], Sean Davis [aut, cre], Marcel Ramos [ctb] Maintainer: Sean Davis URL: https://bioconductor.org/packages/GenomicDataCommons, http://github.com/Bioconductor/GenomicDataCommons, http://bioconductor.github.io/GenomicDataCommons/ VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicDataCommons/issues/new git_url: https://git.bioconductor.org/packages/GenomicDataCommons git_branch: devel git_last_commit: 06edbb7 git_last_commit_date: 2025-11-07 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomicDataCommons_1.35.1.tar.gz vignettes: vignettes/GenomicDataCommons/inst/doc/overview.html, vignettes/GenomicDataCommons/inst/doc/questions-and-answers.html, vignettes/GenomicDataCommons/inst/doc/somatic_mutations.html vignetteTitles: Introduction to Accessing the NCI Genomic Data Commons, Questions and answers from over the years, Somatic Mutation Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicDataCommons/inst/doc/overview.R, vignettes/GenomicDataCommons/inst/doc/questions-and-answers.R, vignettes/GenomicDataCommons/inst/doc/somatic_mutations.R importsMe: GDCRNATools, TCGAutils suggestsMe: autonomics, imageTCGA dependencyCount: 51 Package: GenomicDistributions Version: 1.19.0 Depends: R (>= 4.0), IRanges, GenomicRanges Imports: data.table, ggplot2, reshape2, methods, utils, Biostrings, plyr, dplyr, scales, broom, GenomeInfoDb, stats Suggests: AnnotationFilter, rtracklayer, testthat, knitr, BiocStyle, rmarkdown, GenomicDistributionsData Enhances: BSgenome, extrafont, ensembldb, GenomicFeatures License: BSD_2_clause + file LICENSE MD5sum: c29a5d53ca287700155bd9e1756d1295 NeedsCompilation: no Title: GenomicDistributions: fast analysis of genomic intervals with Bioconductor Description: If you have a set of genomic ranges, this package can help you with visualization and comparison. It produces several kinds of plots, for example: Chromosome distribution plots, which visualize how your regions are distributed over chromosomes; feature distance distribution plots, which visualizes how your regions are distributed relative to a feature of interest, like Transcription Start Sites (TSSs); genomic partition plots, which visualize how your regions overlap given genomic features such as promoters, introns, exons, or intergenic regions. It also makes it easy to compare one set of ranges to another. biocViews: Software, GenomeAnnotation, GenomeAssembly, DataRepresentation, Sequencing, Coverage, FunctionalGenomics, Visualization Author: Kristyna Kupkova [aut, cre], Jose Verdezoto [aut], Tessa Danehy [aut], John Lawson [aut], Jose Verdezoto [aut], Michal Stolarczyk [aut], Jason Smith [aut], Bingjie Xue [aut], Sophia Rogers [aut], John Stubbs [aut], Nathan C. Sheffield [aut] Maintainer: Kristyna Kupkova URL: http://code.databio.org/GenomicDistributions VignetteBuilder: knitr BugReports: http://github.com/databio/GenomicDistributions git_url: https://git.bioconductor.org/packages/GenomicDistributions git_branch: devel git_last_commit: eca5b00 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomicDistributions_1.19.0.tar.gz vignettes: vignettes/GenomicDistributions/inst/doc/full-power.html, vignettes/GenomicDistributions/inst/doc/intro.html vignetteTitles: 2. Full power GenomicDistributions, 1. Getting started with GenomicDistributions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenomicDistributions/inst/doc/intro.R dependencyCount: 60 Package: GenomicFeatures Version: 1.63.2 Depends: R (>= 3.5.0), BiocGenerics (>= 0.51.2), S4Vectors (>= 0.47.6), IRanges (>= 2.37.1), Seqinfo (>= 0.99.2), GenomicRanges (>= 1.61.1), AnnotationDbi (>= 1.41.4) Imports: methods, utils, stats, DBI, XVector, Biostrings (>= 2.77.2), rtracklayer (>= 1.69.1) LinkingTo: S4Vectors, IRanges Suggests: GenomeInfoDb, txdbmaker, org.Mm.eg.db, org.Hs.eg.db, BSgenome, BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17), BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.17), FDb.UCSC.tRNAs, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1), TxDb.Mmusculus.UCSC.mm10.knownGene (>= 3.4.7), TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.4.6), SNPlocs.Hsapiens.dbSNP144.GRCh38, Rsamtools, pasillaBamSubset (>= 0.0.5), GenomicAlignments (>= 1.15.7), ensembldb, AnnotationFilter, RUnit, BiocStyle, knitr, markdown License: Artistic-2.0 MD5sum: 2f0f608b4d702be7a0061c9a18e2c0fc NeedsCompilation: yes Title: Query the gene models of a given organism/assembly Description: Extract the genomic locations of genes, transcripts, exons, introns, and CDS, for the gene models stored in a TxDb object. A TxDb object is a small database that contains the gene models of a given organism/assembly. Bioconductor provides a small collection of TxDb objects in the form of ready-to-install TxDb packages for the most commonly studied organisms. Additionally, the user can easily make a TxDb object (or package) for the organism/assembly of their choice by using the tools from the txdbmaker package. biocViews: Genetics, Infrastructure, Annotation, Sequencing, GenomeAnnotation Author: H. Pagès [aut, cre], M. Carlson [aut], P. Aboyoun [aut], S. Falcon [aut], M. Morgan [aut], D. Sarkar [aut], M. Lawrence [aut], V. Obenchain [aut], S. Arora [ctb], J. MacDonald [ctb], M. Ramos [ctb], S. Saini [ctb], P. Shannon [ctb], L. Shepherd [ctb], D. Tenenbaum [ctb], D. Van Twisk [ctb] Maintainer: H. Pagès URL: https://bioconductor.org/packages/GenomicFeatures VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicFeatures/issues git_url: https://git.bioconductor.org/packages/GenomicFeatures git_branch: devel git_last_commit: a1a980e git_last_commit_date: 2026-04-05 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomicFeatures_1.63.2.tar.gz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.html vignetteTitles: Obtaining and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R dependsOnMe: Cogito, cpvSNP, CRISPRseek, ensembldb, GSReg, Guitar, HelloRanges, mygene, OrganismDbi, OUTRIDER, RareVariantVis, RiboDiPA, SplicingGraphs, txdbmaker, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, Homo.sapiens, Mus.musculus, Rattus.norvegicus, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Cfamiliaris.UCSC.canFam4.refGene, TxDb.Cfamiliaris.UCSC.canFam5.refGene, TxDb.Cfamiliaris.UCSC.canFam6.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg19.refGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg38.refGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, generegulation importsMe: AllelicImbalance, AnnotationHubData, annotatr, APAlyzer, appreci8R, ASpli, atena, bambu, BgeeCall, BindingSiteFinder, biovizBase, bumphunter, BUSpaRse, CAGEfightR, CAGEr, casper, chevreulProcess, ChIPpeakAnno, ChIPseeker, compEpiTools, crisprDesign, crisprViz, crupR, CSSQ, customProDB, Damsel, decompTumor2Sig, derfinder, derfinderPlot, DNAfusion, DOTSeq, doubletrouble, EDASeq, ELMER, ELViS, EpiMix, epimutacions, epiSeeker, EpiTxDb, epivizrData, epivizrStandalone, esATAC, factR, FindIT2, FLAMES, FRASER, GA4GHshiny, gDNAx, geneAttribution, GenomicInteractionNodes, GenomicPlot, GenVisR, ggbio, gmapR, gmoviz, goseq, GUIDEseq, Gviz, gwascat, HiLDA, icetea, InPAS, INSPEcT, IntEREst, karyoploteR, lumi, magpie, mCSEA, metaseqR2, methylumi, msgbsR, multicrispr, musicatk, ORFik, OutSplice, proActiv, proBAMr, ProteoDisco, PureCN, qpgraph, QuasR, raer, RCAS, recoup, rGREAT, RiboCrypt, ribosomeProfilingQC, RITAN, RNAmodR, saseR, scanMiRApp, scruff, SEMPLR, SGSeq, sitadela, SPICEY, SPLINTER, srnadiff, StructuralVariantAnnotation, svaNUMT, svaRetro, TAPseq, TCGAutils, TFEA.ChIP, trackViewer, transcriptR, transmogR, TRESS, txcutr, tximeta, UMI4Cats, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState, Homo.sapiens, Mus.musculus, Rattus.norvegicus, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Hsapiens.BioMart.igis, TxDb.Rnorvegicus.BioMart.igis, DMRcatedata, geneLenDataBase, GenomicDistributionsData, scRNAseq, driveR, lisat suggestsMe: BANDITS, Bioc.gff, biomvRCNS, Biostrings, BSgenomeForge, carnation, chipseq, chromPlot, CrispRVariants, csaw, DEXSeq, eisaR, fishpond, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, HDF5Array, HiContacts, InteractiveComplexHeatmap, IRanges, MiRaGE, MutationalPatterns, pageRank, plotgardener, recount, RNAmodR.ML, Rsamtools, rtracklayer, scPipe, Seqinfo, ShortRead, SummarizedExperiment, systemPipeR, TFutils, tidyCoverage, TnT, VplotR, wiggleplotr, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer7, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Rnorvegicus.UCSC.rn6, curatedAdipoChIP, ObMiTi, Single.mTEC.Transcriptomes, systemPipeRdata, CAGEWorkflow, polyRAD dependencyCount: 74 Package: GenomicFiles Version: 1.47.0 Depends: BiocGenerics, BiocParallel, GenomicRanges, MatrixGenerics, methods, Rsamtools (>= 2.25.1), rtracklayer (>= 1.69.1), SummarizedExperiment (>= 1.39.1) Imports: BiocBaseUtils, GenomeInfoDb (>= 1.45.7), GenomicAlignments (>= 1.45.1), IRanges, S4Vectors, Seqinfo, VariantAnnotation (>= 1.55.1) Suggests: BiocStyle, Biostrings, deepSNV, genefilter, Homo.sapiens, knitr, RNAseqData.HNRNPC.bam.chr14, RUnit, snpStats License: Artistic-2.0 MD5sum: 2409acead52b919cfeaf91ae2fe39918 NeedsCompilation: no Title: Distributed computing by file or by range Description: This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation. biocViews: Genetics, Infrastructure, DataImport, Sequencing, Coverage Author: Bioconductor Package Maintainer [aut, cre], Valerie Obenchain [aut], Michael Love [aut], Lori Shepherd [aut], Martin Morgan [aut], Sonali Kumari [ctb] (Converted 'GenomicFiles' vignettes from Sweave to RMarkdown / HTML.) Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/GenomicFiles VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=3PK_jx44QTs BugReports: https://github.com/Bioconductor/GenomicFiles/issues git_url: https://git.bioconductor.org/packages/GenomicFiles git_branch: devel git_last_commit: 76a7462 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomicFiles_1.47.0.tar.gz vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.html vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R dependsOnMe: IntEREst importsMe: CAGEfightR, derfinder, gDNAx, QuasR, Rqc, TFutils, VCFArray suggestsMe: ldblock, MungeSumstats dependencyCount: 80 Package: genomicInstability Version: 1.17.0 Depends: R (>= 4.1.0), checkmate Imports: mixtools, SummarizedExperiment Suggests: SingleCellExperiment, ExperimentHub, pROC License: file LICENSE MD5sum: 144e2aed86a3f7e33aea86ae09cc876d NeedsCompilation: no Title: Genomic Instability estimation for scRNA-Seq Description: This package contain functions to run genomic instability analysis (GIA) from scRNA-Seq data. GIA estimates the association between gene expression and genomic location of the coding genes. It uses the aREA algorithm to quantify the enrichment of sets of contiguous genes (loci-blocks) on the gene expression profiles and estimates the Genomic Instability Score (GIS) for each analyzed cell. biocViews: SystemsBiology, GeneExpression, SingleCell Author: Mariano Alvarez [aut, cre], Pasquale Laise [aut], DarwinHealth [cph] Maintainer: Mariano Alvarez URL: https://github.com/DarwinHealth/genomicInstability BugReports: https://github.com/DarwinHealth/genomicInstability git_url: https://git.bioconductor.org/packages/genomicInstability git_branch: devel git_last_commit: 17308a2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/genomicInstability_1.17.0.tar.gz vignettes: vignettes/genomicInstability/inst/doc/genomicInstability.pdf vignetteTitles: Using genomicInstability hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genomicInstability/inst/doc/genomicInstability.R dependencyCount: 97 Package: GenomicInteractionNodes Version: 1.15.0 Depends: R (>= 4.2.0), stats Imports: AnnotationDbi, graph, GO.db, GenomicRanges, GenomicFeatures, Seqinfo, methods, IRanges, RBGL, S4Vectors Suggests: RUnit, BiocStyle, knitr, rmarkdown, rtracklayer, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: file LICENSE MD5sum: f7f6c0c1613bbda51c6eb2ded8b545c4 NeedsCompilation: no Title: A R/Bioconductor package to detect the interaction nodes from HiC/HiChIP/HiCAR data Description: The GenomicInteractionNodes package can import interactions from bedpe file and define the interaction nodes, the genomic interaction sites with multiple interaction loops. The interaction nodes is a binding platform regulates one or multiple genes. The detected interaction nodes will be annotated for downstream validation. biocViews: HiC, Sequencing, Software Author: Jianhong Ou [aut, cre] (ORCID: ), Yarui Diao [fnd] Maintainer: Jianhong Ou URL: https://github.com/jianhong/GenomicInteractionNodes VignetteBuilder: knitr BugReports: https://github.com/jianhong/GenomicInteractionNodes/issues git_url: https://git.bioconductor.org/packages/GenomicInteractionNodes git_branch: devel git_last_commit: 8ee0b28 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomicInteractionNodes_1.15.0.tar.gz vignettes: vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.html vignetteTitles: GenomicInteractionNodes Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.R dependencyCount: 78 Package: GenomicInteractions Version: 1.45.0 Depends: R (>= 3.5), InteractionSet Imports: Rsamtools, rtracklayer, GenomicRanges (>= 1.29.6), IRanges, BiocGenerics (>= 0.15.3), data.table, stringr, Seqinfo, ggplot2, grid, gridExtra, methods, igraph, S4Vectors (>= 0.13.13), dplyr, Gviz, Biobase, graphics, stats, utils, grDevices Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 33d60b277ed3be3192635c276e6b0922 NeedsCompilation: no Title: Utilities for handling genomic interaction data Description: Utilities for handling genomic interaction data such as ChIA-PET or Hi-C, annotating genomic features with interaction information, and producing plots and summary statistics. biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard, B. Maintainer: Liz Ing-Simmons URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicInteractions git_branch: devel git_last_commit: c3eb720 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomicInteractions_1.45.0.tar.gz vignettes: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html vignetteTitles: chiapet_vignette.html, GenomicInteractions-HiC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R importsMe: CAGEfightR, HiCaptuRe, OHCA suggestsMe: Chicago, ELMER, extraChIPs, sevenC, chicane dependencyCount: 153 Package: GenomicRanges Version: 1.63.2 Depends: R (>= 4.0.0), methods, stats4, BiocGenerics (>= 0.53.2), S4Vectors (>= 0.45.2), IRanges (>= 2.43.6), Seqinfo (>= 0.99.3) Imports: utils, stats Suggests: GenomeInfoDb, Biobase, AnnotationDbi, annotate, Biostrings (>= 2.77.2), SummarizedExperiment (>= 1.39.1), Rsamtools, GenomicAlignments, BSgenome, GenomicFeatures, UCSC.utils, txdbmaker, Gviz, VariantAnnotation, AnnotationHub, DESeq2, DEXSeq, edgeR, KEGGgraph, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, KEGGREST, hgu95av2.db, hgu95av2probe, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, RUnit, digest, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: ef92284ecbd334f9d6c10ec509f9ad51 NeedsCompilation: no Title: Representation and manipulation of genomic intervals Description: The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. biocViews: Genetics, Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, Coverage Author: Patrick Aboyoun [aut], Hervé Pagès [aut, cre], Michael Lawrence [aut], Sonali Arora [ctb], Martin Morgan [ctb], Kayla Morrell [ctb], Valerie Obenchain [ctb], Marcel Ramos [ctb], Lori Shepherd [ctb], Dan Tenenbaum [ctb], Daniel van Twisk [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomicRanges VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicRanges/issues git_url: https://git.bioconductor.org/packages/GenomicRanges git_branch: devel git_last_commit: 1f405ca git_last_commit_date: 2026-04-05 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomicRanges_1.63.2.tar.gz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf, vignettes/GenomicRanges/inst/doc/Ten_things_slides.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html vignetteTitles: 5. Extending GenomicRanges, 2. GenomicRanges HOWTOs, 3. A quick introduction to GRanges and GRangesList objects (slides), 4. Ten Things You Didn't Know (slides from BioC 2016), 1. An Introduction to the GenomicRanges Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.R, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.R, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.R, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.R, vignettes/GenomicRanges/inst/doc/Ten_things_slides.R dependsOnMe: alabaster.ranges, AllelicImbalance, annmap, AnnotationHubData, BaalChIP, Basic4Cseq, betaHMM, BindingSiteFinder, biomvRCNS, BiSeq, bnbc, breakpointR, BSgenome, bsseq, bumphunter, CAFE, CAGEfightR, casper, chimeraviz, ChIPpeakAnno, chipseq, chromPlot, cn.mops, cnvGSA, CNVPanelizer, CNVRanger, COCOA, Cogito, compEpiTools, consensusSeekeR, CSAR, csaw, CSSQ, deepSNV, DEScan2, DESeq2, DEXSeq, diffHic, DMCFB, DMCHMM, DMRcaller, DNAshapeR, easylift, EnrichedHeatmap, ensembldb, epigenomix, esATAC, ExCluster, extraChIPs, fastseg, fCCAC, FindIT2, fourSynergy, GeneBreak, GenomicAlignments, GenomicCoordinates, GenomicDistributions, GenomicFeatures, GenomicFiles, GenomicOZone, GenomicPlot, GenomicScores, GenomicTuples, gmapR, gmoviz, GOTHiC, GreyListChIP, groHMM, gtrellis, GUIDEseq, Guitar, Gviz, HelloRanges, HERON, HiCDOC, HiTC, IdeoViz, igvR, igvShiny, InTAD, intansv, InteractionSet, IntEREst, IWTomics, karyoploteR, m6Aboost, maser, MBASED, metagene2, methimpute, methodical, methylKit, methylPipe, minfi, MotifDb, motifTestR, msgbsR, MutationalPatterns, NADfinder, oncoscanR, ORFik, periodicDNA, plyranges, podkat, QuasR, r3Cseq, RaggedExperiment, recoup, regioneR, RepViz, rGREAT, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, RNAmodR, RnBeads, Rsamtools, RSVSim, rtracklayer, Scale4C, SCOPE, segmentSeq, seqCAT, SeqGate, SGSeq, SICtools, SMITE, SNPhood, SomaticSignatures, spiky, StructuralVariantAnnotation, SummarizedExperiment, svaNUMT, svaRetro, tadar, TnT, trackViewer, transmogR, traseR, tRNA, tRNAdbImport, tRNAscanImport, txdbmaker, VanillaICE, VarCon, VariantAnnotation, VariantTools, VplotR, vtpnet, wavClusteR, YAPSA, EuPathDB, excluderanges, UCSCRepeatMasker, ChAMPdata, EatonEtAlChIPseq, nullrangesData, RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5, WGSmapp, liftOver, sequencing, PlasmaMutationDetector, rnaCrosslinkOO importsMe: ACE, alabaster.se, ALDEx2, amplican, annoLinker, AnnotationFilter, annotatr, APAlyzer, apeglm, appreci8R, ASpli, AssessORF, ATACseqQC, ATACseqTFEA, atena, BadRegionFinder, ballgown, bambu, bamsignals, baySeq, BBCAnalyzer, beadarray, BEAT, bedbaser, betterChromVAR, BiFET, Bioc.gff, BioTIP, biovizBase, biscuiteer, BiSeq, BOBaFIT, borealis, branchpointer, BREW3R.r, BSgenomeForge, BUSpaRse, cageminer, CAGEr, cardelino, cBioPortalData, CexoR, cfdnakit, cfDNAPro, cfTools, chipenrich, ChIPexoQual, ChIPseeker, chipseq, ChIPseqR, chromDraw, ChromHeatMap, ChromSCape, cicero, circRNAprofiler, cleanUpdTSeq, CleanUpRNAseq, cliProfiler, CNEr, CNVfilteR, CNViz, CNVMetrics, comapr, coMethDMR, conumee, CopyNumberPlots, CoverageView, crisprBase, crisprBowtie, crisprDesign, CRISPRseek, CrispRVariants, crisprViz, crupR, CTexploreR, customProDB, DAMEfinder, damidBind, Damsel, debrowser, decemedip, decompTumor2Sig, deconvR, DEFormats, DegCre, DegNorm, deltaCaptureC, derfinder, derfinderPlot, DEWSeq, diffUTR, dinoR, DMRcaller, DMRcate, dmrseq, DNAfusion, DominoEffect, DOTSeq, doubletrouble, DRIMSeq, DropletUtils, DuplexDiscovereR, easyRNASeq, EDASeq, EDIRquery, eisaR, ELMER, ELViS, enhancerHomologSearch, epialleleR, EpiCompare, epidecodeR, epigraHMM, EpiMix, epimutacions, epiregulon, epiSeeker, epistack, EpiTxDb, epivizr, epivizrData, EventPointer, factR, fastRanges, fcScan, FilterFFPE, fishpond, FLAMES, fRagmentomics, FRASER, G4SNVHunter, GA4GHclient, gcapc, gDNAx, geneAttribution, GENESIS, genomation, GenomAutomorphism, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicInteractionNodes, GenomicInteractions, GenVisR, geomeTriD, ggbio, GOfuncR, GrafGen, GRaNIE, gVenn, gwascat, h5vc, heatmaps, hermes, HicAggR, HiCaptuRe, HiCBricks, HiCcompare, HiCExperiment, HiContacts, HiCool, HiCParser, HiCPotts, hicVennDiagram, HilbertCurve, HiLDA, hummingbird, icetea, ideal, idr2d, iNETgrate, INSPEcT, ipdDb, IsoformSwitchAnalyzeR, isomiRs, IVAS, karyoploteR, katdetectr, knowYourCG, loci2path, LOLA, LoomExperiment, lumi, magpie, mariner, mCSEA, MDTS, MEAL, MEDIPS, megadepth, memes, metaseqR2, methInheritSim, MethReg, methrix, methylCC, methylInheritance, MethylSeekR, methylSig, methylumi, MinimumDistance, MIRA, missMethyl, mitoClone2, MMDiff2, mobileRNA, Modstrings, monaLisa, Moonlight2R, mosaics, Motif2Site, motifmatchr, MotifPeeker, MouseFM, MSA2dist, MultiAssayExperiment, multicrispr, MultiDataSet, multiHiCcompare, MungeSumstats, musicatk, MutSeqR, NanoMethViz, ncRNAtools, nearBynding, normr, nucleR, nullranges, OGRE, oligoClasses, OmaDB, openPrimeR, OrganismDbi, OUTRIDER, OutSplice, packFinder, pageRank, panelcn.mops, partCNV, PAST, pcaExplorer, peakCombiner, pepStat, pgxRpi, PhIPData, PICB, PIPETS, PlinkMatrix, plotgardener, plyinteractions, postNet, pqsfinder, pram, prebs, preciseTAD, primirTSS, proActiv, proBAMr, ProteoDisco, PureCN, Pviz, QDNAseq, qpgraph, qsea, Qtlizer, R3CPET, R453Plus1Toolbox, raer, RAIDS, ramr, RareVariantVis, RBedMethyl, RCAS, recount, recount3, regionalpcs, regioneR, regionReport, regutools, REMP, RESOLVE, rfPred, Rhisat2, RiboCrypt, RiboDiPA, rigvf, Rmmquant, rmspc, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RNAshapeQC, roar, RTCGAToolbox, saseR, scafari, scanMiR, scanMiRApp, scDblFinder, scmeth, scoreInvHap, scPipe, scRNAseqApp, scruff, scuttle, segmenter, SEMPLR, seq2pathway, SeqArray, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, shinyepico, ShortRead, signeR, SigsPack, SimFFPE, SingleCellExperiment, SingleMoleculeFootprinting, sitadela, Site2Target, SMTrackR, snapcount, SOMNiBUS, SparseSignatures, SpectralTAD, SPICEY, SpliceImpactR, SpliceWiz, SplicingGraphs, SPLINTER, srnadiff, STADyUM, strandCheckR, syntenet, systemPipeR, TAPseq, target, TCGAbiolinks, TCGAutils, TCseq, TDbasedUFE, TDbasedUFEadv, TENET, TENxIO, TEQC, terraTCGAdata, TFARM, TFBSTools, TFEA.ChIP, TFHAZ, tidybulk, tidyCoverage, tLOH, tracktables, transcriptR, transite, TRESS, tricycle, triplex, TVTB, txcutr, tximeta, UMI4Cats, uncoverappLib, Uniquorn, UPDhmm, VariantFiltering, VaSP, VCFArray, vmrseq, wiggleplotr, xcore, ZygosityPredictor, BioMartGOGeneSets, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, TENET.AnnotationHub, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, biscuiteerData, chipenrich.data, COSMIC.67, ELMER.data, fourDNData, GenomicDistributionsData, leeBamViews, mCSEAdata, MethylSeqData, pepDat, scMultiome, scRNAseq, sesameData, SomaticCancerAlterations, spatialLIBD, TENET.ExperimentHub, TumourMethData, VariantToolsData, recountWorkflow, seqpac, ActiveDriverWGS, cinaR, cpp11bigwig, crispRdesignR, DESNP, driveR, GencoDymo2, geno2proteo, GenoPop, hahmmr, hicream, karyotapR, lisat, locuszoomr, LoopRig, MitoHEAR, noisyr, numbat, ocrRBBR, PACVr, PopPsiSeqR, RapidoPGS, revert, scPloidy, Signac, tepr, TmCalculator, VALERIE suggestsMe: AlphaMissenseR, AnnotationHub, autonomics, biobroom, BiocGenerics, BiocParallel, CCAFE, Chicago, ComplexHeatmap, DFplyr, epivizrChart, GenomeInfoDb, ggmanh, Glimma, GSReg, GWASTools, HDF5Array, InteractiveComplexHeatmap, IRanges, iscream, iSEE, LACHESIS, maftools, MiRaGE, MIRit, omicsPrint, parglms, recountmethylation, RTCGA, S4Vectors, SeqGSEA, Seqinfo, splatter, TFutils, universalmotif, updateObject, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, CTCF, GenomicState, BeadArrayUseCases, GeuvadisTranscriptExpr, MEDIPSData, MetaScope, nanotubes, RNAmodR.Data, Single.mTEC.Transcriptomes, systemPipeRdata, xcoredata, CAGEWorkflow, chicane, DGEobj, gkmSVM, methFuse, MoBPS, polyRAD, Rgff, rliger, seqmagick, Seurat, sigminer, smer, SNPassoc, updog, valr dependencyCount: 10 Package: GenomicSuperSignature Version: 1.19.0 Depends: R (>= 4.1.0), SummarizedExperiment Imports: ComplexHeatmap, ggplot2, methods, S4Vectors, Biobase, ggpubr, dplyr, plotly, BiocFileCache, grid, flextable, irlba Suggests: knitr, rmarkdown, devtools, roxygen2, pkgdown, usethis, BiocStyle, testthat, forcats, stats, wordcloud, circlize, EnrichmentBrowser, clusterProfiler, msigdbr, cluster, RColorBrewer, reshape2, tibble, BiocManager, bcellViper, readr, utils License: Artistic-2.0 MD5sum: 07fd3665acde756868c8d2849f6411a6 NeedsCompilation: no Title: Interpretation of RNA-seq experiments through robust, efficient comparison to public databases Description: This package provides a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. Through the pre-computed index, users can identify public resources associated with their dataset such as gene sets, MeSH term, and publication. Functions to identify interpretable annotations and intuitive visualization options are implemented in this package. biocViews: Transcriptomics, SystemsBiology, PrincipalComponent, RNASeq, Sequencing, Pathways, Clustering Author: Sehyun Oh [aut, cre], Levi Waldron [aut], Sean Davis [aut] Maintainer: Sehyun Oh URL: https://github.com/shbrief/GenomicSuperSignature VignetteBuilder: knitr BugReports: https://github.com/shbrief/GenomicSuperSignature/issues git_url: https://git.bioconductor.org/packages/GenomicSuperSignature git_branch: devel git_last_commit: 7a9c399 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomicSuperSignature_1.19.0.tar.gz vignettes: vignettes/GenomicSuperSignature/inst/doc/Contents.html, vignettes/GenomicSuperSignature/inst/doc/Quickstart.html vignetteTitles: Introduction on RAVmodel, Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicSuperSignature/inst/doc/Contents.R, vignettes/GenomicSuperSignature/inst/doc/Quickstart.R dependencyCount: 170 Package: GenomicTuples Version: 1.45.0 Depends: R (>= 4.0), GenomicRanges (>= 1.37.4), Seqinfo, S4Vectors (>= 0.17.25) Imports: methods, BiocGenerics (>= 0.21.2), Rcpp (>= 0.11.2), IRanges (>= 2.19.13), data.table, stats4, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, covr, GenomicAlignments, Biostrings, GenomeInfoDb License: Artistic-2.0 MD5sum: f33ffefa63cb9aa728c7ea9e82873301 NeedsCompilation: yes Title: Representation and Manipulation of Genomic Tuples Description: GenomicTuples defines general purpose containers for storing genomic tuples. It aims to provide functionality for tuples of genomic co-ordinates that are analogous to those available for genomic ranges in the GenomicRanges Bioconductor package. biocViews: Infrastructure, DataRepresentation, Sequencing Author: Peter Hickey [aut, cre], Marcin Cieslik [ctb], Hervé Pagès [ctb] Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/GenomicTuples/issues git_url: https://git.bioconductor.org/packages/GenomicTuples git_branch: devel git_last_commit: 8f1b003 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GenomicTuples_1.45.0.tar.gz vignettes: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html vignetteTitles: GenomicTuplesIntroduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R dependencyCount: 13 Package: GenProSeq Version: 1.15.0 Depends: keras, mclust, R (>= 4.2) Imports: tensorflow, word2vec, DeepPINCS, ttgsea, CatEncoders, reticulate, stats Suggests: VAExprs, stringdist, knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: 6f8e8d0a2ffd3af3a20de2378ec0cc80 NeedsCompilation: no Title: Generating Protein Sequences with Deep Generative Models Description: Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. Machine learning has enabled us to generate useful protein sequences on a variety of scales. Generative models are machine learning methods which seek to model the distribution underlying the data, allowing for the generation of novel samples with similar properties to those on which the model was trained. Generative models of proteins can learn biologically meaningful representations helpful for a variety of downstream tasks. Furthermore, they can learn to generate protein sequences that have not been observed before and to assign higher probability to protein sequences that satisfy desired criteria. In this package, common deep generative models for protein sequences, such as variational autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer encoder is applied to protein sequences for the autoregressive model. biocViews: Software, Proteomics Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenProSeq git_branch: devel git_last_commit: ea2d31c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GenProSeq_1.15.0.tar.gz vignettes: vignettes/GenProSeq/inst/doc/GenProSeq.html vignetteTitles: GenProSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenProSeq/inst/doc/GenProSeq.R dependencyCount: 146 Package: GenVisR Version: 1.43.1 Depends: R (>= 3.3.0), methods Imports: AnnotationDbi, biomaRt (>= 2.45.8), BiocGenerics, Biostrings, DBI, GenomicFeatures, GenomicRanges (>= 1.25.4), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0), gtable, gtools, IRanges (>= 2.7.5), plyr (>= 1.8.3), reshape2, Rsamtools, scales, viridis, data.table, BSgenome, Seqinfo, VariantAnnotation Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL, roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, rmarkdown, vdiffr, formatR, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38 License: GPL-3 + file LICENSE MD5sum: 03e2e64442f4fa88e605222b971f06ea NeedsCompilation: no Title: Genomic Visualizations in R Description: Produce highly customizable publication quality graphics for genomic data primarily at the cohort level. biocViews: Infrastructure, DataRepresentation, Classification, DNASeq Author: Zachary Skidmore [aut, cre], Alex Wagner [aut], Robert Lesurf [aut], Katie Campbell [aut], Jason Kunisaki [aut], Obi Griffith [aut], Malachi Griffith [aut] Maintainer: Zachary Skidmore VignetteBuilder: knitr BugReports: https://github.com/griffithlab/GenVisR/issues git_url: https://git.bioconductor.org/packages/GenVisR git_branch: devel git_last_commit: 2ba6596 git_last_commit_date: 2026-01-10 Date/Publication: 2026-04-20 source.ver: src/contrib/GenVisR_1.43.1.tar.gz vignettes: vignettes/GenVisR/inst/doc/Intro.html, vignettes/GenVisR/inst/doc/waterfall_introduction.html vignetteTitles: GenVisR: An introduction, waterfall: function introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenVisR/inst/doc/Intro.R, vignettes/GenVisR/inst/doc/waterfall_introduction.R dependencyCount: 114 Package: GeoDiff Version: 1.17.0 Depends: R (>= 4.1.0), Biobase Imports: Matrix, robust, plyr, lme4, Rcpp (>= 1.0.4.6), withr, methods, graphics, stats, testthat, GeomxTools, NanoStringNCTools LinkingTo: Rcpp, RcppArmadillo, roptim Suggests: knitr, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: 6412159ed89097f05e11fdb2bdf7f658 NeedsCompilation: yes Title: Count model based differential expression and normalization on GeoMx RNA data Description: A series of statistical models using count generating distributions for background modelling, feature and sample QC, normalization and differential expression analysis on GeoMx RNA data. The application of these methods are demonstrated by example data analysis vignette. biocViews: GeneExpression, DifferentialExpression, Normalization Author: Maddy Griswold [cre], Lei Yang [aut], Zhi Yang [aut] Maintainer: Maddy Griswold URL: https://github.com/Nanostring-Biostats/GeoDiff VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/GeoDiff git_url: https://git.bioconductor.org/packages/GeoDiff git_branch: devel git_last_commit: 7ebe2b2 git_last_commit_date: 2026-04-08 Date/Publication: 2026-04-20 source.ver: src/contrib/GeoDiff_1.17.0.tar.gz vignettes: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.html vignetteTitles: Workflow_WTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.R dependencyCount: 145 Package: GEOfastq Version: 1.19.0 Imports: xml2, rvest, stringr, RCurl, doParallel, foreach, plyr Suggests: BiocCheck, roxygen2, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: b079bba46114499a64be6136cc33b83d NeedsCompilation: no Title: Downloads ENA Fastqs With GEO Accessions Description: GEOfastq is used to download fastq files from the European Nucleotide Archive (ENA) starting with an accession from the Gene Expression Omnibus (GEO). To do this, sample metadata is retrieved from GEO and the Sequence Read Archive (SRA). SRA run accessions are then used to construct FTP and aspera download links for fastq files generated by the ENA. biocViews: RNASeq, DataImport Author: Alex Pickering [cre, aut] (ORCID: ) Maintainer: Alex Pickering VignetteBuilder: knitr BugReports: https://github.com/alexvpickering/GEOfastq/issues git_url: https://git.bioconductor.org/packages/GEOfastq git_branch: devel git_last_commit: 8f661e7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GEOfastq_1.19.0.tar.gz vignettes: vignettes/GEOfastq/inst/doc/GEOfastq.html vignetteTitles: Using the GEOfastq Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GEOfastq/inst/doc/GEOfastq.R dependencyCount: 36 Package: GEOmetadb Version: 1.73.2 Depends: R.utils,RSQLite Suggests: knitr, rmarkdown, dplyr, dbplyr, tm, wordcloud License: Artistic-2.0 MD5sum: 567d565237d60035d8aa63d752b03cde NeedsCompilation: no Title: A compilation of metadata from NCBI GEO Description: The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data of interest can be challenging using current tools. GEOmetadb is an attempt to make access to the metadata associated with samples, platforms, and datasets much more feasible. This is accomplished by parsing all the NCBI GEO metadata into a SQLite database that can be stored and queried locally. GEOmetadb is simply a thin wrapper around the SQLite database along with associated documentation. Finally, the SQLite database is updated regularly as new data is added to GEO and can be downloaded at will for the most up-to-date metadata. GEOmetadb paper: http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798 . biocViews: Infrastructure Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEOmetadb git_branch: devel git_last_commit: 958a081 git_last_commit_date: 2026-03-24 Date/Publication: 2026-04-20 source.ver: src/contrib/GEOmetadb_1.73.2.tar.gz vignettes: vignettes/GEOmetadb/inst/doc/GEOmetadb.html vignetteTitles: GEOmetadb hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R suggestsMe: epiSeeker, antiProfilesData, maGUI dependencyCount: 23 Package: GeomxTools Version: 3.15.0 Depends: R (>= 3.6), Biobase, NanoStringNCTools, S4Vectors Imports: BiocGenerics, rjson, readxl, EnvStats, reshape2, methods, utils, stats, data.table, lmerTest, dplyr, stringr, grDevices, graphics, GGally, rlang, ggplot2, SeuratObject Suggests: rmarkdown, knitr, testthat (>= 3.0.0), parallel, ggiraph, Seurat, SpatialExperiment (>= 1.4.0), SpatialDecon, patchwork License: MIT MD5sum: 5ac0fbaa6cb56e9b5a124e9b2528c97f NeedsCompilation: no Title: NanoString GeoMx Tools Description: Tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, mRNAMicroarray, ProprietaryPlatforms, RNASeq, Sequencing, ExperimentalDesign, Normalization, Spatial Author: Maddy Griswold [cre, aut], Nicole Ortogero [aut], Zhi Yang [aut], Ronalyn Vitancol [aut], David Henderson [aut] Maintainer: Maddy Griswold VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeomxTools git_branch: devel git_last_commit: 2ccee05 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeomxTools_3.15.0.tar.gz vignettes: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.html, vignettes/GeomxTools/inst/doc/GeomxSet_coercions.html, vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.html vignetteTitles: Developer Introduction to the NanoStringGeoMxSet, Coercion of GeoMxSet to Seurat and SpatialExperiment Objects, Protein data using GeomxTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.R, vignettes/GeomxTools/inst/doc/GeomxSet_coercions.R, vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.R dependsOnMe: GeoMxWorkflows importsMe: GeoDiff, SpatialDecon, SpatialOmicsOverlay dependencyCount: 123 Package: GEOquery Version: 2.79.0 Depends: R (>= 4.1.0), methods, Biobase Imports: readr (>= 1.3.1), xml2, dplyr, data.table, tidyr, magrittr, limma, curl, rentrez, R.utils, stringr, SummarizedExperiment, S4Vectors, rvest, httr2 Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr, markdown, quarto, DropletUtils, SingleCellExperiment License: MIT + file LICENSE MD5sum: 4a5aeeb7da8eb61c20280cb38935ea5f NeedsCompilation: no Title: Get data from NCBI Gene Expression Omnibus (GEO) Description: The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor. biocViews: Microarray, DataImport, OneChannel, TwoChannel, SAGE Author: Sean Davis [aut, cre] (ORCID: ) Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery, http://seandavi.github.io/GEOquery, http://seandavi.github.io/GEOquery/ VignetteBuilder: quarto BugReports: https://github.com/seandavi/GEOquery/issues/new git_url: https://git.bioconductor.org/packages/GEOquery git_branch: devel git_last_commit: ccaa9cd git_last_commit_date: 2025-11-24 Date/Publication: 2026-04-20 source.ver: src/contrib/GEOquery_2.79.0.tar.gz vignettes: vignettes/GEOquery/inst/doc/GEOquery.html, vignettes/GEOquery/inst/doc/single-cell.html vignetteTitles: GEOquery.html, single-cell.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R, vignettes/GEOquery/inst/doc/single-cell.R dependsOnMe: DrugVsDisease, SCAN.UPC, dyebiasexamples, GSE103322, GSE13015, GSE62944 importsMe: bigmelon, ChIPXpress, DExMA, EGAD, minfi, Moonlight2R, MoonlightR, phantasus, recount, BeadArrayUseCases, BioPlex, GSE13015, healthyControlsPresenceChecker, geneExpressionFromGEO, RCPA suggestsMe: AUCell, autonomics, COTAN, ctsGE, dearseq, diffcoexp, dyebias, EpiDISH, EpiMix, epiSeeker, fgsea, FLAMES, GeneExpressionSignature, GenomicOZone, GeoTcgaData, methylclock, multiClust, MultiDataSet, omicsPrint, PCAtools, phantasusLite, quantiseqr, RegEnrich, RFGeneRank, RGSEA, Rnits, runibic, skewr, spatialHeatmap, TargetScore, zFPKM, ath1121501frmavecs, airway, antiProfilesData, muscData, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, RegParallel, AnnoProbe, BED, CimpleG, easybio, fdrci, maGUI, metaMA, MLML2R, NACHO, scregclust, TcGSA, tinyarray dependencyCount: 74 Package: GEOsubmission Version: 1.63.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: 990436ad1b76b0048093828beeaefdc8 NeedsCompilation: no Title: Prepares microarray data for submission to GEO Description: Helps to easily submit a microarray dataset and the associated sample information to GEO by preparing a single file for upload (direct deposit). biocViews: Microarray Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/GEOsubmission git_branch: devel git_last_commit: 6bd8c28 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GEOsubmission_1.63.0.tar.gz vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf vignetteTitles: GEOsubmission Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOsubmission/inst/doc/GEOsubmission.R dependencyCount: 12 Package: GeoTcgaData Version: 2.11.0 Depends: R (>= 4.2.0) Imports: utils, data.table, plyr, cqn, topconfects, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, DESeq2, S4Vectors, ChAMP, impute, tidyr, clusterProfiler, org.Hs.eg.db, edgeR, limma, quantreg, minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, dearseq, NOISeq, testthat (>= 3.0.0), CATT, TCGAbiolinks, enrichplot, GEOquery, BiocGenerics License: Artistic-2.0 MD5sum: b1b6b18eb12e55774b7d4415bcd79567 NeedsCompilation: no Title: Processing Various Types of Data on GEO and TCGA Description: Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data. biocViews: GeneExpression, DifferentialExpression, RNASeq, CopyNumberVariation, Microarray, Software, DNAMethylation, DifferentialMethylation, SNP, ATACSeq, MethylationArray Author: Erqiang Hu [aut, cre] (ORCID: ) Maintainer: Erqiang Hu <13766876214@163.com> URL: https://github.com/YuLab-SMU/GeoTcgaData VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/GeoTcgaData/issues git_url: https://git.bioconductor.org/packages/GeoTcgaData git_branch: devel git_last_commit: 4541645 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GeoTcgaData_2.11.0.tar.gz vignettes: vignettes/GeoTcgaData/inst/doc/GeoTcgaData.html vignetteTitles: GeoTcgaData hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeoTcgaData/inst/doc/GeoTcgaData.R dependencyCount: 56 Package: getDEE2 Version: 1.21.0 Depends: R (>= 4.4) Imports: stats, utils, SummarizedExperiment, htm2txt Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: b07156db9bdb76d53d99ffdf9dacb470 NeedsCompilation: no Title: Programmatic access to the DEE2 RNA expression dataset Description: Digital Expression Explorer 2 (or DEE2 for short) is a repository of processed RNA-seq data in the form of counts. It was designed so that researchers could undertake re-analysis and meta-analysis of published RNA-seq studies quickly and easily. As of April 2020, over 1 million SRA datasets have been processed. This package provides an R interface to access these expression data. More information about the DEE2 project can be found at the project homepage (http://dee2.io) and main publication (https://doi.org/10.1093/gigascience/giz022). biocViews: GeneExpression, Transcriptomics, Sequencing Author: Mark Ziemann [aut, cre], Antony Kaspi [aut] Maintainer: Mark 0000-0002-7688-6974 Ziemann URL: https://github.com/markziemann/getDEE2 VignetteBuilder: knitr BugReports: https://github.com/markziemann/getDEE2 git_url: https://git.bioconductor.org/packages/getDEE2 git_branch: devel git_last_commit: 5d99578 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/getDEE2_1.21.0.tar.gz vignettes: vignettes/getDEE2/inst/doc/getDEE2.html vignetteTitles: getDEE2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/getDEE2/inst/doc/getDEE2.R dependencyCount: 26 Package: geva Version: 1.19.0 Depends: R (>= 4.1) Imports: grDevices, graphics, methods, stats, utils, dbscan, fastcluster, matrixStats Suggests: devtools, knitr, rmarkdown, roxygen2, limma, topGO, testthat (>= 3.0.0) License: LGPL-3 MD5sum: 223df592e545cf790bb879123768c22c NeedsCompilation: no Title: Gene Expression Variation Analysis (GEVA) Description: Statistic methods to evaluate variations of differential expression (DE) between multiple biological conditions. It takes into account the fold-changes and p-values from previous differential expression (DE) results that use large-scale data (*e.g.*, microarray and RNA-seq) and evaluates which genes would react in response to the distinct experiments. This evaluation involves an unique pipeline of statistical methods, including weighted summarization, quantile detection, cluster analysis, and ANOVA tests, in order to classify a subset of relevant genes whose DE is similar or dependent to certain biological factors. biocViews: Classification, DifferentialExpression, GeneExpression, Microarray, MultipleComparison, RNASeq, SystemsBiology, Transcriptomics Author: Itamar José Guimarães Nunes [aut, cre] (ORCID: ), Murilo Zanini David [ctb], Bruno César Feltes [ctb] (ORCID: ), Marcio Dorn [ctb] (ORCID: ) Maintainer: Itamar José Guimarães Nunes URL: https://github.com/sbcblab/geva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geva git_branch: devel git_last_commit: 4af0647 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/geva_1.19.0.tar.gz vignettes: vignettes/geva/inst/doc/geva.pdf vignetteTitles: GEVA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geva/inst/doc/geva.R dependencyCount: 10 Package: GEWIST Version: 1.55.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 7feb9e868a32e727cf9af5e5c0f49a34 NeedsCompilation: no Title: Gene Environment Wide Interaction Search Threshold Description: This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions. biocViews: MultipleComparison, Genetics Author: Wei Q. Deng, Guillaume Pare Maintainer: Wei Q. Deng git_url: https://git.bioconductor.org/packages/GEWIST git_branch: devel git_last_commit: c4557a9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GEWIST_1.55.0.tar.gz vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf vignetteTitles: GEWIST.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEWIST/inst/doc/GEWIST.R dependencyCount: 78 Package: geyser Version: 1.3.3 Depends: R (>= 3.5.0) Imports: bslib (>= 0.6.0), BiocStyle, ComplexHeatmap, cowplot, dplyr, DT, ggbeeswarm, ggplot2, ggrepel, ggh4x, htmltools, magrittr, pals, RColorBrewer, rlang, R.utils, shiny, shinyjs, S4Vectors, SummarizedExperiment, tibble, tidyselect, tidyr, yaml Suggests: airway, knitr, DESeq2, rmarkdown, stringr, testthat (>= 3.0.0) License: CC0 MD5sum: 54c4141b2ccb0f076d38b01b5d59a4f4 NeedsCompilation: no Title: Gene Expression displaYer of SummarizedExperiment in R Description: Lightweight Expression displaYer (plotter / viewer) of SummarizedExperiment object in R. This package provides a quick and easy Shiny-based GUI to empower a user to use a SummarizedExperiment object to view biocViews: Software, ShinyApps, GUI, GeneExpression Author: David McGaughey [aut, cre] (ORCID: ) Maintainer: David McGaughey URL: https://github.com/davemcg/geyser VignetteBuilder: knitr BugReports: https://github.com/davemcg/geyser/issues git_url: https://git.bioconductor.org/packages/geyser git_branch: devel git_last_commit: 277cf9e git_last_commit_date: 2026-03-06 Date/Publication: 2026-04-20 source.ver: src/contrib/geyser_1.3.3.tar.gz vignettes: vignettes/geyser/inst/doc/Gene_Expression_Plotting_GUI.html vignetteTitles: Gene_Expression_Plotting_GUI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geyser/inst/doc/Gene_Expression_Plotting_GUI.R dependencyCount: 120 Package: gg4way Version: 1.9.0 Depends: R (>= 4.3.0), ggplot2 Imports: DESeq2, dplyr, edgeR, ggrepel, glue, janitor, limma, magrittr, methods, purrr, rlang, scales, stats, stringr, tibble, tidyr Suggests: airway, BiocStyle, knitr, org.Hs.eg.db, rmarkdown, testthat, vdiffr License: MIT + file LICENSE MD5sum: 8fc93c6008288ed225305b5dce583a66 NeedsCompilation: no Title: 4way Plots of Differential Expression Description: 4way plots enable a comparison of the logFC values from two contrasts of differential gene expression. The gg4way package creates 4way plots using the ggplot2 framework and supports popular Bioconductor objects. The package also provides information about the correlation between contrasts and significant genes of interest. biocViews: Software, Visualization, DifferentialExpression, GeneExpression, Transcription, RNASeq, SingleCell, Sequencing Author: Benjamin I Laufer [aut, cre], Brad A Friedman [aut] Maintainer: Benjamin I Laufer URL: https://github.com/ben-laufer/gg4way VignetteBuilder: knitr BugReports: https://github.com/ben-laufer/gg4way/issues git_url: https://git.bioconductor.org/packages/gg4way git_branch: devel git_last_commit: d0f2a7c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gg4way_1.9.0.tar.gz vignettes: vignettes/gg4way/inst/doc/gg4way.html vignetteTitles: gg4way hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gg4way/inst/doc/gg4way.R dependencyCount: 75 Package: ggcyto Version: 1.39.4 Depends: methods, ggplot2(>= 3.5.0), flowCore(>= 1.41.5), ncdfFlow(>= 2.17.1), flowWorkspace(>= 4.3.1) Imports: plyr, scales, hexbin, data.table, RColorBrewer, gridExtra, rlang Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats, openCyto, flowViz, ggridges, vdiffr License: file LICENSE MD5sum: 9b1b90ee338d273e6c179951bffef49b NeedsCompilation: no Title: Visualize Cytometry data with ggplot Description: With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Infrastructure, Visualization Author: Mike Jiang Maintainer: Mike Jiang URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggcyto git_branch: devel git_last_commit: ee14a01 git_last_commit_date: 2026-04-18 Date/Publication: 2026-04-20 source.ver: src/contrib/ggcyto_1.39.4.tar.gz vignettes: vignettes/ggcyto/inst/doc/autoplot.html, vignettes/ggcyto/inst/doc/ggcyto.flowSet.html, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.html, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.html vignetteTitles: Quick plot for cytometry data, Visualize flowSet with ggcyto, Visualize GatingSet with ggcyto, Feature summary of ggcyto hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggcyto/inst/doc/autoplot.R, vignettes/ggcyto/inst/doc/ggcyto.flowSet.R, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.R, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.R dependsOnMe: flowGate importsMe: CompensAID, CytoML, CytoPipeline suggestsMe: CATALYST, flowCore, flowStats, flowTime, flowWorkspace, openCyto, staRgate dependencyCount: 65 Package: ggkegg Version: 1.9.0 Depends: R (>= 4.3.0), ggplot2, ggraph, XML, igraph, tidygraph Imports: BiocFileCache, data.table, dplyr, magick, patchwork, shadowtext, stringr, tibble, methods, utils, stats, grDevices, gtable Suggests: knitr, clusterProfiler, bnlearn, rmarkdown, BiocStyle, AnnotationDbi, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 6cefa2cd5e3c954d37a8625543c260a6 NeedsCompilation: no Title: Analyzing and visualizing KEGG information using the grammar of graphics Description: This package aims to import, parse, and analyze KEGG data such as KEGG PATHWAY and KEGG MODULE. The package supports visualizing KEGG information using ggplot2 and ggraph through using the grammar of graphics. The package enables the direct visualization of the results from various omics analysis packages. biocViews: Pathways, DataImport, KEGG Author: Noriaki Sato [cre, aut] Maintainer: Noriaki Sato URL: https://github.com/noriakis/ggkegg VignetteBuilder: knitr BugReports: https://github.com/noriakis/ggkegg/issues git_url: https://git.bioconductor.org/packages/ggkegg git_branch: devel git_last_commit: cfbc2f3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ggkegg_1.9.0.tar.gz vignettes: vignettes/ggkegg/inst/doc/usage_of_ggkegg.html vignetteTitles: ggkegg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggkegg/inst/doc/usage_of_ggkegg.R importsMe: pathfindR suggestsMe: ReporterScore dependencyCount: 97 Package: ggmanh Version: 1.15.0 Depends: methods, ggplot2 Imports: gdsfmt, ggrepel, grDevices, paletteer, RColorBrewer, rlang, scales, SeqArray (>= 1.32.0), stats, tidyr, dplyr, pals, magrittr Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0), GenomicRanges License: MIT + file LICENSE MD5sum: 8ad454699ef94613be20a25751b6f970 NeedsCompilation: no Title: Visualization Tool for GWAS Result Description: Manhattan plot and QQ Plot are commonly used to visualize the end result of Genome Wide Association Study. The "ggmanh" package aims to keep the generation of these plots simple while maintaining customizability. Main functions include manhattan_plot, qqunif, and thinPoints. biocViews: Visualization, GenomeWideAssociation, Genetics Author: John Lee [aut, cre], John Lee [aut] (AbbVie), Xiuwen Zheng [ctb, dtc] Maintainer: John Lee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggmanh git_branch: devel git_last_commit: 3e4cc5e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ggmanh_1.15.0.tar.gz vignettes: vignettes/ggmanh/inst/doc/ggmanh.html vignetteTitles: Guide to ggmanh Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggmanh/inst/doc/ggmanh.R suggestsMe: SAIGEgds, plotthis dependencyCount: 60 Package: ggmsa Version: 1.17.0 Depends: R (>= 4.1.0) Imports: Biostrings, ggplot2, magrittr, tidyr, utils, stats, aplot, RColorBrewer, ggfun (>= 0.2.0), ggforce, dplyr, R4RNA, grDevices, seqmagick, grid, methods, ggtree (>= 1.17.1) Suggests: ggtreeExtra, ape, cowplot, knitr, rmarkdown, readxl, ggnewscale, kableExtra, gggenes, statebins, prettydoc, testthat (>= 3.0.0), yulab.utils License: Artistic-2.0 MD5sum: ca909d650dfcf34609881fcedaf3be4a NeedsCompilation: no Title: Plot Multiple Sequence Alignment using 'ggplot2' Description: A visual exploration tool for multiple sequence alignment and associated data. Supports MSA of DNA, RNA, and protein sequences using 'ggplot2'. Multiple sequence alignment can easily be combined with other 'ggplot2' plots, such as phylogenetic tree Visualized by 'ggtree', boxplot, genome map and so on. More features: visualization of sequence logos, sequence bundles, RNA secondary structures and detection of sequence recombinations. biocViews: Software, Visualization, Alignment, Annotation, MultipleSequenceAlignment Author: Guangchuang Yu [aut, cre, ths] (ORCID: ), Lang Zhou [aut], Shuangbin Xu [ctb], Huina Huang [ctb] Maintainer: Guangchuang Yu URL: https://doi.org/10.1093/bib/bbac222(paper), https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggmsa/issues git_url: https://git.bioconductor.org/packages/ggmsa git_branch: devel git_last_commit: aa847fd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ggmsa_1.17.0.tar.gz vignettes: vignettes/ggmsa/inst/doc/ggmsa.html vignetteTitles: ggmsa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggmsa/inst/doc/ggmsa.R importsMe: ggaligner dependencyCount: 93 Package: GGPA Version: 1.23.0 Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats Imports: Rcpp (>= 0.11.3) LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle License: GPL (>= 2) MD5sum: 55ab57c32c7fa3c62cf7665c22c6bb86 NeedsCompilation: yes Title: graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture Description: Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Hang J. Kim, Carter Allen Maintainer: Dongjun Chung URL: https://github.com/dongjunchung/GGPA/ SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/GGPA git_branch: devel git_last_commit: 8abab50 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GGPA_1.23.0.tar.gz vignettes: vignettes/GGPA/inst/doc/GGPA-example.pdf vignetteTitles: GGPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGPA/inst/doc/GGPA-example.R dependencyCount: 54 Package: ggsc Version: 1.9.0 Depends: R (>= 4.1.0) Imports: Rcpp, RcppParallel, cli, dplyr, ggfun (>= 0.1.5), ggplot2, grDevices, grid, methods, rlang, scattermore, stats, Seurat, SingleCellExperiment, SummarizedExperiment, tidydr, tidyr, tibble, utils, RColorBrewer, yulab.utils, scales LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: aplot, BiocParallel, forcats, ggforce, ggnewscale, igraph, knitr, ks, Matrix, prettydoc, rmarkdown, scran, scater, scatterpie (>= 0.2.4), scuttle, shadowtext, sf, SeuratObject, SpatialExperiment, STexampleData, testthat (>= 3.0.0), MASS License: Artistic-2.0 MD5sum: 936b9989d96c28de632cf65aec66fc1d NeedsCompilation: yes Title: Visualizing Single Cell and Spatial Transcriptomics Description: Useful functions to visualize single cell and spatial data. It supports visualizing 'Seurat', 'SingleCellExperiment' and 'SpatialExperiment' objects through grammar of graphics syntax implemented in 'ggplot2'. biocViews: DimensionReduction, GeneExpression, SingleCell, Software, Spatial, Transcriptomics,Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Shuangbin Xu [aut] (ORCID: ), Noriaki Sato [ctb] Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/ggsc (devel), https://yulab-smu.top/ggsc/ (docs) SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggsc/issues git_url: https://git.bioconductor.org/packages/ggsc git_branch: devel git_last_commit: 9591222 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ggsc_1.9.0.tar.gz vignettes: vignettes/ggsc/inst/doc/ggsc.html vignetteTitles: Visualizing single cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggsc/inst/doc/ggsc.R suggestsMe: SVP dependencyCount: 169 Package: ggseqalign Version: 1.5.0 Depends: R (>= 4.4.0) Imports: pwalign, dplyr, ggplot2 Suggests: Biostrings, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 7a5b276d08afedfdc0d2591b8991d81d NeedsCompilation: no Title: Minimal Visualization of Sequence Alignments Description: Simple visualizations of alignments of DNA or AA sequences as well as arbitrary strings. Compatible with Biostrings and ggplot2. The plots are fully customizable using ggplot2 modifiers such as theme(). biocViews: Alignment, MultipleSequenceAlignment, Software, Visualization Author: Simeon Lim Rossmann [aut, cre] (ORCID: ) Maintainer: Simeon Lim Rossmann URL: https://github.com/simeross/ggseqalign VignetteBuilder: knitr BugReports: https://github.com/simeross/ggseqalign/issues git_url: https://git.bioconductor.org/packages/ggseqalign git_branch: devel git_last_commit: 2aa88e4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ggseqalign_1.5.0.tar.gz vignettes: vignettes/ggseqalign/inst/doc/ggseqalign.html vignetteTitles: Quickstart Guide to ggseqalign hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggseqalign/inst/doc/ggseqalign.R dependencyCount: 41 Package: ggspavis Version: 1.17.0 Depends: ggplot2 Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, ggside, grid, ggrepel, RColorBrewer, scales, grDevices, methods, stats Suggests: BiocStyle, rmarkdown, knitr, OSTA.data, VisiumIO, arrow, STexampleData, BumpyMatrix, scater, scran, uwot, testthat, patchwork License: MIT + file LICENSE MD5sum: 5d68ba8d06bb1b0a13f7b1f38254b157 NeedsCompilation: no Title: Visualization functions for spatial transcriptomics data Description: Visualization functions for spatial transcriptomics data. Includes functions to generate several types of plots, including spot plots, feature (molecule) plots, reduced dimension plots, spot-level quality control (QC) plots, and feature-level QC plots, for datasets from the 10x Genomics Visium and other technological platforms. Datasets are assumed to be in either SpatialExperiment or SingleCellExperiment format. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, QualityControl, DimensionReduction Author: Lukas M. Weber [aut, cre] (ORCID: ), Helena L. Crowell [aut] (ORCID: ), Yixing E. Dong [aut] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/ggspavis VignetteBuilder: knitr BugReports: https://github.com/lmweber/ggspavis/issues git_url: https://git.bioconductor.org/packages/ggspavis git_branch: devel git_last_commit: 0e5bfdb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ggspavis_1.17.0.tar.gz vignettes: vignettes/ggspavis/inst/doc/ggspavis_overview.html vignetteTitles: ggspavis overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggspavis/inst/doc/ggspavis_overview.R importsMe: OSTA suggestsMe: smoothclust, HCATonsilData dependencyCount: 77 Package: ggtree Version: 4.1.2 Depends: R (>= 4.2.0) Imports: ape, aplot, cli, dplyr, ggfun (>= 0.1.7), ggiraph (>= 0.9.1), ggplot2 (>= 4.0.0), grid, magrittr, methods, purrr, rlang, scales, stats, tidyr, tidytree (>= 0.4.5), treeio (>= 1.8.0), utils, yulab.utils (>= 0.2.3) Suggests: emojifont, ggimage, ggplotify, shadowtext, grDevices, knitr, prettydoc, rmarkdown, igraph, testthat, tibble, glue, Biostrings License: Artistic-2.0 MD5sum: 5274e49e737a8998021a1382706a52ab NeedsCompilation: no Title: an R package for visualization of tree and annotation data Description: 'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data. biocViews: Alignment, Annotation, Clustering, DataImport, MultipleSequenceAlignment, Phylogenetics, ReproducibleResearch, Software, Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Tommy Tsan-Yuk Lam [aut, ths], Shuangbin Xu [aut] (ORCID: ), Lin Li [ctb], Bradley Jones [ctb], Justin Silverman [ctb], Watal M. Iwasaki [ctb], Yonghe Xia [ctb], Ruizhu Huang [ctb] Maintainer: Guangchuang Yu URL: https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book), http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12628 (paper) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtree/issues git_url: https://git.bioconductor.org/packages/ggtree git_branch: devel git_last_commit: c12faf7 git_last_commit_date: 2026-04-09 Date/Publication: 2026-04-20 source.ver: src/contrib/ggtree_4.1.2.tar.gz vignettes: vignettes/ggtree/inst/doc/ggtree.html vignetteTitles: ggtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtree/inst/doc/ggtree.R dependsOnMe: ggtreeDendro, tanggle importsMe: cardelino, cellmig, cogeqc, crumblr, DspikeIn, enrichplot, ggmsa, ggtreeExtra, ggtreeSpace, iSEEtree, lefser, LymphoSeq, MetaboDynamics, miaViz, MicrobiotaProcess, mitology, orthogene, philr, scBubbletree, scDotPlot, SEMPLR, singleCellTK, sitePath, SVP, systemPipeTools, treeclimbR, treekoR, BioVizSeq, DAISIEprep, ddtlcm, delimtools, dowser, EvoPhylo, genBaRcode, harrietr, mycolorsTB, numbat, RevGadgets, scistreer, shinyTempSignal, STraTUS, Sysrecon, TransProR suggestsMe: compcodeR, CrcBiomeScreen, epiSeeker, fastreeR, syntenet, TreeAndLeaf, treeio, TreeSummarizedExperiment, universalmotif, aplot, aplotExtra, DAISIE, deeptime, FossilSim, gggenomes, ggimage, ggtangle, idiogramFISH, MetaNet, nosoi, oppr, PCMBase, pctax, piglet, RAINBOWR, rhierbaps, rphylopic, treestructure dependencyCount: 79 Package: ggtreeDendro Version: 1.13.0 Depends: ggtree (>= 3.5.3) Imports: ggplot2, stats, tidytree, utils Suggests: aplot, cluster, knitr, MASS, mdendro, prettydoc, pvclust, rmarkdown, testthat (>= 3.0.0), treeio, yulab.utils License: Artistic-2.0 MD5sum: e404f94b6c843a52ed1b67942ee4caa4 NeedsCompilation: no Title: Drawing 'dendrogram' using 'ggtree' Description: Offers a set of 'autoplot' methods to visualize tree-like structures (e.g., hierarchical clustering and classification/regression trees) using 'ggtree'. You can adjust graphical parameters using grammar of graphic syntax and integrate external data to the tree. biocViews: Clustering, Classification, DecisionTree, Phylogenetics, Visualization Author: Guangchuang Yu [aut, cre, cph] (ORCID: ), Shuangbin Xu [ctb] (ORCID: ), Chuanjie Zhang [ctb] Maintainer: Guangchuang Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggtreeDendro git_branch: devel git_last_commit: f945905 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ggtreeDendro_1.13.0.tar.gz vignettes: vignettes/ggtreeDendro/inst/doc/ggtreeDendro.html vignetteTitles: Visualizing Dendrogram using ggtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeDendro/inst/doc/ggtreeDendro.R dependencyCount: 80 Package: ggtreeExtra Version: 1.21.1 Imports: ggplot2 (>= 4.0.0), utils, rlang, ggnewscale, stats, ggtree, tidytree (>= 0.3.9), cli, magrittr, yulab.utils Suggests: treeio, ggstar, patchwork, knitr, rmarkdown, prettydoc, markdown, testthat (>= 3.0.0), pillar License: GPL (>= 3) MD5sum: de48b5baff74254a8bc33b2a75c0ac25 NeedsCompilation: no Title: An R Package To Add Geometric Layers On Circular Or Other Layout Tree Of "ggtree" Description: 'ggtreeExtra' extends the method for mapping and visualizing associated data on phylogenetic tree using 'ggtree'. These associated data can be presented on the external panels to circular layout, fan layout, or other rectangular layout tree built by 'ggtree' with the grammar of 'ggplot2'. biocViews: Software, Visualization, Phylogenetics, Annotation Author: Shuangbin Xu [aut, cre] (ORCID: ), Guangchuang Yu [aut, ctb] (ORCID: ) Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/ggtreeExtra/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtreeExtra/issues git_url: https://git.bioconductor.org/packages/ggtreeExtra git_branch: devel git_last_commit: 21c8148 git_last_commit_date: 2026-01-19 Date/Publication: 2026-04-20 source.ver: src/contrib/ggtreeExtra_1.21.1.tar.gz vignettes: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html vignetteTitles: ggtreeExtra hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.R importsMe: DspikeIn, MicrobiotaProcess suggestsMe: enrichplot, ggmsa, pctax, TransProR dependencyCount: 81 Package: ggtreeSpace Version: 1.7.0 Depends: R (>= 4.1.0) Imports: interp, ape, dplyr, GGally, ggplot2, grid, ggtree, phytools, rlang, tibble, tidyr, tidyselect, stats Suggests: knitr, prettydoc, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 25cb8186ece0c28faf6e6368c52787c6 NeedsCompilation: no Title: Visualizing Phylomorphospaces using 'ggtree' Description: This package is a comprehensive visualization tool specifically designed for exploring phylomorphospace. It not only simplifies the process of generating phylomorphospace, but also enhances it with the capability to add graphic layers to the plot with grammar of graphics to create fully annotated phylomorphospaces. It also provide some utilities to help interpret evolutionary patterns. biocViews: Annotation, Visualization, Phylogenetics, Software Author: Guangchuang Yu [aut, cre, ths, cph] (ORCID: ), Li Lin [ctb] Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/ggtreeSpace VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtreeSpace/issues git_url: https://git.bioconductor.org/packages/ggtreeSpace git_branch: devel git_last_commit: 049d55c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ggtreeSpace_1.7.0.tar.gz vignettes: vignettes/ggtreeSpace/inst/doc/ggtreeSpace.html vignetteTitles: Introduction to ggtreeSpace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeSpace/inst/doc/ggtreeSpace.R dependencyCount: 110 Package: GIGSEA Version: 1.29.0 Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: b33aec067a6616a54d4520e7c85c1dbf NeedsCompilation: no Title: Genotype Imputed Gene Set Enrichment Analysis Description: We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal. biocViews: GeneSetEnrichment,SNP,VariantAnnotation,GeneExpression,GeneRegulation,Regression,DifferentialExpression Author: Shijia Zhu Maintainer: Shijia Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GIGSEA git_branch: devel git_last_commit: 456186a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GIGSEA_1.29.0.tar.gz vignettes: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.pdf vignetteTitles: GIGSEA: Genotype Imputed Gene Set Enrichment Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.R suggestsMe: GIGSEAdata dependencyCount: 11 Package: ginmappeR Version: 1.7.0 Imports: KEGGREST, UniProt.ws, XML, rentrez, httr, utils, memoise, cachem, jsonlite, rvest Suggests: RUnit, BiocGenerics, markdown, knitr License: GPL-3 + file LICENSE MD5sum: 6e8598f3a50b27f9d6750acc2258396b NeedsCompilation: no Title: Gene Identifier Mapper Description: Provides functionalities to translate gene or protein identifiers between state-of-art biological databases: CARD (), NCBI Protein, Nucleotide and Gene (), UniProt () and KEGG (). Also offers complementary functionality like NCBI identical proteins or UniProt similar genes clusters retrieval. biocViews: Annotation, KEGG, Genetics, ThirdPartyClient, Software Author: Fernando Sola [aut, cre] (ORCID: ), Daniel Ayala [aut] (ORCID: ), Marina Pulido [aut] (ORCID: ), Rafael Ayala [aut] (ORCID: ), Lorena López-Cerero [aut] (ORCID: ), Inma Hernández [aut] (ORCID: ), David Ruiz [aut] (ORCID: ) Maintainer: Fernando Sola VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ginmappeR git_branch: devel git_last_commit: 78c4175 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ginmappeR_1.7.0.tar.gz vignettes: vignettes/ginmappeR/inst/doc/ginmappeR.html vignetteTitles: ginmappeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ginmappeR/inst/doc/ginmappeR.R dependencyCount: 69 Package: GLAD Version: 2.75.0 Depends: R (>= 2.10) Imports: aws License: GPL-2 MD5sum: aa94b7a40c35ec0899b6d7034e70a6ac NeedsCompilation: yes Title: Gain and Loss Analysis of DNA Description: Analysis of array CGH data : detection of breakpoints in genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified. biocViews: Microarray, CopyNumberVariation Author: Philippe Hupe Maintainer: Philippe Hupe URL: http://bioinfo.curie.fr SystemRequirements: gsl. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. git_url: https://git.bioconductor.org/packages/GLAD git_branch: devel git_last_commit: 8bbc766 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GLAD_2.75.0.tar.gz vignettes: vignettes/GLAD/inst/doc/GLAD.pdf vignetteTitles: GLAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GLAD/inst/doc/GLAD.R dependsOnMe: ITALICS importsMe: ITALICS, MANOR suggestsMe: aroma.cn, aroma.core dependencyCount: 4 Package: GladiaTOX Version: 1.27.0 Depends: R (>= 3.6.0), data.table (>= 1.9.4) Imports: DBI, RMariaDB, RSQLite, numDeriv, RColorBrewer, parallel, stats, methods, graphics, grDevices, xtable, tools, brew, stringr, RJSONIO, ggplot2, ggrepel, tidyr, utils, RCurl, XML Suggests: roxygen2, knitr, rmarkdown, testthat, BiocStyle License: GPL-2 MD5sum: c5dd600cb7fe994a859a32579bb1431e NeedsCompilation: no Title: R Package for Processing High Content Screening data Description: GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the tcpl core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate pdf reports for quality control and data processing. biocViews: Software, WorkflowStep, Normalization, Preprocessing, QualityControl Author: Vincenzo Belcastro [aut, cre], Dayne L Filer [aut], Stephane Cano [aut] Maintainer: PMP S.A. R Support URL: https://github.com/philipmorrisintl/GladiaTOX VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GladiaTOX git_branch: devel git_last_commit: ab512db git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GladiaTOX_1.27.0.tar.gz vignettes: vignettes/GladiaTOX/inst/doc/GladiaTOX.html vignetteTitles: GladiaTOX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GladiaTOX/inst/doc/GladiaTOX.R dependencyCount: 59 Package: GlobalAncova Version: 4.29.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi, Biobase, dendextend, GSEABase, VGAM Suggests: GO.db, golubEsets, hu6800.db, vsn, Rgraphviz License: GPL (>= 2) MD5sum: 960188daeb49f8dad7c25741469b6cf1 NeedsCompilation: yes Title: Global test for groups of variables via model comparisons Description: The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. biocViews: Microarray, OneChannel, DifferentialExpression, Pathways, Regression Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel Maintainer: Manuela Hummel git_url: https://git.bioconductor.org/packages/GlobalAncova git_branch: devel git_last_commit: 342275c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GlobalAncova_4.29.0.tar.gz vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R importsMe: miRtest suggestsMe: GiANT dependencyCount: 70 Package: globalSeq Version: 1.39.0 Depends: R (>= 3.0.0) Suggests: knitr, testthat, SummarizedExperiment, S4Vectors License: GPL-3 MD5sum: 11e2564a91747bcaa7b37c1780e4d62a NeedsCompilation: no Title: Global Test for Counts Description: The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data. biocViews: GeneExpression, ExonArray, DifferentialExpression, GenomeWideAssociation, Transcriptomics, DimensionReduction, Regression, Sequencing, WholeGenome, RNASeq, ExomeSeq, miRNA, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/globalSeq VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/globalSeq/issues git_url: https://git.bioconductor.org/packages/globalSeq git_branch: devel git_last_commit: 50c9aeb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/globalSeq_1.39.0.tar.gz vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf, vignettes/globalSeq/inst/doc/article.html, vignettes/globalSeq/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R dependencyCount: 0 Package: globaltest Version: 5.65.0 Depends: methods, survival Imports: Biobase, AnnotationDbi, annotate, graphics Suggests: vsn, golubEsets, KEGGREST, hu6800.db, Rgraphviz, GO.db, lungExpression, org.Hs.eg.db, GSEABase, penalized, gss, MASS, boot, rpart, mstate License: GPL (>= 2) MD5sum: eb729de0cce79aec5efe7f757ba81aef NeedsCompilation: no Title: Testing Groups of Covariates/Features for Association with a Response Variable, with Applications to Gene Set Testing Description: The global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms. biocViews: Microarray, OneChannel, Bioinformatics, DifferentialExpression, GO, Pathways Author: Jelle Goeman and Jan Oosting, with contributions by Livio Finos, Aldo Solari, Dominic Edelmann Maintainer: Jelle Goeman git_url: https://git.bioconductor.org/packages/globaltest git_branch: devel git_last_commit: f1b6690 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/globaltest_5.65.0.tar.gz vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf vignetteTitles: Global Test hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globaltest/inst/doc/GlobalTest.R dependsOnMe: GlobalAncova importsMe: BiSeq, EGSEA, SIM, miRtest suggestsMe: topGO, GiANT, penalized dependencyCount: 50 Package: GloScope Version: 2.1.2 Depends: R (>= 4.4.0) Imports: utils, stats, MASS, mclust, ggplot2, RANN, FNN, BiocParallel, mvnfast, SingleCellExperiment, rlang, RColorBrewer, pheatmap, vegan, cluster, boot, permute Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown, zellkonverter License: Artistic-2.0 MD5sum: 2632012e3772751bf2d2cc9b42d067ca NeedsCompilation: no Title: Population-level Representation on scRNA-Seq data Description: This package aims at representing and summarizing the entire single-cell profile of a sample. It allows researchers to perform important bioinformatic analyses at the sample-level such as visualization and quality control. The main functions Estimate sample distribution and calculate statistical divergence among samples, and visualize the distance matrix through MDS plots. biocViews: DataRepresentation, QualityControl, RNASeq, Sequencing, Software, SingleCell Author: Elizabeth Purdom [aut, cre], William Torous [aut] (ORCID: ), Hao Wang [aut] (ORCID: ), Boying Gong [aut] Maintainer: Elizabeth Purdom VignetteBuilder: knitr BugReports: https://github.com/epurdom/GloScope/issues git_url: https://git.bioconductor.org/packages/GloScope git_branch: devel git_last_commit: 458539a git_last_commit_date: 2025-11-17 Date/Publication: 2026-04-20 source.ver: src/contrib/GloScope_2.1.2.tar.gz vignettes: vignettes/GloScope/inst/doc/GloScopeTutorial.html vignetteTitles: GloScope hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GloScope/inst/doc/GloScopeTutorial.R dependencyCount: 67 Package: glycoTraitR Version: 0.99.2 Depends: R (>= 4.5.0) Imports: igraph, SummarizedExperiment, pbapply, car, ggplot2, rlang Suggests: knitr, BiocStyle, rmarkdown, markdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 420a2e6f7ae39909ce742d57f2bc5e26 NeedsCompilation: no Title: Compute and analyze the glycan structrual traits from GPSM data Description: GlycoTraitR is an R package for analyzing glycoproteomics data, particularly glycopeptide-spectrum matches (GPSMs). It supports results generated by the pGlyco3 and Glyco-Decipher search engines. The package parses glycan structures, computes monosaccharide compositions and structural traits, and performs differential analysis of glycan heterogeneity. It constructs trait-by-PSM matrices stored in a SummarizedExperiment object, supports user-defined structural motifs, and provides visualization utilities for interpreting glycan trait changes. biocViews: Proteomics, MassSpectrometry, Visualization, Software Author: Bingyuan Zhang [aut, cre] (ORCID: ), Koichi Himori [aut], Yusuke Matsui [aut, fnd] Maintainer: Bingyuan Zhang URL: https://github.com/matsui-lab/glycoTraitR VignetteBuilder: knitr BugReports: https://github.com/matsui-lab/glycoTraitR/issues git_url: https://git.bioconductor.org/packages/glycoTraitR git_branch: devel git_last_commit: 9be6f65 git_last_commit_date: 2026-01-29 Date/Publication: 2026-04-20 source.ver: src/contrib/glycoTraitR_0.99.2.tar.gz vignettes: vignettes/glycoTraitR/inst/doc/quick_start.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/glycoTraitR/inst/doc/quick_start.R dependencyCount: 94 Package: gmapR Version: 1.53.1 Depends: R (>= 2.15.0), methods, Seqinfo, GenomicRanges (>= 1.61.1), Rsamtools (>= 1.31.2) Imports: S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), BiocGenerics (>= 0.25.1), rtracklayer (>= 1.39.7), GenomicFeatures (>= 1.31.3), Biostrings, VariantAnnotation (>= 1.25.11), tools, Biobase, BSgenome, GenomicAlignments (>= 1.15.6), BiocParallel, BiocIO Suggests: GenomeInfoDb, RUnit, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Scerevisiae.UCSC.sacCer3, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, LungCancerLines License: Artistic-2.0 MD5sum: d9180a0436cf4e473fb73ca4caee8fa9 NeedsCompilation: yes Title: An R interface to the GMAP/GSNAP/GSTRUCT suite Description: GSNAP and GMAP are a pair of tools to align short-read data written by Tom Wu. This package provides convenience methods to work with GMAP and GSNAP from within R. In addition, it provides methods to tally alignment results on a per-nucleotide basis using the bam_tally tool. biocViews: Alignment Author: Cory Barr, Thomas Wu, Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/gmapR git_branch: devel git_last_commit: 7b655db git_last_commit_date: 2026-02-03 Date/Publication: 2026-04-20 source.ver: src/contrib/gmapR_1.53.1.tar.gz vignettes: vignettes/gmapR/inst/doc/gmapR.pdf vignetteTitles: gmapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmapR/inst/doc/gmapR.R suggestsMe: VariantTools, VariantToolsData dependencyCount: 77 Package: gmoviz Version: 1.23.0 Depends: circlize, GenomicRanges, graphics, R (>= 4.0) Imports: grid, gridBase, Rsamtools, ComplexHeatmap, BiocGenerics, Biostrings, Seqinfo, methods, GenomicAlignments, GenomicFeatures, IRanges, rtracklayer, pracma, colorspace, S4Vectors Suggests: testthat, knitr, rmarkdown, pasillaBamSubset, BiocStyle, BiocManager, GenomeInfoDb License: GPL-3 MD5sum: d9d66cd17fde1b5cb0b956fd100d5123 NeedsCompilation: no Title: Seamless visualization of complex genomic variations in GMOs and edited cell lines Description: Genetically modified organisms (GMOs) and cell lines are widely used models in all kinds of biological research. As part of characterising these models, DNA sequencing technology and bioinformatics analyses are used systematically to study their genomes. Therefore, large volumes of data are generated and various algorithms are applied to analyse this data, which introduces a challenge on representing all findings in an informative and concise manner. `gmoviz` provides users with an easy way to visualise and facilitate the explanation of complex genomic editing events on a larger, biologically-relevant scale. biocViews: Visualization, Sequencing, GeneticVariability, GenomicVariation, Coverage Author: Kathleen Zeglinski [cre, aut], Arthur Hsu [aut], Monther Alhamdoosh [aut] (ORCID: ), Constantinos Koutsakis [aut] Maintainer: Kathleen Zeglinski VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gmoviz git_branch: devel git_last_commit: 13acb8f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gmoviz_1.23.0.tar.gz vignettes: vignettes/gmoviz/inst/doc/gmoviz_advanced.html, vignettes/gmoviz/inst/doc/gmoviz_overview.html vignetteTitles: Advanced usage of gmoviz, Introduction to gmoviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmoviz/inst/doc/gmoviz_advanced.R, vignettes/gmoviz/inst/doc/gmoviz_overview.R dependencyCount: 90 Package: GMRP Version: 1.39.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics License: GPL (>= 2) MD5sum: c2a17e102e9220c8d6c28e9495491c0f NeedsCompilation: no Title: GWAS-based Mendelian Randomization and Path Analyses Description: Perform Mendelian randomization analysis of multiple SNPs to determine risk factors causing disease of study and to exclude confounding variabels and perform path analysis to construct path of risk factors to the disease. biocViews: Sequencing, Regression, SNP Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/GMRP git_branch: devel git_last_commit: 4cca9a7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GMRP_1.39.0.tar.gz vignettes: vignettes/GMRP/inst/doc/GMRP-manual.pdf, vignettes/GMRP/inst/doc/GMRP.pdf vignetteTitles: GMRP-manual.pdf, Causal Effect Analysis of Risk Factors for Disease with the "GMRP" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GMRP/inst/doc/GMRP.R dependencyCount: 16 Package: goatea Version: 1.99.7 Depends: R (>= 4.5.0), dplyr (>= 1.1.4) Imports: goat (>= 1.0), shiny (>= 1.10.0), shinyjs (>= 2.1.0), shinyjqui (>= 0.4.1), shinydashboard (>= 0.7.2), openxlsx (>= 4.2.7.1), upsetjs (>= 1.11.1), data.table (>= 1.18.2.1), ComplexHeatmap (>= 2.24.0), InteractiveComplexHeatmap (>= 1.12.0), tidyr (>= 1.3.1), purrr (>= 1.0.2), ggplot2 (>= 3.5.1), plotly (>= 4.10.4), igraph (>= 2.1.4), visNetwork (>= 2.1.2), arrow (>= 18.1.0.1), htmltools (>= 0.5.8.1), methods (>= 4.5.0), AnnotationDbi (>= 1.69.1), DT (>= 0.33), plyr (>= 1.8.9), tibble (>= 3.2.1), rlang (>= 1.1.6), DOSE (>= 4.4.0), enrichplot (>= 1.30.4), clusterProfiler (>= 4.18.4), EnhancedVolcano (>= 1.28.2), org.Hs.eg.db (>= 3.22.0), org.Mm.eg.db (>= 3.22.0), org.Dm.eg.db (>= 3.22.0), org.Mmu.eg.db (>= 3.22.0), org.Rn.eg.db (>= 3.22.0), org.Ce.eg.db (>= 3.22.0), org.Pt.eg.db (>= 3.22.0), org.Dr.eg.db (>= 3.22.0) Suggests: knitr, rmarkdown, BiocStyle, magick License: Apache License (>= 2) MD5sum: 23b7eb4aa0e2417e55158a762b8e5d91 NeedsCompilation: no Title: Interactive Exploration of GSEA by the GOAT Method Description: Geneset Ordinal Association Test Enrichment Analysis (GOATEA) provides a 'Shiny' interface with interactive visualizations and utility functions for performing and exploring automated gene set enrichment analysis using the 'GOAT' package. 'GOATEA' is designed to support large-scale and user-friendly enrichment workflows across multiple gene lists and comparisons, with flexible plotting and output options. Visualizations pre-enrichment include interactive 'Volcano' and 'UpSet' (overlap) plots. Visualizations post-enrichment include interactive geneset dotplot, geneset treeplot, gene-effectsize heatmap, gene-geneset heatmap and 'STRING' database of protein-protein-interactions network graph. 'GOAT' reference: Frank Koopmans (2024) . biocViews: GeneSetEnrichment, NetworkEnrichment, Visualization, ShinyApps, GUI, Transcriptomics, Genetics, FunctionalGenomics, DifferentialExpression, Network Author: Maurits Unkel [aut, cre, fnd, cph] (ORCID: ) Maintainer: Maurits Unkel URL: https://github.com/mauritsunkel/goatea, https://mauritsunkel.github.io/goatea/ VignetteBuilder: knitr BugReports: https://github.com/mauritsunkel/goatea/issues git_url: https://git.bioconductor.org/packages/goatea git_branch: devel git_last_commit: 4552cce git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/goatea_1.99.7.tar.gz vignettes: vignettes/goatea/inst/doc/goatea_GUI.html, vignettes/goatea/inst/doc/goatea.html vignetteTitles: 1. GOATEA GUI & installation, 2. GOATEA R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goatea/inst/doc/goatea_GUI.R, vignettes/goatea/inst/doc/goatea.R dependencyCount: 198 Package: GOexpress Version: 1.45.0 Depends: R (>= 3.4), grid, stats, graphics, Biobase (>= 2.22.0) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6), RCurl (>= 1.95) Suggests: BiocStyle License: GPL (>= 3) MD5sum: e04a71786aa4f22f05ef616c74a40e98 NeedsCompilation: no Title: Visualise microarray and RNAseq data using gene ontology annotations Description: The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Clustering, TimeCourse, Microarray, Sequencing, RNASeq, Annotation, MultipleComparison, Pathways, GO, Visualization, ImmunoOncology Author: Kevin Rue-Albrecht [aut, cre], Tharvesh M.L. Ali [ctb], Paul A. McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee [ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen V. Gordon [ths], David E. MacHugh [ths], Hugo Gruson [ctb] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/GOexpress git_url: https://git.bioconductor.org/packages/GOexpress git_branch: devel git_last_commit: f14e6df git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GOexpress_1.45.0.tar.gz vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf vignetteTitles: UsersGuide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.R suggestsMe: InteractiveComplexHeatmap dependencyCount: 80 Package: GOfan Version: 0.99.2 Depends: R (>= 4.5.0), ggplot2 Imports: AnnotationDbi, grid, grDevices, GO.db, igraph, methods, plotly, rlang, stats, scales, vctrs Suggests: BiocStyle, knitr, rmarkdown, testthat, org.Dr.eg.db License: GPL-3 MD5sum: 4bfd87764c72edad508575d0839ccd43 NeedsCompilation: no Title: Sunburst Plot for Enriched Gene Ontology Terms Description: GOfan provides an intuitive and compact visualization of Gene Ontology (GO) enrichment results using a sunburst layout inspired by SynGO, preserving hierarchical relationships among GO terms and allowing color-based encoding of information such as p-values or gene counts. By converting complex GO DAGs into clean, circular representations, it allows researchers to quickly grasp the hierarchical structure and biological significance of enriched terms. The interactive and customizable visualizations facilitate exploration of key GO categories, enhancing interpretation and presentation of enrichment analyses. biocViews: Visualization, GO, Annotation Author: Jianhong Ou [aut, cre] (ORCID: ), Kenneth Poss [aut, fnd] Maintainer: Jianhong Ou URL: https://github.com/jianhong/GOfan VignetteBuilder: knitr BugReports: https://github.com/jianhong/GOfan/issues git_url: https://git.bioconductor.org/packages/GOfan git_branch: devel git_last_commit: dc47477 git_last_commit_date: 2026-03-04 Date/Publication: 2026-04-20 source.ver: src/contrib/GOfan_0.99.2.tar.gz vignettes: vignettes/GOfan/inst/doc/GOfan.html vignetteTitles: GOfan Vignette: GO Enrichment Sunburst Plot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOfan/inst/doc/GOfan.R dependencyCount: 92 Package: goProfiles Version: 1.73.0 Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr Suggests: org.Hs.eg.db License: GPL-2 MD5sum: 57f5ad15cf4ca425d35bc9ce00097302 NeedsCompilation: no Title: goProfiles: an R package for the statistical analysis of functional profiles Description: The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'. biocViews: Annotation, GO, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Microarray, MultipleComparison, Pathways, Software Author: Alex Sanchez, Jordi Ocana and Miquel Salicru Maintainer: Alex Sanchez git_url: https://git.bioconductor.org/packages/goProfiles git_branch: devel git_last_commit: f39fe7a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/goProfiles_1.73.0.tar.gz vignettes: vignettes/goProfiles/inst/doc/goProfiles-comparevisual.pdf, vignettes/goProfiles/inst/doc/goProfiles-plotProfileMF.pdf, vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles-comparevisual.pdf, goProfiles-plotProfileMF.pdf, goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R importsMe: goSorensen dependencyCount: 47 Package: GOSemSim Version: 2.37.2 Depends: R (>= 4.2.0) Imports: AnnotationDbi, DBI, digest, GO.db, methods, rlang, stats, utils, yulab.utils (>= 0.2.3) LinkingTo: Rcpp Suggests: AnnotationHub, BiocManager, clusterProfiler, DOSE, knitr, org.Hs.eg.db, prettydoc, readr, rmarkdown, testthat, tidyr, tidyselect, ROCR License: Artistic-2.0 MD5sum: 6bb4a35f40d02edbbdf6cef7e6cdc90c NeedsCompilation: yes Title: GO-terms Semantic Similarity Measures Description: The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively. biocViews: Annotation, GO, Clustering, Pathways, Network, Software Author: Guangchuang Yu [aut, cre], Alexey Stukalov [ctb], Pingfan Guo [ctb], Chuanle Xiao [ctb], Lluís Revilla Sancho [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/GOSemSim/issues git_url: https://git.bioconductor.org/packages/GOSemSim git_branch: devel git_last_commit: 67e3da1 git_last_commit_date: 2026-01-19 Date/Publication: 2026-04-20 source.ver: src/contrib/GOSemSim_2.37.2.tar.gz vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.html vignetteTitles: GOSemSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R dependsOnMe: tRanslatome importsMe: clusterProfiler, DOSE, enrichplot, meshes, Rcpi, rrvgo, ViSEAGO suggestsMe: BioCor, epiNEM, FELLA, SemDist, genekitr, protr, scDiffCom dependencyCount: 48 Package: goseq Version: 1.63.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase (>= 1.9.2) Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db, BiocGenerics, methods, rtracklayer, GenomicFeatures, Seqinfo Suggests: edgeR, org.Hs.eg.db License: LGPL (>= 2) MD5sum: 8decd7236e9fa42763f0443b1bf39102 NeedsCompilation: no Title: Gene Ontology analyser for RNA-seq and other length biased data Description: Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data. biocViews: ImmunoOncology, Sequencing, GO, GeneExpression, Transcription, RNASeq, DifferentialExpression, Annotation, GeneSetEnrichment, KEGG, Pathways, Software Author: Matthew Young [aut], Nadia Davidson [aut], Federico Marini [ctb, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/goseq BugReports: https://github.com/federicomarini/goseq/issues git_url: https://git.bioconductor.org/packages/goseq git_branch: devel git_last_commit: 9833b83 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/goseq_1.63.0.tar.gz vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goseq/inst/doc/goseq.R dependsOnMe: rgsepd importsMe: Damsel, ideal, mosdef, SMITE suggestsMe: carnation, sparrow dependencyCount: 105 Package: goSorensen Version: 1.13.0 Depends: R (>= 4.4) Imports: clusterProfiler, goProfiles, org.Hs.eg.db, parallel, stats, stringr Suggests: BiocManager, BiocStyle, knitr, rmarkdown, org.At.tair.db, org.Ag.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcSakai.eg.db, org.EcK12.eg.db, org.Gg.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Pt.eg.db, org.Xl.eg.db, GO.db, ggplot2, ggrepel, DT, magick License: GPL-3 MD5sum: d9af792a6b168a3a070d207b7f81b3fe NeedsCompilation: no Title: Statistical inference based on the Sorensen-Dice dissimilarity and the Gene Ontology (GO) Description: This package implements inferential methods to compare gene lists in terms of their biological meaning as expressed in the GO. The compared gene lists are characterized by cross-tabulation frequency tables of enriched GO items. Dissimilarity between gene lists is evaluated using the Sorensen-Dice index. The fundamental guiding principle is that two gene lists are taken as similar if they share a great proportion of common enriched GO items. biocViews: Annotation, GO, GeneSetEnrichment, Software, Microarray, Pathways, GeneExpression, MultipleComparison, GraphAndNetwork, Reactome, Clustering, KEGG Author: Pablo Flores [aut, cre] (), Jordi Ocana [aut, ctb] (0000-0002-4736-699), Alexandre Sanchez-Pla [ctb] (), Miquel Salicru [ctb] () Maintainer: Pablo Flores VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSorensen git_branch: devel git_last_commit: b2f0b05 git_last_commit_date: 2026-03-12 Date/Publication: 2026-04-20 source.ver: src/contrib/goSorensen_1.13.0.tar.gz vignettes: vignettes/goSorensen/inst/doc/Dissimilarities_Matrix.html, vignettes/goSorensen/inst/doc/goSorensen_Introduction.html vignetteTitles: Working with te Irrelevance-threshold Matrix of Dissimilarities., An Introduction to goSorensen R-Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSorensen/inst/doc/Dissimilarities_Matrix.R, vignettes/goSorensen/inst/doc/goSorensen_Introduction.R dependencyCount: 132 Package: goSTAG Version: 1.35.0 Depends: R (>= 3.4) Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 6046addd7e8ef8250090f63d23b696bd NeedsCompilation: no Title: A tool to use GO Subtrees to Tag and Annotate Genes within a set Description: Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Clustering, Microarray, mRNAMicroarray, RNASeq, Visualization, GO, ImmunoOncology Author: Brian D. Bennett and Pierre R. Bushel Maintainer: Brian D. Bennett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSTAG git_branch: devel git_last_commit: 80ac782 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/goSTAG_1.35.0.tar.gz vignettes: vignettes/goSTAG/inst/doc/goSTAG.html vignetteTitles: The goSTAG User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSTAG/inst/doc/goSTAG.R dependencyCount: 64 Package: GOTHiC Version: 1.47.0 Depends: R (>= 3.5.0), methods, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors (>= 0.9.38), IRanges, Rsamtools, ShortRead, rtracklayer, ggplot2, BiocManager, grDevices, utils, stats, Seqinfo Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 MD5sum: cba5df33ec7fd461747ccaf09cfad38a NeedsCompilation: no Title: Binomial test for Hi-C data analysis Description: This is a Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. It takes mapped paired NGS reads as input and gives back the list of significant interactions for a given bin size in the genome. biocViews: ImmunoOncology, Sequencing, Preprocessing, Epigenetics, HiC Author: Borbala Mifsud and Robert Sugar Maintainer: Borbala Mifsud git_url: https://git.bioconductor.org/packages/GOTHiC git_branch: devel git_last_commit: 7af2557 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GOTHiC_1.47.0.tar.gz vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf vignetteTitles: package_vignettes.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOTHiC/inst/doc/package_vignettes.R importsMe: OHCA dependencyCount: 86 Package: goTools Version: 1.85.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: 13343304f94a81ef5fada34816f9b41b NeedsCompilation: no Title: Functions for Gene Ontology database Description: Wraper functions for description/comparison of oligo ID list using Gene Ontology database biocViews: Microarray,GO,Visualization Author: Yee Hwa (Jean) Yang , Agnes Paquet Maintainer: Agnes Paquet git_url: https://git.bioconductor.org/packages/goTools git_branch: devel git_last_commit: 99ac810 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/goTools_1.85.0.tar.gz vignettes: vignettes/goTools/inst/doc/goTools.pdf vignetteTitles: goTools overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/goTools/inst/doc/goTools.R dependencyCount: 43 Package: GPA Version: 1.23.0 Depends: R (>= 4.0.0), methods, graphics, Rcpp Imports: parallel, ggplot2, ggrepel, plyr, vegan, DT, shiny, shinyBS, stats, utils, grDevices LinkingTo: Rcpp Suggests: gpaExample License: GPL (>= 2) MD5sum: 1602acee3599ad9dbfc935b73c55e0da NeedsCompilation: yes Title: GPA (Genetic analysis incorporating Pleiotropy and Annotation) Description: This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Emma Kortemeier, Carter Allen Maintainer: Dongjun Chung URL: http://dongjunchung.github.io/GPA/ SystemRequirements: GNU make BugReports: https://github.com/dongjunchung/GPA/issues git_url: https://git.bioconductor.org/packages/GPA git_branch: devel git_last_commit: 564d115 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GPA_1.23.0.tar.gz vignettes: vignettes/GPA/inst/doc/GPA-example.pdf vignetteTitles: GPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GPA/inst/doc/GPA-example.R dependencyCount: 72 Package: gpls Version: 1.83.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: 0a3cb6bebd93493617b1016e5658ec56 NeedsCompilation: no Title: Classification using generalized partial least squares Description: Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification. biocViews: Classification, Microarray, Regression Author: Beiying Ding Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/gpls git_branch: devel git_last_commit: c44e828 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gpls_1.83.0.tar.gz vignettes: vignettes/gpls/inst/doc/gpls.pdf vignetteTitles: gpls Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpls/inst/doc/gpls.R suggestsMe: MLInterfaces dependencyCount: 1 Package: GrafGen Version: 1.7.0 Depends: R (>= 4.3.0) Imports: stats, utils, graphics, ggplot2, plotly, scales, RColorBrewer, dplyr, grDevices, GenomicRanges, shiny, cowplot, ggpubr, stringr, rlang Suggests: knitr, rmarkdown, RUnit, BiocManager, BiocGenerics, BiocStyle, devtools License: GPL-2 MD5sum: 52830a5717bd53dbf9662255465be1f9 NeedsCompilation: yes Title: Classification of Helicobacter Pylori Genomes Description: To classify Helicobacter pylori genomes according to genetic distance from nine reference populations. The nine reference populations are hpgpAfrica, hpgpAfrica-distant, hpgpAfroamerica, hpgpEuroamerica, hpgpMediterranea, hpgpEurope, hpgpEurasia, hpgpAsia, and hpgpAklavik86-like. The vertex populations are Africa, Europe and Asia. biocViews: Genetics, Software, GenomeAnnotation, Classification Author: William Wheeler [aut, cre], Difei Wang [aut], Isaac Zhao [aut], Yumi Jin [aut], Charles Rabkin [aut] Maintainer: William Wheeler VignetteBuilder: knitr BugReports: https://github.com/wheelerb/GrafGen/issues git_url: https://git.bioconductor.org/packages/GrafGen git_branch: devel git_last_commit: d6f7792 git_last_commit_date: 2026-03-05 Date/Publication: 2026-04-20 source.ver: src/contrib/GrafGen_1.7.0.tar.gz vignettes: vignettes/GrafGen/inst/doc/vignette.html vignetteTitles: GrafGen: Classifying H. pylori genomes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GrafGen/inst/doc/vignette.R dependencyCount: 131 Package: GRaNIE Version: 1.15.0 Depends: R (>= 4.2.0) Imports: futile.logger, checkmate, patchwork (>= 1.2.0), reshape2, data.table, matrixStats, Matrix, GenomicRanges, RColorBrewer, ComplexHeatmap, DESeq2, circlize, progress, utils, methods, stringr, tools, scales, igraph, S4Vectors, ggplot2, rlang, Biostrings, GenomeInfoDb (>= 1.34.8), SummarizedExperiment, forcats, gridExtra, limma, tidyselect, readr, grid, tidyr (>= 1.3.0), dplyr, stats, grDevices, graphics, magrittr, tibble, viridis, colorspace, biomaRt, topGO, AnnotationHub, ensembldb Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Mmulatta.UCSC.rheMac10, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm39.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Dm.eg.db, org.Mmu.eg.db, IHW, clusterProfiler, ReactomePA, DOSE, BiocFileCache, ChIPseeker, testthat (>= 3.0.0), BiocStyle, csaw, BiocParallel, WGCNA, variancePartition, purrr, EDASeq, JASPAR2022, JASPAR2024, RSQLite, TFBSTools, motifmatchr, rbioapi, LDlinkR License: Artistic-2.0 MD5sum: 6b4aec9b1e83f24faf43082380e243a8 NeedsCompilation: no Title: GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using single-cell or bulk chromatin accessibility and RNA-seq data Description: Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using single-cell or bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally (Capture) Hi-C data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach. biocViews: Software, GeneExpression, GeneRegulation, NetworkInference, GeneSetEnrichment, BiomedicalInformatics, Genetics, Transcriptomics, ATACSeq, RNASeq, GraphAndNetwork, Regression, Transcription, ChIPSeq Author: Christian Arnold [cre, aut], Judith Zaugg [aut], Rim Moussa [aut], Armando Reyes-Palomares [ctb], Giovanni Palla [ctb], Maksim Kholmatov [ctb] Maintainer: Christian Arnold URL: https://grp-zaugg.embl-community.io/GRaNIE VignetteBuilder: knitr BugReports: https://git.embl.de/grp-zaugg/GRaNIE/issues git_url: https://git.bioconductor.org/packages/GRaNIE git_branch: devel git_last_commit: 43246b6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GRaNIE_1.15.0.tar.gz vignettes: vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.html, vignettes/GRaNIE/inst/doc/GRaNIE_singleCell_eGRNs.html, vignettes/GRaNIE/inst/doc/GRaNIE_workflow.html vignetteTitles: Package Details, Single-cell eGRN inference, Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.R, vignettes/GRaNIE/inst/doc/GRaNIE_singleCell_eGRNs.R, vignettes/GRaNIE/inst/doc/GRaNIE_workflow.R dependencyCount: 151 Package: graper Version: 1.27.0 Depends: R (>= 3.6) Imports: Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: ba363f18e787b4d127c0e68acf9fcbab NeedsCompilation: yes Title: Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes Description: This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach. biocViews: Regression, Bayesian, Classification Author: Britta Velten [aut, cre], Wolfgang Huber [aut] Maintainer: Britta Velten VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graper git_branch: devel git_last_commit: 95474d7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/graper_1.27.0.tar.gz vignettes: vignettes/graper/inst/doc/example_linear.html, vignettes/graper/inst/doc/example_logistic.html vignetteTitles: example_linear, example_logistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graper/inst/doc/example_linear.R, vignettes/graper/inst/doc/example_logistic.R dependencyCount: 30 Package: graph Version: 1.89.1 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11) Imports: stats, stats4, utils Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster, BiocStyle, knitr Enhances: Rgraphviz License: Artistic-2.0 MD5sum: 4dbfa1aaf715d90f8c09e433840f9948 NeedsCompilation: yes Title: graph: A package to handle graph data structures Description: A package that implements some simple graph handling capabilities. biocViews: GraphAndNetwork Author: R Gentleman [aut], Elizabeth Whalen [aut], W Huber [aut], S Falcon [aut], Jeff Gentry [aut], Paul Shannon [aut], Halimat C. Atanda [ctb] (Converted 'MultiGraphClass' and 'GraphClass' vignettes from Sweave to RMarkdown / HTML.), Paul Villafuerte [ctb] (Converted vignettes from Sweave to RMarkdown / HTML.), Aliyu Atiku Mustapha [ctb] (Converted 'Graph' vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graph git_branch: devel git_last_commit: 5493460 git_last_commit_date: 2025-12-02 Date/Publication: 2026-04-20 source.ver: src/contrib/graph_1.89.1.tar.gz vignettes: vignettes/graph/inst/doc/clusterGraph.html, vignettes/graph/inst/doc/graph.html, vignettes/graph/inst/doc/graphAttributes.html, vignettes/graph/inst/doc/GraphClass.html, vignettes/graph/inst/doc/MultiGraphClass.html vignetteTitles: clusterGraph and distGraph, How to use the graph package, Attributes for Graph Objects, Graph Design, graphBAM and MultiGraph Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graph/inst/doc/clusterGraph.R, vignettes/graph/inst/doc/graph.R, vignettes/graph/inst/doc/graphAttributes.R, vignettes/graph/inst/doc/GraphClass.R, vignettes/graph/inst/doc/MultiGraphClass.R dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, BLMA, CellNOptR, clipper, CNORfeeder, EnrichmentBrowser, GOstats, GraphAT, GSEABase, hypergraph, keggorthology, pathRender, Pigengene, RbcBook1, RBGL, RCyjs, Rgraphviz, ROntoTools, SRAdb, topGO, vtpnet, DLBCL, SNAData, yeastExpData, cyjShiny, dlsem, gridGraphviz, PairViz, PerfMeas importsMe: AnnotationHubData, BgeeDB, BiocCheck, BiocFHIR, biocGraph, BiocPkgTools, biocViews, bnem, CAMERA, Category, categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, consICA, CytoML, DEGraph, DEsubs, EnrichDO, epiNEM, EventPointer, fgga, flowClust, flowWorkspace, gage, GeneNetworkBuilder, GenomicInteractionNodes, GraphAT, graphite, hyperdraw, KEGGgraph, MIRit, mnem, MOSClip, NCIgraph, netresponse, OncoSimulR, ontoProc, openCyto, oposSOM, OrganismDbi, pathview, qpgraph, RCy3, RGraph2js, rsbml, scGraphVerse, SplicingGraphs, VariantFiltering, BioPlex, abn, BayesNetBP, BCDAG, BiDAG, BNrich, CePa, classGraph, clustNet, CodeDepends, cogmapr, ggm, gridDebug, net4pg, netgsa, NetPreProc, pcalg, pcgen, rags2ridges, RANKS, RCPA, rsolr, rSpectral, SEMgraph, stablespec, topologyGSA, tpc, unifDAG suggestsMe: anansi, AnnotationDbi, DAPAR, DEGraph, EBcoexpress, ecolitk, gwascat, KEGGlincs, MLP, NetPathMiner, rBiopaxParser, RCX, rTRM, S4Vectors, SPIA, VariantTools, arulesViz, bnlearn, bnstruct, bsub, caugi, ChoR, gbutils, GeneNet, gMCP, lava, loon, maGUI, netmeta, psych, rCausalMGM, rEMM, rPref, sisal, textplot, tidygraph, zenplots dependencyCount: 7 Package: GraphAlignment Version: 1.75.0 License: file LICENSE License_restricts_use: yes MD5sum: b4b64c58aed9dbadcbb7c1feea4e6a81 NeedsCompilation: yes Title: GraphAlignment Description: Graph alignment is an extension package for the R programming environment which provides functions for finding an alignment between two networks based on link and node similarity scores. (J. Berg and M. Laessig, "Cross-species analysis of biological networks by Bayesian alignment", PNAS 103 (29), 10967-10972 (2006)) biocViews: GraphAndNetwork, Network Author: Joern P. Meier , Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg. Maintainer: Joern P. Meier URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/ git_url: https://git.bioconductor.org/packages/GraphAlignment git_branch: devel git_last_commit: c4f998f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GraphAlignment_1.75.0.tar.gz vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf vignetteTitles: GraphAlignment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GraphAlignment/inst/doc/GraphAlignment.R dependencyCount: 0 Package: GraphAT Version: 1.83.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL MD5sum: 0d4bebc7c3e5ff74ef45cd8c144d515c NeedsCompilation: no Title: Graph Theoretic Association Tests Description: Functions and data used in Balasubramanian, et al. (2004) biocViews: Network, GraphAndNetwork Author: R. Balasubramanian, T. LaFramboise, D. Scholtens Maintainer: Thomas LaFramboise git_url: https://git.bioconductor.org/packages/GraphAT git_branch: devel git_last_commit: 581549b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GraphAT_1.83.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 21 Package: graphite Version: 1.57.0 Depends: R (>= 4.2), methods Imports: AnnotationDbi, graph (>= 1.67.1), httr, rappdirs, stats, utils, graphics, rlang, lifecycle, purrr, dir.expiry Suggests: checkmate, a4Preproc, ALL, BiocStyle, clipper, codetools, hgu133plus2.db, hgu95av2.db, impute, knitr, org.Hs.eg.db, parallel, R.rsp, RCy3, rmarkdown, SPIA (>= 2.2), testthat, topologyGSA (>= 1.4.0) License: AGPL-3 MD5sum: 719e32d14e7e8bb8497a33659391c7e8 NeedsCompilation: no Title: GRAPH Interaction from pathway Topological Environment Description: Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases. biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network, Reactome, KEGG, Metabolomics Author: Gabriele Sales [cre] (ORCID: ), Enrica Calura [aut], Chiara Romualdi [aut] Maintainer: Gabriele Sales URL: https://github.com/sales-lab/graphite VignetteBuilder: R.rsp BugReports: https://github.com/sales-lab/graphite/issues git_url: https://git.bioconductor.org/packages/graphite git_branch: devel git_last_commit: 0b372f1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/graphite_1.57.0.tar.gz vignettes: vignettes/graphite/inst/doc/graphite.pdf, vignettes/graphite/inst/doc/metabolites.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment, metabolites.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graphite/inst/doc/graphite.R importsMe: CBNplot, EnrichmentBrowser, MIRit, mogsa, MOSClip, multiGSEA, ReactomePA, sSNAPPY, netgsa, SEMgraph suggestsMe: clipper, InterCellar, metaboliteIDmapping dependencyCount: 48 Package: GRENITS Version: 1.63.0 Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8), ggplot2 (>= 0.9.0) Imports: graphics, grDevices, reshape2, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: network License: GPL (>= 2) MD5sum: 3913b74a8d997c8ff645854d087c7df4 NeedsCompilation: yes Title: Gene Regulatory Network Inference Using Time Series Description: The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model. biocViews: NetworkInference, GeneRegulation, TimeCourse, GraphAndNetwork, GeneExpression, Network, Bayesian Author: Edward Morrissey Maintainer: Edward Morrissey git_url: https://git.bioconductor.org/packages/GRENITS git_branch: devel git_last_commit: 2c9fcd3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GRENITS_1.63.0.tar.gz vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf vignetteTitles: GRENITS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRENITS/inst/doc/GRENITS_package.R dependencyCount: 31 Package: GreyListChIP Version: 1.43.0 Depends: R (>= 4.0), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, Seqinfo, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, RUnit, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 7e65d0f39ad91d60f438c5f1a32b5ac5 NeedsCompilation: no Title: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs Description: Identify regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling. Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis. biocViews: ChIPSeq, Alignment, Preprocessing, DifferentialPeakCalling, Sequencing, GenomeAnnotation, Coverage Author: Matt Eldridge [cre], Gord Brown [aut] Maintainer: Matt Eldridge git_url: https://git.bioconductor.org/packages/GreyListChIP git_branch: devel git_last_commit: 8633e38 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GreyListChIP_1.43.0.tar.gz vignettes: vignettes/GreyListChIP/inst/doc/GreyList-demo.pdf vignetteTitles: Generating Grey Lists from Input Libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GreyListChIP/inst/doc/GreyList-demo.R importsMe: epigraHMM dependencyCount: 59 Package: GRmetrics Version: 1.37.0 Depends: R (>= 4.0), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors, stats Suggests: knitr, rmarkdown, BiocStyle, tinytex License: GPL-3 MD5sum: b9689ac08fd495535cc1393107680580 NeedsCompilation: no Title: Calculate growth-rate inhibition (GR) metrics Description: Functions for calculating and visualizing growth-rate inhibition (GR) metrics. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Software, TimeCourse, Visualization Author: Nicholas Clark Maintainer: Nicholas Clark , Mario Medvedovic URL: https://github.com/uc-bd2k/GRmetrics VignetteBuilder: knitr BugReports: https://github.com/uc-bd2k/GRmetrics/issues git_url: https://git.bioconductor.org/packages/GRmetrics git_branch: devel git_last_commit: c19a8a1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GRmetrics_1.37.0.tar.gz vignettes: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html vignetteTitles: GRmetrics: an R package for calculation and visualization of dose-response metrics based on growth rate inhibition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.R dependencyCount: 134 Package: groHMM Version: 1.45.0 Depends: R (>= 4.1.0), MASS, parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Seqinfo, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), rtracklayer (>= 1.39.7) Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 55f87cb409e1f6896e686a3a51537cd5 NeedsCompilation: yes Title: GRO-seq Analysis Pipeline Description: A pipeline for the analysis of GRO-seq data. biocViews: Sequencing, Software Author: Charles G. Danko [aut], Minho Chae [aut], Andre Martins [ctb], W. Lee Kraus [aut, fnd], Anusha Nagari [ctb], Tulip Nandu [cre, ctb], Pariksheet Nanda [ctb] (ORCID: ) Maintainer: Tulip Nandu URL: https://github.com/Kraus-Lab/groHMM BugReports: https://github.com/Kraus-Lab/groHMM/issues git_url: https://git.bioconductor.org/packages/groHMM git_branch: devel git_last_commit: a2e7235 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/groHMM_1.45.0.tar.gz vignettes: vignettes/groHMM/inst/doc/groHMM.pdf vignetteTitles: groHMM tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/groHMM/inst/doc/groHMM.R dependencyCount: 58 Package: GSALightning Version: 1.39.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 5a4d05cc3ccc4d9e139ec08ec1fdebf6 NeedsCompilation: no Title: Fast Permutation-based Gene Set Analysis Description: GSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation. biocViews: Software, BiologicalQuestion, GeneSetEnrichment, DifferentialExpression, GeneExpression, Transcription Author: Billy Heung Wing Chang Maintainer: Billy Heung Wing Chang URL: https://github.com/billyhw/GSALightning VignetteBuilder: knitr BugReports: https://github.com/billyhw/GSALightning/issues git_url: https://git.bioconductor.org/packages/GSALightning git_branch: devel git_last_commit: 0a30e78 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GSALightning_1.39.0.tar.gz vignettes: vignettes/GSALightning/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSALightning/inst/doc/vignette.R dependencyCount: 9 Package: GSAR Version: 1.45.0 Depends: R (>= 3.0.1), igraph (>= 0.7.1) Imports: stats, graphics Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate, org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR, BiocStyle License: GPL (>=2) MD5sum: 969160f502f8d3a035393a79c11e30d0 NeedsCompilation: no Title: Gene Set Analysis in R Description: Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure. biocViews: Software, StatisticalMethod, DifferentialExpression Author: Yasir Rahmatallah , Galina Glazko Maintainer: Yasir Rahmatallah , Galina Glazko git_url: https://git.bioconductor.org/packages/GSAR git_branch: devel git_last_commit: 25443bb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GSAR_1.45.0.tar.gz vignettes: vignettes/GSAR/inst/doc/GSAR.pdf vignetteTitles: Gene Set Analysis in R -- the GSAR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSAR/inst/doc/GSAR.R dependencyCount: 17 Package: GSCA Version: 2.41.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 14d75c556d6808f2d53b40f2968c8e16 NeedsCompilation: no Title: GSCA: Gene Set Context Analysis Description: GSCA takes as input several lists of activated and repressed genes. GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression. GSCA provides both traditional R functions and interactive, user-friendly user interface. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji git_url: https://git.bioconductor.org/packages/GSCA git_branch: devel git_last_commit: e6b6b2f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GSCA_2.41.0.tar.gz vignettes: vignettes/GSCA/inst/doc/GSCA.pdf vignetteTitles: GSCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSCA/inst/doc/GSCA.R dependencyCount: 65 Package: gscreend Version: 1.25.0 Depends: R (>= 3.6) Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel, graphics Suggests: knitr, testthat, rmarkdown, BiocStyle License: GPL-3 MD5sum: e031fbd509de7760d1ba4faa3a2cb5b5 NeedsCompilation: no Title: Analysis of pooled genetic screens Description: Package for the analysis of pooled genetic screens (e.g. CRISPR-KO). The analysis of such screens is based on the comparison of gRNA abundances before and after a cell proliferation phase. The gscreend packages takes gRNA counts as input and allows detection of genes whose knockout decreases or increases cell proliferation. biocViews: Software, StatisticalMethod, PooledScreens, CRISPR Author: Katharina Imkeller [cre, aut], Wolfgang Huber [aut] Maintainer: Katharina Imkeller URL: https://github.com/imkeller/gscreend VignetteBuilder: knitr BugReports: https://github.com/imkeller/gscreend/issues git_url: https://git.bioconductor.org/packages/gscreend git_branch: devel git_last_commit: 53c6ec5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gscreend_1.25.0.tar.gz vignettes: vignettes/gscreend/inst/doc/gscreend_simulated_data.html vignetteTitles: Example_simulated hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gscreend/inst/doc/gscreend_simulated_data.R dependencyCount: 49 Package: GSEABase Version: 1.73.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.13.8), Biobase (>= 2.17.8), annotate (>= 1.45.3), methods, graph (>= 1.37.2) Imports: AnnotationDbi, XML Suggests: hgu95av2.db, GO.db, org.Hs.eg.db, Rgraphviz, ReportingTools, testthat, BiocStyle, knitr, RUnit License: Artistic-2.0 MD5sum: 4a64087ceef24a66c57e11e2f2c50949 NeedsCompilation: no Title: Gene set enrichment data structures and methods Description: This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA). biocViews: GeneExpression, GeneSetEnrichment, GraphAndNetwork, GO, KEGG Author: Martin Morgan [aut], Seth Falcon [aut], Robert Gentleman [aut], Paul Villafuerte [ctb] ('GSEABase' vignette translation from Sweave to Rmarkdown / HTML), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEABase git_branch: devel git_last_commit: 40d4be6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GSEABase_1.73.0.tar.gz vignettes: vignettes/GSEABase/inst/doc/GSEABase.html vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R dependsOnMe: AGDEX, BicARE, CCPROMISE, Cepo, cpvSNP, npGSEA, PROMISE, splineTimeR, TissueEnrich, GSVAdata, OSCA.basic importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cosmosR, dreamlet, EnrichmentBrowser, gep2pep, GlobalAncova, GmicR, GSRI, GSVA, mastR, miRSM, mogsa, oppar, PanomiR, phenoTest, postNet, PROMISE, ReportingTools, scTGIF, signatureSearch, singIST, singleCellTK, singscore, slalom, sparrow, TFutils, TMSig, vissE, zenith, msigdb, clustermole suggestsMe: BiocSet, epiregulon.extra, escape, gage, globaltest, GOstats, GSAR, MAST, pathMED, phenoTest, BaseSet dependencyCount: 46 Package: GSEAlm Version: 1.71.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 94416c98d5b6c56ce752bbafd2767ffb NeedsCompilation: no Title: Linear Model Toolset for Gene Set Enrichment Analysis Description: Models and methods for fitting linear models to gene expression data, together with tools for computing and using various regression diagnostics. biocViews: Microarray Author: Assaf Oron, Robert Gentleman (with contributions from S. Falcon and Z. Jiang) Maintainer: Assaf Oron git_url: https://git.bioconductor.org/packages/GSEAlm git_branch: devel git_last_commit: 952cfe6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GSEAlm_1.71.0.tar.gz vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf vignetteTitles: Linear models in GSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEAlm/inst/doc/GSEAlm.R dependencyCount: 7 Package: GSEAmining Version: 1.21.0 Depends: R (>= 4.0) Imports: dplyr, tidytext, dendextend, tibble, ggplot2, ggwordcloud, stringr, gridExtra, rlang, grDevices, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle, clusterProfiler, testthat, tm License: GPL-3 | file LICENSE MD5sum: 9e9a88c979f0d61c46550a72d20a54cd NeedsCompilation: no Title: Make Biological Sense of Gene Set Enrichment Analysis Outputs Description: Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes. Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology (GO) classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging. Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. For these reasons, GSEAmining was born to be an easy tool to create reproducible reports to help researchers make biological sense of GSEA outputs. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge (core) subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets. For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets (as wordclouds) - The most enriched genes in the leading edge subsets (as bar plots). In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study. biocViews: GeneSetEnrichment, Clustering, Visualization Author: Oriol Arqués [aut, cre] Maintainer: Oriol Arqués VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEAmining git_branch: devel git_last_commit: 74ec804 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GSEAmining_1.21.0.tar.gz vignettes: vignettes/GSEAmining/inst/doc/GSEAmining.html vignetteTitles: GSEAmining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSEAmining/inst/doc/GSEAmining.R dependencyCount: 57 Package: gsean Version: 1.31.0 Depends: R (>= 3.5), fgsea, PPInfer Suggests: SummarizedExperiment, pasilla, org.Dm.eg.db, AnnotationDbi, knitr, plotly, WGCNA, rmarkdown License: Artistic-2.0 MD5sum: 84ee94265ad6b8d3369d75aaa025268b NeedsCompilation: yes Title: Gene Set Enrichment Analysis with Networks Description: Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, GeneExpression, NetworkEnrichment, Pathways, DifferentialExpression Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gsean git_branch: devel git_last_commit: f3f93bb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gsean_1.31.0.tar.gz vignettes: vignettes/gsean/inst/doc/gsean.html vignetteTitles: gsean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gsean/inst/doc/gsean.R dependencyCount: 107 Package: GSgalgoR Version: 1.21.0 Imports: cluster, doParallel, foreach, matchingR, nsga2R, survival, proxy, stats, methods, Suggests: knitr, rmarkdown, ggplot2, BiocStyle, genefu, survcomp, Biobase, survminer, breastCancerTRANSBIG, breastCancerUPP, iC10TrainingData, pamr, testthat License: MIT + file LICENSE MD5sum: d4c424e76f950216b8505570cd0a2191 NeedsCompilation: no Title: An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer Description: A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The 'Galgo' framework combines the advantages of clustering algorithms for grouping heterogeneous 'omics' data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high. biocViews: GeneExpression, Transcription, Clustering, Classification, Survival Author: Martin Guerrero [aut], Carlos Catania [cre] Maintainer: Carlos Catania URL: https://github.com/harpomaxx/GSgalgoR VignetteBuilder: knitr BugReports: https://github.com/harpomaxx/GSgalgoR/issues git_url: https://git.bioconductor.org/packages/GSgalgoR git_branch: devel git_last_commit: c93c10d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GSgalgoR_1.21.0.tar.gz vignettes: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.html, vignettes/GSgalgoR/inst/doc/GSgalgoR.html vignetteTitles: GSgalgoR_callbacks.html, GSgalgoR.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.R, vignettes/GSgalgoR/inst/doc/GSgalgoR.R dependencyCount: 22 Package: GSReg Version: 1.45.0 Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures, AnnotationDbi Suggests: GenomicRanges, GSBenchMark License: GPL-2 MD5sum: ee93511ccffb22efd04ba148b56828ac NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA). biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment, AlternativeSplicing Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari , Elana J. Fertig git_url: https://git.bioconductor.org/packages/GSReg git_branch: devel git_last_commit: a73ad30 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GSReg_1.45.0.tar.gz vignettes: vignettes/GSReg/inst/doc/GSReg.pdf vignetteTitles: Working with the GSReg package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSReg/inst/doc/GSReg.R dependencyCount: 83 Package: GSRI Version: 2.59.0 Depends: R (>= 2.14.2), fdrtool Imports: methods, graphics, stats, utils, genefilter, Biobase, GSEABase, les (>= 1.1.6) Suggests: limma, hgu95av2.db Enhances: parallel License: GPL-3 MD5sum: f6bf9c6d66ac53333a118c1be3bb2143 NeedsCompilation: no Title: Gene Set Regulation Index Description: The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI). biocViews: Microarray, Transcription, DifferentialExpression, GeneSetEnrichment, GeneRegulation Author: Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/GSRI git_branch: devel git_last_commit: 6de90a8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GSRI_2.59.0.tar.gz vignettes: vignettes/GSRI/inst/doc/gsri.pdf vignetteTitles: Introduction to the GSRI package: Estimating Regulatory Effects utilizing the Gene Set Regulation Index hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSRI/inst/doc/gsri.R dependencyCount: 64 Package: gtrellis Version: 1.43.1 Depends: R (>= 4.0.0), grid, IRanges, GenomicRanges Imports: circlize (>= 0.4.8), GetoptLong, grDevices, utils Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, rmarkdown, ComplexHeatmap (>= 1.99.0), Cairo, png, jpeg, tiff License: MIT + file LICENSE MD5sum: ee1f19b158244bcb3fbb5f2c6dabfed3 NeedsCompilation: no Title: Genome Level Trellis Layout Description: Genome level Trellis graph visualizes genomic data conditioned by genomic categories (e.g. chromosomes). For each genomic category, multiple dimensional data which are represented as tracks describe different features from different aspects. This package provides high flexibility to arrange genomic categories and to add self-defined graphics in the plot. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/gtrellis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gtrellis git_branch: devel git_last_commit: d0e2aee git_last_commit_date: 2026-01-30 Date/Publication: 2026-04-20 source.ver: src/contrib/gtrellis_1.43.1.tar.gz vignettes: vignettes/gtrellis/inst/doc/gtrellis.html vignetteTitles: The gtrellis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: YAPSA dependencyCount: 20 Package: Guitar Version: 2.27.0 Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges, magrittr, ggplot2, methods, stats,utils ,knitr,dplyr License: GPL-2 MD5sum: ec2b520e57e96ef6c2a2f42a644d8dcb NeedsCompilation: no Title: Guitar Description: The package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site. biocViews: Sequencing, SplicedAlignment, Alignment, DataImport, RNASeq, MethylSeq, QualityControl, Transcription Author: Xiao Du, Hui Liu, Lin Zhang, Jia Meng Maintainer: Jia Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Guitar git_branch: devel git_last_commit: f0b1059 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Guitar_2.27.0.tar.gz vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf vignetteTitles: Guitar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Guitar/inst/doc/Guitar-Overview.R dependencyCount: 95 Package: gVenn Version: 1.1.1 Depends: R (>= 4.5.0) Imports: ComplexHeatmap, eulerr, GenomicRanges, IRanges, lubridate, methods, rtracklayer, stringr, writexl Suggests: testthat (>= 3.0.0), ggplot2, withr, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 1e6fa207cea6056e0c8675b2da12e785 NeedsCompilation: no Title: Proportional Venn and UpSet Diagrams for Gene Sets and Genomic Regions Description: Tools to compute and visualize overlaps between gene sets or genomic regions. Venn diagrams with proportional areas are provided, while UpSet plots are recommended for larger numbers of sets. The package supports GRanges and GRangesList inputs, and integrates with analysis workflows for ChIP-seq, ATAC-seq, and other genomic interval data. It generates clean, interpretable, and publication-ready figures. biocViews: Software, Visualization, ChIPSeq, ATACSeq, Epigenetics, DataRepresentation, Sequencing Author: Christophe Tav [aut, cre] (ORCID: ) Maintainer: Christophe Tav URL: https://github.com/ckntav/gVenn, https://ckntav.github.io/gVenn/ VignetteBuilder: knitr BugReports: https://github.com/ckntav/gVenn/issues git_url: https://git.bioconductor.org/packages/gVenn git_branch: devel git_last_commit: 8b01cda git_last_commit_date: 2025-11-02 Date/Publication: 2026-04-20 source.ver: src/contrib/gVenn_1.1.1.tar.gz vignettes: vignettes/gVenn/inst/doc/gVenn.html vignetteTitles: gVenn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gVenn/inst/doc/gVenn.R dependencyCount: 88 Package: Gviz Version: 1.55.0 Depends: R (>= 4.3), methods, S4Vectors (>= 0.9.25), IRanges (>= 1.99.18), GenomicRanges (>= 1.61.1), grid Imports: XVector (>= 0.5.7), rtracklayer (>= 1.69.1), lattice, RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5), Biobase (>= 2.15.3), GenomicFeatures (>= 1.61.4), ensembldb (>= 2.11.3), BSgenome (>= 1.77.1), Biostrings (>= 2.77.2), biovizBase (>= 1.13.8), Rsamtools (>= 2.25.1), latticeExtra (>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>= 1.45.1), Seqinfo, GenomeInfoDb, BiocGenerics (>= 0.11.3), digest(>= 0.6.8), graphics, grDevices, stats, utils Suggests: BSgenome.Hsapiens.UCSC.hg19, xml2, BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 1491c0df3a8edde9b8dfdaa569b0c80e NeedsCompilation: no Title: Plotting data and annotation information along genomic coordinates Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. biocViews: Visualization, Microarray, Sequencing Author: Florian Hahne [aut], Steffen Durinck [aut], Robert Ivanek [aut, cre] (ORCID: ), Arne Mueller [aut], Steve Lianoglou [aut], Ge Tan [aut], Lance Parsons [aut], Shraddha Pai [aut], Thomas McCarthy [ctb], Felix Ernst [ctb], Mike Smith [ctb] Maintainer: Robert Ivanek URL: https://github.com/ivanek/Gviz VignetteBuilder: knitr BugReports: https://github.com/ivanek/Gviz/issues git_url: https://git.bioconductor.org/packages/Gviz git_branch: devel git_last_commit: 6339846 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Gviz_1.55.0.tar.gz vignettes: vignettes/Gviz/inst/doc/Gviz.html vignetteTitles: The Gviz User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Gviz/inst/doc/Gviz.R dependsOnMe: biomvRCNS, chimeraviz, cicero, Pviz, methylationArrayAnalysis, rnaseqGene, csawBook importsMe: AllelicImbalance, ASpli, CAGEfightR, comapr, crisprViz, DMRcate, DuplexDiscovereR, ELMER, epimutacions, GenomicInteractions, maser, mCSEA, MEAL, methylPipe, OGRE, primirTSS, regutools, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, SPLINTER, srnadiff, tadar, trackViewer, TVTB, uncoverappLib, VariantFiltering, DMRcatedata, TmCalculator suggestsMe: annmap, BindingSiteFinder, cellbaseR, CNEr, CNVRanger, ensembldb, extraChIPs, fishpond, GenomicRanges, gwascat, MIRit, pqsfinder, QuasR, RnBeads, segmenter, SplicingGraphs, TFutils, Single.mTEC.Transcriptomes, CAGEWorkflow, chipseqDB, chicane, GRIN2 dependencyCount: 150 Package: GWAS.BAYES Version: 1.21.0 Depends: R (>= 4.3.0) Imports: GA (>= 3.2), caret (>= 6.0-86), memoise (>= 1.1.0), Matrix (>= 1.2-18), limma (>= 3.54.0), stats (>= 4.2.2), MASS (>= 7.3-58.1) Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP License: GPL-3 + file LICENSE MD5sum: d99423ce17f1e80f0baf3f83f9d76054 NeedsCompilation: no Title: Bayesian analysis of Gaussian GWAS data Description: This package is built to perform GWAS analysis using Bayesian techniques. Currently, GWAS.BAYES has functionality for the implementation of BICOSS (Williams, J., Ferreira, M. A., and Ji, T. (2022). BICOSS: Bayesian iterative conditional stochastic search for GWAS. BMC Bioinformatics), BGWAS (Williams, J., Xu, S., Ferreira, M. A.. (2023) "BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies." BMC Bioinformatics), and GINA. All methods currently are for the analysis of Gaussian phenotypes The research related to this package was supported in part by National Science Foundation awards DMS 1853549, DMS 1853556, and DMS 2054173. biocViews: Bayesian, AssayDomain, SNP, GenomeWideAssociation Author: Jacob Williams [aut, cre] (ORCID: ), Marco Ferreira [aut] (ORCID: ), Tieming Ji [aut] Maintainer: Jacob Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWAS.BAYES git_branch: devel git_last_commit: 131b9a7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GWAS.BAYES_1.21.0.tar.gz vignettes: vignettes/GWAS.BAYES/inst/doc/Vignette_BICOSS.html, vignettes/GWAS.BAYES/inst/doc/Vignette_GINA.html vignetteTitles: BICOSS, GINA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GWAS.BAYES/inst/doc/Vignette_BICOSS.R, vignettes/GWAS.BAYES/inst/doc/Vignette_GINA.R dependencyCount: 91 Package: gwascat Version: 2.43.2 Depends: R (>= 4.1.0), methods Imports: S4Vectors (>= 0.9.25), IRanges, Seqinfo, GenomeInfoDb, GenomicRanges (>= 1.29.6), GenomicFeatures, readr, Biostrings, AnnotationDbi, BiocFileCache, snpStats, VariantAnnotation, AnnotationHub, data.table, tibble Suggests: DO.db, DT, knitr, RBGL, testthat, rmarkdown, dplyr, Gviz, Rsamtools, rtracklayer, graph, ggbio, DelayedArray, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BiocStyle Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37 License: Artistic-2.0 MD5sum: e257421b37c55db1057bbda01e17a9f1 NeedsCompilation: no Title: representing and modeling data in the EMBL-EBI GWAS catalog Description: Represent and model data in the EMBL-EBI GWAS catalog. biocViews: Genetics Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwascat git_branch: devel git_last_commit: f456e2c git_last_commit_date: 2026-01-25 Date/Publication: 2026-04-20 source.ver: src/contrib/gwascat_2.43.2.tar.gz vignettes: vignettes/gwascat/inst/doc/gwascat.html, vignettes/gwascat/inst/doc/gwascatOnt.html vignetteTitles: gwascat: structuring and querying the NHGRI GWAS catalog, gwascat -- GRanges for GWAS hits in EBI catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R, vignettes/gwascat/inst/doc/gwascatOnt.R dependsOnMe: vtpnet, liftOver importsMe: circRNAprofiler suggestsMe: GenomicScores, hmdbQuery, ldblock, parglms, TFutils, grasp2db dependencyCount: 110 Package: GWASTools Version: 1.57.0 Depends: Biobase Imports: graphics, stats, utils, methods, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth, data.table Suggests: ncdf4, GWASdata, BiocGenerics, RUnit, Biostrings, GenomicRanges, IRanges, SNPRelate, snpStats, S4Vectors, VariantAnnotation, parallel, BiocStyle, knitr License: Artistic-2.0 MD5sum: 83324c0ce682f9c57083d752b0c79c37 NeedsCompilation: no Title: Tools for Genome Wide Association Studies Description: Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis. biocViews: SNP, GeneticVariability, QualityControl, Microarray Author: Stephanie M. Gogarten [aut], Cathy Laurie [aut], Tushar Bhangale [aut], Matthew P. Conomos [aut], Cecelia Laurie [aut], Michael Lawrence [aut], Caitlin McHugh [aut], Ian Painter [aut], Xiuwen Zheng [aut], Jess Shen [aut], Rohit Swarnkar [aut], Adrienne Stilp [aut], Sarah Nelson [aut], David Levine [aut], Sonali Kumari [ctb] (Converted vignettes from Sweave to RMarkdown / HTML.), Stephanie M. Gogarten [cre] Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/GWASTools VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWASTools git_branch: devel git_last_commit: 224f71a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GWASTools_1.57.0.tar.gz vignettes: vignettes/GWASTools/inst/doc/DataCleaning.pdf, vignettes/GWASTools/inst/doc/Formats.pdf, vignettes/GWASTools/inst/doc/Affymetrix.html vignetteTitles: GWAS Data Cleaning, Data formats in GWASTools, Preparing Affymetrix Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWASTools/inst/doc/Affymetrix.R, vignettes/GWASTools/inst/doc/DataCleaning.R, vignettes/GWASTools/inst/doc/Formats.R dependsOnMe: mBPCR, GWASdata, snplinkage importsMe: GENESIS, gwasurvivr suggestsMe: podkat dependencyCount: 94 Package: gwasurvivr Version: 1.29.0 Depends: R (>= 3.4.0) Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats, SummarizedExperiment, stats, utils, SNPRelate Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: de01c25bd6875d0c3ad21a8742f76ab4 NeedsCompilation: no Title: gwasurvivr: an R package for genome wide survival analysis Description: gwasurvivr is a package to perform survival analysis using Cox proportional hazard models on imputed genetic data. biocViews: GenomeWideAssociation, Survival, Regression, Genetics, SNP, GeneticVariability, Pharmacogenomics, BiomedicalInformatics Author: Abbas Rizvi, Ezgi Karaesmen, Martin Morgan, Lara Sucheston-Campbell Maintainer: Abbas Rizvi URL: https://github.com/suchestoncampbelllab/gwasurvivr VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwasurvivr git_branch: devel git_last_commit: f8ecda5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gwasurvivr_1.29.0.tar.gz vignettes: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.html vignetteTitles: gwasurvivr Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.R dependencyCount: 143 Package: GWENA Version: 1.21.0 Depends: R (>= 4.1) Imports: WGCNA (>= 1.67), dplyr (>= 0.8.3), dynamicTreeCut (>= 1.63-1), ggplot2 (>= 3.1.1), gprofiler2 (>= 0.1.6), magrittr (>= 1.5), tibble (>= 2.1.1), tidyr (>= 1.0.0), NetRep (>= 1.2.1), igraph (>= 1.2.4.1), RColorBrewer (>= 1.1-2), purrr (>= 0.3.3), rlist (>= 0.4.6.1), matrixStats (>= 0.55.0), SummarizedExperiment (>= 1.14.1), stringr (>= 1.4.0), cluster (>= 2.1.0), grDevices (>= 4.0.4), methods, graphics, stats, utils Suggests: testthat (>= 2.1.0), knitr (>= 1.25), rmarkdown (>= 1.16), prettydoc (>= 0.3.0), httr (>= 1.4.1), S4Vectors (>= 0.22.1), BiocStyle (>= 2.15.8) License: GPL-3 MD5sum: c9c6a0b37c28c538583497197626f54a NeedsCompilation: no Title: Pipeline for augmented co-expression analysis Description: The development of high-throughput sequencing led to increased use of co-expression analysis to go beyong single feature (i.e. gene) focus. We propose GWENA (Gene Whole co-Expression Network Analysis) , a tool designed to perform gene co-expression network analysis and explore the results in a single pipeline. It includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparison of networks configuration between conditions. biocViews: Software, GeneExpression, Network, Clustering, GraphAndNetwork, GeneSetEnrichment, Pathways, Visualization, RNASeq, Transcriptomics, mRNAMicroarray, Microarray, NetworkEnrichment, Sequencing, GO Author: Gwenaëlle Lemoine [aut, cre] (ORCID: ), Marie-Pier Scott-Boyer [ths], Arnaud Droit [fnd] Maintainer: Gwenaëlle Lemoine VignetteBuilder: knitr BugReports: https://github.com/Kumquatum/GWENA/issues git_url: https://git.bioconductor.org/packages/GWENA git_branch: devel git_last_commit: 28d0013 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/GWENA_1.21.0.tar.gz vignettes: vignettes/GWENA/inst/doc/GWENA_guide.html vignetteTitles: GWENA-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWENA/inst/doc/GWENA_guide.R dependencyCount: 123 Package: gypsum Version: 1.7.0 Imports: utils, httr2, jsonlite, parallel, filelock, rappdirs Suggests: knitr, rmarkdown, testthat, BiocStyle, digest, jsonvalidate, DBI, RSQLite, S4Vectors, methods License: MIT + file LICENSE MD5sum: 85820ff43b452f13c114cfbacaf7969c NeedsCompilation: no Title: Interface to the gypsum REST API Description: Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various adminstrative and management tasks. Check out the documentation at https://github.com/ArtifactDB/gypsum-worker for more details. biocViews: DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/ArtifactDB/gypsum-R VignetteBuilder: knitr BugReports: https://github.com/ArtifactDB/gypsum-R/issues git_url: https://git.bioconductor.org/packages/gypsum git_branch: devel git_last_commit: b24f688 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/gypsum_1.7.0.tar.gz vignettes: vignettes/gypsum/inst/doc/userguide.html vignetteTitles: Hitting the gypsum API hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gypsum/inst/doc/userguide.R importsMe: celldex, scRNAseq dependencyCount: 21 Package: h5mread Version: 1.3.3 Depends: R (>= 4.5), methods, rhdf5, BiocGenerics, SparseArray Imports: stats, tools, rhdf5filters, S4Vectors, IRanges, S4Arrays LinkingTo: Rhdf5lib, S4Vectors Suggests: BiocParallel, ExperimentHub, TENxBrainData, HDF5Array, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 5c2da301599159e28411e20804521fad NeedsCompilation: yes Title: A fast HDF5 reader Description: The main function in the h5mread package is h5mread(), which allows reading arbitrary data from an HDF5 dataset into R, similarly to what the h5read() function from the rhdf5 package does. In the case of h5mread(), the implementation has been optimized to make it as fast and memory-efficient as possible. biocViews: Infrastructure, DataRepresentation, DataImport Author: Hervé Pagès [aut, cre] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/h5mread SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/h5mread/issues git_url: https://git.bioconductor.org/packages/h5mread git_branch: devel git_last_commit: 288d616 git_last_commit_date: 2026-04-07 Date/Publication: 2026-04-20 source.ver: src/contrib/h5mread_1.3.3.tar.gz vignettes: vignettes/h5mread/inst/doc/h5mread.html vignetteTitles: The h5mread package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/h5mread/inst/doc/h5mread.R dependsOnMe: HDF5Array importsMe: SpliceWiz suggestsMe: MultiAssayExperiment dependencyCount: 26 Package: h5vc Version: 2.45.0 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>= 2.13.1), methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb LinkingTo: Rhtslib (>= 1.99.1) Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics, rmarkdown License: GPL (>= 3) MD5sum: b1aabe02bc0d9ac7d3896adc1ecbe39e NeedsCompilation: yes Title: Managing alignment tallies using a hdf5 backend Description: This package contains functions to interact with tally data from NGS experiments that is stored in HDF5 files. Author: Paul Theodor Pyl Maintainer: Paul Theodor Pyl SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/h5vc git_branch: devel git_last_commit: 39f8a8c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/h5vc_2.45.0.tar.gz vignettes: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html vignetteTitles: Building a minimal genome browser with h5vc and shiny, h5vc -- Tour hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.R, vignettes/h5vc/inst/doc/h5vc.tour.R suggestsMe: h5vcData dependencyCount: 86 Package: hammers Version: 0.99.9 Imports: cluster, dplyr, ggplot2, ggrepel, grDevices, henna, liver, rlang, scLang, stats, text2vec, Suggests: BiocStyle, knitr, qs2, rmarkdown, scater, scRNAseq, scuttle, testthat (>= 3.0.0), withr License: MIT + file LICENSE MD5sum: f682312726d0a2716d9e1dcfd0979e15 NeedsCompilation: no Title: Utilities for scRNA-seq data analysis Description: hammers is a utilities suite for scRNA-seq data analysis compatible with both Seurat and SingleCellExperiment. It provides simple tools to address tasks such as retrieving aggregate gene statistics, finding and removing rare genes, performing representation analysis, computing the center of mass for the expression of a gene of interest in low-dimensional space, and calculating silhouette and cluster-normalized silhouette. biocViews: Software, SingleCell, GeneExpression, MultipleComparison, Visualization Author: Andrei-Florian Stoica [aut, cre] (ORCID: ) Maintainer: Andrei-Florian Stoica URL: https://github.com/andrei-stoica26/hammers VignetteBuilder: knitr BugReports: https://github.com/andrei-stoica26/hammers/issues git_url: https://git.bioconductor.org/packages/hammers git_branch: devel git_last_commit: 2465038 git_last_commit_date: 2026-03-06 Date/Publication: 2026-04-20 source.ver: src/contrib/hammers_0.99.9.tar.gz vignettes: vignettes/hammers/inst/doc/hammers.html vignetteTitles: Getting started with CSOA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hammers/inst/doc/hammers.R importsMe: GSABenchmark dependencyCount: 113 Package: hapFabia Version: 1.53.0 Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: 7c2255d84b1990a49358e105a4597c5a NeedsCompilation: yes Title: hapFabia: Identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data Description: A package to identify very short IBD segments in large sequencing data by FABIA biclustering. Two haplotypes are identical by descent (IBD) if they share a segment that both inherited from a common ancestor. Current IBD methods reliably detect long IBD segments because many minor alleles in the segment are concordant between the two haplotypes. However, many cohort studies contain unrelated individuals which share only short IBD segments. This package provides software to identify short IBD segments in sequencing data. Knowledge of short IBD segments are relevant for phasing of genotyping data, association studies, and for population genetics, where they shed light on the evolutionary history of humans. The package supports VCF formats, is based on sparse matrix operations, and provides visualization of haplotype clusters in different formats. biocViews: Genetics, GeneticVariability, SNP, Sequencing, Sequencing, Visualization, Clustering, SequenceMatching, Software Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html git_url: https://git.bioconductor.org/packages/hapFabia git_branch: devel git_last_commit: 273bb8f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hapFabia_1.53.0.tar.gz vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf vignetteTitles: hapFabia: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hapFabia/inst/doc/hapfabia.R dependencyCount: 9 Package: Harman Version: 1.39.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.11.2), graphics, stats, Ckmeans.1d.dp, parallel, methods, matrixStats LinkingTo: Rcpp Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit, RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA, affydata, minfiData, sva License: GPL-3 + file LICENCE MD5sum: ce6ff3f163d62ef32fddb4940efd8476 NeedsCompilation: yes Title: The removal of batch effects from datasets using a PCA and constrained optimisation based technique Description: Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user. biocViews: BatchEffect, Microarray, MultipleComparison, PrincipalComponent, Normalization, Preprocessing, DNAMethylation, Transcription, Software, StatisticalMethod Author: Yalchin Oytam [aut], Josh Bowden [aut], Jason Ross [aut, cre] Maintainer: Jason Ross URL: http://www.bioinformatics.csiro.au/harman/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Harman git_branch: devel git_last_commit: 857a365 git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/Harman_1.39.0.tar.gz vignettes: vignettes/Harman/inst/doc/IntroductionToHarman.html vignetteTitles: IntroductionToHarman hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R importsMe: BatchQC, debrowser suggestsMe: HarmanData dependencyCount: 11 Package: HarmonizR Version: 1.9.0 Depends: R (>= 4.2.0) Imports: doParallel (>= 1.0.16), foreach (>= 1.5.1), janitor (>= 2.1.0), plyr (>= 1.8.6), sva (>= 3.36.0), seriation (>= 1.3.5), limma (>= 3.46.0), SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: f6eadffefeb2d2ba36c27cbf831a10e9 NeedsCompilation: no Title: Handles missing values and makes more data available Description: An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss. biocViews: BatchEffect Author: Simon Schlumbohm [aut, cre], Julia Neumann [aut], Philipp Neumann [aut] Maintainer: Simon Schlumbohm VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HarmonizR git_branch: devel git_last_commit: 17b1b32 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HarmonizR_1.9.0.tar.gz vignettes: vignettes/HarmonizR/inst/doc/HarmonizR_Vignette.html vignetteTitles: HarmonizR_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HarmonizR/inst/doc/HarmonizR_Vignette.R dependencyCount: 107 Package: HDTD Version: 1.45.0 Depends: R (>= 4.1) Imports: stats, Rcpp (>= 1.0.1) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 19f513e7699848b172fb66cf4bfa6231 NeedsCompilation: yes Title: Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD) Description: Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables. biocViews: DifferentialExpression, Genetics, GeneExpression, Microarray, Sequencing, StatisticalMethod, Software Author: Anestis Touloumis [cre, aut] (ORCID: ), John C. Marioni [aut] (ORCID: ), Simon Tavar\'{e} [aut] (ORCID: ) Maintainer: Anestis Touloumis URL: http://github.com/AnestisTouloumis/HDTD VignetteBuilder: knitr BugReports: http://github.com/AnestisTouloumis/HDTD/issues git_url: https://git.bioconductor.org/packages/HDTD git_branch: devel git_last_commit: ec245d8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HDTD_1.45.0.tar.gz vignettes: vignettes/HDTD/inst/doc/HDTD.html vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/HDTD.R dependencyCount: 5 Package: hdxmsqc Version: 1.7.0 Depends: R(>= 4.3), QFeatures, S4Vectors, Spectra Imports: dplyr, tidyr, ggplot2, BiocStyle, knitr, methods, grDevices, stats, MsCoreUtils Suggests: RColorBrewer, pheatmap, MASS, patchwork, testthat License: file LICENSE MD5sum: 51224a1a6bf4ac2cab5d4916eed55e55 NeedsCompilation: no Title: An R package for quality Control for hydrogen deuterium exchange mass spectrometry experiments Description: The hdxmsqc package enables us to analyse and visualise the quality of HDX-MS experiments. Either as a final quality check before downstream analysis and publication or as part of a interative procedure to determine the quality of the data. The package builds on the QFeatures and Spectra packages to integrate with other mass-spectrometry data. biocViews: QualityControl,DataImport, Proteomics, MassSpectrometry, Metabolomics Author: Oliver M. Crook [aut, cre] (ORCID: ) Maintainer: Oliver M. Crook URL: http://github.com/ococrook/hdxmsqc VignetteBuilder: knitr BugReports: https://github.com/ococrook/hdxmsqc/issues git_url: https://git.bioconductor.org/packages/hdxmsqc git_branch: devel git_last_commit: 1520631 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hdxmsqc_1.7.0.tar.gz vignettes: vignettes/hdxmsqc/inst/doc/qc-streamlined.html vignetteTitles: Qualityt control for differential hydrogen deuterium exchange mass spectrometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hdxmsqc/inst/doc/qc-streamlined.R dependencyCount: 114 Package: heatmaps Version: 1.35.0 Depends: R (>= 3.5.0) Imports: methods, grDevices, graphics, stats, Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix, Matrix, EBImage, RColorBrewer, BiocGenerics, Seqinfo Suggests: BSgenome.Drerio.UCSC.danRer7, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: ed03e58b8f3a8c7d5f092a1448a39ca2 NeedsCompilation: no Title: Flexible Heatmaps for Functional Genomics and Sequence Features Description: This package provides functions for plotting heatmaps of genome-wide data across genomic intervals, such as ChIP-seq signals at peaks or across promoters. Many functions are also provided for investigating sequence features. biocViews: Visualization, SequenceMatching, FunctionalGenomics Author: Malcolm Perry Maintainer: Malcolm Perry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/heatmaps git_branch: devel git_last_commit: dd77fbc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/heatmaps_1.35.0.tar.gz vignettes: vignettes/heatmaps/inst/doc/heatmaps.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/heatmaps/inst/doc/heatmaps.R dependencyCount: 57 Package: Heatplus Version: 3.19.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: f90a6b3d771f45f8cd23a0f29f8b3a92 NeedsCompilation: no Title: Heatmaps with row and/or column covariates and colored clusters Description: Display a rectangular heatmap (intensity plot) of a data matrix. By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. biocViews: Microarray, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: https://github.com/alexploner/Heatplus BugReports: https://github.com/alexploner/Heatplus/issues git_url: https://git.bioconductor.org/packages/Heatplus git_branch: devel git_last_commit: 5195d9f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Heatplus_3.19.0.tar.gz vignettes: vignettes/Heatplus/inst/doc/annHeatmap.pdf, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf, vignettes/Heatplus/inst/doc/oldHeatplus.pdf vignetteTitles: Annotated and regular heatmaps, Commented package source, Old functions (deprecated) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Heatplus/inst/doc/annHeatmap.R, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R, vignettes/Heatplus/inst/doc/oldHeatplus.R dependsOnMe: phenoTest, tRanslatome, heatmapFlex suggestsMe: mtbls2, RforProteomics dependencyCount: 4 Package: HelloRanges Version: 1.37.0 Depends: methods, BiocGenerics, S4Vectors (>= 0.17.39), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.10), Biostrings (>= 2.41.3), BSgenome, GenomicFeatures (>= 1.31.5), VariantAnnotation (>= 1.19.3), Rsamtools, GenomicAlignments (>= 1.15.7), rtracklayer (>= 1.33.8), Seqinfo, SummarizedExperiment, BiocIO Imports: docopt, stats, tools, utils Suggests: GenomeInfoDb, HelloRangesData, BiocStyle, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 7fabc05ef866f35b6f40960e6b7958d8 NeedsCompilation: no Title: Introduce *Ranges to bedtools users Description: Translates bedtools command-line invocations to R code calling functions from the Bioconductor *Ranges infrastructure. This is intended to educate novice Bioconductor users and to compare the syntax and semantics of the two frameworks. biocViews: Sequencing, Annotation, Coverage, GenomeAnnotation, DataImport, SequenceMatching, VariantAnnotation Author: Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/HelloRanges git_branch: devel git_last_commit: 38fc888 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HelloRanges_1.37.0.tar.gz vignettes: vignettes/HelloRanges/inst/doc/tutorial.pdf vignetteTitles: HelloRanges Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HelloRanges/inst/doc/tutorial.R importsMe: OMICsPCA suggestsMe: plyranges dependencyCount: 78 Package: HELP Version: 1.69.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: 08a013a3b890811b16c46580a0a276be NeedsCompilation: no Title: Tools for HELP data analysis Description: The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications. biocViews: CpGIsland, DNAMethylation, Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, Visualization Author: Reid F. Thompson , John M. Greally , with contributions from Mark Reimers Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/HELP git_branch: devel git_last_commit: c8a9b3d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HELP_1.69.0.tar.gz vignettes: vignettes/HELP/inst/doc/HELP.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HELP/inst/doc/HELP.R dependencyCount: 8 Package: HEM Version: 1.83.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) MD5sum: 96c6bfadc22f29f166a72e21030f9055 NeedsCompilation: yes Title: Heterogeneous error model for identification of differentially expressed genes under multiple conditions Description: This package fits heterogeneous error models for analysis of microarray data biocViews: Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: HyungJun Cho URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/HEM git_branch: devel git_last_commit: f81d7e9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HEM_1.83.0.tar.gz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: hermes Version: 1.15.0 Depends: ggfortify, R (>= 4.1), SummarizedExperiment (>= 1.16) Imports: assertthat, Biobase, BiocGenerics, biomaRt, checkmate (>= 2.1), circlize, ComplexHeatmap, DESeq2, dplyr, edgeR, EnvStats, forcats (>= 1.0.0), GenomicRanges, ggplot2, ggrepel (>= 0.9), IRanges, limma, magrittr, matrixStats (>= 1.5.0), methods, MultiAssayExperiment, purrr, R6, Rdpack (>= 2.6.2), rlang, S4Vectors, stats, tidyr, utils Suggests: BiocStyle, DelayedArray, DT, grid, httr, knitr, rmarkdown, statmod, testthat (>= 3.2.2), vdiffr (>= 1.0.8) License: Apache License 2.0 MD5sum: 988971327c8e86bbb61b611d79908cef NeedsCompilation: no Title: Preprocessing, analyzing, and reporting of RNA-seq data Description: Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed `RNA-seq` data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from `BioMart`. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including cpm, rpkm and tpm can be used, and 'DESeq2` as well as voom differential expression analyses are available. biocViews: RNASeq, DifferentialExpression, Normalization, Preprocessing, QualityControl Author: Daniel Sabanés Bové [aut, cre], Namrata Bhatia [aut], Stefanie Bienert [aut], Benoit Falquet [aut], Haocheng Li [aut], Jeff Luong [aut], Lyndsee Midori Zhang [aut], Alex Richardson [aut], Simona Rossomanno [aut], Tim Treis [aut], Mark Yan [aut], Naomi Chang [aut], Chendi Liao [aut], Carolyn Zhang [aut], Joseph N. Paulson [aut], F. Hoffmann-La Roche AG [cph, fnd] Maintainer: Daniel Sabanés Bové URL: https://insightsengineering.github.io/hermes/ VignetteBuilder: knitr BugReports: https://github.com/insightsengineering/hermes/issues git_url: https://git.bioconductor.org/packages/hermes git_branch: devel git_last_commit: da21eaa git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hermes_1.15.0.tar.gz vignettes: vignettes/hermes/inst/doc/hermes.html vignetteTitles: Introduction to `hermes` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hermes/inst/doc/hermes.R dependencyCount: 126 Package: HERON Version: 1.9.0 Depends: R (>= 4.4.0), SummarizedExperiment (>= 1.1.6), GenomicRanges, IRanges, S4Vectors Imports: matrixStats, stats, data.table, harmonicmeanp, metap, cluster, spdep, Matrix, limma, methods Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: d007da5bd2af2468ee83720edbe9ead3 NeedsCompilation: no Title: Hierarchical Epitope pROtein biNding Description: HERON is a software package for analyzing peptide binding array data. In addition to identifying significant binding probes, HERON also provides functions for finding epitopes (string of consecutive peptides within a protein). HERON also calculates significance on the probe, epitope, and protein level by employing meta p-value methods. HERON is designed for obtaining calls on the sample level and calculates fractions of hits for different conditions. biocViews: Microarray, Software Author: Sean McIlwain [aut, cre] (ORCID: ), Irene Ong [aut] (ORCID: ) Maintainer: Sean McIlwain URL: https://github.com/Ong-Research/HERON VignetteBuilder: knitr BugReports: https://github.com/Ong-Research/HERON/issues git_url: https://git.bioconductor.org/packages/HERON git_branch: devel git_last_commit: 4b63635 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HERON_1.9.0.tar.gz vignettes: vignettes/HERON/inst/doc/full_analysis.html vignetteTitles: Analyzing High Density Peptide Array Data using HERON hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HERON/inst/doc/full_analysis.R dependencyCount: 74 Package: Herper Version: 1.21.0 Depends: R (>= 4.0), reticulate Imports: utils, rjson, withr, stats Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 3bf9493ecc83d2e0695f5523accc3c6c NeedsCompilation: no Title: The Herper package is a simple toolset to install and manage conda packages and environments from R Description: Many tools for data analysis are not available in R, but are present in public repositories like conda. The Herper package provides a comprehensive set of functions to interact with the conda package managament system. With Herper users can install, manage and run conda packages from the comfort of their R session. Herper also provides an ad-hoc approach to handling external system requirements for R packages. For people developing packages with python conda dependencies we recommend using basilisk (https://bioconductor.org/packages/release/bioc/html/basilisk.html) to internally support these system requirments pre-hoc. biocViews: Infrastructure, Software Author: Matt Paul [aut] (ORCID: ), Thomas Carroll [aut, cre] (ORCID: ), Doug Barrows [aut], Kathryn Rozen-Gagnon [ctb] Maintainer: Thomas Carroll URL: https://github.com/RockefellerUniversity/Herper VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Herper git_branch: devel git_last_commit: 2c2b888 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Herper_1.21.0.tar.gz vignettes: vignettes/Herper/inst/doc/QuickStart.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Herper/inst/doc/QuickStart.R dependencyCount: 19 Package: HGC Version: 1.19.0 Depends: R (>= 4.1.0) Imports: Rcpp (>= 1.0.0), RcppEigen(>= 0.3.2.0), Matrix, RANN, ape, dendextend, ggplot2, mclust, patchwork, dplyr, grDevices, methods, stats LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 50cb0697a221d1b045b874e82616b3e2 NeedsCompilation: yes Title: A fast hierarchical graph-based clustering method Description: HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6. biocViews: SingleCell, Software, Clustering, RNASeq, GraphAndNetwork, DNASeq Author: Zou Ziheng [aut], Hua Kui [aut], XGlab [cre, cph] Maintainer: XGlab SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HGC git_branch: devel git_last_commit: 8ba086b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HGC_1.19.0.tar.gz vignettes: vignettes/HGC/inst/doc/HGC.html vignetteTitles: HGC package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HGC/inst/doc/HGC.R dependencyCount: 46 Package: HIBAG Version: 1.47.2 Depends: R (>= 3.2.0) Imports: methods, RcppParallel LinkingTo: RcppParallel (>= 5.0.0) Suggests: parallel, ggplot2, reshape2, gdsfmt, SNPRelate, SeqArray, knitr, markdown, rmarkdown, Rsamtools License: GPL-3 MD5sum: e2c881254f03ca77a7d32724e03b5edd NeedsCompilation: yes Title: HLA Genotype Imputation with Attribute Bagging Description: Imputes HLA classical alleles using GWAS SNP data, and it relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection. biocViews: Genetics, StatisticalMethod Author: Xiuwen Zheng [aut, cre, cph] (ORCID: ), Bruce Weir [ctb, ths] (ORCID: ) Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/HIBAG, https://hibag.s3.amazonaws.com/index.html SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIBAG git_branch: devel git_last_commit: dd2c9ff git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/HIBAG_1.47.2.tar.gz vignettes: vignettes/HIBAG/inst/doc/HIBAG.html, vignettes/HIBAG/inst/doc/HLA_Association.html, vignettes/HIBAG/inst/doc/Implementation.html vignetteTitles: HIBAG vignette html, HLA association vignette html, HIBAG algorithm implementation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIBAG/inst/doc/HIBAG.R, vignettes/HIBAG/inst/doc/HLA_Association.R, vignettes/HIBAG/inst/doc/Implementation.R dependencyCount: 2 Package: HicAggR Version: 1.7.0 Depends: R (>= 4.2.0) Imports: InteractionSet, BiocGenerics, BiocParallel, dplyr, Seqinfo, GenomicRanges, ggplot2, grDevices, IRanges, Matrix, methods, rhdf5, rlang, rtracklayer, S4Vectors, stats, utils, strawr, tibble, stringr, tidyr, gridExtra, data.table, reshape, checkmate, purrr, withr Suggests: GenomeInfoDb, covr, tools, kableExtra (>= 1.3.4), knitr (>= 1.45), rmarkdown, testthat (>= 3.0.0), BiocFileCache (>= 2.6.1) License: MIT + file LICENSE MD5sum: cd58deb00d5f3540de056f524d12565f NeedsCompilation: no Title: Set of 3D genomic interaction analysis tools Description: This package provides a set of functions useful in the analysis of 3D genomic interactions. It includes the import of standard HiC data formats into R and HiC normalisation procedures. The main objective of this package is to improve the visualization and quantification of the analysis of HiC contacts through aggregation. The package allows to import 1D genomics data, such as peaks from ATACSeq, ChIPSeq, to create potential couples between features of interest under user-defined parameters such as distance between pairs of features of interest. It allows then the extraction of contact values from the HiC data for these couples and to perform Aggregated Peak Analysis (APA) for visualization, but also to compare normalized contact values between conditions. Overall the package allows to integrate 1D genomics data with 3D genomics data, providing an easy access to HiC contact values. biocViews: Software, HiC, DataImport, DataRepresentation, Normalization, Visualization, DNA3DStructure, ATACSeq, ChIPSeq, DNaseSeq, RNASeq Author: Robel Tesfaye [aut, ctb] (ORCID: ), David Depierre [aut], Naomi Schickele [ctb], Nicolas Chanard [aut], Refka Askri [ctb], Stéphane Schaak [aut, ctb], Pascal Martin [ctb], Olivier Cuvier [cre, ctb] (ORCID: ) Maintainer: Olivier Cuvier URL: https://bioconductor.org/packages/HicAggR, https://cuvierlab.github.io/HicAggR/, https://github.com/CuvierLab/HicAggR VignetteBuilder: knitr BugReports: https://github.com/CuvierLab/HicAggR/issues git_url: https://git.bioconductor.org/packages/HicAggR git_branch: devel git_last_commit: 7e29b61 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HicAggR_1.7.0.tar.gz vignettes: vignettes/HicAggR/inst/doc/HicAggR.html vignetteTitles: HicAggR - In depth tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HicAggR/inst/doc/HicAggR.R dependencyCount: 98 Package: HiCaptuRe Version: 1.1.0 Depends: R (>= 4.5.0) Imports: Biostrings, BSgenome, cli, data.table, dplyr, GenomeInfoDb, GenomicInteractions, GenomicRanges, InteractionSet, ggplot2, ggpubr, ggVennDiagram, gplots, igraph, IRanges, memoise, methods, S4Vectors, stringr, tibble, tidyr, UpSetR, utils Suggests: BSgenome.Hsapiens.NCBI.GRCh38, knitr, rmarkdown, DT, testthat, BiocStyle, kableExtra License: GPL-3 MD5sum: 25893ab5b2720dc57e8ddfa26ce71787 NeedsCompilation: no Title: HiCaptuRe: Manipulating and integrating Capture Hi-C data Description: Capture Hi-C is a set of techniques that enable the detection of genomic interactions involving regions of interest, known as baits. By focusing on selected loci, these approaches reduce sequencing costs while maintaining high resolution at the level of restriction fragments. HiCaptuRe provides tools to import, annotate, manipulate, and export Capture Hi-C data. The package accounts for the specific structure of bait–otherEnd interactions, facilitates integration with other omics datasets, and enables comparison across samples and conditions. biocViews: Epigenetics, HiC, Sequencing, DataImport, Software Author: Laureano Tomas-Daza [aut, cre] (ORCID: ) Maintainer: Laureano Tomas-Daza URL: https://github.com/LaureTomas/HiCaptuRe VignetteBuilder: knitr BugReports: https://github.com/LaureTomas/HiCaptuRe/issues git_url: https://git.bioconductor.org/packages/HiCaptuRe git_branch: devel git_last_commit: 342bae3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HiCaptuRe_1.1.0.tar.gz vignettes: vignettes/HiCaptuRe/inst/doc/vignette_functions.html, vignettes/HiCaptuRe/inst/doc/vignetteIntroduction.html vignetteTitles: Functions, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCaptuRe/inst/doc/vignette_functions.R, vignettes/HiCaptuRe/inst/doc/vignetteIntroduction.R dependencyCount: 208 Package: HiCcompare Version: 1.33.0 Depends: R (>= 3.5.0), dplyr Imports: data.table, ggplot2, gridExtra, mgcv, stats, InteractionSet, GenomicRanges, IRanges, S4Vectors, BiocParallel, KernSmooth, methods, utils, graphics, pheatmap, gtools, rhdf5 Suggests: knitr, rmarkdown, testthat, multiHiCcompare License: MIT + file LICENSE MD5sum: 761ce402677eb1b74677c394f1b7cc43 NeedsCompilation: no Title: HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets Description: HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets. biocViews: Software, HiC, Sequencing, Normalization Author: Mikhail Dozmorov [aut, cre] (ORCID: ), Kellen Cresswell [aut], John Stansfield [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/HiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/HiCcompare/issues git_url: https://git.bioconductor.org/packages/HiCcompare git_branch: devel git_last_commit: cd8aee7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HiCcompare_1.33.0.tar.gz vignettes: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.html vignetteTitles: HiCcompare Usage Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.R importsMe: multiHiCcompare, scHiCcompare, SpectralTAD, TADCompare dependencyCount: 74 Package: HiCDCPlus Version: 1.19.0 Imports: Rcpp,InteractionSet,GenomicInteractions,bbmle,pscl,BSgenome,data.table,dplyr,tidyr,GenomeInfoDb,rlang,splines,MASS,GenomicRanges,IRanges,tibble,R.utils,Biostrings,rtracklayer,methods,S4Vectors LinkingTo: Rcpp Suggests: BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, RUnit, BiocGenerics, knitr, rmarkdown, HiTC, DESeq2, Matrix, BiocFileCache, rappdirs Enhances: parallel License: GPL-3 MD5sum: a0f7bac97162251ce7c8b7c61d24a2ac NeedsCompilation: yes Title: Hi-C Direct Caller Plus Description: Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw contact matrix for each chromosome up to a specified genomic distance, binned by uniform genomic intervals or restriction enzyme fragments, by training a background model to account for random polymer ligation and systematic sources of read count variation. biocViews: HiC, DNA3DStructure, Software, Normalization Author: Merve Sahin [cre, aut] (ORCID: ) Maintainer: Merve Sahin SystemRequirements: JRE 8+ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCDCPlus git_branch: devel git_last_commit: 720bb53 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HiCDCPlus_1.19.0.tar.gz vignettes: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.html vignetteTitles: Analyzing Hi-C and HiChIP data with HiCDCPlus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.R dependencyCount: 164 Package: HiCDOC Version: 1.13.0 Depends: InteractionSet, GenomicRanges, SummarizedExperiment, R (>= 4.1.0) Imports: methods, ggplot2, Rcpp (>= 0.12.8), stats, S4Vectors, gtools, pbapply, BiocParallel, BiocGenerics, grid, cowplot, gridExtra, data.table, multiHiCcompare, Seqinfo LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, BiocManager, rhdf5 License: LGPL-3 MD5sum: bb4064052d3bd9a6555104bdb40989ce NeedsCompilation: yes Title: A/B compartment detection and differential analysis Description: HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular `.tsv` files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files. biocViews: HiC, DNA3DStructure, Normalization, Sequencing, Software, Clustering Author: Kurylo Cyril [aut], Zytnicki Matthias [aut], Foissac Sylvain [aut], Maigné Élise [aut, cre] Maintainer: Maigné Élise URL: https://github.com/mzytnicki/HiCDOC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/mzytnicki/HiCDOC/issues git_url: https://git.bioconductor.org/packages/HiCDOC git_branch: devel git_last_commit: 6ee95b8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HiCDOC_1.13.0.tar.gz vignettes: vignettes/HiCDOC/inst/doc/HiCDOC.html vignetteTitles: HiCDOC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCDOC/inst/doc/HiCDOC.R importsMe: treediff dependencyCount: 96 Package: HiCExperiment Version: 1.11.1 Depends: R (>= 4.2) Imports: InteractionSet, strawr, Seqinfo, GenomicRanges, IRanges, S4Vectors, BiocGenerics, BiocIO, BiocParallel, methods, rhdf5, Matrix, vroom, dplyr, stats Suggests: HiContacts, HiContactsData, BiocFileCache, rtracklayer, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 87050ae3b79c4fba6423d56db07e967f NeedsCompilation: no Title: Bioconductor class for interacting with Hi-C files in R Description: R generic interface to Hi-C contact matrices in `.(m)cool`, `.hic` or HiC-Pro derived formats, as well as other Hi-C processed file formats. Contact matrices can be partially parsed using a random access method, allowing a memory-efficient representation of Hi-C data in R. The `HiCExperiment` class stores the Hi-C contacts parsed from local contact matrix files. `HiCExperiment` instances can be further investigated in R using the `HiContacts` analysis package. biocViews: HiC, DNA3DStructure, DataImport Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/HiCExperiment VignetteBuilder: knitr BugReports: https://github.com/js2264/HiCExperiment/issues git_url: https://git.bioconductor.org/packages/HiCExperiment git_branch: devel git_last_commit: 11da24a git_last_commit_date: 2026-04-19 Date/Publication: 2026-04-20 source.ver: src/contrib/HiCExperiment_1.11.1.tar.gz vignettes: vignettes/HiCExperiment/inst/doc/HiCExperiment.html vignetteTitles: Introduction to HiCExperiment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCExperiment/inst/doc/HiCExperiment.R dependsOnMe: HiContacts, HiCool, DNAZooData importsMe: HiSpaR, fourDNData, OHCA dependencyCount: 67 Package: HiContacts Version: 1.13.1 Depends: R (>= 4.2), HiCExperiment Imports: InteractionSet, SummarizedExperiment, GenomicRanges, IRanges, GenomeInfoDb, S4Vectors, methods, BiocGenerics, BiocIO, BiocParallel, RSpectra, Matrix, tibble, tidyr, dplyr, readr, stringr, ggplot2, ggrastr, scales, stats, utils Suggests: HiContactsData, rtracklayer, GenomicFeatures, Biostrings, BSgenome.Scerevisiae.UCSC.sacCer3, WGCNA, Rfast, terra, patchwork, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 1c09e6923624cd02aeafa8d5ed80091f NeedsCompilation: no Title: Analysing cool files in R with HiContacts Description: HiContacts provides a collection of tools to analyse and visualize Hi-C datasets imported in R by HiCExperiment. biocViews: HiC, DNA3DStructure Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/HiContacts VignetteBuilder: knitr BugReports: https://github.com/js2264/HiContacts/issues git_url: https://git.bioconductor.org/packages/HiContacts git_branch: devel git_last_commit: 8d361a6 git_last_commit_date: 2026-04-19 Date/Publication: 2026-04-20 source.ver: src/contrib/HiContacts_1.13.1.tar.gz vignettes: vignettes/HiContacts/inst/doc/HiContacts.html vignetteTitles: Introduction to HiContacts hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiContacts/inst/doc/HiContacts.R importsMe: OHCA suggestsMe: HiCExperiment, HiCool, HiSpaR dependencyCount: 104 Package: HiCParser Version: 1.3.0 Imports: data.table, InteractionSet, GenomicRanges, SummarizedExperiment, Rcpp (>= 1.0.12), S4Vectors, gtools, pbapply, BiocGenerics, Seqinfo LinkingTo: Rcpp Suggests: rhdf5, BiocStyle, knitr, sessioninfo, testthat (>= 3.0.0) License: LGPL MD5sum: 00bddf9f595f1ea38b61b5d6bb331dcb NeedsCompilation: yes Title: Parser for HiC data in R Description: This package is a parser to import HiC data into R. It accepts several type of data: tabular files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files. The HiC data can be several files, for several replicates and conditions. The data is formated in an InteractionSet object. biocViews: Software, HiC, DataImport Author: Zytnicki Matthias [aut], Maigné Élise [aut, cre] Maintainer: Maigné Élise URL: https://github.com/emaigne/HiCParser VignetteBuilder: knitr BugReports: https://github.com/emaigne/HiCParser/issues git_url: https://git.bioconductor.org/packages/HiCParser git_branch: devel git_last_commit: a19d833 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HiCParser_1.3.0.tar.gz vignettes: vignettes/HiCParser/inst/doc/HiCParser.html vignetteTitles: Introduction to HiCParser hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCParser/inst/doc/HiCParser.R dependencyCount: 31 Package: HiCPotts Version: 1.1.0 Depends: R (>= 4.5) Imports: Rcpp(>= 0.11.0), parallel, stats, Biostrings, GenomicRanges, rtracklayer, strawr, rhdf5, BSgenome,IRanges, S4Vectors, BSgenome.Dmelanogaster.UCSC.dm6 LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr (>= 1.30), rmarkdown (>= 2.10), ggplot2 (>= 3.5.0), reshape2 (>= 1.4.4), testthat (>= 3.0.0), BiocManager License: GPL-3 MD5sum: 45b0a021e7f5ada94571dbdf0fca0ea6 NeedsCompilation: yes Title: HiCPotts: Hierarchical Modeling to Identify and Correct Genomic Biases in Hi-C Description: The HiCPotts package provides a comprehensive Bayesian framework for analyzing Hi-C interaction data, integrating both spatial and genomic biases within a probabilistic modeling framework. At its core, HiCPotts leverages the Potts model (Wu, 1982)—a well-established graphical model—to capture and quantify spatial dependencies across interaction loci arranged on a genomic lattice. By treating each interaction as a spatially correlated random variable, the Potts model enables robust segmentation of the genomic landscape into meaningful components, such as noise, true signals, and false signals. To model the influence of various genomic biases, HiCPotts employs a regression-based approach incorporating multiple covariates: Genomic distance (D): The distance between interacting loci, recognized as a fundamental driver of contact frequency. GC-content (GC): The local GC composition around the interacting loci, which can influence chromatin structure and interaction patterns. Transposable elements (TEs): The presence and abundance of repetitive elements that may shape contact probability through chromatin organization. Accessibility score (Acc): A measure of chromatin openness, informing how accessible certain genomic regions are to interaction. By embedding these covariates into a hierarchical mixture model, HiCPotts characterizes each interaction’s probability of belonging to one of several latent components. The model parameters, including regression coefficients, zero-inflation parameters (for ZIP/ZINB distributions), and dispersion terms (for NB/ZINB distributions), are inferred via a MCMC sampler. This algorithm draws samples from the joint posterior distribution, allowing for flexible posterior inference on model parameters and hidden states. From these posterior samples, HiCPotts computes posterior means of regression parameters and other quantities of interest. These posterior estimates are then used to calculate the posterior probabilities that assign each interaction to a specific component. The resulting classification sheds light on the underlying structure: distinguishing genuine high-confidence interactions (signal) from background noise and potential false signals, while simultaneously quantifying the impact of genomic biases on observed interaction frequencies. In summary, HiCPotts seamlessly integrates spatial modeling, bias correction, and probabilistic classification into a unified Bayesian inference framework. It provides rich posterior summaries and interpretable, model-based assignments of interaction states, enabling researchers to better understand the interplay between genomic organization, biases, and spatial correlation in Hi-C data. biocViews: StatisticalMethod, FunctionalGenomics, GenomeAnnotation, GenomeWideAssociation, PeakDetection, DataImport, Spatial, Bayesian, Classification, HiddenMarkovModel, Regression Author: Itunu. Godwin Osuntoki [aut, cre] (ORCID: ), Nicolae. Radu Zabet [aut] Maintainer: Itunu. Godwin Osuntoki URL: https://github.com/igosungithub/HiCPotts VignetteBuilder: knitr BugReports: https://github.com/igosungithub/HiCPotts/issues git_url: https://git.bioconductor.org/packages/HiCPotts git_branch: devel git_last_commit: a37a5fa git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HiCPotts_1.1.0.tar.gz vignettes: vignettes/HiCPotts/inst/doc/HiCPotts_vignette.html vignetteTitles: Bayesian Analysis of Hi-C Interactions with HiCPotts hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCPotts/inst/doc/HiCPotts_vignette.R dependencyCount: 68 Package: hicVennDiagram Version: 1.9.0 Depends: R (>= 4.3.0) Imports: Seqinfo, GenomicRanges, IRanges, InteractionSet, rtracklayer, ggplot2, ComplexUpset, reshape2, eulerr, S4Vectors, methods, utils, htmlwidgets, svglite Suggests: BiocStyle, knitr, rmarkdown, testthat, ChIPpeakAnno, grid, TxDb.Hsapiens.UCSC.hg38.knownGene License: GPL-3 MD5sum: 83d5c48b4e879f93c705d4470f9333f9 NeedsCompilation: no Title: Venn Diagram for genomic interaction data Description: A package to generate high-resolution Venn and Upset plots for genomic interaction data from HiC, ChIA-PET, HiChIP, PLAC-Seq, Hi-TrAC, HiCAR and etc. The package generates plots specifically crafted to eliminate the deceptive visual representation caused by the counts method. biocViews: DNA3DStructure, HiC, Visualization Author: Jianhong Ou [aut, cre] (ORCID: ) Maintainer: Jianhong Ou URL: https://github.com/jianhong/hicVennDiagram VignetteBuilder: knitr BugReports: https://github.com/jianhong/hicVennDiagram/issues git_url: https://git.bioconductor.org/packages/hicVennDiagram git_branch: devel git_last_commit: 09d3176 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hicVennDiagram_1.9.0.tar.gz vignettes: vignettes/hicVennDiagram/inst/doc/hicVennDiagram.html vignetteTitles: hicVennDiagram Vignette: overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hicVennDiagram/inst/doc/hicVennDiagram.R dependencyCount: 109 Package: hierGWAS Version: 1.41.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: e82aeab884f0ac5f571f69d66af67ae6 NeedsCompilation: no Title: Asessing statistical significance in predictive GWA studies Description: Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers. biocViews: SNP, LinkageDisequilibrium, Clustering Author: Laura Buzdugan Maintainer: Laura Buzdugan git_url: https://git.bioconductor.org/packages/hierGWAS git_branch: devel git_last_commit: 76af363 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hierGWAS_1.41.0.tar.gz vignettes: vignettes/hierGWAS/inst/doc/hierGWAS.pdf vignetteTitles: User manual for R-Package hierGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hierGWAS/inst/doc/hierGWAS.R dependencyCount: 19 Package: hierinf Version: 1.29.0 Depends: R (>= 3.6.0) Imports: fmsb, glmnet, methods, parallel, stats Suggests: knitr, MASS, testthat License: GPL-3 | file LICENSE MD5sum: 1f7f0cdcbbc8035ad1873779db2a03c8 NeedsCompilation: no Title: Hierarchical Inference Description: Tools to perform hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised) linear models. A possible application is to perform hierarchical inference for GWA studies to find significant groups or single SNPs (if the signal is strong) in a data-driven and automated procedure. The method is based on an efficient hierarchical multiple testing correction and controls the FWER. The functions can easily be run in parallel. biocViews: Clustering, GenomeWideAssociation, LinkageDisequilibrium, Regression, SNP Author: Claude Renaux [aut, cre], Laura Buzdugan [aut], Markus Kalisch [aut], Peter Bühlmann [aut] Maintainer: Claude Renaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hierinf git_branch: devel git_last_commit: 6b86f16 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hierinf_1.29.0.tar.gz vignettes: vignettes/hierinf/inst/doc/vignette-hierinf.pdf vignetteTitles: vignette-hierinf.Rnw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hierinf/inst/doc/vignette-hierinf.R dependencyCount: 19 Package: HilbertCurve Version: 2.5.2 Depends: R (>= 4.0.0), grid Imports: methods, utils, png, grDevices, circlize (>= 0.3.3), IRanges, GenomicRanges, polylabelr, Rcpp LinkingTo: Rcpp Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.99.0), markdown, RColorBrewer, RCurl, GetoptLong, rmarkdown License: MIT + file LICENSE MD5sum: babcfc65f39cd9c8699c4172e9511887 NeedsCompilation: yes Title: Making 2D Hilbert Curve Description: Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve. biocViews: Software, Visualization, Sequencing, Coverage, GenomeAnnotation Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/HilbertCurve, https://jokergoo.github.io/HilbertCurve/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HilbertCurve git_branch: devel git_last_commit: 74fb1e2 git_last_commit_date: 2026-01-30 Date/Publication: 2026-04-20 source.ver: src/contrib/HilbertCurve_2.5.2.tar.gz vignettes: vignettes/HilbertCurve/inst/doc/HilbertCurve.html vignetteTitles: The HilbertCurve package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: InteractiveComplexHeatmap dependencyCount: 20 Package: HilbertVis Version: 1.69.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) MD5sum: e1149f3e6c75a4469cc7028cd8b183af NeedsCompilation: yes Title: Hilbert curve visualization Description: Functions to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert git_url: https://git.bioconductor.org/packages/HilbertVis git_branch: devel git_last_commit: 7dda7ff git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HilbertVis_1.69.0.tar.gz vignettes: vignettes/HilbertVis/inst/doc/HilbertVis.pdf vignetteTitles: Visualising very long data vectors with the Hilbert curve hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HilbertVis/inst/doc/HilbertVis.R dependsOnMe: HilbertVisGUI importsMe: ChIPseqR dependencyCount: 6 Package: HilbertVisGUI Version: 1.69.1 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) MD5sum: d74de9e4765d54df1ddb9e595b1d21c0 NeedsCompilation: yes Title: HilbertVisGUI Description: An interactive tool to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert SystemRequirements: gtkmm-2.4, GNU make git_url: https://git.bioconductor.org/packages/HilbertVisGUI git_branch: devel git_last_commit: fa90178 git_last_commit_date: 2026-03-28 Date/Publication: 2026-04-20 source.ver: src/contrib/HilbertVisGUI_1.69.1.tar.gz vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE dependencyCount: 7 Package: HiLDA Version: 1.25.0 Depends: R(>= 4.1), ggplot2 Imports: R2jags, abind, cowplot, grid, forcats, stringr, GenomicRanges, S4Vectors, XVector, Biostrings, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, BiocGenerics, tidyr, grDevices, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: a86afcd7854465b403516d9ff7036392 NeedsCompilation: yes Title: Conducting statistical inference on comparing the mutational exposures of mutational signatures by using hierarchical latent Dirichlet allocation Description: A package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation. It statistically tests whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. The package also provides inference and visualization. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Bayesian Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/HiLDA, https://doi.org/10.1101/577452 SystemRequirements: JAGS 4.0.0 VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/issues git_url: https://git.bioconductor.org/packages/HiLDA git_branch: devel git_last_commit: 0c7e75a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HiLDA_1.25.0.tar.gz vignettes: vignettes/HiLDA/inst/doc/HiLDA.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/HiLDA/inst/doc/HiLDA.R importsMe: selectKSigs dependencyCount: 106 Package: hipathia Version: 3.11.2 Depends: R (>= 4.1), igraph (>= 1.0.1), zen4R(>= 0.10.4), MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1) Imports: coin, stats, limma, grDevices, utils, graphics, preprocessCore, servr, DelayedArray, matrixStats, methods, S4Vectors, ggplot2, ggpubr, dplyr, tibble, visNetwork, reshape2, MetBrewer Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 182fe69835117ab20d20a8b1cf16adc4 NeedsCompilation: no Title: HiPathia: High-throughput Pathway Analysis Description: Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway. biocViews: Pathways, GraphAndNetwork, GeneExpression, GeneSignaling, GO Author: Marta R. Hidalgo [aut, cre], José Carbonell-Caballero [ctb], Francisco Salavert [ctb], Alicia Amadoz [ctb], Çankut Cubuk [ctb], Joaquin Dopazo [ctb] Maintainer: Marta R. Hidalgo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hipathia git_branch: devel git_last_commit: 2281546 git_last_commit_date: 2026-03-25 Date/Publication: 2026-04-20 source.ver: src/contrib/hipathia_3.11.2.tar.gz vignettes: vignettes/hipathia/inst/doc/hipathia-vignette.pdf vignetteTitles: Hipathia Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hipathia/inst/doc/hipathia-vignette.R dependencyCount: 155 Package: HIPPO Version: 1.23.0 Depends: R (>= 3.6.0) Imports: ggplot2, graphics, stats, reshape2, gridExtra, Rtsne, umap, dplyr, rlang, magrittr, irlba, Matrix, SingleCellExperiment, ggrepel Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 78964e3d832ef395ea0f7915325bee38 NeedsCompilation: no Title: Heterogeneity-Induced Pre-Processing tOol Description: For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering. biocViews: Sequencing, SingleCell, GeneExpression, DifferentialExpression, Clustering Author: Tae Kim [aut, cre], Mengjie Chen [aut] Maintainer: Tae Kim URL: https://github.com/tk382/HIPPO VignetteBuilder: knitr BugReports: https://github.com/tk382/HIPPO/issues git_url: https://git.bioconductor.org/packages/HIPPO git_branch: devel git_last_commit: dd797d4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HIPPO_1.23.0.tar.gz vignettes: vignettes/HIPPO/inst/doc/example.html vignetteTitles: Example analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIPPO/inst/doc/example.R dependencyCount: 72 Package: HIREewas Version: 1.29.0 Depends: R (>= 3.5.0) Imports: quadprog, gplots, grDevices, stats Suggests: BiocStyle, knitr, BiocGenerics License: GPL (>= 2) MD5sum: 2ec75eb8a25afbe03c37a312df86e475 NeedsCompilation: yes Title: Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies Description: In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. The "HIREewas" R package is to implement HIRE model in R. biocViews: DNAMethylation, DifferentialMethylation, FeatureExtraction Author: Xiangyu Luo , Can Yang , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIREewas git_branch: devel git_last_commit: ff8d945 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HIREewas_1.29.0.tar.gz vignettes: vignettes/HIREewas/inst/doc/HIREewas.pdf vignetteTitles: HIREewas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIREewas/inst/doc/HIREewas.R dependencyCount: 10 Package: HiSpaR Version: 0.99.6 Depends: R (>= 4.5.0) Imports: Rcpp (>= 1.0.0), utils, stats, Matrix, HiCExperiment LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, rgl, HiContactsData, HiContacts, plotly, callr License: MIT + file LICENSE MD5sum: 8da9b29d717301e8edaa176c754b0290 NeedsCompilation: yes Title: Hierarchical Inference of Spatial Positions from Hi-C Data Description: Provides R bindings for HiSpa, a hierarchical Bayesian model for inferring three-dimensional chromatin structures from Hi-C contact matrices using Markov Chain Monte Carlo (MCMC) sampling. The package implements a cluster-based hierarchical approach that efficiently handles large-scale Hi-C datasets. It uses Rcpp and RcppArmadillo for efficient C++ integration with the original HiSpa C++ implementation, enabling fast computation of chromatin structure inference through parallel MCMC sampling. biocViews: Software, Epigenetics, HiC, StructuralPrediction, Bayesian, Spatial Author: Yingcheng Luo [aut, cre] Maintainer: Yingcheng Luo URL: https://github.com/masterStormtrooper/HiSpaR SystemRequirements: C++17, GNU make, Armadillo (>= 9.0), OpenMP VignetteBuilder: knitr BugReports: https://github.com/masterStormtrooper/HiSpaR/issues git_url: https://git.bioconductor.org/packages/HiSpaR git_branch: devel git_last_commit: de0d420 git_last_commit_date: 2026-03-26 Date/Publication: 2026-04-20 source.ver: src/contrib/HiSpaR_0.99.6.tar.gz vignettes: vignettes/HiSpaR/inst/doc/getting-started.html vignetteTitles: Getting Started with HiSpaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiSpaR/inst/doc/getting-started.R dependencyCount: 69 Package: HiTC Version: 1.55.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix, parallel, Seqinfo Suggests: BiocStyle, HiCDataHumanIMR90, BSgenome.Hsapiens.UCSC.hg18 License: Artistic-2.0 MD5sum: 17895039419a2e115fa7f02d191183a1 NeedsCompilation: no Title: High Throughput Chromosome Conformation Capture analysis Description: The HiTC package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided. biocViews: Sequencing, HighThroughputSequencing, HiC Author: Nicolas Servant Maintainer: Nicolas Servant git_url: https://git.bioconductor.org/packages/HiTC git_branch: devel git_last_commit: c174dd2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HiTC_1.55.0.tar.gz vignettes: vignettes/HiTC/inst/doc/HiC_analysis.pdf, vignettes/HiTC/inst/doc/HiTC.pdf vignetteTitles: Hi-C data analysis using HiTC, Introduction to HiTC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiTC/inst/doc/HiC_analysis.R, vignettes/HiTC/inst/doc/HiTC.R suggestsMe: HiCDCPlus, HiCDataHumanIMR90, adjclust dependencyCount: 58 Package: HMMcopy Version: 1.53.0 Depends: R (>= 2.10.0), data.table (>= 1.11.8) License: GPL-3 MD5sum: c38d39aced974c47e3c958c8a0f6c8fd NeedsCompilation: yes Title: Copy number prediction with correction for GC and mappability bias for HTS data Description: Corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for furthur analysis. Designed for rapid correction of high coverage whole genome tumour and normal samples. biocViews: Sequencing, Preprocessing, Visualization, CopyNumberVariation, Microarray Author: Daniel Lai, Gavin Ha, Sohrab Shah Maintainer: Daniel Lai git_url: https://git.bioconductor.org/packages/HMMcopy git_branch: devel git_last_commit: bd66926 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HMMcopy_1.53.0.tar.gz vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf vignetteTitles: HMMcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HMMcopy/inst/doc/HMMcopy.R importsMe: qsea dependencyCount: 2 Package: HoloFoodR Version: 1.5.0 Depends: R(>= 4.4.0), MultiAssayExperiment, TreeSummarizedExperiment Imports: dplyr, httr2, jsonlite, S4Vectors, stringi, stats, SummarizedExperiment, utils Suggests: BiocStyle, DT, ggh4x, ggsignif, knitr, MGnifyR, mia, miaViz, MOFA2, patchwork, reticulate, rmarkdown, scater, shadowtext, testthat, UpSetR License: Artistic-2.0 | file LICENSE MD5sum: edb1f50113121304bb582e951c54be02 NeedsCompilation: no Title: R interface to EBI HoloFood resource Description: Utility package to facilitate integration and analysis of EBI HoloFood data in R. This package streamlines access to the resource, allowing for direct loading of data into formats optimized for downstream analytics. biocViews: Software, Infrastructure, DataImport, Microbiome, MicrobiomeData Author: Tuomas Borman [aut, cre] (ORCID: ), Artur Sannikov [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ) Maintainer: Tuomas Borman URL: https://github.com/EBI-Metagenomics/HoloFoodR VignetteBuilder: knitr BugReports: https://github.com/EBI-Metagenomics/HoloFoodR/issues git_url: https://git.bioconductor.org/packages/HoloFoodR git_branch: devel git_last_commit: 53c2191 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HoloFoodR_1.5.0.tar.gz vignettes: vignettes/HoloFoodR/inst/doc/HoloFoodR.html vignetteTitles: HoloFoodR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HoloFoodR/inst/doc/HoloFoodR.R dependencyCount: 75 Package: hoodscanR Version: 1.9.0 Depends: R (>= 4.3) Imports: knitr, rmarkdown, SpatialExperiment, SummarizedExperiment, circlize, ComplexHeatmap, scico, rlang, utils, ggplot2, grid, methods, stats, RANN, Rcpp (>= 1.0.9) LinkingTo: Rcpp Suggests: testthat (>= 3.0.0), BiocStyle License: GPL-3 + file LICENSE MD5sum: 79e66dfed51f325e5233bc0d1052d521 NeedsCompilation: yes Title: Spatial cellular neighbourhood scanning in R Description: hoodscanR is an user-friendly R package providing functions to assist cellular neighborhood analysis of any spatial transcriptomics data with single-cell resolution. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. The package can result in cell-level neighborhood annotation output, along with funtions to perform neighborhood colocalization analysis and neighborhood-based cell clustering. biocViews: Spatial, Transcriptomics, SingleCell, Clustering Author: Ning Liu [aut, cre] (ORCID: ), Jarryd Martin [aut] Maintainer: Ning Liu URL: https://github.com/DavisLaboratory/hoodscanR, https://davislaboratory.github.io/hoodscanR/ VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/hoodscanR/issues git_url: https://git.bioconductor.org/packages/hoodscanR git_branch: devel git_last_commit: fbbd399 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hoodscanR_1.9.0.tar.gz vignettes: vignettes/hoodscanR/inst/doc/Quick_start.html vignetteTitles: hoodscanR_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hoodscanR/inst/doc/Quick_start.R importsMe: OSTA dependencyCount: 109 Package: hopach Version: 2.71.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) MD5sum: 24fa8e921d735a89eb272e0d39bb958d NeedsCompilation: yes Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH) Description: The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). biocViews: Clustering Author: Katherine S. Pollard, with Mark J. van der Laan and Greg Wall Maintainer: Katherine S. Pollard URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/ git_url: https://git.bioconductor.org/packages/hopach git_branch: devel git_last_commit: b2873e5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hopach_2.71.0.tar.gz vignettes: vignettes/hopach/inst/doc/hopach.pdf vignetteTitles: hopach hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hopach/inst/doc/hopach.R importsMe: phenoTest, scClassify, treekoR suggestsMe: MicrobiotaProcess dependencyCount: 9 Package: HPAanalyze Version: 1.29.1 Depends: R (>= 3.5.0) Imports: dplyr, openxlsx, ggplot2, tibble, xml2, stats, utils, gridExtra Suggests: knitr, rmarkdown, devtools, BiocStyle License: GPL-3 + file LICENSE MD5sum: 492d05eb17948042f1526549dc010535 NeedsCompilation: no Title: Retrieve and analyze data from the Human Protein Atlas Description: Provide functions for retrieving, exploratory analyzing and visualizing the Human Protein Atlas data. HPAanalyze is designed to fullfill 3 main tasks: (1) Import, subsetting and export downloadable datasets; (2) Visualization of downloadable datasets for exploratory analysis; and (3) Working with the individual XML files. This package aims to serve researchers with little programming experience, but also allow power users to use the imported data as desired. biocViews: Proteomics, CellBiology, Visualization, Software Author: Anh Nhat Tran [aut, cre] Maintainer: Anh Nhat Tran URL: https://github.com/anhtr/HPAanalyze VignetteBuilder: knitr BugReports: https://github.com/anhtr/HPAanalyze/issues git_url: https://git.bioconductor.org/packages/HPAanalyze git_branch: devel git_last_commit: 2d4c3f2 git_last_commit_date: 2025-12-07 Date/Publication: 2026-04-20 source.ver: src/contrib/HPAanalyze_1.29.1.tar.gz vignettes: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.html, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.html, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.html, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.html, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.html, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.html, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.html vignetteTitles: "1. Quick-start guide: Acquire and visualize the Human Protein Atlas (HPA) data in one function with HPAanalyze", "2. In-depth: Working with Human Protein Atlas (HPA) data in R with HPAanalyze", "3. Tutorial: Combine HPAanalyze with your Human Protein Atlas (HPA) queries", "4. Tutorial: Working with Human Protein Atlas (HPA) xml files offline", "5. Tutorial: Export Human Protein Atlas (HPA) data as JSON", "6. Tutorial: Download histology images from the Human Protein Atlas", "99. Code for figures from HPAanalyze paper" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.R, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.R, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.R, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.R, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.R, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.R, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.R dependencyCount: 38 Package: hpar Version: 1.53.0 Depends: R (>= 3.5.0) Imports: utils, ExperimentHub Suggests: org.Hs.eg.db, GO.db, AnnotationDbi, knitr, BiocStyle, testthat, rmarkdown, dplyr, DT License: Artistic-2.0 MD5sum: b006cfafda86ec8e1d8172922b0cdb73 NeedsCompilation: no Title: Human Protein Atlas in R Description: The hpar package provides a simple R interface to and data from the Human Protein Atlas project. biocViews: Proteomics, CellBiology, DataImport, FunctionalGenomics, SystemsBiology, ExperimentHubSoftware Author: Laurent Gatto [cre, aut] (ORCID: ), Manon Martin [aut] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hpar git_branch: devel git_last_commit: 55567a4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hpar_1.53.0.tar.gz vignettes: vignettes/hpar/inst/doc/hpar.html vignetteTitles: Human Protein Atlas in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hpar/inst/doc/hpar.R importsMe: MetaboSignal suggestsMe: pRoloc, RforProteomics dependencyCount: 64 Package: HPiP Version: 1.17.2 Depends: R (>= 4.1) Imports: dplyr (>= 1.0.6), httr (>= 1.4.2), readr, tidyr, tibble, utils, stringr, magrittr, caret, corrplot, ggplot2, pROC, PRROC, igraph, graphics, stats, purrr, grDevices, protr, MCL Suggests: rmarkdown, colorspace, e1071, kernlab, ranger, SummarizedExperiment, Biostrings, randomForest, gprofiler2, gridExtra, ggthemes, BiocStyle, BiocGenerics, RUnit, tools, knitr License: MIT + file LICENSE MD5sum: 0eac8dbc1812bfea3080330ab094e31e NeedsCompilation: no Title: Host-Pathogen Interaction Prediction Description: HPiP (Host-Pathogen Interaction Prediction) uses an ensemble learning algorithm for prediction of host-pathogen protein-protein interactions (HP-PPIs) using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins.The proposed package can effectively address data shortages and data unavailability for HP-PPI network reconstructions. Moreover, establishing computational frameworks in that regard will reveal mechanistic insights into infectious diseases and suggest potential HP-PPI targets, thus narrowing down the range of possible candidates for subsequent wet-lab experimental validations. biocViews: Proteomics, SystemsBiology, NetworkInference, StructuralPrediction, GenePrediction, Network Author: Matineh Rahmatbakhsh [aut, trl, cre], Mohan Babu [led] Maintainer: Matineh Rahmatbakhsh URL: https://github.com/mrbakhsh/HPiP VignetteBuilder: knitr BugReports: https://github.com/mrbakhsh/HPiP/issues git_url: https://git.bioconductor.org/packages/HPiP git_branch: devel git_last_commit: 68bd9a1 git_last_commit_date: 2025-12-28 Date/Publication: 2026-04-20 source.ver: src/contrib/HPiP_1.17.2.tar.gz vignettes: vignettes/HPiP/inst/doc/HPiP_tutorial.html vignetteTitles: Introduction to HPiP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HPiP/inst/doc/HPiP_tutorial.R dependencyCount: 105 Package: HTqPCR Version: 1.65.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 MD5sum: ae9b1844852f692aaaebb5fc72f5ef5d NeedsCompilation: no Title: Automated analysis of high-throughput qPCR data Description: Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs). biocViews: MicrotitrePlateAssay, DifferentialExpression, GeneExpression, DataImport, QualityControl, Preprocessing, Visualization, MultipleComparison, qPCR Author: Heidi Dvinge, Paul Bertone Maintainer: Matthew N. McCall URL: http://www.ebi.ac.uk/bertone/software git_url: https://git.bioconductor.org/packages/HTqPCR git_branch: devel git_last_commit: dad1902 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HTqPCR_1.65.0.tar.gz vignettes: vignettes/HTqPCR/inst/doc/HTqPCR.pdf vignetteTitles: qPCR analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTqPCR/inst/doc/HTqPCR.R importsMe: nondetects dependencyCount: 21 Package: HTSFilter Version: 1.51.0 Depends: R (>= 4.0.0) Imports: edgeR, DESeq2, BiocParallel, Biobase, utils, stats, grDevices, graphics, methods Suggests: EDASeq, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: a880f142051080985fbe88634b5f8c19 NeedsCompilation: no Title: Filter replicated high-throughput transcriptome sequencing data Description: This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions. biocViews: Sequencing, RNASeq, Preprocessing, DifferentialExpression, GeneExpression, Normalization, ImmunoOncology Author: Andrea Rau [cre, aut] (ORCID: ), Melina Gallopin [ctb], Gilles Celeux [ctb], Florence Jaffrézic [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HTSFilter git_branch: devel git_last_commit: 675388a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HTSFilter_1.51.0.tar.gz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.html vignetteTitles: HTSFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R importsMe: coseq suggestsMe: HTSCluster, inDAGO dependencyCount: 58 Package: HuBMAPR Version: 1.5.1 Depends: R (>= 4.4.0) Imports: httr2, dplyr, tidyr, tibble, rjsoncons, utils, stringr, whisker, purrr, rlang Suggests: testthat (>= 3.0.0), knitr, ggplot2, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: c7afed4d727cb7008c03fe71a28c4105 NeedsCompilation: no Title: Interface to 'HuBMAP' Description: 'HuBMAP' provides an open, global bio-molecular atlas of the human body at the cellular level. The `datasets()`, `samples()`, `donors()`, `publications()`, and `collections()` functions retrieves the information for each of these entity types. `*_details()` are available for individual entries of each entity type. `*_derived()` are available for retrieving derived datasets or samples for individual entries of each entity type. Data files can be accessed using `bulk_data_transfer()`. biocViews: Software, SingleCell, DataImport, ThirdPartyClient, Spatial, Infrastructure Author: Christine Hou [aut, cre] (ORCID: ), Martin Morgan [aut] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Christine Hou URL: https://christinehou11.github.io/HuBMAPR/, https://github.com/christinehou11/HuBMAPR VignetteBuilder: knitr BugReports: https://github.com/christinehou11/HuBMAPR/issues git_url: https://git.bioconductor.org/packages/HuBMAPR git_branch: devel git_last_commit: d489fca git_last_commit_date: 2026-04-06 Date/Publication: 2026-04-20 source.ver: src/contrib/HuBMAPR_1.5.1.tar.gz vignettes: vignettes/HuBMAPR/inst/doc/hubmapr_vignettes.html vignetteTitles: Accessing Human Cell Atlas Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HuBMAPR/inst/doc/hubmapr_vignettes.R dependencyCount: 34 Package: HubPub Version: 1.19.0 Imports: available, usethis, biocthis, dplyr, aws.s3, fs, BiocManager, utils Suggests: AnnotationHubData, ExperimentHubData, GenomeInfoDbData, testthat, knitr, rmarkdown, BiocStyle, License: Artistic-2.0 MD5sum: b281ed498ce4a5596088f243289ad336 NeedsCompilation: no Title: Utilities to create and use Bioconductor Hubs Description: HubPub provides users with functionality to help with the Bioconductor Hub structures. The package provides the ability to create a skeleton of a Hub style package that the user can then populate with the necessary information. There are also functions to help add resources to the Hub package metadata files as well as publish data to the Bioconductor S3 bucket. biocViews: DataImport, Infrastructure, Software, ThirdPartyClient Author: Kayla Interdonato [aut, cre], Martin Morgan [aut], Lori Shepherd [ctb] Maintainer: Kayla Interdonato VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HubPub/issues git_url: https://git.bioconductor.org/packages/HubPub git_branch: devel git_last_commit: 78b6e76 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HubPub_1.19.0.tar.gz vignettes: vignettes/HubPub/inst/doc/CreateAHubPackage.html, vignettes/HubPub/inst/doc/HubPub.html vignetteTitles: Creating A Hub Package: ExperimentHub or AnnotationHub, HubPub: Help with publication of Hub packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HubPub/inst/doc/CreateAHubPackage.R, vignettes/HubPub/inst/doc/HubPub.R suggestsMe: AnnotationHub, AnnotationHubData, ExperimentHub, ExperimentHubData dependencyCount: 76 Package: hummingbird Version: 1.21.0 Depends: R (>= 4.0) Imports: Rcpp, graphics, GenomicRanges, SummarizedExperiment, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=2) MD5sum: a6a258513629819009e464c59f1918ec NeedsCompilation: yes Title: Bayesian Hidden Markov Model for the detection of differentially methylated regions Description: A package for detecting differential methylation. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations. Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance. At last, our methodology does not require replication in either or both of the two comparison groups. biocViews: HiddenMarkovModel, Bayesian, DNAMethylation, BiomedicalInformatics, Sequencing, GeneExpression, DifferentialExpression, DifferentialMethylation Author: Eleni Adam [aut, cre], Tieming Ji [aut], Desh Ranjan [aut] Maintainer: Eleni Adam VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hummingbird git_branch: devel git_last_commit: dd35632 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hummingbird_1.21.0.tar.gz vignettes: vignettes/hummingbird/inst/doc/hummingbird.html vignetteTitles: hummingbird hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hummingbird/inst/doc/hummingbird.R dependencyCount: 26 Package: HVP Version: 1.1.0 Imports: Matrix, methods, stats Suggests: SingleCellExperiment, SummarizedExperiment, Seurat, SeuratObject, ggplot2, progress, testthat, splatter, scater, devtools, knitr, rmarkdown, BiocStyle, ExperimentHub License: MIT + file LICENSE MD5sum: 70d8491ef003b28b60ecbb1b562b7650 NeedsCompilation: no Title: Hierarchical Variance Partitioning Description: HVP is a quantitative batch effect metric that estimates the proportion of variance associated with batch effects in a data set. biocViews: SingleCell, Transcriptomics, GeneExpression, BatchEffect Author: Wei Xin Chan [aut, cre] (ORCID: ) Maintainer: Wei Xin Chan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HVP git_branch: devel git_last_commit: e1749af git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HVP_1.1.0.tar.gz vignettes: vignettes/HVP/inst/doc/HVP.html vignetteTitles: Quantifying batch effects with HVP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HVP/inst/doc/HVP.R dependencyCount: 8 Package: HybridExpress Version: 1.7.0 Depends: R (>= 4.3.0) Imports: ggplot2, patchwork, rlang, DESeq2, SummarizedExperiment, stats, methods, RColorBrewer, ComplexHeatmap, grDevices, BiocParallel Suggests: BiocStyle, knitr, sessioninfo, testthat (>= 3.0.0) License: GPL-3 MD5sum: f3051a0f524976ebccfa37e597592bb7 NeedsCompilation: no Title: Comparative analysis of RNA-seq data for hybrids and their progenitors Description: HybridExpress can be used to perform comparative transcriptomics analysis of hybrids (or allopolyploids) relative to their progenitor species. The package features functions to perform exploratory analyses of sample grouping, identify differentially expressed genes in hybrids relative to their progenitors, classify genes in expression categories (N = 12) and classes (N = 5), and perform functional analyses. We also provide users with graphical functions for the seamless creation of publication-ready figures that are commonly used in the literature. biocViews: Software, FunctionalGenomics, GeneExpression, Transcriptomics, RNASeq, Classification, DifferentialExpression Author: Fabricio Almeida-Silva [aut, cre] (ORCID: ), Lucas Prost-Boxoen [aut] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabricio Almeida-Silva URL: https://github.com/almeidasilvaf/HybridExpress VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/HybridExpress git_url: https://git.bioconductor.org/packages/HybridExpress git_branch: devel git_last_commit: 0f48849 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HybridExpress_1.7.0.tar.gz vignettes: vignettes/HybridExpress/inst/doc/HybridExpress.html vignetteTitles: Comparative transcriptomic analysis of hybrids and their progenitors hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridExpress/inst/doc/HybridExpress.R dependencyCount: 71 Package: HybridMTest Version: 1.55.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: 0a2ddff4621a0268d81355ff08c83d3d NeedsCompilation: no Title: Hybrid Multiple Testing Description: Performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using empirical Bayes probability (EBP) estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weigth. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs. biocViews: GeneExpression, Genetics, Microarray Author: Stan Pounds , Demba Fofana Maintainer: Demba Fofana git_url: https://git.bioconductor.org/packages/HybridMTest git_branch: devel git_last_commit: 5c8ba0e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/HybridMTest_1.55.0.tar.gz vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf vignetteTitles: Hybrid Multiple Testing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridMTest/inst/doc/HybridMTest.R suggestsMe: APAlyzer dependencyCount: 15 Package: hypeR Version: 2.9.2 Depends: R (>= 3.6.0) Imports: ggplot2, ggforce, R6, magrittr, dplyr, purrr, stats, stringr, scales, rlang, httr, openxlsx, htmltools, reshape2, reactable, msigdbr, kableExtra, rmarkdown, igraph, visNetwork, shiny, BiocStyle Suggests: tidyverse, devtools, testthat, knitr License: GPL-3 + file LICENSE MD5sum: 64ebd52ddcb38f953b37763f753a23b2 NeedsCompilation: no Title: An R Package For Geneset Enrichment Workflows Description: An R Package for Geneset Enrichment Workflows. biocViews: GeneSetEnrichment, Annotation, Pathways Author: Anthony Federico [aut], Andrew Chen [aut, cre], Stefano Monti [aut] Maintainer: Andrew Chen URL: https://github.com/montilab/hypeR VignetteBuilder: knitr BugReports: https://github.com/montilab/hypeR/issues git_url: https://git.bioconductor.org/packages/hypeR git_branch: devel git_last_commit: fd8fff7 git_last_commit_date: 2025-12-01 Date/Publication: 2026-04-20 source.ver: src/contrib/hypeR_2.9.2.tar.gz vignettes: vignettes/hypeR/inst/doc/hypeR.html vignetteTitles: hypeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hypeR/inst/doc/hypeR.R dependencyCount: 97 Package: hyperdraw Version: 1.63.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: 3f31fd162ba6431efcee641d8a8441e2 NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz git_url: https://git.bioconductor.org/packages/hyperdraw git_branch: devel git_last_commit: 5f06f53 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hyperdraw_1.63.0.tar.gz vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf vignetteTitles: Hyperdraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hyperdraw/inst/doc/hyperdraw.R dependencyCount: 12 Package: hypergraph Version: 1.83.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 78b7e05e670700f8d861049d2c942750 NeedsCompilation: no Title: A package providing hypergraph data structures Description: A package that implements some simple capabilities for representing and manipulating hypergraphs. biocViews: GraphAndNetwork Author: Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/hypergraph git_branch: devel git_last_commit: febfb63 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/hypergraph_1.83.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs importsMe: hyperdraw dependencyCount: 8 Package: iASeq Version: 1.55.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 760991a312c667e6f20f687656194058 NeedsCompilation: no Title: iASeq: integrating multiple sequencing datasets for detecting allele-specific events Description: It fits correlation motif model to multiple RNAseq or ChIPseq studies to improve detection of allele-specific events and describe correlation patterns across studies. biocViews: ImmunoOncology, SNP, RNASeq, ChIPSeq Author: Yingying Wei, Hongkai Ji Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/iASeq git_branch: devel git_last_commit: b35bc1a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iASeq_1.55.0.tar.gz vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf vignetteTitles: iASeq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iASeq/inst/doc/iASeqVignette.R dependencyCount: 2 Package: iasva Version: 1.29.0 Depends: R (>= 3.5), Imports: irlba, stats, cluster, graphics, SummarizedExperiment, BiocParallel Suggests: knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot, DescTools, RColorBrewer License: GPL-2 MD5sum: 13d516eb4997e1aa943ba913c2b34c7a NeedsCompilation: no Title: Iteratively Adjusted Surrogate Variable Analysis Description: Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors. biocViews: Preprocessing, QualityControl, BatchEffect, RNASeq, Software, StatisticalMethod, FeatureExtraction, ImmunoOncology Author: Donghyung Lee [aut, cre], Anthony Cheng [aut], Nathan Lawlor [aut], Duygu Ucar [aut] Maintainer: Donghyung Lee , Anthony Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iasva git_branch: devel git_last_commit: b521934 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iasva_1.29.0.tar.gz vignettes: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.html vignetteTitles: "Detecting hidden heterogeneity in single cell RNA-Seq data" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.R dependencyCount: 37 Package: ibh Version: 1.59.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: d1ee9bc86c3d925c594e7268485389e6 NeedsCompilation: no Title: Interaction Based Homogeneity for Evaluating Gene Lists Description: This package contains methods for calculating Interaction Based Homogeneity to evaluate fitness of gene lists to an interaction network which is useful for evaluation of clustering results and gene list analysis. BioGRID interactions are used in the calculation. The user can also provide their own interactions. biocViews: QualityControl, DataImport, GraphAndNetwork, NetworkEnrichment Author: Kircicegi Korkmaz, Volkan Atalay, Rengul Cetin Atalay. Maintainer: Kircicegi Korkmaz git_url: https://git.bioconductor.org/packages/ibh git_branch: devel git_last_commit: 04c0df4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ibh_1.59.0.tar.gz vignettes: vignettes/ibh/inst/doc/ibh.pdf vignetteTitles: ibh hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ibh/inst/doc/ibh.R dependencyCount: 1 Package: iBMQ Version: 1.51.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 MD5sum: 2ca34beea93d3386d1b4c4ad85f0d259 NeedsCompilation: yes Title: integrated Bayesian Modeling of eQTL data Description: integrated Bayesian Modeling of eQTL data biocViews: Microarray, Preprocessing, GeneExpression, SNP Author: Marie-Pier Scott-Boyer and Greg Imholte Maintainer: Greg Imholte URL: http://www.rglab.org SystemRequirements: GSL and OpenMP git_url: https://git.bioconductor.org/packages/iBMQ git_branch: devel git_last_commit: 2683ae2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iBMQ_1.51.0.tar.gz vignettes: vignettes/iBMQ/inst/doc/iBMQ.pdf vignetteTitles: iBMQ: An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBMQ/inst/doc/iBMQ.R dependencyCount: 26 Package: iCARE Version: 1.39.0 Depends: R (>= 3.3.0), plotrix, gtools, Hmisc Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE MD5sum: e7654f3b2271a934cd4f0a710b8ad8c6 NeedsCompilation: yes Title: Individualized Coherent Absolute Risk Estimation (iCARE) Description: An R package to build, validate and apply absolute risk models biocViews: Software, StatisticalMethod, GenomeWideAssociation Author: Parichoy Pal Choudhury, Paige Maas, William Wheeler, Nilanjan Chatterjee Maintainer: Parichoy Pal Choudhury git_url: https://git.bioconductor.org/packages/iCARE git_branch: devel git_last_commit: 5d81f64 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iCARE_1.39.0.tar.gz vignettes: vignettes/iCARE/inst/doc/vignette_model_validation.pdf, vignettes/iCARE/inst/doc/vignette.pdf vignetteTitles: iCARE Vignette Model Validation, iCARE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iCARE/inst/doc/vignette_model_validation.R, vignettes/iCARE/inst/doc/vignette.R dependencyCount: 64 Package: Icens Version: 1.83.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: fe5210ef1cfa014c455abec9e1080eed NeedsCompilation: no Title: NPMLE for Censored and Truncated Data Description: Many functions for computing the NPMLE for censored and truncated data. biocViews: Infrastructure Author: R. Gentleman and Alain Vandal Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Icens git_branch: devel git_last_commit: 572deff git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Icens_1.83.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess, icensBKL, interval importsMe: PROcess suggestsMe: LTRCtrees, ReIns dependencyCount: 10 Package: icetea Version: 1.29.0 Depends: R (>= 4.0) Imports: stats, utils, methods, graphics, grDevices, ggplot2, GenomicFeatures, ShortRead, BiocParallel, Biostrings, S4Vectors, Rsamtools, BiocGenerics, IRanges, GenomicAlignments, GenomicRanges, rtracklayer, SummarizedExperiment, VariantAnnotation, limma, edgeR, csaw, DESeq2, TxDb.Dmelanogaster.UCSC.dm6.ensGene Suggests: GenomeInfoDb, knitr, rmarkdown, Rsubread (>= 1.29.0), testthat License: GPL-3 + file LICENSE MD5sum: af8ee62a4f3215f0e4ccc9c505365777 NeedsCompilation: no Title: Integrating Cap Enrichment with Transcript Expression Analysis Description: icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis. biocViews: ImmunoOncology, Transcription, GeneExpression, Sequencing, RNASeq, Transcriptomics, DifferentialExpression Author: Vivek Bhardwaj [aut, cre] Maintainer: Vivek Bhardwaj URL: https://github.com/vivekbhr/icetea VignetteBuilder: knitr BugReports: https://github.com/vivekbhr/icetea/issues git_url: https://git.bioconductor.org/packages/icetea git_branch: devel git_last_commit: 2bfbdfd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/icetea_1.29.0.tar.gz vignettes: vignettes/icetea/inst/doc/mapcap_analysis.html vignetteTitles: Analysing transcript 5'-profiling data using icetea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/icetea/inst/doc/mapcap_analysis.R dependencyCount: 106 Package: iChip Version: 1.65.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) MD5sum: b7b0d943e4f08295aba4cab26b710337 NeedsCompilation: yes Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models Description: Hidden Ising models are implemented to identify enriched genomic regions in ChIP-chip data. They can be used to analyze the data from multiple platforms (e.g., Affymetrix, Agilent, and NimbleGen), and the data with single to multiple replicates. biocViews: ChIPchip, OneChannel, AgilentChip, Microarray Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iChip git_branch: devel git_last_commit: 25217b9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iChip_1.65.0.tar.gz vignettes: vignettes/iChip/inst/doc/iChip.pdf vignetteTitles: iChip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iChip/inst/doc/iChip.R dependencyCount: 7 Package: iClusterPlus Version: 1.47.3 Depends: R (>= 4.1.0), irlba, parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: f8324c140d9a1f7ce6f742429906d7bd NeedsCompilation: yes Title: Integrative clustering of multi-type genomic data Description: Integrative clustering of multiple genomic data using a joint latent variable model. biocViews: Multi-omics, Clustering Author: Qianxing Mo, Ronglai Shen Maintainer: Qianxing Mo , Ronglai Shen git_url: https://git.bioconductor.org/packages/iClusterPlus git_branch: devel git_last_commit: 954aabd git_last_commit_date: 2026-03-20 Date/Publication: 2026-04-20 source.ver: src/contrib/iClusterPlus_1.47.3.tar.gz vignettes: vignettes/iClusterPlus/inst/doc/iClusterPlus.pdf, vignettes/iClusterPlus/inst/doc/iManual.pdf, vignettes/iClusterPlus/inst/doc/Tutorial_iCluster2b_singleCell.html, vignettes/iClusterPlus/inst/doc/Tutorial_iClusterBayes2_GBM.html, vignettes/iClusterPlus/inst/doc/Tutorial_iClusterPlus2_UM.html vignetteTitles: iClusterPlus, iManual.pdf, iCluster analysis of single cell multi-omics data, iCluster analysis of glioblastoma multiforme multi-omics data, iCluster analysis of uveal melanoma multi-omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MultiDataSet dependencyCount: 10 Package: iCNV Version: 1.31.0 Depends: R (>= 3.3.1), CODEX Imports: fields, ggplot2, truncnorm, tidyr, data.table, dplyr, grDevices, graphics, stats, utils, rlang Suggests: knitr, rmarkdown, WES.1KG.WUGSC License: GPL-2 MD5sum: 248fc0e45b44fb80cc98669b789c01b6 NeedsCompilation: no Title: Integrated Copy Number Variation detection Description: Integrative copy number variation (CNV) detection from multiple platform and experimental design. biocViews: ImmunoOncology, ExomeSeq, WholeGenome, SNP, CopyNumberVariation, HiddenMarkovModel Author: Zilu Zhou, Nancy Zhang Maintainer: Zilu Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iCNV git_branch: devel git_last_commit: f950001 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iCNV_1.31.0.tar.gz vignettes: vignettes/iCNV/inst/doc/iCNV-vignette.html vignetteTitles: iCNV Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCNV/inst/doc/iCNV-vignette.R dependencyCount: 95 Package: iCOBRA Version: 1.39.2 Depends: R (>= 4.4.0) Imports: shiny (>= 0.9.1.9008), shinydashboard, reshape2, ggplot2 (>= 3.4.0), scales, ROCR, dplyr, DT, limma, methods, UpSetR, utils, rlang, prompter Suggests: knitr, markdown, rmarkdown, testthat License: GPL (>=2) MD5sum: 5eea59305828f79072f0ace6c55b7002 NeedsCompilation: no Title: Comparison and Visualization of Ranking and Assignment Methods Description: This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. Various types of performance plots can be generated programmatically. The package also contains a shiny application for interactive exploration of results. biocViews: Classification, Visualization Author: Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://csoneson.github.io/iCOBRA/ VignetteBuilder: knitr BugReports: https://github.com/csoneson/iCOBRA/issues git_url: https://git.bioconductor.org/packages/iCOBRA git_branch: devel git_last_commit: ca78dfe git_last_commit_date: 2025-12-13 Date/Publication: 2026-04-20 source.ver: src/contrib/iCOBRA_1.39.2.tar.gz vignettes: vignettes/iCOBRA/inst/doc/iCOBRA.html vignetteTitles: iCOBRA User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R suggestsMe: muscat dependencyCount: 82 Package: IdeoViz Version: 1.47.0 Depends: R (>= 3.5.0), Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer, graphics, GenomeInfoDb License: GPL-2 MD5sum: 5dfebac95954ac7b875ee494c67ca867 NeedsCompilation: no Title: Plots data (continuous/discrete) along chromosomal ideogram Description: Plots data associated with arbitrary genomic intervals along chromosomal ideogram. biocViews: Visualization,Microarray Author: Shraddha Pai , Jingliang Ren Maintainer: Shraddha Pai git_url: https://git.bioconductor.org/packages/IdeoViz git_branch: devel git_last_commit: 847e77c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IdeoViz_1.47.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 60 Package: idiogram Version: 1.87.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: 8d877e2fe446929c1ba824f012aea475 NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema git_url: https://git.bioconductor.org/packages/idiogram git_branch: devel git_last_commit: 495914b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/idiogram_1.87.0.tar.gz vignettes: vignettes/idiogram/inst/doc/idiogram.pdf vignetteTitles: HOWTO: idiogram hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idiogram/inst/doc/idiogram.R dependencyCount: 46 Package: idpr Version: 1.21.0 Depends: R (>= 4.1.0) Imports: ggplot2 (>= 3.3.0), magrittr (>= 1.5), dplyr (>= 0.8.5), plyr (>= 1.8.6), jsonlite (>= 1.6.1), rlang (>= 0.4.6), Biostrings (>= 2.56.0), methods (>= 4.0.0) Suggests: knitr, rmarkdown, pwalign, msa, ape, testthat, seqinr License: LGPL (>= 3) MD5sum: 9aab78e94a403499b280db4aac8fd270 NeedsCompilation: no Title: Profiling and Analyzing Intrinsically Disordered Proteins in R Description: ‘idpr’ aims to integrate tools for the computational analysis of intrinsically disordered proteins (IDPs) within R. This package is used to identify known characteristics of IDPs for a sequence of interest with easily reported and dynamic results. Additionally, this package includes tools for IDP-based sequence analysis to be used in conjunction with other R packages. Described in McFadden WM & Yanowitz JL (2022). "idpr: A package for profiling and analyzing Intrinsically Disordered Proteins in R." PloS one, 17(4), e0266929. . biocViews: StructuralPrediction, Proteomics, CellBiology Author: William M. McFadden [cre, aut], Judith L. Yanowitz [aut, fnd], Michael Buszczak [ctb, fnd] Maintainer: William M. McFadden VignetteBuilder: knitr BugReports: https://github.com/wmm27/idpr/issues git_url: https://git.bioconductor.org/packages/idpr git_branch: devel git_last_commit: 2d2d99d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/idpr_1.21.0.tar.gz vignettes: vignettes/idpr/inst/doc/chargeHydropathy-vignette.html, vignettes/idpr/inst/doc/disorderedMatrices-vignette.html, vignettes/idpr/inst/doc/idpr-vignette.html, vignettes/idpr/inst/doc/iupred-vignette.html, vignettes/idpr/inst/doc/sequenceMAP-vignette.html, vignettes/idpr/inst/doc/structuralTendency-vignette.html vignetteTitles: Charge and Hydropathy Vignette, Disordered Matrices Vignette, idpr Package Overview Vignette, IUPred Vignette, Sequence Map Vignette, Structural Tendency Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idpr/inst/doc/chargeHydropathy-vignette.R, vignettes/idpr/inst/doc/disorderedMatrices-vignette.R, vignettes/idpr/inst/doc/idpr-vignette.R, vignettes/idpr/inst/doc/iupred-vignette.R, vignettes/idpr/inst/doc/sequenceMAP-vignette.R, vignettes/idpr/inst/doc/structuralTendency-vignette.R dependencyCount: 43 Package: idr2d Version: 1.25.0 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.6), futile.logger (>= 1.4.3), GenomeInfoDb (>= 1.14.0), GenomicRanges (>= 1.30), ggplot2 (>= 3.1.1), grDevices, grid, idr (>= 1.2), IRanges (>= 2.18.0), magrittr (>= 1.5), methods, reticulate (>= 1.13), scales (>= 1.0.0), stats, stringr (>= 1.3.1), utils Suggests: DT (>= 0.4), htmltools (>= 0.3.6), knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: b69c76bc1f41425a9fd557953ec3e6b2 NeedsCompilation: no Title: Irreproducible Discovery Rate for Genomic Interactions Data Description: A tool to measure reproducibility between genomic experiments that produce two-dimensional peaks (interactions between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an extension of the original idr package, which is intended for (one-dimensional) ChIP-seq peaks. biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics, FunctionalGenomics, Classification, HiC Author: Konstantin Krismer [aut, cre, cph] (ORCID: ), David Gifford [ths, cph] (ORCID: ) Maintainer: Konstantin Krismer URL: https://idr2d.mit.edu SystemRequirements: Python (>= 3.5.0), hic-straw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/idr2d git_branch: devel git_last_commit: 3edc0a1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/idr2d_1.25.0.tar.gz vignettes: vignettes/idr2d/inst/doc/idr1d.html, vignettes/idr2d/inst/doc/idr2d.html vignetteTitles: Identify reproducible genomic peaks from replicate ChIP-seq experiments, Identify reproducible genomic interactions from replicate ChIA-PET experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/idr2d/inst/doc/idr1d.R, vignettes/idr2d/inst/doc/idr2d.R dependencyCount: 63 Package: IFAA Version: 1.13.0 Depends: R (>= 4.2.0), Imports: mathjaxr, doRNG, foreach (>= 1.4.3), Matrix (>= 1.4-0), HDCI (>= 1.0-2), parallel (>= 3.3.0), doParallel (>= 1.0.11), parallelly , glmnet, stats, utils, SummarizedExperiment, stringr, S4Vectors, DescTools, MatrixExtra, methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: a9d3583096996fa58ec85baf6fc275d8 NeedsCompilation: no Title: Robust Inference for Absolute Abundance in Microbiome Analysis Description: This package offers a robust approach to make inference on the association of covariates with the absolute abundance (AA) of microbiome in an ecosystem. It can be also directly applied to relative abundance (RA) data to make inference on AA because the ratio of two RA is equal to the ratio of their AA. This algorithm can estimate and test the associations of interest while adjusting for potential confounders. The estimates of this method have easy interpretation like a typical regression analysis. High-dimensional covariates are handled with regularization and it is implemented by parallel computing. False discovery rate is automatically controlled by this approach. Zeros do not need to be imputed by a positive value for the analysis. The IFAA package also offers the 'MZILN' function for estimating and testing associations of abundance ratios with covariates. biocViews: Software, Technology, Sequencing, Microbiome, Regression Author: Quran Wu [aut], Zhigang Li [aut, cre] Maintainer: Zhigang Li URL: https://pubmed.ncbi.nlm.nih.gov/35241863/, https://pubmed.ncbi.nlm.nih.gov/30923584/, https://github.com/quranwu/IFAA VignetteBuilder: knitr BugReports: https://github.com/quranwu/IFAA/issues git_url: https://git.bioconductor.org/packages/IFAA git_branch: devel git_last_commit: 4c864fa git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IFAA_1.13.0.tar.gz vignettes: vignettes/IFAA/inst/doc/IFAA.pdf vignetteTitles: IFAA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IFAA/inst/doc/IFAA.R dependencyCount: 100 Package: igblastr Version: 1.1.28 Depends: R (>= 4.2.0), tibble, Biostrings Imports: methods, utils, stats, tools, R.utils, curl, httr, xml2, rvest, xtable, jsonlite, S4Vectors, IRanges, GenomeInfoDb Suggests: GenomicAlignments, parallel, testthat, knitr, rmarkdown, BiocStyle, ggplot2, dplyr, scales, ggseqlogo License: Artistic-2.0 MD5sum: f0811ee1e4f354f80bf7d74a043ddb67 NeedsCompilation: no Title: User-friendly R Wrapper to IgBLAST Description: The igblastr package provides functions to conveniently install and use a local IgBLAST installation from within R. The package also includes a set of built-in IgBLAST-compatible germline databases from OGRDB, the AIRR Community’s Open Germline Receptor Database, for various organisms. It provides functions to create additional IgBLAST-compatible germline databases using reference sequences retrieved from IMGT/V-QUEST or local FASTA files supplied by the user. When possible, the FWR/CDR boundaries on the V alleles (a.k.a "internal data") are computed and stored in the germline database, so can be used as a replacement for the internal data shipped with IgBLAST. IgBLAST is described at . IgBLAST web interface: . OGRDB: . IMGT/V-QUEST download site: . biocViews: Immunology, Immunogenetics, ImmunoOncology, CellBiology Author: Hervé Pagès [aut, cre] (ORCID: ), Ollivier Hyrien [aut, fnd] (ORCID: ), Kellie MacPhee [ctb] (ORCID: ), Michael Duff [ctb] (ORCID: ), Jason Taylor [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/igblastr VignetteBuilder: knitr BugReports: https://github.com/HyrienLab/igblastr/issues git_url: https://git.bioconductor.org/packages/igblastr git_branch: devel git_last_commit: 0456d02 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/igblastr_1.1.28.tar.gz vignettes: vignettes/igblastr/inst/doc/igblastr_overview.html vignetteTitles: igblastr overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/igblastr/inst/doc/igblastr_overview.R dependencyCount: 44 Package: iGC Version: 1.41.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: 8bf80b61b5945333f58822624a4992a2 NeedsCompilation: no Title: An integrated analysis package of Gene expression and Copy number alteration Description: This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data. biocViews: Software, Biological Question, DifferentialExpression, GenomicVariation, AssayDomain, CopyNumberVariation, GeneExpression, ResearchField, Genetics, Technology, Microarray, Sequencing, WorkflowStep, MultipleComparison Author: Yi-Pin Lai [aut], Liang-Bo Wang [aut, cre], Tzu-Pin Lu [aut], Eric Y. Chuang [aut] Maintainer: Liang-Bo Wang URL: http://github.com/ccwang002/iGC VignetteBuilder: knitr BugReports: http://github.com/ccwang002/iGC/issues git_url: https://git.bioconductor.org/packages/iGC git_branch: devel git_last_commit: 1a2bc2a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iGC_1.41.0.tar.gz vignettes: vignettes/iGC/inst/doc/Introduction.html vignetteTitles: Introduction to iGC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iGC/inst/doc/Introduction.R dependencyCount: 5 Package: IgGeneUsage Version: 1.25.0 Depends: R (>= 4.2.0) Imports: methods, reshape2 (>= 1.4.3), Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), rstantools (>= 2.2.0), SummarizedExperiment, tidyr LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), ggplot2, ggforce, ggrepel, patchwork License: MIT + file LICENSE MD5sum: 6189db5a7330d6dbb062ce7d5cd730cd NeedsCompilation: yes Title: Differential gene usage in immune repertoires Description: Detection of biases in the usage of immunoglobulin (Ig) genes is an important task in immune repertoire profiling. IgGeneUsage detects aberrant Ig gene usage between biological conditions using a probabilistic model which is analyzed computationally by Bayes inference. With this IgGeneUsage also avoids some common problems related to the current practice of null-hypothesis significance testing. biocViews: DifferentialExpression, Regression, Genetics, Bayesian, BiomedicalInformatics, ImmunoOncology, MathematicalBiology Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski URL: https://github.com/snaketron/IgGeneUsage SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/snaketron/IgGeneUsage/issues git_url: https://git.bioconductor.org/packages/IgGeneUsage git_branch: devel git_last_commit: 03134f1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IgGeneUsage_1.25.0.tar.gz vignettes: vignettes/IgGeneUsage/inst/doc/User_Manual.html vignetteTitles: User Manual: IgGeneUsage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IgGeneUsage/inst/doc/User_Manual.R dependencyCount: 78 Package: igvR Version: 1.31.0 Depends: R (>= 3.5.0), GenomicRanges, GenomicAlignments, BrowserViz (>= 2.17.1) Imports: methods, BiocGenerics, httpuv, utils, rtracklayer, VariantAnnotation, RColorBrewer, httr Suggests: RUnit, BiocStyle, knitr, rmarkdown, MotifDb, seqLogo License: MIT + file LICENSE MD5sum: 1209181834a90e3c7032ace786d89bcf NeedsCompilation: no Title: igvR: integrative genomics viewer Description: Access to igv.js, the Integrative Genomics Viewer running in a web browser. biocViews: Visualization, ThirdPartyClient, GenomeBrowsers Author: Paul Shannon Maintainer: Arkadiusz Gladki URL: https://gladkia.github.io/igvR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/igvR git_branch: devel git_last_commit: 62be310 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/igvR_1.31.0.tar.gz vignettes: vignettes/igvR/inst/doc/v00.basicIntro.html, vignettes/igvR/inst/doc/v01.stockGenome.html, vignettes/igvR/inst/doc/v02.customGenome.html, vignettes/igvR/inst/doc/v03.ctcfChIP.html, vignettes/igvR/inst/doc/v04.pairedEnd.html, vignettes/igvR/inst/doc/v05.ucscTableBrowser.html, vignettes/igvR/inst/doc/v06.annotationHub.html, vignettes/igvR/inst/doc/v07.gwas.html vignetteTitles: "Introduction: a simple demo", "Use a Stock Genome", "Use a Custom Genome", "Explore CTCF ChIP-seq alignments,, MACS2 narrowPeaks,, Motif Matching and H3K4me3 methylation", "Paired-end Interaction Tracks", "Obtain and Display H3K4Me3 K562 track from UCSC table browser", "Obtain and Display H3K27ac K562 track from the AnnotationHub", "GWAS Tracks" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvR/inst/doc/v00.basicIntro.R, vignettes/igvR/inst/doc/v01.stockGenome.R, vignettes/igvR/inst/doc/v02.customGenome.R, vignettes/igvR/inst/doc/v03.ctcfChIP.R, vignettes/igvR/inst/doc/v04.pairedEnd.R, vignettes/igvR/inst/doc/v05.ucscTableBrowser.R, vignettes/igvR/inst/doc/v06.annotationHub.R, vignettes/igvR/inst/doc/v07.gwas.R dependencyCount: 85 Package: igvShiny Version: 1.7.0 Depends: R (>= 3.5.0), GenomicRanges, methods, shiny Imports: BiocGenerics, checkmate, futile.logger, GenomeInfoDbData, htmlwidgets, httr, jsonlite, randomcoloR, utils Suggests: BiocStyle, GenomicAlignments, knitr, Rsamtools, rtracklayer, RUnit, shinytest2, VariantAnnotation License: MIT + file LICENSE MD5sum: 391b7191d283590de7891c1cb8aa139a NeedsCompilation: no Title: igvShiny: a wrapper of Integrative Genomics Viewer (IGV - an interactive tool for visualization and exploration integrated genomic data) Description: This package is a wrapper of Integrative Genomics Viewer (IGV). It comprises an htmlwidget version of IGV. It can be used as a module in Shiny apps. biocViews: Software, ShinyApps, Sequencing, Coverage Author: Paul Shannon [aut], Arkadiusz Gladki [aut, cre] (ORCID: ), Karolina Scigocka [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gladkia/igvShiny, https://gladkia.github.io/igvShiny/ VignetteBuilder: knitr BugReports: https://github.com/gladkia/igvShiny/issues git_url: https://git.bioconductor.org/packages/igvShiny git_branch: devel git_last_commit: bda3441 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/igvShiny_1.7.0.tar.gz vignettes: vignettes/igvShiny/inst/doc/igvShiny.html vignetteTitles: igvShiny Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvShiny/inst/doc/igvShiny.R importsMe: damidBind dependencyCount: 76 Package: IHW Version: 1.39.0 Depends: R (>= 3.3.0) Imports: methods, slam, lpsymphony, fdrtool, BiocGenerics Suggests: ggplot2, dplyr, gridExtra, scales, DESeq2, airway, testthat, Matrix, BiocStyle, knitr, rmarkdown, devtools License: Artistic-2.0 MD5sum: b876173870454b086fe73af2e26eb21d NeedsCompilation: no Title: Independent Hypothesis Weighting Description: Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis. biocViews: ImmunoOncology, MultipleComparison, RNASeq Author: Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut] Maintainer: Nikos Ignatiadis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IHW git_branch: devel git_last_commit: 129deec git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IHW_1.39.0.tar.gz vignettes: vignettes/IHW/inst/doc/introduction_to_ihw.html vignetteTitles: "Introduction to IHW" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R dependsOnMe: IHWpaper importsMe: ideal, scp suggestsMe: DEWSeq, GRaNIE, metagenomeSeq, muscat, BisRNA, DGEobj.utils, readyomics dependencyCount: 10 Package: illuminaio Version: 0.53.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 MD5sum: c1c1a5faed7bb568e78bdde60005c5a2 NeedsCompilation: yes Title: Parsing Illumina Microarray Output Files Description: Tools for parsing Illumina's microarray output files, including IDAT. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms Author: Keith Baggerly [aut], Henrik Bengtsson [aut], Kasper Daniel Hansen [aut, cre], Matt Ritchie [aut], Mike L. Smith [aut], Tim Triche Jr. [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/illuminaio BugReports: https://github.com/HenrikBengtsson/illuminaio/issues git_url: https://git.bioconductor.org/packages/illuminaio git_branch: devel git_last_commit: 9c358b7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/illuminaio_0.53.0.tar.gz vignettes: vignettes/illuminaio/inst/doc/EncryptedFormat.pdf, vignettes/illuminaio/inst/doc/illuminaio.pdf vignetteTitles: Description of Encrypted IDAT Format, Introduction to illuminaio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/illuminaio/inst/doc/illuminaio.R dependsOnMe: normalize450K, RnBeads, wateRmelon, EGSEA123 importsMe: bigmelon, crlmm, methylumi, minfi suggestsMe: limma dependencyCount: 4 Package: ILoReg Version: 1.21.0 Depends: R (>= 4.0.0) Imports: Matrix, parallel, foreach, aricode, LiblineaR, SparseM, ggplot2, cowplot, RSpectra, umap, Rtsne, fastcluster, parallelDist, cluster, dendextend, DescTools, plyr, scales, pheatmap, reshape2, dplyr, doRNG, SingleCellExperiment, SummarizedExperiment, S4Vectors, methods, stats, doSNOW, utils Suggests: knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 83385f7b8eacef4ab325fc5f7bf729ef NeedsCompilation: no Title: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data Description: ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided. biocViews: SingleCell, Software, Clustering, DimensionReduction, RNASeq, Visualization, Transcriptomics, DataRepresentation, DifferentialExpression, Transcription, GeneExpression Author: Johannes Smolander [cre, aut], Sini Junttila [aut], Mikko S Venäläinen [aut], Laura L Elo [aut] Maintainer: Johannes Smolander URL: https://github.com/elolab/ILoReg VignetteBuilder: knitr BugReports: https://github.com/elolab/ILoReg/issues git_url: https://git.bioconductor.org/packages/ILoReg git_branch: devel git_last_commit: 676b5a1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ILoReg_1.21.0.tar.gz vignettes: vignettes/ILoReg/inst/doc/ILoReg.html vignetteTitles: ILoReg package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ILoReg/inst/doc/ILoReg.R dependencyCount: 126 Package: imageTCGA Version: 1.3.0 Depends: R (>= 3.5.0), shiny Imports: DT, dplyr, bslib, bsicons, ggplot2, viridis, tidyr, leaflet, clipr, rlang Suggests: BiocManager, BiocStyle, knitr, curl, glue, rmarkdown, sessioninfo, testthat, tibble, GenomicDataCommons License: Artistic-2.0 MD5sum: f78b6d807938c3d1e4f2dcb52ba792c7 NeedsCompilation: no Title: TCGA Diagnostic Image Database Explorer Description: A Shiny application to explore the TCGA Diagnostic Image Database. biocViews: ShinyApps Author: Ilaria Billato [aut, cre] (ORCID: , affiliation: Department of Biology, University of Padova), Marcel Ramos [ctb] (affiliation: CUNY Graduate School of Public Health and Health Policy, New York, NY USA), Mohamed Omar [ctb] (affiliation: Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California), Sehyun Oh [ctb] (affiliation: CUNY Graduate School of Public Health and Health Policy, New York, NY USA), Levi Waldron [ctb] (affiliation: CUNY Graduate School of Public Health and Health Policy, New York, NY USA), Davide Risso [ctb] (affiliation: Department of Statistical Sciences, University of Padova), Chiara Romualdi [ctb] (affiliation: Department of Biology, University of Padova) Maintainer: Ilaria Billato URL: https://github.com/billila/imageTCGA VignetteBuilder: knitr BugReports: https://github.com/billila/imageTCGA/issues git_url: https://git.bioconductor.org/packages/imageTCGA git_branch: devel git_last_commit: e8f9d8f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/imageTCGA_1.3.0.tar.gz vignettes: vignettes/imageTCGA/inst/doc/imageTCGA.html vignetteTitles: imageTCGA: A Shiny application to explore the TCGA Diagnostic Images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imageTCGA/inst/doc/imageTCGA.R dependencyCount: 92 Package: imageTCGAutils Version: 0.99.24 Depends: R (>= 4.5.0) Imports: BiocBaseUtils, data.table, dplyr, grDevices, methods, rlang, S4Vectors, SpatialExperiment, SummarizedExperiment Suggests: anndataR, BiocStyle, imageFeatureTCGA, ggplot2, knitr, paws, rhdf5, rmarkdown, sfdep, spdep, SpatialFeatureExperiment, tinytest License: Artistic-2.0 MD5sum: 40ca6a628905a496e478a91d8ad8aeff NeedsCompilation: no Title: Utility functions for working with histopathology images Description: Utility functions for working with CONCH data, listing remote files. One function assigns HoverNet nuclei to ProvGigaPath tiles with a scale factor to align coordinates. Provides internal utility functions for 'imageFeatureTCGA' and most functions are not meant for end users. biocViews: Software, WorkflowStep, Preprocessing Author: Marcel Ramos [aut] (ORCID: , affiliation: CUNY Graduate School of Public Health and Health Policy, New York, NY USA), Ilaria Billato [aut, cre] (ORCID: , affiliation: Department of Biology, University of Padova), Eslam Abousamra [ctb] (affiliation: CUNY Graduate School of Public Health and Health Policy, New York, NY USA) Maintainer: Ilaria Billato URL: https://github.com/waldronlab/imageTCGAutils VignetteBuilder: knitr BugReports: https://github.com/waldronlab/imageTCGAutils/issues git_url: https://git.bioconductor.org/packages/imageTCGAutils git_branch: devel git_last_commit: b8249cb git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/imageTCGAutils_0.99.24.tar.gz vignettes: vignettes/imageTCGAutils/inst/doc/imageTCGAutils.html vignetteTitles: imageTCGAutils hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imageTCGAutils/inst/doc/imageTCGAutils.R suggestsMe: imageFeatureTCGA dependencyCount: 68 Package: IMMAN Version: 1.31.0 Imports: STRINGdb, pwalign, igraph, graphics, utils, seqinr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 0752bd4d70753e5ff377e4ccb0f92706 NeedsCompilation: no Title: Interlog protein network reconstruction by Mapping and Mining ANalysis Description: Reconstructing Interlog Protein Network (IPN) integrated from several Protein protein Interaction Networks (PPINs). Using this package, overlaying different PPINs to mine conserved common networks between diverse species will be applicable. biocViews: SequenceMatching, Alignment, SystemsBiology, GraphAndNetwork, Network, Proteomics Author: Minoo Ashtiani, Payman Nickchi, Abdollah Safari, Mehdi Mirzaie, Mohieddin Jafari Maintainer: Minoo Ashtiani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IMMAN git_branch: devel git_last_commit: 839767a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IMMAN_1.31.0.tar.gz vignettes: vignettes/IMMAN/inst/doc/IMMAN.html vignetteTitles: IMMAN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMMAN/inst/doc/IMMAN.R dependencyCount: 68 Package: immApex Version: 1.5.4 Depends: R (>= 4.3.0) Imports: immReferent, Matrix, matrixStats, methods, Rcpp, SingleCellExperiment, stats, stringr, utils LinkingTo: Rcpp Suggests: BiocStyle, dplyr, ggraph, ggplot2, igraph, knitr, markdown, Peptides, randomForest, rmarkdown, scRepertoire, spelling, testthat, tidygraph, viridis License: MIT + file LICENSE MD5sum: a4f233122f6a357b9d36571aaf4ebae1 NeedsCompilation: yes Title: Tools for Adaptive Immune Receptor Sequence-Based Machine and Deep Learning Description: A set of tools to for machine and deep learning in R from amino acid and nucleotide sequences focusing on adaptive immune receptors. The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing, MotifAnnotation Author: Nick Borcherding [aut, cre, cph], Qile Yang [ctb] (ORCID: ) Maintainer: Nick Borcherding URL: https://github.com/BorchLab/immApex/ VignetteBuilder: knitr BugReports: https://github.com/BorchLab/immApex/issues git_url: https://git.bioconductor.org/packages/immApex git_branch: devel git_last_commit: bd6eb8d git_last_commit_date: 2026-04-03 Date/Publication: 2026-04-20 source.ver: src/contrib/immApex_1.5.4.tar.gz vignettes: vignettes/immApex/inst/doc/immApex.html vignetteTitles: Machine and Deep Learning Models with immApex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/immApex/inst/doc/immApex.R importsMe: Ibex, scRepertoire dependencyCount: 54 Package: immReferent Version: 0.99.7 Depends: R (>= 4.5.0) Imports: Biostrings, httr, jsonlite, methods, rvest, tibble, yaml Suggests: BiocManager, BiocStyle, knitr, mockery, spelling, rmarkdown, testthat (>= 3.0.0), withr License: MIT + file LICENSE MD5sum: 98606977299111bf9a307691bfa116f8 NeedsCompilation: no Title: An Interface for Immune Receptor and HLA Gene Reference Data Description: Provides a consistent interface for downloading, storing, and accessing immune receptor (TCR/BCR) and HLA sequences from IMGT, IPD-IMGT/HLA, and OGRDB (AIRR-C). Supports export to popular analysis tools including MiXCR, TRUST4, Cell Ranger, and IgBLAST. This package serves as a core dependency for immunogenomics packages, ensuring reliable and high-quality sequence access with local caching for reproducibility. biocViews: Software, Annotation, Sequencing Author: Nick Borcherding [aut, cre] Maintainer: Nick Borcherding URL: https://github.com/BorchLab/immReferent/ VignetteBuilder: knitr BugReports: https://github.com/BorchLab/immReferent/issues git_url: https://git.bioconductor.org/packages/immReferent git_branch: devel git_last_commit: ec10da3 git_last_commit_date: 2026-04-03 Date/Publication: 2026-04-20 source.ver: src/contrib/immReferent_0.99.7.tar.gz vignettes: vignettes/immReferent/inst/doc/caching.html, vignettes/immReferent/inst/doc/immReferent.html vignetteTitles: Caching and Offline Usage of Reference Sets (IMGT & OGRDB), Getting Started with immReferent hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/immReferent/inst/doc/caching.R, vignettes/immReferent/inst/doc/immReferent.R importsMe: immApex dependencyCount: 39 Package: immunoClust Version: 1.43.0 Depends: R(>= 4.2), flowCore Imports: methods, stats, graphics, grid, lattice, grDevices Suggests: BiocStyle, utils, testthat License: Artistic-2.0 MD5sum: c26b117e3cdce7ab1f48a2645e420182 NeedsCompilation: yes Title: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry Description: immunoClust is a model based clustering approach for Flow Cytometry samples. The cell-events of single Flow Cytometry samples are modelled by a mixture of multinominal normal- or t-distributions. The cell-event clusters of several samples are modelled by a mixture of multinominal normal-distributions aiming stable co-clusters across these samples. biocViews: Clustering, FlowCytometry, SingleCell, CellBasedAssays, ImmunoOncology Author: Till Soerensen [aut, cre] Maintainer: Till Soerensen git_url: https://git.bioconductor.org/packages/immunoClust git_branch: devel git_last_commit: 166e477 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/immunoClust_1.43.0.tar.gz vignettes: vignettes/immunoClust/inst/doc/immunoClust.pdf vignetteTitles: immunoClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunoClust/inst/doc/immunoClust.R dependencyCount: 24 Package: immunogenViewer Version: 1.5.0 Depends: R (>= 4.0) Imports: ggplot2, httr, jsonlite, patchwork, UniProt.ws Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), DT License: Apache License (>= 2) MD5sum: 0149de81c50812382ab7479c76ab1c96 NeedsCompilation: no Title: Visualization and evaluation of protein immunogens Description: Plots protein properties and visualizes position of peptide immunogens within protein sequence. Allows evaluation of immunogens based on structural and functional annotations to infer suitability for antibody-based methods aiming to detect native proteins. biocViews: FeatureExtraction, Proteomics, Software, Visualization Author: Katharina Waury [aut, cre] (ORCID: ) Maintainer: Katharina Waury URL: https://github.com/kathiwaury/immunogenViewer VignetteBuilder: knitr BugReports: https://github.com/kathiwaury/immunogenViewer/issues git_url: https://git.bioconductor.org/packages/immunogenViewer git_branch: devel git_last_commit: 24aec9b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/immunogenViewer_1.5.0.tar.gz vignettes: vignettes/immunogenViewer/inst/doc/immunogenViewer_vignette.html vignetteTitles: Using immunogenViewer to evaluate and choose antibodies hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunogenViewer/inst/doc/immunogenViewer_vignette.R dependencyCount: 75 Package: immunotation Version: 1.19.0 Depends: R (>= 4.1) Imports: stringr, ontologyIndex, curl, ggplot2, readr, rvest, tidyr, xml2, maps, rlang Suggests: BiocGenerics, rmarkdown, BiocStyle, knitr, testthat, DT License: GPL-3 MD5sum: 4ba7a8b118a9d3b114896a0f4205b9f8 NeedsCompilation: no Title: Tools for working with diverse immune genes Description: MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus. More than 28,000 different HLA alleles have been reported, with significant differences in allele frequencies between human populations worldwide. Reproducible and consistent annotation of HLA alleles in large-scale bioinformatics workflows remains challenging, because the available reference databases and software tools often use different HLA naming schemes. The package immunotation provides tools for consistent annotation of HLA genes in typical immunoinformatics workflows such as for example the prediction of MHC-presented peptides in different human donors. Converter functions that provide mappings between different HLA naming schemes are based on the MHC restriction ontology (MRO). The package also provides automated access to HLA alleles frequencies in worldwide human reference populations stored in the Allele Frequency Net Database. biocViews: Software, ImmunoOncology, BiomedicalInformatics, Genetics, Annotation Author: Katharina Imkeller [cre, aut] Maintainer: Katharina Imkeller VignetteBuilder: knitr BugReports: https://github.com/imkeller/immunotation/issues git_url: https://git.bioconductor.org/packages/immunotation git_branch: devel git_last_commit: 0c20023 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/immunotation_1.19.0.tar.gz vignettes: vignettes/immunotation/inst/doc/immunotation_vignette.html vignetteTitles: User guide immunotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunotation/inst/doc/immunotation_vignette.R dependencyCount: 58 Package: iModMix Version: 1.1.1 Depends: R (>= 4.5.0) Imports: config (>= 0.3.2), golem (>= 0.4.1), shiny (>= 1.7.5), ComplexHeatmap, DT, RColorBrewer, WGCNA, corrplot, cowplot, dynamicTreeCut, ggplot2, glassoFast, impute, purrr, stringr, tidyr, visNetwork, shinyBS, httr, dplyr, stats, iModMixData, SummarizedExperiment, ExperimentHub (>= 2.99.0) Suggests: testthat (>= 3.0.0), ggfortify, shinyWidgets, pROC, tuneR, knitr, curl, readxl, reshape2, vroom, here, enrichR, rmarkdown License: GPL-3 MD5sum: 66c1f1c1e400af26bace36f7755c4d0e NeedsCompilation: no Title: Integrative Modules for Multi-Omics Data Description: The iModMix network-based method offers an integrated framework for analyzing multi-omics data, including metabolomics, proteomics, and transcriptomics data, enabling the exploration of intricate molecular associations within heterogeneous biological systems. biocViews: Software, Network, Clustering, Visualization, Transcriptomics, Proteomics, Metabolomics, GeneExpression, PrincipalComponent Author: Isis Narvaez-Bandera [aut, cre] (ORCID: ) Maintainer: Isis Narvaez-Bandera URL: https://github.com/biodatalab/iModMix VignetteBuilder: knitr BugReports: https://github.com/biodatalab/iModMix/issues git_url: https://git.bioconductor.org/packages/iModMix git_branch: devel git_last_commit: d554604 git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/iModMix_1.1.1.tar.gz vignettes: vignettes/iModMix/inst/doc/iModMixTutorial.html vignetteTitles: Publication-ready integration omics data including unidentified metabolites hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iModMix/inst/doc/iModMixTutorial.R dependencyCount: 154 Package: IMPCdata Version: 1.47.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: d58085fb9ee6e6c0135d126361a02ee1 NeedsCompilation: no Title: Retrieves data from IMPC database Description: Package contains methods for data retrieval from IMPC Database. biocViews: ExperimentData Author: Natalja Kurbatova, Jeremy Mason Maintainer: Jeremy Mason git_url: https://git.bioconductor.org/packages/IMPCdata git_branch: devel git_last_commit: f877a20 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IMPCdata_1.47.0.tar.gz vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf vignetteTitles: IMPCdata Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IMPCdata/inst/doc/IMPCdata.R dependencyCount: 1 Package: impute Version: 1.85.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: 7841ed8caf904eb811c0015807b09511 NeedsCompilation: yes Title: impute: Imputation for microarray data Description: Imputation for microarray data (currently KNN only) biocViews: Microarray Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu Maintainer: Balasubramanian Narasimhan git_url: https://git.bioconductor.org/packages/impute git_branch: devel git_last_commit: 8e9dc99 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/impute_1.85.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AMARETTO, CGHcall, TIN, curatedBreastData, imputeLCMD, moduleColor, swamp importsMe: biscuiteer, BreastSubtypeR, cola, DExMA, doppelgangR, EGAD, EpiMix, fastLiquidAssociation, genefu, genomation, iModMix, MAGAR, MatrixQCvis, MEAT, methylclock, MethylMix, miRLAB, MSnbase, netboost, Pigengene, pmp, POMA, REMP, RNAAgeCalc, Rnits, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, DIscBIO, ePCR, GSEMA, iC10, lilikoi, metamorphr, mi4p, PCAPAM50, PINSPlus, Rnmr1D, samr, speaq, WGCNA suggestsMe: BioNet, DAPAR, GeoTcgaData, graphite, MethPed, MsCoreUtils, QFeatures, qmtools, RnBeads, scp, TBSignatureProfiler, TCGAutils, tidyexposomics, corrselect, GSA, maGUI, metabodecon, romic dependencyCount: 0 Package: INDEED Version: 2.25.0 Depends: glasso (>= 1.8), R (>= 3.5) Imports: devtools (>= 1.13.0), graphics (>= 3.3.1), stats (>= 3.3.1), utils (>= 3.3.1), igraph (>= 1.2.4), visNetwork(>= 2.0.6) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8), testthat (>= 2.0.0) License: Artistic-2.0 MD5sum: cd56a29e4028f1b084ec39aacfdccb3b NeedsCompilation: no Title: Interactive Visualization of Integrated Differential Expression and Differential Network Analysis for Biomarker Candidate Selection Package Description: An R package for integrated differential expression and differential network analysis based on omic data for cancer biomarker discovery. Both correlation and partial correlation can be used to generate differential network to aid the traditional differential expression analysis to identify changes between biomolecules on both their expression and pairwise association levels. A detailed description of the methodology has been published in Methods journal (PMID: 27592383). An interactive visualization feature allows for the exploration and selection of candidate biomarkers. biocViews: ImmunoOncology, Software, ResearchField, BiologicalQuestion, StatisticalMethod, DifferentialExpression, MassSpectrometry, Metabolomics Author: Yiming Zuo , Kian Ghaffari , Zhenzhi Li Maintainer: Ressom group , Yiming Zuo URL: http://github.com/ressomlab/INDEED VignetteBuilder: knitr BugReports: http://github.com/ressomlab/INDEED/issues git_url: https://git.bioconductor.org/packages/INDEED git_branch: devel git_last_commit: 4ba8f5c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/INDEED_2.25.0.tar.gz vignettes: vignettes/INDEED/inst/doc/Introduction_to_INDEED.pdf vignetteTitles: INDEED R package for cancer biomarker discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INDEED/inst/doc/Introduction_to_INDEED.R dependencyCount: 107 Package: infercnv Version: 1.27.0 Depends: R(>= 4.0) Imports: graphics, grDevices, RColorBrewer, gplots, futile.logger, stats, utils, methods, ape, phyclust, Matrix, fastcluster, parallelDist, dplyr, HiddenMarkov, ggplot2, edgeR, coin, caTools, digest, RANN, igraph, reshape2, rjags, fitdistrplus, future, foreach, doParallel, Seurat, BiocGenerics, SummarizedExperiment, SingleCellExperiment, tidyr, parallel, coda, gridExtra, argparse Suggests: BiocStyle, knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: a7201dd609bc0394683e8485a681a598 NeedsCompilation: no Title: Infer Copy Number Variation from Single-Cell RNA-Seq Data Description: Using single-cell RNA-Seq expression to visualize CNV in cells. biocViews: Software, CopyNumberVariation, VariantDetection, StructuralVariation, GenomicVariation, Genetics, Transcriptomics, StatisticalMethod, Bayesian, HiddenMarkovModel, SingleCell Author: Timothy Tickle [aut], Itay Tirosh [aut], Christophe Georgescu [aut, cre], Maxwell Brown [aut], Brian Haas [aut] Maintainer: Christophe Georgescu URL: https://github.com/broadinstitute/inferCNV/wiki SystemRequirements: JAGS 4.x.y VignetteBuilder: knitr BugReports: https://github.com/broadinstitute/inferCNV/issues git_url: https://git.bioconductor.org/packages/infercnv git_branch: devel git_last_commit: 4e0174e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/infercnv_1.27.0.tar.gz vignettes: vignettes/infercnv/inst/doc/inferCNV.html vignetteTitles: Visualizing Large-scale Copy Number Variation in Single-Cell RNA-Seq Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/infercnv/inst/doc/inferCNV.R suggestsMe: SCpubr dependencyCount: 193 Package: infinityFlow Version: 1.21.2 Depends: R (>= 4.0.0), flowCore Imports: stats, grDevices, utils, graphics, pbapply, matlab, png, raster, grid, uwot, gtools, Biobase, generics, parallel, methods, xgboost (>= 3.0.0) Suggests: knitr, rmarkdown, keras, tensorflow, glmnetUtils, e1071 License: GPL-3 MD5sum: 13025f1070b3d2bece15312295c06ff3 NeedsCompilation: no Title: Augmenting Massively Parallel Cytometry Experiments Using Multivariate Non-Linear Regressions Description: Pipeline to analyze and merge data files produced by BioLegend's LEGENDScreen or BD Human Cell Surface Marker Screening Panel (BD Lyoplates). biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell, Proteomics Author: Etienne Becht [cre, aut] Maintainer: Etienne Becht VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/infinityFlow git_branch: devel git_last_commit: bc17a32 git_last_commit_date: 2025-12-19 Date/Publication: 2026-04-20 source.ver: src/contrib/infinityFlow_1.21.2.tar.gz vignettes: vignettes/infinityFlow/inst/doc/basic_usage.html, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.html vignetteTitles: Basic usage of the infinityFlow package, Training non default regression models hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/infinityFlow/inst/doc/basic_usage.R, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.R dependencyCount: 45 Package: Informeasure Version: 1.21.0 Depends: R (>= 4.0) Imports: entropy Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), SummarizedExperiment License: Artistic-2.0 MD5sum: cf15502c6bc434a6269cdafc3bd2ead8 NeedsCompilation: no Title: R implementation of information measures Description: This package consolidates a comprehensive set of information measurements, encompassing mutual information, conditional mutual information, interaction information, partial information decomposition, and part mutual information. biocViews: GeneExpression, NetworkInference, Network, Software Author: Chu Pan [aut, cre] Maintainer: Chu Pan URL: https://github.com/chupan1218/Informeasure VignetteBuilder: knitr BugReports: https://github.com/chupan1218/Informeasure/issues git_url: https://git.bioconductor.org/packages/Informeasure git_branch: devel git_last_commit: efa47cf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Informeasure_1.21.0.tar.gz vignettes: vignettes/Informeasure/inst/doc/Informeasure.html vignetteTitles: Informeasure: a tool to quantify nonlinear dependence between variables in biological regulatory networks from an information theory perspective hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Informeasure/inst/doc/Informeasure.R dependencyCount: 1 Package: InPAS Version: 2.19.1 Depends: R (>= 3.5) Imports: AnnotationDbi,batchtools,Biobase,Biostrings,BSgenome,cleanUpdTSeq, depmixS4,dplyr,flock,future,future.apply,GenomeInfoDb,GenomicRanges, GenomicFeatures, ggplot2, IRanges, limma, magrittr,methods,parallelly, plyranges, preprocessCore, readr,reshape2, RSQLite, Seqinfo, stats, S4Vectors, utils Suggests: BiocGenerics,BiocManager, BiocStyle, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.UCSC.hg19, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v79, knitr, markdown, rmarkdown, rtracklayer, RUnit, grDevices, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL (>= 2) MD5sum: a0a4a01baf110714d28e7c1aee874b36 NeedsCompilation: no Title: Identify Novel Alternative PolyAdenylation Sites (PAS) from RNA-seq data Description: Alternative polyadenylation (APA) is one of the important post- transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites and the differential usage of APA sites from RNA-Seq data. It leverages cleanUpdTSeq to fine tune identified APA sites by removing false sites. biocViews: Alternative Polyadenylation, Differential Polyadenylation Site Usage, RNA-seq, Gene Regulation, Transcription Author: Jianhong Ou [aut, cre], Haibo Liu [aut], Lihua Julie Zhu [aut], Sungmi M. Park [aut], Michael R. Green [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InPAS git_branch: devel git_last_commit: f16f642 git_last_commit_date: 2025-11-20 Date/Publication: 2026-04-20 source.ver: src/contrib/InPAS_2.19.1.tar.gz vignettes: vignettes/InPAS/inst/doc/InPAS.html vignetteTitles: InPAS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InPAS/inst/doc/InPAS.R dependencyCount: 143 Package: INPower Version: 1.47.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 0e1e6300ec6537c5c584cb4a5197f19b NeedsCompilation: no Title: An R package for computing the number of susceptibility SNPs Description: An R package for computing the number of susceptibility SNPs and power of future studies biocViews: SNP Author: Ju-Hyun Park Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/INPower git_branch: devel git_last_commit: 1897b5a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/INPower_1.47.0.tar.gz vignettes: vignettes/INPower/inst/doc/vignette.pdf vignetteTitles: INPower Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INPower/inst/doc/vignette.R dependencyCount: 2 Package: INSPEcT Version: 1.41.0 Depends: R (>= 3.6), methods, Biobase, BiocParallel Imports: pROC, deSolve, rootSolve, KernSmooth, readxl, GenomicFeatures, GenomicRanges, IRanges, BiocGenerics, GenomicAlignments, Rsamtools, S4Vectors, Seqinfo, DESeq2, plgem, rtracklayer, SummarizedExperiment, TxDb.Mmusculus.UCSC.mm9.knownGene, shiny Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 9580853a05095efcb9b43716b8a4c16c NeedsCompilation: no Title: Modeling RNA synthesis, processing and degradation with RNA-seq data Description: INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data. biocViews: Sequencing, RNASeq, GeneRegulation, TimeCourse, SystemsBiology Author: Stefano de Pretis Maintainer: Stefano de Pretis , Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/INSPEcT git_branch: devel git_last_commit: 54a4fbd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/INSPEcT_1.41.0.tar.gz vignettes: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html, vignettes/INSPEcT/inst/doc/INSPEcT.html vignetteTitles: INSPEcT_GUI.html, INSPEcT - INference of Synthesis,, Processing and dEgradation rates from Transcriptomic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.R, vignettes/INSPEcT/inst/doc/INSPEcT.R dependencyCount: 123 Package: INTACT Version: 1.11.0 Depends: R (>= 4.3.0) Imports: SQUAREM, bdsmatrix, numDeriv, stats, tidyr, ggplot2 Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 + file LICENSE MD5sum: 807e8e4c28ae9b667c32551c21d54995 NeedsCompilation: no Title: Integrate TWAS and Colocalization Analysis for Gene Set Enrichment Analysis Description: This package integrates colocalization probabilities from colocalization analysis with transcriptome-wide association study (TWAS) scan summary statistics to implicate genes that may be biologically relevant to a complex trait. The probabilistic framework implemented in this package constrains the TWAS scan z-score-based likelihood using a gene-level colocalization probability. Given gene set annotations, this package can estimate gene set enrichment using posterior probabilities from the TWAS-colocalization integration step. biocViews: Bayesian, GeneSetEnrichment Author: Jeffrey Okamoto [aut, cre] (ORCID: ), Xiaoquan Wen [aut] (ORCID: ) Maintainer: Jeffrey Okamoto URL: https://github.com/jokamoto97/INTACT VignetteBuilder: knitr BugReports: https://github.com/jokamoto97/INTACT/issues git_url: https://git.bioconductor.org/packages/INTACT git_branch: devel git_last_commit: 1b812db git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/INTACT_1.11.0.tar.gz vignettes: vignettes/INTACT/inst/doc/INTACT.html vignetteTitles: INTACT: Integrate TWAS and Colocalization Analysis for Gene Set Enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INTACT/inst/doc/INTACT.R dependencyCount: 39 Package: InTAD Version: 1.31.0 Depends: R (>= 3.5), methods, S4Vectors, IRanges, GenomicRanges, MultiAssayExperiment, SummarizedExperiment,stats Imports: BiocGenerics,Biobase,rtracklayer,parallel,graphics,mclust,qvalue, ggplot2,utils,ggpubr Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: acef1ec1207ce0be10b64a18dfc852fd NeedsCompilation: no Title: Search for correlation between epigenetic signals and gene expression in TADs Description: The package is focused on the detection of correlation between expressed genes and selected epigenomic signals (i.e. enhancers obtained from ChIP-seq data) either within topologically associated domains (TADs) or between chromatin contact loop anchors. Various parameters can be controlled to investigate the influence of external factors and visualization plots are available for each analysis step. biocViews: Epigenetics, Sequencing, ChIPSeq, RNASeq, HiC, GeneExpression,ImmunoOncology Author: Konstantin Okonechnikov, Serap Erkek, Lukas Chavez Maintainer: Konstantin Okonechnikov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InTAD git_branch: devel git_last_commit: c1355bb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/InTAD_1.31.0.tar.gz vignettes: vignettes/InTAD/inst/doc/InTAD.html vignetteTitles: Correlation of epigenetic signals and genes in TADs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InTAD/inst/doc/InTAD.R dependencyCount: 135 Package: interacCircos Version: 1.21.0 Depends: R (>= 4.1) Imports: RColorBrewer, htmlwidgets, plyr, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: d933176519dd92ef4dc4a5bab04606f1 NeedsCompilation: no Title: The Generation of Interactive Circos Plot Description: Implement in an efficient approach to display the genomic data, relationship, information in an interactive circular genome(Circos) plot. 'interacCircos' are inspired by 'circosJS', 'BioCircos.js' and 'NG-Circos' and we integrate the modules of 'circosJS', 'BioCircos.js' and 'NG-Circos' into this R package, based on 'htmlwidgets' framework. biocViews: Visualization Author: Zhe Cui [aut, cre] Maintainer: Zhe Cui VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interacCircos git_branch: devel git_last_commit: c590f9b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/interacCircos_1.21.0.tar.gz vignettes: vignettes/interacCircos/inst/doc/interacCircos.html vignetteTitles: interacCircos hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interacCircos/inst/doc/interacCircos.R dependencyCount: 34 Package: InteractionSet Version: 1.39.0 Depends: GenomicRanges, SummarizedExperiment Imports: methods, Matrix, Rcpp, BiocGenerics, S4Vectors (>= 0.27.12), IRanges, Seqinfo LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: afbc4116b2d088bfa64b6fcea89f420e NeedsCompilation: yes Title: Base Classes for Storing Genomic Interaction Data Description: Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments. biocViews: Infrastructure, DataRepresentation, Software, HiC Author: Aaron Lun [aut, cre], Malcolm Perry [aut], Elizabeth Ing-Simmons [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InteractionSet git_branch: devel git_last_commit: 3862179 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/InteractionSet_1.39.0.tar.gz vignettes: vignettes/InteractionSet/inst/doc/interactions.html vignetteTitles: Genomic interaction classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InteractionSet/inst/doc/interactions.R dependsOnMe: diffHic, DuplexDiscovereR, GenomicInteractions, HiCDOC, plyinteractions, sevenC, nullrangesData importsMe: annoLinker, CAGEfightR, ChIPpeakAnno, DegCre, DMRcaller, EDIRquery, extraChIPs, GenomicCoordinates, geomeTriD, HicAggR, HiCaptuRe, HiCcompare, HiCExperiment, HiContacts, HiCool, HiCParser, hicVennDiagram, mariner, nullranges, trackViewer, hicream, treediff suggestsMe: plotgardener, transmogR, updateObject, CAGEWorkflow dependencyCount: 26 Package: InteractiveComplexHeatmap Version: 1.19.1 Depends: R (>= 4.0.0), ComplexHeatmap (>= 2.11.0) Imports: grDevices, stats, shiny, grid, GetoptLong, S4Vectors (>= 0.26.1), digest, IRanges, kableExtra (>= 1.3.1), utils, svglite, htmltools, clisymbols, jsonlite, RColorBrewer, fontawesome Suggests: knitr, rmarkdown, testthat, EnrichedHeatmap, GenomicRanges, data.table, circlize, GenomicFeatures, tidyverse, tidyHeatmap, cluster, org.Hs.eg.db, simplifyEnrichment, GO.db, SC3, GOexpress, SingleCellExperiment, scater, gplots, pheatmap, airway, DESeq2, DT, cola, BiocManager, gridtext, HilbertCurve (>= 1.21.1), shinydashboard, SummarizedExperiment, pkgndep, ks License: MIT + file LICENSE MD5sum: 3567c13d82b84b255c316cfd1390937e NeedsCompilation: no Title: Make Interactive Complex Heatmaps Description: This package can easily make heatmaps which are produced by the ComplexHeatmap package into interactive applications. It provides two types of interactivities: 1. on the interactive graphics device, and 2. on a Shiny app. It also provides functions for integrating the interactive heatmap widgets for more complex Shiny app development. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/InteractiveComplexHeatmap VignetteBuilder: knitr BugReports: https://github.com/jokergoo/InteractiveComplexHeatmap/issues git_url: https://git.bioconductor.org/packages/InteractiveComplexHeatmap git_branch: devel git_last_commit: 8b617e7 git_last_commit_date: 2026-01-30 Date/Publication: 2026-04-20 source.ver: src/contrib/InteractiveComplexHeatmap_1.19.1.tar.gz vignettes: vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.html vignetteTitles: The InteractiveComplexHeatmap package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: CRISPRball, goatea, mineSweepR suggestsMe: CTexploreR, simona, simplifyEnrichment, dtGAP, metasnf dependencyCount: 82 Package: InterCellar Version: 2.17.1 Depends: R (>= 4.1) Imports: config, golem, shiny, DT, shinydashboard, shinyFiles, shinycssloaders, data.table, fs, dplyr, tidyr, circlize, colourpicker, dendextend, factoextra, ggplot2, plotly, plyr, shinyFeedback, shinyalert, tibble, umap, visNetwork, wordcloud2, readxl, htmlwidgets, colorspace, signal, scales, htmltools, ComplexHeatmap, grDevices, stats, tools, utils, biomaRt, rlang, fmsb, igraph Suggests: testthat (>= 3.0.0), knitr, rmarkdown, glue, graphite, processx, attempt, BiocStyle, httr License: MIT + file LICENSE MD5sum: 477f1bd9b7025ae370d82b10cc042494 NeedsCompilation: no Title: InterCellar: an R-Shiny app for interactive analysis and exploration of cell-cell communication in single-cell transcriptomics Description: InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell communication in one or multiple conditions. biocViews: Software, SingleCell, Visualization, GO, Transcriptomics Author: Marta Interlandi [cre, aut] (ORCID: ) Maintainer: Marta Interlandi URL: https://github.com/martaint/InterCellar VignetteBuilder: knitr BugReports: https://github.com/martaint/InterCellar/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/InterCellar git_branch: devel git_last_commit: bbff9df git_last_commit_date: 2026-04-20 Date/Publication: 2026-04-20 source.ver: src/contrib/InterCellar_2.17.1.tar.gz vignettes: vignettes/InterCellar/inst/doc/user_guide.html vignetteTitles: InterCellar User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InterCellar/inst/doc/user_guide.R dependencyCount: 205 Package: IntramiRExploreR Version: 1.33.0 Depends: R (>= 3.4) Imports: igraph (>= 1.0.1), FGNet (>= 3.0.7), knitr (>= 1.12.3), stats, utils, grDevices, graphics Suggests: gProfileR, topGO, org.Dm.eg.db, rmarkdown, testthat License: GPL-2 MD5sum: d661110f9f84b6e480d90ff93246a65a NeedsCompilation: no Title: Predicting Targets for Drosophila Intragenic miRNAs Description: Intra-miR-ExploreR, an integrative miRNA target prediction bioinformatics tool, identifies targets combining expression and biophysical interactions of a given microRNA (miR). Using the tool, we have identified targets for 92 intragenic miRs in D. melanogaster, using available microarray expression data, from Affymetrix 1 and Affymetrix2 microarray array platforms, providing a global perspective of intragenic miR targets in Drosophila. Predicted targets are grouped according to biological functions using the DAVID Gene Ontology tool and are ranked based on a biologically relevant scoring system, enabling the user to identify functionally relevant targets for a given miR. biocViews: Software, Microarray, GeneTarget, StatisticalMethod, GeneExpression, GenePrediction Author: Surajit Bhattacharya and Daniel Cox Maintainer: Surajit Bhattacharya URL: https://github.com/VilainLab/IntramiRExploreR VignetteBuilder: knitr BugReports: https://github.com/VilainLab/IntramiRExploreR git_url: https://git.bioconductor.org/packages/IntramiRExploreR git_branch: devel git_last_commit: a28504f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IntramiRExploreR_1.33.0.tar.gz vignettes: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR.pdf, vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.html vignetteTitles: IntramiRExploreR.pdf, IntramiRExploreR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.R dependencyCount: 37 Package: iPath Version: 1.17.0 Depends: R (>= 4.1), mclust, BiocParallel, survival Imports: Rcpp (>= 1.0.5), matrixStats, ggpubr, ggplot2, survminer, stats LinkingTo: Rcpp, RcppArmadillo Suggests: rmarkdown, BiocStyle, knitr License: GPL-2 MD5sum: dfed7eb7b515536b9803b9e326aa3263 NeedsCompilation: yes Title: iPath pipeline for detecting perturbed pathways at individual level Description: iPath is the Bioconductor package used for calculating personalized pathway score and test the association with survival outcomes. Abundant single-gene biomarkers have been identified and used in the clinics. However, hundreds of oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. We believe individual-level expression patterns of pre-defined pathways or gene sets are better biomarkers than single genes. In this study, we devised a computational method named iPath to identify prognostic biomarker pathways, one sample at a time. To test its utility, we conducted a pan-cancer analysis across 14 cancer types from The Cancer Genome Atlas and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor stage classifications. We found that pathway-based biomarkers are more robust and effective than single genes. biocViews: Pathways, Software, GeneExpression, Survival Author: Kenong Su [aut, cre], Zhaohui Qin [aut] Maintainer: Kenong Su SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/suke18/iPath/issues git_url: https://git.bioconductor.org/packages/iPath git_branch: devel git_last_commit: f49c338 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iPath_1.17.0.tar.gz vignettes: vignettes/iPath/inst/doc/iPath.html vignetteTitles: The iPath User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPath/inst/doc/iPath.R dependencyCount: 110 Package: ipdDb Version: 1.29.0 Depends: R (>= 3.5.0), methods, AnnotationDbi (>= 1.43.1), AnnotationHub Imports: Biostrings, GenomicRanges, RSQLite, DBI, IRanges, stats, assertthat Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: bee2c4b5a5abb8ed1e9f48d5f7137177 NeedsCompilation: no Title: IPD IMGT/HLA and IPD KIR database for Homo sapiens Description: All alleles from the IPD IMGT/HLA and IPD KIR database for Homo sapiens. Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA KIR Nomenclature in non-human species Immunogenetics (2018), in preparation. biocViews: GenomicVariation, SequenceMatching, VariantAnnotation, DataRepresentation,AnnotationHubSoftware Author: Steffen Klasberg Maintainer: Steffen Klasberg URL: https://github.com/DKMS-LSL/ipdDb organism: Homo sapiens VignetteBuilder: knitr BugReports: https://github.com/DKMS-LSL/ipdDb/issues/new git_url: https://git.bioconductor.org/packages/ipdDb git_branch: devel git_last_commit: 1180c38 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ipdDb_1.29.0.tar.gz vignettes: vignettes/ipdDb/inst/doc/Readme.html vignetteTitles: ipdDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ipdDb/inst/doc/Readme.R dependencyCount: 65 Package: IRanges Version: 2.45.0 Depends: R (>= 4.0.0), methods, utils, stats, BiocGenerics (>= 0.53.2), S4Vectors (>= 0.47.6) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 MD5sum: 00844a628f6e231b8aa1bacd39732593 NeedsCompilation: yes Title: Foundation of integer range manipulation in Bioconductor Description: Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre], Patrick Aboyoun [aut], Michael Lawrence [aut] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/IRanges BugReports: https://github.com/Bioconductor/IRanges/issues git_url: https://git.bioconductor.org/packages/IRanges git_branch: devel git_last_commit: 167f22b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IRanges_2.45.0.tar.gz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Overview of the IRanges package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R dependsOnMe: AnnotationDbi, AnnotationHubData, BaalChIP, bambu, biomvRCNS, Biostrings, BiSeq, BSgenome, BSgenomeForge, bumphunter, CAFE, casper, CexoR, 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phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, fourDNData, leeBamViews, MethylSeqData, pd.atdschip.tiling, sesameData, SomaticCancerAlterations, spatialLIBD, seqpac, ActiveDriverWGS, alakazam, cpp11bigwig, crispRdesignR, cubar, DESNP, GencoDymo2, geno2proteo, GenoPop, hahmmr, iimi, karyotapR, lisat, locuszoomr, longreadvqs, LoopRig, MitoHEAR, noisyr, numbat, PACVr, RapidoPGS, refseqR, revert, rnaCrosslinkOO, Signac, TmCalculator, VALERIE suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, BREW3R.r, CCAFE, Chicago, Chromatograms, ClassifyR, DFplyr, easylift, epivizrChart, gDRcore, gDRutils, Glimma, GWASTools, HilbertVis, HilbertVisGUI, iscream, LACHESIS, maftools, martini, MiRaGE, multicrispr, partCNV, plyxp, regionalpcs, regionReport, RTCGA, S4Vectors, scrapper, SEMPLR, SigsPack, splatter, svaNUMT, svaRetro, systemPipeR, TFutils, tidybulk, MetaScope, scMultiome, systemPipeRdata, xcoredata, yeastRNASeq, fuzzyjoin, gggenomes, gkmSVM, MiscMetabar, MoBPS, polyRAD, pQTLdata, rliger, scPloidy, seqmagick, Seurat, sigminer, SNPassoc, updog, valr linksToMe: Bioc.gff, Biostrings, cigarillo, CNEr, DECIPHER, GenomicAlignments, GenomicFeatures, kebabs, MatrixRider, posDemux, pwalign, Rsamtools, rtracklayer, ShortRead, SparseArray, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 8 Package: iscream Version: 1.1.7 Depends: R (>= 4.4) Imports: Rcpp, Matrix, data.table, methods, pbapply, parallelly, stringfish, LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppSpdlog, Rhtslib, stringfish Suggests: BiocFileCache, BiocStyle, bsseq, ggplot2, ggridges, knitr, microbenchmark, rmarkdown, GenomicRanges, IRanges, Rsamtools, SummarizedExperiment, S4Vectors, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: c2fba5e27fe9c044f58fa00f4fea0448 NeedsCompilation: yes Title: Make fast and memory efficient BED file queries, summaries and matrices Description: BED files store ranged genomic data that can be queried even when the files are compressed. iscream can query data from BED files and return them in muliple formats: parsed records or their summary statistics as data frames or GenomicRanges objects, and matrices as matrix, GenomicRanges, or SummarizedExperiment objects. iscream also provides specialized support for importing methylation data. biocViews: DataImport, Software, Sequencing, SingleCell, DNAMethylation Author: James Eapen [aut, cre] (ORCID: ), Jacob Morrison [aut] (ORCID: ), Nathan Spix [ctb], Hui Shen [aut, ths, fnd] (ORCID: ) Maintainer: James Eapen URL: https://huishenlab.github.io/iscream/, https://github.com/huishenlab/iscream/ SystemRequirements: htslib: htslib-devel (rpm) or libhts-dev (deb) & tabix: htslib-tools (rpm) or tabix (deb) & GNU make VignetteBuilder: knitr BugReports: https://github.com/huishenlab/iscream/issues/ git_url: https://git.bioconductor.org/packages/iscream git_branch: devel git_last_commit: b072efa git_last_commit_date: 2026-04-03 Date/Publication: 2026-04-20 source.ver: src/contrib/iscream_1.1.7.tar.gz vignettes: vignettes/iscream/inst/doc/data_structures.html, vignettes/iscream/inst/doc/htslib.html, vignettes/iscream/inst/doc/iscream.html, vignettes/iscream/inst/doc/manuscript_data.html, vignettes/iscream/inst/doc/performance.html, vignettes/iscream/inst/doc/tabix.html, vignettes/iscream/inst/doc/TSS.html vignetteTitles: iscream compatible data structures, htslib.html, An introduction to iscream, Manuscript data availabiliy, Improving iscream performance, iscream vs Rsamtools::scanTabix, Plotting TSS methylation profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/iscream/inst/doc/data_structures.R, vignettes/iscream/inst/doc/htslib.R, vignettes/iscream/inst/doc/iscream.R dependencyCount: 20 Package: iSEE Version: 2.23.1 Depends: SummarizedExperiment, SingleCellExperiment Imports: methods, BiocGenerics, S4Vectors, utils, stats, shiny, shinydashboard, shinyAce, shinyjs, DT, rintrojs, ggplot2 (>= 3.4.0), ggrepel, colourpicker, igraph, vipor, mgcv, graphics, grDevices, viridisLite, shinyWidgets, listviewer, ComplexHeatmap, circlize, grid Suggests: testthat, covr, BiocStyle, knitr, rmarkdown, scRNAseq, TENxPBMCData, scater, DelayedArray, HDF5Array, RColorBrewer, viridis, htmltools, GenomicRanges License: MIT + file LICENSE MD5sum: 02c378ee3e83adf85eb6a1de7027a72e NeedsCompilation: no Title: Interactive SummarizedExperiment Explorer Description: Create an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects, including row- and column-level metadata. The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results. biocViews: CellBasedAssays, Clustering, DimensionReduction, FeatureExtraction, GeneExpression, GUI, ImmunoOncology, ShinyApps, SingleCell, Transcription, Transcriptomics, Visualization Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Aaron Lun [aut] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://isee.github.io/iSEE/ VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEE/issues git_url: https://git.bioconductor.org/packages/iSEE git_branch: devel git_last_commit: 827fb90 git_last_commit_date: 2025-11-16 Date/Publication: 2026-04-20 source.ver: src/contrib/iSEE_2.23.1.tar.gz vignettes: vignettes/iSEE/inst/doc/basic.html, vignettes/iSEE/inst/doc/bigdata.html, vignettes/iSEE/inst/doc/configure.html, vignettes/iSEE/inst/doc/custom.html, vignettes/iSEE/inst/doc/ecm.html, vignettes/iSEE/inst/doc/links.html, vignettes/iSEE/inst/doc/voice.html vignetteTitles: 1. The iSEE User's Guide, 6. Using iSEE with big data, 3. Configuring iSEE apps, 5. Deploying custom panels, 4. The ExperimentColorMap Class, 2. Sharing information across panels, 7. Speech recognition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEE/inst/doc/basic.R, vignettes/iSEE/inst/doc/bigdata.R, vignettes/iSEE/inst/doc/configure.R, vignettes/iSEE/inst/doc/custom.R, vignettes/iSEE/inst/doc/ecm.R, vignettes/iSEE/inst/doc/links.R, vignettes/iSEE/inst/doc/voice.R dependsOnMe: iSEEde, iSEEhex, iSEEpathways, iSEEtree, iSEEu, miaDash importsMe: iSEEfier, iSEEhub, iSEEindex suggestsMe: schex, DuoClustering2018, HCAData, HCATonsilData, TabulaMurisData, TabulaMurisSenisData dependencyCount: 109 Package: iSEEde Version: 1.9.0 Depends: iSEE Imports: DESeq2, edgeR, methods, S4Vectors, shiny, SummarizedExperiment Suggests: airway, BiocStyle, covr, knitr, limma, org.Hs.eg.db, RefManageR, rmarkdown, scuttle, sessioninfo, statmod, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: bd945321d59b25b29d93f9d06667e9d9 NeedsCompilation: no Title: iSEE extension for panels related to differential expression analysis Description: This package contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels or modes facilitating the analysis of differential expression results. This package does not perform differential expression. Instead, it provides methods to embed precomputed differential expression results in a SummarizedExperiment object, in a manner that is compatible with interactive visualisation in iSEE applications. biocViews: Software, Infrastructure, DifferentialExpression Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Thomas Sandmann [ctb] (ORCID: ), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEde VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEde git_url: https://git.bioconductor.org/packages/iSEEde git_branch: devel git_last_commit: b1d08fe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iSEEde_1.9.0.tar.gz vignettes: vignettes/iSEEde/inst/doc/annotations.html, vignettes/iSEEde/inst/doc/iSEEde.html, vignettes/iSEEde/inst/doc/methods.html, vignettes/iSEEde/inst/doc/rounding.html vignetteTitles: Using annotations to facilitate interactive exploration, Introduction to iSEEde, Supported differential expression methods, Rounding numeric values hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEde/inst/doc/annotations.R, vignettes/iSEEde/inst/doc/iSEEde.R, vignettes/iSEEde/inst/doc/methods.R, vignettes/iSEEde/inst/doc/rounding.R suggestsMe: iSEEpathways dependencyCount: 123 Package: iSEEfier Version: 1.7.0 Depends: R (>= 4.1.0) Imports: iSEE, iSEEu, methods, ggplot2, igraph, rlang, stats, SummarizedExperiment, SingleCellExperiment, visNetwork, BiocBaseUtils Suggests: knitr, rmarkdown, scater, scRNAseq, org.Mm.eg.db, scuttle, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: d91d9b5ad838b4e673c3fb59fb963ecf NeedsCompilation: no Title: Streamlining the creation of initial states for starting an iSEE instance Description: iSEEfier provides a set of functionality to quickly and intuitively create, inspect, and combine initial configuration objects. These can be conveniently passed in a straightforward manner to the function call to launch iSEE() with the specified configuration. This package currently works seamlessly with the sets of panels provided by the iSEE and iSEEu packages, but can be extended to accommodate the usage of any custom panel (e.g. from iSEEde, iSEEpathways, or any panel developed independently by the user). biocViews: CellBasedAssays, Clustering, DimensionReduction, FeatureExtraction, GUI, GeneExpression, ImmunoOncology, ShinyApps, SingleCell, Software, Transcription, Transcriptomics, Visualization Author: Najla Abassi [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Najla Abassi URL: https://github.com/NajlaAbassi/iSEEfier VignetteBuilder: knitr BugReports: https://github.com/NajlaAbassi/iSEEfier/issues git_url: https://git.bioconductor.org/packages/iSEEfier git_branch: devel git_last_commit: a000139 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iSEEfier_1.7.0.tar.gz vignettes: vignettes/iSEEfier/inst/doc/iSEEfier_userguide.html vignetteTitles: iSEEfier_userguide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEEfier/inst/doc/iSEEfier_userguide.R dependencyCount: 115 Package: iSEEhex Version: 1.13.0 Depends: SummarizedExperiment, iSEE Imports: ggplot2, hexbin, methods, shiny Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), scRNAseq, scater License: Artistic-2.0 MD5sum: 09ce286e34e825e99efecfcaef2b99e7 NeedsCompilation: no Title: iSEE extension for summarising data points in hexagonal bins Description: This package provides panels summarising data points in hexagonal bins for `iSEE`. It is part of `iSEEu`, the iSEE universe of panels that extend the `iSEE` package. biocViews: Software, Infrastructure Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Federico Marini [aut] (ORCID: ), Aaron Lun [aut] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEhex VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEhex git_url: https://git.bioconductor.org/packages/iSEEhex git_branch: devel git_last_commit: 2ce63cf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iSEEhex_1.13.0.tar.gz vignettes: vignettes/iSEEhex/inst/doc/iSEEhex.html vignetteTitles: The iSEEhex package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEhex/inst/doc/iSEEhex.R dependsOnMe: iSEEu dependencyCount: 111 Package: iSEEhub Version: 1.13.0 Depends: SummarizedExperiment, SingleCellExperiment, ExperimentHub Imports: AnnotationHub, BiocManager, DT, iSEE, methods, rintrojs, S4Vectors, shiny, shinydashboard, shinyjs, utils Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), nullrangesData Enhances: BioPlex, biscuiteerData, bodymapRat, CLLmethylation, CopyNeutralIMA, curatedAdipoArray, curatedAdipoChIP, curatedMetagenomicData, curatedTCGAData, DMRcatedata, DuoClustering2018, easierData, emtdata, epimutacionsData, FieldEffectCrc, GenomicDistributionsData, GSE103322, GSE13015, GSE62944, HDCytoData, HMP16SData, HumanAffyData, imcdatasets, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, MethylSeqData, muscData, NxtIRFdata, ObMiTi, quantiseqr, restfulSEData, RLHub, sesameData, SimBenchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialDmelxsim, STexampleData, TabulaMurisData, TabulaMurisSenisData, TENxVisiumData, tissueTreg, VectraPolarisData, xcoredata License: Artistic-2.0 MD5sum: cf0d2f0b2da20461105d5d2076c89ce1 NeedsCompilation: no Title: iSEE for the Bioconductor ExperimentHub Description: This package defines a custom landing page for an iSEE app interfacing with the Bioconductor ExperimentHub. The landing page allows users to browse the ExperimentHub, select a data set, download and cache it, and import it directly into a Bioconductor iSEE app. biocViews: DataImport, ImmunoOncology Infrastructure, ShinyApps, SingleCell, Software Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEhub VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEhub git_url: https://git.bioconductor.org/packages/iSEEhub git_branch: devel git_last_commit: f5f8921 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iSEEhub_1.13.0.tar.gz vignettes: vignettes/iSEEhub/inst/doc/contributing.html, vignettes/iSEEhub/inst/doc/iSEEhub.html vignetteTitles: Contributing to iSEEhub, Introduction to iSEEhub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEhub/inst/doc/contributing.R, vignettes/iSEEhub/inst/doc/iSEEhub.R dependencyCount: 141 Package: iSEEindex Version: 1.9.0 Depends: SummarizedExperiment, SingleCellExperiment Imports: BiocFileCache, DT, iSEE, methods, paws.storage, rintrojs, shiny, shinydashboard, shinyjs, stringr, urltools, utils Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, markdown, scRNAseq, sessioninfo, testthat (>= 3.0.0), yaml License: Artistic-2.0 MD5sum: 42933ee7f0a473fd8d6e4725c1bb0c05 NeedsCompilation: no Title: iSEE extension for a landing page to a custom collection of data sets Description: This package provides an interface to any collection of data sets within a single iSEE web-application. The main functionality of this package is to define a custom landing page allowing app maintainers to list a custom collection of data sets that users can selected from and directly load objects into an iSEE web-application. biocViews: Software, Infrastructure Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Thomas Sandmann [ctb] (ORCID: ), Federico Marini [aut] (ORCID: ), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEindex VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEindex git_url: https://git.bioconductor.org/packages/iSEEindex git_branch: devel git_last_commit: 8de1724 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iSEEindex_1.9.0.tar.gz vignettes: vignettes/iSEEindex/inst/doc/header.html, vignettes/iSEEindex/inst/doc/iSEEindex.html, vignettes/iSEEindex/inst/doc/resources.html vignetteTitles: Adding custom header and footer to the landing page, Introduction to iSEEindex, Implementing custom iSEEindex resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEindex/inst/doc/header.R, vignettes/iSEEindex/inst/doc/iSEEindex.R, vignettes/iSEEindex/inst/doc/resources.R dependencyCount: 137 Package: iSEEpathways Version: 1.9.0 Depends: iSEE Imports: ggplot2, methods, S4Vectors, shiny, shinyWidgets, stats, SummarizedExperiment Suggests: airway, BiocStyle, covr, edgeR, fgsea, GO.db, iSEEde, knitr, org.Hs.eg.db, RefManageR, rmarkdown, scater, scuttle, sessioninfo, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 3907cbab925f043d1f44cf36e2715fa4 NeedsCompilation: no Title: iSEE extension for panels related to pathway analysis Description: This package contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels or modes facilitating the analysis of pathway analysis results. This package does not perform pathway analysis. Instead, it provides methods to embed precomputed pathway analysis results in a SummarizedExperiment object, in a manner that is compatible with interactive visualisation in iSEE applications. biocViews: Software, Infrastructure, DifferentialExpression, GeneExpression, GUI, Visualization, Pathways, GeneSetEnrichment, GO, ShinyApps Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Thomas Sandmann [ctb] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Federico Marini [ctb] (ORCID: ), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEpathways VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEpathways git_url: https://git.bioconductor.org/packages/iSEEpathways git_branch: devel git_last_commit: 2f932af git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iSEEpathways_1.9.0.tar.gz vignettes: vignettes/iSEEpathways/inst/doc/gene-ontology.html, vignettes/iSEEpathways/inst/doc/integration.html, vignettes/iSEEpathways/inst/doc/iSEEpathways.html vignetteTitles: Working with the Gene Ontology, Integration with other panels, Introduction to iSEEpathways hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEpathways/inst/doc/gene-ontology.R, vignettes/iSEEpathways/inst/doc/integration.R, vignettes/iSEEpathways/inst/doc/iSEEpathways.R dependencyCount: 110 Package: iSEEtree Version: 1.5.0 Depends: R (>= 4.4.0), iSEE (>= 2.19.4) Imports: ape, ggplot2, ggtree, grDevices, methods, miaViz, purrr, S4Vectors, shiny, mia, shinyWidgets, SingleCellExperiment, SummarizedExperiment, tidygraph, TreeSummarizedExperiment, utils Suggests: biomformat, BiocStyle, knitr, RefManageR, remotes, rmarkdown, scater, testthat (>= 3.0.0), vegan License: Artistic-2.0 MD5sum: 3332998c56e0c2a707b7dbce5c0aa49e NeedsCompilation: no Title: Interactive visualisation for microbiome data Description: iSEEtree is an extension of iSEE for the TreeSummarizedExperiment data container. It provides interactive panel designs to explore hierarchical datasets, such as the microbiome and cell lines. biocViews: Software, Visualization, Microbiome, GUI, ShinyApps, DataImport Author: Giulio Benedetti [aut, cre] (ORCID: ), Ely Seraidarian [ctb] (ORCID: ), Leo Lahti [aut] (ORCID: ) Maintainer: Giulio Benedetti URL: https://github.com/microbiome/iSEEtree VignetteBuilder: knitr BugReports: https://github.com/microbiome/iSEEtree/issues git_url: https://git.bioconductor.org/packages/iSEEtree git_branch: devel git_last_commit: 63da959 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iSEEtree_1.5.0.tar.gz vignettes: vignettes/iSEEtree/inst/doc/iSEEtree.html, vignettes/iSEEtree/inst/doc/panels.html vignetteTitles: iSEEtree, iSEEtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEtree/inst/doc/iSEEtree.R, vignettes/iSEEtree/inst/doc/panels.R importsMe: miaDash dependencyCount: 201 Package: iSEEu Version: 1.23.0 Depends: iSEE, iSEEhex Imports: methods, S4Vectors, IRanges, shiny, SummarizedExperiment, SingleCellExperiment, ggplot2 (>= 3.4.0), DT, stats, colourpicker, shinyAce Suggests: scRNAseq, scater, scran, airway, edgeR, AnnotationDbi, org.Hs.eg.db, GO.db, KEGGREST, knitr, igraph, rmarkdown, BiocStyle, htmltools, Rtsne, uwot, testthat (>= 2.1.0), covr License: MIT + file LICENSE MD5sum: 43b261a0f11cfd7574ce7747b0d3f2a5 NeedsCompilation: no Title: iSEE Universe Description: iSEEu (the iSEE universe) contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels, or modes allowing easy configuration of iSEE applications. biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Federico Marini [aut] (ORCID: ), Aaron Lun [aut] (ORCID: ), Michael Stadler [ctb] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEu VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEEu/issues git_url: https://git.bioconductor.org/packages/iSEEu git_branch: devel git_last_commit: 2f5ca87 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iSEEu_1.23.0.tar.gz vignettes: vignettes/iSEEu/inst/doc/iSEEu.html vignetteTitles: Panel universe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEEu/inst/doc/iSEEu.R importsMe: iSEEfier dependencyCount: 112 Package: iSeq Version: 1.63.0 Depends: R (>= 2.10.0) License: GPL (>= 2) MD5sum: 046e07ff634751e409be7ac6c5a95538 NeedsCompilation: yes Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models Description: Bayesian hidden Ising models are implemented to identify IP-enriched genomic regions from ChIP-seq data. They can be used to analyze ChIP-seq data with and without controls and replicates. biocViews: ChIPSeq, Sequencing Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iSeq git_branch: devel git_last_commit: c340405 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iSeq_1.63.0.tar.gz vignettes: vignettes/iSeq/inst/doc/iSeq.pdf vignetteTitles: iSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSeq/inst/doc/iSeq.R dependencyCount: 0 Package: ISLET Version: 1.13.0 Depends: R(>= 4.2.0), Matrix, parallel, BiocParallel, SummarizedExperiment, BiocGenerics, lme4, nnls Imports: stats, methods, purrr, abind Suggests: BiocStyle, knitr, rmarkdown, htmltools, RUnit, dplyr License: GPL-2 MD5sum: d9a76d0d9dfeaee9fa912ff6bbe0e71a NeedsCompilation: no Title: Individual-Specific ceLl typE referencing Tool Description: ISLET is a method to conduct signal deconvolution for general -omics data. It can estimate the individual-specific and cell-type-specific reference panels, when there are multiple samples observed from each subject. It takes the input of the observed mixture data (feature by sample matrix), and the cell type mixture proportions (sample by cell type matrix), and the sample-to-subject information. It can solve for the reference panel on the individual-basis and conduct test to identify cell-type-specific differential expression (csDE) genes. It also improves estimated cell type mixture proportions by integrating personalized reference panels. biocViews: Software, RNASeq, Transcriptomics, Transcription, Sequencing, GeneExpression, DifferentialExpression, DifferentialMethylation Author: Hao Feng [aut, cre] (ORCID: ), Qian Li [aut], Guanqun Meng [aut] Maintainer: Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ISLET git_branch: devel git_last_commit: 6b4e816 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ISLET_1.13.0.tar.gz vignettes: vignettes/ISLET/inst/doc/ISLET.html vignetteTitles: Individual-specific and cell-type-specific deconvolution using ISLET hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ISLET/inst/doc/ISLET.R dependencyCount: 55 Package: islify Version: 1.3.0 Depends: R (>= 4.5) Imports: autothresholdr (>= 1.4.2), Matrix (>= 1.6.1), RBioFormats (>= 1.0.0), tiff (>= 0.1.12), png (>= 0.1.8), dbscan (>= 1.1.12), abind (>= 1.4.8), methods (>= 4.3.3), stats (>= 4.3.3) Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 5ed72c32426bdb47ac1fc75644a86cbe NeedsCompilation: no Title: Automatic scoring and classification of cell-based assay images Description: This software is meant to be used for classification of images of cell-based assays for neuronal surface autoantibody detection or similar techniques. It takes imaging files as input and creates a composite score from these, that for example can be used to classify samples as negative or positive for a certain antibody-specificity. The reason for its name is that I during its creation have thought about the individual picture as an archielago where we with different filters control the water level as well as ground characteristica, thereby finding islands of interest. biocViews: Software,CellBasedAssays,BiomedicalInformatics,FeatureExtraction, Visualization,Pathways,Classification Author: Jakob Theorell [aut, cre, fnd] (ORCID: , Funding provided by the Swedish Wenner-Gren Foundations) Maintainer: Jakob Theorell URL: https://github.com/Bioconductor/islify VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/islify/issues git_url: https://git.bioconductor.org/packages/islify git_branch: devel git_last_commit: 91f9839 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/islify_1.3.0.tar.gz vignettes: vignettes/islify/inst/doc/islify_usage.html vignetteTitles: Typical usage of islify hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/islify/inst/doc/islify_usage.R dependencyCount: 83 Package: isobar Version: 1.57.0 Depends: R (>= 2.10.0), Biobase, stats, methods Imports: distr, plyr, biomaRt, ggplot2 Suggests: MSnbase, OrgMassSpecR, XML, RJSONIO, Hmisc, gplots, RColorBrewer, gridExtra, limma, boot, DBI, MASS License: LGPL-2 MD5sum: 1e0522306c2195af0667ba23ae708193 NeedsCompilation: no Title: Analysis and quantitation of isobarically tagged MSMS proteomics data Description: isobar provides methods for preprocessing, normalization, and report generation for the analysis of quantitative mass spectrometry proteomics data labeled with isobaric tags, such as iTRAQ and TMT. Features modules for integrating and validating PTM-centric datasets (isobar-PTM). More information on http://www.ms-isobar.org. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Bioinformatics, MultipleComparisons, QualityControl Author: Florian P Breitwieser and Jacques Colinge , with contributions from Alexey Stukalov , Xavier Robin and Florent Gluck Maintainer: Florian P Breitwieser URL: https://github.com/fbreitwieser/isobar BugReports: https://github.com/fbreitwieser/isobar/issues git_url: https://git.bioconductor.org/packages/isobar git_branch: devel git_last_commit: 0225060 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/isobar_1.57.0.tar.gz vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf, vignettes/isobar/inst/doc/isobar-ptm.pdf, vignettes/isobar/inst/doc/isobar-usecases.pdf, vignettes/isobar/inst/doc/isobar.pdf vignetteTitles: isobar for developers, isobar for quantification of PTM datasets, Usecases for isobar package, isobar package for iTRAQ and TMT protein quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/isobar/inst/doc/isobar-devel.R, vignettes/isobar/inst/doc/isobar-ptm.R, vignettes/isobar/inst/doc/isobar-usecases.R, vignettes/isobar/inst/doc/isobar.R dependencyCount: 79 Package: IsoBayes Version: 1.9.0 Depends: R (>= 4.3.0) Imports: methods, Rcpp, data.table, glue, stats, doParallel, parallel, doRNG, foreach, iterators, ggplot2, HDInterval, SummarizedExperiment, S4Vectors LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 64aba887c9f463fe7602820ed91c2227 NeedsCompilation: yes Title: IsoBayes: Single Isoform protein inference Method via Bayesian Analyses Description: IsoBayes is a Bayesian method to perform inference on single protein isoforms. Our approach infers the presence/absence of protein isoforms, and also estimates their abundance; additionally, it provides a measure of the uncertainty of these estimates, via: i) the posterior probability that a protein isoform is present in the sample; ii) a posterior credible interval of its abundance. IsoBayes inputs liquid cromatography mass spectrometry (MS) data, and can work with both PSM counts, and intensities. When available, trascript isoform abundances (i.e., TPMs) are also incorporated: TPMs are used to formulate an informative prior for the respective protein isoform relative abundance. We further identify isoforms where the relative abundance of proteins and transcripts significantly differ. We use a two-layer latent variable approach to model two sources of uncertainty typical of MS data: i) peptides may be erroneously detected (even when absent); ii) many peptides are compatible with multiple protein isoforms. In the first layer, we sample the presence/absence of each peptide based on its estimated probability of being mistakenly detected, also known as PEP (i.e., posterior error probability). In the second layer, for peptides that were estimated as being present, we allocate their abundance across the protein isoforms they map to. These two steps allow us to recover the presence and abundance of each protein isoform. biocViews: StatisticalMethod, Bayesian, Proteomics, MassSpectrometry, AlternativeSplicing, Sequencing, RNASeq, GeneExpression, Genetics, Visualization, Software Author: Jordy Bollon [aut], Simone Tiberi [aut, cre] (ORCID: ) Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/IsoBayes SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/IsoBayes/issues git_url: https://git.bioconductor.org/packages/IsoBayes git_branch: devel git_last_commit: 2a6c228 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IsoBayes_1.9.0.tar.gz vignettes: vignettes/IsoBayes/inst/doc/IsoBayes.html vignetteTitles: IsoBayes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IsoBayes/inst/doc/IsoBayes.R dependencyCount: 54 Package: IsoCorrectoR Version: 1.29.0 Depends: R (>= 3.5) Imports: dplyr, magrittr, methods, quadprog, readr, readxl, stringr, tibble, tools, utils, pracma, WriteXLS Suggests: IsoCorrectoRGUI, knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: b8a73454e2ca00764d959a711f8879ca NeedsCompilation: no Title: Correction for natural isotope abundance and tracer purity in MS and MS/MS data from stable isotope labeling experiments Description: IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as ultra-high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). See the Bioconductor package IsoCorrectoRGUI for a graphical user interface to IsoCorrectoR. NOTE: With R version 4.0.0, writing correction results to Excel files may currently not work on Windows. However, writing results to csv works as before. biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoR git_branch: devel git_last_commit: 9553234 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IsoCorrectoR_1.29.0.tar.gz vignettes: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.R importsMe: IsoCorrectoRGUI dependencyCount: 40 Package: IsoCorrectoRGUI Version: 1.27.0 Depends: R (>= 3.6) Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 510bbb3dde73f1f2bfd12b1a093d27cb NeedsCompilation: no Title: Graphical User Interface for IsoCorrectoR Description: IsoCorrectoRGUI is a Graphical User Interface for the IsoCorrectoR package. IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, GUI, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Kuerner [aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoRGUI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoRGUI git_branch: devel git_last_commit: 5dd4785 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IsoCorrectoRGUI_1.27.0.tar.gz vignettes: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.R suggestsMe: IsoCorrectoR dependencyCount: 43 Package: ISoLDE Version: 1.39.0 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) MD5sum: 08a7e5b53a2c25e9b81d9a9c4d2a709b NeedsCompilation: yes Title: Integrative Statistics of alleLe Dependent Expression Description: This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below. biocViews: ImmunoOncology, GeneExpression, Transcription, GeneSetEnrichment, Genetics, Sequencing, RNASeq, MultipleComparison, SNP, GeneticVariability, Epigenetics, MathematicalBiology, GeneRegulation Author: Christelle Reynès [aut, cre], Marine Rohmer [aut], Guilhem Kister [aut] Maintainer: Christelle Reynès URL: www.r-project.org git_url: https://git.bioconductor.org/packages/ISoLDE git_branch: devel git_last_commit: e5f684d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ISoLDE_1.39.0.tar.gz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: isomiRs Version: 1.39.1 Depends: R (>= 4.4), SummarizedExperiment Imports: AnnotationDbi, BiocGenerics, Biobase, broom, cluster, cowplot, DEGreport, DESeq2, IRanges, dplyr, GenomicRanges, gplots, ggplot2, gtools, gridExtra, grid, grDevices, graphics, GGally, limma, methods, RColorBrewer, readr, reshape, rlang, stats, stringr, S4Vectors, tidyr, tibble Suggests: knitr, rmarkdown, org.Mm.eg.db, pheatmap, BiocStyle, testthat License: MIT + file LICENSE MD5sum: a741c522e32a2115f57cc9e479abbb2a NeedsCompilation: no Title: Analyze isomiRs and miRNAs from small RNA-seq Description: Characterization of miRNAs and isomiRs, clustering and differential expression. biocViews: miRNA, RNASeq, DifferentialExpression, Clustering, ImmunoOncology Author: Lorena Pantano [aut, cre], Georgia Escaramis [aut] (CIBERESP - CIBER Epidemiologia y Salud Publica) Maintainer: Lorena Pantano VignetteBuilder: knitr BugReports: https://github.com/lpantano/isomiRs/issues git_url: https://git.bioconductor.org/packages/isomiRs git_branch: devel git_last_commit: a053d30 git_last_commit_date: 2025-12-23 Date/Publication: 2026-04-20 source.ver: src/contrib/isomiRs_1.39.1.tar.gz vignettes: vignettes/isomiRs/inst/doc/isomiRs.html vignetteTitles: miRNA and isomiR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/isomiRs/inst/doc/isomiRs.R dependencyCount: 143 Package: ITALICS Version: 2.71.0 Depends: R (>= 2.0.0), GLAD, ITALICSData, oligo, affxparser, pd.mapping50k.xba240 Imports: affxparser, DBI, GLAD, oligo, oligoClasses, stats Suggests: pd.mapping50k.hind240, pd.mapping250k.sty, pd.mapping250k.nsp License: GPL-2 MD5sum: 9d4d26c188a96c5cc5002f5e12dd1102 NeedsCompilation: no Title: ITALICS Description: A Method to normalize of Affymetrix GeneChip Human Mapping 100K and 500K set biocViews: Microarray, CopyNumberVariation Author: Guillem Rigaill, Philippe Hupe Maintainer: Guillem Rigaill URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/ITALICS git_branch: devel git_last_commit: afe983f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ITALICS_2.71.0.tar.gz vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf vignetteTitles: ITALICS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ITALICS/inst/doc/ITALICS.R dependencyCount: 59 Package: iterativeBMA Version: 1.69.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: fb13b0fda80e3640d7c09a734ccf40a5 NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) algorithm Description: The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402). biocViews: Microarray, Classification Author: Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter Maintainer: Ka Yee Yeung URL: http://faculty.washington.edu/kayee/research.html git_url: https://git.bioconductor.org/packages/iterativeBMA git_branch: devel git_last_commit: 01a26fb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iterativeBMA_1.69.0.tar.gz vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMA/inst/doc/iterativeBMA.R dependencyCount: 22 Package: iterativeBMAsurv Version: 1.69.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: 4fd4313d789d88d18a583f2de459745d NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis Description: The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data. biocViews: Microarray Author: Amalia Annest, University of Washington, Tacoma, WA Ka Yee Yeung, University of Washington, Seattle, WA Maintainer: Ka Yee Yeung URL: http://expression.washington.edu/ibmasurv/protected git_url: https://git.bioconductor.org/packages/iterativeBMAsurv git_branch: devel git_last_commit: 184eb1d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/iterativeBMAsurv_1.69.0.tar.gz vignettes: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm For Survival Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.R dependencyCount: 19 Package: IVAS Version: 2.31.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, Biobase Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, Seqinfo, ggfortify, grDevices, methods, Matrix, BiocParallel,utils, stats Suggests: BiocStyle License: GPL-2 MD5sum: 7cf38f1e4a8734bb7b726178ff5c479a NeedsCompilation: no Title: Identification of genetic Variants affecting Alternative Splicing Description: Identification of genetic variants affecting alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Sangsoo Kim Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IVAS git_branch: devel git_last_commit: 0b3f358 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/IVAS_2.31.0.tar.gz vignettes: vignettes/IVAS/inst/doc/IVAS.pdf vignetteTitles: IVAS : Identification of genetic Variants affecting Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IVAS/inst/doc/IVAS.R dependencyCount: 112 Package: ivygapSE Version: 1.33.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: shiny, survival, survminer, hwriter, plotly, ggplot2, S4Vectors, graphics, stats, utils, UpSetR Suggests: knitr, png, limma, grid, DT, randomForest, digest, testthat, rmarkdown, BiocStyle, magick, statmod, codetools License: Artistic-2.0 MD5sum: 529626a1a8adc273042ee313ff1c7af0 NeedsCompilation: no Title: A SummarizedExperiment for Ivy-GAP data Description: Define a SummarizedExperiment and exploratory app for Ivy-GAP glioblastoma image, expression, and clinical data. biocViews: Transcription, Software, Visualization, Survival, GeneExpression, Sequencing Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ivygapSE git_branch: devel git_last_commit: e007a21 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ivygapSE_1.33.0.tar.gz vignettes: vignettes/ivygapSE/inst/doc/ivygapSE.html vignetteTitles: ivygapSE -- SummarizedExperiment for Ivy-GAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ivygapSE/inst/doc/ivygapSE.R dependencyCount: 153 Package: IWTomics Version: 1.35.1 Depends: R (>= 3.5.0), GenomicRanges Imports: parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools Suggests: XVector, knitr License: GPL (>=2) MD5sum: be96f89d3bef6220862484dd340484f3 NeedsCompilation: no Title: Interval-Wise Testing for Omics Data Description: Implementation of the Interval-Wise Testing (IWT) for omics data. This inferential procedure tests for differences in "Omics" data between two groups of genomic regions (or between a group of genomic regions and a reference center of symmetry), and does not require fixing location and scale at the outset. biocViews: StatisticalMethod, MultipleComparison, DifferentialExpression, DifferentialMethylation, DifferentialPeakCalling, GenomeAnnotation, DataImport Author: Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini Maintainer: Marzia A Cremona VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IWTomics git_branch: devel git_last_commit: 22ec111 git_last_commit_date: 2026-01-05 Date/Publication: 2026-04-20 source.ver: src/contrib/IWTomics_1.35.1.tar.gz vignettes: vignettes/IWTomics/inst/doc/IWTomics.pdf vignetteTitles: Introduction to IWTomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IWTomics/inst/doc/IWTomics.R dependencyCount: 59 Package: jazzPanda Version: 1.3.0 Depends: R (>= 4.5.0) Imports: spatstat.geom, dplyr, glmnet, caret, foreach, stats, magrittr, doParallel, BiocParallel, methods, BumpyMatrix,SpatialExperiment Suggests: BiocStyle, knitr, rmarkdown, spatstat, Seurat, statmod, corrplot, ggplot2, ggraph, ggrepel, gridExtra, reshape2, igraph, jsonlite, vdiffr, patchwork, ggpubr, tidyr, SpatialFeatureExperiment, ExperimentHub, TENxXeniumData, SingleCellExperiment, SFEData, Matrix, data.table, scran, scater, grid, GenomeInfoDb, testthat (>= 3.0.0) License: GPL-3 MD5sum: f3c137e8725d0f99bf00db79c5b5df3a NeedsCompilation: no Title: Finding spatially relevant marker genes in image based spatial transcriptomics data Description: This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise. biocViews: Spatial, GeneExpression, DifferentialExpression, StatisticalMethod, Transcriptomics Author: Melody Jin [aut, cre] (ORCID: ) Maintainer: Melody Jin URL: https://github.com/phipsonlab/jazzPanda, https://bhuvad.github.io/jazzPanda/ VignetteBuilder: knitr BugReports: https://github.com/phipsonlab/jazzPanda/issues git_url: https://git.bioconductor.org/packages/jazzPanda git_branch: devel git_last_commit: f913a8b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/jazzPanda_1.3.0.tar.gz vignettes: vignettes/jazzPanda/inst/doc/jazzPanda.html vignetteTitles: jazzPanda example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/jazzPanda/inst/doc/jazzPanda.R dependencyCount: 135 Package: jvecfor Version: 0.99.7 Depends: R (>= 4.6.0) Imports: BiocNeighbors, BiocParallel, Matrix, bluster, data.table, methods, processx Suggests: BiocStyle, igraph, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: ab1f02abcd0612227a08ff38ad5c5bb0 NeedsCompilation: no Title: Fast K-Nearest Neighbor Search for Single-Cell Analysis Description: Drop-in replacement for BiocNeighbors::findKNN using the jvecfor Java library, which builds on the jvector library to leverage the Java Vector API for portable SIMD acceleration across AVX2, AVX-512, and ARM NEON hardware. jvecfor/jvector implements HNSW-DiskANN approximate search and VP-tree exact search. The package achieves approximately 2x speedup over Annoy-based search at n >= 50K cells while returning output structurally identical to BiocNeighbors, making it suitable for seamless integration into existing Bioconductor single-cell workflows. Convenience wrappers delegate shared nearest-neighbor (SNN) and k-nearest-neighbor (KNN) graph construction to the bluster package. biocViews: SingleCell, GraphAndNetwork, Clustering, Classification Author: Anestis Gkanogiannis [aut, cre] (ORCID: ) Maintainer: Anestis Gkanogiannis URL: https://github.com/gkanogiannis/jvecfor SystemRequirements: Java (>= 20) VignetteBuilder: knitr BugReports: https://github.com/gkanogiannis/jvecfor/issues git_url: https://git.bioconductor.org/packages/jvecfor git_branch: devel git_last_commit: 375f4ff git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/jvecfor_0.99.7.tar.gz vignettes: vignettes/jvecfor/inst/doc/jvecfor_vignette.html vignetteTitles: jvecfor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/jvecfor/inst/doc/jvecfor_vignette.R dependencyCount: 49 Package: katdetectr Version: 1.13.0 Depends: R (>= 4.2) Imports: Biobase (>= 2.54.0), BiocParallel (>= 1.26.2), BSgenome (>= 1.62.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.3), BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.4), changepoint (>= 2.2.3), changepoint.np (>= 1.0.3), checkmate (>= 2.0.0), dplyr (>= 1.0.8), GenomeInfoDb (>= 1.28.4), GenomicRanges (>= 1.44.0), ggplot2 (>= 3.3.5), ggtext (>= 0.1.1), IRanges (>= 2.26.0), maftools (>= 2.10.5), methods (>= 4.1.3), plyranges (>= 1.17.0), Rdpack (>= 2.3.1), rlang (>= 1.0.2), S4Vectors (>= 0.30.2), scales (>= 1.2.0), tibble (>= 3.1.6), tidyr (>= 1.2.0), tools, utils, VariantAnnotation (>= 1.38.0) Suggests: BiocStyle (>= 2.26.0), knitr (>= 1.37), rmarkdown (>= 2.13), stats, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: 5eb68eaf06b42ea696fff6b00fc2893d NeedsCompilation: no Title: Detection, Characterization and Visualization of Kataegis in Sequencing Data Description: Kataegis refers to the occurrence of regional hypermutation and is a phenomenon observed in a wide range of malignancies. Using changepoint detection katdetectr aims to identify putative kataegis foci from common data-formats housing genomic variants. Katdetectr has shown to be a robust package for the detection, characterization and visualization of kataegis. biocViews: WholeGenome, Software, SNP, Sequencing, Classification, VariantAnnotation Author: Daan Hazelaar [aut, cre] (ORCID: ), Job van Riet [aut] (ORCID: ), Harmen van de Werken [ths] (ORCID: ) Maintainer: Daan Hazelaar URL: https://doi.org/doi:10.18129/B9.bioc.katdetectr VignetteBuilder: knitr BugReports: https://github.com/ErasmusMC-CCBC/katdetectr/issues git_url: https://git.bioconductor.org/packages/katdetectr git_branch: devel git_last_commit: 01624d8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/katdetectr_1.13.0.tar.gz vignettes: vignettes/katdetectr/inst/doc/General_overview.html vignetteTitles: Overview_katdetectr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/katdetectr/inst/doc/General_overview.R dependencyCount: 124 Package: KBoost Version: 1.19.0 Depends: R (>= 4.1), stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 | GPL-3 MD5sum: 48b0ba84d52cc37f9bdd6496e1115d8d NeedsCompilation: no Title: Inference of gene regulatory networks from gene expression data Description: Reconstructing gene regulatory networks and transcription factor activity is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-art algorithm are often not able to handle large amounts of data. Furthermore, many of the present methods predict numerous false positives and are unable to integrate other sources of information such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. KBoost can also use a prior network built on previously known transcription factor targets. We have benchmarked KBoost using three different datasets against other high performing algorithms. The results show that our method compares favourably to other methods across datasets. biocViews: Network, GraphAndNetwork, Bayesian, NetworkInference, GeneRegulation, Transcriptomics, SystemsBiology, Transcription, GeneExpression, Regression, PrincipalComponent Author: Luis F. Iglesias-Martinez [aut, cre] (ORCID: ), Barbara de Kegel [aut], Walter Kolch [aut] Maintainer: Luis F. Iglesias-Martinez URL: https://github.com/Luisiglm/KBoost VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KBoost git_branch: devel git_last_commit: 6bffde0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/KBoost_1.19.0.tar.gz vignettes: vignettes/KBoost/inst/doc/KBoost.html vignetteTitles: KBoost hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KBoost/inst/doc/KBoost.R dependencyCount: 2 Package: KCsmart Version: 2.69.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: df92710a3f83c029e947d031360d99a8 NeedsCompilation: no Title: Multi sample aCGH analysis package using kernel convolution Description: Multi sample aCGH analysis package using kernel convolution biocViews: CopyNumberVariation, Visualization, aCGH, Microarray Author: Jorma de Ronde, Christiaan Klijn, Arno Velds Maintainer: Jorma de Ronde git_url: https://git.bioconductor.org/packages/KCsmart git_branch: devel git_last_commit: fc58ed6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/KCsmart_2.69.0.tar.gz vignettes: vignettes/KCsmart/inst/doc/KCS.pdf vignetteTitles: KCsmart example session hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KCsmart/inst/doc/KCS.R dependencyCount: 19 Package: kebabs Version: 1.45.0 Depends: R (>= 3.3.0), Biostrings (>= 2.35.5), kernlab Imports: methods, stats, Rcpp (>= 0.11.2), Matrix (>= 1.5-0), XVector (>= 0.7.3), S4Vectors (>= 0.27.3), e1071, LiblineaR, graphics, grDevices, utils, apcluster LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, Biobase, BiocGenerics, knitr License: GPL (>= 2.1) MD5sum: ba4be0af4a31cb94d96b1d1c7218a5c7 NeedsCompilation: yes Title: Kernel-Based Analysis of Biological Sequences Description: The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions. biocViews: SupportVectorMachine, Classification, Clustering, Regression Author: Johannes Palme [aut], Ulrich Bodenhofer [aut, cre, ths] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/kebabs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kebabs git_branch: devel git_last_commit: 3ed2a80 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/kebabs_1.45.0.tar.gz vignettes: vignettes/kebabs/inst/doc/kebabs.pdf vignetteTitles: KeBABS - An R Package for Kernel Based Analysis of Biological Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kebabs/inst/doc/kebabs.R dependsOnMe: procoil importsMe: odseq dependencyCount: 26 Package: KEGGgraph Version: 1.71.0 Depends: R (>= 3.5.0) Imports: methods, XML (>= 2.3-0), graph, utils, RCurl, Rgraphviz Suggests: RBGL, testthat, RColorBrewer, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) MD5sum: 1c3ced27c2205922f1fa22be0abfa244 NeedsCompilation: no Title: KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor Description: KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: Jitao David Zhang, with inputs from Paul Shannon and Hervé Pagès Maintainer: Jitao David Zhang URL: https://accio.github.io/research/#software git_url: https://git.bioconductor.org/packages/KEGGgraph git_branch: devel git_last_commit: 09a8170 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/KEGGgraph_1.71.0.tar.gz vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf vignetteTitles: KEGGgraph: graph approach to KEGG PATHWAY, KEGGgraph: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraph.R, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R dependsOnMe: lpNet, ROntoTools, SPIA importsMe: clipper, DEGraph, EnrichmentBrowser, MetaboSignal, MWASTools, NCIgraph, pathview, iCARH suggestsMe: DEGraph, GenomicRanges, kangar00, maGUI, rags2ridges dependencyCount: 14 Package: KEGGlincs Version: 1.37.0 Depends: R (>= 3.3), KOdata, hgu133a.db, org.Hs.eg.db (>= 3.3.0) Imports: AnnotationDbi,KEGGgraph,igraph,plyr,gtools,httr,RJSONIO,KEGGREST, methods,graphics,stats,utils, XML, grDevices Suggests: BiocManager (>= 1.20.3), knitr, graph License: GPL-3 MD5sum: 7fa17a11438791102c606cac66acea8a NeedsCompilation: no Title: Visualize all edges within a KEGG pathway and overlay LINCS data Description: See what is going on 'under the hood' of KEGG pathways by explicitly re-creating the pathway maps from information obtained from KGML files. biocViews: NetworkInference, GeneExpression, DataRepresentation, ThirdPartyClient,CellBiology,GraphAndNetwork,Pathways,KEGG,Network Author: Shana White Maintainer: Shana White , Mario Medvedovic SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGlincs git_branch: devel git_last_commit: 0de0797 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/KEGGlincs_1.37.0.tar.gz vignettes: vignettes/KEGGlincs/inst/doc/Example-workflow.html vignetteTitles: KEGGlincs Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGlincs/inst/doc/Example-workflow.R dependencyCount: 60 Package: keggorthology Version: 2.63.0 Depends: R (>= 2.5.0), hgu95av2.db, graph Imports: AnnotationDbi, DBI, grDevices, methods, tools, utils Suggests: RBGL,ALL License: Artistic-2.0 MD5sum: 9f463dc1415afda03f48e8adbc78125f NeedsCompilation: no Title: graph support for KO, KEGG Orthology Description: graphical representation of the Feb 2010 KEGG Orthology. The KEGG orthology is a set of pathway IDs that are not to be confused with the KEGG ortholog IDs. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/keggorthology git_branch: devel git_last_commit: 83d2b0c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/keggorthology_2.63.0.tar.gz vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf vignetteTitles: keggorthology overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/keggorthology/inst/doc/keggorth.R suggestsMe: MLInterfaces dependencyCount: 45 Package: KEGGREST Version: 1.51.1 Depends: R (>= 3.5.0) Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, BiocStyle, knitr, markdown License: Artistic-2.0 MD5sum: b3ccf13082f97712f6c2c29bc79c830a NeedsCompilation: no Title: Client-side REST access to the Kyoto Encyclopedia of Genes and Genomes (KEGG) Description: A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see ). Note that KEGGREST is based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. biocViews: Annotation, Pathways, ThirdPartyClient, KEGG Author: Dan Tenenbaum [aut], Bioconductor Package Maintainer [aut, cre], Martin Morgan [ctb], Kozo Nishida [ctb], Marcel Ramos [ctb], Kristina Riemer [ctb], Lori Shepherd [ctb], Jeremy Volkening [ctb] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/KEGGREST VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/KEGGREST/issues git_url: https://git.bioconductor.org/packages/KEGGREST git_branch: devel git_last_commit: 70af70e git_last_commit_date: 2025-11-17 Date/Publication: 2026-04-20 source.ver: src/contrib/KEGGREST_1.51.1.tar.gz vignettes: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html vignetteTitles: Accessing the KEGG REST API hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R dependsOnMe: ROntoTools, Hiiragi2013 importsMe: ADAM, adSplit, AnnotationDbi, attract, BiocSet, ChIPpeakAnno, CNEr, EnrichmentBrowser, famat, FELLA, funOmics, gage, ginmappeR, MetaboDynamics, MetaboSignal, MWASTools, PADOG, pairkat, pathview, SBGNview, SMITE, terapadog, transomics2cytoscape, YAPSA, WayFindR suggestsMe: anansi, Category, categoryCompare, dmGsea, gatom, GenomicRanges, globaltest, iSEEu, MetMashR, MLP, padma, rGREAT, RTopper, SomaScan.db, CALANGO, ggpicrust2, maGUI, phoenics, ReporterScore, scDiffCom dependencyCount: 24 Package: KinSwingR Version: 1.29.0 Depends: R (>= 3.5) Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 3e3c59ef6406eb4056a385a1bc262519 NeedsCompilation: no Title: KinSwingR: network-based kinase activity prediction Description: KinSwingR integrates phosphosite data derived from mass-spectrometry data and kinase-substrate predictions to predict kinase activity. Several functions allow the user to build PWM models of kinase-subtrates, statistically infer PWM:substrate matches, and integrate these data to infer kinase activity. biocViews: Proteomics, SequenceMatching, Network Author: Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KinSwingR git_branch: devel git_last_commit: 3ec41cc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/KinSwingR_1.29.0.tar.gz vignettes: vignettes/KinSwingR/inst/doc/KinSwingR.html vignetteTitles: KinSwingR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KinSwingR/inst/doc/KinSwingR.R dependencyCount: 35 Package: kmcut Version: 1.5.0 Imports: survival, tools, methods, pracma, doParallel, foreach, parallel, SummarizedExperiment, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, License: Artistic-2.0 MD5sum: 3a864f4a8b8b19ccd7ad002bb5783fde NeedsCompilation: no Title: Optimized Kaplan Meier analysis and identification and validation of prognostic biomarkers Description: The purpose of the package is to identify prognostic biomarkers and an optimal numeric cutoff for each biomarker that can be used to stratify a group of test subjects (samples) into two sub-groups with significantly different survival (better vs. worse). The package was developed for the analysis of gene expression data, such as RNA-seq. However, it can be used with any quantitative variable that has a sufficiently large proportion of unique values. biocViews: Software, StatisticalMethod, GeneExpression, Survival Author: Igor Kuznetsov [aut, cre], Javed Khan [aut] Maintainer: Igor Kuznetsov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kmcut git_branch: devel git_last_commit: 2654e48 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/kmcut_1.5.0.tar.gz vignettes: vignettes/kmcut/inst/doc/kmcut_intro.html vignetteTitles: kmcut_intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kmcut/inst/doc/kmcut_intro.R dependencyCount: 33 Package: knowYourCG Version: 1.7.15 Depends: R (>= 4.4.0) Imports: sesameData, ExperimentHub, AnnotationHub, dplyr, methods, rlang, GenomicRanges, IRanges, reshape2, S4Vectors, stats, stringr, utils, ggplot2, ggrepel, tibble, wheatmap, magrittr, readr Suggests: testthat (>= 3.0.0), SummarizedExperiment, rmarkdown, knitr, sesame, gprofiler2, ggrastr License: AGPL-3 MD5sum: 1484e710c3fbf50d56964c6267f711cf NeedsCompilation: yes Title: Functional analysis of DNA methylome datasets Description: KnowYourCG (KYCG) is a supervised learning framework designed for the functional analysis of DNA methylation data. Unlike existing tools that focus on genes or genomic intervals, KnowYourCG directly targets CpG dinucleotides, featuring automated supervised screenings of diverse biological and technical influences, including sequence motifs, transcription factor binding, histone modifications, replication timing, cell-type-specific methylation, and trait-epigenome associations. KnowYourCG addresses the challenges of data sparsity in various methylation datasets, including low-pass Nanopore sequencing, single-cell DNA methylomes, 5-hydroxymethylation profiles, spatial DNA methylation maps, and array-based datasets for epigenome-wide association studies and epigenetic clocks (). biocViews: Epigenetics, DNAMethylation, Sequencing, SingleCell, Spatial, Transcription, MethylationArray Author: Wanding Zhou [aut, fnd] (ORCID: ), David Goldberg [aut, cre] (ORCID: ), Hongxiang Fu [ctb] Maintainer: David Goldberg URL: https://github.com/zhou-lab/knowYourCG VignetteBuilder: knitr BugReports: https://github.com/zhou-lab/knowYourCG/issues git_url: https://git.bioconductor.org/packages/knowYourCG git_branch: devel git_last_commit: 7c12644 git_last_commit_date: 2026-04-03 Date/Publication: 2026-04-20 source.ver: src/contrib/knowYourCG_1.7.15.tar.gz vignettes: vignettes/knowYourCG/inst/doc/Array.html, vignettes/knowYourCG/inst/doc/Continuous.html, vignettes/knowYourCG/inst/doc/Sequencing.html, vignettes/knowYourCG/inst/doc/visualization.html vignetteTitles: "2. Array Data Analysis", "3. Continuous Variable Enrichment Analysis", "1. Sequencing Data Analysis", "4. Enrichment Visualization" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/knowYourCG/inst/doc/Array.R, vignettes/knowYourCG/inst/doc/Continuous.R, vignettes/knowYourCG/inst/doc/Sequencing.R, vignettes/knowYourCG/inst/doc/visualization.R dependencyCount: 89 Package: LACE Version: 2.15.1 Depends: R (>= 4.2.0) Imports: curl, igraph, foreach, doParallel, sortable, dplyr, forcats, data.tree, graphics, grDevices, parallel, RColorBrewer, Rfast, stats, SummarizedExperiment, utils, purrr, stringi, stringr, Matrix, tidyr, jsonlite, readr, configr, DT, tools, fs, data.table, htmltools, htmlwidgets, bsplus, shinyvalidate, shiny, shinythemes, shinyFiles, shinyjs, shinyBS, shinydashboard, biomaRt, callr, logr, ggplot2, svglite Suggests: BiocGenerics, BiocStyle, testthat, knitr, rmarkdown License: file LICENSE MD5sum: abfac28eba7ecc41d77323cccf8c2a77 NeedsCompilation: no Title: Longitudinal Analysis of Cancer Evolution (LACE) Description: LACE is an algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a weighed likelihood function computed on multiple time points. biocViews: BiomedicalInformatics, SingleCell, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] (ORCID: ), Gianluca Ascolani [aut] Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/LACE VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/LACE git_url: https://git.bioconductor.org/packages/LACE git_branch: devel git_last_commit: 1ce1000 git_last_commit_date: 2026-03-11 Date/Publication: 2026-04-20 source.ver: src/contrib/LACE_2.15.1.tar.gz vignettes: vignettes/LACE/inst/doc/v1_introduction.html, vignettes/LACE/inst/doc/v2_running_LACE.html, vignettes/LACE/inst/doc/v3_LACE_interface.html vignetteTitles: Introduction, Running LACE, LACE-interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LACE/inst/doc/v1_introduction.R, vignettes/LACE/inst/doc/v2_running_LACE.R, vignettes/LACE/inst/doc/v3_LACE_interface.R dependencyCount: 157 Package: LACHESIS Version: 0.99.5 Depends: R (>= 4.3) Imports: data.table, vcfR, tidyr, stats, utils, graphics, grDevices, ggplot2, gridExtra, survival, survminer, RColorBrewer, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19, BiocStyle, Cairo, rmarkdown, knitr, R.utils, tinytest, GenomeInfoDb, GenomicRanges, IRanges, MutationalPatterns, magick License: GPL (>= 3) MD5sum: bcd83ed9745f25cf09b4ac6a7fe35b21 NeedsCompilation: no Title: Functions used to analyze early tumor evolution from whole genome sequencing data Description: This package provides modalities to analyze tumor evolution from whole genome sequencing data. In particular, it provides estimates of mutation densities at genomic segments and uses these to time the origin of the tumor. biocViews: Software, StatisticalMethod, TimeCourse, Sequencing, WholeGenome, Survival, SomaticMutation Author: Verena Körber [aut, cre] (ORCID: ), Anand Mayakonda [aut], Maximilia Eggle [aut] Maintainer: Verena Körber URL: https://github.com/VerenaK90/LACHESIS VignetteBuilder: knitr BugReports: https://github.com/VerenaK90/LACHESIS/issues git_url: https://git.bioconductor.org/packages/LACHESIS git_branch: devel git_last_commit: 1828e01 git_last_commit_date: 2026-03-16 Date/Publication: 2026-04-20 source.ver: src/contrib/LACHESIS_0.99.5.tar.gz vignettes: vignettes/LACHESIS/inst/doc/vignette_LACHESIS.html vignetteTitles: LACHESIS - Real-time inference of evolutionary dynamics during tumor initiation based on whole genome sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LACHESIS/inst/doc/vignette_LACHESIS.R dependencyCount: 117 Package: LBE Version: 1.79.0 Depends: stats Imports: graphics, stats, utils Suggests: qvalue License: GPL-2 MD5sum: 77e7408ff75069c1df4a335b134d8c3b NeedsCompilation: no Title: Estimation of the false discovery rate Description: LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. biocViews: MultipleComparison Author: Cyril Dalmasso Maintainer: Cyril Dalmasso git_url: https://git.bioconductor.org/packages/LBE git_branch: devel git_last_commit: 7859860 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/LBE_1.79.0.tar.gz vignettes: vignettes/LBE/inst/doc/LBE.pdf vignetteTitles: LBE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LBE/inst/doc/LBE.R dependencyCount: 3 Package: ldblock Version: 1.41.0 Depends: R (>= 3.5), methods, rlang Imports: BiocGenerics (>= 0.25.1), Seqinfo, httr, Matrix Suggests: RUnit, knitr, BiocStyle, gwascat, rmarkdown, snpStats, VariantAnnotation, GenomeInfoDb, ensembldb, EnsDb.Hsapiens.v75, Rsamtools, GenomicFiles (>= 1.13.6) License: Artistic-2.0 MD5sum: a21a674038434257a184b2b1f8cf67bc NeedsCompilation: no Title: data structures for linkage disequilibrium measures in populations Description: Define data structures for linkage disequilibrium measures in populations. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ldblock git_branch: devel git_last_commit: 4c2fa2e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ldblock_1.41.0.tar.gz vignettes: vignettes/ldblock/inst/doc/ldblock.html vignetteTitles: ldblock package: linkage disequilibrium data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ldblock/inst/doc/ldblock.R dependencyCount: 24 Package: LEA Version: 3.23.0 Depends: R (>= 3.3.0), methods, stats, utils, graphics Suggests: knitr License: GPL-3 MD5sum: 0a28fd5d9f6e04690b1d7f06a872a263 NeedsCompilation: yes Title: LEA: an R package for Landscape and Ecological Association Studies Description: LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset statistics based on new or predicted environments (genetic.gap, genetic.offset). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets. biocViews: Software, Statistical Method, Clustering, Regression Author: Eric Frichot , Olivier Francois , Clement Gain Maintainer: Olivier Francois URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LEA git_branch: devel git_last_commit: f812da2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/LEA_3.23.0.tar.gz vignettes: vignettes/LEA/inst/doc/LEA.pdf vignetteTitles: LEA: An R Package for Landscape and Ecological Association Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LEA/inst/doc/LEA.R importsMe: dartR.popgen suggestsMe: tidypopgen dependencyCount: 4 Package: leapR Version: 0.99.9 Depends: R (>= 4.5.0) Imports: stats, gplots, readr, tibble, gplots, methods, ggplot2, dplyr, stringr, tidyr, SummarizedExperiment, BiocStyle Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 3d3be73cf4b6d3d9c82202f5d4663547 NeedsCompilation: no Title: Layered enrichment analysis of pathways R Description: leapR is a package that identifies pathways that are enriched across diverse 'omics experiments. It leverages any tabular expression data (proteomics, transcriptomics) using the `SummarizedExperiment` object. It works with any pathway in the .gct file format. biocViews: GeneSetEnrichment, Proteomics, Pathways, GeneExpression, Transcriptomics Author: Sara Gosline [aut, cre] (ORCID: ), Jason McDermott [aut], Jeremy Jacobson [aut], Vincent Danna [ctb], National Institutes of Health [fnd] Maintainer: Sara Gosline VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/leapR git_branch: devel git_last_commit: 0006002 git_last_commit_date: 2026-03-13 Date/Publication: 2026-04-20 source.ver: src/contrib/leapR_0.99.9.tar.gz vignettes: vignettes/leapR/inst/doc/examples.html, vignettes/leapR/inst/doc/leapR.html, vignettes/leapR/inst/doc/order-enrichment.html vignetteTitles: leapR, leapR, leapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/leapR/inst/doc/examples.R, vignettes/leapR/inst/doc/leapR.R, vignettes/leapR/inst/doc/order-enrichment.R dependencyCount: 92 Package: LedPred Version: 1.45.0 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: cf3a81418a65330a45ae3e4bbc8ce86a NeedsCompilation: no Title: Learning from DNA to Predict Enhancers Description: This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences. biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq, Sequencing, Classification Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez Maintainer: Aitor Gonzalez BugReports: https://github.com/aitgon/LedPred/issues git_url: https://git.bioconductor.org/packages/LedPred git_branch: devel git_last_commit: f513d31 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/LedPred_1.45.0.tar.gz vignettes: vignettes/LedPred/inst/doc/LedPred.pdf vignetteTitles: LedPred Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LedPred/inst/doc/LedPred.R dependencyCount: 63 Package: lefser Version: 1.21.7 Depends: SummarizedExperiment, R (>= 4.5.0) Imports: coin, MASS, ggplot2, S4Vectors, stats, methods, utils, dplyr, testthat, tibble, tidyr, forcats, stringr, ggtree, BiocGenerics, ape, ggrepel, mia, purrr, tidyselect, treeio Suggests: knitr, rmarkdown, curatedMetagenomicData, BiocStyle, phyloseq, pkgdown, covr, withr License: Artistic-2.0 MD5sum: 0cc995465becc90fd83a3526cde610a4 NeedsCompilation: no Title: R implementation of the LEfSE method for microbiome biomarker discovery Description: lefser is the R implementation of the popular microbiome biomarker discovery too, LEfSe. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers from two-level classes (and optional sub-classes). biocViews: Software, Sequencing, DifferentialExpression, Microbiome, StatisticalMethod, Classification Author: Sehyun Oh [cre, ctb] (ORCID: ), Asya Khleborodova [aut], Samuel Gamboa-Tuz [ctb], Marcel Ramos [ctb] (ORCID: ), Ludwig Geistlinger [ctb] (ORCID: ), Levi Waldron [ctb] (ORCID: ) Maintainer: Sehyun Oh URL: https://github.com/waldronlab/lefser VignetteBuilder: knitr BugReports: https://github.com/waldronlab/lefser/issues git_url: https://git.bioconductor.org/packages/lefser git_branch: devel git_last_commit: 333fd24 git_last_commit_date: 2025-11-17 Date/Publication: 2026-04-20 source.ver: src/contrib/lefser_1.21.7.tar.gz vignettes: vignettes/lefser/inst/doc/lefser.html vignetteTitles: Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lefser/inst/doc/lefser.R suggestsMe: dar, curatedMetagenomicData, ggpicrust2 dependencyCount: 180 Package: les Version: 1.61.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 217c9d1aeb6568f76ed30bf3a29e9d3e NeedsCompilation: no Title: Identifying Differential Effects in Tiling Microarray Data Description: The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes. biocViews: Microarray, DifferentialExpression, ChIPchip, DNAMethylation, Transcription Author: Julian Gehring, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/les git_branch: devel git_last_commit: 60f7581 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/les_1.61.0.tar.gz vignettes: vignettes/les/inst/doc/les.pdf vignetteTitles: Introduction to the les package: Identifying Differential Effects in Tiling Microarray Data with the Loci of Enhanced Significance Framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/les/inst/doc/les.R importsMe: GSRI dependencyCount: 13 Package: levi Version: 1.29.0 Imports: DT(>= 0.4), RColorBrewer(>= 1.1-2), colorspace(>= 1.3-2), dplyr(>= 0.7.4), ggplot2(>= 2.2.1), httr(>= 1.3.1), igraph(>= 1.2.1), reshape2(>= 1.4.3), shiny(>= 1.0.5), shinydashboard(>= 0.7.0), shinyjs(>= 1.0), xml2(>= 1.2.0), knitr, Rcpp (>= 0.12.18), grid, grDevices, stats, utils, testthat, methods, rmarkdown LinkingTo: Rcpp Suggests: rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: 467da60b253e18e77bc0273a5e6a091d NeedsCompilation: yes Title: Landscape Expression Visualization Interface Description: The tool integrates data from biological networks with transcriptomes, displaying a heatmap with surface curves to evidence the altered regions. biocViews: GeneExpression, Sequencing, Network, Software Author: Rafael Pilan [aut], Isabelle Silva [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/levi git_branch: devel git_last_commit: c626489 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/levi_1.29.0.tar.gz vignettes: vignettes/levi/inst/doc/levi.html vignetteTitles: "Using levi" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/levi/inst/doc/levi.R dependencyCount: 95 Package: lfa Version: 2.11.3 Depends: R (>= 4.0) Imports: methods, corpcor, RSpectra, BEDMatrix, genio Suggests: knitr, rmarkdown, ggplot2, testthat License: GPL (>= 3) MD5sum: b78894bebdf3717181974dfc4c9a4d79 NeedsCompilation: yes Title: Logistic Factor Analysis for Categorical Data Description: Logistic Factor Analysis is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. The main method estimates genetic population structure from genotype data. There are also methods for estimating individual-specific allele frequencies using the population structure. Lastly, a structured Hardy-Weinberg equilibrium (HWE) test is developed, which quantifies the goodness of fit of the genotype data to the estimated population structure, via the estimated individual-specific allele frequencies (all of which generalizes traditional HWE tests). biocViews: SNP, DimensionReduction, PrincipalComponent, Regression Author: Wei Hao [aut], Minsun Song [aut], Alejandro Ochoa [aut, cre] (ORCID: ), John D. Storey [aut] (ORCID: ) Maintainer: Alejandro Ochoa URL: https://github.com/StoreyLab/lfa VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/lfa/issues git_url: https://git.bioconductor.org/packages/lfa git_branch: devel git_last_commit: 21a47b5 git_last_commit_date: 2026-01-29 Date/Publication: 2026-04-20 source.ver: src/contrib/lfa_2.11.3.tar.gz vignettes: vignettes/lfa/inst/doc/lfa.html vignetteTitles: lfa Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lfa/inst/doc/lfa.R importsMe: gcatest dependencyCount: 41 Package: Lheuristic Version: 1.3.0 Depends: R (>= 4.4.0) Imports: Hmisc, stats, energy, grDevices, graphics, utils, MultiAssayExperiment, ggplot2, ggpubr Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 3779300ff401f7a016b652b74af7cabe NeedsCompilation: no Title: Detection of scatterplots with L-shaped pattern Description: The Lheuristic package identifies scatterpots that follow and L-shaped, negative distribution. It can be used to identify genes regulated by methylation by integration of an expression and a methylation array. The package uses two different methods to detect expression and methyaltion L- shapped scatterplots. The parameters can be changed to detect other scatterplot patterns. biocViews: DNAMethylation, StatisticalMethod, MethylationArray Author: Sanchez Pla Alex [aut, cre] (ORCID: ), Miro Cau Berta [aut] (ORCID: ) Maintainer: Sanchez Pla Alex URL: https://github.com/ASPresearch/Lheuristic VignetteBuilder: knitr BugReports: https://github.com/ASPresearch/Lheuristic/issues git_url: https://git.bioconductor.org/packages/Lheuristic git_branch: devel git_last_commit: 9435958 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Lheuristic_1.3.0.tar.gz vignettes: vignettes/Lheuristic/inst/doc/vignette.html vignetteTitles: L-shaped selection from methylation and expression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/Lheuristic/inst/doc/vignette.R dependencyCount: 134 Package: limma Version: 3.67.1 Depends: R (>= 3.6.0) Imports: grDevices, graphics, stats, utils, methods, statmod Suggests: BiasedUrn, ellipse, gplots, knitr, locfit, MASS, splines, affy, AnnotationDbi, Biobase, BiocStyle, GO.db, illuminaio, org.Hs.eg.db, vsn License: GPL (>=2) MD5sum: 4c6e6023d5b707ff9441e5712bb5b180 NeedsCompilation: yes Title: Linear Models for Microarray and Omics Data Description: Data analysis, linear models and differential expression for omics data. biocViews: ExonArray, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, DataImport, Bayesian, Clustering, Regression, TimeCourse, Microarray, MicroRNAArray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel, Sequencing, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl, BiomedicalInformatics, CellBiology, Cheminformatics, Epigenetics, FunctionalGenomics, Genetics, ImmunoOncology, Metabolomics, Proteomics, SystemsBiology, Transcriptomics Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Goknur Giner [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb], Charity Law [ctb], Mengbo Li [ctb], Lizhong Chen [ctb] Maintainer: Gordon Smyth URL: https://bioinf.wehi.edu.au/limma/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/limma git_branch: devel git_last_commit: c949ba8 git_last_commit_date: 2026-04-11 Date/Publication: 2026-04-20 source.ver: src/contrib/limma_3.67.1.tar.gz vignettes: vignettes/limma/inst/doc/usersguide.pdf, vignettes/limma/inst/doc/intro.html vignetteTitles: limma User's Guide, A brief introduction to limma hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limma/inst/doc/intro.R dependsOnMe: ASpli, BLMA, cghMCR, codelink, convert, Cormotif, DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, HTqPCR, IsoformSwitchAnalyzeR, limpa, marray, metagenomeSeq, metaseqR2, mpra, NanoTube, octad, protGear, qpcrNorm, qusage, RBM, RnBeads, Rnits, splineTimeR, TMSig, TOAST, tRanslatome, TurboNorm, variancePartition, wateRmelon, zenith, CCl4, Fletcher2013a, HD2013SGI, ReactomeGSA.data, EGSEA123, methylationArrayAnalysis, RNAseq123, OSCA.basic, OSCA.workflows, BALLI, BioInsight, CEDA, cp4p, DAAGbio, DRomics, fmt, PerfMeas importsMe: a4Base, ABSSeq, affycoretools, affylmGUI, AMARETTO, animalcules, ArrayExpress, arrayQuality, arrayQualityMetrics, artMS, ATACseqQC, ATACseqTFEA, attract, autonomics, AWFisher, ballgown, barbieQ, BatchQC, beadarray, benchdamic, BERT, biotmle, BloodGen3Module, bnem, bsseq, bumphunter, casper, ChAMP, CleanUpRNAseq, clusterExperiment, CNVRanger, combi, compcodeR, consensusOV, crlmm, csaw, cTRAP, ctsGE, DAMEfinder, damidBind, DaMiRseq, debrowser, DeeDeeExperiment, DELocal, derfinderPlot, DESpace, DEsubs, DExMA, diffcyt, diffHic, diffUTR, distinct, DMRcate, Doscheda, dreamlet, DRIMSeq, DspikeIn, EGAD, EGSEA, eisaR, EnrichmentBrowser, epigraHMM, EpiMix, erccdashboard, EventPointer, EWCE, ExploreModelMatrix, flowBin, gCrisprTools, GDCRNATools, genefu, GeneSelectMMD, GEOquery, gg4way, Glimma, GRaNIE, GWAS.BAYES, HarmonizR, hermes, HERON, hipathia, HTqPCR, icetea, iCheck, iChip, iCOBRA, ideal, InPAS, isomiRs, KnowSeq, lemur, limmaGUI, LimROTS, Linnorm, lipidr, lmdme, markeR, mastR, MatrixQCvis, MBECS, MBQN, mCSEA, MEAL, MetaProViz, methylKit, MethylMix, microbiomeExplorer, miloR, minfi, MIRit, miRLAB, missMethyl, MLSeq, moanin, monocle, MoonlightR, msImpute, mspms, msqrob2, MSstats, MSstatsTMT, MultiDataSet, muscat, mutscan, NADfinder, NanoMethViz, nethet, nondetects, NormalyzerDE, notameViz, OLIN, omicRexposome, OVESEG, PAA, PADOG, pairedGSEA, PanomiR, PathoStat, pcaExplorer, PECA, PepSetTest, pepStat, phantasus, phenoTest, PhosR, PolySTest, POMA, POWSC, proBatch, projectR, PRONE, psichomics, qmtools, qPLEXanalyzer, qsea, RegEnrich, regsplice, RFGeneRank, RFLOMICS, RNAseqCovarImpute, roastgsa, ROSeq, RTN, RTopper, saseR, satuRn, scClassify, scone, scQTLtools, scran, ScreenR, scviR, seqsetvis, shinyDSP, shinyepico, singleCellTK, SmartPhos, sparrow, speckle, SpNeigh, SPsimSeq, standR, STATegRa, Statial, structToolbox, sva, tidyexposomics, timecourse, TOP, ToxicoGx, TPP, TPP2D, transcriptogramer, TVTB, tweeDEseq, unifiedWMWqPCR, VISTA, vsclust, vsn, weitrix, Wrench, XAItest, yamss, yarn, BeadArrayUseCases, signatureSearchData, spatialLIBD, ExpressionNormalizationWorkflow, recountWorkflow, batchtma, BPM, Cascade, cinaR, DiPALM, dsb, eLNNpairedCov, GSEMA, GWASbyCluster, hicream, lfproQC, lilikoi, limorhyde2, lipidomeR, MetAlyzer, metaMA, mi4p, miRtest, MKmisc, MKomics, MSclassifR, newIMVC, nlcv, OncoSubtype, Patterns, plfMA, promor, RANKS, RCPA, RPPanalyzer, scBio, scGOclust, scROSHI, ssizeRNA, tinyarray, TransProR, treediff, wrProteo, XYomics suggestsMe: ABarray, ADaCGH2, Biobase, biobroom, BiocSet, BioNet, BioQC, blase, broadSeq, Category, categoryCompare, celaref, CellBench, CellMixS, ChIPpeakAnno, ClassifyR, CMA, coGPS, CONSTANd, cydar, Damsel, DAPAR, dar, dearseq, DEGreport, derfinder, DEScan2, dyebias, easyreporting, EnMCB, extraChIPs, fgsea, fishpond, gage, GeoTcgaData, geva, glmGamPoi, GSRI, GSVA, Harman, Heatplus, iSEEde, isobar, ivygapSE, les, lumi, MAST, methylumi, MLP, npGSEA, oligo, oppar, piano, PREDA, proDA, puma, QFeatures, qsvaR, raer, randRotation, recountmethylation, ribosomeProfilingQC, rtracklayer, Rvisdiff, scFeatures, signifinder, spatialHeatmap, SpliceWiz, stageR, subSeq, systemPipeR, tadar, TCGAbiolinks, TFEA.ChIP, tidybulk, topconfects, tximeta, tximport, ViSEAGO, zFPKM, BloodCancerMultiOmics2017, bugphyzz, GeuvadisTranscriptExpr, mammaPrintData, msigdb, seventyGeneData, arrays, CAGEWorkflow, fluentGenomics, simpleSingleCell, AnnoProbe, aroma.affymetrix, canvasXpress, corncob, DGEobj.utils, easybio, ggpicrust2, GiANT, hexbin, inDAGO, limorhyde, maGUI, NACHO, pctax, pmartR, protti, RepeatedHighDim, SCdeconR, seqgendiff, Seurat, SeuratExplorer, simphony, st, volcano3D, wrGraph, wrMisc dependencyCount: 6 Package: limmaGUI Version: 1.87.0 Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot, xtable, utils License: GPL (>=2) MD5sum: 8c0ea6b0e91b1fd3f297d65d138c7870 NeedsCompilation: no Title: GUI for limma Package With Two Color Microarrays Description: A Graphical User Interface for differential expression analysis of two-color microarray data using the limma package. biocViews: GUI, GeneExpression, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, TwoChannel, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [aut], Gordon Smyth [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limmaGUI/ git_url: https://git.bioconductor.org/packages/limmaGUI git_branch: devel git_last_commit: 5172739 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/limmaGUI_1.87.0.tar.gz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf, vignettes/limmaGUI/inst/doc/LinModIntro.pdf, vignettes/limmaGUI/inst/doc/about.html, vignettes/limmaGUI/inst/doc/CustMenu.html, vignettes/limmaGUI/inst/doc/import.html, vignettes/limmaGUI/inst/doc/index.html, vignettes/limmaGUI/inst/doc/InputFiles.html, vignettes/limmaGUI/inst/doc/lgDevel.html, vignettes/limmaGUI/inst/doc/windowsFocus.html vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette, LinModIntro.pdf, about.html, CustMenu.html, import.html, index.html, InputFiles.html, lgDevel.html, windowsFocus.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R dependencyCount: 11 Package: limpa Version: 1.3.12 Depends: limma Imports: methods, stats, data.table, statmod Suggests: arrow, knitr, BiocStyle License: GPL (>=2) MD5sum: 2424e566b116f373047ef628ca6bb48a NeedsCompilation: no Title: Quantification and Differential Analysis of Proteomics Data Description: Quantification and differential analysis of mass-spectrometry proteomics data, with probabilistic recovery of information from missing values. Avoids the need for imputation. Estimates the detection probability curve (DPC), which relates the probability of successful detection to the underlying log-intensity of each precursor ion, and uses it to incorporate missing values into protein quantification and into subsequent differential expression analyses. The package produces objects suitable for downstream analysis in limma. The package accepts precursor (or peptide) intensities including missing values and produces complete protein quantifications without the need for imputation. The uncertainty of the protein quantifications is propagated through to the limma analyses using variance modeling and precision weights, ensuring accurate error rate control. The analysis pipeline can alternatively work with PTM or protein level data. The package name "limpa" is an acronym for "Linear Models for Proteomics Data". biocViews: Bayesian, BiologicalQuestion, DataImport, DifferentialExpression, GeneExpression, MassSpectrometry, Preprocessing, Proteomics, Regression, Software Author: Mengbo Li [aut] (ORCID: ), Pedro Baldoni [ctb] (ORCID: ), Gordon Smyth [cre, aut] (ORCID: ) Maintainer: Gordon Smyth URL: https://github.com/SmythLab/limpa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/limpa git_branch: devel git_last_commit: 7a47eff git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/limpa_1.3.12.tar.gz vignettes: vignettes/limpa/inst/doc/limpa.html vignetteTitles: Analyzing mass spectrometry data with limpa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limpa/inst/doc/limpa.R dependencyCount: 8 Package: limpca Version: 1.7.0 Depends: R (>= 3.5.0) Imports: ggplot2, stringr, plyr, ggrepel, reshape2, grDevices, graphics, doParallel, parallel, dplyr, tibble, tidyr, ggsci, tidyverse, methods, stats, SummarizedExperiment, S4Vectors Suggests: BiocStyle, pander, rmarkdown, car, gridExtra, knitr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: bc051dbbaa2e0a03793ebb7c8bf7f3c3 NeedsCompilation: no Title: An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods Description: This package has for objectives to provide a method to make Linear Models for high-dimensional designed data. limpca applies a GLM (General Linear Model) version of ASCA and APCA to analyse multivariate sample profiles generated by an experimental design. ASCA/APCA provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design and contrarily to MANOVA, it can deal with mutlivariate datasets having more variables than observations. This method can handle unbalanced design. biocViews: StatisticalMethod, PrincipalComponent, Regression, Visualization, ExperimentalDesign, MultipleComparison, GeneExpression, Metabolomics Author: Bernadette Govaerts [aut, ths], Sebastien Franceschini [ctb], Robin van Oirbeek [ctb], Michel Thiel [aut], Pascal de Tullio [dtc], Manon Martin [aut, cre] (ORCID: ), Nadia Benaiche [ctb] Maintainer: Manon Martin URL: https://github.com/ManonMartin/limpca, https://manonmartin.github.io/limpca/ VignetteBuilder: knitr BugReports: https://github.com/ManonMartin/limpca/issues git_url: https://git.bioconductor.org/packages/limpca git_branch: devel git_last_commit: 224acb2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/limpca_1.7.0.tar.gz vignettes: vignettes/limpca/inst/doc/limpca.html, vignettes/limpca/inst/doc/Trout.html, vignettes/limpca/inst/doc/UCH.html vignetteTitles: Get started with limpca, Analysis of the Trout dataset with limpca, Analysis of the UCH dataset with limpca hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limpca/inst/doc/limpca.R, vignettes/limpca/inst/doc/Trout.R, vignettes/limpca/inst/doc/UCH.R dependencyCount: 133 Package: lineagespot Version: 1.15.0 Imports: VariantAnnotation, MatrixGenerics, SummarizedExperiment, data.table, stringr, httr, utils Suggests: BiocStyle, RefManageR, rmarkdown, knitr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: edb298e11ec2970f2e7e1beccadb13c6 NeedsCompilation: no Title: Detection of SARS-CoV-2 lineages in wastewater samples using next-generation sequencing Description: Lineagespot is a framework written in R, and aims to identify SARS-CoV-2 related mutations based on a single (or a list) of variant(s) file(s) (i.e., variant calling format). The method can facilitate the detection of SARS-CoV-2 lineages in wastewater samples using next generation sequencing, and attempts to infer the potential distribution of the SARS-CoV-2 lineages. biocViews: VariantDetection, VariantAnnotation, Sequencing Author: Nikolaos Pechlivanis [aut, cre] (ORCID: ), Maria Tsagiopoulou [aut], Maria Christina Maniou [aut], Anastasis Togkousidis [aut], Evangelia Mouchtaropoulou [aut], Taxiarchis Chassalevris [aut], Serafeim Chaintoutis [aut], Chrysostomos Dovas [aut], Maria Petala [aut], Margaritis Kostoglou [aut], Thodoris Karapantsios [aut], Stamatia Laidou [aut], Elisavet Vlachonikola [aut], Aspasia Orfanou [aut], Styliani-Christina Fragkouli [aut], Sofoklis Keisaris [aut], Anastasia Chatzidimitriou [aut], Agis Papadopoulos [aut], Nikolaos Papaioannou [aut], Anagnostis Argiriou [aut], Fotis E. Psomopoulos [aut] Maintainer: Nikolaos Pechlivanis URL: https://github.com/BiodataAnalysisGroup/lineagespot VignetteBuilder: knitr BugReports: https://github.com/BiodataAnalysisGroup/lineagespot/issues git_url: https://git.bioconductor.org/packages/lineagespot git_branch: devel git_last_commit: 409c851 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/lineagespot_1.15.0.tar.gz vignettes: vignettes/lineagespot/inst/doc/lineagespot.html vignetteTitles: lineagespot User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lineagespot/inst/doc/lineagespot.R dependencyCount: 81 Package: LinkHD Version: 1.25.0 Depends: R(>= 3.6.0), methods, ggplot2, stats Imports: scales, cluster, graphics, ggpubr, gridExtra, vegan, rio, MultiAssayExperiment, emmeans, reshape2, data.table Suggests: MASS (>= 7.3.0), knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 66f2e687233db1233611c7ca7af53b49 NeedsCompilation: no Title: LinkHD: a versatile framework to explore and integrate heterogeneous data Description: Here we present Link-HD, an approach to integrate heterogeneous datasets, as a generalization of STATIS-ACT (“Structuration des Tableaux A Trois Indices de la Statistique–Analyse Conjointe de Tableaux”), a family of methods to join and compare information from multiple subspaces. However, STATIS-ACT has some drawbacks since it only allows continuous data and it is unable to establish relationships between samples and features. In order to tackle these constraints, we incorporate multiple distance options and a linear regression based Biplot model in order to stablish relationships between observations and variable and perform variable selection. biocViews: Classification,MultipleComparison,Regression,Software Author: Laura M. Zingaretti [aut, cre] Maintainer: "Laura M Zingaretti" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinkHD git_branch: devel git_last_commit: ceb49e3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/LinkHD_1.25.0.tar.gz vignettes: vignettes/LinkHD/inst/doc/LinkHD.html vignetteTitles: Annotating Genomic Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LinkHD/inst/doc/LinkHD.R dependencyCount: 133 Package: Linnorm Version: 2.35.0 Depends: R(>= 4.1.0) Imports: Rcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap, Rtsne, gmodels LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, markdown, gplots, RColorBrewer, moments, testthat, matrixStats License: MIT + file LICENSE MD5sum: 63ad60bf5540276302340f6866380e4c NeedsCompilation: yes Title: Linear model and normality based normalization and transformation method (Linnorm) Description: Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. biocViews: ImmunoOncology, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Genetics, Normalization, Software, Transcription, BatchEffect, PeakDetection, Clustering, Network, SingleCell Author: Shun Hang Yip Maintainer: Shun Hang Yip URL: https://doi.org/10.1093/nar/gkx828 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Linnorm git_branch: devel git_last_commit: 85dd5e1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Linnorm_2.35.0.tar.gz vignettes: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.pdf vignetteTitles: Linnorm User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.R importsMe: mnem suggestsMe: SCdeconR dependencyCount: 62 Package: lionessR Version: 1.25.0 Depends: R (>= 3.6.0) Imports: stats, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, igraph, reshape2, limma, License: MIT + file LICENSE MD5sum: 7ff901d9aad1203c19d1a8ef320667f7 NeedsCompilation: no Title: Modeling networks for individual samples using LIONESS Description: LIONESS, or Linear Interpolation to Obtain Network Estimates for Single Samples, can be used to reconstruct single-sample networks (https://arxiv.org/abs/1505.06440). This code implements the LIONESS equation in the lioness function in R to reconstruct single-sample networks. The default network reconstruction method we use is based on Pearson correlation. However, lionessR can run on any network reconstruction algorithms that returns a complete, weighted adjacency matrix. lionessR works for both unipartite and bipartite networks. biocViews: Network, NetworkInference, GeneExpression Author: Marieke Lydia Kuijjer [aut] (ORCID: ), Ping-Han Hsieh [cre] (ORCID: ) Maintainer: Ping-Han Hsieh URL: https://github.com/mararie/lionessR VignetteBuilder: knitr BugReports: https://github.com/mararie/lionessR/issues git_url: https://git.bioconductor.org/packages/lionessR git_branch: devel git_last_commit: e10cf52 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/lionessR_1.25.0.tar.gz vignettes: vignettes/lionessR/inst/doc/lionessR.html vignetteTitles: lionessR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lionessR/inst/doc/lionessR.R dependencyCount: 25 Package: LipidTrend Version: 1.1.0 Depends: R (>= 4.5.0) Imports: dplyr, ggnewscale, ggplot2, magrittr, methods, rlang, SummarizedExperiment, MKmisc, matrixTests Suggests: BiocStyle, devtools, knitr, roxygen2, rmarkdown, testthat (>= 3.0.0), S4Vectors, Enhances: data.table, License: MIT + file LICENSE MD5sum: ffc4d33cc8e7e53242cf9ab9a6dfa26e NeedsCompilation: no Title: LipidTrend: Analysis and Visualization of Lipid Feature Tendencies Description: "LipidTrend" is an R package that implements a permutation-based statistical test to identify significant differences in lipidomic features between groups. The test incorporates Gaussian kernel smoothing of region statistics to improve stability and accuracy, particularly when dealing with small sample sizes. This package also includes two plotting functions for visualizing significant tendencies in 1D and 2D feature data, respectively. biocViews: Software, Lipidomics, StatisticalMethod, DifferentialExpression, Visualization Author: Wei-Chung Cheng [aut, cre, cph] (ORCID: ), Chia-Hsin Liu [aut, ctb], Pei-Chun Shen [aut, ctb], Wen-Jen Lin [aut, ctb], Hung-Ching Chang [aut, ctb], Meng-Hsin Tsai [aut, ctb] Maintainer: Wei-Chung Cheng URL: https://github.com/BioinfOMICS/LipidTrend VignetteBuilder: knitr BugReports: https://github.com/BioinfOMICS/LipidTrend/issues git_url: https://git.bioconductor.org/packages/LipidTrend git_branch: devel git_last_commit: 8eef251 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/LipidTrend_1.1.0.tar.gz vignettes: vignettes/LipidTrend/inst/doc/LipidTrend.html vignetteTitles: LipidTrend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LipidTrend/inst/doc/LipidTrend.R dependencyCount: 56 Package: LiquidAssociation Version: 1.65.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: 97853a694c3a0bf9576bcf885413a37f NeedsCompilation: no Title: LiquidAssociation Description: The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data. biocViews: Pathways, GeneExpression, CellBiology, Genetics, Network, TimeCourse Author: Yen-Yi Ho Maintainer: Yen-Yi Ho git_url: https://git.bioconductor.org/packages/LiquidAssociation git_branch: devel git_last_commit: 03aceab git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/LiquidAssociation_1.65.0.tar.gz vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf vignetteTitles: LiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.R dependsOnMe: fastLiquidAssociation dependencyCount: 59 Package: lmdme Version: 1.53.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: 761705285637240937200dbac3bbbc26 NeedsCompilation: no Title: Linear Model decomposition for Designed Multivariate Experiments Description: linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS. biocViews: Microarray, OneChannel, TwoChannel, Visualization, DifferentialExpression, ExperimentData, Cancer Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar/?page_id=38 git_url: https://git.bioconductor.org/packages/lmdme git_branch: devel git_last_commit: 885461c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/lmdme_1.53.0.tar.gz vignettes: vignettes/lmdme/inst/doc/lmdme-vignette.pdf vignetteTitles: lmdme: linear model framework for PCA/PLS analysis of ANOVA decomposition on Designed Multivariate Experiments in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lmdme/inst/doc/lmdme-vignette.R dependencyCount: 9 Package: loci2path Version: 1.31.0 Depends: R (>= 3.5) Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods, grDevices, stats, graphics, GenomicRanges, BiocParallel, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: cf5b4302428d633daada0d475b01f8c3 NeedsCompilation: no Title: Loci2path: regulatory annotation of genomic intervals based on tissue-specific expression QTLs Description: loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB. biocViews: FunctionalGenomics, Genetics, GeneSetEnrichment, Software, GeneExpression, Sequencing, Coverage, BioCarta Author: Tianlei Xu Maintainer: Tianlei Xu URL: https://github.com/StanleyXu/loci2path VignetteBuilder: knitr BugReports: https://github.com/StanleyXu/loci2path/issues git_url: https://git.bioconductor.org/packages/loci2path git_branch: devel git_last_commit: 4d9f769 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/loci2path_1.31.0.tar.gz vignettes: vignettes/loci2path/inst/doc/loci2path-vignette.html vignetteTitles: loci2path hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/loci2path/inst/doc/loci2path-vignette.R dependencyCount: 38 Package: logicFS Version: 2.31.0 Depends: LogicReg, mcbiopi, survival Imports: graphics, methods, stats Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: c0cff7d75e38a986b67e0744bda51efb NeedsCompilation: no Title: Identification of SNP Interactions Description: Identification of interactions between binary variables using Logic Regression. Can, e.g., be used to find interesting SNP interactions. Contains also a bagging version of logic regression for classification. biocViews: SNP, Classification, Genetics Author: Holger Schwender, Tobias Tietz Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/logicFS git_branch: devel git_last_commit: 5889bdc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/logicFS_2.31.0.tar.gz vignettes: vignettes/logicFS/inst/doc/logicFS.pdf vignetteTitles: logicFS Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logicFS/inst/doc/logicFS.R suggestsMe: trio dependencyCount: 12 Package: LOLA Version: 1.41.1 Depends: R (>= 3.5.0) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, reshape2, utils, stats, methods Suggests: parallel, XVector, testthat, knitr, BiocStyle, rmarkdown Enhances: simpleCache, qvalue, ggplot2 License: GPL-3 MD5sum: 19ec3ec26496beb5feb7304d95eefe20 NeedsCompilation: no Title: Locus overlap analysis for enrichment of genomic ranges Description: Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data. biocViews: GeneSetEnrichment, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing Author: Nathan Sheffield [aut, cre], Christoph Bock [ctb] Maintainer: Nathan Sheffield URL: http://code.databio.org/LOLA VignetteBuilder: knitr BugReports: http://github.com/nsheff/LOLA git_url: https://git.bioconductor.org/packages/LOLA git_branch: devel git_last_commit: 5459337 git_last_commit_date: 2026-01-05 Date/Publication: 2026-04-20 source.ver: src/contrib/LOLA_1.41.1.tar.gz vignettes: vignettes/LOLA/inst/doc/choosingUniverse.html, vignettes/LOLA/inst/doc/gettingStarted.html, vignettes/LOLA/inst/doc/usingLOLACore.html vignetteTitles: 3. Choosing a Universe, 1. Getting Started with LOLA, 2. Using LOLA Core hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LOLA/inst/doc/choosingUniverse.R, vignettes/LOLA/inst/doc/gettingStarted.R, vignettes/LOLA/inst/doc/usingLOLACore.R suggestsMe: COCOA, MAGAR, MIRA, ramr dependencyCount: 24 Package: looking4clusters Version: 1.1.0 Depends: R (>= 4.5.0) Imports: stats, utils, SummarizedExperiment, SingleCellExperiment, BiocBaseUtils, jsonlite Suggests: knitr, rmarkdown, Seurat, parallelDist, uwot, NMF, fpc, dendextend, cluster, Rtsne, scRNAseq, Matrix License: GPL-2 | GPL-3 MD5sum: 47560d0eb36845e20ded7bb05c250a86 NeedsCompilation: no Title: Interactive Visualization of scRNA-Seq Description: Enables the interactive visualization of dimensional reduction, clustering, and cell properties for scRNA-Seq results. It generates an interactive HTML page using either a numeric matrix, SummarizedExperiment, SingleCellExperiment or Seurat objects as input. The input data can be projected into two-dimensional representations by applying dimensionality reduction methods such as PCA, MDS, t-SNE, UMAP, and NMF. Displaying multiple dimensionality reduction results within the same interface, with interconnected graphs, provides different perspectives that facilitate accurate cell classification. The package also integrates unsupervised clustering techniques, whose results that can be viewed interactively in the graphical interface. In addition to visualization, this interface allows manual selection of groups, labeling of cell entities based on processed meta-information, generation of new graphs displaying gene expression values for each cell, sample identification, and visual comparison of samples and clusters. biocViews: Software, Visualization, DataRepresentation, GeneExpression, MultipleComparison, Classification, Clustering Author: David Barrios [aut, cre] (ORCID: ), Angela Villaverde [aut] (ORCID: ), Carlos Prieto [aut] (ORCID: ) Maintainer: David Barrios URL: https://github.com/BioinfoUSAL/looking4clusters/ VignetteBuilder: knitr BugReports: https://github.com/BioinfoUSAL/looking4clusters/issues/ git_url: https://git.bioconductor.org/packages/looking4clusters git_branch: devel git_last_commit: 7fb974e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/looking4clusters_1.1.0.tar.gz vignettes: vignettes/looking4clusters/inst/doc/looking4clusters.html vignetteTitles: scRNA-Seq,, Dimensional Reduction,, Clustering and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/looking4clusters/inst/doc/looking4clusters.R dependencyCount: 28 Package: LPE Version: 1.85.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: 859c770519979e42cbe1cad6e05daa9d NeedsCompilation: no Title: Methods for analyzing microarray data using Local Pooled Error (LPE) method Description: This LPE library is used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional 'BH' or 'BY' procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions. To use it for paired data, see LPEP library. For using LPE in multiple conditions, use HEM library. biocViews: Microarray, DifferentialExpression Author: Nitin Jain , Michael O'Connell , Jae K. Lee . Includes R source code contributed by HyungJun Cho Maintainer: Nitin Jain URL: http://www.r-project.org, http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/, http://sourceforge.net/projects/r-lpe/ git_url: https://git.bioconductor.org/packages/LPE git_branch: devel git_last_commit: 0dc97ce git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/LPE_1.85.0.tar.gz vignettes: vignettes/LPE/inst/doc/LPE.pdf vignetteTitles: LPE test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPE/inst/doc/LPE.R dependsOnMe: PLPE suggestsMe: ABarray dependencyCount: 1 Package: lpNet Version: 2.43.0 Depends: lpSolve, KEGGgraph License: Artistic License 2.0 MD5sum: 8e6df1ae365e7a7fb8d0b705ad015f58 NeedsCompilation: no Title: Linear Programming Model for Network Inference Description: lpNet aims at infering biological networks, in particular signaling and gene networks. For that it takes perturbation data, either steady-state or time-series, as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used. biocViews: NetworkInference Author: Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali Maintainer: Lars Kaderali git_url: https://git.bioconductor.org/packages/lpNet git_branch: devel git_last_commit: 6f7eee9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/lpNet_2.43.0.tar.gz vignettes: vignettes/lpNet/inst/doc/vignette_lpNet.pdf vignetteTitles: lpNet,, network inference with a linear optimization program. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpNet/inst/doc/vignette_lpNet.R dependencyCount: 16 Package: lpsymphony Version: 1.39.0 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr, testthat Enhances: slam License: EPL MD5sum: 66b9cfdddd942c544ec42ab44ae39fec NeedsCompilation: yes Title: Symphony integer linear programming solver in R Description: This package was derived from Rsymphony_0.1-17 from CRAN. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY. For Windows, precompiled DLLs are included in this package. biocViews: Infrastructure, ThirdPartyClient Author: Vladislav Kim [aut, cre], Ted Ralphs [ctb], Menal Guzelsoy [ctb], Ashutosh Mahajan [ctb], Reinhard Harter [ctb], Kurt Hornik [ctb], Cyrille Szymanski [ctb], Stefan Theussl [ctb], Mike Smith [ctb] (ORCID: ) Maintainer: Vladislav Kim URL: http://R-Forge.R-project.org/projects/rsymphony, https://projects.coin-or.org/SYMPHONY, http://www.coin-or.org/download/source/SYMPHONY/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/lpsymphony/issues git_url: https://git.bioconductor.org/packages/lpsymphony git_branch: devel git_last_commit: 83b7089 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/lpsymphony_1.39.0.tar.gz vignettes: vignettes/lpsymphony/inst/doc/lpsymphony.pdf vignetteTitles: Introduction to lpsymphony hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpsymphony/inst/doc/lpsymphony.R importsMe: IHW suggestsMe: oppr, prioritizr dependencyCount: 0 Package: LRBaseDbi Version: 2.21.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: testthat, BiocStyle, AnnotationHub License: Artistic-2.0 MD5sum: 0d263eedd806443968e71cb764b24bc5 NeedsCompilation: no Title: DBI to construct LRBase-related package Description: Interface to construct LRBase package (LRBase.XXX.eg.db). biocViews: Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki VignetteBuilder: utils git_url: https://git.bioconductor.org/packages/LRBaseDbi git_branch: devel git_last_commit: e5322a2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/LRBaseDbi_2.21.0.tar.gz vignettes: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.pdf vignetteTitles: LRBaseDbi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.R suggestsMe: scTensor dependencyCount: 42 Package: LRcell Version: 1.19.0 Depends: R (>= 4.1), ExperimentHub, AnnotationHub Imports: BiocParallel, dplyr, ggplot2, ggrepel, magrittr, stats, utils Suggests: LRcellTypeMarkers, BiocStyle, knitr, rmarkdown, roxygen2, testthat License: MIT + file LICENSE MD5sum: c10ac6f38ce539fa9bae61a46ca19bd6 NeedsCompilation: no Title: Differential cell type change analysis using Logistic/linear Regression Description: The goal of LRcell is to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. To achieve this, LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing (scRNA-seq) as indicators for various cell types in the tissue of interest. Next, for each cell type, using its marker genes as indicators, we apply Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. Finally, these p-values are compared to predict which one(s) are likely to be responsible for the differential gene expression pattern observed in the bulk RNA-seq experiments. LRcell is inspired by the LRpath[@sartor2009lrpath] algorithm developed by Sartor et al., originally designed for pathway/gene set enrichment analysis. LRcell contains three major components: LRcell analysis, plot generation and marker gene selection. All modules in this package are written in R. This package also provides marker genes in the Prefrontal Cortex (pFC) human brain region, human PBMC and nine mouse brain regions (Frontal Cortex, Cerebellum, Globus Pallidus, Hippocampus, Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra and Thalamus). biocViews: SingleCell, GeneSetEnrichment, Sequencing, Regression, GeneExpression, DifferentialExpression Author: Wenjing Ma [cre, aut] (ORCID: ) Maintainer: Wenjing Ma VignetteBuilder: knitr BugReports: https://github.com/marvinquiet/LRcell/issues git_url: https://git.bioconductor.org/packages/LRcell git_branch: devel git_last_commit: fb27f27 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/LRcell_1.19.0.tar.gz vignettes: vignettes/LRcell/inst/doc/LRcell-vignette.html vignetteTitles: LRcell Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LRcell/inst/doc/LRcell-vignette.R suggestsMe: LRcellTypeMarkers dependencyCount: 85 Package: LymphoSeq Version: 1.39.0 Depends: R (>= 3.3), LymphoSeqDB Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq, RColorBrewer, circlize, grid, utils, stats, ggtree, msa, Biostrings, phangorn, stringdist, UpSetR Suggests: knitr, pheatmap, wordcloud, rmarkdown License: Artistic-2.0 MD5sum: d106f00990397d13d3130b2502ac5315 NeedsCompilation: no Title: Analyze high-throughput sequencing of T and B cell receptors Description: This R package analyzes high-throughput sequencing of T and B cell receptor complementarity determining region 3 (CDR3) sequences generated by Adaptive Biotechnologies' ImmunoSEQ assay. Its input comes from tab-separated value (.tsv) files exported from the ImmunoSEQ analyzer. biocViews: Software, Technology, Sequencing, TargetedResequencing, Alignment, MultipleSequenceAlignment Author: David Coffey Maintainer: David Coffey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LymphoSeq git_branch: devel git_last_commit: be99fc7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/LymphoSeq_1.39.0.tar.gz vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.html vignetteTitles: Analysis of high-throughput sequencing of T and B cell receptors with LymphoSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LymphoSeq/inst/doc/LymphoSeq.R dependencyCount: 111 Package: M3C Version: 1.33.0 Depends: R (>= 3.5.0) Imports: ggplot2, Matrix, doSNOW, cluster, parallel, foreach, doParallel, matrixcalc, Rtsne, corpcor, umap Suggests: knitr, rmarkdown License: AGPL-3 MD5sum: d59c6083090fe9778081c72dbacb18c8 NeedsCompilation: no Title: Monte Carlo Reference-based Consensus Clustering Description: M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1. biocViews: Clustering, GeneExpression, Transcription, RNASeq, Sequencing, ImmunoOncology Author: Christopher John, David Watson Maintainer: Christopher John VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/M3C git_branch: devel git_last_commit: 8703a74 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/M3C_1.33.0.tar.gz vignettes: vignettes/M3C/inst/doc/M3Cvignette.pdf vignetteTitles: M3C hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3C/inst/doc/M3Cvignette.R importsMe: lilikoi suggestsMe: parameters dependencyCount: 50 Package: M3Drop Version: 1.37.0 Depends: R (>= 3.4), numDeriv Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics, stats, matrixStats, Matrix, irlba, reldist, Hmisc, methods, scater Suggests: ROCR, knitr, M3DExampleData, SingleCellExperiment, Seurat, Biobase License: GPL (>=2) MD5sum: ed30a4c9dd3b1d20f993cd2a0a34d9df NeedsCompilation: no Title: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq Description: This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model. biocViews: RNASeq, Sequencing, Transcriptomics, GeneExpression, Software, DifferentialExpression, DimensionReduction, FeatureExtraction Author: Tallulah Andrews Maintainer: Tallulah Andrews URL: https://github.com/tallulandrews/M3Drop VignetteBuilder: knitr BugReports: https://github.com/tallulandrews/M3Drop/issues git_url: https://git.bioconductor.org/packages/M3Drop git_branch: devel git_last_commit: 85bdb7c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/M3Drop_1.37.0.tar.gz vignettes: vignettes/M3Drop/inst/doc/M3Drop_Vignette.pdf vignetteTitles: Introduction to M3Drop hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3Drop/inst/doc/M3Drop_Vignette.R importsMe: scMerge dependencyCount: 158 Package: m6Aboost Version: 1.17.0 Depends: S4Vectors, adabag, GenomicRanges, R (>= 4.1) Imports: dplyr, rtracklayer, BSgenome, Biostrings, utils, methods, IRanges, ExperimentHub Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 MD5sum: d3fa16d62bf37c62e1dfc95e5e29e084 NeedsCompilation: no Title: m6Aboost Description: This package can help user to run the m6Aboost model on their own miCLIP2 data. The package includes functions to assign the read counts and get the features to run the m6Aboost model. The miCLIP2 data should be stored in a GRanges object. More details can be found in the vignette. biocViews: Sequencing, Epigenetics, Genetics, ExperimentHubSoftware Author: You Zhou [aut, cre] (ORCID: ), Kathi Zarnack [aut] (ORCID: ) Maintainer: You Zhou URL: https://github.com/ZarnackGroup/m6Aboost VignetteBuilder: knitr BugReports: https://github.com/ZarnackGroup/m6Aboost/issues git_url: https://git.bioconductor.org/packages/m6Aboost git_branch: devel git_last_commit: c8944d2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/m6Aboost_1.17.0.tar.gz vignettes: vignettes/m6Aboost/inst/doc/m6AboosVignettes.html vignetteTitles: m6Aboost Vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/m6Aboost/inst/doc/m6AboosVignettes.R dependencyCount: 152 Package: Maaslin2 Version: 1.25.1 Depends: R (>= 3.6) Imports: robustbase, biglm, pcaPP, edgeR, metagenomeSeq, pbapply, car, dplyr, vegan, chemometrics, ggplot2, pheatmap, logging, data.table, lmerTest, hash, optparse, grDevices, stats, utils, glmmTMB, MASS, cplm, pscl, lme4, tibble Suggests: knitr, testthat (>= 2.1.0), rmarkdown, markdown License: MIT + file LICENSE MD5sum: 5e9c5108e4ec71fab804e59bea908098 NeedsCompilation: no Title: "Multivariable Association Discovery in Population-scale Meta-omics Studies" Description: MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin. biocViews: Metagenomics, Software, Microbiome, Normalization Author: Himel Mallick [aut], Ali Rahnavard [aut], Thomas Kuntz [aut], Sagun Maharjan [aut, cre] Maintainer: Sagun Maharjan URL: http://huttenhower.sph.harvard.edu/maaslin2 VignetteBuilder: knitr BugReports: https://github.com/biobakery/maaslin2/issues git_url: https://git.bioconductor.org/packages/Maaslin2 git_branch: devel git_last_commit: 05f7fa4 git_last_commit_date: 2026-02-27 Date/Publication: 2026-04-20 source.ver: src/contrib/Maaslin2_1.25.1.tar.gz vignettes: vignettes/Maaslin2/inst/doc/maaslin2.html vignetteTitles: Maaslin2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Maaslin2/inst/doc/maaslin2.R importsMe: benchdamic, Macarron suggestsMe: dar dependencyCount: 133 Package: maaslin3 Version: 1.3.0 Depends: R (>= 4.4) Imports: dplyr, plyr, pbapply, lmerTest, parallel, lme4, optparse, logging, multcomp, ggplot2, RColorBrewer, patchwork, scales, rlang, tibble, ggnewscale, survival, methods, BiocGenerics, reformulas Suggests: knitr, testthat (>= 2.1.0), rmarkdown, markdown, kableExtra, SummarizedExperiment, TreeSummarizedExperiment License: MIT + file LICENSE MD5sum: 4ab50a613dac427a2743b0e698a162cb NeedsCompilation: no Title: "Refining and extending generalized multivariate linear models for meta-omic association discovery" Description: MaAsLin 3 refines and extends generalized multivariate linear models for meta-omicron association discovery. It finds abundance and prevalence associations between microbiome meta-omics features and complex metadata in population-scale epidemiological studies. The software includes multiple analysis methods (including support for multiple covariates, repeated measures, and ordered predictors), filtering, normalization, and transform options to customize analysis for your specific study. biocViews: Metagenomics, Software, Microbiome, Normalization, MultipleComparison Author: William Nickols [aut, cre] (ORCID: ), Jacob Nearing [aut] Maintainer: William Nickols URL: http://huttenhower.sph.harvard.edu/maaslin3 VignetteBuilder: knitr BugReports: https://github.com/biobakery/maaslin3/issues git_url: https://git.bioconductor.org/packages/maaslin3 git_branch: devel git_last_commit: d4f80f8 git_last_commit_date: 2026-02-09 Date/Publication: 2026-04-20 source.ver: src/contrib/maaslin3_1.3.0.tar.gz vignettes: vignettes/maaslin3/inst/doc/maaslin3_manual.html, vignettes/maaslin3/inst/doc/maaslin3_tutorial.html vignetteTitles: Manual, Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maaslin3/inst/doc/maaslin3_manual.R, vignettes/maaslin3/inst/doc/maaslin3_tutorial.R importsMe: benchdamic, MMUPHin suggestsMe: miaViz dependencyCount: 101 Package: Macarron Version: 1.15.1 Depends: R (>= 4.5.0), SummarizedExperiment Imports: BiocParallel, DelayedArray, WGCNA, ff, data.table, dynamicTreeCut, Maaslin2, plyr, stats, psych, logging, methods, utils Suggests: knitr, BiocStyle, optparse, testthat (>= 2.1.0), rmarkdown, markdown License: MIT + file LICENSE MD5sum: 37e43de5ff7135495a0f55f80b5d18d0 NeedsCompilation: no Title: Prioritization of potentially bioactive metabolic features from epidemiological and environmental metabolomics datasets Description: Macarron is a workflow for the prioritization of potentially bioactive metabolites from metabolomics experiments. Prioritization integrates strengths of evidences of bioactivity such as covariation with a known metabolite, abundance relative to a known metabolite and association with an environmental or phenotypic indicator of bioactivity. Broadly, the workflow consists of stratified clustering of metabolic spectral features which co-vary in abundance in a condition, transfer of functional annotations, estimation of relative abundance and differential abundance analysis to identify associations between features and phenotype/condition. biocViews: Sequencing, Metabolomics, Coverage, FunctionalPrediction, Clustering Author: Amrisha Bhosle [aut], Ludwig Geistlinger [aut], Sagun Maharjan [aut, cre] Maintainer: Sagun Maharjan URL: http://huttenhower.sph.harvard.edu/macarron VignetteBuilder: knitr BugReports: https://forum.biobakery.org/c/microbial-community-profiling/macarron git_url: https://git.bioconductor.org/packages/Macarron git_branch: devel git_last_commit: 729ef2c git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/Macarron_1.15.1.tar.gz vignettes: vignettes/Macarron/inst/doc/Macarron.html vignetteTitles: Macarron hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Macarron/inst/doc/Macarron.R dependencyCount: 190 Package: maCorrPlot Version: 1.81.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: c9fea035d4ceb1e19586070518ed33b9 NeedsCompilation: no Title: Visualize artificial correlation in microarray data Description: Graphically displays correlation in microarray data that is due to insufficient normalization biocViews: Microarray, Preprocessing, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=15799785 git_url: https://git.bioconductor.org/packages/maCorrPlot git_branch: devel git_last_commit: 788060f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/maCorrPlot_1.81.0.tar.gz vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf vignetteTitles: maCorrPlot Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maCorrPlot/inst/doc/maCorrPlot.R dependencyCount: 6 Package: MACSQuantifyR Version: 1.25.0 Imports: readxl, graphics, tools, utils, grDevices, ggplot2, ggrepel, methods, stats, latticeExtra, lattice, rmarkdown, png, grid, gridExtra, prettydoc, rvest, xml2 Suggests: knitr, testthat, R.utils, spelling License: Artistic-2.0 MD5sum: 6e894e264a4f8fabc4d3ee9f4f9383d9 NeedsCompilation: no Title: Fast treatment of MACSQuantify FACS data Description: Automatically process the metadata of MACSQuantify FACS sorter. It runs multiple modules: i) imports of raw file and graphical selection of duplicates in well plate, ii) computes statistics on data and iii) can compute combination index. biocViews: DataImport, Preprocessing, Normalization, FlowCytometry, DataRepresentation, GUI Author: Raphaël Bonnet [aut, cre], Marielle Nebout [dtc],Giulia Biondani [dtc], Jean-François Peyron[aut,ths], Inserm [fnd] Maintainer: Raphaël Bonnet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSQuantifyR git_branch: devel git_last_commit: 1dffa00 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MACSQuantifyR_1.25.0.tar.gz vignettes: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.html vignetteTitles: MACSQuantifyR_step_by_step_analysis, MACSQuantifyR_simple_pipeline, MACSQuantifyR_quick_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.R dependencyCount: 79 Package: made4 Version: 1.85.0 Depends: RColorBrewer,gplots,scatterplot3d, Biobase, SummarizedExperiment Imports: ade4 Suggests: affy, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 5f40cd79d12b09422303e15eaa661a2a NeedsCompilation: no Title: Multivariate analysis of microarray data using ADE4 Description: Multivariate data analysis and graphical display of microarray data. Functions include for supervised dimension reduction (between group analysis) and joint dimension reduction of 2 datasets (coinertia analysis). It contains functions that require R package ade4. biocViews: Clustering, Classification, DimensionReduction, PrincipalComponent,Transcriptomics, MultipleComparison, GeneExpression, Sequencing, Microarray Author: Aedin Culhane Maintainer: Aedin Culhane URL: http://www.hsph.harvard.edu/aedin-culhane/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/made4 git_branch: devel git_last_commit: 9e87f7f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/made4_1.85.0.tar.gz vignettes: vignettes/made4/inst/doc/introduction.html vignetteTitles: Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/made4/inst/doc/introduction.R importsMe: omicade4 dependencyCount: 38 Package: maftools Version: 2.27.0 Depends: R (>= 4.1.0) Imports: data.table, grDevices, methods, RColorBrewer, Rhtslib, survival, DNAcopy, pheatmap LinkingTo: Rhtslib Suggests: berryFunctions, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomicRanges, IRanges, knitr, mclust, MultiAssayExperiment, NMF, R.utils, RaggedExperiment, rmarkdown, S4Vectors License: MIT + file LICENSE MD5sum: aaf4b2fa70bfd888df05d9f19c237aa8 NeedsCompilation: yes Title: Summarize, Analyze and Visualize MAF Files Description: Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort. biocViews: DataRepresentation, DNASeq, Visualization, DriverMutation, VariantAnnotation, FeatureExtraction, Classification, SomaticMutation, Sequencing, FunctionalGenomics, Survival Author: Anand Mayakonda [aut, cre] (ORCID: ) Maintainer: Anand Mayakonda URL: https://github.com/PoisonAlien/maftools SystemRequirements: GNU make, curl VignetteBuilder: knitr BugReports: https://github.com/PoisonAlien/maftools/issues git_url: https://git.bioconductor.org/packages/maftools git_branch: devel git_last_commit: 5abf2c6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/maftools_2.27.0.tar.gz vignettes: vignettes/maftools/inst/doc/cancer_hotspots.html, vignettes/maftools/inst/doc/cnv_analysis.html, vignettes/maftools/inst/doc/maftools.html, vignettes/maftools/inst/doc/oncoplots.html vignetteTitles: 03: Cancer report, 04: Copy number analysis, 01: Summarize,, Analyze,, and Visualize MAF Files, 02: Customizing oncoplots hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maftools/inst/doc/cancer_hotspots.R, vignettes/maftools/inst/doc/cnv_analysis.R, vignettes/maftools/inst/doc/maftools.R, vignettes/maftools/inst/doc/oncoplots.R dependsOnMe: GNOSIS importsMe: CaMutQC, CIMICE, katdetectr, musicatk, aplotExtra, Rediscover, sigminer, SMDIC, ssMutPA suggestsMe: GenomicDataCommons, MultiAssayExperiment, survtype, TCGAbiolinks dependencyCount: 26 Package: magpie Version: 1.11.0 Depends: R (>= 4.3.0) Imports: utils, rtracklayer, Matrix, matrixStats, stats, S4Vectors, methods, graphics, GenomicRanges, GenomicFeatures, IRanges, Rsamtools, AnnotationDbi, aod, BiocParallel, DESeq2, openxlsx, RColorBrewer, reshape2, TRESS Suggests: knitr, rmarkdown, kableExtra, RUnit, TBX20BamSubset, BiocGenerics, BiocStyle License: MIT + file LICENSE MD5sum: 77cc38d5dbb750b29bac4e2d9b0a4f6c NeedsCompilation: no Title: MeRIP-Seq data Analysis for Genomic Power Investigation and Evaluation Description: This package aims to perform power analysis for the MeRIP-seq study. It calculates FDR, FDC, power, and precision under various study design parameters, including but not limited to sample size, sequencing depth, and testing method. It can also output results into .xlsx files or produce corresponding figures of choice. biocViews: Epitranscriptomics, DifferentialMethylation, Sequencing, RNASeq, Software Author: Daoyu Duan [aut, cre], Zhenxing Guo [aut] Maintainer: Daoyu Duan URL: https://github.com/dxd429/magpie VignetteBuilder: knitr BugReports: https://github.com/dxd429/magpie/issues git_url: https://git.bioconductor.org/packages/magpie git_branch: devel git_last_commit: 48f31cd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/magpie_1.11.0.tar.gz vignettes: vignettes/magpie/inst/doc/magpie.html vignetteTitles: magpie Package User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/magpie/inst/doc/magpie.R dependencyCount: 98 Package: magrene Version: 1.13.0 Depends: R (>= 4.2.0) Imports: utils, stats, BiocParallel Suggests: BiocStyle, covr, knitr, rmarkdown, ggplot2, sessioninfo, testthat (>= 3.0.0) License: GPL-3 MD5sum: 10ccc3a06d4c2b8ba7f0344b704bee3b NeedsCompilation: no Title: Motif Analysis In Gene Regulatory Networks Description: magrene allows the identification and analysis of graph motifs in (duplicated) gene regulatory networks (GRNs), including lambda, V, PPI V, delta, and bifan motifs. GRNs can be tested for motif enrichment by comparing motif frequencies to a null distribution generated from degree-preserving simulated GRNs. Motif frequencies can be analyzed in the context of gene duplications to explore the impact of small-scale and whole-genome duplications on gene regulatory networks. Finally, users can calculate interaction similarity for gene pairs based on the Sorensen-Dice similarity index. biocViews: Software, MotifDiscovery, NetworkEnrichment, SystemsBiology, GraphAndNetwork Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/magrene VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/magrene git_url: https://git.bioconductor.org/packages/magrene git_branch: devel git_last_commit: 5d26ccd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/magrene_1.13.0.tar.gz vignettes: vignettes/magrene/inst/doc/magrene.html vignetteTitles: Introduction to magrene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/magrene/inst/doc/magrene.R dependencyCount: 13 Package: MAI Version: 1.17.0 Depends: R (>= 3.5.0) Imports: caret, parallel, doParallel, foreach, e1071, future.apply, future, missForest, pcaMethods, tidyverse, stats, utils, methods, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: ff396a23435d1191cbe47ce6621a8d01 NeedsCompilation: no Title: Mechanism-Aware Imputation Description: A two-step approach to imputing missing data in metabolomics. Step 1 uses a random forest classifier to classify missing values as either Missing Completely at Random/Missing At Random (MCAR/MAR) or Missing Not At Random (MNAR). MCAR/MAR are combined because it is often difficult to distinguish these two missing types in metabolomics data. Step 2 imputes the missing values based on the classified missing mechanisms, using the appropriate imputation algorithms. Imputation algorithms tested and available for MCAR/MAR include Bayesian Principal Component Analysis (BPCA), Multiple Imputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and Random Forest. Imputation algorithms tested and available for MNAR include nsKNN and a single imputation approach for imputation of metabolites where left-censoring is present. biocViews: Software, Metabolomics, StatisticalMethod, Classification Author: Jonathan Dekermanjian [aut, cre], Elin Shaddox [aut], Debmalya Nandy [aut], Debashis Ghosh [aut], Katerina Kechris [aut] Maintainer: Jonathan Dekermanjian URL: https://github.com/KechrisLab/MAI VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/MAI/issues git_url: https://git.bioconductor.org/packages/MAI git_branch: devel git_last_commit: ca7ec3d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MAI_1.17.0.tar.gz vignettes: vignettes/MAI/inst/doc/UsingMAI.html vignetteTitles: Utilizing Mechanism-Aware Imputation (MAI) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MAI/inst/doc/UsingMAI.R dependencyCount: 173 Package: makecdfenv Version: 1.87.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils License: GPL (>= 2) MD5sum: bc10bde1b8ec646654df07d9a1a4f372 NeedsCompilation: yes Title: CDF Environment Maker Description: This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment. biocViews: OneChannel, DataImport, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Wolfgang Huber , Ben Bolstad Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/makecdfenv git_branch: devel git_last_commit: 7eb6d83 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/makecdfenv_1.87.0.tar.gz vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf vignetteTitles: makecdfenv primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/makecdfenv/inst/doc/makecdfenv.R dependsOnMe: altcdfenvs dependencyCount: 12 Package: MANOR Version: 1.83.0 Depends: R (>= 2.10) Imports: GLAD, graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, bookdown License: GPL-2 MD5sum: 4de506c3399d1c029fa4122c8f03db94 NeedsCompilation: yes Title: CGH Micro-Array NORmalization Description: Importation, normalization, visualization, and quality control functions to correct identified sources of variability in array-CGH experiments. biocViews: Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, CopyNumberVariation, Normalization Author: Pierre Neuvial , Philippe Hupé Maintainer: Pierre Neuvial URL: http://bioinfo.curie.fr/projects/manor/index.html VignetteBuilder: knitr BugReports: https://github.com/pneuvial/MANOR/issues git_url: https://git.bioconductor.org/packages/MANOR git_branch: devel git_last_commit: 4e71ccf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MANOR_1.83.0.tar.gz vignettes: vignettes/MANOR/inst/doc/MANOR.html vignetteTitles: Overview of the MANOR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MANOR/inst/doc/MANOR.R dependencyCount: 9 Package: MantelCorr Version: 1.81.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) MD5sum: c629127497ef9d3a04715fcc66b2f357 NeedsCompilation: no Title: Compute Mantel Cluster Correlations Description: Computes Mantel cluster correlations from a (p x n) numeric data matrix (e.g. microarray gene-expression data). biocViews: Clustering Author: Brian Steinmeyer and William Shannon Maintainer: Brian Steinmeyer git_url: https://git.bioconductor.org/packages/MantelCorr git_branch: devel git_last_commit: 1950492 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MantelCorr_1.81.0.tar.gz vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf vignetteTitles: MantelCorrVignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MantelCorr/inst/doc/MantelCorrVignette.R dependencyCount: 1 Package: MAPFX Version: 1.7.1 Depends: R (>= 4.4.0) Imports: flowCore, Biobase, stringr, uwot, iCellR, igraph, ggplot2, RColorBrewer, Rfast, ComplexHeatmap, circlize, glmnetUtils, e1071, xgboost (>= 3.0.0), parallel, pbapply, reshape2, gtools, utils, stats, cowplot, methods, grDevices, graphics Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 44337604be5e6c83dd3876f334d4bcd4 NeedsCompilation: no Title: MAssively Parallel Flow cytometry Xplorer (MAPFX): A Toolbox for Analysing Data from the Massively-Parallel Cytometry Experiments Description: MAPFX is an end-to-end toolbox that pre-processes the raw data from MPC experiments (e.g., BioLegend's LEGENDScreen and BD Lyoplates assays), and further imputes the ‘missing’ infinity markers in the wells without those measurements. The pipeline starts by performing background correction on raw intensities to remove the noise from electronic baseline restoration and fluorescence compensation by adapting a normal-exponential convolution model. Unwanted technical variation, from sources such as well effects, is then removed using a log-normal model with plate, column, and row factors, after which infinity markers are imputed using the informative backbone markers as predictors. The completed dataset can then be used for clustering and other statistical analyses. Additionally, MAPFX can be used to normalise data from FFC assays as well. biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell, Proteomics, Clustering Author: Hsiao-Chi Liao [aut, cre] (ORCID: ), Agus Salim [ctb], infinityFlow [ctb] Maintainer: Hsiao-Chi Liao URL: https://github.com/HsiaoChiLiao/MAPFX VignetteBuilder: knitr BugReports: https://github.com/HsiaoChiLiao/MAPFX/issues git_url: https://git.bioconductor.org/packages/MAPFX git_branch: devel git_last_commit: 158ad54 git_last_commit_date: 2025-12-19 Date/Publication: 2026-04-20 source.ver: src/contrib/MAPFX_1.7.1.tar.gz vignettes: vignettes/MAPFX/inst/doc/MAPFX_Vignette.html vignetteTitles: MAPFX: MAssively Parallel Flow cytometry Xplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAPFX/inst/doc/MAPFX_Vignette.R dependencyCount: 196 Package: maPredictDSC Version: 1.49.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: 0960fe5c0db9128866380cba24d648e5 NeedsCompilation: no Title: Phenotype prediction using microarray data: approach of the best overall team in the IMPROVER Diagnostic Signature Challenge Description: This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types. biocViews: Microarray, Classification Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC git_url: https://git.bioconductor.org/packages/maPredictDSC git_branch: devel git_last_commit: befed4f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/maPredictDSC_1.49.0.tar.gz vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf vignetteTitles: maPredictDSC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maPredictDSC/inst/doc/maPredictDSC.R dependencyCount: 130 Package: mapscape Version: 1.35.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), base64enc (>= 0.1-3), stringr (>= 1.0.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 9153305466c2167751f2bd38b5286832 NeedsCompilation: no Title: mapscape Description: MapScape integrates clonal prevalence, clonal hierarchy, anatomic and mutational information to provide interactive visualization of spatial clonal evolution. There are four inputs to MapScape: (i) the clonal phylogeny, (ii) clonal prevalences, (iii) an image reference, which may be a medical image or drawing and (iv) pixel locations for each sample on the referenced image. Optionally, MapScape can accept a data table of mutations for each clone and their variant allele frequencies in each sample. The output of MapScape consists of a cropped anatomical image surrounded by two representations of each tumour sample. The first, a cellular aggregate, visually displays the prevalence of each clone. The second shows a skeleton of the clonal phylogeny while highlighting only those clones present in the sample. Together, these representations enable the analyst to visualize the distribution of clones throughout anatomic space. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mapscape git_branch: devel git_last_commit: cb371d5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mapscape_1.35.0.tar.gz vignettes: vignettes/mapscape/inst/doc/mapscape_vignette.html vignetteTitles: MapScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mapscape/inst/doc/mapscape_vignette.R dependencyCount: 36 Package: markeR Version: 1.1.2 Depends: R (>= 4.5.0) Imports: circlize, edgeR, ComplexHeatmap, ggh4x, ggplot2, ggpubr, grid, gridExtra, pROC, RColorBrewer, reshape2, rstatix, scales, stats, utils, fgsea, limma, ggrepel, effectsize, msigdbr, tibble Suggests: devtools, markdown, renv, testthat, BiocManager, knitr, rmarkdown, roxygen2, mockery, covr, magick, BiocStyle License: Artistic-2.0 MD5sum: 2674ca7c12ed3a88ba85397cbb9bb8d4 NeedsCompilation: no Title: An R Toolkit for Evaluating Gene Signatures as Phenotypic Markers Description: markeR is an R package that provides a modular and extensible framework for the systematic evaluation of gene sets as phenotypic markers using transcriptomic data. The package is designed to support both quantitative analyses and visual exploration of gene set behaviour across experimental and clinical phenotypes. It implements multiple methods, including score-based and enrichment approaches, and also allows the exploration of expression behaviour of individual genes. In addition, users can assess the similarity of their own gene sets against established collections (e.g., those from MSigDB), facilitating biological interpretation. biocViews: GeneExpression, Transcriptomics, Visualization, Software, GeneSetEnrichment, Classification Author: Rita Martins-Silva [aut, cre] (ORCID: ), Alexandre Kaizeler [aut, ctb] (ORCID: ), Nuno Luís Barbosa-Morais [aut, led, ths] (ORCID: ) Maintainer: Rita Martins-Silva URL: https://diseasetranscriptomicslab.github.io/markeR/, https://github.com/DiseaseTranscriptomicsLab/markeR VignetteBuilder: knitr BugReports: https://github.com/DiseaseTranscriptomicsLab/markeR/issues git_url: https://git.bioconductor.org/packages/markeR git_branch: devel git_last_commit: 3356098 git_last_commit_date: 2026-03-12 Date/Publication: 2026-04-20 source.ver: src/contrib/markeR_1.1.2.tar.gz vignettes: vignettes/markeR/inst/doc/markeR.html vignetteTitles: Introduction to markeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/markeR/inst/doc/markeR.R dependencyCount: 134 Package: marr Version: 1.21.0 Depends: R (>= 4.0) Imports: Rcpp, SummarizedExperiment, utils, methods, ggplot2, dplyr, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, covr License: GPL (>= 3) MD5sum: e5dc34cca25bf51c9aac33b3bd99d772 NeedsCompilation: yes Title: Maximum rank reproducibility Description: marr (Maximum Rank Reproducibility) is a nonparametric approach that detects reproducible signals using a maximal rank statistic for high-dimensional biological data. In this R package, we implement functions that measures the reproducibility of features per sample pair and sample pairs per feature in high-dimensional biological replicate experiments. The user-friendly plot functions in this package also plot histograms of the reproducibility of features per sample pair and sample pairs per feature. Furthermore, our approach also allows the users to select optimal filtering threshold values for the identification of reproducible features and sample pairs based on output visualization checks (histograms). This package also provides the subset of data filtered by reproducible features and/or sample pairs. biocViews: QualityControl, Metabolomics, MassSpectrometry, RNASeq, ChIPSeq Author: Tusharkanti Ghosh [aut, cre], Max McGrath [aut], Daisy Philtron [aut], Katerina Kechris [aut], Debashis Ghosh [aut, cph] Maintainer: Tusharkanti Ghosh VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/marr/issues git_url: https://git.bioconductor.org/packages/marr git_branch: devel git_last_commit: 9d1d483 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/marr_1.21.0.tar.gz vignettes: vignettes/marr/inst/doc/MarrVignette.html vignetteTitles: The marr user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/marr/inst/doc/MarrVignette.R dependencyCount: 50 Package: marray Version: 1.89.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: e9e14ea297f1b0a223631e5ff073af6e NeedsCompilation: no Title: Exploratory analysis for two-color spotted microarray data Description: Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking. biocViews: Microarray, TwoChannel, Preprocessing Author: Yee Hwa (Jean) Yang with contributions from Agnes Paquet and Sandrine Dudoit. Maintainer: Yee Hwa (Jean) Yang URL: http://www.maths.usyd.edu.au/u/jeany/ git_url: https://git.bioconductor.org/packages/marray git_branch: devel git_last_commit: 7dc98b0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/marray_1.89.0.tar.gz vignettes: vignettes/marray/inst/doc/marray.pdf, vignettes/marray/inst/doc/marrayClasses.pdf, vignettes/marray/inst/doc/marrayClassesShort.pdf, vignettes/marray/inst/doc/marrayInput.pdf, vignettes/marray/inst/doc/marrayNorm.pdf, vignettes/marray/inst/doc/marrayPlots.pdf vignetteTitles: marray Overview, marrayClasses Overview, marrayClasses Tutorial (short), marrayInput Introduction, marray Normalization, marrayPlots Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/marray/inst/doc/marray.R, vignettes/marray/inst/doc/marrayClasses.R, vignettes/marray/inst/doc/marrayClassesShort.R, vignettes/marray/inst/doc/marrayInput.R, vignettes/marray/inst/doc/marrayNorm.R, vignettes/marray/inst/doc/marrayPlots.R dependsOnMe: CGHbase, convert, dyebias, nnNorm, OLIN, RBM, stepNorm, TurboNorm, beta7, dyebiasexamples importsMe: arrayQuality, ChAMP, methylPipe, MSstats, MSstatsShiny, nnNorm, OLIN, OLINgui, piano, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz, hexbin dependencyCount: 7 Package: martini Version: 1.31.0 Depends: R (>= 4.0) Imports: igraph (>= 1.0.1), Matrix, memoise (>= 2.0.0), methods (>= 3.3.2), Rcpp (>= 0.12.8), snpStats (>= 1.20.0), stats, utils, LinkingTo: Rcpp, RcppEigen (>= 0.3.3.5.0) Suggests: biomaRt (>= 2.34.1), circlize (>= 0.4.11), STRINGdb (>= 2.2.0), httr (>= 1.2.1), IRanges (>= 2.8.2), S4Vectors (>= 0.12.2), knitr, testthat, readr, rmarkdown License: GPL-3 MD5sum: 621fc323054d2828cb78f0072c30e3c2 NeedsCompilation: yes Title: GWAS Incorporating Networks Description: martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre] (ORCID: ), Chloe-Agathe Azencott [aut] (ORCID: ) Maintainer: Hector Climente-Gonzalez URL: https://github.com/hclimente/martini VignetteBuilder: knitr BugReports: https://github.com/hclimente/martini/issues git_url: https://git.bioconductor.org/packages/martini git_branch: devel git_last_commit: 59da883 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/martini_1.31.0.tar.gz vignettes: vignettes/martini/inst/doc/scones_usage.html, vignettes/martini/inst/doc/simulate_phenotype.html vignetteTitles: Running SConES, Simulating SConES-based phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/martini/inst/doc/scones_usage.R, vignettes/martini/inst/doc/simulate_phenotype.R dependencyCount: 27 Package: maser Version: 1.29.0 Depends: R (>= 3.5.0), ggplot2, GenomicRanges Imports: dplyr, rtracklayer, reshape2, Gviz, DT, Seqinfo, stats, utils, IRanges, methods, BiocGenerics, parallel, data.table Suggests: testthat, knitr, rmarkdown, BiocStyle, AnnotationHub License: MIT + file LICENSE MD5sum: 266117b712b718085874f335275e5d28 NeedsCompilation: no Title: Mapping Alternative Splicing Events to pRoteins Description: This package provides functionalities for downstream analysis, annotation and visualizaton of alternative splicing events generated by rMATS. biocViews: AlternativeSplicing, Transcriptomics, Visualization Author: Diogo F.T. Veiga [aut, cre] Maintainer: Diogo F.T. Veiga URL: https://github.com/DiogoVeiga/maser VignetteBuilder: knitr BugReports: https://github.com/DiogoVeiga/maser/issues git_url: https://git.bioconductor.org/packages/maser git_branch: devel git_last_commit: a5a5f37 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/maser_1.29.0.tar.gz vignettes: vignettes/maser/inst/doc/Introduction.html, vignettes/maser/inst/doc/Protein_mapping.html vignetteTitles: Introduction, Mapping protein features to splicing events hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maser/inst/doc/Introduction.R, vignettes/maser/inst/doc/Protein_mapping.R dependencyCount: 158 Package: maSigPro Version: 1.83.0 Depends: R (>= 2.3.1) Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS License: GPL (>= 2) MD5sum: ee17cceaedf4c743fcd3248812b646c2 NeedsCompilation: no Title: Significant Gene Expression Profile Differences in Time Course Gene Expression Data Description: maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. biocViews: Microarray, RNA-Seq, Differential Expression, TimeCourse Author: Ana Conesa and Maria Jose Nueda Maintainer: Maria Jose Nueda git_url: https://git.bioconductor.org/packages/maSigPro git_branch: devel git_last_commit: 99d470c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/maSigPro_1.83.0.tar.gz vignettes: vignettes/maSigPro/inst/doc/maSigPro.pdf, vignettes/maSigPro/inst/doc/maSigProUsersGuide.pdf vignetteTitles: maSigPro Vignette, maSigProUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: maskBAD Version: 1.55.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe, hgu95av2cdf License: GPL (>= 2) MD5sum: 53eafddc8cd7e56d5227036acc860b3d NeedsCompilation: no Title: Masking probes with binding affinity differences Description: Package includes functions to analyze and mask microarray expression data. biocViews: Microarray Author: Michael Dannemann Maintainer: Michael Dannemann git_url: https://git.bioconductor.org/packages/maskBAD git_branch: devel git_last_commit: f7b7871 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/maskBAD_1.55.0.tar.gz vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf vignetteTitles: Package maskBAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maskBAD/inst/doc/maskBAD.R dependencyCount: 22 Package: MassArray Version: 1.63.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, stats, utils License: GPL (>=2) MD5sum: 6d59420ead8a2f82ae74f3f9ee0f147d NeedsCompilation: no Title: Analytical Tools for MassArray Data Description: This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis. biocViews: ImmunoOncology, DNAMethylation, SNP, MassSpectrometry, Genetics, DataImport, Visualization Author: Reid F. Thompson , John M. Greally Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/MassArray git_branch: devel git_last_commit: 4b22465 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MassArray_1.63.0.tar.gz vignettes: vignettes/MassArray/inst/doc/MassArray.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassArray/inst/doc/MassArray.R dependencyCount: 5 Package: massiR Version: 1.47.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: 80f5eeff4cfb4289f2c3f33a5b90b8a1 NeedsCompilation: no Title: massiR: MicroArray Sample Sex Identifier Description: Predicts the sex of samples in gene expression microarray datasets biocViews: Software, Microarray, GeneExpression, Clustering, Classification, QualityControl Author: Sam Buckberry Maintainer: Sam Buckberry git_url: https://git.bioconductor.org/packages/massiR git_branch: devel git_last_commit: 0501645 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/massiR_1.47.0.tar.gz vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf vignetteTitles: massiR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/massiR/inst/doc/massiR_Vignette.R dependencyCount: 15 Package: MassSpecWavelet Version: 1.77.0 Suggests: signal, waveslim, BiocStyle, knitr, rmarkdown, RUnit, bench License: LGPL (>= 2) MD5sum: 153ef1b804af24e258881d348b1ebce3 NeedsCompilation: yes Title: Peak Detection for Mass Spectrometry data using wavelet-based algorithms Description: Peak Detection in Mass Spectrometry data is one of the important preprocessing steps. The performance of peak detection affects subsequent processes, including protein identification, profile alignment and biomarker identification. Using Continuous Wavelet Transform (CWT), this package provides a reliable algorithm for peak detection that does not require any type of smoothing or previous baseline correction method, providing more consistent results for different spectra. See ) Maintainer: Sergio Oller Moreno URL: https://github.com/zeehio/MassSpecWavelet VignetteBuilder: knitr BugReports: http://github.com/zeehio/MassSpecWavelet/issues git_url: https://git.bioconductor.org/packages/MassSpecWavelet git_branch: devel git_last_commit: 0fdc2a4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MassSpecWavelet_1.77.0.tar.gz vignettes: vignettes/MassSpecWavelet/inst/doc/FindingLocalMaxima.html, vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.html vignetteTitles: Finding local maxima, Using the MassSpecWavelet package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/FindingLocalMaxima.R, vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq, xcms, NMRphasing, Rnmr1D, speaq suggestsMe: downlit, metabodecon dependencyCount: 0 Package: MAST Version: 1.37.0 Depends: SingleCellExperiment (>= 1.2.0), R(>= 3.5) Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr, stringr, abind, methods, parallel, reshape2, stats, stats4, graphics, utils, SummarizedExperiment(>= 1.5.3), progress, Matrix Suggests: knitr, rmarkdown, testthat, lme4(>= 1.0), blme, roxygen2(> 6.0.0), numDeriv, car, gdata, lattice, GGally, GSEABase, NMF, TxDb.Hsapiens.UCSC.hg19.knownGene, rsvd, limma, RColorBrewer, BiocStyle, scater, DelayedArray, HDF5Array, zinbwave, dplyr License: GPL(>= 2) MD5sum: 64b13aa9900a8786d490529030f53c99 NeedsCompilation: no Title: Model-based Analysis of Single Cell Transcriptomics Description: Methods and models for handling zero-inflated single cell assay data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, RNASeq, Transcriptomics, SingleCell Author: Andrew McDavid [aut, cre], Greg Finak [aut], Masanao Yajima [aut] Maintainer: Andrew McDavid URL: https://github.com/RGLab/MAST/ VignetteBuilder: knitr BugReports: https://github.com/RGLab/MAST/issues git_url: https://git.bioconductor.org/packages/MAST git_branch: devel git_last_commit: 29ffa2b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MAST_1.37.0.tar.gz vignettes: vignettes/MAST/inst/doc/MAITAnalysis.html, vignettes/MAST/inst/doc/MAST-interoperability.html, vignettes/MAST/inst/doc/MAST-Intro.html vignetteTitles: Using MAST for filtering,, differential expression and gene set enrichment in MAIT cells, Interoptability between MAST and SingleCellExperiment-derived packages, An Introduction to MAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAST/inst/doc/MAITAnalysis.R, vignettes/MAST/inst/doc/MAST-interoperability.R, vignettes/MAST/inst/doc/MAST-Intro.R dependsOnMe: POWSC importsMe: benchdamic, celaref, singleCellTK, DWLS suggestsMe: clusterExperiment, EWCE, Seurat, SeuratExplorer dependencyCount: 56 Package: mastR Version: 1.11.2 Depends: R (>= 4.3.0) Imports: AnnotationDbi, Biobase, dplyr, edgeR, ggplot2, ggpubr, graphics, grDevices, GSEABase, limma, Matrix, methods, msigdb, org.Hs.eg.db, patchwork, SeuratObject (> 5.0.0), SingleCellExperiment, stats, SummarizedExperiment, tidyr, utils Suggests: BiocManager, BiocStyle, clusterProfiler, ComplexHeatmap, depmap, enrichplot, ggrepel, ggvenn, Glimma, gridExtra, jsonlite, knitr, rmarkdown, RobustRankAggreg, rvest, scuttle, singscore, splatter, testthat (>= 3.0.0), UpSetR License: MIT + file LICENSE MD5sum: a06e83ee0311121874a601fae645e377 NeedsCompilation: no Title: Markers Automated Screening Tool in R Description: mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of group markers in various biological and medical applications, including cancer research and developmental biology. biocViews: Software, GeneExpression, Transcriptomics, DifferentialExpression, Visualization Author: Jinjin Chen [aut, cre] (ORCID: ), Ahmed Mohamed [aut, ctb] (ORCID: ), Chin Wee Tan [ctb] (ORCID: ) Maintainer: Jinjin Chen URL: https://davislaboratory.github.io/mastR VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/mastR/issues git_url: https://git.bioconductor.org/packages/mastR git_branch: devel git_last_commit: 5407010 git_last_commit_date: 2026-01-25 Date/Publication: 2026-04-20 source.ver: src/contrib/mastR_1.11.2.tar.gz vignettes: vignettes/mastR/inst/doc/mastR_customized_design.html, vignettes/mastR/inst/doc/mastR_Demo.html vignetteTitles: mastR_Demo, mastR_Demo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mastR/inst/doc/mastR_customized_design.R, vignettes/mastR/inst/doc/mastR_Demo.R dependencyCount: 157 Package: matchBox Version: 1.53.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: ebe9a7bbd2f99b8a675f0430408595d7 NeedsCompilation: no Title: Utilities to compute, compare, and plot the agreement between ordered vectors of features (ie. distinct genomic experiments). The package includes Correspondence-At-the-TOP (CAT) analysis. Description: The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance. biocViews: Software, Annotation, Microarray, MultipleComparison, Visualization Author: Luigi Marchionni , Anuj Gupta Maintainer: Luigi Marchionni , Anuj Gupta git_url: https://git.bioconductor.org/packages/matchBox git_branch: devel git_last_commit: b245774 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/matchBox_1.53.0.tar.gz vignettes: vignettes/matchBox/inst/doc/matchBox.pdf vignetteTitles: Working with the matchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matchBox/inst/doc/matchBox.R dependencyCount: 0 Package: MatrixGenerics Version: 1.23.0 Depends: matrixStats (>= 1.4.1) Imports: methods Suggests: Matrix, sparseMatrixStats, SparseArray, DelayedArray, DelayedMatrixStats, SummarizedExperiment, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: 288ecc582d984b8d4075aa76fac87d05 NeedsCompilation: no Title: S4 Generic Summary Statistic Functions that Operate on Matrix-Like Objects Description: S4 generic functions modeled after the 'matrixStats' API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities. biocViews: Infrastructure, Software Author: Constantin Ahlmann-Eltze [aut] (ORCID: ), Peter Hickey [aut, cre] (ORCID: ), Hervé Pagès [aut] Maintainer: Peter Hickey URL: https://bioconductor.org/packages/MatrixGenerics BugReports: https://github.com/Bioconductor/MatrixGenerics/issues git_url: https://git.bioconductor.org/packages/MatrixGenerics git_branch: devel git_last_commit: 4bb40d2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MatrixGenerics_1.23.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: DelayedArray, DelayedMatrixStats, GenomicFiles, SparseArray, sparseMatrixStats, SummarizedExperiment, VariantAnnotation importsMe: atena, Banksy, blase, carnation, CoreGx, crisprDesign, CTexploreR, DeconvoBuddies, demuxSNP, DESeq2, dreamlet, escape, FLAMES, gCrisprTools, genefilter, glmGamPoi, GSVA, imcRtools, lemur, lineagespot, methodical, mia, miaSim, miloR, MinimumDistance, MultiAssayExperiment, muscat, omicsGMF, PDATK, plaid, RaggedExperiment, RAIDS, RCSL, SanityR, saseR, scater, scFeatures, scone, scPCA, scran, scuttle, scviR, Seqtometry, shinyMethyl, StabMap, tadar, TENxIO, tLOH, tpSVG, transformGamPoi, universalmotif, VanillaICE, Voyager, zitools, spatialLIBD suggestsMe: bnem, cypress, MungeSumstats, MoBPS dependencyCount: 2 Package: MatrixQCvis Version: 1.19.0 Depends: R (>= 4.1.0), DT (>= 0.33), SummarizedExperiment (>= 1.20.0), plotly (>= 4.9.3), shiny (>= 1.6.0) Imports: ComplexHeatmap (>= 2.7.9), dplyr (>= 1.0.5), ExperimentHub (>= 2.6.0), ggplot2 (>= 3.3.3), grDevices (>= 4.1.0), Hmisc (>= 4.5-0), htmlwidgets (>= 1.5.3), impute (>= 1.65.0), imputeLCMD (>= 2.0), limma (>= 3.47.12), MASS (>= 7.3-58.1), methods (>= 4.1.0), pcaMethods (>= 1.83.0), proDA (>= 1.5.0), rlang (>= 0.4.10), rmarkdown (>= 2.7), Rtsne (>= 0.15), shinydashboard (>= 0.7.1), shinyhelper (>= 0.3.2), shinyjs (>= 2.0.0), stats (>= 4.1.0), sva (>= 3.52.0), tibble (>= 3.1.1), tidyr (>= 1.1.3), umap (>= 0.2.7.0), UpSetR (>= 1.4.0), vsn (>= 3.59.1) Suggests: BiocGenerics (>= 0.37.4), BiocStyle (>= 2.19.2), hexbin (>= 1.28.2), httr (>= 1.4.7), jpeg (>= 0.1-10), knitr (>= 1.33), statmod (>= 1.5.0), testthat (>= 3.0.2) License: GPL-3 MD5sum: 156ae2a2c44cd88a0781bc363d3bbd0f NeedsCompilation: no Title: Shiny-based interactive data-quality exploration for omics data Description: Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object. biocViews: Visualization, ShinyApps, GUI, QualityControl, DimensionReduction, Metabolomics, Proteomics, Transcriptomics Author: Thomas Naake [aut, cre] (ORCID: ), Wolfgang Huber [aut] (ORCID: ) Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MatrixQCvis git_branch: devel git_last_commit: 0b99d49 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MatrixQCvis_1.19.0.tar.gz vignettes: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.html vignetteTitles: Shiny-based interactive data quality exploration of omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.R dependencyCount: 188 Package: MatrixRider Version: 1.43.0 Depends: R (>= 3.1.2) Imports: methods, TFBSTools, IRanges, XVector, Biostrings LinkingTo: IRanges, XVector, Biostrings, S4Vectors Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014 License: GPL-3 MD5sum: 15aa2ccf486ee7376af58a597728bbe2 NeedsCompilation: yes Title: Obtain total affinity and occupancies for binding site matrices on a given sequence Description: Calculates a single number for a whole sequence that reflects the propensity of a DNA binding protein to interact with it. The DNA binding protein has to be described with a PFM matrix, for example gotten from Jaspar. biocViews: GeneRegulation, Genetics, MotifAnnotation Author: Elena Grassi Maintainer: Elena Grassi git_url: https://git.bioconductor.org/packages/MatrixRider git_branch: devel git_last_commit: c9dff82 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MatrixRider_1.43.0.tar.gz vignettes: vignettes/MatrixRider/inst/doc/MatrixRider.pdf vignetteTitles: Total affinity and occupancies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixRider/inst/doc/MatrixRider.R dependencyCount: 80 Package: matter Version: 2.13.5 Depends: R (>= 4.4), BiocParallel, Matrix, methods Imports: BiocGenerics, ProtGenerics, digest, irlba, stats, stats4, graphics, grDevices, parallel, utils LinkingTo: BH Suggests: BiocStyle, knitr, testthat, plotly License: Artistic-2.0 | file LICENSE MD5sum: 09e0617a7852abe63f3c14e4b9cd0e33 NeedsCompilation: yes Title: Out-of-core statistical computing and signal processing Description: Toolbox for larger-than-memory scientific computing and visualization, providing efficient out-of-core data structures using files or shared memory, for dense and sparse vectors, matrices, and arrays, with applications to nonuniformly sampled signals and images. biocViews: Infrastructure, DataRepresentation, DataImport, DimensionReduction, Preprocessing Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: https://github.com/kuwisdelu/matter VignetteBuilder: knitr BugReports: https://github.com/kuwisdelu/matter/issues git_url: https://git.bioconductor.org/packages/matter git_branch: devel git_last_commit: 016f311 git_last_commit_date: 2026-04-10 Date/Publication: 2026-04-20 source.ver: src/contrib/matter_2.13.5.tar.gz vignettes: vignettes/matter/inst/doc/matter2-guide.html, vignettes/matter/inst/doc/matter2-signal.html vignetteTitles: 1. Matter 2: User guide for flexible out-of-memory data structures, 2. Matter 2: Signal and image processing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/matter/inst/doc/matter2-guide.R, vignettes/matter/inst/doc/matter2-signal.R dependsOnMe: CardinalIO importsMe: Cardinal dependencyCount: 24 Package: MBAmethyl Version: 1.45.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: 91865ca23099b2a88e0c821434817460 NeedsCompilation: no Title: Model-based analysis of DNA methylation data Description: This package provides a function for reconstructing DNA methylation values from raw measurements. It iteratively implements the group fused lars to smooth related-by-location methylation values and the constrained least squares to remove probe affinity effect across multiple sequences. biocViews: DNAMethylation, MethylationArray Author: Tao Wang, Mengjie Chen Maintainer: Tao Wang git_url: https://git.bioconductor.org/packages/MBAmethyl git_branch: devel git_last_commit: a70414d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MBAmethyl_1.45.0.tar.gz vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf vignetteTitles: MBAmethyl Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBAmethyl/inst/doc/MBAmethyl.R dependencyCount: 0 Package: MBASED Version: 1.45.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 MD5sum: 12fabb8fe1ef021f5e775df08b02d805 NeedsCompilation: no Title: Package containing functions for ASE analysis using Meta-analysis Based Allele-Specific Expression Detection Description: The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE. biocViews: Sequencing, GeneExpression, Transcription Author: Oleg Mayba, Houston Gilbert Maintainer: Oleg Mayba git_url: https://git.bioconductor.org/packages/MBASED git_branch: devel git_last_commit: cd4e120 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MBASED_1.45.0.tar.gz vignettes: vignettes/MBASED/inst/doc/MBASED.pdf vignetteTitles: MBASED hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBASED/inst/doc/MBASED.R dependencyCount: 36 Package: MBCB Version: 1.65.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>=2) MD5sum: 8d732a59c636e80dc53ec1cf416a1c88 NeedsCompilation: no Title: MBCB (Model-based Background Correction for Beadarray) Description: This package provides a model-based background correction method, which incorporates the negative control beads to pre-process Illumina BeadArray data. biocViews: Microarray, Preprocessing Author: Yang Xie Maintainer: Bo Yao URL: https://qbrc.swmed.edu/ git_url: https://git.bioconductor.org/packages/MBCB git_branch: devel git_last_commit: 4aa3893 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MBCB_1.65.0.tar.gz vignettes: vignettes/MBCB/inst/doc/MBCB.pdf vignetteTitles: MBCB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBCB/inst/doc/MBCB.R dependencyCount: 5 Package: MBECS Version: 1.15.0 Depends: R (>= 4.1) Imports: methods, magrittr, phyloseq, limma, lme4, lmerTest, pheatmap, rmarkdown, cluster, dplyr, ggplot2, gridExtra, ruv, sva, tibble, tidyr, vegan, stats, utils, Matrix Suggests: knitr, markdown, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 662793f32d6ea131c84bfc1050fa4f66 NeedsCompilation: no Title: Evaluation and correction of batch effects in microbiome data-sets Description: The Microbiome Batch Effect Correction Suite (MBECS) provides a set of functions to evaluate and mitigate unwated noise due to processing in batches. To that end it incorporates a host of batch correcting algorithms (BECA) from various packages. In addition it offers a correction and reporting pipeline that provides a preliminary look at the characteristics of a data-set before and after correcting for batch effects. biocViews: BatchEffect, Microbiome, ReportWriting, Visualization, Normalization, QualityControl Author: Michael Olbrich [aut, cre] (ORCID: ) Maintainer: Michael Olbrich URL: https://github.com/rmolbrich/MBECS VignetteBuilder: knitr BugReports: https://github.com/rmolbrich/MBECS/issues/new git_url: https://git.bioconductor.org/packages/MBECS git_branch: devel git_last_commit: 796859e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MBECS_1.15.0.tar.gz vignettes: vignettes/MBECS/inst/doc/mbecs_vignette.html vignetteTitles: MBECS introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBECS/inst/doc/mbecs_vignette.R dependencyCount: 137 Package: mbkmeans Version: 1.27.1 Depends: R (>= 3.6) Imports: methods, DelayedArray, Rcpp, S4Vectors, SingleCellExperiment, SummarizedExperiment, ClusterR, benchmarkme, Matrix, BiocParallel LinkingTo: Rcpp, RcppArmadillo (>= 0.7.2), Rhdf5lib, beachmat, ClusterR Suggests: beachmat, HDF5Array, Rhdf5lib, BiocStyle, TENxPBMCData, scater, DelayedMatrixStats, bluster, knitr, testthat, rmarkdown License: MIT + file LICENSE MD5sum: 2d720e1d99c456af4cde631245e241e0 NeedsCompilation: yes Title: Mini-batch K-means Clustering for Single-Cell RNA-seq Description: Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation. biocViews: Clustering, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Yuwei Ni [aut, cph], Davide Risso [aut, cre, cph], Stephanie Hicks [aut, cph], Elizabeth Purdom [aut, cph] Maintainer: Davide Risso SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/drisso/mbkmeans/issues git_url: https://git.bioconductor.org/packages/mbkmeans git_branch: devel git_last_commit: 4af827b git_last_commit_date: 2026-02-10 Date/Publication: 2026-04-20 source.ver: src/contrib/mbkmeans_1.27.1.tar.gz vignettes: vignettes/mbkmeans/inst/doc/Vignette.html vignetteTitles: mbkmeans vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mbkmeans/inst/doc/Vignette.R dependsOnMe: OSCA.basic importsMe: clusterExperiment suggestsMe: bluster, concordexR, scDblFinder dependencyCount: 84 Package: mBPCR Version: 1.65.0 Depends: oligoClasses, GWASTools Imports: Biobase, graphics, methods, utils, grDevices Suggests: xtable License: GPL (>= 2) MD5sum: 669b4c4ab19d9976af102babdbe1a842 NeedsCompilation: no Title: Bayesian Piecewise Constant Regression for DNA copy number estimation Description: It contains functions for estimating the DNA copy number profile using mBPCR with the aim of detecting regions with copy number changes. biocViews: aCGH, SNP, Microarray, CopyNumberVariation Author: P.M.V. Rancoita , with contributions from M. Hutter Maintainer: P.M.V. Rancoita URL: http://www.idsia.ch/~paola/mBPCR git_url: https://git.bioconductor.org/packages/mBPCR git_branch: devel git_last_commit: 1aad86f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mBPCR_1.65.0.tar.gz vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf vignetteTitles: mBPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mBPCR/inst/doc/mBPCR.R dependencyCount: 113 Package: MBQN Version: 2.23.0 Depends: R (>= 3.6) Imports: stats, graphics, utils, limma (>= 3.30.13), SummarizedExperiment (>= 1.10.0), preprocessCore (>= 1.36.0), BiocFileCache, rappdirs, xml2, RCurl, ggplot2, PairedData, rmarkdown Suggests: knitr License: GPL-3 + file LICENSE MD5sum: 8c846e68da30b14d003fa6a9dd504413 NeedsCompilation: no Title: Mean/Median-balanced quantile normalization Description: Modified quantile normalization for omics or other matrix-like data distorted in location and scale. biocViews: Normalization, Preprocessing, Proteomics, Software Author: Eva Brombacher [aut, cre] (ORCID: ), Clemens Kreutz [aut, ctb] (ORCID: ), Ariane Schad [aut, ctb] (ORCID: ) Maintainer: Eva Brombacher URL: https://github.com/arianeschad/mbqn VignetteBuilder: knitr BugReports: https://github.com/arianeschad/MBQN/issues git_url: https://git.bioconductor.org/packages/MBQN git_branch: devel git_last_commit: 6c11f8c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MBQN_2.23.0.tar.gz vignettes: vignettes/MBQN/inst/doc/MBQNpackage.html vignetteTitles: MBQN Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MBQN/inst/doc/MBQNpackage.R dependencyCount: 101 Package: mbQTL Version: 1.11.0 Depends: R (>= 4.3.0) Imports: MatrixEQTL, dplyr, ggplot2, readxl, stringr, tidyr, metagenomeSeq, pheatmap, broom, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 77ac916f04f1d2a59fb59f727332e886 NeedsCompilation: no Title: mbQTL: A package for SNP-Taxa mGWAS analysis Description: mbQTL is a statistical R package for simultaneous 16srRNA,16srDNA (microbial) and variant, SNP, SNV (host) relationship, correlation, regression studies. We apply linear, logistic and correlation based statistics to identify the relationships of taxa, genus, species and variant, SNP, SNV in the infected host. We produce various statistical significance measures such as P values, FDR, BC and probability estimation to show significance of these relationships. Further we provide various visualization function for ease and clarification of the results of these analysis. The package is compatible with dataframe, MRexperiment and text formats. biocViews: SNP, Microbiome, WholeGenome, Metagenomics, StatisticalMethod, Regression Author: Mercedeh Movassagh [aut, cre] (ORCID: ), Steven Schiff [aut], Joseph N Paulson [aut] Maintainer: Mercedeh Movassagh URL: "https://github.com/Mercedeh66/mbQTL" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mbQTL git_branch: devel git_last_commit: 22bccdc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mbQTL_1.11.0.tar.gz vignettes: vignettes/mbQTL/inst/doc/mbQTL_Vignette.html vignetteTitles: MbQTL_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mbQTL/inst/doc/mbQTL_Vignette.R dependencyCount: 72 Package: MBttest Version: 1.39.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 994305137fe1a1e6bf32879d116b5bc0 NeedsCompilation: no Title: Multiple Beta t-Tests Description: MBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE, GeneExpression, Transcription, AlternativeSplicing,Coverage, DifferentialSplicing Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/MBttest git_branch: devel git_last_commit: 30609ce git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MBttest_1.39.0.tar.gz vignettes: vignettes/MBttest/inst/doc/MBttest-manual.pdf, vignettes/MBttest/inst/doc/MBttest.pdf vignetteTitles: MBttest-manual.pdf, Analysing RNA-Seq count data with the "MBttest" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBttest/inst/doc/MBttest.R dependencyCount: 11 Package: MCbiclust Version: 1.35.0 Depends: R (>= 3.4) Imports: BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db, org.Hs.eg.db, GGally, ggplot2, scales, cluster, WGCNA Suggests: gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr, pander, devtools, testthat, GSVA License: GPL-2 MD5sum: 441438df9ce37e892852f8af744f8529 NeedsCompilation: no Title: Massive correlating biclusters for gene expression data and associated methods Description: Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set. biocViews: ImmunoOncology, Clustering, Microarray, StatisticalMethod, Software, RNASeq, GeneExpression Author: Robert Bentham Maintainer: Robert Bentham VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MCbiclust git_branch: devel git_last_commit: 538a41b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MCbiclust_1.35.0.tar.gz vignettes: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.html vignetteTitles: Introduction to MCbiclust hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.R dependencyCount: 125 Package: mdp Version: 1.31.0 Depends: R (>= 4.0) Imports: ggplot2, gridExtra, grid, stats, utils Suggests: testthat, knitr, rmarkdown, fgsea, BiocManager License: GPL-3 MD5sum: 7f42b57e65bea2a0283c21bddf8d6117 NeedsCompilation: no Title: Molecular Degree of Perturbation calculates scores for transcriptome data samples based on their perturbation from controls Description: The Molecular Degree of Perturbation webtool quantifies the heterogeneity of samples. It takes a data.frame of omic data that contains at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. It is based on the Molecular Distance to Health (Pankla et al. 2009), and expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes. biocViews: BiomedicalInformatics, QualityControl, Transcriptomics, SystemsBiology, Microarray, QualityControl Author: Melissa Lever [aut], Pedro Russo [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya URL: https://mdp.sysbio.tools/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mdp git_branch: devel git_last_commit: d77d61e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mdp_1.31.0.tar.gz vignettes: vignettes/mdp/inst/doc/my-vignette.html vignetteTitles: Running the mdp package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdp/inst/doc/my-vignette.R dependencyCount: 24 Package: mdqc Version: 1.73.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 3886e672c1f63303cb93ee023c8baf65 NeedsCompilation: no Title: Mahalanobis Distance Quality Control for microarrays Description: MDQC is a multivariate quality assessment method for microarrays based on quality control (QC) reports. The Mahalanobis distance of an array's quality attributes is used to measure the similarity of the quality of that array against the quality of the other arrays. Then, arrays with unusually high distances can be flagged as potentially low-quality. biocViews: Microarray, QualityControl Author: Justin Harrington Maintainer: Gabriela Cohen-Freue git_url: https://git.bioconductor.org/packages/mdqc git_branch: devel git_last_commit: 2335174 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mdqc_1.73.0.tar.gz vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf vignetteTitles: Introduction to MDQC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdqc/inst/doc/mdqcvignette.R importsMe: arrayMvout dependencyCount: 7 Package: MDSvis Version: 0.99.7 Depends: R (>= 4.6) Imports: CytoMDS (>= 1.3.5), rlang, ggplot2, plotly, shiny, shinyjs, methods Suggests: knitr, rmarkdown, BiocStyle, HDCytoData, flowCore, testthat (>= 3.0.0) License: GPL-3 MD5sum: e337ed6d0cdab6bd0eb8b47ee4457e44 NeedsCompilation: no Title: Plots of Multi Dimensional Scaling (MDS) results Description: This package implements visulization of Multi Dimensional Scaling (MDS) results. biocViews: FlowCytometry, QualityControl, DimensionReduction, MultidimensionalScaling, Software, Visualization Author: Andrea Vicini [aut] (ORCID: ), Philippe Hauchamps [aut, cre] (ORCID: ), Shabnam Zaman [ctb] (ORCID: ), Laurent Gatto [aut] (ORCID: ) Maintainer: Philippe Hauchamps URL: https://uclouvain-cbio.github.io/MDSvis VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/MDSvis/issues git_url: https://git.bioconductor.org/packages/MDSvis git_branch: devel git_last_commit: ea20a50 git_last_commit_date: 2026-01-15 Date/Publication: 2026-04-20 source.ver: src/contrib/MDSvis_0.99.7.tar.gz vignettes: vignettes/MDSvis/inst/doc/MDSvis_Input_From_Distance.html, vignettes/MDSvis/inst/doc/MDSvis.html vignetteTitles: Preparing input objects from a distance matrix and sample properties, Visualization of Multi Dimensional Scaling (MDS) objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MDSvis/inst/doc/MDSvis_Input_From_Distance.R, vignettes/MDSvis/inst/doc/MDSvis.R dependencyCount: 206 Package: MDTS Version: 1.31.0 Depends: R (>= 3.5.0) Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings, DNAcopy, Rsamtools, parallel, stringr Suggests: testthat, knitr License: Artistic-2.0 MD5sum: 3540a66068089657d68614c4c99390db NeedsCompilation: no Title: Detection of de novo deletion in targeted sequencing trios Description: A package for the detection of de novo copy number deletions in targeted sequencing of trios with high sensitivity and positive predictive value. biocViews: StatisticalMethod, Technology, Sequencing, TargetedResequencing, Coverage, DataImport Author: Jack M.. Fu [aut, cre] Maintainer: Jack M.. Fu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MDTS git_branch: devel git_last_commit: c8a8ac4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MDTS_1.31.0.tar.gz vignettes: vignettes/MDTS/inst/doc/mdts.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MDTS/inst/doc/mdts.R dependencyCount: 51 Package: MeasurementError.cor Version: 1.83.0 License: LGPL MD5sum: b7d43e097ef7227c6befb6087f1e96c4 NeedsCompilation: no Title: Measurement Error model estimate for correlation coefficient Description: Two-stage measurement error model for correlation estimation with smaller bias than the usual sample correlation biocViews: StatisticalMethod Author: Beiying Ding Maintainer: Beiying Ding git_url: https://git.bioconductor.org/packages/MeasurementError.cor git_branch: devel git_last_commit: 5d8e435 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MeasurementError.cor_1.83.0.tar.gz vignettes: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf vignetteTitles: MeasurementError.cor Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.R dependencyCount: 0 Package: MEB Version: 1.25.0 Depends: R (>= 3.6.0) Imports: e1071, edgeR, scater, stats, wrswoR, SummarizedExperiment, SingleCellExperiment Suggests: knitr,rmarkdown,BiocStyle License: GPL-2 MD5sum: 80cfb3632160744f214472361ced36e7 NeedsCompilation: no Title: A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq and scRNA-seq data Description: This package provides a method to identify differential expression genes in the same or different species. Given that non-DE genes have some similarities in features, a scaling-free minimum enclosing ball (SFMEB) model is built to cover those non-DE genes in feature space, then those DE genes, which are enormously different from non-DE genes, being regarded as outliers and rejected outside the ball. The method on this package is described in the article 'A minimum enclosing ball method to detect differential expression genes for RNA-seq data'. The SFMEB method is extended to the scMEB method that considering two or more potential types of cells or unknown labels scRNA-seq dataset DEGs identification. biocViews: DifferentialExpression, GeneExpression, Normalization, Classification, Sequencing Author: Yan Zhou, Jiadi Zhu Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>, Yan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEB git_branch: devel git_last_commit: b767e6c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MEB_1.25.0.tar.gz vignettes: vignettes/MEB/inst/doc/NIMEB.html vignetteTitles: MEB Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEB/inst/doc/NIMEB.R dependencyCount: 98 Package: MEDIPS Version: 1.63.0 Depends: R (>= 3.0), BSgenome, Rsamtools Imports: GenomicRanges, Biostrings, graphics, gtools, IRanges, methods, stats, utils, edgeR, DNAcopy, biomaRt, rtracklayer, preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, BiocStyle License: GPL (>=2) MD5sum: d8e27032366cdc98c4fa0bd9483c33dc NeedsCompilation: no Title: DNA IP-seq data analysis Description: MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis. biocViews: DNAMethylation, CpGIsland, DifferentialExpression, Sequencing, ChIPSeq, Preprocessing, QualityControl, Visualization, Microarray, Genetics, Coverage, GenomeAnnotation, CopyNumberVariation, SequenceMatching Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich, Isaac Lopez Moyado Maintainer: Lukas Chavez git_url: https://git.bioconductor.org/packages/MEDIPS git_branch: devel git_last_commit: b8a28dc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MEDIPS_1.63.0.tar.gz vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf vignetteTitles: MEDIPS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDIPS/inst/doc/MEDIPS.R importsMe: decemedip suggestsMe: MEDIPSData dependencyCount: 103 Package: MEDME Version: 1.71.0 Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils Imports: Biostrings, MASS, drc Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9 License: GPL (>= 2) MD5sum: 76ccdeadcb2a3bd9ea804401bba214db NeedsCompilation: yes Title: Modelling Experimental Data from MeDIP Enrichment Description: MEDME allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments biocViews: Microarray, CpGIsland, DNAMethylation Author: Mattia Pelizzola and Annette Molinaro Maintainer: Mattia Pelizzola git_url: https://git.bioconductor.org/packages/MEDME git_branch: devel git_last_commit: dcafb76 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MEDME_1.71.0.tar.gz vignettes: vignettes/MEDME/inst/doc/MEDME.pdf vignetteTitles: MEDME.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDME/inst/doc/MEDME.R dependencyCount: 94 Package: megadepth Version: 1.21.0 Imports: xfun, utils, fs, GenomicRanges, readr, cmdfun, dplyr, magrittr Suggests: covr, knitr, BiocStyle, sessioninfo, rmarkdown, rtracklayer, derfinder, GenomeInfoDb, tools, RefManageR, testthat License: Artistic-2.0 MD5sum: fe3b333c4bae579b37cdd8349ef4a848 NeedsCompilation: no Title: megadepth: BigWig and BAM related utilities Description: This package provides an R interface to Megadepth by Christopher Wilks available at https://github.com/ChristopherWilks/megadepth. It is particularly useful for computing the coverage of a set of genomic regions across bigWig or BAM files. With this package, you can build base-pair coverage matrices for regions or annotations of your choice from BigWig files. Megadepth was used to create the raw files provided by https://bioconductor.org/packages/recount3. biocViews: Software, Coverage, DataImport, Transcriptomics, RNASeq, Preprocessing Author: Leonardo Collado-Torres [aut] (ORCID: ), David Zhang [aut, cre] (ORCID: ) Maintainer: David Zhang URL: https://github.com/LieberInstitute/megadepth SystemRequirements: megadepth () VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/megadepth git_url: https://git.bioconductor.org/packages/megadepth git_branch: devel git_last_commit: ae67061 git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/megadepth_1.21.0.tar.gz vignettes: vignettes/megadepth/inst/doc/megadepth.html vignetteTitles: megadepth quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/megadepth/inst/doc/megadepth.R importsMe: chevreulProcess dependencyCount: 75 Package: MEIGOR Version: 1.45.0 Depends: R (>= 4.0), Rsolnp, snowfall, deSolve, CNORode Suggests: CellNOptR, knitr, BiocStyle License: GPL-3 MD5sum: 30f95a675485e842b90ab7ecec7db830 NeedsCompilation: no Title: MEIGOR - MEtaheuristics for bIoinformatics Global Optimization Description: MEIGOR provides a comprehensive environment for performing global optimization tasks in bioinformatics and systems biology. It leverages advanced metaheuristic algorithms to efficiently search the solution space and is specifically tailored to handle the complexity and high-dimensionality of biological datasets. This package supports various optimization routines and is integrated with Bioconductor's infrastructure for a seamless analysis workflow. biocViews: SystemsBiology, Optimization, Software Author: Jose A. Egea [aut, cre], David Henriques [aut], Alexandre Fdez. Villaverde [aut], Thomas Cokelaer [aut] Maintainer: Jose A. Egea VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEIGOR git_branch: devel git_last_commit: 2caec25 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MEIGOR_1.45.0.tar.gz vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.html vignetteTitles: MEIGOR: a software suite based on metaheuristics for global optimization in systems biology and bioinformatics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R dependencyCount: 80 Package: MeLSI Version: 0.99.9 Depends: R (>= 4.5.0) Imports: vegan, ggplot2, phyloseq, stats, utils Suggests: testthat, knitr, rmarkdown, BiocManager, BiocStyle, BiocParallel, Matrix, microbiome License: MIT + file LICENSE MD5sum: 5a72bbb6f13db5229dcdf888ea805136 NeedsCompilation: no Title: Metric Learning for Statistical Inference in Microbiome Analysis Description: MeLSI (Metric Learning for Statistical Inference) is a novel machine learning method for microbiome data analysis that learns optimal distance metrics to improve statistical power in detecting group differences. Unlike traditional distance metrics (Bray-Curtis, Euclidean, Jaccard), MeLSI adapts to the specific characteristics of your dataset to maximize separation between groups. The method uses an ensemble of weak learners to identify which microbial features drive group differences, providing both improved statistical power and biological interpretability through feature importance weights. biocViews: Software, StatisticalMethod, Microbiome Author: Nathan Bresette [aut, cre] (ORCID: ), Aaron C. Ericsson [aut] (ORCID: ), Carter Woods [aut] (ORCID: ), Ai-Ling Lin [aut, fnd] (ORCID: ) Maintainer: Nathan Bresette URL: https://github.com/NathanBresette/MeLSI VignetteBuilder: knitr BugReports: https://github.com/NathanBresette/MeLSI/issues git_url: https://git.bioconductor.org/packages/MeLSI git_branch: devel git_last_commit: dd590e4 git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/MeLSI_0.99.9.tar.gz vignettes: vignettes/MeLSI/inst/doc/melsi_tutorial.html vignetteTitles: MeLSI Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MeLSI/inst/doc/melsi_tutorial.R dependencyCount: 67 Package: memes Version: 1.19.1 Depends: R (>= 4.1) Imports: Biostrings, dplyr, cmdfun (>= 1.0.2), GenomicRanges, ggplot2, magrittr, matrixStats, methods, patchwork, processx, purrr, rlang, readr, stats, tools, tibble, tidyr, utils, usethis, universalmotif (>= 1.9.3), xml2 Suggests: cowplot, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm6, forcats, testthat (>= 2.1.0), knitr, MotifDb, pheatmap, PMCMRplus, plyranges (>= 1.9.1), rmarkdown, covr License: MIT + file LICENSE MD5sum: e285f59dbd190fba048058c8c8a1c61b NeedsCompilation: no Title: motif matching, comparison, and de novo discovery using the MEME Suite Description: A seamless interface to the MEME Suite family of tools for motif analysis. 'memes' provides data aware utilities for using GRanges objects as entrypoints to motif analysis, data structures for examining & editing motif lists, and novel data visualizations. 'memes' functions and data structures are amenable to both base R and tidyverse workflows. biocViews: DataImport, FunctionalGenomics, GeneRegulation, MotifAnnotation, MotifDiscovery, SequenceMatching, Software Author: Spencer Nystrom [aut, cre, cph] (ORCID: ) Maintainer: Spencer Nystrom URL: https://snystrom.github.io/memes/, https://github.com/snystrom/memes SystemRequirements: Meme Suite (v5.3.3 or above) VignetteBuilder: knitr BugReports: https://github.com/snystrom/memes/issues git_url: https://git.bioconductor.org/packages/memes git_branch: devel git_last_commit: 7c48d1d git_last_commit_date: 2026-01-06 Date/Publication: 2026-04-20 source.ver: src/contrib/memes_1.19.1.tar.gz vignettes: vignettes/memes/inst/doc/core_ame.html, vignettes/memes/inst/doc/core_dreme.html, vignettes/memes/inst/doc/core_fimo.html, vignettes/memes/inst/doc/core_tomtom.html, vignettes/memes/inst/doc/install_guide.html, vignettes/memes/inst/doc/integrative_analysis.html, vignettes/memes/inst/doc/tidy_motifs.html vignetteTitles: Motif Enrichment Testing using AME, Denovo Motif Discovery Using DREME, Motif Scanning using FIMO, Motif Comparison using TomTom, Install MEME, ChIP-seq Analysis, Tidying Motif Metadata hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/memes/inst/doc/core_ame.R, vignettes/memes/inst/doc/core_dreme.R, vignettes/memes/inst/doc/core_fimo.R, vignettes/memes/inst/doc/core_tomtom.R, vignettes/memes/inst/doc/install_guide.R, vignettes/memes/inst/doc/integrative_analysis.R, vignettes/memes/inst/doc/tidy_motifs.R importsMe: MotifPeeker, postNet dependencyCount: 97 Package: Mergeomics Version: 1.39.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 4fef8c54f5b17b525ac8e654a1230c05 NeedsCompilation: no Title: Integrative network analysis of omics data Description: The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA). biocViews: Software Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang Maintainer: Zeyneb Kurt git_url: https://git.bioconductor.org/packages/Mergeomics git_branch: devel git_last_commit: 5d079fe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Mergeomics_1.39.0.tar.gz vignettes: vignettes/Mergeomics/inst/doc/Mergeomics.pdf vignetteTitles: Mergeomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mergeomics/inst/doc/Mergeomics.R dependencyCount: 0 Package: MeSHDbi Version: 1.47.0 Depends: R (>= 3.0.1) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: testthat License: Artistic-2.0 MD5sum: 57172ad39951568ad87a00f54b551d9f NeedsCompilation: no Title: DBI to construct MeSH-related package from sqlite file Description: The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db. biocViews: Annotation, AnnotationData, Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/MeSHDbi git_branch: devel git_last_commit: 1fa151c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MeSHDbi_1.47.0.tar.gz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: meshes, meshr, scTensor dependencyCount: 42 Package: meshes Version: 1.37.1 Depends: R (>= 4.1.0) Imports: enrichit, gson, AnnotationDbi, GOSemSim (> 2.37.0), methods, utils, AnnotationHub, MeSHDbi, yulab.utils (>= 0.1.5) Suggests: knitr, rmarkdown, prettydoc License: Artistic-2.0 MD5sum: 0d9c4376e0c9627c69a2d699de7b9559 NeedsCompilation: no Title: MeSH Enrichment and Semantic analyses Description: MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH terms were associated by Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH. This association is fundamental for enrichment and semantic analyses. meshes supports enrichment analysis (over-representation and gene set enrichment analysis) of gene list or whole expression profile. The semantic comparisons of MeSH terms provide quantitative ways to compute similarities between genes and gene groups. meshes implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively and supports more than 70 species. biocViews: Annotation, Clustering, MultipleComparison, Software Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/meshes/issues git_url: https://git.bioconductor.org/packages/meshes git_branch: devel git_last_commit: 4dbc7cb git_last_commit_date: 2025-12-22 Date/Publication: 2026-04-20 source.ver: src/contrib/meshes_1.37.1.tar.gz vignettes: vignettes/meshes/inst/doc/meshes.html vignetteTitles: meshes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshes/inst/doc/meshes.R dependencyCount: 72 Package: MesKit Version: 1.21.0 Depends: R (>= 4.0.0) Imports: methods, data.table, Biostrings, dplyr, tidyr (>= 1.0.0), ape (>= 5.4.1), ggrepel, pracma, ggridges, AnnotationDbi, IRanges, circlize, cowplot, mclust, phangorn, ComplexHeatmap (>= 1.9.3), ggplot2, RColorBrewer, grDevices, stats, utils, S4Vectors Suggests: shiny, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), org.Hs.eg.db, clusterProfiler, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: b1ee5c76051c356895744e9006d141f2 NeedsCompilation: no Title: A tool kit for dissecting cancer evolution from multi-region derived tumor biopsies via somatic alterations Description: MesKit provides commonly used analysis and visualization modules based on mutational data generated by multi-region sequencing (MRS). This package allows to depict mutational profiles, measure heterogeneity within or between tumors from the same patient, track evolutionary dynamics, as well as characterize mutational patterns on different levels. Shiny application was also developed for a need of GUI-based analysis. As a handy tool, MesKit can facilitate the interpretation of tumor heterogeneity and the understanding of evolutionary relationship between regions in MRS study. Author: Mengni Liu [aut, cre] (ORCID: ), Jianyu Chen [aut, ctb] (ORCID: ), Xin Wang [aut, ctb] (ORCID: ) Maintainer: Mengni Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MesKit git_branch: devel git_last_commit: 15660f6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MesKit_1.21.0.tar.gz vignettes: vignettes/MesKit/inst/doc/MesKit.html vignetteTitles: Analyze and Visualize Multi-region Whole-exome Sequencing Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MesKit/inst/doc/MesKit.R importsMe: CaMutQC dependencyCount: 94 Package: messina Version: 1.47.0 Depends: R (>= 3.1.0), survival (>= 2.37-4), methods Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics LinkingTo: Rcpp Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>= 2.22.0), BiocStyle Enhances: doMC (>= 1.3.3) License: EPL (>= 1.0) MD5sum: 0a023a75d5e6e83b7a5b913b086671dc NeedsCompilation: yes Title: Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems Description: Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Survival Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph] Maintainer: Mark Pinese VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/messina git_branch: devel git_last_commit: 49bc1b6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/messina_1.47.0.tar.gz vignettes: vignettes/messina/inst/doc/messina.pdf vignetteTitles: Using Messina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/messina/inst/doc/messina.R dependencyCount: 32 Package: metabCombiner Version: 1.21.0 Depends: R (>= 4.0) Imports: dplyr (>= 1.0), methods, mgcv, caret, S4Vectors, stats, utils, rlang, graphics, matrixStats, tidyr Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 39148e0704552969870c8df5166c4feb NeedsCompilation: yes Title: Method for Combining LC-MS Metabolomics Feature Measurements Description: This package aligns LC-HRMS metabolomics datasets acquired from biologically similar specimens analyzed under similar, but not necessarily identical, conditions. Peak-picked and simply aligned metabolomics feature tables (consisting of m/z, rt, and per-sample abundance measurements, plus optional identifiers & adduct annotations) are accepted as input. The package outputs a combined table of feature pair alignments, organized into groups of similar m/z, and ranked by a similarity score. Input tables are assumed to be acquired using similar (but not necessarily identical) analytical methods. biocViews: Software, MassSpectrometry, Metabolomics Author: Hani Habra [aut, cre], Alla Karnovsky [ths] Maintainer: Hani Habra VignetteBuilder: knitr BugReports: https://www.github.com/hhabra/metabCombiner/issues git_url: https://git.bioconductor.org/packages/metabCombiner git_branch: devel git_last_commit: 0e1b012 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/metabCombiner_1.21.0.tar.gz vignettes: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.html vignetteTitles: Combine LC-MS Metabolomics Datasets with metabCombiner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.R dependencyCount: 87 Package: metabinR Version: 1.13.0 Depends: R (>= 4.3) Imports: methods, rJava Suggests: BiocStyle, cvms, data.table, dplyr, ggplot2, gridExtra, knitr, R.utils, rmarkdown, sabre, spelling, testthat (>= 3.0.0) License: GPL-3 MD5sum: e8903ca17a0bafe56e43f5932cc5a20b NeedsCompilation: no Title: Abundance and Compositional Based Binning of Metagenomes Description: Provide functions for performing abundance and compositional based binning on metagenomic samples, directly from FASTA or FASTQ files. Functions are implemented in Java and called via rJava. Parallel implementation that operates directly on input FASTA/FASTQ files for fast execution. biocViews: Classification, Clustering, Microbiome, Sequencing, Software Author: Anestis Gkanogiannis [aut, cre] (ORCID: ) Maintainer: Anestis Gkanogiannis URL: https://github.com/gkanogiannis/metabinR SystemRequirements: Java (>= 8) VignetteBuilder: knitr BugReports: https://github.com/gkanogiannis/metabinR/issues git_url: https://git.bioconductor.org/packages/metabinR git_branch: devel git_last_commit: e276457 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/metabinR_1.13.0.tar.gz vignettes: vignettes/metabinR/inst/doc/metabinR_vignette.html vignetteTitles: metabinR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabinR/inst/doc/metabinR_vignette.R dependencyCount: 2 Package: MetaboAnnotation Version: 1.15.3 Depends: R (>= 4.0.0) Imports: BiocGenerics, MsCoreUtils, MetaboCoreUtils, ProtGenerics, methods, S4Vectors, Spectra (>= 1.21.5), BiocParallel, SummarizedExperiment, QFeatures, AnnotationHub, graphics, CompoundDb Suggests: testthat, knitr, MsDataHub, BiocStyle, rmarkdown, plotly, shiny, shinyjs, msentropy, DT, microbenchmark, mzR Enhances: RMariaDB, RSQLite License: Artistic-2.0 MD5sum: 78518e564dddd4da386f55fa26588ebc NeedsCompilation: no Title: Utilities for Annotation of Metabolomics Data Description: High level functions to assist in annotation of (metabolomics) data sets. These include functions to perform simple tentative annotations based on mass matching but also functions to consider m/z and retention times for annotation of LC-MS features given that respective reference values are available. In addition, the function provides high-level functions to simplify matching of LC-MS/MS spectra against spectral libraries and objects and functionality to represent and manage such matched data. biocViews: Infrastructure, Metabolomics, MassSpectrometry Author: Michael Witting [aut] (ORCID: ), Johannes Rainer [aut, cre] (ORCID: ), Andrea Vicini [aut] (ORCID: ), Carolin Huber [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Nir Shachaf [ctb] Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MetaboAnnotation VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MetaboAnnotation/issues git_url: https://git.bioconductor.org/packages/MetaboAnnotation git_branch: devel git_last_commit: 34e5ba4 git_last_commit_date: 2026-03-19 Date/Publication: 2026-04-20 source.ver: src/contrib/MetaboAnnotation_1.15.3.tar.gz vignettes: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.html vignetteTitles: Annotation of MS-based Metabolomics Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.R dependencyCount: 137 Package: MetaboCoreUtils Version: 1.19.3 Depends: R (>= 4.0) Imports: utils, MsCoreUtils, BiocParallel, methods, stats Suggests: BiocStyle, testthat, knitr, rmarkdown, robustbase License: Artistic-2.0 MD5sum: 1c3dffeab5e42ccf07d2cca60a901e2c NeedsCompilation: no Title: Core Utils for Metabolomics Data Description: MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound mass-to-charge ratios and masses or functions to work with chemical formulas. The package provides also a set of adduct definitions and information on some commercially available internal standard mixes commonly used in MS experiments. biocViews: Infrastructure, Metabolomics, MassSpectrometry Author: Johannes Rainer [aut, cre] (ORCID: ), Michael Witting [aut] (ORCID: ), Andrea Vicini [aut], Liesa Salzer [ctb] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Michael Stravs [ctb] (ORCID: ), Roger Gine [aut] (ORCID: ), William Kumler [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Gabriele Tomè [aut] (ORCID: , fnd: MetaRbolomics4Galaxy project (CUP: D53C25001030003) co-funded by the Autonomous Province of Bolzano under the Joint Projects South Tyrol–Germany 2025 program.) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MetaboCoreUtils VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MetaboCoreUtils/issues git_url: https://git.bioconductor.org/packages/MetaboCoreUtils git_branch: devel git_last_commit: 1daa859 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/MetaboCoreUtils_1.19.3.tar.gz vignettes: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.html vignetteTitles: Core Utils for Metabolomics Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.R importsMe: CompoundDb, MetaboAnnotation, MsQuality, Spectra, xcms, pubchem.bio dependencyCount: 24 Package: MetaboDynamics Version: 2.1.104 Depends: R (>= 4.4.0) Imports: dplyr, ggplot2, KEGGREST, methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), rstantools (>= 2.4.0), S4Vectors, stringr, SummarizedExperiment, tidyr, dynamicTreeCut, rlang, ape, ggtree, patchwork LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: a9470adf4c73c51c831cdb29985118bd NeedsCompilation: yes Title: Bayesian analysis of longitudinal metabolomics data Description: MetaboDynamics is an R-package that provides a framework of probabilistic models to analyze longitudinal metabolomics data. It enables robust estimation of mean concentrations despite varying spread between timepoints and reports differences between timepoints as well as metabolite specific dynamics profiles that can be used for identifying "dynamics clusters" of metabolites of similar dynamics. Provides probabilistic over-representation analysis of KEGG functional modules and pathways as well as comparison between clusters of different experimental conditions. biocViews: Software,Metabolomics,Bayesian,FunctionalPrediction,MultipleComparison,KEGG,Pathways,TimeCourse, Clustering Author: Katja Danielzik [aut, cre] (ORCID: ), Simo Kitanovski [ctb] (ORCID: ), Johann Matschke [ctb] (ORCID: ), Daniel Hoffmann [ctb] (ORCID: ) Maintainer: Katja Danielzik URL: https://github.com/KatjaDanielzik/MetaboDynamics SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/KatjaDanielzik/MetaboDynamics/issues git_url: https://git.bioconductor.org/packages/MetaboDynamics git_branch: devel git_last_commit: afe7589 git_last_commit_date: 2026-04-13 Date/Publication: 2026-04-20 source.ver: src/contrib/MetaboDynamics_2.1.104.tar.gz vignettes: vignettes/MetaboDynamics/inst/doc/MetaboDynamics_dataframes.html, vignettes/MetaboDynamics/inst/doc/MetaboDynamics.html vignetteTitles: 2. MetaboDynamics_dataframes, 1. MetaboDynamics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboDynamics/inst/doc/MetaboDynamics_dataframes.R, vignettes/MetaboDynamics/inst/doc/MetaboDynamics.R dependencyCount: 127 Package: metabolomicsWorkbenchR Version: 1.21.0 Depends: R (>= 4.0) Imports: data.table, httr, jsonlite, methods, MultiAssayExperiment, struct, SummarizedExperiment, utils Suggests: BiocStyle, covr, knitr, HDF5Array, httptest, rmarkdown, structToolbox, testthat, pmp, grid, png License: GPL-3 MD5sum: 3c6284f86cd2cb44d004536bd2dc6346 NeedsCompilation: no Title: Metabolomics Workbench in R Description: This package provides functions for interfacing with the Metabolomics Workbench RESTful API. Study, compound, protein and gene information can be searched for using the API. Methods to obtain study data in common Bioconductor formats such as SummarizedExperiment and MultiAssayExperiment are also included. biocViews: Software, Metabolomics Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metabolomicsWorkbenchR git_branch: devel git_last_commit: d4ae095 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/metabolomicsWorkbenchR_1.21.0.tar.gz vignettes: vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.html, vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.html vignetteTitles: Example using structToolbox, Introduction_to_metabolomicsWorkbenchR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.R, vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.R suggestsMe: fobitools, MetMashR dependencyCount: 63 Package: metabomxtr Version: 1.45.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2 Suggests: xtable, reshape2 License: GPL-2 MD5sum: 48e50d23ef07a4c0520869248ae60e53 NeedsCompilation: no Title: A package to run mixture models for truncated metabolomics data with normal or lognormal distributions Description: The functions in this package return optimized parameter estimates and log likelihoods for mixture models of truncated data with normal or lognormal distributions. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens Maintainer: Michael Nodzenski git_url: https://git.bioconductor.org/packages/metabomxtr git_branch: devel git_last_commit: 832ac4e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/metabomxtr_1.45.0.tar.gz vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.pdf vignetteTitles: metabomxtr, mixnorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.R dependencyCount: 49 Package: metaCCA Version: 1.39.0 Suggests: knitr License: MIT + file LICENSE MD5sum: c476e41d46c6e320c9f4abf608d48560 NeedsCompilation: no Title: Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis Description: metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. biocViews: GenomeWideAssociation, SNP, Genetics, Regression, StatisticalMethod, Software Author: Anna Cichonska Maintainer: Anna Cichonska URL: https://doi.org/10.1093/bioinformatics/btw052 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metaCCA git_branch: devel git_last_commit: 09a5ea1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/metaCCA_1.39.0.tar.gz vignettes: vignettes/metaCCA/inst/doc/metaCCA.pdf vignetteTitles: metaCCA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metaCCA/inst/doc/metaCCA.R dependencyCount: 0 Package: MetaCyto Version: 1.33.0 Depends: R (>= 3.4) Imports: flowCore (>= 1.4),tidyr (>= 0.7),fastcluster,ggplot2,metafor,cluster,FlowSOM, grDevices, graphics, stats, utils Suggests: knitr, dplyr, rmarkdown License: GPL (>= 2) MD5sum: 4ab4fd7a92288dd813a614b12b6ee142 NeedsCompilation: no Title: MetaCyto: A package for meta-analysis of cytometry data Description: This package provides functions for preprocessing, automated gating and meta-analysis of cytometry data. It also provides functions that facilitate the collection of cytometry data from the ImmPort database. biocViews: ImmunoOncology, CellBiology, FlowCytometry, Clustering, StatisticalMethod, Software, CellBasedAssays, Preprocessing Author: Zicheng Hu, Chethan Jujjavarapu, Sanchita Bhattacharya, Atul J. Butte Maintainer: Zicheng Hu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/MetaCyto git_branch: devel git_last_commit: 5669afb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MetaCyto_1.33.0.tar.gz vignettes: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.R dependencyCount: 120 Package: MetaDICT Version: 1.1.0 Depends: R (>= 4.2.0) Imports: stats, RANN, igraph, vegan, edgeR, ecodist, ggplot2, viridis, ggpubr, ape, cluster, matrixStats Suggests: BiocStyle, knitr, rmarkdown, DT, ggraph, tidyverse, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 74bd1af23c04cf0acfa320550a347b1c NeedsCompilation: no Title: Microbiome data integration method via shared dictionary learning Description: MetaDICT is a method for the integration of microbiome data. This method is designed to remove batch effects and preserve biological variation while integrating heterogeneous datasets. MetaDICT can better avoid overcorrection when unobserved confounding variables are present. biocViews: Microbiome, BatchEffect, Sequencing, Clustering, Software Author: Bo Yuan [aut, cre] (ORCID: ), Shulei Wang [aut] Maintainer: Bo Yuan URL: https://github.com/BoYuan07/MetaDICT VignetteBuilder: knitr BugReports: https://github.com/BoYuan07/MetaDICT/issues git_url: https://git.bioconductor.org/packages/MetaDICT git_branch: devel git_last_commit: b0ced9b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MetaDICT_1.1.0.tar.gz vignettes: vignettes/MetaDICT/inst/doc/MetaDICT.html vignetteTitles: MetaDICT Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaDICT/inst/doc/MetaDICT.R dependencyCount: 100 Package: metagene2 Version: 1.27.0 Depends: R (>= 4.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, tools, GenomicAlignments, GenomeInfoDb, IRanges, ggplot2, Rsamtools, purrr, data.table, methods, dplyr, magrittr, reshape2 Suggests: BiocGenerics, RUnit, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: d70173d7d1d621add51d85b03976a920 NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare coverages of sequencing experiments at selected groups of genomic regions. It can be used for such analyses as assessing the binding of DNA-interacting proteins at promoter regions or surveying antisense transcription over the length of a gene. The metagene2 package can manage all aspects of the analysis, from normalization of coverages to plot facetting according to experimental metadata. Bootstraping analysis is used to provide confidence intervals of per-sample mean coverages. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Eric Fournier [cre, aut], Charles Joly Beauparlant [aut], Cedric Lippens [aut], Arnaud Droit [aut] Maintainer: Eric Fournier URL: https://github.com/ArnaudDroitLab/metagene2 VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/metagene2/issues git_url: https://git.bioconductor.org/packages/metagene2 git_branch: devel git_last_commit: a49814f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/metagene2_1.27.0.tar.gz vignettes: vignettes/metagene2/inst/doc/metagene2.html vignetteTitles: Introduction to metagene2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagene2/inst/doc/metagene2.R dependencyCount: 88 Package: metagenomeSeq Version: 1.53.2 Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer Imports: parallel, matrixStats, foreach, Matrix, gplots, graphics, grDevices, stats, utils, Wrench Suggests: annotate, BiocGenerics, biomformat, knitr, gss, testthat (>= 0.8), vegan, IHW, SparseArray License: Artistic-2.0 MD5sum: 678af87b3840389225e198c4365eecb2 NeedsCompilation: no Title: Statistical analysis for sparse high-throughput sequencing Description: metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. biocViews: ImmunoOncology, Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software Author: Joseph Nathaniel Paulson, Nathan D. Olson, Domenick J. Braccia, Justin Wagner, Hisham Talukder, Mihai Pop, Hector Corrada Bravo Maintainer: Joseph N. Paulson URL: https://github.com/nosson/metagenomeSeq/ VignetteBuilder: knitr BugReports: https://github.com/nosson/metagenomeSeq/issues git_url: https://git.bioconductor.org/packages/metagenomeSeq git_branch: devel git_last_commit: b36eb0b git_last_commit_date: 2026-04-08 Date/Publication: 2026-04-20 source.ver: src/contrib/metagenomeSeq_1.53.2.tar.gz vignettes: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.pdf, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf vignetteTitles: fitTimeSeries: differential abundance analysis through time or location, metagenomeSeq: statistical analysis for sparse high-throughput sequencing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.R, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.R dependsOnMe: microbiomeExplorer, etec16s importsMe: benchdamic, Maaslin2, mbQTL, microbiomeDASim suggestsMe: dar, phyloseq, scTreeViz, Wrench dependencyCount: 32 Package: metahdep Version: 1.69.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 MD5sum: e502e893008d20306f6dcd972373669b NeedsCompilation: yes Title: Hierarchical Dependence in Meta-Analysis Description: Tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies biocViews: Microarray, DifferentialExpression Author: John R. Stevens, Gabriel Nicholas Maintainer: John R. Stevens git_url: https://git.bioconductor.org/packages/metahdep git_branch: devel git_last_commit: 0135fb7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/metahdep_1.69.0.tar.gz vignettes: vignettes/metahdep/inst/doc/metahdep.pdf vignetteTitles: metahdep Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metahdep/inst/doc/metahdep.R dependencyCount: 1 Package: MetaPhOR Version: 1.13.0 Depends: R (>= 4.2.0) Imports: utils, ggplot2, ggrepel, stringr, pheatmap, grDevices, stats, clusterProfiler, RecordLinkage, RCy3 Suggests: BiocStyle, knitr, rmarkdown, kableExtra License: Artistic-2.0 MD5sum: 51533d2c0d9632e32fc4a963c7f24698 NeedsCompilation: no Title: Metabolic Pathway Analysis of RNA Description: MetaPhOR was developed to enable users to assess metabolic dysregulation using transcriptomic-level data (RNA-sequencing and Microarray data) and produce publication-quality figures. A list of differentially expressed genes (DEGs), which includes fold change and p value, from DESeq2 or limma, can be used as input, with sample size for MetaPhOR, and will produce a data frame of scores for each KEGG pathway. These scores represent the magnitude and direction of transcriptional change within the pathway, along with estimated p-values.MetaPhOR then uses these scores to visualize metabolic profiles within and between samples through a variety of mechanisms, including: bubble plots, heatmaps, and pathway models. biocViews: Metabolomics, RNASeq, Pathways, GeneExpression, DifferentialExpression, KEGG, Sequencing, Microarray Author: Emily Isenhart [aut, cre], Spencer Rosario [aut] Maintainer: Emily Isenhart SystemRequirements: Cytoscape (>= 3.9.0) for the cytoPath() examples VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaPhOR git_branch: devel git_last_commit: d88197a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MetaPhOR_1.13.0.tar.gz vignettes: vignettes/MetaPhOR/inst/doc/MetaPhOR-vignette.html vignetteTitles: MetaPhOR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaPhOR/inst/doc/MetaPhOR-vignette.R dependencyCount: 172 Package: metapod Version: 1.19.2 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 15af2d1e940529320d3a365f9c1ac522 NeedsCompilation: yes Title: Meta-Analyses on P-Values of Differential Analyses Description: Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate. biocViews: MultipleComparison, DifferentialPeakCalling Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/LTLA/metapod SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/LTLA/metapod/issues git_url: https://git.bioconductor.org/packages/metapod git_branch: devel git_last_commit: 2a14a2c git_last_commit_date: 2026-02-17 Date/Publication: 2026-04-20 source.ver: src/contrib/metapod_1.19.2.tar.gz vignettes: vignettes/metapod/inst/doc/overview.html vignetteTitles: Meta-analysis strategies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metapod/inst/doc/overview.R importsMe: csaw, mumosa, scp, scran suggestsMe: TSCAN dependencyCount: 3 Package: metapone Version: 1.17.0 Depends: R (>= 4.1.0), BiocParallel, fields, markdown, fdrtool, fgsea, ggplot2, ggrepel Imports: methods Suggests: rmarkdown, knitr License: Artistic-2.0 MD5sum: 6c1ba42459eee3784aa875913ca82d90 NeedsCompilation: no Title: Conducts pathway test of metabolomics data using a weighted permutation test Description: The package conducts pathway testing from untargetted metabolomics data. It requires the user to supply feature-level test results, from case-control testing, regression, or other suitable feature-level tests for the study design. Weights are given to metabolic features based on how many metabolites they could potentially match to. The package can combine positive and negative mode results in pathway tests. biocViews: Technology, MassSpectrometry, Metabolomics, Pathways Author: Leqi Tian [aut], Tianwei Yu [aut], Tianwei Yu [cre] Maintainer: Tianwei Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapone git_branch: devel git_last_commit: 0eb50eb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/metapone_1.17.0.tar.gz vignettes: vignettes/metapone/inst/doc/metapone.html vignetteTitles: metapone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metapone/inst/doc/metapone.R dependencyCount: 50 Package: metaSeq Version: 1.51.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: c2e7e0bed84ab2542bd979f3b8e688ea NeedsCompilation: no Title: Meta-analysis of RNA-Seq count data in multiple studies Description: The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method biocViews: RNASeq, DifferentialExpression, Sequencing, ImmunoOncology Author: Koki Tsuyuzaki, Itoshi Nikaido Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/metaSeq git_branch: devel git_last_commit: 7de2dd7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/metaSeq_1.51.0.tar.gz vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf vignetteTitles: metaSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaSeq/inst/doc/metaSeq.R dependencyCount: 15 Package: MetCirc Version: 1.41.0 Depends: R (>= 4.4), amap (>= 0.8), circlize (>= 0.4.16), scales (>= 1.3.0), shiny (>= 1.8.1.1), Spectra (>= 1.15.3) Imports: ggplot2 (>= 3.5.1), MsCoreUtils (>= 1.17.0), S4Vectors (>= 0.43.1) Suggests: BiocGenerics, graphics (>= 4.4), grDevices (>= 4.4), knitr (>= 1.48), testthat (>= 3.2.1.1) License: GPL (>= 3) MD5sum: c3045df459a3e4451716f0d873f46b6b NeedsCompilation: no Title: Navigating mass spectral similarity in high-resolution MS/MS metabolomics data metabolomics data Description: MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectra object infrastructure defined in the package Spectra that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features. biocViews: ShinyApps, Metabolomics, MassSpectrometry, Visualization Author: Thomas Naake , Johannes Rainer and Emmanuel Gaquerel Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetCirc git_branch: devel git_last_commit: ed1872d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MetCirc_1.41.0.tar.gz vignettes: vignettes/MetCirc/inst/doc/MetCirc.html vignetteTitles: Workflow for Metabolomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetCirc/inst/doc/MetCirc.R dependencyCount: 75 Package: methimpute Version: 1.33.0 Depends: R (>= 3.5.0), GenomicRanges, ggplot2 Imports: Rcpp (>= 0.12.4.5), methods, utils, grDevices, stats, GenomeInfoDb, IRanges, Biostrings, reshape2, minpack.lm, data.table LinkingTo: Rcpp Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: 705cc2be3ada122b979a626bea304872 NeedsCompilation: yes Title: Imputation-guided re-construction of complete methylomes from WGBS data Description: This package implements functions for calling methylation for all cytosines in the genome. biocViews: ImmunoOncology, Software, DNAMethylation, Epigenetics, HiddenMarkovModel, Sequencing, Coverage Author: Aaron Taudt Maintainer: Aaron Taudt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methimpute git_branch: devel git_last_commit: ad2d2ed git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/methimpute_1.33.0.tar.gz vignettes: vignettes/methimpute/inst/doc/methimpute.pdf vignetteTitles: Methylation status calling with METHimpute hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methimpute/inst/doc/methimpute.R dependencyCount: 51 Package: methInheritSim Version: 1.33.0 Depends: R (>= 3.5.0) Imports: methylKit, GenomicRanges, Seqinfo, parallel, BiocGenerics, S4Vectors, methods, stats, IRanges, msm Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance License: Artistic-2.0 MD5sum: 8f5265aa360913293aaab325b0cc0b0c NeedsCompilation: no Title: Simulating Whole-Genome Inherited Bisulphite Sequencing Data Description: Simulate a multigeneration methylation case versus control experiment with inheritance relation using a real control dataset. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Pascal Belleau, Astrid Deschênes and Arnaud Droit Maintainer: Pascal Belleau URL: https://github.com/belleau/methInheritSim VignetteBuilder: knitr BugReports: https://github.com/belleau/methInheritSim/issues git_url: https://git.bioconductor.org/packages/methInheritSim git_branch: devel git_last_commit: 6ebe1c9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/methInheritSim_1.33.0.tar.gz vignettes: vignettes/methInheritSim/inst/doc/methInheritSim.html vignetteTitles: Simulating Whole-Genome Inherited Bisulphite Sequencing Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methInheritSim/inst/doc/methInheritSim.R suggestsMe: methylInheritance dependencyCount: 107 Package: MethPed Version: 1.39.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 MD5sum: 3074288953afb44cf30839e47d8bb6e2 NeedsCompilation: no Title: A DNA methylation classifier tool for the identification of pediatric brain tumor subtypes Description: Classification of pediatric tumors into biologically defined subtypes is challenging and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro). biocViews: ImmunoOncology, DNAMethylation, Classification, Epigenetics Author: Mohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut], Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph] Maintainer: Helena Carén VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethPed git_branch: devel git_last_commit: 1e8d04d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MethPed_1.39.0.tar.gz vignettes: vignettes/MethPed/inst/doc/MethPed-vignette.html vignetteTitles: MethPed User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R dependencyCount: 9 Package: MethTargetedNGS Version: 1.43.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings, pwalign Imports: utils, graphics, stats License: Artistic-2.0 MD5sum: e7e7e7f05be0b8ae129e322de235dfe4 NeedsCompilation: no Title: Perform Methylation Analysis on Next Generation Sequencing Data Description: Perform step by step methylation analysis of Next Generation Sequencing data. biocViews: ResearchField, Genetics, Sequencing, Alignment, SequenceMatching, DataImport Author: Muhammad Ahmer Jamil with Contribution of Prof. Holger Frohlich and Priv.-Doz. Dr. Osman El-Maarri Maintainer: Muhammad Ahmer Jamil SystemRequirements: HMMER3 git_url: https://git.bioconductor.org/packages/MethTargetedNGS git_branch: devel git_last_commit: 53cf09a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MethTargetedNGS_1.43.0.tar.gz vignettes: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.pdf vignetteTitles: Introduction to MethTargetedNGS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.R dependencyCount: 40 Package: methyLImp2 Version: 1.7.1 Depends: R (>= 4.3.0), ChAMPdata Imports: BiocParallel, parallel, stats, methods, corpcor, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, spelling, testthat (>= 3.0.0) License: GPL-3 MD5sum: 101ab6be3d5bfd1ef140a65abc816354 NeedsCompilation: no Title: Missing value estimation of DNA methylation data Description: This package allows to estimate missing values in DNA methylation data. methyLImp method is based on linear regression since methylation levels show a high degree of inter-sample correlation. Implementation is parallelised over chromosomes since probes on different chromosomes are usually independent. Mini-batch approach to reduce the runtime in case of large number of samples is available. biocViews: DNAMethylation, Microarray, Software, MethylationArray, Regression Author: Pietro Di Lena [aut] (ORCID: ), Anna Plaksienko [aut, cre] (ORCID: ), Claudia Angelini [aut] (ORCID: ), Christine Nardini [aut] (ORCID: ) Maintainer: Anna Plaksienko URL: https://github.com/annaplaksienko/methyLImp2 VignetteBuilder: knitr BugReports: https://github.com/annaplaksienko/methyLImp2/issues git_url: https://git.bioconductor.org/packages/methyLImp2 git_branch: devel git_last_commit: 579f07b git_last_commit_date: 2025-11-12 Date/Publication: 2026-04-20 source.ver: src/contrib/methyLImp2_1.7.1.tar.gz vignettes: vignettes/methyLImp2/inst/doc/methyLImp2_vignette.html vignetteTitles: methyLImp2 vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methyLImp2/inst/doc/methyLImp2_vignette.R dependencyCount: 37 Package: methylInheritance Version: 1.35.0 Depends: R (>= 3.5) Imports: methylKit, BiocParallel, GenomicRanges, IRanges, S4Vectors, methods, parallel, ggplot2, gridExtra, rebus Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit, methInheritSim, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 99738d18c432241a98b4dd1d65015c78 NeedsCompilation: no Title: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect Description: Permutation analysis, based on Monte Carlo sampling, for testing the hypothesis that the number of conserved differentially methylated elements, between several generations, is associated to an effect inherited from a treatment and that stochastic effect can be dismissed. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Astrid Deschênes [cre, aut] (ORCID: ), Pascal Belleau [aut] (ORCID: ), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/methylInheritance VignetteBuilder: knitr BugReports: https://github.com/adeschen/methylInheritance/issues git_url: https://git.bioconductor.org/packages/methylInheritance git_branch: devel git_last_commit: ffbee45 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/methylInheritance_1.35.0.tar.gz vignettes: vignettes/methylInheritance/inst/doc/methylInheritance.html vignetteTitles: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylInheritance/inst/doc/methylInheritance.R suggestsMe: methInheritSim dependencyCount: 106 Package: methylKit Version: 1.37.0 Depends: R (>= 3.5.0), GenomicRanges (>= 1.18.1), methods Imports: IRanges, data.table (>= 1.9.6), parallel, S4Vectors (>= 0.13.13), Seqinfo, KernSmooth, qvalue, emdbook, Rsamtools, gtools, fastseg, rtracklayer, mclust, mgcv, Rcpp, R.utils, limma, grDevices, graphics, stats, utils LinkingTo: Rcpp, Rhtslib (>= 1.13.1) Suggests: testthat (>= 2.1.0), knitr, rmarkdown, genomation, BiocManager License: Artistic-2.0 MD5sum: 8447b5c8866871a302b3c395812a5c5b NeedsCompilation: yes Title: DNA methylation analysis from high-throughput bisulfite sequencing results Description: methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files. biocViews: DNAMethylation, Sequencing, MethylSeq Author: Altuna Akalin [aut, cre], Matthias Kormaksson [aut], Sheng Li [aut], Arsene Wabo [ctb], Adrian Bierling [aut], Alexander Blume [aut], Katarzyna Wreczycka [ctb] Maintainer: Altuna Akalin , Alexander Blume URL: https://github.com/al2na/methylKit SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/al2na/methylKit/issues git_url: https://git.bioconductor.org/packages/methylKit git_branch: devel git_last_commit: 1192e2b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/methylKit_1.37.0.tar.gz vignettes: vignettes/methylKit/inst/doc/methylKit.html vignetteTitles: methylKit: User Guide v`r packageVersion('methylKit')` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylKit/inst/doc/methylKit.R importsMe: deconvR, methInheritSim, methylInheritance dependencyCount: 99 Package: MethylMix Version: 2.41.0 Depends: R (>= 3.2.0) Imports: foreach, RPMM, RColorBrewer, ggplot2, RCurl, impute, data.table, limma, R.matlab, digest Suggests: BiocStyle, doParallel, testthat, knitr, rmarkdown License: GPL-2 MD5sum: 0147f8bab32d917cec5909b9af17d881 NeedsCompilation: no Title: MethylMix: Identifying methylation driven cancer genes Description: MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,DifferentialExpression,Pathways,Network Author: Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylMix git_branch: devel git_last_commit: 4e0a470 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MethylMix_2.41.0.tar.gz vignettes: vignettes/MethylMix/inst/doc/vignettes.html vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/vignettes.R dependencyCount: 40 Package: methylMnM Version: 1.49.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 MD5sum: 2b4615a26a747177893ada0fb8e15d4c NeedsCompilation: yes Title: detect different methylation level (DMR) Description: To give the exactly p-value and q-value of MeDIP-seq and MRE-seq data for different samples comparation. biocViews: Software, DNAMethylation, Sequencing Author: Yan Zhou, Bo Zhang, Nan Lin, BaoXue Zhang and Ting Wang Maintainer: Yan Zhou git_url: https://git.bioconductor.org/packages/methylMnM git_branch: devel git_last_commit: 0c70661 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/methylMnM_1.49.0.tar.gz vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf vignetteTitles: methylMnM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylMnM/inst/doc/methylMnM.R importsMe: SIMD dependencyCount: 11 Package: methylPipe Version: 1.45.0 Depends: R (>= 3.5.0), methods, grDevices, graphics, stats, utils, GenomicRanges, SummarizedExperiment (>= 0.2.0), Rsamtools Imports: marray, gplots, IRanges, BiocGenerics, Gviz, GenomicAlignments, Biostrings, parallel, data.table, Seqinfo, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg18, TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR License: GPL(>=2) MD5sum: 1925f5cb755192c07e87f84b352c4fe3 NeedsCompilation: yes Title: Base resolution DNA methylation data analysis Description: Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data. biocViews: MethylSeq, DNAMethylation, Coverage, Sequencing Author: Mattia Pelizzola [aut], Kamal Kishore [aut], Mattia Furlan [ctb, cre] Maintainer: Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylPipe git_branch: devel git_last_commit: 1246c88 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/methylPipe_1.45.0.tar.gz vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf vignetteTitles: methylPipe.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylPipe/inst/doc/methylPipe.R dependsOnMe: ListerEtAlBSseq importsMe: compEpiTools dependencyCount: 158 Package: methylscaper Version: 1.19.0 Depends: R (>= 4.4.0) Imports: shiny, shinyjs, seriation, BiocParallel, seqinr, Biostrings, pwalign, Rfast, grDevices, graphics, stats, utils, tools, methods, shinyFiles, data.table, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, devtools, R.utils License: GPL-2 MD5sum: 8dcd50547b49ee7afb90b374012ef161 NeedsCompilation: no Title: Visualization of Methylation Data Description: methylscaper is an R package for processing and visualizing data jointly profiling methylation and chromatin accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The package supports both single-cell and single-molecule data, and a common interface for jointly visualizing both data types through the generation of ordered representational methylation-state matrices. The Shiny app allows for an interactive seriation process of refinement and re-weighting that optimally orders the cells or DNA molecules to discover methylation patterns and nucleosome positioning. biocViews: DNAMethylation, Epigenetics, Sequencing, Visualization, SingleCell, NucleosomePositioning Author: Bacher Rhonda [aut, cre], Parker Knight [aut] Maintainer: Bacher Rhonda URL: https://github.com/rhondabacher/methylscaper/ VignetteBuilder: knitr BugReports: https://github.com/rhondabacher/methylscaper/issues git_url: https://git.bioconductor.org/packages/methylscaper git_branch: devel git_last_commit: 2426d32 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/methylscaper_1.19.0.tar.gz vignettes: vignettes/methylscaper/inst/doc/methylScaper.html vignetteTitles: Using methylscaper to visualize joint methylation and nucleosome occupancy data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylscaper/inst/doc/methylScaper.R dependencyCount: 101 Package: MethylSeekR Version: 1.51.0 Depends: rtracklayer (>= 1.16.3), parallel (>= 2.15.1), mhsmm (>= 0.4.4) Imports: IRanges (>= 1.16.3), BSgenome (>= 1.26.1), GenomicRanges (>= 1.10.5), geneplotter (>= 1.34.0), graphics (>= 2.15.2), grDevices (>= 2.15.2), parallel (>= 2.15.2), stats (>= 2.15.2), utils (>= 2.15.2), GenomeInfoDb Suggests: BSgenome.Hsapiens.UCSC.hg38 License: GPL (>=2) MD5sum: 2e54ecd425030603a7148628376a4cbd NeedsCompilation: no Title: Segmentation of Bis-seq data Description: This is a package for the discovery of regulatory regions from Bis-seq data biocViews: Sequencing, MethylSeq, DNAMethylation Author: Lukas Burger, Dimos Gaidatzis, Dirk Schubeler and Michael Stadler Maintainer: Lukas Burger git_url: https://git.bioconductor.org/packages/MethylSeekR git_branch: devel git_last_commit: 045c8f7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MethylSeekR_1.51.0.tar.gz vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf vignetteTitles: MethylSeekR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylSeekR/inst/doc/MethylSeekR.R suggestsMe: methylPipe, RnBeads dependencyCount: 83 Package: MetID Version: 1.29.0 Depends: R (>= 3.5) Imports: utils (>= 3.3.1), stats (>= 3.4.2), devtools (>= 1.13.0), stringr (>= 1.3.0), Matrix (>= 1.2-12), igraph (>= 1.2.1), ChemmineR (>= 2.30.2) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8) License: Artistic-2.0 MD5sum: b822512a702c0a1ccd4473453255c076 NeedsCompilation: no Title: Network-based prioritization of putative metabolite IDs Description: This package uses an innovative network-based approach that will enhance our ability to determine the identities of significant ions detected by LC-MS. biocViews: AssayDomain, BiologicalQuestion, Infrastructure, ResearchField, StatisticalMethod, Technology, WorkflowStep, Network, KEGG Author: Zhenzhi Li Maintainer: Zhenzhi Li URL: https://github.com/ressomlab/MetID VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetID git_branch: devel git_last_commit: 2d4e012 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MetID_1.29.0.tar.gz vignettes: vignettes/MetID/inst/doc/Introduction_to_MetID.html vignetteTitles: Introduction to MetID hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetID/inst/doc/Introduction_to_MetID.R dependencyCount: 128 Package: MetMashR Version: 1.5.1 Depends: R (>= 4.3.0), struct Imports: dplyr, methods, httr, scales, ggthemes, utils, rlang, stats, ggplot2 Suggests: covr, httptest, knitr, rmarkdown, testthat (>= 3.0.0), rgoslin, DT, RSQLite, CompoundDb, BiocStyle, BiocFileCache, msPurity, rsvg, metabolomicsWorkbenchR, KEGGREST, plyr, magick, structToolbox, ggVennDiagram, patchwork, XML, GO.db, tidytext, tidyr, tidyselect, ComplexUpset, jsonlite, openxlsx, ggplotify, cowplot License: GPL-3 MD5sum: 50b9657662daaf847c6e8a065b2ebc00 NeedsCompilation: no Title: Metabolite Mashing with R Description: A package to merge, filter sort, organise and otherwise mash together metabolite annotation tables. Metabolite annotations can be imported from multiple sources (software) and combined using workflow steps based on S4 class templates derived from the `struct` package. Other modular workflow steps such as filtering, merging, splitting, normalisation and rest-api queries are included. biocViews: WorkflowStep, Metabolomics, KEGG Author: Gavin Rhys Lloyd [aut, cre] (ORCID: ), Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd URL: https://computational-metabolomics.github.io/MetMashR/ VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/MetMashR/issues git_url: https://git.bioconductor.org/packages/MetMashR git_branch: devel git_last_commit: d3308d4 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/MetMashR_1.5.1.tar.gz vignettes: vignettes/MetMashR/inst/doc/annotate_mixtures.html, vignettes/MetMashR/inst/doc/exploring_mtox.html, vignettes/MetMashR/inst/doc/Extending_MetMashR.html, vignettes/MetMashR/inst/doc/using_MetMashR.html vignetteTitles: Annotation of mixtures of standards, Exploring the MTox700+ library, Extending MetMashR, Using MetMashR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetMashR/inst/doc/annotate_mixtures.R, vignettes/MetMashR/inst/doc/exploring_mtox.R, vignettes/MetMashR/inst/doc/Extending_MetMashR.R, vignettes/MetMashR/inst/doc/using_MetMashR.R dependencyCount: 69 Package: MetNet Version: 1.29.0 Depends: R (>= 4.1), S4Vectors (>= 0.28.1), SummarizedExperiment (>= 1.20.0) Imports: bnlearn (>= 4.3), BiocParallel (>= 1.12.0), corpcor (>= 1.6.10), dplyr (>= 1.0.3), ggplot2 (>= 3.3.3), GeneNet (>= 1.2.15), GENIE3 (>= 1.7.0), methods (>= 4.1), parmigene (>= 1.0.2), psych (>= 2.1.6), rlang (>= 0.4.10), stabs (>= 0.6), stats (>= 4.1), tibble (>= 3.0.5), tidyr (>= 1.1.2) Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), glmnet (>= 4.1-1), igraph (>= 1.1.2), knitr (>= 1.11), rmarkdown (>= 1.15), testthat (>= 2.2.1), Spectra (>= 1.4.1), MsCoreUtils (>= 1.6.0) License: GPL (>= 3) MD5sum: d151a74ce6267f049d4bf96d25511114 NeedsCompilation: no Title: Inferring metabolic networks from untargeted high-resolution mass spectrometry data Description: MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Network, Regression Author: Thomas Naake [aut, cre], Liesa Salzer [ctb], Elva Maria Novoa-del-Toro [ctb] (ORCID: ) Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetNet git_branch: devel git_last_commit: e4fd4ac git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MetNet_1.29.0.tar.gz vignettes: vignettes/MetNet/inst/doc/MetNet.html vignetteTitles: Workflow for high-resolution metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetNet/inst/doc/MetNet.R dependencyCount: 77 Package: Mfuzz Version: 2.71.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: 17cac16522a032da9c86b37a0045821a NeedsCompilation: no Title: Soft clustering of omics time series data Description: The Mfuzz package implements noise-robust soft clustering of omics time-series data, including transcriptomic, proteomic or metabolomic data. It is based on the use of c-means clustering. For convenience, it includes a graphical user interface. biocViews: Microarray, Clustering, TimeCourse, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://mfuzz.sysbiolab.eu/ git_url: https://git.bioconductor.org/packages/Mfuzz git_branch: devel git_last_commit: f44a57a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Mfuzz_2.71.0.tar.gz vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf vignetteTitles: Introduction to Mfuzz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mfuzz/inst/doc/Mfuzz.R dependsOnMe: cycle, MultiRNAflow importsMe: Patterns suggestsMe: DAPAR, pctax dependencyCount: 17 Package: MGFM Version: 1.45.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: 052287f996b308b85a807d53ca2c13ba NeedsCompilation: no Title: Marker Gene Finder in Microarray gene expression data Description: The package is designed to detect marker genes from Microarray gene expression data sets biocViews: Genetics, GeneExpression, Microarray Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFM git_branch: devel git_last_commit: 86143b0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MGFM_1.45.0.tar.gz vignettes: vignettes/MGFM/inst/doc/MGFM.pdf vignetteTitles: Using MGFM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFM/inst/doc/MGFM.R dependsOnMe: sampleClassifier dependencyCount: 45 Package: MGnifyR Version: 1.7.2 Depends: R(>= 4.4.0), MultiAssayExperiment, TreeSummarizedExperiment, SummarizedExperiment, BiocGenerics Imports: mia, ape, dplyr, httr, methods, plyr, reshape2, S4Vectors, urltools, utils Suggests: biomformat, broom, ggplot2, knitr, rmarkdown, testthat, xml2, BiocStyle, miaViz, vegan, scater, phyloseq, magick License: Artistic-2.0 | file LICENSE MD5sum: 157fe07d06281bad6a78e65ef6917d51 NeedsCompilation: no Title: R interface to EBI MGnify metagenomics resource Description: Utility package to facilitate integration and analysis of EBI MGnify data in R. The package can be used to import microbial data for instance into TreeSummarizedExperiment (TreeSE). In TreeSE format, the data is directly compatible with miaverse framework. biocViews: Infrastructure, DataImport, Metagenomics, Microbiome, MicrobiomeData Author: Tuomas Borman [aut, cre] (ORCID: ), Ben Allen [aut], Leo Lahti [aut] (ORCID: ) Maintainer: Tuomas Borman URL: https://github.com/EBI-Metagenomics/MGnifyR VignetteBuilder: knitr BugReports: https://github.com/EBI-Metagenomics/MGnifyR/issues git_url: https://git.bioconductor.org/packages/MGnifyR git_branch: devel git_last_commit: 5a30a78 git_last_commit_date: 2026-02-13 Date/Publication: 2026-04-20 source.ver: src/contrib/MGnifyR_1.7.2.tar.gz vignettes: vignettes/MGnifyR/inst/doc/MGnify_course.html, vignettes/MGnifyR/inst/doc/MGnifyR_long.html, vignettes/MGnifyR/inst/doc/MGnifyR.html vignetteTitles: MGnifyR,, extended vignette, MGnifyR,, extended vignette, MGnifyR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MGnifyR/inst/doc/MGnify_course.R, vignettes/MGnifyR/inst/doc/MGnifyR_long.R, vignettes/MGnifyR/inst/doc/MGnifyR.R suggestsMe: HoloFoodR dependencyCount: 138 Package: mgsa Version: 1.59.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 MD5sum: bef2c3332c437ed3625efe9af5a66b93 NeedsCompilation: yes Title: Model-based gene set analysis Description: Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology. biocViews: Pathways, GO, GeneSetEnrichment Author: Sebastian Bauer , Julien Gagneur Maintainer: Sebastian Bauer URL: https://github.com/sba1/mgsa-bioc git_url: https://git.bioconductor.org/packages/mgsa git_branch: devel git_last_commit: 5257341 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mgsa_1.59.0.tar.gz vignettes: vignettes/mgsa/inst/doc/mgsa.pdf vignetteTitles: Overview of the mgsa package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mgsa/inst/doc/mgsa.R dependencyCount: 9 Package: mia Version: 1.19.8 Depends: R (>= 4.1.0), MultiAssayExperiment, SingleCellExperiment, SummarizedExperiment, TreeSummarizedExperiment (>= 1.99.3) Imports: ape, BiocGenerics, BiocParallel, Biostrings, bluster, DECIPHER, decontam, DelayedArray, DelayedMatrixStats, DirichletMultinomial, dplyr, IRanges, MASS, MatrixGenerics, methods, ecodive, rlang, S4Vectors, scater, stats, stringr, tibble, tidyr, utils, vegan, Rcpp LinkingTo: Rcpp Suggests: ade4, BiocStyle, biomformat, dada2, knitr, mediation, miaTime, miaViz, microbiomeDataSets, NMF, patchwork, philr, phyloseq, reldist, rhdf5, rmarkdown, scuttle, testthat, topicdoc, topicmodels, yaml License: Artistic-2.0 | file LICENSE MD5sum: 9663908445760124243e74aa7310a39c NeedsCompilation: yes Title: Microbiome analysis Description: mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization. biocViews: Microbiome, Software, DataImport Author: Tuomas Borman [aut, cre] (ORCID: ), Felix G.M. Ernst [aut] (ORCID: ), Sudarshan A. Shetty [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Yang Cao [ctb], Nathan D. Olson [ctb], Levi Waldron [ctb], Marcel Ramos [ctb], Héctor Corrada Bravo [ctb], Jayaram Kancherla [ctb], Domenick Braccia [ctb], Basil Courbayre [ctb], Geraldson Muluh [ctb], Giulio Benedetti [ctb], Moritz Emanuel Beber [ctb] (ORCID: ), Chouaib Benchraka [ctb], Akewak Jeba [ctb] (ORCID: ), Himmi Lindgren [ctb], Noah De Gunst [ctb], Théotime Pralas [ctb], Shadman Ishraq [ctb], Eineje Ameh [ctb], Artur Sannikov [ctb] (ORCID: ), Hervé Pagès [ctb], Rajesh Shigdel [ctb], Katariina Pärnänen [ctb], Pande Erawijantari [ctb], Danielle Callan [ctb], Sam Hillman [ctb], Jesse Pasanen [ctb], Eetu Tammi [ctb], Aituar Bektanov [ctb], Rasmus Hindström [ctb] Maintainer: Tuomas Borman URL: https://microbiome.github.io/mia/, https://github.com/microbiome/mia VignetteBuilder: knitr BugReports: https://github.com/microbiome/mia/issues git_url: https://git.bioconductor.org/packages/mia git_branch: devel git_last_commit: f81d66a git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/mia_1.19.8.tar.gz vignettes: vignettes/mia/inst/doc/mia.html vignetteTitles: mia hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mia/inst/doc/mia.R dependsOnMe: miaTime, miaViz importsMe: dar, iSEEtree, lefser, MGnifyR, miaDash, curatedMetagenomicData suggestsMe: anansi, ANCOMBC, DspikeIn, HoloFoodR, LimROTS, miaSim, philr, TaxSEA, bugphyzz, MicrobiomeBenchmarkData, MiscMetabar dependencyCount: 129 Package: miaDash Version: 1.3.0 Depends: R (>= 4.4.0), iSEE (>= 2.19.4), shiny Imports: ape, bluster, htmltools, iSEEtree (>= 1.1.4), mia, rintrojs, scater, scuttle, shinydashboard, shinyjs, shinyWidgets, S4Vectors, SingleCellExperiment, SummarizedExperiment, TreeSummarizedExperiment, utils, vegan Suggests: BiocStyle, knitr, philr, remotes, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 7a35209a5b31e35e0818daa52ec85ea4 NeedsCompilation: no Title: Dashboard for the interactive analysis and exploration of microbiome data Description: miaDash provides a Graphical User Interface for the exploration of microbiome data. This way, no knowledge of programming is required to perform analyses. Datasets can be imported, manipulated, analysed and visualised with a user-friendly interface. biocViews: Microbiome, Software, Visualization, GUI, ShinyApps, DataImport Author: Giulio Benedetti [aut, cre] (ORCID: ), Akewak Jeba [ctb] (ORCID: ), Leo Lahti [aut] (ORCID: ) Maintainer: Giulio Benedetti URL: https://github.com/microbiome/miaDash VignetteBuilder: knitr BugReports: https://github.com/microbiome/miaDash/issues git_url: https://git.bioconductor.org/packages/miaDash git_branch: devel git_last_commit: 320315c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/miaDash_1.3.0.tar.gz vignettes: vignettes/miaDash/inst/doc/miaDash.html vignetteTitles: miaDash hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miaDash/inst/doc/miaDash.R dependencyCount: 202 Package: miaSim Version: 1.17.0 Depends: TreeSummarizedExperiment Imports: SummarizedExperiment, deSolve, stats, poweRlaw, MatrixGenerics, S4Vectors Suggests: ape, cluster, foreach, doParallel, dplyr, GGally, ggplot2, igraph, network, reshape2, sna, vegan, rmarkdown, knitr, BiocStyle, testthat, mia, miaViz, colourvalues, philentropy License: Artistic-2.0 | file LICENSE MD5sum: b7e8be2e41ac8f5feb553b9baa683818 NeedsCompilation: no Title: Microbiome Data Simulation Description: Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects. biocViews: Microbiome, Software, Sequencing, DNASeq, ATACSeq, Coverage, Network Author: Yagmur Simsek [cre, aut], Karoline Faust [aut], Yu Gao [aut], Emma Gheysen [aut], Daniel Rios Garza [aut], Tuomas Borman [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Geraldson Muluh [ctb], Akewak Jeba [ctb] (ORCID: ) Maintainer: Yagmur Simsek URL: https://github.com/microbiome/miaSim VignetteBuilder: knitr BugReports: https://github.com/microbiome/miaSim/issues git_url: https://git.bioconductor.org/packages/miaSim git_branch: devel git_last_commit: 253acb3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/miaSim_1.17.0.tar.gz vignettes: vignettes/miaSim/inst/doc/miaSim.html vignetteTitles: miaSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaSim/inst/doc/miaSim.R dependencyCount: 71 Package: miaTime Version: 1.1.0 Depends: R (>= 4.5.0), mia Imports: dplyr, methods, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tidyr, TreeSummarizedExperiment Suggests: BiocStyle, devtools, ggplot2, knitr, lubridate, miaViz, rmarkdown, scater, testthat, vegan License: Artistic-2.0 | file LICENSE MD5sum: 598bc71bc15d17a77a0943d8e64e8668 NeedsCompilation: no Title: Microbiome Time Series Analysis Description: The `miaTime` package provides tools for microbiome time series analysis based on (Tree)SummarizedExperiment infrastructure. biocViews: Microbiome, Software, Sequencing Author: Leo Lahti [aut] (ORCID: ), Tuomas Borman [aut, cre] (ORCID: ), Yagmur Simsek [aut], Sudarshan Shetty [ctb], Chouaib Benchraka [ctb], Muluh Muluh [ctb], Ali Hajj [ctb] Maintainer: Tuomas Borman URL: https://github.com/microbiome/miaTime VignetteBuilder: knitr BugReports: https://github.com/microbiome/miaTime/issues git_url: https://git.bioconductor.org/packages/miaTime git_branch: devel git_last_commit: 411484f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/miaTime_1.1.0.tar.gz vignettes: vignettes/miaTime/inst/doc/miaTime.html vignetteTitles: miaTime hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaTime/inst/doc/miaTime.R suggestsMe: LimROTS, mia, miaViz dependencyCount: 130 Package: miaViz Version: 1.19.6 Depends: R (>= 4.1.0), ggplot2, ggraph (>= 2.0), mia (>= 1.13.0), SummarizedExperiment, TreeSummarizedExperiment Imports: ape, BiocGenerics, BiocParallel, DelayedArray, DirichletMultinomial, dplyr, ggnewscale, ggrepel, ggtree, methods, patchwork, rlang, S4Vectors, scales, scater, SingleCellExperiment, stats, tibble, tidygraph, tidyr, tidytext, tidytree, viridis Suggests: beeswarm, BiocStyle, bluster, circlize, ComplexHeatmap, ggh4x, ggpubr, knitr, maaslin3, mediation, miaTime, patchwork, rmarkdown, rstatix, shadowtext, testthat, vegan, vipor License: Artistic-2.0 | file LICENSE MD5sum: dc984b9987ae4d3cd0ea146e17507bca NeedsCompilation: no Title: Microbiome Analysis Plotting and Visualization Description: The miaViz package implements functions to visualize TreeSummarizedExperiment objects especially in the context of microbiome analysis. Part of the mia family of R/Bioconductor packages. biocViews: Microbiome, Software, Visualization Author: Tuomas Borman [aut, cre] (ORCID: ), Felix G.M. Ernst [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Basil Courbayre [ctb], Giulio Benedetti [ctb] (ORCID: ), Théotime Pralas [ctb], Chouaib Benchraka [ctb], Sam Hillman [ctb], Geraldson Muluh [ctb], Noah De Gunst [ctb], Ely Seraidarian [ctb], Himmi Lindgren [ctb], Akewak Jeba [ctb] (ORCID: ), Vivian Ikeh [ctb] Maintainer: Tuomas Borman URL: https://github.com/microbiome/miaViz VignetteBuilder: knitr BugReports: https://github.com/microbiome/miaViz/issues git_url: https://git.bioconductor.org/packages/miaViz git_branch: devel git_last_commit: 032ed91 git_last_commit_date: 2026-03-23 Date/Publication: 2026-04-20 source.ver: src/contrib/miaViz_1.19.6.tar.gz vignettes: vignettes/miaViz/inst/doc/miaViz.html vignetteTitles: miaViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaViz/inst/doc/miaViz.R importsMe: iSEEtree suggestsMe: HoloFoodR, MGnifyR, mia, miaSim, miaTime dependencyCount: 170 Package: MiChip Version: 1.65.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: c83eb1f6b65ea0deb65f94cc14920554 NeedsCompilation: no Title: MiChip Parsing and Summarizing Functions Description: This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages. biocViews: Microarray, Preprocessing Author: Jonathon Blake Maintainer: Jonathon Blake git_url: https://git.bioconductor.org/packages/MiChip git_branch: devel git_last_commit: 5d190b4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MiChip_1.65.0.tar.gz vignettes: vignettes/MiChip/inst/doc/MiChip.pdf vignetteTitles: MiChip miRNA Microarray Processing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiChip/inst/doc/MiChip.R dependencyCount: 7 Package: microbiome Version: 1.33.0 Depends: R (>= 3.6.0), phyloseq, ggplot2 Imports: Biostrings, compositions, dplyr, reshape2, Rtsne, scales, stats, tibble, tidyr, utils, vegan Suggests: BiocGenerics, BiocStyle, Cairo, knitr, rmarkdown, testthat License: BSD_2_clause + file LICENSE MD5sum: 68bcb9105592cd21af704ea12e9ffdb9 NeedsCompilation: no Title: Microbiome Analytics Description: Utilities for microbiome analysis. biocViews: Metagenomics,Microbiome,Sequencing,SystemsBiology Author: Leo Lahti [aut, cre] (ORCID: ), Sudarshan Shetty [aut] (ORCID: ) Maintainer: Leo Lahti URL: http://microbiome.github.io/microbiome VignetteBuilder: knitr BugReports: https://github.com/microbiome/microbiome/issues git_url: https://git.bioconductor.org/packages/microbiome git_branch: devel git_last_commit: 4c97d9e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/microbiome_1.33.0.tar.gz vignettes: vignettes/microbiome/inst/doc/vignette.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiome/inst/doc/vignette.R importsMe: benchdamic, DspikeIn, MicrobiomeSurv suggestsMe: ANCOMBC, CrcBiomeScreen, dar, MeLSI, zitools dependencyCount: 80 Package: microbiomeDASim Version: 1.25.0 Depends: R (>= 3.6.0) Imports: graphics, ggplot2, MASS, tmvtnorm, Matrix, mvtnorm, pbapply, stats, phyloseq, metagenomeSeq, Biobase Suggests: testthat (>= 2.1.0), knitr, devtools License: MIT + file LICENSE MD5sum: bb347bd5a54963ac51a63afcbd12bbd8 NeedsCompilation: no Title: Microbiome Differential Abundance Simulation Description: A toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance. biocViews: Microbiome, Visualization, Software Author: Justin Williams, Hector Corrada Bravo, Jennifer Tom, Joseph Nathaniel Paulson Maintainer: Justin Williams URL: https://github.com/williazo/microbiomeDASim VignetteBuilder: knitr BugReports: https://github.com/williazo/microbiomeDASim/issues git_url: https://git.bioconductor.org/packages/microbiomeDASim git_branch: devel git_last_commit: 5e863d9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/microbiomeDASim_1.25.0.tar.gz vignettes: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.pdf vignetteTitles: microbiomeDASim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.R dependencyCount: 87 Package: microbiomeExplorer Version: 1.21.0 Depends: shiny, magrittr, metagenomeSeq, Biobase Imports: shinyjs (>= 2.0.0), shinydashboard, shinycssloaders, shinyWidgets, rmarkdown (>= 1.9.0), DESeq2, RColorBrewer, dplyr, tidyr, purrr, rlang, knitr, readr, DT (>= 0.12.0), biomformat, tools, stringr, vegan, matrixStats, heatmaply, car, broom, limma, reshape2, tibble, forcats, lubridate, methods, plotly (>= 4.9.1) Suggests: V8, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: e3b7f083777e59c406ca5285523a2c78 NeedsCompilation: no Title: Microbiome Exploration App Description: The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation. biocViews: Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software, ImmunoOncology Author: Joseph Paulson [aut], Janina Reeder [aut, cre], Mo Huang [aut], Genentech [cph, fnd] Maintainer: Janina Reeder VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/microbiomeExplorer git_branch: devel git_last_commit: 6045c09 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/microbiomeExplorer_1.21.0.tar.gz vignettes: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.html vignetteTitles: microbiomeExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.R dependencyCount: 195 Package: MicrobiotaProcess Version: 1.23.1 Depends: R (>= 4.0.0) Imports: ape, tidyr, ggplot2, magrittr, dplyr, Biostrings, ggrepel, vegan, zoo, ggtree, tidytree (>= 0.4.2), MASS, methods, rlang, tibble, grDevices, stats, utils, coin, ggsignif, patchwork, ggstar, tidyselect, SummarizedExperiment, foreach, treeio (>= 1.17.2), pillar, cli, plyr, dtplyr, ggtreeExtra, data.table, ggfun (>= 0.1.1) Suggests: rmarkdown, prettydoc, testthat, knitr, nlme, phangorn, DECIPHER, randomForest, jsonlite, biomformat, scales, yaml, withr, S4Vectors, purrr, seqmagick, glue, ggupset, ggVennDiagram, ggalluvial (>= 0.11.1), forcats, phyloseq, aplot, ggnewscale, ggside, ggh4x, hopach, parallel, shadowtext, DirichletMultinomial, ggpp, BiocManager, rhdf5 License: GPL (>= 3.0) MD5sum: bd690ded496bf72047e6195f0931a049 NeedsCompilation: no Title: A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework Description: MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework). biocViews: Visualization, Microbiome, Software, MultipleComparison, FeatureExtraction Author: Shuangbin Xu [aut, cre] (ORCID: ), Guangchuang Yu [aut, ctb] (ORCID: ) Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/MicrobiotaProcess/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiotaProcess/issues git_url: https://git.bioconductor.org/packages/MicrobiotaProcess git_branch: devel git_last_commit: 916270c git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/MicrobiotaProcess_1.23.1.tar.gz vignettes: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.html vignetteTitles: Introduction to MicrobiotaProcess hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.R dependencyCount: 124 Package: microRNA Version: 1.69.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) License: Artistic-2.0 MD5sum: d673fb2a49d8f6686a5962a4e93b7270 NeedsCompilation: yes Title: Data and functions for dealing with microRNAs Description: Different data resources for microRNAs and some functions for manipulating them. biocViews: Infrastructure, GenomeAnnotation, SequenceMatching Author: R. Gentleman, S. Falcon Maintainer: "Michael Lawrence" git_url: https://git.bioconductor.org/packages/microRNA git_branch: devel git_last_commit: 8b807df git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/microRNA_1.69.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: rtracklayer dependencyCount: 15 Package: MICSQTL Version: 1.9.0 Depends: R (>= 4.3.0), SummarizedExperiment, stats Imports: TCA, nnls, purrr, TOAST, magrittr, BiocParallel, ggplot2, ggpubr, ggridges, glue, S4Vectors, dirmult Suggests: testthat (>= 3.0.0), rmarkdown, knitr, BiocStyle License: GPL-3 MD5sum: c59a64c54ad8b17faf4381431876026f NeedsCompilation: no Title: MICSQTL (Multi-omic deconvolution, Integration and Cell-type-specific Quantitative Trait Loci) Description: Our pipeline, MICSQTL, utilizes scRNA-seq reference and bulk transcriptomes to estimate cellular composition in the matched bulk proteomes. The expression of genes and proteins at either bulk level or cell type level can be integrated by Angle-based Joint and Individual Variation Explained (AJIVE) framework. Meanwhile, MICSQTL can perform cell-type-specic quantitative trait loci (QTL) mapping to proteins or transcripts based on the input of bulk expression data and the estimated cellular composition per molecule type, without the need for single cell sequencing. We use matched transcriptome-proteome from human brain frontal cortex tissue samples to demonstrate the input and output of our tool. biocViews: GeneExpression, Genetics, Proteomics, RNASeq, Sequencing, SingleCell, Software, Visualization, CellBasedAssays, Coverage Author: Yue Pan [aut] (ORCID: ), Qian Li [aut, cre] (ORCID: ), Iain Carmichael [ctb] Maintainer: Qian Li URL: https://bioconductor.org/packages/MICSQTL, https://github.com/YuePan027/MICSQTL VignetteBuilder: knitr BugReports: https://github.com/YuePan027/MICSQTL/issues git_url: https://git.bioconductor.org/packages/MICSQTL git_branch: devel git_last_commit: b9763ce git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MICSQTL_1.9.0.tar.gz vignettes: vignettes/MICSQTL/inst/doc/MICSQTL.html vignetteTitles: MICSQTL: Multi-omic deconvolution,, Integration and Cell-type-specific Quantitative Trait Loci hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MICSQTL/inst/doc/MICSQTL.R dependencyCount: 166 Package: midasHLA Version: 1.19.0 Depends: R (>= 4.1), MultiAssayExperiment (>= 1.8.3) Imports: assertthat (>= 0.2.0), broom (>= 0.5.1), dplyr (>= 0.8.0.1), formattable (>= 0.2.0.1), HardyWeinberg (>= 1.6.3), kableExtra (>= 1.1.0), knitr (>= 1.21), magrittr (>= 1.5), methods, stringi (>= 1.2.4), rlang (>= 0.3.1), S4Vectors (>= 0.20.1), stats, SummarizedExperiment (>= 1.12.0), tibble (>= 2.0.1), utils, qdapTools (>= 1.3.3) Suggests: broom.mixed (>= 0.2.4), cowplot (>= 1.0.0), devtools (>= 2.0.1), ggplot2 (>= 3.1.0), ggpubr (>= 0.2.5), rmarkdown, seqinr (>= 3.4-5), survival (>= 2.43-3), testthat (>= 2.0.1), tidyr (>= 1.1.2) License: MIT + file LICENCE MD5sum: 0fe43714d7d755f8657131062f42be64 NeedsCompilation: no Title: R package for immunogenomics data handling and association analysis Description: MiDAS is a R package for immunogenetics data transformation and statistical analysis. MiDAS accepts input data in the form of HLA alleles and KIR types, and can transform it into biologically meaningful variables, enabling HLA amino acid fine mapping, analyses of HLA evolutionary divergence, KIR gene presence, as well as validated HLA-KIR interactions. Further, it allows comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS closes a gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to T cell, Natural Killer cell, and disease biology. biocViews: CellBiology, Genetics, StatisticalMethod Author: Christian Hammer [aut], Maciej Migdał [aut, cre] Maintainer: Maciej Migdał VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/midasHLA git_branch: devel git_last_commit: f95a648 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/midasHLA_1.19.0.tar.gz vignettes: vignettes/midasHLA/inst/doc/MiDAS_tutorial.html, vignettes/midasHLA/inst/doc/MiDAS_vignette.html vignetteTitles: MiDAS tutorial, MiDAS quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/midasHLA/inst/doc/MiDAS_tutorial.R, vignettes/midasHLA/inst/doc/MiDAS_vignette.R dependencyCount: 138 Package: miloR Version: 2.7.1 Depends: R (>= 4.0.0), edgeR Imports: BiocNeighbors, BiocGenerics, SingleCellExperiment, Matrix (>= 1.3-0), MatrixGenerics, S4Vectors, stats, stringr, methods, igraph, irlba, utils, cowplot, BiocParallel, BiocSingular, limma, ggplot2, tibble, matrixStats, ggraph, gtools, SummarizedExperiment, patchwork, tidyr, dplyr, ggrepel, ggbeeswarm, RColorBrewer, grDevices, Rcpp, pracma, numDeriv LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, mvtnorm, scater, scran, covr, knitr, rmarkdown, uwot, scuttle, BiocStyle, MouseGastrulationData, MouseThymusAgeing, magick, RCurl, MASS, curl, scRNAseq, graphics, sparseMatrixStats License: GPL-3 + file LICENSE MD5sum: 2c7502ba6bc67a0f23ef3ca3f0ca59c7 NeedsCompilation: yes Title: Differential neighbourhood abundance testing on a graph Description: Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using either a negative bionomial generalized linear model or negative binomial generalized linear mixed model. biocViews: SingleCell, MultipleComparison, FunctionalGenomics, Software Author: Mike Morgan [aut, cre] (ORCID: ), Emma Dann [aut, ctb] Maintainer: Mike Morgan URL: https://marionilab.github.io/miloR VignetteBuilder: knitr BugReports: https://github.com/MarioniLab/miloR/issues git_url: https://git.bioconductor.org/packages/miloR git_branch: devel git_last_commit: 25cd117 git_last_commit_date: 2026-04-20 Date/Publication: 2026-04-20 source.ver: src/contrib/miloR_2.7.1.tar.gz vignettes: vignettes/miloR/inst/doc/milo_contrasts.html, vignettes/miloR/inst/doc/milo_demo.html, vignettes/miloR/inst/doc/milo_gastrulation.html, vignettes/miloR/inst/doc/milo_glmm.html vignetteTitles: Using contrasts for differential abundance testing, Differential abundance testing with Milo, Differential abundance testing with Milo - Mouse gastrulation example, Mixed effect models for Milo DA testing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miloR/inst/doc/milo_contrasts.R, vignettes/miloR/inst/doc/milo_demo.R, vignettes/miloR/inst/doc/milo_gastrulation.R, vignettes/miloR/inst/doc/milo_glmm.R importsMe: dandelionR dependencyCount: 101 Package: mimager Version: 1.35.0 Depends: Biobase Imports: BiocGenerics, S4Vectors, preprocessCore, grDevices, methods, grid, gtable, scales, DBI, affy, affyPLM, oligo, oligoClasses Suggests: knitr, rmarkdown, BiocStyle, testthat, lintr, Matrix, abind, affydata, hgu95av2cdf, oligoData, pd.hugene.1.0.st.v1 License: MIT + file LICENSE MD5sum: 27dee40e8f6d4ea72df186a82b63b752 NeedsCompilation: no Title: mimager: The Microarray Imager Description: Easily visualize and inspect microarrays for spatial artifacts. biocViews: Infrastructure, Visualization, Microarray Author: Aaron Wolen [aut, cre, cph] Maintainer: Aaron Wolen URL: https://github.com/aaronwolen/mimager VignetteBuilder: knitr BugReports: https://github.com/aaronwolen/mimager/issues git_url: https://git.bioconductor.org/packages/mimager git_branch: devel git_last_commit: 86a8e49 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mimager_1.35.0.tar.gz vignettes: vignettes/mimager/inst/doc/introduction.html vignetteTitles: mimager overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mimager/inst/doc/introduction.R dependencyCount: 63 Package: mina Version: 1.19.0 Depends: R (>= 4.0.0) Imports: methods, stats, Rcpp, MCL, RSpectra, apcluster, bigmemory, foreach, ggplot2, parallel, parallelDist, reshape2, plyr, biganalytics, stringr, Hmisc, utils LinkingTo: Rcpp, RcppParallel, RcppArmadillo Suggests: knitr, rmarkdown Enhances: doMC License: GPL MD5sum: e5e1c249bcc9803198fbf8b6542f88ae NeedsCompilation: yes Title: Microbial community dIversity and Network Analysis Description: An increasing number of microbiome datasets have been generated and analyzed with the help of rapidly developing sequencing technologies. At present, analysis of taxonomic profiling data is mainly conducted using composition-based methods, which ignores interactions between community members. Besides this, a lack of efficient ways to compare microbial interaction networks limited the study of community dynamics. To better understand how community diversity is affected by complex interactions between its members, we developed a framework (Microbial community dIversity and Network Analysis, mina), a comprehensive framework for microbial community diversity analysis and network comparison. By defining and integrating network-derived community features, we greatly reduce noise-to-signal ratio for diversity analyses. A bootstrap and permutation-based method was implemented to assess community network dissimilarities and extract discriminative features in a statistically principled way. biocViews: Software, WorkflowStep Author: Rui Guan [aut, cre], Ruben Garrido-Oter [ctb] Maintainer: Rui Guan VignetteBuilder: knitr BugReports: https://github.com/Guan06/mina git_url: https://git.bioconductor.org/packages/mina git_branch: devel git_last_commit: 7f01d56 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mina_1.19.0.tar.gz vignettes: vignettes/mina/inst/doc/mina.html vignetteTitles: Microbial dIversity and Network Analysis with MINA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mina/inst/doc/mina.R dependencyCount: 86 Package: minet Version: 3.69.0 Imports: infotheo License: Artistic-2.0 MD5sum: d7d46c7ca683beba67ad1b01425dc178 NeedsCompilation: yes Title: Mutual Information NETworks Description: This package implements various algorithms for inferring mutual information networks from data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference Author: Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi Maintainer: Patrick E. Meyer URL: http://minet.meyerp.com git_url: https://git.bioconductor.org/packages/minet git_branch: devel git_last_commit: 46d68a0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/minet_3.69.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: BioNERO, epiNEM, RTN, PRANA, TGS suggestsMe: CNORfeeder, TCGAbiolinks, WGCNA dependencyCount: 1 Package: MinimumDistance Version: 1.55.0 Depends: R (>= 3.5.0), VanillaICE (>= 1.47.1) Imports: methods, BiocGenerics, MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges, Seqinfo, GenomicRanges (>= 1.17.16), SummarizedExperiment (>= 1.15.4), oligoClasses, DNAcopy, ff, foreach, matrixStats, lattice, data.table, grid, stats, utils Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, RUnit Enhances: snow, doSNOW License: Artistic-2.0 MD5sum: f5a138ec1b067c54c628f54b3cd2bea9 NeedsCompilation: no Title: A Package for De Novo CNV Detection in Case-Parent Trios Description: Analysis of de novo copy number variants in trios from high-dimensional genotyping platforms. biocViews: Microarray, SNP, CopyNumberVariation Author: Robert B Scharpf and Ingo Ruczinski Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/MinimumDistance git_branch: devel git_last_commit: 3e07ba2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MinimumDistance_1.55.0.tar.gz vignettes: vignettes/MinimumDistance/inst/doc/MinimumDistance.pdf vignetteTitles: Detection of de novo copy number alterations in case-parent trios hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MinimumDistance/inst/doc/MinimumDistance.R dependencyCount: 97 Package: MiPP Version: 1.83.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: b3cc5487b257ee8d53ff2f2edfc1d8f7 NeedsCompilation: no Title: Misclassification Penalized Posterior Classification Description: This package finds optimal sets of genes that seperate samples into two or more classes. biocViews: Microarray, Classification Author: HyungJun Cho , Sukwoo Kim , Mat Soukup , and Jae K. Lee Maintainer: Sukwoo Kim URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/MiPP git_branch: devel git_last_commit: b118e70 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MiPP_1.83.0.tar.gz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: miQC Version: 1.19.0 Depends: R (>= 3.5.0) Imports: SingleCellExperiment, flexmix, ggplot2, splines Suggests: scRNAseq, scater, BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 66e5ee59125881db7d5773119706f9dc NeedsCompilation: no Title: Flexible, probabilistic metrics for quality control of scRNA-seq data Description: Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. biocViews: SingleCell, QualityControl, GeneExpression, Preprocessing, Sequencing Author: Ariel Hippen [aut, cre], Stephanie Hicks [aut] Maintainer: Ariel Hippen URL: https://github.com/greenelab/miQC VignetteBuilder: knitr BugReports: https://github.com/greenelab/miQC/issues git_url: https://git.bioconductor.org/packages/miQC git_branch: devel git_last_commit: 08e5f55 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/miQC_1.19.0.tar.gz vignettes: vignettes/miQC/inst/doc/miQC.html vignetteTitles: miQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miQC/inst/doc/miQC.R dependencyCount: 47 Package: MiRaGE Version: 1.53.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: BiocGenerics, S4Vectors, AnnotationDbi, BiocManager Suggests: seqinr (>= 3.0.7), biomaRt (>= 2.19.1), GenomicFeatures (>= 1.15.4), Biostrings (>= 2.31.3), BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, miRNATarget, humanStemCell, IRanges, GenomicRanges (>= 1.8.3), BSgenome, beadarrayExampleData License: GPL MD5sum: 8594eaf773532cd0ef5b46f5885d89fd NeedsCompilation: no Title: MiRNA Ranking by Gene Expression Description: The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile. biocViews: ImmunoOncology, Microarray, GeneExpression, RNASeq, Sequencing, SAGE Author: Y-h. Taguchi Maintainer: Y-h. Taguchi git_url: https://git.bioconductor.org/packages/MiRaGE git_branch: devel git_last_commit: 700ec14 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MiRaGE_1.53.0.tar.gz vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf vignetteTitles: How to use MiRaGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R dependencyCount: 43 Package: miRBaseConverter Version: 1.35.0 Depends: R (>= 3.4) Imports: stats Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils, rmarkdown License: GPL (>= 2) MD5sum: 2b01d99332cabcecf482821d44c856fa NeedsCompilation: no Title: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions Description: A comprehensive tool for converting and retrieving the miRNA Name, Accession, Sequence, Version, History and Family information in different miRBase versions. It can process a huge number of miRNAs in a short time without other depends. biocViews: Software, miRNA Author: Taosheng Xu Taosheng Xu [aut, cre] (ORCID: ) Maintainer: Taosheng Xu Taosheng Xu URL: https://github.com/taoshengxu/miRBaseConverter VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/miRBaseConverter/issues git_url: https://git.bioconductor.org/packages/miRBaseConverter git_branch: devel git_last_commit: 326974e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/miRBaseConverter_1.35.0.tar.gz vignettes: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.html vignetteTitles: "miRBaseConverter: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.R suggestsMe: EpiMix dependencyCount: 1 Package: miRcomp Version: 1.41.0 Depends: R (>= 3.5.0), Biobase (>= 2.22.0), miRcompData Imports: utils, methods, graphics, KernSmooth, stats Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, shiny License: GPL-3 | file LICENSE MD5sum: 3db22f6381ae52559e0402c8db64ae33 NeedsCompilation: no Title: Tools to assess and compare miRNA expression estimatation methods Description: Based on a large miRNA dilution study, this package provides tools to read in the raw amplification data and use these data to assess the performance of methods that estimate expression from the amplification curves. biocViews: Software, qPCR, Preprocessing, QualityControl Author: Matthew N. McCall , Lauren Kemperman Maintainer: Matthew N. McCall VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRcomp git_branch: devel git_last_commit: 0114b71 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/miRcomp_1.41.0.tar.gz vignettes: vignettes/miRcomp/inst/doc/miRcomp.html vignetteTitles: Assessment and comparison of miRNA expression estimation methods (miRcomp) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R dependencyCount: 9 Package: mirIntegrator Version: 1.41.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: 0a1d18f4edbc365e386de038899fe85a NeedsCompilation: no Title: Integrating microRNA expression into signaling pathways for pathway analysis Description: Tools for augmenting signaling pathways to perform pathway analysis of microRNA and mRNA expression levels. biocViews: Network, Microarray, GraphAndNetwork, Pathways, KEGG Author: Diana Diaz Maintainer: Diana Diaz URL: http://datad.github.io/mirIntegrator/ git_url: https://git.bioconductor.org/packages/mirIntegrator git_branch: devel git_last_commit: 85fa48a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mirIntegrator_1.41.0.tar.gz vignettes: vignettes/mirIntegrator/inst/doc/mirIntegrator.pdf vignetteTitles: mirIntegrator Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mirIntegrator/inst/doc/mirIntegrator.R dependencyCount: 62 Package: MIRit Version: 1.7.4 Depends: MultiAssayExperiment, R (>= 4.4.0) Imports: AnnotationDbi, BiocFileCache, BiocParallel, DESeq2, edgeR, fgsea, genekitr, geneset, ggplot2, ggpubr, graph, graphics, graphite, grDevices, httr, limma, methods, Rcpp, Rgraphviz (>= 2.44.0), rlang, stats, utils LinkingTo: Rcpp Suggests: BiocStyle, biomaRt, BSgenome.Hsapiens.UCSC.hg38, GenomicRanges, ggrepel, ggridges, Gviz, gwasrapidd, knitr, MonoPoly, org.Hs.eg.db, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: e39606bce7cb6875a74aa88bc710c4a9 NeedsCompilation: yes Title: Integrate microRNA and gene expression to decipher pathway complexity Description: MIRit is an R package that provides several methods for investigating the relationships between miRNAs and genes in different biological conditions. In particular, MIRit allows to explore the functions of dysregulated miRNAs, and makes it possible to identify miRNA-gene regulatory axes that control biological pathways, thus enabling the users to unveil the complexity of miRNA biology. MIRit is an all-in-one framework that aims to help researchers in all the central aspects of an integrative miRNA-mRNA analyses, from differential expression analysis to network characterization. biocViews: Software, GeneRegulation, NetworkEnrichment, NetworkInference, Epigenetics, FunctionalGenomics, SystemsBiology, Network, Pathways, GeneExpression, DifferentialExpression Author: Jacopo Ronchi [aut, cre] (ORCID: ), Maria Foti [fnd] (ORCID: ) Maintainer: Jacopo Ronchi URL: https://jacopo-ronchi.github.io/MIRit/, https://github.com/jacopo-ronchi/MIRit VignetteBuilder: knitr BugReports: https://github.com/jacopo-ronchi/MIRit/issues git_url: https://git.bioconductor.org/packages/MIRit git_branch: devel git_last_commit: 88f700f git_last_commit_date: 2026-02-20 Date/Publication: 2026-04-20 source.ver: src/contrib/MIRit_1.7.4.tar.gz vignettes: vignettes/MIRit/inst/doc/MIRit.html vignetteTitles: Integrate miRNA and gene expression data with MIRit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIRit/inst/doc/MIRit.R dependencyCount: 223 Package: miRNAmeConverter Version: 1.39.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi, reshape2 Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 30c45948231641ec73f46cbee561a1c4 NeedsCompilation: no Title: Convert miRNA Names to Different miRBase Versions Description: Translating mature miRNA names to different miRBase versions, sequence retrieval, checking names for validity and detecting miRBase version of a given set of names (data from http://www.mirbase.org/). biocViews: Preprocessing, miRNA Author: Stefan Haunsberger [aut, cre] Maintainer: Stefan J. Haunsberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRNAmeConverter git_branch: devel git_last_commit: 6f9e554 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/miRNAmeConverter_1.39.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: TCGAutils dependencyCount: 50 Package: miRNApath Version: 1.71.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: 06f872eed90e3fbf71d2e26b642add6f NeedsCompilation: no Title: miRNApath: Pathway Enrichment for miRNA Expression Data Description: This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes. biocViews: Annotation, Pathways, DifferentialExpression, NetworkEnrichment, miRNA Author: James M. Ward with contributions from Yunling Shi, Cindy Richards, John P. Cogswell Maintainer: James M. Ward git_url: https://git.bioconductor.org/packages/miRNApath git_branch: devel git_last_commit: 111d188 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/miRNApath_1.71.0.tar.gz vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNApath/inst/doc/miRNApath.R dependencyCount: 1 Package: miRNAtap Version: 1.45.0 Depends: R (>= 3.3.0), AnnotationDbi Imports: DBI, RSQLite, stringr, sqldf, plyr, methods Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat License: GPL-2 MD5sum: 02affb1ce40a2a68bee63cedb7cf81c7 NeedsCompilation: no Title: miRNAtap: microRNA Targets - Aggregated Predictions Description: The package facilitates implementation of workflows requiring miRNA predictions, it allows to integrate ranked miRNA target predictions from multiple sources available online and aggregate them with various methods which improves quality of predictions above any of the single sources. Currently predictions are available for Homo sapiens, Mus musculus and Rattus norvegicus (the last one through homology translation). biocViews: Software, Classification, Microarray, Sequencing, miRNA Author: Maciej Pajak, T. Ian Simpson Maintainer: T. Ian Simpson git_url: https://git.bioconductor.org/packages/miRNAtap git_branch: devel git_last_commit: c086840 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/miRNAtap_1.45.0.tar.gz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R dependsOnMe: miRNAtap.db importsMe: miRNAtap.db dependencyCount: 51 Package: miRSM Version: 2.7.1 Depends: R (>= 4.4.0) Imports: WGCNA, flashClust, dynamicTreeCut, GFA, igraph, RColorBrewer, grid, MCL, fabia, NMF, BicARE, isa2, methods, rJava, Biobase, PMA, stats, dbscan, mclust, SOMbrero, ppclust, Rcpp, utils, SummarizedExperiment, GSEABase, org.Hs.eg.db, clusterProfiler, ReactomePA, DOSE, MatrixCorrelation, energy Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 8c1db4e1edbfb8c51f4246dfa6aca5bb NeedsCompilation: yes Title: Inferring miRNA sponge modules in heterogeneous data Description: The package aims to identify miRNA sponge or ceRNA modules in heterogeneous data. It provides several functions to study miRNA sponge modules at single-sample and multi-sample levels, including popular methods for inferring gene modules (candidate miRNA sponge or ceRNA modules), and two functions to identify miRNA sponge modules at single-sample and multi-sample levels, as well as several functions to conduct modular analysis of miRNA sponge modules. biocViews: GeneExpression, BiomedicalInformatics, Clustering, GeneSetEnrichment, Microarray, Software, GeneRegulation, GeneTarget Author: Junpeng Zhang [aut, cre] Maintainer: Junpeng Zhang URL: https://github.com/zhangjunpeng411/miRSM VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRSM/issues git_url: https://git.bioconductor.org/packages/miRSM git_branch: devel git_last_commit: 50c7f3c git_last_commit_date: 2026-01-15 Date/Publication: 2026-04-20 source.ver: src/contrib/miRSM_2.7.1.tar.gz vignettes: vignettes/miRSM/inst/doc/miRSM.html vignetteTitles: miRSM: inferring miRNA sponge modules in heterogeneous data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRSM/inst/doc/miRSM.R dependencyCount: 238 Package: mirTarRnaSeq Version: 1.19.0 Depends: R (>= 4.1.0), ggplot2 Imports: purrr, MASS, pscl, assertthat, caTools, dplyr, pheatmap, reshape2, corrplot, grDevices, graphics, stats, utils, data.table, R.utils, viridis Suggests: BiocStyle, knitr, rmarkdown, R.cache, SPONGE License: MIT + file LICENSE MD5sum: 4b2bc6d7e7457ef126ba1ba3729270ba NeedsCompilation: no Title: mirTarRnaSeq Description: mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments. biocViews: miRNA, Regression, Software, Sequencing, SmallRNA, TimeCourse, DifferentialExpression Author: Mercedeh Movassagh [aut, cre] (ORCID: ), Sarah Morton [aut], Rafael Irizarry [aut], Jeffrey Bailey [aut], Joseph N Paulson [aut] Maintainer: Mercedeh Movassagh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mirTarRnaSeq git_branch: devel git_last_commit: c07b0d0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mirTarRnaSeq_1.19.0.tar.gz vignettes: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.pdf vignetteTitles: mirTarRnaSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.R dependencyCount: 52 Package: missRows Version: 1.31.0 Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment Imports: plyr, stats, gtools, S4Vectors Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: 01bbfc07993b502dba9fb5feb4fe6e61 NeedsCompilation: no Title: Handling Missing Individuals in Multi-Omics Data Integration Description: The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values. biocViews: Software, StatisticalMethod, DimensionReduction, PrincipalComponent, MathematicalBiology, Visualization Author: Ignacio Gonzalez and Valentin Voillet Maintainer: Gonzalez Ignacio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missRows git_branch: devel git_last_commit: b3e59a2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/missRows_1.31.0.tar.gz vignettes: vignettes/missRows/inst/doc/missRows.pdf vignetteTitles: missRows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missRows/inst/doc/missRows.R dependencyCount: 58 Package: mist Version: 1.3.3 Depends: R (>= 4.5.0) Imports: BiocParallel, MCMCpack, Matrix, S4Vectors, methods, rtracklayer, car, mvtnorm, SummarizedExperiment, SingleCellExperiment, BiocGenerics, stats, rlang Suggests: knitr, rmarkdown, RUnit, ggplot2, BiocStyle License: MIT + file LICENSE MD5sum: 738f73157785123f66cc6c0ab839d071 NeedsCompilation: no Title: Differential Methylation Analysis for scDNAm Data Description: mist (Methylation Inference for Single-cell along Trajectory) is a hierarchical Bayesian framework for modeling DNA methylation trajectories and performing differential methylation (DM) analysis in single-cell DNA methylation (scDNAm) data. It estimates developmental-stage-specific variations, identifies genomic features with drastic changes along pseudotime, and, for two phenotypic groups, detects features with distinct temporal methylation patterns. mist uses Gibbs sampling to estimate parameters for temporal changes and stage-specific variations. biocViews: Epigenetics, DifferentialMethylation, DNAMethylation, SingleCell, Software Author: Daoyu Duan [aut, cre] (ORCID: ) Maintainer: Daoyu Duan URL: https://https://github.com/dxd429/mist VignetteBuilder: knitr BugReports: https://https://github.com/dxd429/mist/issues git_url: https://git.bioconductor.org/packages/mist git_branch: devel git_last_commit: d475aad git_last_commit_date: 2026-02-06 Date/Publication: 2026-04-20 source.ver: src/contrib/mist_1.3.3.tar.gz vignettes: vignettes/mist/inst/doc/mist_vignette.html vignetteTitles: mist_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mist/inst/doc/mist_vignette.R dependencyCount: 126 Package: mistyR Version: 1.19.0 Depends: R (>= 4.0) Imports: assertthat, caret, deldir, digest, distances, dplyr (>= 1.1.0), filelock, furrr (>= 0.2.0), ggplot2, methods, purrr, ranger, readr (>= 2.0.0), ridge, rlang, rlist, R.utils, stats, stringr, tibble, tidyr, tidyselect (>= 1.2.0), utils, withr Suggests: BiocStyle, covr, earth, future, igraph (>= 1.2.7), iml, kernlab, knitr, MASS, rmarkdown, RSNNS, testthat (>= 3.0.0), xgboost License: GPL-3 MD5sum: f307eeef454bffa9ac5b3f4c9747ff13 NeedsCompilation: no Title: Multiview Intercellular SpaTial modeling framework Description: mistyR is an implementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution. biocViews: Software, BiomedicalInformatics, CellBiology, SystemsBiology, Regression, DecisionTree, SingleCell, Spatial Author: Jovan Tanevski [cre, aut] (ORCID: ), Ricardo Omar Ramirez Flores [ctb] (ORCID: ), Philipp Schäfer [ctb] Maintainer: Jovan Tanevski URL: https://saezlab.github.io/mistyR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/mistyR/issues git_url: https://git.bioconductor.org/packages/mistyR git_branch: devel git_last_commit: f1bc6d2 git_last_commit_date: 2026-01-12 Date/Publication: 2026-04-20 source.ver: src/contrib/mistyR_1.19.0.tar.gz vignettes: vignettes/mistyR/inst/doc/mistyR.html vignetteTitles: Getting started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mistyR/inst/doc/mistyR.R dependencyCount: 107 Package: mitch Version: 1.23.1 Depends: R (>= 4.4) Imports: stats, grDevices, graphics, utils, MASS, plyr, reshape2, parallel, GGally, grid, gridExtra, knitr, rmarkdown, ggplot2, gplots, beeswarm, echarts4r, kableExtra, dplyr, network Suggests: stringi, testthat (>= 2.1.0), HGNChelper, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 License: CC BY-SA 4.0 + file LICENSE MD5sum: 03bdf157d761705292413a529ede0ea9 NeedsCompilation: no Title: Multi-Contrast Gene Set Enrichment Analysis Description: mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. We have an established routine for pathway enrichment of Infinium Methylation Array data (see vignette). The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments. biocViews: GeneExpression, GeneSetEnrichment, SingleCell, Transcriptomics, Epigenetics, Proteomics, DifferentialExpression, Reactome, DNAMethylation, MethylationArray, DataImport Author: Mark Ziemann [aut, cre, cph] (ORCID: ), Antony Kaspi [aut, cph] Maintainer: Mark Ziemann URL: https://github.com/markziemann/mitch VignetteBuilder: knitr BugReports: https://github.com/markziemann/mitch git_url: https://git.bioconductor.org/packages/mitch git_branch: devel git_last_commit: 560f5e5 git_last_commit_date: 2026-01-08 Date/Publication: 2026-04-20 source.ver: src/contrib/mitch_1.23.1.tar.gz vignettes: vignettes/mitch/inst/doc/infiniumMethArrayWorkflow.html, vignettes/mitch/inst/doc/mitchWorkflow.html vignetteTitles: Applying mitch to pathway analysis of Infinium Methylation array data, mitch Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mitch/inst/doc/infiniumMethArrayWorkflow.R, vignettes/mitch/inst/doc/mitchWorkflow.R dependencyCount: 102 Package: mitoClone2 Version: 1.17.0 Depends: R (>= 4.4.0) Imports: reshape2, GenomicRanges, pheatmap, deepSNV, grDevices, Matrix, graphics, stats, utils, S4Vectors, Rhtslib, parallel, methods, ggplot2 LinkingTo: Rhtslib (>= 1.13.1) Suggests: knitr, rmarkdown, Biostrings, testthat License: GPL-3 MD5sum: 74392aa181b4527dfa96964f0ad0da41 NeedsCompilation: yes Title: Clonal Population Identification in Single-Cell RNA-Seq Data using Mitochondrial and Somatic Mutations Description: This package primarily identifies variants in mitochondrial genomes from BAM alignment files. It filters these variants to remove RNA editing events then estimates their evolutionary relationship (i.e. their phylogenetic tree) and groups single cells into clones. It also visualizes the mutations and providing additional genomic context. biocViews: Annotation, DataImport, Genetics, SNP, Software, SingleCell, Alignment Author: Benjamin Story [aut, cre], Lars Velten [aut], Gregor Mönke [aut] Maintainer: Benjamin Story URL: https://github.com/benstory/mitoClone2 SystemRequirements: GNU make, PhISCS (optional) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mitoClone2 git_branch: devel git_last_commit: 0ee32de git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mitoClone2_1.17.0.tar.gz vignettes: vignettes/mitoClone2/inst/doc/clustering.html, vignettes/mitoClone2/inst/doc/overview.html vignetteTitles: Computation of phylogenetic trees and clustering of mutations, Variant Calling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitoClone2/inst/doc/clustering.R, vignettes/mitoClone2/inst/doc/overview.R dependencyCount: 97 Package: mitology Version: 1.3.0 Depends: R (>= 4.5.0) Imports: AnnotationDbi, ape, circlize, clusterProfiler, ComplexHeatmap, ggplot2, ggtree, magrittr, org.Hs.eg.db, ReactomePA, scales Suggests: Biobase, BiocStyle, GSVA, methods, rmarkdown, knitr, SummarizedExperiment, testthat License: AGPL-3 MD5sum: 0138c33183893b43d71123695f9cd669 NeedsCompilation: no Title: Study of mitochondrial activity from RNA-seq data Description: mitology allows to study the mitochondrial activity throught high-throughput RNA-seq data. It is based on a collection of genes whose proteins localize in to the mitochondria. From these, mitology provides a reorganization of the pathways related to mitochondria activity from Reactome and Gene Ontology. Further a ready-to-use implementation of MitoCarta3.0 pathways is included. biocViews: GeneExpression, RNASeq, Visualization, SingleCell, Spatial, Pathways, Reactome, GO Author: Stefania Pirrotta [cre, aut] (ORCID: ), Enrica Calura [aut, fnd] (ORCID: ) Maintainer: Stefania Pirrotta URL: https://github.com/CaluraLab/mitology VignetteBuilder: knitr BugReports: https://github.com/CaluraLab/mitology/issues git_url: https://git.bioconductor.org/packages/mitology git_branch: devel git_last_commit: ada0939 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mitology_1.3.0.tar.gz vignettes: vignettes/mitology/inst/doc/mitology.html vignetteTitles: mitology vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitology/inst/doc/mitology.R dependencyCount: 155 Package: mixOmics Version: 6.35.1 Depends: R (>= 4.4.0), MASS, lattice, ggplot2 Imports: igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra, grDevices, graphics, stats, ggrepel, BiocParallel, utils, gsignal, rgl, rlang Suggests: BiocStyle, knitr, rmarkdown, mime, testthat, microbenchmark, magick, vdiffr, kableExtra, devtools License: GPL (>= 2) MD5sum: 1f360d6be88fcba21924b97ccf364388 NeedsCompilation: no Title: Omics Data Integration Project Description: Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares. biocViews: ImmunoOncology, Microarray, Sequencing, Metabolomics, Metagenomics, Proteomics, GenePrediction, MultipleComparison, Classification, Regression Author: Kim-Anh Le Cao [aut], Florian Rohart [aut], Ignacio Gonzalez [aut], Sebastien Dejean [aut], Al J Abadi [ctb], Max Bladen [ctb], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb], Eva Hamrud [ctb], Derek Lei [ctb, cre] Maintainer: Derek Lei URL: http://www.mixOmics.org VignetteBuilder: knitr BugReports: https://github.com/mixOmicsTeam/mixOmics/issues/ git_url: https://git.bioconductor.org/packages/mixOmics git_branch: devel git_last_commit: 30ec7af git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/mixOmics_6.35.1.tar.gz vignettes: vignettes/mixOmics/inst/doc/vignette.html vignetteTitles: mixOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mixOmics/inst/doc/vignette.R dependsOnMe: timeOmics, mixKernel, sgPLS importsMe: AlpsNMR, benchdamic, DepecheR, PLSDAbatch, POMA, RFLOMICS, tidyexposomics, Coxmos, CytoProfile, Holomics, iTensor, plsmod, plsRcox, SISIR suggestsMe: autonomics, notameStats, planet, eoPredData, MetabolomicsBasics, MSclassifR, OmicNetR, pctax, RVAideMemoire, SelectBoost, sharp dependencyCount: 85 Package: MLInterfaces Version: 1.91.0 Depends: R (>= 3.5), Rcpp, methods, BiocGenerics (>= 0.13.11), Biobase, annotate, cluster Imports: gdata, pls, sfsmisc, MASS, rpart, genefilter, fpc, ggvis, shiny, gbm, RColorBrewer, hwriter, threejs (>= 0.2.2), mlbench, stats4, tools, grDevices, graphics, stats, magrittr, SummarizedExperiment Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, hgu95av2.db, som, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party, klaR, BiocStyle, knitr, testthat, airway Enhances: parallel License: LGPL MD5sum: 57ababfa8ff20138dcc0d0334bc6a9f3 NeedsCompilation: no Title: Uniform interfaces to R machine learning procedures for data in Bioconductor containers Description: This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers. biocViews: Classification, Clustering Author: Vincent Carey [cre, aut] (ORCID: ), Jess Mar [aut], Jason Vertrees [ctb], Laurent Gatto [ctb], Phylis Atieno [ctb] (Translated vignettes from Sweave to Rmarkdown / HTML.) Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLInterfaces git_branch: devel git_last_commit: e9c6af4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MLInterfaces_1.91.0.tar.gz vignettes: vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf, vignettes/MLInterfaces/inst/doc/MLint_devel.html, vignettes/MLInterfaces/inst/doc/MLprac2_2.html vignetteTitles: MLInterfaces Computer Cluster, MLInterfaces 2.0 -- a new design, A machine learning tutorial tutorial: applications of the Bioconductor MLInterfaces package to gene expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R, vignettes/MLInterfaces/inst/doc/MLprac2_2.R, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R dependsOnMe: pRoloc, SigCheck, nlcv dependencyCount: 120 Package: MLP Version: 1.59.0 Imports: AnnotationDbi, gplots, graphics, stats, utils Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Cf.eg.db, org.Mmu.eg.db, KEGGREST, annotate, Rgraphviz, GOstats, graph, limma, mouse4302.db, reactome.db License: GPL-3 MD5sum: a4263c4824e00cc4e123e4022d27894a NeedsCompilation: no Title: Mean Log P Analysis Description: Pathway analysis based on p-values associated to genes from a genes expression analysis of interest. Utility functions enable to extract pathways from the Gene Ontology Biological Process (GOBP), Molecular Function (GOMF) and Cellular Component (GOCC), Kyoto Encyclopedia of Genes of Genomes (KEGG) and Reactome databases. Methodology, and helper functions to display the results as a table, barplot of pathway significance, Gene Ontology graph and pathway significance are available. biocViews: Genetics, GeneExpression, Pathways, Reactome, KEGG, GO Author: Nandini Raghavan [aut], Tobias Verbeke [aut], An De Bondt [aut], Javier Cabrera [ctb], Dhammika Amaratunga [ctb], Tine Casneuf [ctb], Willem Ligtenberg [ctb], Laure Cougnaud [cre], Katarzyna Gorczak [ctb] Maintainer: Tobias Verbeke git_url: https://git.bioconductor.org/packages/MLP git_branch: devel git_last_commit: b806436 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MLP_1.59.0.tar.gz vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf vignetteTitles: UsingMLP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLP/inst/doc/UsingMLP.R importsMe: esetVis suggestsMe: a4 dependencyCount: 47 Package: MLSeq Version: 2.29.0 Depends: caret, ggplot2 Imports: testthat, VennDiagram, pamr, methods, DESeq2, edgeR, limma, Biobase, SummarizedExperiment, plyr, foreach, utils, sSeq, xtable Suggests: knitr, e1071, kernlab License: GPL(>=2) MD5sum: 74fa89770ef17948fe0e74fbf09bc4e9 NeedsCompilation: no Title: Machine Learning Interface for RNA-Seq Data Description: This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, Classification, Clustering Author: Gokmen Zararsiz [aut, cre], Dincer Goksuluk [aut], Selcuk Korkmaz [aut], Vahap Eldem [aut], Izzet Parug Duru [ctb], Ahmet Ozturk [aut], Ahmet Ergun Karaagaoglu [aut, ths] Maintainer: Gokmen Zararsiz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLSeq git_branch: devel git_last_commit: b1aac87 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MLSeq_2.29.0.tar.gz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: Beginner's guide to the "MLSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R importsMe: GARS dependencyCount: 133 Package: MMDiff2 Version: 1.39.0 Depends: R (>= 3.5.0), Rsamtools, Biobase Imports: GenomicRanges, locfit, BSgenome, Biostrings, shiny, ggplot2, RColorBrewer, graphics, grDevices, parallel, S4Vectors, methods Suggests: MMDiffBamSubset, MotifDb, knitr, BiocStyle, BSgenome.Mmusculus.UCSC.mm9 License: Artistic-2.0 MD5sum: 60df2fc40d79d08ff68c589d418dabde NeedsCompilation: no Title: Statistical Testing for ChIP-Seq data sets Description: This package detects statistically significant differences between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD). biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Software Author: Gabriele Schweikert [cre, aut], David Kuo [aut] Maintainer: Gabriele Schweikert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMDiff2 git_branch: devel git_last_commit: d528d04 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MMDiff2_1.39.0.tar.gz vignettes: vignettes/MMDiff2/inst/doc/MMDiff2.pdf vignetteTitles: An Introduction to the MMDiff2 method hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMDiff2/inst/doc/MMDiff2.R suggestsMe: MMDiffBamSubset dependencyCount: 96 Package: MMUPHin Version: 1.99.3 Depends: R (>= 3.6) Imports: maaslin3, metafor, fpc, igraph, ggplot2, dplyr, tidyr, stringr, cowplot, utils, stats, grDevices Suggests: testthat, BiocStyle, knitr, rmarkdown, magrittr, vegan, phyloseq, curatedMetagenomicData, genefilter License: MIT + file LICENSE MD5sum: 2aa95026e432076e4e66c81c3f11db5b NeedsCompilation: no Title: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies Description: MMUPHin is an R package for meta-analysis tasks of microbiome cohorts. It has function interfaces for: a) covariate-controlled batch- and cohort effect adjustment, b) meta-analysis differential abundance testing, c) meta-analysis unsupervised discrete structure (clustering) discovery, and d) meta-analysis unsupervised continuous structure discovery. biocViews: Metagenomics, Microbiome, BatchEffect Author: Siyuan Ma Maintainer: Siyuan MA SystemRequirements: glpk (>= 4.57) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMUPHin git_branch: devel git_last_commit: 2c6cd74 git_last_commit_date: 2026-01-31 Date/Publication: 2026-04-20 source.ver: src/contrib/MMUPHin_1.99.3.tar.gz vignettes: vignettes/MMUPHin/inst/doc/MMUPHin.html vignetteTitles: MMUPHin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MMUPHin/inst/doc/MMUPHin.R dependencyCount: 119 Package: mnem Version: 1.27.0 Depends: R (>= 4.1) Imports: cluster, graph, Rgraphviz, flexclust, lattice, naturalsort, snowfall, stats4, tsne, methods, graphics, stats, utils, Linnorm, data.table, Rcpp, RcppEigen, matrixStats, grDevices, e1071, ggplot2, wesanderson LinkingTo: Rcpp, RcppEigen Suggests: knitr, devtools, rmarkdown, BiocGenerics, RUnit, epiNEM, BiocStyle License: GPL-3 MD5sum: ef832e4696efa5b0dc2a64f0856793c6 NeedsCompilation: yes Title: Mixture Nested Effects Models Description: Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm. biocViews: Pathways, SystemsBiology, NetworkInference, Network, RNASeq, PooledScreens, SingleCell, CRISPR, ATACSeq, DNASeq, GeneExpression Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/mnem/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/mnem/issues git_url: https://git.bioconductor.org/packages/mnem git_branch: devel git_last_commit: 0fd323e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mnem_1.27.0.tar.gz vignettes: vignettes/mnem/inst/doc/mnem.html vignetteTitles: mnem hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mnem/inst/doc/mnem.R dependsOnMe: nempi importsMe: bnem, epiNEM dependencyCount: 78 Package: mobileRNA Version: 1.7.1 Depends: R (>= 4.3.0) Imports: dplyr, tidyr, ggplot2, BiocGenerics, DESeq2, edgeR, ggrepel, grDevices, pheatmap, utils, tidyselect, progress, RColorBrewer, GenomicRanges, rtracklayer, data.table, SimDesign, scales, IRanges, stats, methods, Biostrings, reticulate, S4Vectors, GenomeInfoDb, SummarizedExperiment, rlang, bioseq, grid Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: e6dc945f9dabb324ed7833c234736a9e NeedsCompilation: no Title: mobileRNA: Investigate the RNA mobilome & population-scale changes Description: Genomic analysis can be utilised to identify differences between RNA populations in two conditions, both in production and abundance. This includes the identification of RNAs produced by multiple genomes within a biological system. For example, RNA produced by pathogens within a host or mobile RNAs in plant graft systems. The mobileRNA package provides methods to pre-process, analyse and visualise the sRNA and mRNA populations based on the premise of mapping reads to all genotypes at the same time. biocViews: Visualization, RNASeq, Sequencing, SmallRNA, GenomeAssembly, Clustering, ExperimentalDesign, QualityControl, WorkflowStep, Alignment, Preprocessing Author: Katie Jeynes-Cupper [aut, cre] (ORCID: ), Marco Catoni [aut] (ORCID: ) Maintainer: Katie Jeynes-Cupper SystemRequirements: GNU make, ShortStack (>= 4.0), HTSeq, HISAT2, SAMtools, Conda VignetteBuilder: knitr BugReports: https://github.com/KJeynesCupper/mobileRNA/issues git_url: https://git.bioconductor.org/packages/mobileRNA git_branch: devel git_last_commit: b58cca2 git_last_commit_date: 2025-11-03 Date/Publication: 2026-04-20 source.ver: src/contrib/mobileRNA_1.7.1.tar.gz vignettes: vignettes/mobileRNA/inst/doc/mobileRNA.html vignetteTitles: mobileRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mobileRNA/inst/doc/mobileRNA.R dependencyCount: 151 Package: MODA Version: 1.37.0 Depends: R (>= 3.3) Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut, igraph, cluster, AMOUNTAIN, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 08fa28f9be682fb93e311a8737137b0a NeedsCompilation: no Title: MODA: MOdule Differential Analysis for weighted gene co-expression network Description: MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, James B. Brown, Luisa Orsini, Zhisong Pan, Guyu Hu and Shan He Maintainer: Dong Li git_url: https://git.bioconductor.org/packages/MODA git_branch: devel git_last_commit: 32634a3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MODA_1.37.0.tar.gz vignettes: vignettes/MODA/inst/doc/MODA.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 81 Package: ModCon Version: 1.19.0 Depends: data.table, parallel, utils, stats, R (>= 4.1) Suggests: testthat, knitr, rmarkdown, dplyr, shinycssloaders, shiny, shinyFiles, shinydashboard, shinyjs License: GPL-3 + file LICENSE MD5sum: bb65e0569150df13ebc4d2947249e7f1 NeedsCompilation: no Title: Modifying splice site usage by changing the mRNP code, while maintaining the genetic code Description: Collection of functions to calculate a nucleotide sequence surrounding for splice donors sites to either activate or repress donor usage. The proposed alternative nucleotide sequence encodes the same amino acid and could be applied e.g. in reporter systems to silence or activate cryptic splice donor sites. biocViews: FunctionalGenomics, AlternativeSplicing Author: Johannes Ptok [aut, cre] (ORCID: ) Maintainer: Johannes Ptok URL: https://github.com/caggtaagtat/ModCon SystemRequirements: Perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ModCon git_branch: devel git_last_commit: b159276 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ModCon_1.19.0.tar.gz vignettes: vignettes/ModCon/inst/doc/ModCon.html vignetteTitles: Designing SD context with ModCon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ModCon/inst/doc/ModCon.R dependencyCount: 5 Package: Modstrings Version: 1.27.2 Depends: R (>= 3.6), Biostrings (>= 2.79.3) Imports: methods, BiocGenerics, GenomicRanges, S4Vectors, IRanges, XVector, stringi, stringr, crayon, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: efa874319b8bd6a08cb42d502f028f68 NeedsCompilation: no Title: Working with modified nucleotide sequences Description: Representing nucleotide modifications in a nucleotide sequence is usually done via special characters from a number of sources. This represents a challenge to work with in R and the Biostrings package. The Modstrings package implements this functionallity for RNA and DNA sequences containing modified nucleotides by translating the character internally in order to work with the infrastructure of the Biostrings package. For this the ModRNAString and ModDNAString classes and derivates and functions to construct and modify these objects despite the encoding issues are implemenented. In addition the conversion from sequences to list like location information (and the reverse operation) is implemented as well. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Modstrings/issues git_url: https://git.bioconductor.org/packages/Modstrings git_branch: devel git_last_commit: f830d42 git_last_commit_date: 2025-12-24 Date/Publication: 2026-04-20 source.ver: src/contrib/Modstrings_1.27.2.tar.gz vignettes: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.html, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.html, vignettes/Modstrings/inst/doc/Modstrings.html vignetteTitles: Modstrings-DNA-alphabet, Modstrings-RNA-alphabet, Modstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.R, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.R, vignettes/Modstrings/inst/doc/Modstrings.R dependsOnMe: EpiTxDb, RNAmodR, tRNAdbImport importsMe: tRNA suggestsMe: EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3 dependencyCount: 24 Package: MOGAMUN Version: 1.21.0 Imports: stats, utils, RCy3, stringr, graphics, grDevices, RUnit, BiocParallel, igraph Suggests: knitr, markdown License: GPL-3 + file LICENSE MD5sum: 1560f4fe823aad0bd9af9a2ef5bbb6d4 NeedsCompilation: no Title: MOGAMUN: A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks Description: MOGAMUN is a multi-objective genetic algorithm that identifies active modules in a multiplex biological network. This allows analyzing different biological networks at the same time. MOGAMUN is based on NSGA-II (Non-Dominated Sorting Genetic Algorithm, version II), which we adapted to work on networks. biocViews: SystemsBiology, GraphAndNetwork, DifferentialExpression, BiomedicalInformatics, Transcriptomics, Clustering, Network Author: Elva-María Novoa-del-Toro [aut, cre] (ORCID: ) Maintainer: Elva-María Novoa-del-Toro URL: https://github.com/elvanov/MOGAMUN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MOGAMUN git_branch: devel git_last_commit: 9dca90c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MOGAMUN_1.21.0.tar.gz vignettes: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.html vignetteTitles: Finding active modules with MOGAMUN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.R dependencyCount: 68 Package: mogsa Version: 1.45.0 Depends: R (>= 3.4.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase, parallel, corpcor, svd, cluster, grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, org.Hs.eg.db License: GPL-2 MD5sum: db56312d9fbc09f21630ff394c256da6 NeedsCompilation: no Title: Multiple omics data integrative clustering and gene set analysis Description: This package provide a method for doing gene set analysis based on multiple omics data. biocViews: GeneExpression, PrincipalComponent, StatisticalMethod, Clustering, Software Author: Chen Meng Maintainer: Chen Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mogsa git_branch: devel git_last_commit: 51a848b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mogsa_1.45.0.tar.gz vignettes: vignettes/mogsa/inst/doc/moCluster-knitr.pdf, vignettes/mogsa/inst/doc/mogsa-knitr.pdf vignetteTitles: moCluster: Integrative clustering using multiple omics data, mogsa: gene set analysis on multiple omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mogsa/inst/doc/moCluster-knitr.R, vignettes/mogsa/inst/doc/mogsa-knitr.R dependencyCount: 70 Package: MoleculeExperiment Version: 1.11.0 Depends: R (>= 4.1.0) Imports: SpatialExperiment, Matrix, purrr, data.table, dplyr (>= 1.1.1), magrittr, rjson, utils, methods, terra, ggplot2, rlang, cli, EBImage, rhdf5, BiocParallel, S4Vectors, stats Suggests: knitr, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: b62c5fcf6eacfe0dabc40e2984f23504 NeedsCompilation: no Title: Prioritising a molecule-level storage of Spatial Transcriptomics Data Description: MoleculeExperiment contains functions to create and work with objects from the new MoleculeExperiment class. We introduce this class for analysing molecule-based spatial transcriptomics data (e.g., Xenium by 10X, Cosmx SMI by Nanostring, and Merscope by Vizgen). This allows researchers to analyse spatial transcriptomics data at the molecule level, and to have standardised data formats accross vendors. biocViews: DataImport, DataRepresentation, Infrastructure, Software, Spatial, Transcriptomics Author: Bárbara Zita Peters Couto [aut], Nicholas Robertson [aut], Ellis Patrick [aut], Shila Ghazanfar [aut, cre] Maintainer: Shila Ghazanfar URL: https://github.com/SydneyBioX/MoleculeExperiment VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/MoleculeExperiment/issues git_url: https://git.bioconductor.org/packages/MoleculeExperiment git_branch: devel git_last_commit: 35b0ff3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MoleculeExperiment_1.11.0.tar.gz vignettes: vignettes/MoleculeExperiment/inst/doc/MoleculeExperiment.html vignetteTitles: "Introduction to MoleculeExperiment" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MoleculeExperiment/inst/doc/MoleculeExperiment.R dependencyCount: 117 Package: MOMA Version: 1.23.0 Depends: R (>= 4.0) Imports: circlize, cluster, ComplexHeatmap, dplyr, ggplot2, graphics, grid, grDevices, magrittr, methods, MKmisc, MultiAssayExperiment, parallel, qvalue, RColorBrewer, readr, reshape2, rlang, stats, stringr, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, viper License: GPL-3 MD5sum: 32c3854c4c5e0697116eadc26ebd4659 NeedsCompilation: no Title: Multi Omic Master Regulator Analysis Description: This package implements the inference of candidate master regulator proteins from multi-omics' data (MOMA) algorithm, as well as ancillary analysis and visualization functions. biocViews: Software, NetworkEnrichment, NetworkInference, Network, FeatureExtraction, Clustering, FunctionalGenomics, Transcriptomics, SystemsBiology Author: Evan Paull [aut], Sunny Jones [aut, cre], Mariano Alvarez [aut] Maintainer: Sunny Jones VignetteBuilder: knitr BugReports: https://github.com/califano-lab/MOMA/issues git_url: https://git.bioconductor.org/packages/MOMA git_branch: devel git_last_commit: 099cb9e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MOMA_1.23.0.tar.gz vignettes: vignettes/MOMA/inst/doc/moma.html vignetteTitles: MOMA - Multi Omic Master Regulator Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOMA/inst/doc/moma.R dependencyCount: 91 Package: monaLisa Version: 1.17.1 Depends: R (>= 4.1) Imports: BiocGenerics, BiocParallel, Biostrings, BSgenome, circlize, ComplexHeatmap (>= 2.11.1), Seqinfo, GenomicRanges, cli, ggplot2 (>= 4.0.0), glmnet, grDevices, grid, IRanges, methods, rlang, RSQLite, stabs, stats, SummarizedExperiment, S4Vectors, TFBSTools, tidyr, tools, utils, XVector Suggests: BiocManager, BiocStyle, BSgenome.Mmusculus.UCSC.mm10, ggrepel, gridExtra, JASPAR2020, JASPAR2024, knitr, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL (>= 3) MD5sum: 69046971d1762e5e5741776731b0dd6f NeedsCompilation: no Title: Binned Motif Enrichment Analysis and Visualization Description: Useful functions to work with sequence motifs in the analysis of genomics data. These include methods to annotate genomic regions or sequences with predicted motif hits and to identify motifs that drive observed changes in accessibility or expression. Functions to produce informative visualizations of the obtained results are also provided. biocViews: MotifAnnotation, Visualization, FeatureExtraction, Epigenetics Author: Dania Machlab [aut] (ORCID: ), Lukas Burger [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Dany Mukesha [ctb] (ORCID: ), Michael Stadler [aut, cre] (ORCID: ) Maintainer: Michael Stadler URL: https://github.com/fmicompbio/monaLisa, https://bioconductor.org/packages/monaLisa/, https://fmicompbio.github.io/monaLisa/ VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/monaLisa/issues git_url: https://git.bioconductor.org/packages/monaLisa git_branch: devel git_last_commit: 7ce123c git_last_commit_date: 2026-01-27 Date/Publication: 2026-04-20 source.ver: src/contrib/monaLisa_1.17.1.tar.gz vignettes: vignettes/monaLisa/inst/doc/monaLisa.html, vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.html vignetteTitles: monaLisa - MOtif aNAlysis with Lisa, selecting_motifs_with_randLassoStabSel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monaLisa/inst/doc/monaLisa.R, vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.R dependencyCount: 118 Package: monocle Version: 2.39.0 Depends: R (>= 2.10.0), methods, Matrix (>= 1.2-6), Biobase, ggplot2 (>= 1.0.0), VGAM (>= 1.0-6), DDRTree (>= 0.1.4), Imports: parallel, igraph (>= 1.0.1), BiocGenerics, HSMMSingleCell (>= 0.101.5), plyr, cluster, combinat, fastICA, grid, irlba (>= 2.0.0), matrixStats, Rtsne, MASS, reshape2, leidenbase (>= 0.1.9), limma, tibble, dplyr, pheatmap, stringr, proxy, slam, viridis, stats, biocViews, RANN(>= 2.5), Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: destiny, Hmisc, knitr, Seurat, scater, testthat License: Artistic-2.0 MD5sum: 4a2f500b5469e46f22203a7b18a64d3f NeedsCompilation: yes Title: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq Description: Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: Cole Trapnell Maintainer: Cole Trapnell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/monocle git_branch: devel git_last_commit: 1349852 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/monocle_2.39.0.tar.gz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Cell counting,, differential expression,, and trajectory analysis for single-cell RNA-Seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R dependsOnMe: cicero importsMe: uSORT, scPOEM suggestsMe: ClusterGVis, sincell, Seurat dependencyCount: 74 Package: MoonlightR Version: 1.37.0 Depends: R (>= 3.5), doParallel, foreach Imports: parmigene, randomForest, SummarizedExperiment, gplots, circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase, limma, grDevices, graphics, TCGAbiolinks, GEOquery, stats, RISmed, grid, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, png, edgeR License: GPL (>= 3) MD5sum: 414d1a755d60de657cb21799ae882891 NeedsCompilation: no Title: Identify oncogenes and tumor suppressor genes from omics data Description: Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Antonio Colaprico [aut], Catharina Olsen [aut], Matthew H. Bailey [aut], Gabriel J. Odom [aut], Thilde Terkelsen [aut], Mona Nourbakhsh [aut], Astrid Saksager [aut], Tiago C. Silva [aut], André V. Olsen [aut], Laura Cantini [aut], Andrei Zinovyev [aut], Emmanuel Barillot [aut], Houtan Noushmehr [aut], Gloria Bertoli [aut], Isabella Castiglioni [aut], Claudia Cava [aut], Gianluca Bontempi [aut], Xi Steven Chen [aut], Elena Papaleo [aut], Matteo Tiberti [cre, aut] Maintainer: Matteo Tiberti URL: https://github.com/ELELAB/MoonlightR VignetteBuilder: knitr BugReports: https://github.com/ELELAB/MoonlightR/issues git_url: https://git.bioconductor.org/packages/MoonlightR git_branch: devel git_last_commit: 9303ed6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MoonlightR_1.37.0.tar.gz vignettes: vignettes/MoonlightR/inst/doc/Moonlight.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoonlightR/inst/doc/Moonlight.R dependencyCount: 184 Package: MOSClip Version: 1.5.0 Depends: R (>= 4.4.0) Imports: MultiAssayExperiment, methods, survminer, graph, graphite, AnnotationDbi, checkmate, ggplot2, gridExtra, igraph, pheatmap, survival, RColorBrewer, SuperExactTest, reshape, NbClust, S4Vectors, grDevices, graphics, stats, utils, ComplexHeatmap, FactoMineR, circlize, corpcor, coxrobust, elasticnet, gRbase, ggplotify, qpgraph, org.Hs.eg.db, Matrix Suggests: RUnit, BiocGenerics, MASS, BiocStyle, knitr, EDASeq, rmarkdown, kableExtra, testthat (>= 3.0.0) License: AGPL-3 MD5sum: c3a87e7f017413b17975566dc5be70b3 NeedsCompilation: no Title: Multi Omics Survival Clip Description: Topological pathway analysis tool able to integrate multi-omics data. It finds survival-associated modules or significant modules for two-class analysis. This tool have two main methods: pathway tests and module tests. The latter method allows the user to dig inside the pathways itself. biocViews: Software, StatisticalMethod, GraphAndNetwork, Survival, Regression, DimensionReduction, Pathways, Reactome Author: Paolo Martini [aut, cre] (ORCID: ), Anna Bortolato [aut] (ORCID: ), Anna Tanada [aut] (ORCID: ), Enrica Calura [aut] (ORCID: ), Stefania Pirrotta [aut] (ORCID: ), Federico Agostinis [aut] Maintainer: Paolo Martini URL: https://github.com/CaluraLab/MOSClip/ VignetteBuilder: knitr BugReports: https://github.com/CaluraLab/MOSClip/issues git_url: https://git.bioconductor.org/packages/MOSClip git_branch: devel git_last_commit: feb3c8b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MOSClip_1.5.0.tar.gz vignettes: vignettes/MOSClip/inst/doc/mosclip_vignette.html vignetteTitles: MOSClip hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSClip/inst/doc/mosclip_vignette.R dependencyCount: 219 Package: mosdef Version: 1.7.0 Depends: R (>= 4.4.0) Imports: DT, ggplot2, ggforce, ggrepel, graphics, grDevices, htmltools, methods, AnnotationDbi, topGO, GO.db, clusterProfiler, goseq, utils, RColorBrewer, rlang, DESeq2, scales, SummarizedExperiment, S4Vectors, stats Suggests: knitr, rmarkdown, macrophage, org.Hs.eg.db, GeneTonic, testthat (>= 3.0.0), TxDb.Hsapiens.UCSC.hg38.knownGene, BiocStyle License: MIT + file LICENSE MD5sum: a8926b5000130bee307356b73e93c241 NeedsCompilation: no Title: MOSt frequently used and useful Differential Expression Functions Description: This package provides functionality to run a number of tasks in the differential expression analysis workflow. This encompasses the most widely used steps, from running various enrichment analysis tools with a unified interface to creating plots and beautifying table components linking to external websites and databases. This streamlines the generation of comprehensive analysis reports. biocViews: GeneExpression, Software, Transcription, Transcriptomics, DifferentialExpression, Visualization, ReportWriting, GeneSetEnrichment, GO Author: Leon Dammer [aut] (ORCID: ), Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/imbeimainz/mosdef VignetteBuilder: knitr BugReports: https://github.com/imbeimainz/mosdef/issues git_url: https://git.bioconductor.org/packages/mosdef git_branch: devel git_last_commit: 06b8376 git_last_commit_date: 2025-12-01 Date/Publication: 2026-04-20 source.ver: src/contrib/mosdef_1.7.0.tar.gz vignettes: vignettes/mosdef/inst/doc/mosdef_userguide.html vignetteTitles: The mosdef User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mosdef/inst/doc/mosdef_userguide.R importsMe: GeneTonic, ideal, pcaExplorer suggestsMe: DeeDeeExperiment, GeDi dependencyCount: 183 Package: MOSim Version: 2.7.0 Depends: R (>= 4.2.0) Imports: HiddenMarkov, zoo, IRanges, S4Vectors, dplyr, ggplot2, lazyeval, matrixStats, methods, rlang, stringi, stringr, scran, Seurat, Signac, edgeR, Rcpp LinkingTo: cpp11, Rcpp Suggests: testthat, knitr, rmarkdown, codetools, BiocStyle, stats, utils, purrr, scales, tibble, tidyr, Biobase, scater, SingleCellExperiment, decor, markdown, Rsamtools, igraph, leiden, bluster License: GPL-3 MD5sum: e19d8ce511a09a09e1e477e9850eb92a NeedsCompilation: yes Title: Multi-Omics Simulation (MOSim) Description: MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups. biocViews: Software, TimeCourse, ExperimentalDesign, RNASeq Author: Carolina Monzó [aut], Carlos Martínez [aut], Sonia Tarazona [cre, aut] Maintainer: Sonia Tarazona URL: https://github.com/ConesaLab/MOSim VignetteBuilder: knitr BugReports: https://github.com/ConesaLab/MOSim/issues git_url: https://git.bioconductor.org/packages/MOSim git_branch: devel git_last_commit: ce3195c git_last_commit_date: 2026-02-11 Date/Publication: 2026-04-20 source.ver: src/contrib/MOSim_2.7.0.tar.gz vignettes: vignettes/MOSim/inst/doc/MOSim.html, vignettes/MOSim/inst/doc/scMOSim.html vignetteTitles: Wiki of how to use mosim, Wiki of how to use sc_mosim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSim/inst/doc/MOSim.R, vignettes/MOSim/inst/doc/scMOSim.R dependencyCount: 196 Package: Motif2Site Version: 1.15.0 Depends: R (>= 4.1) Imports: S4Vectors, stats, utils, methods, grDevices, graphics, BiocGenerics, BSgenome, GenomeInfoDb, MASS, IRanges, GenomicRanges, Biostrings, GenomicAlignments, edgeR, mixtools Suggests: BiocStyle, rmarkdown, knitr, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Ecoli.NCBI.20080805 License: GPL-2 MD5sum: d1b0c81315b0a7bc810ff76327412d41 NeedsCompilation: no Title: Detect binding sites from motifs and ChIP-seq experiments, and compare binding sites across conditions Description: Detect binding sites using motifs IUPAC sequence or bed coordinates and ChIP-seq experiments in bed or bam format. Combine/compare binding sites across experiments, tissues, or conditions. All normalization and differential steps are done using TMM-GLM method. Signal decomposition is done by setting motifs as the centers of the mixture of normal distribution curves. biocViews: Software, Sequencing, ChIPSeq, DifferentialPeakCalling, Epigenetics, SequenceMatching Author: Peyman Zarrineh [cre, aut] (ORCID: ) Maintainer: Peyman Zarrineh VignetteBuilder: knitr BugReports: https://github.com/fls-bioinformatics-core/Motif2Site/issues git_url: https://git.bioconductor.org/packages/Motif2Site git_branch: devel git_last_commit: c1fd6d7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Motif2Site_1.15.0.tar.gz vignettes: vignettes/Motif2Site/inst/doc/Motif2Site.html vignetteTitles: Motif2Site hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Motif2Site/inst/doc/Motif2Site.R dependencyCount: 124 Package: MotifDb Version: 1.53.0 Depends: R (>= 3.5.0), methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings Imports: rtracklayer, splitstackshape Suggests: RUnit, seqLogo, BiocStyle, knitr, rmarkdown, formatR, markdown License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: 0081903c3fe6e73fdf944ed441bf5345 NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 9900 annotated position frequency matrices from 14 public sources, for multiple organisms. biocViews: MotifAnnotation Author: Paul Shannon, Matt Richards Maintainer: Paul Shannon VignetteBuilder: knitr, rmarkdown, formatR, markdown git_url: https://git.bioconductor.org/packages/MotifDb git_branch: devel git_last_commit: 69a2754 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MotifDb_1.53.0.tar.gz vignettes: vignettes/MotifDb/inst/doc/MotifDb.html vignetteTitles: "A collection of PWMs" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R dependsOnMe: generegulation importsMe: rTRMui, TENET suggestsMe: ATACseqQC, DiffLogo, enhancerHomologSearch, igvR, memes, MMDiff2, motifcounter, motifStack, motifTestR, profileScoreDist, PWMEnrich, rTRM, TFutils, universalmotif, vtpnet dependencyCount: 59 Package: motifmatchr Version: 1.33.0 Depends: R (>= 3.3) Imports: Matrix, Rcpp, methods, TFBSTools, Biostrings, BSgenome, S4Vectors, SummarizedExperiment, GenomicRanges, IRanges, Rsamtools, Seqinfo LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 + file LICENSE MD5sum: 7b66e29f258f2b10d83649b39d7006b7 NeedsCompilation: yes Title: Fast Motif Matching in R Description: Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. biocViews: MotifAnnotation Author: Alicia Schep [aut, cre], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifmatchr git_branch: devel git_last_commit: c2e55a2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/motifmatchr_1.33.0.tar.gz vignettes: vignettes/motifmatchr/inst/doc/motifmatchr.html vignetteTitles: motifmatchr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/motifmatchr/inst/doc/motifmatchr.R importsMe: ATACseqTFEA, enhancerHomologSearch, epiregulon, esATAC, pageRank suggestsMe: GRaNIE, MethReg, CAGEWorkflow, Signac dependencyCount: 81 Package: MotifPeeker Version: 1.3.3 Depends: R (>= 4.6.0) Imports: BiocFileCache, BiocParallel, DT, ggplot2, plotly, universalmotif, GenomicRanges, IRanges, rtracklayer, tools, htmltools, rmarkdown, viridis, SummarizedExperiment, htmlwidgets, Rsamtools, GenomicAlignments, Seqinfo, Biostrings, BSgenome, memes, S4Vectors, dplyr, purrr, tidyr, heatmaply, stats, utils Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm39, downloadthis, knitr, markdown, methods, remotes, rworkflows, testthat (>= 3.0.0), withr, emoji, curl, jsonlite License: GPL (>= 3) MD5sum: 0fb96fb2ad2ec42616117905904e1c7a NeedsCompilation: no Title: Benchmarking Epigenomic Profiling Methods Using Motif Enrichment Description: MotifPeeker is used to compare and analyse datasets from epigenomic profiling methods with motif enrichment as the key benchmark. The package outputs an HTML report consisting of three sections: (1. General Metrics) Overview of peaks-related general metrics for the datasets (FRiP scores, peak widths and motif-summit distances). (2. Known Motif Enrichment Analysis) Statistics for the frequency of user-provided motifs enriched in the datasets. (3. Motif Discovery Enrichment Analysis) Statistics for the frequency of ab-initio discovered motifs enriched in the datasets and compared with known motifs. biocViews: Epigenetics, Genetics, QualityControl, ChIPSeq, MultipleComparison, FunctionalGenomics, MotifDiscovery, SequenceMatching, Software, Alignment Author: Hiranyamaya Dash [cre, aut] (ORCID: ), Thomas Roberts [aut] (ORCID: ), Maria Weinert [aut] (ORCID: ), Nathan Skene [aut] (ORCID: ) Maintainer: Hiranyamaya Dash URL: https://github.com/neurogenomics/MotifPeeker SystemRequirements: MEME Suite (v5.3.3 or above) VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/MotifPeeker/issues git_url: https://git.bioconductor.org/packages/MotifPeeker git_branch: devel git_last_commit: 5d5ee22 git_last_commit_date: 2026-04-20 Date/Publication: 2026-04-20 source.ver: src/contrib/MotifPeeker_1.3.3.tar.gz vignettes: vignettes/MotifPeeker/inst/doc/MotifPeeker.html, vignettes/MotifPeeker/inst/doc/troubleshooting.html vignetteTitles: MotifPeeker, troubleshooting hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/MotifPeeker/inst/doc/MotifPeeker.R, vignettes/MotifPeeker/inst/doc/troubleshooting.R dependencyCount: 184 Package: motifStack Version: 1.55.0 Depends: R (>= 2.15.1), methods, grid Imports: ade4, Biostrings, ggplot2, grDevices, graphics, htmlwidgets, stats, stats4, utils, XML, TFBSTools Suggests: Cairo, grImport, grImport2, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr, RUnit, rmarkdown, JASPAR2020 License: GPL (>= 2) MD5sum: a9fb4eb405c99c69f2cb24604be34060 NeedsCompilation: no Title: Plot stacked logos for single or multiple DNA, RNA and amino acid sequence Description: The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motif and amino acid sequence motif. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors. biocViews: SequenceMatching, Visualization, Sequencing, Microarray, Alignment, ChIPchip, ChIPSeq, MotifAnnotation, DataImport Author: Jianhong Ou, Michael Brodsky, Scot Wolfe and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifStack git_branch: devel git_last_commit: 27317e0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/motifStack_1.55.0.tar.gz vignettes: vignettes/motifStack/inst/doc/motifStack_HTML.html vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R dependsOnMe: generegulation importsMe: ATACseqQC, atSNP, dagLogo, ribosomeProfilingQC suggestsMe: ChIPpeakAnno, TFutils, trackViewer, tripr, universalmotif dependencyCount: 111 Package: motifTestR Version: 1.7.0 Depends: Biostrings, GenomicRanges, ggplot2 (>= 4.0.0), R (>= 4.5.0), Imports: Seqinfo, graphics, harmonicmeanp, IRanges, matrixStats, methods, parallel, patchwork, rlang, S4Vectors, stats, universalmotif, Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, extraChIPs (>= 1.13.3), ggdendro, knitr, MASS, MotifDb, rmarkdown, rtracklayer, SimpleUpset, testthat (>= 3.0.0), VGAM License: GPL-3 MD5sum: 856c994a932287b43ec28bffa2d9b119 NeedsCompilation: no Title: Perform key tests for binding motifs in sequence data Description: Taking a set of sequence motifs as PWMs, test a set of sequences for over-representation of these motifs, as well as any positional features within the set of motifs. Enrichment analysis can be undertaken using multiple statistical approaches. The package also contains core functions to prepare data for analysis, and to visualise results. biocViews: MotifAnnotation, ChIPSeq, ChipOnChip, SequenceMatching, Software Author: Stevie Pederson [aut, cre] (ORCID: ) Maintainer: Stevie Pederson URL: https://github.com/smped/motifTestR VignetteBuilder: knitr BugReports: https://github.com/smped/motifTestR/issues git_url: https://git.bioconductor.org/packages/motifTestR git_branch: devel git_last_commit: 26a47a7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/motifTestR_1.7.0.tar.gz vignettes: vignettes/motifTestR/inst/doc/motifAnalysis.html vignetteTitles: Motif Analysis Using motifTestR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifTestR/inst/doc/motifAnalysis.R dependencyCount: 45 Package: MouseFM Version: 1.21.1 Depends: R (>= 4.0.0) Imports: httr, curl, GenomicRanges, dplyr, ggplot2, reshape2, scales, gtools, tidyr, data.table, jsonlite, rlist, Seqinfo, methods, biomaRt, stats, IRanges Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: b10ad7cdad3acbb3e61ef98bedc1f4ad NeedsCompilation: no Title: In-silico methods for genetic finemapping in inbred mice Description: This package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%). biocViews: Genetics, SNP, GeneTarget, VariantAnnotation, GenomicVariation, MultipleComparison, SystemsBiology, MathematicalBiology, PatternLogic, GenePrediction, BiomedicalInformatics, FunctionalGenomics Author: Matthias Munz [aut, cre] (ORCID: ), Inken Wohlers [aut] (ORCID: ), Hauke Busch [aut] (ORCID: ) Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/MouseFM/issues git_url: https://git.bioconductor.org/packages/MouseFM git_branch: devel git_last_commit: 6cd9eac git_last_commit_date: 2026-03-22 Date/Publication: 2026-04-20 source.ver: src/contrib/MouseFM_1.21.1.tar.gz vignettes: vignettes/MouseFM/inst/doc/fetch.html, vignettes/MouseFM/inst/doc/finemap.html, vignettes/MouseFM/inst/doc/prio.html vignetteTitles: Fetch, Finemapping, Prioritization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MouseFM/inst/doc/fetch.R, vignettes/MouseFM/inst/doc/finemap.R, vignettes/MouseFM/inst/doc/prio.R dependencyCount: 82 Package: MPAC Version: 1.5.0 Depends: R (>= 4.4.0) Imports: data.table (>= 1.14.2), SummarizedExperiment (>= 1.30.2), BiocParallel (>= 1.28.3), fitdistrplus (>= 1.1), igraph (>= 1.4.3), BiocSingular (>= 1.10.0), S4Vectors (>= 0.32.3), SingleCellExperiment (>= 1.16.0), bluster (>= 1.4.0), fgsea (>= 1.20.0), scran (>= 1.22.1), ComplexHeatmap (>= 2.16.0), circlize (>= 0.4.16), scales (>= 1.3.0), stringr (>= 1.5.1), viridis (>= 0.6.5), ggplot2 (>= 3.5.1), ggraph (>= 2.2.1), survival (>= 3.7), survminer (>= 0.4.9), grid, stats Suggests: rmarkdown, knitr, svglite, bookdown(>= 0.34), testthat (>= 3.0.0) License: GPL-3 MD5sum: 21f19f9668f9de1ca034d73893322fb4 NeedsCompilation: no Title: Multi-omic Pathway Analysis of Cells Description: Multi-omic Pathway Analysis of Cells (MPAC), integrates multi-omic data for understanding cellular mechanisms. It predicts novel patient groups with distinct pathway profiles as well as identifying key pathway proteins with potential clinical associations. From CNA and RNA-seq data, it determines genes’ DNA and RNA states (i.e., repressed, normal, or activated), which serve as the input for PARADIGM to calculate Inferred Pathway Levels (IPLs). It also permutes DNA and RNA states to create a background distribution to filter IPLs as a way to remove events observed by chance. It provides multiple methods for downstream analysis and visualization. biocViews: Software, Technology, Sequencing, RNASeq, Survival, Clustering, ImmunoOncology Author: Peng Liu [aut, cre] (ORCID: ), Paul Ahlquist [aut], Irene Ong [aut], Anthony Gitter [aut] Maintainer: Peng Liu URL: https://github.com/pliu55/MPAC VignetteBuilder: knitr BugReports: https://github.com/pliu55/MPAC/issues git_url: https://git.bioconductor.org/packages/MPAC git_branch: devel git_last_commit: f287ddc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MPAC_1.5.0.tar.gz vignettes: vignettes/MPAC/inst/doc/MPAC.html vignetteTitles: MPAC: Multi-omic Pathway Analysis of Cells hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPAC/inst/doc/MPAC.R dependencyCount: 171 Package: MPFE Version: 1.47.0 License: GPL (>= 3) MD5sum: 1e8b649cc2af35be4dfa73a5a6d7a3b2 NeedsCompilation: no Title: Estimation of the amplicon methylation pattern distribution from bisulphite sequencing data Description: Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and read error rate. biocViews: HighThroughputSequencingData, DNAMethylation, MethylSeq Author: Peijie Lin, Sylvain Foret, Conrad Burden Maintainer: Conrad Burden git_url: https://git.bioconductor.org/packages/MPFE git_branch: devel git_last_commit: 8e3d15d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MPFE_1.47.0.tar.gz vignettes: vignettes/MPFE/inst/doc/MPFE.pdf vignetteTitles: MPFE hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPFE/inst/doc/MPFE.R dependencyCount: 0 Package: mpra Version: 1.33.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, limma Imports: S4Vectors, scales, stats, graphics, statmod Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: c17d52166a3bfce96d68e21b823d6ec3 NeedsCompilation: no Title: Analyze massively parallel reporter assays Description: Tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA). biocViews: Software, GeneRegulation, Sequencing, FunctionalGenomics Author: Leslie Myint [cre, aut], Kasper D. Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/mpra VignetteBuilder: knitr BugReports: https://github.com/hansenlab/mpra/issues git_url: https://git.bioconductor.org/packages/mpra git_branch: devel git_last_commit: df59fd8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mpra_1.33.0.tar.gz vignettes: vignettes/mpra/inst/doc/mpra.html vignetteTitles: mpra User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mpra/inst/doc/mpra.R dependencyCount: 37 Package: MPRAnalyze Version: 1.29.0 Imports: BiocParallel, methods, progress, stats, SummarizedExperiment Suggests: knitr License: GPL-3 MD5sum: f1e36c134855158b3ac4e0443c8126a8 NeedsCompilation: no Title: Statistical Analysis of MPRA data Description: MPRAnalyze provides statistical framework for the analysis of data generated by Massively Parallel Reporter Assays (MPRAs), used to directly measure enhancer activity. MPRAnalyze can be used for quantification of enhancer activity, classification of active enhancers and comparative analyses of enhancer activity between conditions. MPRAnalyze construct a nested pair of generalized linear models (GLMs) to relate the DNA and RNA observations, easily adjustable to various experimental designs and conditions, and provides a set of rigorous statistical testig schemes. biocViews: ImmunoOncology, Software, StatisticalMethod, Sequencing, GeneExpression, CellBiology, CellBasedAssays, DifferentialExpression, ExperimentalDesign, Classification Author: Tal Ashuach [aut, cre], David S Fischer [aut], Anat Kriemer [ctb], Fabian J Theis [ctb], Nir Yosef [ctb], Maintainer: Tal Ashuach URL: https://github.com/YosefLab/MPRAnalyze VignetteBuilder: knitr BugReports: https://github.com/YosefLab/MPRAnalyze git_url: https://git.bioconductor.org/packages/MPRAnalyze git_branch: devel git_last_commit: e893d06 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MPRAnalyze_1.29.0.tar.gz vignettes: vignettes/MPRAnalyze/inst/doc/vignette.html vignetteTitles: Analyzing MPRA data with MPRAnalyze hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPRAnalyze/inst/doc/vignette.R dependencyCount: 46 Package: msa Version: 1.43.1 Depends: R (>= 3.3.0), methods, Biostrings (>= 2.40.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr, seqinr, ape (>= 5.1), phangorn, pwalign License: GPL (>= 2) MD5sum: 23b00dc7945285f10b8fe2fd42e43614 NeedsCompilation: yes Title: Multiple Sequence Alignment Description: The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade. biocViews: MultipleSequenceAlignment, Alignment, MultipleComparison, Sequencing Author: Enrico Bonatesta [aut], Christoph Kainrath [aut], Ulrich Bodenhofer [aut, cre, ths] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/msa SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/msa git_branch: devel git_last_commit: 5fe136a git_last_commit_date: 2025-11-11 Date/Publication: 2026-04-20 source.ver: src/contrib/msa_1.43.1.tar.gz vignettes: vignettes/msa/inst/doc/msa.pdf vignetteTitles: msa - An R Package for Multiple Sequence Alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msa/inst/doc/msa.R importsMe: ClustIRR, DspikeIn, LymphoSeq, odseq, rhinotypeR, surfaltr suggestsMe: idpr, AntibodyForests, bio3d, BOLDconnectR, SVAlignR dependencyCount: 16 Package: MSA2dist Version: 1.15.0 Depends: R (>= 4.4.0) Imports: Rcpp, Biostrings, GenomicRanges, IRanges, ape, doParallel, dplyr, foreach, methods, parallel, pwalign, rlang, seqinr, stats, stringi, stringr, tibble, tidyr, utils LinkingTo: Rcpp, RcppThread Suggests: rmarkdown, knitr, devtools, testthat, ggplot2, BiocStyle License: GPL-3 + file LICENSE MD5sum: a1230a614ad2190f588921a86a550599 NeedsCompilation: yes Title: MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis Description: MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis. It uses scoring matrices to be used in these pairwise distance calculations which can be adapted to any scoring for DNA or AA characters. E.g. by using literal distances MSA2dist calculates pairwise IUPAC distances. DNAStringSet alignments can be analysed as codon alignments to look for synonymous and nonsynonymous substitutions (dN/dS) in a parallelised fashion using a variety of substitution models. Non-aligned coding sequences can be directly used to construct pairwise codon alignments (global/local) and calculate dN/dS without any external dependencies. biocViews: Alignment, Sequencing, Genetics, GO Author: Kristian K Ullrich [aut, cre] (ORCID: ) Maintainer: Kristian K Ullrich URL: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist, https://mpievolbio-it.pages.gwdg.de/MSA2dist/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist/issues git_url: https://git.bioconductor.org/packages/MSA2dist git_branch: devel git_last_commit: 662eead git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MSA2dist_1.15.0.tar.gz vignettes: vignettes/MSA2dist/inst/doc/MSA2dist.html vignetteTitles: MSA2dist Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MSA2dist/inst/doc/MSA2dist.R importsMe: doubletrouble, rhinotypeR dependencyCount: 55 Package: MsBackendMassbank Version: 1.19.3 Depends: R (>= 4.0), Spectra (>= 1.21.5) Imports: BiocParallel, S4Vectors, IRanges, methods, ProtGenerics (>= 1.35.3), MsCoreUtils, DBI, utils Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), RSQLite, rmarkdown License: Artistic-2.0 MD5sum: 612c876c3519013385da68c557f226d4 NeedsCompilation: no Title: Mass Spectrometry Data Backend for MassBank record Files Description: Mass spectrometry (MS) data backend supporting import and export of MS/MS library spectra from MassBank record files. Different backends are available that allow handling of data in plain MassBank text file format or allow also to interact directly with MassBank SQL databases. Objects from this package are supposed to be used with the Spectra Bioconductor package. This package thus adds MassBank support to the Spectra package. biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport Author: RforMassSpectrometry Package Maintainer [cre], Michael Witting [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Michael Stravs [ctb] Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsBackendMassbank VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMassbank/issues git_url: https://git.bioconductor.org/packages/MsBackendMassbank git_branch: devel git_last_commit: b93ed94 git_last_commit_date: 2026-03-18 Date/Publication: 2026-04-20 source.ver: src/contrib/MsBackendMassbank_1.19.3.tar.gz vignettes: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.html vignetteTitles: Description and Usage of MsBackendMassbank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.R dependencyCount: 32 Package: MsBackendMetaboLights Version: 1.5.3 Depends: R (>= 4.2.0), Spectra (>= 1.15.12) Imports: curl, ProtGenerics, BiocFileCache, S4Vectors, methods, progress, utils, MsCoreUtils (>= 1.23.8) Suggests: testthat, rmarkdown, mzR, knitr, BiocStyle License: Artistic-2.0 MD5sum: 89d01b96f0445ac57f03ac4a4b641b4a NeedsCompilation: no Title: Retrieve Mass Spectrometry Data from MetaboLights Description: MetaboLights is one of the main public repositories for storage of metabolomics experiments, which includes analysis results as well as raw data. The MsBackendMetaboLights package provides functionality to retrieve and represent mass spectrometry (MS) data from MetaboLights. Data files are downloaded and cached locally avoiding repetitive downloads. MS data from metabolomics experiments can thus be directly and seamlessly integrated into R-based analysis workflows with the Spectra and MsBackendMetaboLights package. biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport, Proteomics Author: Johannes Rainer [aut, cre] (ORCID: ), Philippine Louail [aut] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsBackendMetaboLights VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMetaboLights/issues git_url: https://git.bioconductor.org/packages/MsBackendMetaboLights git_branch: devel git_last_commit: 8d83a33 git_last_commit_date: 2026-04-13 Date/Publication: 2026-04-20 source.ver: src/contrib/MsBackendMetaboLights_1.5.3.tar.gz vignettes: vignettes/MsBackendMetaboLights/inst/doc/MsBackendMetaboLights.html vignetteTitles: Retrieve and Use Mass Spectrometry Data from MetaboLights hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMetaboLights/inst/doc/MsBackendMetaboLights.R suggestsMe: Chromatograms dependencyCount: 70 Package: MsBackendMgf Version: 1.19.1 Depends: R (>= 4.0), Spectra (>= 1.5.14) Imports: ProtGenerics (>= 1.35.3), BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats, data.table Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 MD5sum: 18d67f57c6e55ffd2caa7c15cd9925ab NeedsCompilation: no Title: Mass Spectrometry Data Backend for Mascot Generic Format (mgf) Files Description: Mass spectrometry (MS) data backend supporting import and export of MS/MS spectra data from Mascot Generic Format (mgf) files. Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, DataImport Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Michael Witting [ctb] (ORCID: ), Adriano Rutz [ctb] (ORCID: ), Corey Broeckling [ctb] (ORCID: ) Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsBackendMgf VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMgf/issues git_url: https://git.bioconductor.org/packages/MsBackendMgf git_branch: devel git_last_commit: 213d796 git_last_commit_date: 2026-03-03 Date/Publication: 2026-04-20 source.ver: src/contrib/MsBackendMgf_1.19.1.tar.gz vignettes: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.html vignetteTitles: Description and usage of MsBackendMgf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.R suggestsMe: CompoundDb, MsBackendRawFileReader, SpectriPy, xcms dependencyCount: 31 Package: MsBackendMsp Version: 1.15.1 Depends: R (>= 4.1.0), Spectra (>= 1.5.14) Imports: ProtGenerics (>= 1.35.3), BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats, data.table Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 MD5sum: 899da939da2d6881824439801447ae5f NeedsCompilation: no Title: Mass Spectrometry Data Backend for NIST msp Files Description: Mass spectrometry (MS) data backend supporting import and handling of MS/MS spectra from NIST MSP Format (msp) files. Import of data from files with different MSP *flavours* is supported. Objects from this package add support for MSP files to Bioconductor's Spectra package. This package is thus not supposed to be used without the Spectra package that provides a complete infrastructure for MS data handling. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, DataImport Author: Neumann Steffen [aut] (ORCID: ), Johannes Rainer [aut, cre] (ORCID: ), Michael Witting [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsBackendMsp VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMsp/issues git_url: https://git.bioconductor.org/packages/MsBackendMsp git_branch: devel git_last_commit: 506e13c git_last_commit_date: 2026-03-03 Date/Publication: 2026-04-20 source.ver: src/contrib/MsBackendMsp_1.15.1.tar.gz vignettes: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.html vignetteTitles: MsBackendMsp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.R importsMe: lcmsPlot dependencyCount: 31 Package: MsBackendRawFileReader Version: 1.17.0 Depends: R (>= 4.1), methods, Spectra (>= 1.15.10) Imports: ProtGenerics (>= 1.35.3), MsCoreUtils, S4Vectors, IRanges, rawrr (>= 1.17.2), utils, BiocParallel Suggests: BiocStyle (>= 2.5), ExperimentHub, MsBackendMgf, knitr, lattice, mzR, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 MD5sum: 9824f47b62ee8dba32b87b351cd3c9b2 NeedsCompilation: yes Title: Mass Spectrometry Backend for Reading Thermo Fisher Scientific raw Files Description: implements a MsBackend for the Spectra package using Thermo Fisher Scientific's NewRawFileReader .Net libraries. The package is generalizing the functionality introduced by the rawrr package Methods defined in this package are supposed to extend the Spectra Bioconductor package. biocViews: MassSpectrometry, Proteomics, Metabolomics Author: Christian Panse [aut, cre] (ORCID: ), Tobias Kockmann [aut] (ORCID: ), Roger Gine Bertomeu [ctb] (ORCID: ) Maintainer: Christian Panse URL: https://github.com/fgcz/MsBackendRawFileReader SystemRequirements: mono-runtime 4.x or higher (including System.Data library) on Linux/macOS, .Net Framework (>= 4.5.1) on Microsoft Windows. VignetteBuilder: knitr BugReports: https://github.com/fgcz/MsBackendRawFileReader/issues git_url: https://git.bioconductor.org/packages/MsBackendRawFileReader git_branch: devel git_last_commit: c509d56 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MsBackendRawFileReader_1.17.0.tar.gz vignettes: vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.html vignetteTitles: On Using and Extending the `MsBackendRawFileReader` Backend. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.R dependencyCount: 32 Package: MsBackendSql Version: 1.11.3 Depends: R (>= 4.2.0), Spectra (>= 1.19.8) Imports: BiocParallel, S4Vectors, methods, ProtGenerics (>= 1.35.3), DBI, MsCoreUtils, IRanges, data.table, progress, stringi, fastmatch, BiocGenerics Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), RSQLite, MsDataHub, rmarkdown, microbenchmark, mzR License: Artistic-2.0 MD5sum: 7d9aec25f1daf29aa81d5be45a04d6e6 NeedsCompilation: no Title: SQL-based Mass Spectrometry Data Backend Description: SQL-based mass spectrometry (MS) data backend supporting also storange and handling of very large data sets. Objects from this package are supposed to be used with the Spectra Bioconductor package. Through the MsBackendSql with its minimal memory footprint, this package thus provides an alternative MS data representation for very large or remote MS data sets. biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport, Proteomics Author: Johannes Rainer [aut, cre] (ORCID: ), Chong Tang [ctb], Laurent Gatto [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsBackendSql VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendSql/issues git_url: https://git.bioconductor.org/packages/MsBackendSql git_branch: devel git_last_commit: 9b5f2fe git_last_commit_date: 2026-02-05 Date/Publication: 2026-04-20 source.ver: src/contrib/MsBackendSql_1.11.3.tar.gz vignettes: vignettes/MsBackendSql/inst/doc/MsBackendSql.html vignetteTitles: Storing Mass Spectrometry Data in SQL Databases hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendSql/inst/doc/MsBackendSql.R suggestsMe: MsExperiment dependencyCount: 45 Package: MsCoreUtils Version: 1.23.9 Depends: R (>= 3.6.0) Imports: methods, S4Vectors, MASS, stats, clue LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, roxygen2, imputeLCMD, impute, norm, pcaMethods, vsn, Matrix, preprocessCore, missForest, rlang Enhances: HDF5Array License: Artistic-2.0 MD5sum: db59a1c726374d229cc3da6ad55df65e NeedsCompilation: yes Title: Core Utils for Mass Spectrometry Data Description: MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...), misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Adriano Rutz [aut] (ORCID: ), Adriaan Sticker [ctb], Sigurdur Smarason [ctb], Thomas Naake [ctb], Josep Maria Badia Aparicio [ctb] (ORCID: ), Michael Witting [ctb] (ORCID: ), Samuel Wieczorek [ctb], Roger Gine Bertomeu [ctb] (ORCID: ), Mar Garcia-Aloy [ctb] (ORCID: ), Gabriele Tomè [ctb] (ORCID: ) Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsCoreUtils VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsCoreUtils/issues git_url: https://git.bioconductor.org/packages/MsCoreUtils git_branch: devel git_last_commit: 2772946 git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/MsCoreUtils_1.23.9.tar.gz vignettes: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.html vignetteTitles: Core Utils for Mass Spectrometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.R importsMe: Chromatograms, CompoundDb, hdxmsqc, MetaboAnnotation, MetaboCoreUtils, MetCirc, MsBackendMassbank, MsBackendMetaboLights, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MsFeatures, MSnbase, MsQuality, PSMatch, QFeatures, qmtools, scp, SmartPhos, Spectra, SpectraQL, SpectriPy, xcms suggestsMe: MetNet, msqrob2, sfi dependencyCount: 13 Package: MsDataHub Version: 1.11.3 Imports: ExperimentHub, utils Suggests: ExperimentHubData, DT, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), Spectra, mzR, PSMatch, QFeatures (>= 1.13.3) License: Artistic-2.0 MD5sum: c5afee6702f9af45731d2f33dfb2bb1b NeedsCompilation: no Title: Mass Spectrometry Data on ExperimentHub Description: The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments. biocViews: ExperimentHubSoftware, MassSpectrometry, Proteomics, Metabolomics Author: Laurent Gatto [aut, cre] (ORCID: ), Kristina Gomoryova [ctb] (ORCID: ), Johannes Rainer [aut] (ORCID: ) Maintainer: Laurent Gatto URL: https://rformassspectrometry.github.io/MsDataHub VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsDataHub/issues git_url: https://git.bioconductor.org/packages/MsDataHub git_branch: devel git_last_commit: 0075d61 git_last_commit_date: 2026-04-05 Date/Publication: 2026-04-20 source.ver: src/contrib/MsDataHub_1.11.3.tar.gz vignettes: vignettes/MsDataHub/inst/doc/MsDataHub.html vignetteTitles: Mass Spectrometry Data on ExperimentHub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsDataHub/inst/doc/MsDataHub.R importsMe: MsQuality suggestsMe: MetaboAnnotation, MetaboAnnotatoR, MsBackendSql, MsExperiment, msqrob2, mzR, PSMatch, QFeatures, scp, Spectra, SpectraQL, SpectriPy dependencyCount: 64 Package: MsExperiment Version: 1.13.1 Depends: R (>= 4.2), ProtGenerics (>= 1.35.2), Imports: methods, S4Vectors, IRanges, Spectra, SummarizedExperiment, QFeatures, DBI, BiocGenerics Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown, rpx, mzR, MsDataHub, MsBackendSql (>= 1.3.2), RSQLite License: Artistic-2.0 MD5sum: 1a9096a16da3da75c02f1a6f7ba6c794 NeedsCompilation: no Title: Infrastructure for Mass Spectrometry Experiments Description: Infrastructure to store and manage all aspects related to a complete proteomics or metabolomics mass spectrometry (MS) experiment. The MsExperiment package provides light-weight and flexible containers for MS experiments building on the new MS infrastructure provided by the Spectra, QFeatures and related packages. Along with raw data representations, links to original data files and sample annotations, additional metadata or annotations can also be stored within the MsExperiment container. To guarantee maximum flexibility only minimal constraints are put on the type and content of the data within the containers. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, ExperimentalDesign, DataImport Author: Laurent Gatto [aut, cre] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Tuomas Borman [ctb] (ORCID: ) Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/MsExperiment VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsExperiment/issues git_url: https://git.bioconductor.org/packages/MsExperiment git_branch: devel git_last_commit: 19ec01c git_last_commit_date: 2026-02-06 Date/Publication: 2026-04-20 source.ver: src/contrib/MsExperiment_1.13.1.tar.gz vignettes: vignettes/MsExperiment/inst/doc/MsExperiment.html vignetteTitles: Managing Mass Spectrometry Experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsExperiment/inst/doc/MsExperiment.R importsMe: lcmsPlot, MsQuality, squallms, xcms dependencyCount: 112 Package: MsFeatures Version: 1.19.0 Depends: R (>= 4.0) Imports: methods, ProtGenerics (>= 1.23.5), MsCoreUtils, SummarizedExperiment, stats Suggests: testthat, roxygen2, BiocStyle, pheatmap, knitr, rmarkdown License: Artistic-2.0 MD5sum: 88660e33122cbf6c22373f8cb88ab5e3 NeedsCompilation: no Title: Functionality for Mass Spectrometry Features Description: The MsFeature package defines functionality for Mass Spectrometry features. This includes functions to group (LC-MS) features based on some of their properties, such as retention time (coeluting features), or correlation of signals across samples. This packge hence allows to group features, and its results can be used as an input for the `QFeatures` package which allows to aggregate abundance levels of features within each group. This package defines concepts and functions for base and common data types, implementations for more specific data types are expected to be implemented in the respective packages (such as e.g. `xcms`). All functionality of this package is implemented in a modular way which allows combination of different grouping approaches and enables its re-use in other R packages. biocViews: Infrastructure, MassSpectrometry, Metabolomics Author: Johannes Rainer [aut, cre] (ORCID: ), Johan Lassen [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsFeatures VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsFeatures/issues git_url: https://git.bioconductor.org/packages/MsFeatures git_branch: devel git_last_commit: e8548f3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MsFeatures_1.19.0.tar.gz vignettes: vignettes/MsFeatures/inst/doc/MsFeatures.html vignetteTitles: Grouping Mass Spectrometry Features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsFeatures/inst/doc/MsFeatures.R importsMe: xcms suggestsMe: qmtools dependencyCount: 31 Package: msImpute Version: 1.21.0 Depends: R (>= 3.5.0) Imports: softImpute, methods, stats, graphics, pdist, reticulate, scran, data.table, FNN, matrixStats, limma, mvtnorm, tidyr, dplyr Suggests: BiocStyle, knitr, rmarkdown, ComplexHeatmap, imputeLCMD License: GPL (>=2) MD5sum: 60723056e6d89054c73d8b8e02b7e724 NeedsCompilation: no Title: Imputation of label-free mass spectrometry peptides Description: MsImpute is a package for imputation of peptide intensity in proteomics experiments. It additionally contains tools for MAR/MNAR diagnosis and assessment of distortions to the probability distribution of the data post imputation. The missing values are imputed by low-rank approximation of the underlying data matrix if they are MAR (method = "v2"), by Barycenter approach if missingness is MNAR ("v2-mnar"), or by Peptide Identity Propagation (PIP). biocViews: MassSpectrometry, Proteomics, Software Author: Soroor Hediyeh-zadeh [aut, cre] (ORCID: ) Maintainer: Soroor Hediyeh-zadeh SystemRequirements: python VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/msImpute/issues git_url: https://git.bioconductor.org/packages/msImpute git_branch: devel git_last_commit: 1f62ae8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/msImpute_1.21.0.tar.gz vignettes: vignettes/msImpute/inst/doc/msImpute-vignette.html vignetteTitles: msImpute: proteomics missing values imputation and diagnosis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msImpute/inst/doc/msImpute-vignette.R dependencyCount: 86 Package: mslp Version: 1.13.0 Depends: R (>= 4.2.0) Imports: data.table (>= 1.13.0), doRNG, fmsb, foreach, magrittr, org.Hs.eg.db, pROC, randomForest, RankProd, stats, utils Suggests: BiocStyle, doFuture, future, knitr, rmarkdown, roxygen2, tinytest License: GPL-3 MD5sum: 479940cee87a94cdaac6429817de6c14 NeedsCompilation: no Title: Predict synthetic lethal partners of tumour mutations Description: An integrated pipeline to predict the potential synthetic lethality partners (SLPs) of tumour mutations, based on gene expression, mutation profiling and cell line genetic screens data. It has builtd-in support for data from cBioPortal. The primary SLPs correlating with muations in WT and compensating for the loss of function of mutations are predicted by random forest based methods (GENIE3) and Rank Products, respectively. Genetic screens are employed to identfy consensus SLPs leads to reduced cell viability when perturbed. biocViews: Pharmacogenetics, Pharmacogenomics Author: Chunxuan Shao [aut, cre] Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mslp git_branch: devel git_last_commit: d9bef21 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/mslp_1.13.0.tar.gz vignettes: vignettes/mslp/inst/doc/mslp.html vignetteTitles: mslp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mslp/inst/doc/mslp.R dependencyCount: 59 Package: mspms Version: 1.3.1 Depends: R (>= 4.4.0) Imports: QFeatures, limma, SummarizedExperiment, magrittr, rlang, dplyr, purrr, stats, tidyr, stringr, ggplot2, ggseqlogo, heatmaply, readr, rstatix, tibble, ggpubr, imputeLCMD Suggests: knitr, testthat (>= 3.0.0), downloadthis, DT, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 34a0efd270a01d1be3bd63134cc28560 NeedsCompilation: no Title: Tools for the analysis of MSP-MS data Description: This package provides functions for the analysis of data generated by the multiplex substrate profiling by mass spectrometry for proteases (MSP-MS) method. Data exported from upstream proteomics software is accepted as input and subsequently processed for analysis. Tools for statistical analysis, visualization, and interpretation of the data are provided. biocViews: Proteomics, MassSpectrometry, Preprocessing Author: Charlie Bayne [aut, cre] (ORCID: ) Maintainer: Charlie Bayne URL: https://github.com/baynec2/mspms VignetteBuilder: knitr BugReports: https://github.com/baynec2/mspms/issues git_url: https://git.bioconductor.org/packages/mspms git_branch: devel git_last_commit: 20f2c4b git_last_commit_date: 2025-11-12 Date/Publication: 2026-04-20 source.ver: src/contrib/mspms_1.3.1.tar.gz vignettes: vignettes/mspms/inst/doc/mspms_vignette.html vignetteTitles: mspms_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mspms/inst/doc/mspms_vignette.R dependencyCount: 187 Package: msPurity Version: 1.37.3 Depends: Rcpp Imports: plyr, dplyr, dbplyr, magrittr, foreach, parallel, doSNOW, stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite, BiocFileCache Suggests: MSnbase, testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData, CAMERA, RPostgres, RMySQL License: GPL-3 + file LICENSE MD5sum: 891615b3c0cea6a3bf9ebb205c67ae00 NeedsCompilation: no Title: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics Description: msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the "precursor ion purity". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call "Precursor ion purity" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation. biocViews: MassSpectrometry, Metabolomics, Software Author: Thomas N. Lawson [aut, cre] (ORCID: ), Ralf Weber [ctb], Martin Jones [ctb], Julien Saint-Vanne [ctb], Andris Jankevics [ctb], Ossama Edbali [ctb] (ORCID: ), Mark Viant [ths], Warwick Dunn [ths] Maintainer: Thomas N. Lawson URL: https://github.com/computational-metabolomics/msPurity/ VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/msPurity/issues/new git_url: https://git.bioconductor.org/packages/msPurity git_branch: devel git_last_commit: d893bfb git_last_commit_date: 2026-03-12 Date/Publication: 2026-04-20 source.ver: src/contrib/msPurity_1.37.3.tar.gz vignettes: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.html, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.html, vignettes/msPurity/inst/doc/msPurity-vignette.html vignetteTitles: msPurity spectral matching, msPurity spectral database schema, msPurity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.R, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.R, vignettes/msPurity/inst/doc/msPurity-vignette.R suggestsMe: MetMashR dependencyCount: 71 Package: msqrob2 Version: 1.19.2 Depends: R (>= 4.1), QFeatures (>= 1.1.2) Imports: stats, methods, lme4, purrr, BiocParallel, Matrix, MASS, limma, SummarizedExperiment, MultiAssayExperiment, codetools, matrixStats, ggplot2, assertthat, dplyr, grDevices, utils, rlang Suggests: stringr, ExploreModelMatrix, kableExtra, ComplexHeatmap, scater, multcomp, gridExtra, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown, testthat, tidyverse, tidyr, plotly, MsDataHub, MSnbase, MsCoreUtils, covr, arrow, data.table, ggcorrplot, iq License: Artistic-2.0 MD5sum: 37f6d7e020eef79d431904033a296344 NeedsCompilation: no Title: Robust statistical inference for quantitative LC-MS proteomics Description: msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data. biocViews: Proteomics, Metabolomics, MassSpectrometry, DifferentialExpression, MultipleComparison, Regression, ExperimentalDesign, Software, ImmunoOncology, Normalization, TimeCourse, Preprocessing Author: Lieven Clement [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Oliver M. Crook [aut] (ORCID: ), Adriaan Sticker [ctb], Ludger Goeminne [ctb], Milan Malfait [ctb] (ORCID: ), Stijn Vandenbulcke [aut] Maintainer: Lieven Clement URL: https://github.com/statOmics/msqrob2 VignetteBuilder: knitr BugReports: https://github.com/statOmics/msqrob2/issues git_url: https://git.bioconductor.org/packages/msqrob2 git_branch: devel git_last_commit: ba24bdb git_last_commit_date: 2026-04-20 Date/Publication: 2026-04-20 source.ver: src/contrib/msqrob2_1.19.2.tar.gz vignettes: vignettes/msqrob2/inst/doc/cptac.html, vignettes/msqrob2/inst/doc/staesSpikein-DIA-NN.html, vignettes/msqrob2/inst/doc/staesSpikein-spectronaut.html vignetteTitles: A. label-free workflow with two group design, B. label-free DIA workflow - 3 groups design - DIA-NN, C. label-free DIA workflow - 3 groups design - Spectronaut hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msqrob2/inst/doc/cptac.R, vignettes/msqrob2/inst/doc/staesSpikein-DIA-NN.R, vignettes/msqrob2/inst/doc/staesSpikein-spectronaut.R dependencyCount: 122 Package: MsQuality Version: 1.11.3 Depends: R (>= 4.2.0) Imports: BiocParallel (>= 1.32.0), Chromatograms (>= 1.1.5), ggplot2 (>= 3.3.5), htmlwidgets (>= 1.5.3), methods (>= 4.2.0), MsDataHub (>= 1.10.0), MsExperiment (>= 0.99.0), plotly (>= 4.9.4.1), ProtGenerics (>= 1.24.0), rlang (>= 1.1.1), rmzqc (>= 0.7.0), shiny (>= 1.6.0), shinydashboard (>= 0.7.1), Spectra (>= 1.13.2), stats (>= 4.2.0), stringr (>= 1.4.0), tibble (>= 3.1.4), tidyr (>= 1.1.3), utils (>= 4.2.0), MsCoreUtils (>= 1.19.0), MetaboCoreUtils (>= 1.19.2) Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), dplyr (>= 1.0.5), knitr (>= 1.11), mzR (>= 2.32.0), rmarkdown (>= 2.7), S4Vectors (>= 0.29.17), testthat (>= 2.2.1) License: GPL-3 MD5sum: f7f67c34caf0aaf639597184709b04fc NeedsCompilation: no Title: MsQuality - Quality metric calculation from Spectra, MsExperiment and Chromatograms objects Description: The MsQuality provides functionality to calculate quality metrics for mass spectrometry-derived, spectral data at the per-sample level. MsQuality relies on the mzQC framework of quality metrics defined by the Human Proteom Organization-Proteomics Standards Initiative (HUPO-PSI). These metrics quantify the quality of spectral raw files using a controlled vocabulary. The package is especially addressed towards users that acquire mass spectrometry data on a large scale (e.g. data sets from clinical settings consisting of several thousands of samples). The MsQuality package allows to calculate low-level quality metrics that require minimum information on mass spectrometry data: retention time, m/z values, and associated intensities. MsQuality relies on the Spectra package, or alternatively the MsExperiment package, and its infrastructure to store spectral data. Additionally, MsQuality supports Chromatograms objects from the Chromatograms package for chromatographic quality metrics. biocViews: Metabolomics, Proteomics, MassSpectrometry, QualityControl Author: Thomas Naake [aut, cre] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Helge Hecht [ctb], Philippine Louail [aut] (ORCID: ) Maintainer: Thomas Naake URL: https://www.github.com/tnaake/MsQuality/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MsQuality git_branch: devel git_last_commit: 7a88336 git_last_commit_date: 2026-03-30 Date/Publication: 2026-04-20 source.ver: src/contrib/MsQuality_1.11.3.tar.gz vignettes: vignettes/MsQuality/inst/doc/MsQuality.html vignetteTitles: QC for metabolomics and proteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsQuality/inst/doc/MsQuality.R dependencyCount: 156 Package: MSstats Version: 4.19.2 Depends: R (>= 4.0) Imports: MSstatsConvert, data.table, checkmate, MASS, htmltools, limma, lme4, preprocessCore, survival, utils, Rcpp, ggplot2, ggrepel, gplots, plotly, marray, stats, grDevices, graphics, methods, statmod, parallel, rlang LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, tinytest, covr, markdown, mockery, kableExtra License: Artistic-2.0 MD5sum: a4591707741de8717b836a20d6b68ea9 NeedsCompilation: yes Title: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments Description: A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, Normalization, QualityControl, TimeCourse Author: Meena Choi [aut, cre], Mateusz Staniak [aut], Devon Kohler [aut], Tony Wu [aut], Deril Raju [aut], Tsung-Heng Tsai [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Meena Choi URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstats git_branch: devel git_last_commit: 1c96555 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/MSstats_4.19.2.tar.gz vignettes: vignettes/MSstats/inst/doc/MSstats.html, vignettes/MSstats/inst/doc/MSstatsPlus.html, vignettes/MSstats/inst/doc/MSstatsWorkflow.html vignetteTitles: MSstats: Protein/Peptide significance analysis, MSstats+: Peak quality-weighted differential analysis, MSstats: End to End Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MSstats/inst/doc/MSstats.R, vignettes/MSstats/inst/doc/MSstatsPlus.R, vignettes/MSstats/inst/doc/MSstatsWorkflow.R dependsOnMe: MSstatsBioNet importsMe: artMS, MSstatsBig, MSstatsLiP, MSstatsPTM, MSstatsTMT suggestsMe: MSstatsResponse dependencyCount: 101 Package: MSstatsBig Version: 1.9.3 Imports: arrow, DBI, dplyr, MSstats, MSstatsConvert, readr, sparklyr, utils Suggests: testthat, mockery, knitr, rmarkdown License: Artistic-2.0 MD5sum: 1e6ebfcf69cb0e33dfdb8d4f4205e2bf NeedsCompilation: no Title: MSstats Preprocessing for Larger than Memory Data Description: MSstats package provide tools for preprocessing, summarization and differential analysis of mass spectrometry (MS) proteomics data. Recently, some MS protocols enable acquisition of data sets that result in larger than memory quantitative data. MSstats functions are not able to process such data. MSstatsBig package provides additional converter functions that enable processing larger than memory data sets. biocViews: MassSpectrometry, Proteomics, Software Author: Anthony Wu [aut, cre], Mateusz Staniak [aut], Devon Kohler [aut] Maintainer: Anthony Wu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSstatsBig git_branch: devel git_last_commit: 15a1b7f git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/MSstatsBig_1.9.3.tar.gz vignettes: vignettes/MSstatsBig/inst/doc/MSstatsBig_Workflow.html vignetteTitles: MSstatsBig Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsBig/inst/doc/MSstatsBig_Workflow.R dependencyCount: 124 Package: MSstatsBioNet Version: 1.3.6 Depends: R (>= 4.4.0), MSstats Imports: httr, jsonlite, r2r, tidyr, htmlwidgets, grDevices, stats, text2vec, stopwords, xml2, rentrez Suggests: data.table, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), mockery, MSstatsConvert, shiny License: file LICENSE MD5sum: 254f0d8f4a0cd4e03a36b649ecea2ed9 NeedsCompilation: no Title: Network Analysis for MS-based Proteomics Experiments Description: A set of tools for network analysis using mass spectrometry-based proteomics data and network databases. The package takes as input the output of MSstats differential abundance analysis and provides functions to perform enrichment analysis and visualization in the context of prior knowledge from past literature. Notably, this package integrates with INDRA, which is a database of biological networks extracted from the literature using text mining techniques. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, QualityControl, NetworkEnrichment, Network Author: Anthony Wu [aut, cre] (ORCID: ), Olga Vitek [aut] (ORCID: ) Maintainer: Anthony Wu URL: http://msstats.org, https://vitek-lab.github.io/MSstatsBioNet/ VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsBioNet git_branch: devel git_last_commit: a480faa git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/MSstatsBioNet_1.3.6.tar.gz vignettes: vignettes/MSstatsBioNet/inst/doc/Cytoscape-Visualization.html, vignettes/MSstatsBioNet/inst/doc/Filter-By-Context.html, vignettes/MSstatsBioNet/inst/doc/MSstatsBioNet.html, vignettes/MSstatsBioNet/inst/doc/PTM-Analysis.html vignetteTitles: MSstatsBioNet: Visualization Engine with CytoscapeJS, Filtering Subnetworks by Biological Context, MSstatsBioNet: Introduction, MSstatsBioNet: PTM Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MSstatsBioNet/inst/doc/Cytoscape-Visualization.R, vignettes/MSstatsBioNet/inst/doc/Filter-By-Context.R, vignettes/MSstatsBioNet/inst/doc/MSstatsBioNet.R, vignettes/MSstatsBioNet/inst/doc/PTM-Analysis.R importsMe: MSstatsShiny dependencyCount: 115 Package: MSstatsConvert Version: 1.21.3 Depends: R (>= 4.0) Imports: data.table, log4r, methods, checkmate, utils, stringi, Rcpp, parallel LinkingTo: Rcpp Suggests: tinytest, covr, knitr, arrow, rmarkdown License: Artistic-2.0 MD5sum: 9c9b3695dce5d421b0e5876a00a33feb NeedsCompilation: yes Title: Import Data from Various Mass Spectrometry Signal Processing Tools to MSstats Format Description: MSstatsConvert provides tools for importing reports of Mass Spectrometry data processing tools into R format suitable for statistical analysis using the MSstats and MSstatsTMT packages. biocViews: MassSpectrometry, Proteomics, Software, DataImport, QualityControl Author: Mateusz Staniak [aut], Devon Kohler [aut], Anthony Wu [aut, cre], Meena Choi [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Anthony Wu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSstatsConvert git_branch: devel git_last_commit: e0eb2c4 git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/MSstatsConvert_1.21.3.tar.gz vignettes: vignettes/MSstatsConvert/inst/doc/msstats_data_format.html vignetteTitles: Working with MSstatsConvert hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsConvert/inst/doc/msstats_data_format.R importsMe: MSstats, MSstatsBig, MSstatsLiP, MSstatsPTM, MSstatsShiny, MSstatsTMT suggestsMe: MSstatsBioNet dependencyCount: 11 Package: MSstatsLiP Version: 1.17.1 Depends: R (>= 4.1) Imports: dplyr, gridExtra, stringr, ggplot2, grDevices, MSstats, MSstatsConvert, data.table, Biostrings, MSstatsPTM (>= 2.12.0), Rcpp, checkmate, factoextra, ggpubr, purrr, tibble, tidyr, tidyverse, scales, stats, plotly, htmltools LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, gghighlight License: Artistic-2.0 MD5sum: 95c250703ddb3e1674ef6d3df41e3081 NeedsCompilation: yes Title: LiP Significance Analysis in shotgun mass spectrometry-based proteomic experiments Description: Tools for LiP peptide and protein significance analysis. Provides functions for summarization, estimation of LiP peptide abundance, and detection of changes across conditions. Utilizes functionality across the MSstats family of packages. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut], Anthony Wu [aut, cre], Tsung-Heng Tsai [aut], Deril Raju [aut], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Valentina Cappelletti [aut], Liliana Malinovska [aut], Olga Vitek [aut] Maintainer: Anthony Wu VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLiP/issues git_url: https://git.bioconductor.org/packages/MSstatsLiP git_branch: devel git_last_commit: 7131096 git_last_commit_date: 2026-04-07 Date/Publication: 2026-04-20 source.ver: src/contrib/MSstatsLiP_1.17.1.tar.gz vignettes: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.html, vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.html vignetteTitles: MSstatsLiP Workflow: An example workflow and analysis of the MSstatsLiP package, MSstatsLiP Proteolytic Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.R, vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.R dependencyCount: 198 Package: MSstatsLOBD Version: 1.19.0 Depends: R (>= 4.0) Imports: minpack.lm, ggplot2, utils, stats, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, dplyr License: Artistic-2.0 MD5sum: 29fc9b87ca4081eca5caac96cf2dcb4b NeedsCompilation: no Title: Assay characterization: estimation of limit of blanc(LoB) and limit of detection(LOD) Description: The MSstatsLOBD package allows calculation and visualization of limit of blac (LOB) and limit of detection (LOD). We define the LOB as the highest apparent concentration of a peptide expected when replicates of a blank sample containing no peptides are measured. The LOD is defined as the measured concentration value for which the probability of falsely claiming the absence of a peptide in the sample is 0.05, given a probability 0.05 of falsely claiming its presence. These functionalities were previously a part of the MSstats package. The methodology is described in Galitzine (2018) . biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Mateusz Staniak [aut], Cyril Galitzine [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLODQ/issues git_url: https://git.bioconductor.org/packages/MSstatsLOBD git_branch: devel git_last_commit: 3747330 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MSstatsLOBD_1.19.0.tar.gz vignettes: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.html vignetteTitles: LOB/LOD Estimation Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.R dependencyCount: 25 Package: MSstatsPTM Version: 2.13.2 Depends: R (>= 4.3) Imports: dplyr, gridExtra, stringr, stats, ggplot2, stringi, grDevices, MSstatsTMT, MSstatsConvert (>= 1.19.1), MSstats, data.table, Rcpp, Biostrings, checkmate, ggrepel, plotly, htmltools, rlang LinkingTo: Rcpp Suggests: knitr, rmarkdown, tinytest, covr, mockery, arrow, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 352d8ba3d573840e335d3988346fcb79 NeedsCompilation: yes Title: Statistical Characterization of Post-translational Modifications Description: MSstatsPTM provides general statistical methods for quantitative characterization of post-translational modifications (PTMs). Supports DDA, DIA, SRM, and tandem mass tag (TMT) labeling. Typically, the analysis involves the quantification of PTM sites (i.e., modified residues) and their corresponding proteins, as well as the integration of the quantification results. MSstatsPTM provides functions for summarization, estimation of PTM site abundance, and detection of changes in PTMs across experimental conditions. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut], Tsung-Heng Tsai [aut], Anthony Wu [aut, cre], Deril Raju [aut], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Anthony Wu URL: https://vitek-lab.github.io/MSstatsPTM/ VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsPTM/issues git_url: https://git.bioconductor.org/packages/MSstatsPTM git_branch: devel git_last_commit: c753b85 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/MSstatsPTM_2.13.2.tar.gz vignettes: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.html, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.html vignetteTitles: MSstatsPTM LabelFree Workflow, MSstatsPTM TMT Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.R, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.R importsMe: MSstatsLiP, MSstatsShiny dependencyCount: 114 Package: MSstatsResponse Version: 1.1.0 Depends: R (>= 4.5.0) Imports: BiocParallel, ggplot2, dplyr, stats, parallel, data.table Suggests: MSstats, MSstatsTMT, tidyverse, boot, purrr, gridExtra, knitr, rmarkdown, BiocStyle, testthat License: Artistic-2.0 MD5sum: c7c6e61816f156a477047c04b5603880 NeedsCompilation: no Title: Statistical Methods for Chemoproteomics Dose-Response Analysis Description: Tools for detecting drug-protein interactions and estimating IC50 values from chemoproteomics data. Implements semi-parametric isotonic regression, bootstrapping, and curve fitting to evaluate compound effects on protein abundance. biocViews: Proteomics, MassSpectrometry, StatisticalMethod, Software, Regression Author: Sarah Szvetecz [aut, cre], Devon Kohler [aut], Olga Vitek [aut] Maintainer: Sarah Szvetecz URL: https://github.com/Vitek-Lab/MSstatsResponse VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsResponse/issues git_url: https://git.bioconductor.org/packages/MSstatsResponse git_branch: devel git_last_commit: 048d39d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MSstatsResponse_1.1.0.tar.gz vignettes: vignettes/MSstatsResponse/inst/doc/MSstatsResponse.html vignetteTitles: MSstatsResponse User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsResponse/inst/doc/MSstatsResponse.R importsMe: MSstatsShiny dependencyCount: 41 Package: MSstatsTMT Version: 2.19.0 Depends: R (>= 4.2) Imports: limma, lme4, lmerTest, methods, data.table, stats, utils, ggplot2, grDevices, graphics, MSstats, MSstatsConvert, checkmate, plotly, htmltools Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 4338d7f7b85965ff43764ad5497323b2 NeedsCompilation: no Title: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling Description: The package provides statistical tools for detecting differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. It provides multiple functionalities, including aata visualization, protein quantification and normalization, and statistical modeling and inference. Furthermore, it is inter-operable with other data processing tools, such as Proteome Discoverer, MaxQuant, OpenMS and SpectroMine. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software Author: Devon Kohler [aut, cre], Ting Huang [aut], Meena Choi [aut], Mateusz Staniak [aut], Tony Wu [aut], Deril Raju [aut], Sicheng Hao [aut], Olga Vitek [aut] Maintainer: Devon Kohler URL: http://msstats.org/msstatstmt/ VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsTMT git_branch: devel git_last_commit: f287ad0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MSstatsTMT_2.19.0.tar.gz vignettes: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.html vignetteTitles: MSstatsTMT User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.R importsMe: MSstatsPTM, MSstatsShiny suggestsMe: MSstatsResponse dependencyCount: 104 Package: MuData Version: 1.15.0 Depends: Matrix, S4Vectors, rhdf5 (>= 2.45) Imports: methods, stats, MultiAssayExperiment, SingleCellExperiment, SummarizedExperiment, DelayedArray, S4Vectors Suggests: HDF5Array, rmarkdown, knitr, fs, testthat, BiocStyle, covr, SingleCellMultiModal, CiteFuse, scater License: GPL-3 MD5sum: ffd8233a0c022234ee4aa2fad9ed6183 NeedsCompilation: no Title: Serialization for MultiAssayExperiment Objects Description: Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage. biocViews: DataImport Author: Danila Bredikhin [aut] (ORCID: ), Ilia Kats [aut, cre] (ORCID: ) Maintainer: Ilia Kats URL: https://github.com/ilia-kats/MuData VignetteBuilder: knitr BugReports: https://github.com/ilia-kats/MuData/issues git_url: https://git.bioconductor.org/packages/MuData git_branch: devel git_last_commit: 186e639 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MuData_1.15.0.tar.gz vignettes: vignettes/MuData/inst/doc/Blood-CITE-seq.html, vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.html, vignettes/MuData/inst/doc/Getting-Started.html vignetteTitles: Blood CITE-seq with MuData, Cord Blood CITE-seq with MuData, Getting started with MuDataMae hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MuData/inst/doc/Blood-CITE-seq.R, vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.R, vignettes/MuData/inst/doc/Getting-Started.R dependencyCount: 53 Package: Mulcom Version: 1.61.0 Depends: R (>= 2.10), Biobase Imports: graphics, grDevices, stats, methods, fields License: GPL-2 MD5sum: c2778a65eef3f07bf0f073b994d446ed NeedsCompilation: yes Title: Calculates Mulcom test Description: Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test. biocViews: StatisticalMethod, MultipleComparison, Microarray, DifferentialExpression, GeneExpression Author: Claudio Isella Maintainer: Claudio Isella git_url: https://git.bioconductor.org/packages/Mulcom git_branch: devel git_last_commit: 7d6df9a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Mulcom_1.61.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 16 Package: MultiAssayExperiment Version: 1.37.4 Depends: SummarizedExperiment, R (>= 4.5.0) Imports: Biobase, BiocBaseUtils, BiocGenerics, DelayedArray, GenomicRanges, IRanges, MatrixGenerics, methods, S4Vectors, tidyr, utils Suggests: BiocStyle, HDF5Array, h5mread, knitr, maftools, RaggedExperiment, reshape2, rmarkdown, survival, survminer, testthat, UpSetR License: Artistic-2.0 MD5sum: 8d8744249d9483ec39e57f53972e5468 NeedsCompilation: no Title: Software for the integration of multi-omics experiments in Bioconductor Description: Harmonize data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. Facilities are provided for reshaping data into wide and long formats for adaptability to graphing and downstream analysis. biocViews: Infrastructure, DataRepresentation Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut, ctb], Lori Shepherd [ctb], Hervé Pagès [ctb], Vincent J Carey [aut, ctb], Levi Waldron [aut], MultiAssay SIG [ctb], NCI [fnd] (GrantNo.: U24CA289073) Maintainer: Marcel Ramos URL: http://waldronlab.io/MultiAssayExperiment/ VignetteBuilder: knitr Video: https://youtu.be/w6HWAHaDpyk, https://youtu.be/Vh0hVVUKKFM BugReports: https://github.com/waldronlab/MultiAssayExperiment/issues git_url: https://git.bioconductor.org/packages/MultiAssayExperiment git_branch: devel git_last_commit: 2e22d1f git_last_commit_date: 2026-03-28 Date/Publication: 2026-04-20 source.ver: src/contrib/MultiAssayExperiment_1.37.4.tar.gz vignettes: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment_cheatsheet.html, vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.html vignetteTitles: MultiAssayExperiment Cheatsheet, Coordinating Analysis of Multi-Assay Experiments, Quick-start Guide, HDF5Array and MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.R, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.R, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.R dependsOnMe: alabaster.mae, CAGEr, cBioPortalData, ClassifyR, evaluomeR, hipathia, HoloFoodR, InTAD, MGnifyR, mia, midasHLA, MIRit, missRows, QFeatures, RFLOMICS, terraTCGAdata, tidyexposomics, curatedPCaData, curatedTCGAData, microbiomeDataSets, OMICsPCAdata, scMultiome, SingleCellMultiModal importsMe: AffiXcan, AMARETTO, anansi, animalcules, autonomics, biosigner, CoreGx, corral, ELMER, FindIT2, gDRcore, gDRimport, gDRutils, glmSparseNet, GOpro, hermes, Lheuristic, LinkHD, metabolomicsWorkbenchR, MOMA, MOSClip, msqrob2, MuData, MultiBaC, MultimodalExperiment, nipalsMCIA, OMICsPCA, omicsPrint, padma, PDATK, PharmacoGx, ropls, scGraphVerse, scp, scPipe, SmartPhos, survClust, TCGAutils, TENET, vsclust, xcore, curatedTBData, HMP2Data, LegATo, MetaScope, TENET.ExperimentHub suggestsMe: BatchQC, BiocGenerics, CNVRanger, funOmics, maftools, MOFA2, MultiDataSet, RaggedExperiment, updateObject, brgedata, MOFAdata, teal, teal.slice dependencyCount: 45 Package: multiClust Version: 1.41.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, rmarkdown, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) MD5sum: 94cd8be89c350e344fb115a0ce41e40e NeedsCompilation: no Title: multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles Description: Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies. biocViews: FeatureExtraction, Clustering, GeneExpression, Survival Author: Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri [aut], Krish Karuturi [aut], Joshy George [aut] Maintainer: Nathan Lawlor VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiClust git_branch: devel git_last_commit: 9d7c5f5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/multiClust_1.41.0.tar.gz vignettes: vignettes/multiClust/inst/doc/multiClust.html vignetteTitles: "A Guide to multiClust" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiClust/inst/doc/multiClust.R dependencyCount: 36 Package: MultiDataSet Version: 1.39.0 Depends: R (>= 4.1), Biobase Imports: BiocGenerics, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, methods, utils, ggplot2, ggrepel, qqman, limma Suggests: brgedata, minfi, minfiData, knitr, rmarkdown, testthat, omicade4, iClusterPlus, GEOquery, MultiAssayExperiment, BiocStyle, RaggedExperiment License: file LICENSE MD5sum: 6a05b04a8bd6b3d7af51340e16c6c620 NeedsCompilation: no Title: Implementation of MultiDataSet and ResultSet Description: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages. biocViews: Software, DataRepresentation Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut] Maintainer: Xavier Escrib Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiDataSet git_branch: devel git_last_commit: 5045884 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MultiDataSet_1.39.0.tar.gz vignettes: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.html, vignettes/MultiDataSet/inst/doc/MultiDataSet.html vignetteTitles: Adding a new type of data to MultiDataSet objects, Introduction to MultiDataSet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.R, vignettes/MultiDataSet/inst/doc/MultiDataSet.R dependsOnMe: MEAL importsMe: biosigner, omicRexposome, ropls dependencyCount: 49 Package: multiGSEA Version: 1.21.0 Depends: R (>= 4.0.0) Imports: magrittr, graphite, AnnotationDbi, metaboliteIDmapping, dplyr, fgsea, metap, rappdirs, rlang, methods Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Ss.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Xl.eg.db, org.Cf.eg.db, knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: GPL-3 MD5sum: 8237633a45a18a793342b3b56b34d9d7 NeedsCompilation: no Title: Combining GSEA-based pathway enrichment with multi omics data integration Description: Extracted features from pathways derived from 8 different databases (KEGG, Reactome, Biocarta, etc.) can be used on transcriptomic, proteomic, and/or metabolomic level to calculate a combined GSEA-based enrichment score. biocViews: GeneSetEnrichment, Pathways, Reactome, BioCarta Author: Sebastian Canzler [aut, cre] (ORCID: ), Jörg Hackermüller [aut] (ORCID: ) Maintainer: Sebastian Canzler URL: https://github.com/yigbt/multiGSEA VignetteBuilder: knitr BugReports: https://github.com/yigbt/multiGSEA/issues git_url: https://git.bioconductor.org/packages/multiGSEA git_branch: devel git_last_commit: 7e0f16c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/multiGSEA_1.21.0.tar.gz vignettes: vignettes/multiGSEA/inst/doc/multiGSEA.html vignetteTitles: multiGSEA.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiGSEA/inst/doc/multiGSEA.R dependencyCount: 118 Package: multiHiCcompare Version: 1.29.0 Depends: R (>= 4.0.0) Imports: data.table, dplyr, HiCcompare, edgeR, BiocParallel, qqman, pheatmap, methods, GenomicRanges, graphics, stats, utils, pbapply, GenomeInfoDbData, GenomeInfoDb, aggregation Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: a1d0b5f53ce35ac2ef7ec2732544030a NeedsCompilation: no Title: Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available Description: multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner. biocViews: Software, HiC, Sequencing, Normalization Author: Mikhail Dozmorov [aut, cre] (ORCID: ), John Stansfield [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/multiHiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/multiHiCcompare/issues git_url: https://git.bioconductor.org/packages/multiHiCcompare git_branch: devel git_last_commit: 31f6781 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/multiHiCcompare_1.29.0.tar.gz vignettes: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.html, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.html vignetteTitles: juiceboxVisualization, multiHiCcompare hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.R, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.R importsMe: HiCDOC, OHCA suggestsMe: HiCcompare dependencyCount: 94 Package: MultiMed Version: 2.33.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: f201ed10f620547221d4a7690d21ae97 NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements methods for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Ruth Heller, Joshua N. Sampson Maintainer: Simina M. Boca git_url: https://git.bioconductor.org/packages/MultiMed git_branch: devel git_last_commit: 8d1a5fe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MultiMed_2.33.0.tar.gz vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf vignetteTitles: MultiMedTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiMed/inst/doc/MultiMed.R dependencyCount: 0 Package: MultimodalExperiment Version: 1.11.0 Depends: R (>= 4.3.0), IRanges, S4Vectors Imports: BiocGenerics, MultiAssayExperiment, methods, utils Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 1c75afbc83a9e78c4956afbbc5f4445f NeedsCompilation: no Title: Integrative Bulk and Single-Cell Experiment Container Description: MultimodalExperiment is an S4 class that integrates bulk and single-cell experiment data; it is optimally storage-efficient, and its methods are exceptionally fast. It effortlessly represents multimodal data of any nature and features normalized experiment, subject, sample, and cell annotations, which are related to underlying biological experiments through maps. Its coordination methods are opt-in and employ database-like join operations internally to deliver fast and flexible management of multimodal data. biocViews: DataRepresentation, Infrastructure, SingleCell Author: Lucas Schiffer [aut, cre] (ORCID: ) Maintainer: Lucas Schiffer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultimodalExperiment git_branch: devel git_last_commit: 14f0df6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MultimodalExperiment_1.11.0.tar.gz vignettes: vignettes/MultimodalExperiment/inst/doc/MultimodalExperiment.html vignetteTitles: MultimodalExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultimodalExperiment/inst/doc/MultimodalExperiment.R dependencyCount: 46 Package: MultiRNAflow Version: 1.9.0 Depends: Mfuzz (>= 2.64.0), R (>= 4.4) Imports: Biobase (>= 2.54.0), ComplexHeatmap (>= 2.20.0), DESeq2 (>= 1.44.0), factoextra (>= 1.0.7), FactoMineR (>= 2.11), ggalluvial (>= 0.12.5), ggplot2 (>= 3.5.1), ggplotify (>= 0.1.2), ggrepel (>= 0.9.5), gprofiler2 (>= 0.2.3), graphics (>= 4.2.2), grDevices (>= 4.2.2), grid (>= 4.2.2), plot3D (>= 1.4.1), plot3Drgl (>= 1.0.4), reshape2 (>= 1.4.4), rlang (>= 1.1.6), S4Vectors (>= 0.42.0), stats (>= 4.2.2), SummarizedExperiment (>= 1.34.0), UpSetR (>= 1.4.0), utils (>= 4.2.2) Suggests: BiocGenerics (>= 0.40.0), BiocStyle (>= 2.32.1), e1071 (>= 1.7.12), knitr (>= 1.47), rmarkdown (>= 2.27), testthat (>= 3.0.0) License: GPL-3 | file LICENSE MD5sum: b4947054836ee86a5376879f564ba9d3 NeedsCompilation: no Title: An R package for integrated analysis of temporal RNA-seq data with multiple biological conditions Description: Our R package MultiRNAflow provides an easy to use unified framework allowing to automatically make both unsupervised and supervised (DE) analysis for datasets with an arbitrary number of biological conditions and time points. In particular, our code makes a deep downstream analysis of DE information, e.g. identifying temporal patterns across biological conditions and DE genes which are specific to a biological condition for each time. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, TimeCourse, Preprocessing, Visualization, Normalization, PrincipalComponent, Clustering, DifferentialExpression, GeneSetEnrichment, Pathways Author: Rodolphe Loubaton [aut, cre] (ORCID: ), Nicolas Champagnat [aut, ths] (ORCID: ), Laurent Vallat [aut, ths] (ORCID: ), Pierre Vallois [aut] (ORCID: ), Région Grand Est [fnd], Cancéropôle Est [fnd] Maintainer: Rodolphe Loubaton URL: https://github.com/loubator/MultiRNAflow VignetteBuilder: knitr BugReports: https://github.com/loubator/MultiRNAflow/issues git_url: https://git.bioconductor.org/packages/MultiRNAflow git_branch: devel git_last_commit: 3c2e28d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MultiRNAflow_1.9.0.tar.gz vignettes: vignettes/MultiRNAflow/inst/doc/MultiRNAflow_vignette-knitr.pdf, vignettes/MultiRNAflow/inst/doc/Running_analysis_with_MultiRNAflow.html vignetteTitles: MultiRNAflow: A R package for analysing RNA-seq raw counts with different time points and several biological conditions., Running_analysis_with_MultiRNAflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiRNAflow/inst/doc/MultiRNAflow_vignette-knitr.R, vignettes/MultiRNAflow/inst/doc/Running_analysis_with_MultiRNAflow.R dependencyCount: 193 Package: multiscan Version: 1.71.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) MD5sum: b135860c0ca574b56202b1a35b57591d NeedsCompilation: yes Title: R package for combining multiple scans Description: Estimates gene expressions from several laser scans of the same microarray biocViews: Microarray, Preprocessing Author: Mizanur Khondoker , Chris Glasbey, Bruce Worton. Maintainer: Mizanur Khondoker git_url: https://git.bioconductor.org/packages/multiscan git_branch: devel git_last_commit: 6267e46 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/multiscan_1.71.0.tar.gz vignettes: vignettes/multiscan/inst/doc/multiscan.pdf vignetteTitles: An R Package for Estimating Gene Expressions using Multiple Scans hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiscan/inst/doc/multiscan.R dependencyCount: 7 Package: multistateQTL Version: 2.3.0 Depends: QTLExperiment, SummarizedExperiment, ComplexHeatmap, collapse Imports: methods, S4Vectors, data.table, grid, dplyr, tidyr, matrixStats, stats, fitdistrplus, viridis, ggplot2, circlize, mashr, grDevices Suggests: testthat, BiocStyle, knitr, covr, rmarkdown License: GPL-3 MD5sum: 389cb744d649f7bedfc2ea3674a734f9 NeedsCompilation: no Title: Toolkit for the analysis of multi-state QTL data Description: A collection of tools for doing various analyses of multi-state QTL data, with a focus on visualization and interpretation. The package 'multistateQTL' contains functions which can remove or impute missing data, identify significant associations, as well as categorise features into global, multi-state or unique. The analysis results are stored in a 'QTLExperiment' object, which is based on the 'SummarisedExperiment' framework. biocViews: FunctionalGenomics, GeneExpression, Sequencing, Visualization, SNP, Software Author: Christina Del Azodi [aut], Davis McCarthy [ctb], Amelia Dunstone [cre, aut] (ORCID: ) Maintainer: Amelia Dunstone URL: https://github.com/dunstone-a/multistateQTL VignetteBuilder: knitr BugReports: https://github.com/dunstone-a/multistateQTL/issues git_url: https://git.bioconductor.org/packages/multistateQTL git_branch: devel git_last_commit: e4b2944 git_last_commit_date: 2026-01-25 Date/Publication: 2026-04-20 source.ver: src/contrib/multistateQTL_2.3.0.tar.gz vignettes: vignettes/multistateQTL/inst/doc/multistateQTL.html vignetteTitles: multistateQTL: Orchestrating multi-state QTL analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multistateQTL/inst/doc/multistateQTL.R dependencyCount: 104 Package: multiWGCNA Version: 1.9.3 Depends: R (>= 4.3.0), ggalluvial Imports: stringr, readr, WGCNA, magrittr, dplyr, reshape2, data.table, patchwork, scales, igraph, flashClust, ggplot2, dcanr, cowplot, ggrepel, methods, SummarizedExperiment, ggraph, tidyr Suggests: BiocStyle, doParallel, ExperimentHub, knitr, markdown, rmarkdown, testthat (>= 3.0.0), vegan License: GPL-3 MD5sum: 3413c216c918d3a590697a5e513bad50 NeedsCompilation: no Title: multiWGCNA Description: An R package for deeping mining gene co-expression networks in multi-trait expression data. Provides functions for analyzing, comparing, and visualizing WGCNA networks across conditions. multiWGCNA was designed to handle the common case where there are multiple biologically meaningful sample traits, such as disease vs wildtype across development or anatomical region. biocViews: Sequencing, RNASeq, GeneExpression, DifferentialExpression, Regression, Clustering Author: Dario Tommasini [aut, cre] (ORCID: ), Brent Fogel [aut, ctb] Maintainer: Dario Tommasini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiWGCNA git_branch: devel git_last_commit: 42c77b5 git_last_commit_date: 2026-01-07 Date/Publication: 2026-04-20 source.ver: src/contrib/multiWGCNA_1.9.3.tar.gz vignettes: vignettes/multiWGCNA/inst/doc/astrocyte_map_v2.html, vignettes/multiWGCNA/inst/doc/autism_full_workflow.html vignetteTitles: Astrocyte multiWGCNA network, General Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiWGCNA/inst/doc/astrocyte_map_v2.R, vignettes/multiWGCNA/inst/doc/autism_full_workflow.R suggestsMe: multiWGCNAdata dependencyCount: 136 Package: multtest Version: 2.67.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL MD5sum: 9017c2039bfde3f4a6319a7ac39fd76a NeedsCompilation: yes Title: Resampling-based multiple hypothesis testing Description: Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra Taylor, Sandrine Dudoit Maintainer: Katherine S. Pollard git_url: https://git.bioconductor.org/packages/multtest git_branch: devel git_last_commit: 877b1b4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/multtest_2.67.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: aCGH, BicARE, KCsmart, PREDA, rain, REDseq, siggenes, webbioc, cp4p, DiffCorr, PCS importsMe: a4Base, ABarray, adSplit, ALDEx2, anota, BulkSignalR, ChIPpeakAnno, GUIDEseq, metabomxtr, nethet, OCplus, phyloseq, RTopper, singleCellTK, webbioc, mutoss, nlcv, pRF, Qploidy, structSSI, TcGSA suggestsMe: annaffy, CAMERA, ecolitk, factDesign, GOstats, GSEAlm, ropls, topGO, xcms, cherry, POSTm dependencyCount: 15 Package: mumosa Version: 1.19.1 Depends: SingleCellExperiment Imports: stats, utils, methods, igraph, Matrix, BiocGenerics, BiocParallel, IRanges, S4Vectors, DelayedArray, DelayedMatrixStats, SummarizedExperiment, BiocNeighbors, BiocSingular, ScaledMatrix, beachmat, scuttle, metapod, scran, batchelor, uwot Suggests: testthat, knitr, BiocStyle, rmarkdown, scater, bluster, DropletUtils, scRNAseq License: GPL-3 MD5sum: 5d62bf99066f4b6d2ab753d3ec1c6622 NeedsCompilation: no Title: Multi-Modal Single-Cell Analysis Methods Description: Assorted utilities for multi-modal analyses of single-cell datasets. Includes functions to combine multiple modalities for downstream analysis, perform MNN-based batch correction across multiple modalities, and to compute correlations between assay values for different modalities. biocViews: ImmunoOncology, SingleCell, RNASeq Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: http://bioconductor.org/packages/mumosa VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/mumosa git_branch: devel git_last_commit: accca5c git_last_commit_date: 2026-03-04 Date/Publication: 2026-04-20 source.ver: src/contrib/mumosa_1.19.1.tar.gz vignettes: vignettes/mumosa/inst/doc/overview.html vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mumosa/inst/doc/overview.R suggestsMe: Ibex dependencyCount: 73 Package: MungeSumstats Version: 1.19.6 Depends: R(>= 4.1) Imports: data.table, utils, R.utils, dplyr, stats, GenomicRanges, GenomeInfoDb, IRanges, ieugwasr(>= 1.0.1), BSgenome, Biostrings, stringr, VariantAnnotation, methods, parallel, rtracklayer(>= 1.59.1), RCurl Suggests: SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BiocGenerics, S4Vectors, rmarkdown, markdown, knitr, testthat (>= 3.0.0), UpSetR, BiocStyle, covr, Rsamtools, MatrixGenerics, badger, BiocParallel, GenomicFiles License: Artistic-2.0 MD5sum: cf5b32b76d6392e15ceb02a81d022f70 NeedsCompilation: no Title: Standardise summary statistics from GWAS Description: The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences. biocViews: SNP, WholeGenome, Genetics, ComparativeGenomics, GenomeWideAssociation, GenomicVariation, Preprocessing Author: Alan Murphy [aut, cre] (ORCID: ), Brian Schilder [aut, ctb] (ORCID: ), Nathan Skene [aut] (ORCID: ) Maintainer: Alan Murphy URL: https://github.com/neurogenomics/MungeSumstats, https://al-murphy.github.io/MungeSumstats/ VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/MungeSumstats/issues git_url: https://git.bioconductor.org/packages/MungeSumstats git_branch: devel git_last_commit: 5dfb8a6 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/MungeSumstats_1.19.6.tar.gz vignettes: vignettes/MungeSumstats/inst/doc/docker.html, vignettes/MungeSumstats/inst/doc/MungeSumstats.html, vignettes/MungeSumstats/inst/doc/OpenGWAS.html vignetteTitles: docker, MungeSumstats, OpenGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MungeSumstats/inst/doc/docker.R, vignettes/MungeSumstats/inst/doc/MungeSumstats.R, vignettes/MungeSumstats/inst/doc/OpenGWAS.R dependencyCount: 93 Package: muscle Version: 3.53.0 Depends: Biostrings License: Unlimited MD5sum: 9a2e2094121ebae89c5de3fb3975d384 NeedsCompilation: yes Title: Multiple Sequence Alignment with MUSCLE Description: MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences. biocViews: MultipleSequenceAlignment, Alignment, Sequencing, Genetics, SequenceMatching, DataImport Author: Algorithm by Robert C. Edgar. R port by Alex T. Kalinka. Maintainer: Alex T. Kalinka URL: http://www.drive5.com/muscle/ git_url: https://git.bioconductor.org/packages/muscle git_branch: devel git_last_commit: e0bf042 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/muscle_3.53.0.tar.gz vignettes: vignettes/muscle/inst/doc/muscle-vignette.pdf vignetteTitles: A guide to using muscle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscle/inst/doc/muscle-vignette.R suggestsMe: BOLDconnectR, orthGS, seqmagick dependencyCount: 15 Package: musicatk Version: 2.5.0 Depends: R (>= 4.4.0), NMF Imports: SummarizedExperiment, VariantAnnotation, Biostrings, base, methods, magrittr, tibble, tidyr, gtools, gridExtra, MCMCprecision, MASS, matrixTests, data.table, dplyr, rlang, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, S4Vectors, uwot, ggplot2, stringr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, decompTumor2Sig, topicmodels, ggrepel, plotly, utils, factoextra, cluster, ComplexHeatmap, philentropy, maftools, shiny, stringi, tidyverse, ggpubr, Matrix (>= 1.6.1), scales Suggests: TCGAbiolinks, shinyBS, shinyalert, shinybusy, shinydashboard, shinyjs, shinyjqui, sortable, testthat, BiocStyle, knitr, rmarkdown, survival, XVector, qpdf, covr, shinyWidgets, cowplot, withr License: LGPL-3 MD5sum: d80becf90e0fdc3032c006d4ed04b6dd NeedsCompilation: no Title: Mutational Signature Comprehensive Analysis Toolkit Description: Mutational signatures are carcinogenic exposures or aberrant cellular processes that can cause alterations to the genome. We created musicatk (MUtational SIgnature Comprehensive Analysis ToolKit) to address shortcomings in versatility and ease of use in other pre-existing computational tools. Although many different types of mutational data have been generated, current software packages do not have a flexible framework to allow users to mix and match different types of mutations in the mutational signature inference process. Musicatk enables users to count and combine multiple mutation types, including SBS, DBS, and indels. Musicatk calculates replication strand, transcription strand and combinations of these features along with discovery from unique and proprietary genomic feature associated with any mutation type. Musicatk also implements several methods for discovery of new signatures as well as methods to infer exposure given an existing set of signatures. Musicatk provides functions for visualization and downstream exploratory analysis including the ability to compare signatures between cohorts and find matching signatures in COSMIC V2 or COSMIC V3. biocViews: Software, BiologicalQuestion, SomaticMutation, VariantAnnotation Author: Aaron Chevalier [aut] (ORCHID: 0000-0002-3968-9250), Natasha Gurevich [aut] (ORCHID: 0000-0002-0747-6840), Tao Guo [aut] (ORCHID: 0009-0005-8960-9203), Joshua D. Campbell [aut, cre] (ORCID: ) Maintainer: Joshua D. Campbell URL: https://www.camplab.net/musicatk/ VignetteBuilder: knitr BugReports: https://github.com/campbio/musicatk/issues git_url: https://git.bioconductor.org/packages/musicatk git_branch: devel git_last_commit: ec6bf01 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/musicatk_2.5.0.tar.gz vignettes: vignettes/musicatk/inst/doc/musicatk.html vignetteTitles: Mutational Signature Comprehensive Analysis Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/musicatk/inst/doc/musicatk.R dependencyCount: 273 Package: MutationalPatterns Version: 3.21.1 Depends: R (>= 4.2.0), GenomicRanges (>= 1.24.0), NMF (>= 0.20.6) Imports: stats, S4Vectors, BiocGenerics (>= 0.18.0), BSgenome (>= 1.40.0), VariantAnnotation (>= 1.18.1), dplyr (>= 0.8.3), tibble(>= 2.1.3), purrr (>= 0.3.2), tidyr (>= 1.0.0), stringr (>= 1.4.0), magrittr (>= 1.5), ggplot2 (>= 2.1.0), pracma (>= 1.8.8), IRanges (>= 2.6.0), Seqinfo, GenomeInfoDb (>= 1.45.9), Biostrings (>= 2.40.0), ggdendro (>= 0.1-20), cowplot (>= 0.9.2), ggalluvial (>= 0.12.2), RColorBrewer, methods Suggests: BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), BiocStyle (>= 2.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), biomaRt (>= 2.28.0), gridExtra (>= 2.2.1), rtracklayer (>= 1.32.2), ccfindR (>= 1.6.0), GenomicFeatures, AnnotationDbi, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 27139d6ee0f82ddff86c4ddd06f4ad7e NeedsCompilation: no Title: Comprehensive genome-wide analysis of mutational processes Description: Mutational processes leave characteristic footprints in genomic DNA. This package provides a comprehensive set of flexible functions that allows researchers to easily evaluate and visualize a multitude of mutational patterns in base substitution catalogues of e.g. healthy samples, tumour samples, or DNA-repair deficient cells. The package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes. The package works with single nucleotide variants (SNVs), insertions and deletions (Indels), double base substitutions (DBSs) and larger multi base substitutions (MBSs). The package provides functionalities for both extracting mutational signatures de novo and determining the contribution of previously identified mutational signatures on a single sample level. MutationalPatterns integrates with common R genomic analysis workflows and allows easy association with (publicly available) annotation data. biocViews: Genetics, SomaticMutation Author: Freek Manders [aut] (ORCID: ), Francis Blokzijl [aut] (ORCID: ), Roel Janssen [aut] (ORCID: ), Jurrian de Kanter [ctb] (ORCID: ), Rurika Oka [ctb] (ORCID: ), Mark van Roosmalen [cre], Ruben van Boxtel [aut, cph] (ORCID: ), Edwin Cuppen [aut] (ORCID: ) Maintainer: Mark van Roosmalen URL: https://doi.org/doi:10.1186/s12864-022-08357-3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MutationalPatterns git_branch: devel git_last_commit: e9acc28 git_last_commit_date: 2026-02-16 Date/Publication: 2026-04-20 source.ver: src/contrib/MutationalPatterns_3.21.1.tar.gz vignettes: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.html vignetteTitles: Introduction to MutationalPatterns hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.R importsMe: RESOLVE suggestsMe: LACHESIS, SUITOR dependencyCount: 119 Package: mutscan Version: 1.1.1 Depends: R (>= 4.5.0) Imports: BiocGenerics, S4Vectors, methods, SummarizedExperiment, Rcpp, edgeR (>= 3.42.0), dplyr, Matrix, limma, tidyr, stats, GGally, ggplot2, tidyselect (>= 1.2.0), tibble, rlang, grDevices, csaw, rmarkdown, xfun, DT, ggrepel, IRanges, utils, DelayedArray, tools LinkingTo: Rcpp Suggests: testthat (>= 3.0.0), BiocStyle, knitr, Biostrings, pwalign, plotly, scattermore, BiocManager License: MIT + file LICENSE MD5sum: 307b02eea5ca769817bc277d12e54d2a NeedsCompilation: yes Title: Preprocessing and Analysis of Deep Mutational Scanning Data Description: Provides functionality for processing and statistical analysis of multiplexed assays of variant effect (MAVE) and similar data. The package contains functions covering the full workflow from raw FASTQ files to publication-ready visualizations. A broad range of library designs can be processed with a single, unified interface. biocViews: GeneticVariability, GenomicVariation, Preprocessing Author: Charlotte Soneson [aut, cre] (ORCID: ), Michael Stadler [aut] (ORCID: ), Friedrich Miescher Institute for Biomedical Research [cph] Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/mutscan SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/mutscan/issues git_url: https://git.bioconductor.org/packages/mutscan git_branch: devel git_last_commit: 971f0ec git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/mutscan_1.1.1.tar.gz vignettes: vignettes/mutscan/inst/doc/mutscan.html vignetteTitles: mutscan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mutscan/inst/doc/mutscan.R dependencyCount: 110 Package: MutSeqR Version: 0.99.9 Depends: R (>= 4.5.0) Imports: BiocGenerics, Biostrings, BSgenome, data.table, dplyr, GenomicRanges, ggplot2, here, IRanges, ggdendro, magrittr, methods, plyranges, rlang, S4Vectors, Seqinfo, stringr, SummarizedExperiment, tidyr, VariantAnnotation Suggests: binom, BiocManager, BiocStyle, bs4Dash, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, car, colorspace, dendsort, doBy, DT, ExperimentHub, fmsb, fs, ggrepel, gtools, htmltools, httr, knitr, lme4, magick, MutSeqRData, openxlsx, packcircles, patchwork, RColorBrewer, reticulate, rmarkdown, scales, shiny, testthat (>= 3.0.0), trackViewer, withr, yaml, xml2 License: MIT + file LICENSE MD5sum: 9782c7bbb21b5b3fb6abd5b883deb4a9 NeedsCompilation: no Title: Analysis of Error-Corrected Sequencing Data for Mutation Detection Description: Standard methods for analysis of mutation data following error- corrected sequencing (ECS) for the purpose of mutagencity assessment. Functions include importing the mutation lists provided by a variant caller, and a set of analytical tools for statistical testing and visualization of mutation data; comparison to COSMIC and/or germline signatures; etc. biocViews: Sequencing, SomaticMutation, Visualization, GenomicVariation, DriverMutation, StatisticalMethod, GeneTarget Author: Annette E. Dodge [aut] (ORCID: ), Andrew Williams [aut] (ORCID: ), Danielle P.M. LeBlanc [aut] (ORCID: ), David M. Schuster [aut] (ORCID: ), Elena Esina [aut] (ORCID: ), Clint C. Valentine [aut] (ORCID: ), Jesse J. Salk [aut] (ORCID: ), Alexander Y. Maslov [aut], Christopher Bradley [aut], Carole L. Yauk [aut] (ORCID: ), Francesco Marchetti [aut] (ORCID: ), Matthew J. Meier [aut, cre] (ORCID: ), Geronimo Matteo [ctb] (ORCID: ), Health Canada's Genomics Research and Development Initiative [fnd], Canada Research Chairs Program [fnd] (CRC-2020-00060), Burroughs Wellcome Fund [fnd] Maintainer: Matthew J. Meier URL: https://ehsrb-bsrse-bioinformatics.github.io/MutSeqR/ VignetteBuilder: knitr BugReports: https://github.com/EHSRB-BSRSE-Bioinformatics/MutSeqR/issues git_url: https://git.bioconductor.org/packages/MutSeqR git_branch: devel git_last_commit: 04943b4 git_last_commit_date: 2026-02-06 Date/Publication: 2026-04-20 source.ver: src/contrib/MutSeqR_0.99.9.tar.gz vignettes: vignettes/MutSeqR/inst/doc/MutSeqR_introduction.html vignetteTitles: MutSeqR: Error-Corrected Sequencing (ECS) Analysis For Mutagenicity Assessment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MutSeqR/inst/doc/MutSeqR_introduction.R dependencyCount: 103 Package: MVCClass Version: 1.85.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: 37f71c58cf5654f0d090d77a5ce28e44 NeedsCompilation: no Title: Model-View-Controller (MVC) Classes Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/MVCClass git_branch: devel git_last_commit: 91675ba git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MVCClass_1.85.0.tar.gz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BioMVCClass dependencyCount: 1 Package: MWASTools Version: 1.35.0 Depends: R (>= 3.5.0) Imports: glm2, ppcor, qvalue, car, boot, grid, ggplot2, gridExtra, igraph, SummarizedExperiment, KEGGgraph, RCurl, KEGGREST, ComplexHeatmap, stats, utils Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: CC BY-NC-ND 4.0 MD5sum: 2d0be3050dfc68e9e0edd998698293e6 NeedsCompilation: no Title: MWASTools: an integrated pipeline to perform metabolome-wide association studies Description: MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results. biocViews: Metabolomics, Lipidomics, Cheminformatics, SystemsBiology, QualityControl Author: Andrea Rodriguez-Martinez, Joram M. Posma, Rafael Ayala, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MWASTools git_branch: devel git_last_commit: 8cc5c70 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/MWASTools_1.35.0.tar.gz vignettes: vignettes/MWASTools/inst/doc/MWASTools.html vignetteTitles: MWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MWASTools/inst/doc/MWASTools.R importsMe: MetaboSignal dependencyCount: 129 Package: myvariant Version: 1.41.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, Seqinfo, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: 9175a27922f80c7143ccb31e45783280 NeedsCompilation: no Title: Accesses MyVariant.info variant query and annotation services Description: MyVariant.info is a comprehensive aggregation of variant annotation resources. myvariant is a wrapper for querying MyVariant.info services biocViews: VariantAnnotation, Annotation, GenomicVariation Author: Adam Mark Maintainer: Adam Mark, Chunlei Wu git_url: https://git.bioconductor.org/packages/myvariant git_branch: devel git_last_commit: a081fd4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/myvariant_1.41.0.tar.gz vignettes: vignettes/myvariant/inst/doc/myvariant.pdf vignetteTitles: Using MyVariant.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/myvariant/inst/doc/myvariant.R dependencyCount: 123 Package: mzID Version: 1.49.1 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 9508c2d3933ae3a69bbe882aeac09cb3 NeedsCompilation: no Title: An mzIdentML parser for R Description: A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly. biocViews: ImmunoOncology, DataImport, MassSpectrometry, Proteomics Author: Laurent Gatto [ctb, cre] (ORCID: ), Thomas Pedersen [aut] (ORCID: ), Vladislav Petyuk [ctb] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mzID git_branch: devel git_last_commit: 6be4883 git_last_commit_date: 2026-04-05 Date/Publication: 2026-04-20 source.ver: src/contrib/mzID_1.49.1.tar.gz vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf vignetteTitles: Using mzID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzID/inst/doc/HOWTO_mzID.R importsMe: MSnbase, MSnID, TargetDecoy suggestsMe: mzR, PSMatch, RforProteomics, PepMapViz dependencyCount: 11 Package: mzR Version: 2.45.1 Depends: R (>= 4.0.0), Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.17.3), ncdf4 LinkingTo: Rcpp, Rhdf5lib (>= 1.1.4) Suggests: MsDataHub, RUnit, mzID, BiocStyle (>= 2.5.19), knitr, XML, rmarkdown License: Artistic-2.0 MD5sum: cc64f087cdf39b202527430e1213e1fc NeedsCompilation: yes Title: parser for netCDF, mzXML and mzML and mzIdentML files (mass spectrometry data) Description: mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. biocViews: ImmunoOncology, Infrastructure, DataImport, Proteomics, Metabolomics, MassSpectrometry Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou, Johannes Rainer Maintainer: Steffen Neumann URL: https://github.com/sneumann/mzR/ SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/sneumann/mzR/issues/ git_url: https://git.bioconductor.org/packages/mzR git_branch: devel git_last_commit: f768fca git_last_commit_date: 2026-03-22 Date/Publication: 2026-04-20 source.ver: src/contrib/mzR_2.45.1.tar.gz vignettes: vignettes/mzR/inst/doc/mzR.html vignetteTitles: Accessin raw mass spectrometry and identification data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzR/inst/doc/mzR.R dependsOnMe: MSnbase importsMe: adductomicsR, Aerith, CluMSID, lcmsPlot, MSnID, msPurity, peakPantheR, RMassBank, sfi, SIMAT, TargetDecoy, topdownr, xcms, yamss suggestsMe: AnnotationHub, Chromatograms, koinar, MetaboAnnotation, MsBackendMetaboLights, MsBackendRawFileReader, MsBackendSql, MsDataHub, MsExperiment, MsQuality, PSMatch, qcmetrics, Spectra, SpectraQL, SpectriPy, msdata, RforProteomics, chromConverter, erah dependencyCount: 15 Package: NanoStringDiff Version: 1.41.0 Depends: Biobase Imports: matrixStats, methods, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle License: GPL MD5sum: 21320d17c64cdea85ef96eb2d3b8358e NeedsCompilation: yes Title: Differential Expression Analysis of NanoString nCounter Data Description: This Package utilizes a generalized linear model(GLM) of the negative binomial family to characterize count data and allows for multi-factor design. NanoStrongDiff incorporate size factors, calculated from positive controls and housekeeping controls, and background level, obtained from negative controls, in the model framework so that all the normalization information provided by NanoString nCounter Analyzer is fully utilized. biocViews: DifferentialExpression, Normalization Author: hong wang , tingting zhai , chi wang Maintainer: tingting zhai ,hong wang git_url: https://git.bioconductor.org/packages/NanoStringDiff git_branch: devel git_last_commit: b94a57b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NanoStringDiff_1.41.0.tar.gz vignettes: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf vignetteTitles: NanoStringDiff Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.R suggestsMe: NanoTube dependencyCount: 9 Package: NanoStringNCTools Version: 1.19.0 Depends: R (>= 3.6), Biobase, S4Vectors, ggplot2 Imports: BiocGenerics, Biostrings, ggbeeswarm, ggiraph, ggthemes, grDevices, IRanges, methods, pheatmap, RColorBrewer, stats, utils Suggests: biovizBase, ggbio, RUnit, rmarkdown, knitr, qpdf License: MIT MD5sum: bedbe7a4f84ff9446f719ec80f4de3ed NeedsCompilation: no Title: NanoString nCounter Tools Description: Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, mRNAMicroarray, ProprietaryPlatforms, RNASeq Author: Patrick Aboyoun [aut], Nicole Ortogero [aut], Maddy Griswold [cre], Zhi Yang [ctb] Maintainer: Maddy Griswold VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringNCTools git_branch: devel git_last_commit: 47660a8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NanoStringNCTools_1.19.0.tar.gz vignettes: vignettes/NanoStringNCTools/inst/doc/Introduction.html vignetteTitles: Introduction to the NanoStringRCCSet Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NanoStringNCTools/inst/doc/Introduction.R dependsOnMe: GeomxTools, GeoMxWorkflows importsMe: GeoDiff dependencyCount: 79 Package: NanoTube Version: 1.17.0 Depends: R (>= 4.1), Biobase, ggplot2, limma Imports: fgsea, methods, reshape, stats, utils Suggests: grid, kableExtra, knitr, NanoStringDiff, pheatmap, plotly, rlang, rmarkdown, ruv, RUVSeq, shiny, testthat, xlsx License: GPL-3 + file LICENSE MD5sum: c64aa6e767531155a90ba8a3830ec308 NeedsCompilation: no Title: An Easy Pipeline for NanoString nCounter Data Analysis Description: NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages. biocViews: Software, GeneExpression, DifferentialExpression, QualityControl Author: Caleb Class [cre, aut] (ORCID: ), Caiden Lukan [ctb] Maintainer: Caleb Class VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoTube git_branch: devel git_last_commit: 6e52811 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NanoTube_1.17.0.tar.gz vignettes: vignettes/NanoTube/inst/doc/NanoTube.html vignetteTitles: NanoTube Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NanoTube/inst/doc/NanoTube.R dependencyCount: 46 Package: NBAMSeq Version: 1.27.0 Depends: R (>= 3.6), SummarizedExperiment, S4Vectors Imports: DESeq2, mgcv(>= 1.8-24), BiocParallel, genefilter, methods, stats, Suggests: knitr, rmarkdown, testthat, ggplot2 License: GPL-2 MD5sum: 9c157fa8e3f0838d5c89c3d7c031b631 NeedsCompilation: no Title: Negative Binomial Additive Model for RNA-Seq Data Description: High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. biocViews: RNASeq, DifferentialExpression, GeneExpression, Sequencing, Coverage Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/NBAMSeq VignetteBuilder: knitr BugReports: https://github.com/reese3928/NBAMSeq/issues git_url: https://git.bioconductor.org/packages/NBAMSeq git_branch: devel git_last_commit: e827f50 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NBAMSeq_1.27.0.tar.gz vignettes: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.html vignetteTitles: Negative Binomial Additive Model for RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.R dependencyCount: 84 Package: ncdfFlow Version: 2.57.0 Depends: R (>= 2.14.0), flowCore(>= 1.51.7), methods, BH Imports: Biobase,BiocGenerics,flowCore LinkingTo: cpp11,BH, Rhdf5lib Suggests: testthat,parallel,flowStats,knitr License: AGPL-3.0-only MD5sum: 473e5366f9e1e457bdad9bf4685d729e NeedsCompilation: yes Title: ncdfFlow: A package that provides HDF5 based storage for flow cytometry data. Description: Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. biocViews: ImmunoOncology, FlowCytometry Author: Mike Jiang,Greg Finak,N. Gopalakrishnan Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ncdfFlow git_branch: devel git_last_commit: 1d39b7d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ncdfFlow_2.57.0.tar.gz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R dependsOnMe: ggcyto importsMe: flowStats, flowWorkspace, openCyto suggestsMe: COMPASS, cydar dependencyCount: 22 Package: NCIgraph Version: 1.59.0 Depends: R (>= 4.0.0) Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.oo Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: b4bbeb350c972b003246322212c850c3 NeedsCompilation: no Title: Pathways from the NCI Pathways Database Description: Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them. biocViews: Pathways, GraphAndNetwork Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/NCIgraph git_branch: devel git_last_commit: 5a91ea4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NCIgraph_1.59.0.tar.gz vignettes: vignettes/NCIgraph/inst/doc/NCIgraph.pdf vignetteTitles: NCIgraph: networks from the NCI pathway integrated database as graphNEL objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NCIgraph/inst/doc/NCIgraph.R importsMe: DEGraph suggestsMe: DEGraph dependencyCount: 57 Package: ndexr Version: 1.33.0 Depends: RCX Imports: httr, jsonlite, plyr, tidyr Suggests: BiocStyle, testthat, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 91a812b21e9697aa6eba4bd9ceadbef3 NeedsCompilation: no Title: NDEx R client library Description: This package offers an interface to NDEx servers, e.g. the public server at http://ndexbio.org/. It can retrieve and save networks via the API. Networks are offered as RCX object and as igraph representation. biocViews: Pathways, DataImport, Network Author: Florian Auer [cre, aut] (ORCID: ), Frank Kramer [ctb], Alex Ishkin [ctb], Dexter Pratt [ctb] Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/ndexr VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/ndexr/issues git_url: https://git.bioconductor.org/packages/ndexr git_branch: devel git_last_commit: be353bc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ndexr_1.33.0.tar.gz vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html vignetteTitles: NDExR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R dependencyCount: 40 Package: Nebulosa Version: 1.21.1 Depends: R (>= 4.0), ggplot2, patchwork Imports: SingleCellExperiment, SummarizedExperiment, SeuratObject, ks, Matrix, stats, methods, ggrastr Suggests: testthat, BiocStyle, knitr, rmarkdown, covr, scater, scran, DropletUtils, igraph, BiocFileCache, Seurat License: GPL-3 MD5sum: 829125670146dbea9b209c940fe22af6 NeedsCompilation: no Title: Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation Description: This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa. biocViews: Software, GeneExpression, SingleCell, Visualization, DimensionReduction Author: Jose Alquicira-Hernandez [aut, cre] (ORCID: ) Maintainer: Jose Alquicira-Hernandez URL: https://github.com/powellgenomicslab/Nebulosa VignetteBuilder: knitr BugReports: https://github.com/powellgenomicslab/Nebulosa/issues git_url: https://git.bioconductor.org/packages/Nebulosa git_branch: devel git_last_commit: f2276c0 git_last_commit_date: 2026-02-05 Date/Publication: 2026-04-20 source.ver: src/contrib/Nebulosa_1.21.1.tar.gz vignettes: vignettes/Nebulosa/inst/doc/introduction.html, vignettes/Nebulosa/inst/doc/nebulosa_seurat.html vignetteTitles: Visualization of gene expression with Nebulosa, Visualization of gene expression with Nebulosa (in Seurat) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Nebulosa/inst/doc/introduction.R, vignettes/Nebulosa/inst/doc/nebulosa_seurat.R suggestsMe: scCustomize, SCpubr dependencyCount: 82 Package: nempi Version: 1.19.0 Depends: R (>= 4.1), mnem Imports: e1071, nnet, randomForest, naturalsort, graphics, stats, utils, matrixStats, epiNEM Suggests: knitr, BiocGenerics, rmarkdown, RUnit, BiocStyle License: GPL-3 MD5sum: 5f8915e82db2d4a4ff0754f5be48ff18 NeedsCompilation: no Title: Inferring unobserved perturbations from gene expression data Description: Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models. biocViews: Software, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneSignaling, Pathways, Network, Classification, NeuralNetwork, NetworkInference, ATACSeq, DNASeq, RNASeq, PooledScreens, CRISPR, SingleCell, SystemsBiology Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/nempi/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/nempi/issues git_url: https://git.bioconductor.org/packages/nempi git_branch: devel git_last_commit: 45e9506 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/nempi_1.19.0.tar.gz vignettes: vignettes/nempi/inst/doc/nempi.html vignetteTitles: nempi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nempi/inst/doc/nempi.R dependencyCount: 109 Package: NetActivity Version: 1.13.0 Depends: R (>= 4.1.0) Imports: airway, DelayedArray, DelayedMatrixStats, DESeq2, methods, methods, NetActivityData, SummarizedExperiment, utils Suggests: AnnotationDbi, BiocStyle, Fletcher2013a, knitr, org.Hs.eg.db, rmarkdown, testthat (>= 3.0.0), tidyverse License: MIT + file LICENSE MD5sum: f7480ead9f652de1d035ab87a2a1a33a NeedsCompilation: no Title: Compute gene set scores from a deep learning framework Description: #' NetActivity enables to compute gene set scores from previously trained sparsely-connected autoencoders. The package contains a function to prepare the data (`prepareSummarizedExperiment`) and a function to compute the gene set scores (`computeGeneSetScores`). The package `NetActivityData` contains different pre-trained models to be directly applied to the data. Alternatively, the users might use the package to compute gene set scores using custom models. biocViews: RNASeq, Microarray, Transcription, FunctionalGenomics, GO, GeneExpression, Pathways, Software Author: Carlos Ruiz-Arenas [aut, cre] Maintainer: Carlos Ruiz-Arenas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetActivity git_branch: devel git_last_commit: 0cad5d9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NetActivity_1.13.0.tar.gz vignettes: vignettes/NetActivity/inst/doc/NetActivity.html vignetteTitles: "Gene set scores computation with NetActivity" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NetActivity/inst/doc/NetActivity.R dependencyCount: 59 Package: netboost Version: 2.19.3 Depends: R (>= 4.0.0) Imports: Rcpp, RcppParallel, parallel, grDevices, graphics, stats, utils, dynamicTreeCut, WGCNA, impute, colorspace, methods, R.utils LinkingTo: Rcpp, RcppParallel Suggests: knitr, rmarkdown, BiocStyle License: GPL-3 OS_type: unix MD5sum: dc594f741fe3e8ca0a23b0c34ebb1761 NeedsCompilation: yes Title: Network Analysis Supported by Boosting Description: Boosting supported network analysis for high-dimensional omics applications. This package comes bundled with the MC-UPGMA clustering package by Yaniv Loewenstein. biocViews: Software, StatisticalMethod, GraphAndNetwork, Network, Clustering, DimensionReduction, BiomedicalInformatics, Epigenetics, Metabolomics, Transcriptomics Author: Pascal Schlosser [aut, cre] (ORCID: ), Jochen Knaus [aut, ctb], Yaniv Loewenstein [aut] Maintainer: Pascal Schlosser URL: https://bioconductor.org/packages/release/bioc/html/netboost.html SystemRequirements: GNU make, Bash, Perl, Gzip VignetteBuilder: knitr BugReports: mailto:pascal.schlosser@uniklinik-freiburg.de git_url: https://git.bioconductor.org/packages/netboost git_branch: devel git_last_commit: 4e016d8 git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/netboost_2.19.3.tar.gz vignettes: vignettes/netboost/inst/doc/netboost.html vignetteTitles: The Netboost users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netboost/inst/doc/netboost.R dependencyCount: 82 Package: nethet Version: 1.43.0 Imports: glasso, mvtnorm, GeneNet, huge, CompQuadForm, ggm, mclust, parallel, GSA, limma, multtest, ICSNP, glmnet, network, ggplot2, grDevices, graphics, stats, utils Suggests: knitr, xtable, BiocStyle, testthat License: GPL-2 MD5sum: 15a63dac5eb5a7e65b35ce028c46aef5 NeedsCompilation: yes Title: A bioconductor package for high-dimensional exploration of biological network heterogeneity Description: Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013). biocViews: Clustering, GraphAndNetwork Author: Nicolas Staedler, Frank Dondelinger Maintainer: Nicolas Staedler , Frank Dondelinger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nethet git_branch: devel git_last_commit: 603f933 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/nethet_1.43.0.tar.gz vignettes: vignettes/nethet/inst/doc/nethet.pdf vignetteTitles: nethet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nethet/inst/doc/nethet.R dependencyCount: 74 Package: NetPathMiner Version: 1.47.0 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, graph, knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: 2e947fdb75ed2273063567fd75b8d87d NeedsCompilation: yes Title: NetPathMiner for Biological Network Construction, Path Mining and Visualization Description: NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation. biocViews: GraphAndNetwork, Pathways, Network, Clustering, Classification Author: Ahmed Mohamed [aut, cre] (ORCID: ), Tim Hancock [aut], Tim Hancock [aut] Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/NetPathMiner SystemRequirements: libxml2, libSBML (>= 5.5) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetPathMiner git_branch: devel git_last_commit: e081c30 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NetPathMiner_1.47.0.tar.gz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.html vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R dependencyCount: 17 Package: netresponse Version: 1.71.0 Depends: R (>= 2.15.1), BiocStyle, Rgraphviz, rmarkdown, methods, minet, mclust, reshape2 Imports: ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer Suggests: knitr License: GPL (>=2) MD5sum: 55b42bdea70fd908a3a30fff46ed0d97 NeedsCompilation: yes Title: Functional Network Analysis Description: Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling. biocViews: CellBiology, Clustering, GeneExpression, Genetics, Network, GraphAndNetwork, DifferentialExpression, Microarray, NetworkInference, Transcription Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso Parkkinen Maintainer: Leo Lahti URL: https://github.com/antagomir/netresponse VignetteBuilder: knitr BugReports: https://github.com/antagomir/netresponse/issues git_url: https://git.bioconductor.org/packages/netresponse git_branch: devel git_last_commit: 8295054 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/netresponse_1.71.0.tar.gz vignettes: vignettes/netresponse/inst/doc/NetResponse.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netresponse/inst/doc/NetResponse.R dependencyCount: 69 Package: NetSAM Version: 1.51.0 Depends: R (>= 3.0.0), seriation (>= 1.0-6), igraph (>= 2.0.0), tools (>= 3.0.0), WGCNA (>= 1.34.0), biomaRt (>= 2.18.0) Imports: methods, AnnotationDbi (>= 1.28.0), doParallel (>= 1.0.10), foreach (>= 1.4.0), survival (>= 2.37-7), GO.db (>= 2.10.0), R2HTML (>= 2.2.0), DBI (>= 0.5-1) Suggests: RUnit, BiocGenerics, org.Sc.sgd.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Dr.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.At.tair.db, rmarkdown, knitr, markdown License: LGPL MD5sum: 06fae2be02965e81e595cd736b4dfd02 NeedsCompilation: no Title: Network Seriation And Modularization Description: The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a weighted or unweighted network as an input, performs network seriation and modularization analysis, and generates as files that can be used as an input for the one-dimensional network visualization tool NetGestalt (http://www.netgestalt.org) or other network analysis. The NetSAM package can also generate correlation network (e.g. co-expression network) based on the input matrix data, perform seriation and modularization analysis for the correlation network and calculate the associations between the sample features and modules or identify the associated GO terms for the modules. Author: Jing Wang Maintainer: Zhiao Shi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetSAM git_branch: devel git_last_commit: e7c982a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NetSAM_1.51.0.tar.gz vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf vignetteTitles: NetSAM User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R dependencyCount: 132 Package: ngsReports Version: 2.13.1 Depends: R (>= 4.2.0), BiocGenerics, ggplot2 (>= 4.0.0), patchwork (>= 1.1.1), tibble (>= 1.3.1) Imports: Biostrings, checkmate, dplyr (>= 1.1.0), forcats, ggdendro, grDevices (>= 3.6.0), grid, jsonlite, lifecycle, lubridate, methods, plotly (>= 4.9.4), rlang, rmarkdown, scales, stats, stringr, tidyr, tidyselect (>= 0.2.3), utils, zoo Suggests: BiocStyle, Cairo, DT, knitr, pander, readr, testthat, truncnorm License: LGPL-3 MD5sum: 75912eca6f50f24b970790afc8fe80c6 NeedsCompilation: no Title: Load FastqQC reports and other NGS related files Description: This package provides methods and object classes for parsing FastQC reports and output summaries from other NGS tools into R. As well as parsing files, multiple plotting methods have been implemented for visualising the parsed data. Plots can be generated as static ggplot objects or interactive plotly objects. biocViews: QualityControl, ReportWriting Author: Stevie Pederson [aut, cre] (ORCID: ), Christopher Ward [aut], Thu-Hien To [aut] Maintainer: Stevie Pederson URL: https://github.com/smped/ngsReports VignetteBuilder: knitr BugReports: https://github.com/smped/ngsReports/issues git_url: https://git.bioconductor.org/packages/ngsReports git_branch: devel git_last_commit: aec27b7 git_last_commit_date: 2025-11-09 Date/Publication: 2026-04-20 source.ver: src/contrib/ngsReports_2.13.1.tar.gz vignettes: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.html vignetteTitles: An Introduction To ngsReports hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.R dependencyCount: 89 Package: nipalsMCIA Version: 1.9.1 Depends: R (>= 4.3.0) Imports: ComplexHeatmap, dplyr, fgsea, ggplot2 (>= 3.0.0), graphics, grid, methods, MultiAssayExperiment, SummarizedExperiment, pracma, rlang, RSpectra, scales, stats Suggests: BiocFileCache, BiocStyle, circlize, ggpubr, KernSmooth, knitr, piggyback, reshape2, rmarkdown, rpart, Seurat (>= 4.0.0), spatstat.explore, stringr, survival, tidyverse, testthat (>= 3.0.0) License: GPL-3 MD5sum: 3b068f6b4f0bb1a8fdc9f4fc7583b062 NeedsCompilation: no Title: Multiple Co-Inertia Analysis via the NIPALS Method Description: Computes Multiple Co-Inertia Analysis (MCIA), a dimensionality reduction (jDR) algorithm, for a multi-block dataset using a modification to the Nonlinear Iterative Partial Least Squares method (NIPALS) proposed in (Hanafi et. al, 2010). Allows multiple options for row- and table-level preprocessing, and speeds up computation of variance explained. Vignettes detail application to bulk- and single cell- multi-omics studies. biocViews: Software, Clustering, Classification, MultipleComparison, Normalization, Preprocessing, SingleCell Author: Maximilian Mattessich [cre] (ORCID: ), Joaquin Reyna [aut] (ORCID: ), Edel Aron [aut] (ORCID: ), Ferhat Ay [aut] (ORCID: ), Steven Kleinstein [aut] (ORCID: ), Anna Konstorum [aut] (ORCID: ) Maintainer: Maximilian Mattessich URL: https://github.com/Muunraker/nipalsMCIA VignetteBuilder: knitr BugReports: https://github.com/Muunraker/nipalsMCIA/issues git_url: https://git.bioconductor.org/packages/nipalsMCIA git_branch: devel git_last_commit: cd033e5 git_last_commit_date: 2026-01-19 Date/Publication: 2026-04-20 source.ver: src/contrib/nipalsMCIA_1.9.1.tar.gz vignettes: vignettes/nipalsMCIA/inst/doc/Analysis-of-MCIA-Decomposition.html, vignettes/nipalsMCIA/inst/doc/Predicting-New-Scores.html, vignettes/nipalsMCIA/inst/doc/Single-Cell-Analysis.html vignetteTitles: Analysis of MCIA Decomposition, Predicting New MCIA scores, Single Cell Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nipalsMCIA/inst/doc/Analysis-of-MCIA-Decomposition.R, vignettes/nipalsMCIA/inst/doc/Predicting-New-Scores.R, vignettes/nipalsMCIA/inst/doc/Single-Cell-Analysis.R suggestsMe: tidyexposomics dependencyCount: 87 Package: nnNorm Version: 2.75.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: 63e72a4909f24d4e396d1b332ab3e596 NeedsCompilation: no Title: Spatial and intensity based normalization of cDNA microarray data based on robust neural nets Description: This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting. biocViews: Microarray, TwoChannel, Preprocessing Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/tarca/ git_url: https://git.bioconductor.org/packages/nnNorm git_branch: devel git_last_commit: f06fb71 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/nnNorm_2.75.0.tar.gz vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf vignetteTitles: nnNorm Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nnNorm/inst/doc/nnNorm.R dependencyCount: 9 Package: nnSVG Version: 1.15.0 Depends: R (>= 4.2) Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, BRISC, BiocParallel, Matrix, matrixStats, stats, methods Suggests: BiocStyle, knitr, rmarkdown, STexampleData, WeberDivechaLCdata, scran, ggplot2, testthat License: MIT + file LICENSE MD5sum: 5cbd4cdce8cb3a7ebdcf2cd2ebd2f0a2 NeedsCompilation: no Title: Scalable identification of spatially variable genes in spatially-resolved transcriptomics data Description: Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Preprocessing Author: Lukas M. Weber [aut, cre] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/nnSVG VignetteBuilder: knitr BugReports: https://github.com/lmweber/nnSVG/issues git_url: https://git.bioconductor.org/packages/nnSVG git_branch: devel git_last_commit: 2a226ef git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/nnSVG_1.15.0.tar.gz vignettes: vignettes/nnSVG/inst/doc/nnSVG.html vignetteTitles: nnSVG Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nnSVG/inst/doc/nnSVG.R importsMe: spoon, OSTA suggestsMe: SEraster, tpSVG dependencyCount: 80 Package: NOISeq Version: 2.55.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: 49dcd55ff75112835ba877e50aa27464 NeedsCompilation: no Title: Exploratory analysis and differential expression for RNA-seq data Description: Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. biocViews: ImmunoOncology, RNASeq, DifferentialExpression, Visualization, Sequencing Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto Ferrer and Ana Conesa Maintainer: Sonia Tarazona git_url: https://git.bioconductor.org/packages/NOISeq git_branch: devel git_last_commit: ad38b2f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NOISeq_2.55.0.tar.gz vignettes: vignettes/NOISeq/inst/doc/NOISeq.pdf, vignettes/NOISeq/inst/doc/QCreport.pdf vignetteTitles: NOISeq User's Guide, QCreport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NOISeq/inst/doc/NOISeq.R dependsOnMe: metaSeq importsMe: benchdamic, broadSeq, CNVPanelizer, damidBind suggestsMe: compcodeR, GeoTcgaData dependencyCount: 12 Package: nondetects Version: 2.41.0 Depends: R (>= 3.2), Biobase (>= 2.22.0) Imports: limma, mvtnorm, utils, methods, arm, HTqPCR (>= 1.16.0) Suggests: knitr, rmarkdown, BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL-3 MD5sum: e6a1fbe2216b46bc84023fc164888ba4 NeedsCompilation: no Title: Non-detects in qPCR data Description: Methods to model and impute non-detects in the results of qPCR experiments. biocViews: Software, AssayDomain, GeneExpression, Technology, qPCR, WorkflowStep, Preprocessing Author: Matthew N. McCall , Valeriia Sherina Maintainer: Valeriia Sherina VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nondetects git_branch: devel git_last_commit: 9d43cbc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/nondetects_2.41.0.tar.gz vignettes: vignettes/nondetects/inst/doc/nondetects.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nondetects/inst/doc/nondetects.R dependencyCount: 44 Package: NoRCE Version: 1.23.0 Depends: R (>= 4.4.0) Imports: KEGGREST,png,dplyr,graphics,RSQLite,DBI,tidyr,grDevices,stringr,Seqinfo, S4Vectors,SummarizedExperiment,reactome.db,rWikiPathways,RCurl, dbplyr,utils,ggplot2,igraph,stats,reshape2,readr, GO.db, biomaRt,rtracklayer,IRanges,GenomicRanges,GenomicFeatures,AnnotationDbi,methods Suggests: knitr, TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Mmusculus.UCSC.mm10.knownGene,TxDb.Dmelanogaster.UCSC.dm6.ensGene, testthat,TxDb.Celegans.UCSC.ce11.refGene,rmarkdown, TxDb.Rnorvegicus.UCSC.rn6.refGene,TxDb.Hsapiens.UCSC.hg19.knownGene, org.Mm.eg.db, org.Rn.eg.db,org.Hs.eg.db,org.Dr.eg.db,BiocGenerics, org.Sc.sgd.db, org.Ce.eg.db,org.Dm.eg.db, markdown License: MIT + file LICENSE MD5sum: 04afd580c560f8fd5780cd410f9ff601 NeedsCompilation: no Title: NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment Description: While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint to a functional association. We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. biocViews: BiologicalQuestion, DifferentialExpression, GenomeAnnotation, GeneSetEnrichment, GeneTarget, GenomeAssembly, GO Author: Gulden Olgun [aut, cre] Maintainer: Gulden Olgun VignetteBuilder: knitr BugReports: https://github.com/guldenolgun/NoRCE/issues git_url: https://git.bioconductor.org/packages/NoRCE git_branch: devel git_last_commit: 1e7412c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NoRCE_1.23.0.tar.gz vignettes: vignettes/NoRCE/inst/doc/NoRCE.html vignetteTitles: Noncoding RNA Set Cis Annotation and Enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NoRCE/inst/doc/NoRCE.R dependencyCount: 119 Package: normalize450K Version: 1.39.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: c55d7bf760b82208fbd1168b246f66cd NeedsCompilation: no Title: Preprocessing of Illumina Infinium 450K data Description: Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K '.idat' files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values. biocViews: Normalization, DNAMethylation, Microarray, TwoChannel, Preprocessing, MethylationArray Author: Jonathan Alexander Heiss Maintainer: Jonathan Alexander Heiss git_url: https://git.bioconductor.org/packages/normalize450K git_branch: devel git_last_commit: 62981d2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/normalize450K_1.39.0.tar.gz vignettes: vignettes/normalize450K/inst/doc/read_and_normalize450K.pdf vignetteTitles: Normalization of 450K data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/normalize450K/inst/doc/read_and_normalize450K.R dependencyCount: 13 Package: NormqPCR Version: 1.57.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: 381843b573af5be4215b048da1443b36 NeedsCompilation: no Title: Functions for normalisation of RT-qPCR data Description: Functions for the selection of optimal reference genes and the normalisation of real-time quantitative PCR data. biocViews: MicrotitrePlateAssay, GeneExpression, qPCR Author: Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html git_url: https://git.bioconductor.org/packages/NormqPCR git_branch: devel git_last_commit: 0d5f960 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NormqPCR_1.57.0.tar.gz vignettes: vignettes/NormqPCR/inst/doc/NormqPCR.pdf vignetteTitles: NormqPCR: Functions for normalisation of RT-qPCR data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormqPCR/inst/doc/NormqPCR.R suggestsMe: OAtools dependencyCount: 47 Package: notame Version: 1.1.4 Depends: R (>= 4.5.0), ggplot2, SummarizedExperiment Imports: BiocGenerics, BiocParallel, dplyr, futile.logger, methods, openxlsx, S4Vectors, scales, stringr, tidyr, utils Suggests: BiocStyle, fpc, igraph, knitr, missForest, notameViz, notameStats, pcaMethods, RUVSeq, testthat License: MIT + file LICENSE MD5sum: 016ac8c36f0b918530860beaf6c490bf NeedsCompilation: no Title: Workflow for non-targeted LC-MS metabolic profiling Description: Provides functionality for untargeted LC-MS metabolomics research as specified in the associated protocol article in the 'Metabolomics Data Processing and Data Analysis—Current Best Practices' special issue of the Metabolites journal (2020). This includes tabular data preprocessing and quality control, uni- and multivariate analysis as well as quality control visualizations, feature-wise visualizations and results visualizations. Raw data preprocessing and functionality related to biological context, such as pathway analysis, is not included. biocViews: BiomedicalInformatics, Metabolomics, DataImport, MassSpectrometry, BatchEffect, MultipleComparison, Normalization, QualityControl, Visualization, Preprocessing Author: Anton Klåvus [aut, cph] (ORCID: ), Jussi Paananen [aut, cph] (ORCID: ), Oskari Timonen [aut, cph] (ORCID: ), Atte Lihtamo [aut], Vilhelm Suksi [aut, cre] (ORCID: ), Retu Haikonen [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Kati Hanhineva [aut] (ORCID: ), Ville Koistinen [ctb] (ORCID: ), Olli Kärkkäinen [ctb] (ORCID: ), Artur Sannikov [ctb] (ORCID: ) Maintainer: Vilhelm Suksi URL: https://github.com/hanhineva-lab/notame, https://hanhineva-lab.github.io/notame/ VignetteBuilder: knitr BugReports: https://github.com/hanhineva-lab/notame/issues git_url: https://git.bioconductor.org/packages/notame git_branch: devel git_last_commit: ceb2b0f git_last_commit_date: 2026-02-26 Date/Publication: 2026-04-20 source.ver: src/contrib/notame_1.1.4.tar.gz vignettes: vignettes/notame/inst/doc/introduction.html, vignettes/notame/inst/doc/project_example.html vignetteTitles: Non-targeted metabolomics preprocessing and wrangling, Project example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/notame/inst/doc/introduction.R, vignettes/notame/inst/doc/project_example.R importsMe: notameStats, notameViz dependencyCount: 65 Package: notameStats Version: 1.1.2 Depends: R (>= 4.5.0), SummarizedExperiment, Imports: BiocGenerics, BiocParallel, broom, dplyr, methods, notame, stats, tibble, tidyr, utils Suggests: BiocStyle, car, knitr, lmerTest, missForest, mixOmics, MuMIn, MUVR2, notameViz, PERMANOVA, PK, randomForest, rmcorr, testthat License: MIT + file LICENSE MD5sum: 251880f9e938435509f9fe1870f16e7d NeedsCompilation: no Title: Workflow for non-targeted LC-MS metabolic profiling Description: Provides univariate and multivariate statistics for feature prioritization in untargeted LC-MS metabolomics research. biocViews: BiomedicalInformatics, Metabolomics, DataImport, MassSpectrometry, BatchEffect, MultipleComparison, Normalization, QualityControl, Visualization, Preprocessing Author: Anton Klåvus [aut, cph] (ORCID: ), Jussi Paananen [aut, cph] (ORCID: ), Oskari Timonen [aut, cph] (ORCID: ), Atte Lihtamo [aut], Vilhelm Suksi [aut, cre] (ORCID: ), Retu Haikonen [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Kati Hanhineva [aut] (ORCID: ), Ville Koistinen [ctb] (ORCID: ), Olli Kärkkäinen [ctb] (ORCID: ), Artur Sannikov [ctb] Maintainer: Vilhelm Suksi URL: https://github.com/hanhineva-lab/notameStats VignetteBuilder: knitr BugReports: https://github.com/hanhineva-lab/notameStats/issues git_url: https://git.bioconductor.org/packages/notameStats git_branch: devel git_last_commit: a0396f2 git_last_commit_date: 2026-04-20 Date/Publication: 2026-04-20 source.ver: src/contrib/notameStats_1.1.2.tar.gz vignettes: vignettes/notameStats/inst/doc/Non-targeted_metabolomics_feature_prioritization.html vignetteTitles: Non-targeted metabolomics feature prioritization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/notameStats/inst/doc/Non-targeted_metabolomics_feature_prioritization.R suggestsMe: notame, notameViz dependencyCount: 68 Package: notameViz Version: 1.1.4 Depends: R (>= 4.5.0), ggplot2, SummarizedExperiment Imports: BiocGenerics, cowplot, devEMF, dplyr, ggbeeswarm, ggdendro, ggrepel, grDevices, limma, methods, notame, pcaMethods, qpdf, Rtsne, scales, stringr, stats, tibble, tidyr, utils Suggests: batchCorr, BiocStyle, igraph, knitr, notameStats, testthat License: MIT + file LICENSE MD5sum: 0d8730ddab270c1e056750b94dd124fe NeedsCompilation: no Title: Workflow for non-targeted LC-MS metabolic profiling Description: Provides visualization functionality for untargeted LC-MS metabolomics research. Includes quality control visualizations, feature-wise visualizations and results visualizations. biocViews: BiomedicalInformatics, Metabolomics, DataImport, MassSpectrometry, BatchEffect, MultipleComparison, Normalization, QualityControl, Visualization, Preprocessing Author: Anton Klåvus [aut, cph] (ORCID: ), Jussi Paananen [aut, cph] (ORCID: ), Oskari Timonen [aut, cph] (ORCID: ), Atte Lihtamo [aut], Vilhelm Suksi [aut, cre] (ORCID: ), Retu Haikonen [aut] (ORCID: ), Leo Lahti [aut] (ORCID: ), Kati Hanhineva [aut] (ORCID: ), Ville Koistinen [ctb] (ORCID: ), Olli Kärkkäinen [ctb] (ORCID: ), Artur Sannikov [ctb] Maintainer: Vilhelm Suksi URL: https://github.com/hanhineva-lab/notameViz VignetteBuilder: knitr BugReports: https://github.com/hanhineva-lab/notameViz/issues git_url: https://git.bioconductor.org/packages/notameViz git_branch: devel git_last_commit: 7604681 git_last_commit_date: 2026-02-17 Date/Publication: 2026-04-20 source.ver: src/contrib/notameViz_1.1.4.tar.gz vignettes: vignettes/notameViz/inst/doc/Non_targeted_metabolomics_visualization.html vignetteTitles: Non-targeted metabolomics visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/notameViz/inst/doc/Non_targeted_metabolomics_visualization.R suggestsMe: notame, notameStats dependencyCount: 82 Package: NPARC Version: 1.23.0 Depends: R (>= 4.0.0) Imports: dplyr, tidyr, BiocParallel, broom, MASS, rlang, magrittr, stats, methods Suggests: testthat, devtools, knitr, rprojroot, rmarkdown, ggplot2, BiocStyle License: GPL-3 MD5sum: efb2ecd3d7ce60b99eb858bc03d92653 NeedsCompilation: no Title: Non-parametric analysis of response curves for thermal proteome profiling experiments Description: Perform non-parametric analysis of response curves as described by Childs, Bach, Franken et al. (2019): Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins. biocViews: Software, Proteomics Author: Dorothee Childs, Nils Kurzawa Maintainer: Nils Kurzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NPARC git_branch: devel git_last_commit: fcf70e4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NPARC_1.23.0.tar.gz vignettes: vignettes/NPARC/inst/doc/NPARC.html vignetteTitles: Analysing thermal proteome profiling data with the NPARC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NPARC/inst/doc/NPARC.R dependencyCount: 38 Package: npGSEA Version: 1.47.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: f166bc552f429353825acb708cc9301b NeedsCompilation: no Title: Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA) Description: Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results. biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways Author: Jessica Larson and Art Owen Maintainer: Jessica Larson git_url: https://git.bioconductor.org/packages/npGSEA git_branch: devel git_last_commit: 3e7356f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/npGSEA_1.47.0.tar.gz vignettes: vignettes/npGSEA/inst/doc/npGSEA.pdf vignetteTitles: Running gene set enrichment analysis with the "npGSEA" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/npGSEA/inst/doc/npGSEA.R dependencyCount: 47 Package: NTW Version: 1.61.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: eec326c4d2eb2aac438f2fa84cbf8bb6 NeedsCompilation: no Title: Predict gene network using an Ordinary Differential Equation (ODE) based method Description: This package predicts the gene-gene interaction network and identifies the direct transcriptional targets of the perturbation using an ODE (Ordinary Differential Equation) based method. biocViews: Preprocessing Author: Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang, Yuanhua Liu, Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/NTW git_branch: devel git_last_commit: 7531a70 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NTW_1.61.0.tar.gz vignettes: vignettes/NTW/inst/doc/NTW.pdf vignetteTitles: NTW vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NTW/inst/doc/NTW.R dependencyCount: 3 Package: nucleoSim Version: 1.39.0 Imports: stats, IRanges, S4Vectors, graphics, methods Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: f815a292a2070bb3445aa03e3bf58762 NeedsCompilation: no Title: Generate synthetic nucleosome maps Description: This package can generate a synthetic map with reads covering the nucleosome regions as well as a synthetic map with forward and reverse reads emulating next-generation sequencing. The synthetic hybridization data of “Tiling Arrays” can also be generated. The user has choice between three different distributions for the read positioning: Normal, Student and Uniform. In addition, a visualization tool is provided to explore the synthetic nucleosome maps. biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment Author: Rawane Samb [aut], Astrid Deschênes [cre, aut] (ORCID: ), Pascal Belleau [aut] (ORCID: ), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/arnauddroitlab/nucleoSim VignetteBuilder: knitr BugReports: https://github.com/arnauddroitlab/nucleoSim/issues git_url: https://git.bioconductor.org/packages/nucleoSim git_branch: devel git_last_commit: 792586e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/nucleoSim_1.39.0.tar.gz vignettes: vignettes/nucleoSim/inst/doc/nucleoSim.html vignetteTitles: Generate synthetic nucleosome maps hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R suggestsMe: RJMCMCNucleosomes dependencyCount: 9 Package: nucleR Version: 2.43.0 Depends: R (>= 3.5.0), methods Imports: Biobase, BiocGenerics, Biostrings, Seqinfo, GenomicRanges, IRanges, Rsamtools, S4Vectors, ShortRead, dplyr, ggplot2, magrittr, parallel, stats, utils, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat License: LGPL (>= 3) MD5sum: 12f1b746e0a31074a22bf9f631f41c35 NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and NGS experiments. biocViews: NucleosomePositioning, Coverage, ChIPSeq, Microarray, Sequencing, Genetics, QualityControl, DataImport Author: Oscar Flores, Ricard Illa Maintainer: Alba Sala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nucleR git_branch: devel git_last_commit: 3db28f8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/nucleR_2.43.0.tar.gz vignettes: vignettes/nucleR/inst/doc/nucleR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleR/inst/doc/nucleR.R dependencyCount: 76 Package: nuCpos Version: 1.29.0 Depends: R (>= 4.2.0) Imports: graphics, methods Suggests: NuPoP, Biostrings, testthat License: GPL-2 MD5sum: 913c6ccf912a69bd322402c1e3685620 NeedsCompilation: yes Title: An R package for prediction of nucleosome positions Description: nuCpos, a derivative of NuPoP, is an R package for prediction of nucleosome positions. nuCpos calculates local and whole nucleosomal histone binding affinity (HBA) scores for a given 147-bp sequence. Note: This package was designed to demonstrate the use of chemical maps in prediction. As the parental package NuPoP now provides chemical-map-based prediction, the function for dHMM-based prediction was removed from this package. nuCpos continues to provide functions for HBA calculation. biocViews: Genetics, Epigenetics, NucleosomePositioning Author: Hiroaki Kato, Takeshi Urano Maintainer: Hiroaki Kato git_url: https://git.bioconductor.org/packages/nuCpos git_branch: devel git_last_commit: e871f9d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/nuCpos_1.29.0.tar.gz vignettes: vignettes/nuCpos/inst/doc/nuCpos-intro.pdf vignetteTitles: An R package for prediction of nucleosome positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nuCpos/inst/doc/nuCpos-intro.R dependencyCount: 2 Package: nullranges Version: 1.17.3 Depends: R (>= 4.2.0) Imports: stats, IRanges, GenomicRanges, Seqinfo, methods, rlang, S4Vectors, scales, InteractionSet, ggplot2, grDevices, plyranges, data.table, progress, ggridges Suggests: testthat, knitr, rmarkdown, ks, DNAcopy, RcppHMM, AnnotationHub, ExperimentHub, GenomeInfoDb, nullrangesData, ensembldb, EnsDb.Hsapiens.v86, BSgenome.Hsapiens.UCSC.hg38, patchwork, plotgardener, dplyr, magrittr, tidyr, cobalt, DiagrammeR, MatchIt, mariner License: GPL-3 MD5sum: 590abbc86b2022929c356d1a2e2bbdda NeedsCompilation: no Title: Generation of null ranges via bootstrapping or covariate matching Description: Modular package for generation of sets of ranges representing the null hypothesis. These can take the form of bootstrap samples of ranges (using the block bootstrap framework of Bickel et al 2010), or sets of control ranges that are matched across one or more covariates. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package. biocViews: Visualization, GeneSetEnrichment, FunctionalGenomics, Epigenetics, GeneRegulation, GeneTarget, GenomeAnnotation, Annotation, GenomeWideAssociation, HistoneModification, ChIPSeq, ATACSeq, DNaseSeq, RNASeq, HiddenMarkovModel Author: Michael Love [aut, cre] (ORCID: ), Wancen Mu [aut] (ORCID: ), Eric Davis [aut] (ORCID: ), Douglas Phanstiel [aut] (ORCID: ), Stuart Lee [aut] (ORCID: ), Mikhail Dozmorov [ctb], Tim Triche [ctb], CZI [fnd] Maintainer: Michael Love URL: https://nullranges.github.io/nullranges, https://github.com/nullranges/nullranges VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/nullranges/ git_url: https://git.bioconductor.org/packages/nullranges git_branch: devel git_last_commit: ffc311b git_last_commit_date: 2025-11-20 Date/Publication: 2026-04-20 source.ver: src/contrib/nullranges_1.17.3.tar.gz vignettes: vignettes/nullranges/inst/doc/bootRanges.html, vignettes/nullranges/inst/doc/matching_ginteractions.html, vignettes/nullranges/inst/doc/matching_granges.html, vignettes/nullranges/inst/doc/matching_pool_set.html, vignettes/nullranges/inst/doc/matchRanges.html, vignettes/nullranges/inst/doc/nullranges.html vignetteTitles: 1. Introduction to bootRanges, 4. Matching case study II: CTCF orientation, 3. Matching case study I: CTCF occupancy, 5. Creating a pool set for matchRanges, 2. Introduction to matchRanges, 0. Introduction to nullranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nullranges/inst/doc/bootRanges.R, vignettes/nullranges/inst/doc/matching_ginteractions.R, vignettes/nullranges/inst/doc/matching_granges.R, vignettes/nullranges/inst/doc/matching_pool_set.R, vignettes/nullranges/inst/doc/matchRanges.R, vignettes/nullranges/inst/doc/nullranges.R suggestsMe: tidyomics dependencyCount: 87 Package: NuPoP Version: 2.19.0 Depends: R (>= 4.0) Imports: graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 894459c0e85fdb30186c3df58d6a8423 NeedsCompilation: yes Title: An R package for nucleosome positioning prediction Description: NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at https://github.com/jipingw. The Fortran codes have complete functonality as the R package. Note: NuPoP has two separate functions for prediction of nucleosome positioning, one for MNase-map trained models and the other for chemical map-trained models. The latter was implemented for four species including yeast, S.pombe, mouse and human, trained based on our recent publications. We noticed there is another package nuCpos by another group for prediction of nucleosome positioning trained with chemicals. A report to compare recent versions of NuPoP with nuCpos can be found at https://github.com/jiping/NuPoP_doc. Some more information can be found and will be posted at https://github.com/jipingw/NuPoP. biocViews: Genetics,Visualization,Classification,NucleosomePositioning, HiddenMarkovModel Author: Ji-Ping Wang ; Liqun Xi ; Oscar Zarate Maintainer: Ji-Ping Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NuPoP git_branch: devel git_last_commit: 095144b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/NuPoP_2.19.0.tar.gz vignettes: vignettes/NuPoP/inst/doc/NuPoP.html vignetteTitles: NuPoP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NuPoP/inst/doc/NuPoP.R suggestsMe: nuCpos dependencyCount: 2 Package: occugene Version: 1.71.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: 2784614b8cfaf0e003886900d1d1b614 NeedsCompilation: no Title: Functions for Multinomial Occupancy Distribution Description: Statistical tools for building random mutagenesis libraries for prokaryotes. The package has functions for handling the occupancy distribution for a multinomial and for estimating the number of essential genes in random transposon mutagenesis libraries. biocViews: Annotation, Pathways Author: Oliver Will Maintainer: Oliver Will git_url: https://git.bioconductor.org/packages/occugene git_branch: devel git_last_commit: 6d04278 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/occugene_1.71.0.tar.gz vignettes: vignettes/occugene/inst/doc/occugene.pdf vignetteTitles: occugene hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/occugene/inst/doc/occugene.R dependencyCount: 0 Package: OCplus Version: 1.85.0 Depends: R (>= 2.1.0) Imports: multtest (>= 1.7.3), graphics, grDevices, stats, interp License: LGPL MD5sum: 7f09bdeba7264c23741b67b526be16e9 NeedsCompilation: no Title: Operating characteristics plus sample size and local fdr for microarray experiments Description: This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes). biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yudi Pawitan and Alexander Ploner Maintainer: Alexander Ploner git_url: https://git.bioconductor.org/packages/OCplus git_branch: devel git_last_commit: 57b9a1a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OCplus_1.85.0.tar.gz vignettes: vignettes/OCplus/inst/doc/OCplus.pdf vignetteTitles: OCplus Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OCplus/inst/doc/OCplus.R dependencyCount: 20 Package: odseq Version: 1.39.0 Depends: R (>= 3.2.3) Imports: msa (>= 1.2.1), kebabs (>= 1.4.1), mclust (>= 5.1) Suggests: knitr(>= 1.11) License: MIT + file LICENSE MD5sum: 83efa619206a355a21bd654b154377ac NeedsCompilation: no Title: Outlier detection in multiple sequence alignments Description: Performs outlier detection of sequences in a multiple sequence alignment using bootstrap of predefined distance metrics. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This package implements the OD-seq algorithm proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for aligned sequences and a variant using string kernels for unaligned sequences. biocViews: Alignment, MultipleSequenceAlignment Author: José Jiménez Maintainer: José Jiménez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/odseq git_branch: devel git_last_commit: 0ecb197 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/odseq_1.39.0.tar.gz vignettes: vignettes/odseq/inst/doc/vignette.pdf vignetteTitles: A quick tutorial to outlier detection in MSAs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/odseq/inst/doc/vignette.R dependencyCount: 29 Package: OGRE Version: 1.15.0 Depends: R (>= 4.2.0), S4Vectors Imports: GenomicRanges, methods, data.table, assertthat, ggplot2, Gviz, IRanges, AnnotationHub, grDevices, stats, Seqinfo, GenomeInfoDb, shiny, shinyFiles, DT, rtracklayer, shinydashboard, shinyBS,tidyr Suggests: testthat (>= 3.0.0), knitr (>= 1.36), rmarkdown (>= 2.11) License: Artistic-2.0 MD5sum: 036dec1270163991871a37aa62fafea0 NeedsCompilation: no Title: Calculate, visualize and analyse overlap between genomic regions Description: OGRE calculates overlap between user defined genomic region datasets. Any regions can be supplied i.e. genes, SNPs, or reads from sequencing experiments. Key numbers help analyse the extend of overlaps which can also be visualized at a genomic level. biocViews: Software, WorkflowStep, BiologicalQuestion, Annotation, Metagenomics, Visualization, Sequencing Author: Sven Berres [aut, cre], Jörg Gromoll [ctb], Marius Wöste [ctb], Sarah Sandmann [ctb], Sandra Laurentino [ctb] Maintainer: Sven Berres URL: https://github.com/svenbioinf/OGRE/ VignetteBuilder: knitr BugReports: https://github.com/svenbioinf/OGRE/issues git_url: https://git.bioconductor.org/packages/OGRE git_branch: devel git_last_commit: eb5e234 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OGRE_1.15.0.tar.gz vignettes: vignettes/OGRE/inst/doc/OGRE.html vignetteTitles: OGRE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OGRE/inst/doc/OGRE.R dependencyCount: 169 Package: oligo Version: 1.75.0 Depends: R (>= 3.2.0), BiocGenerics (>= 0.13.11), oligoClasses (>= 1.29.6), Biobase (>= 2.27.3), Biostrings (>= 2.35.12) Imports: affyio (>= 1.35.0), affxparser (>= 1.39.4), DBI (>= 0.3.1), ff, graphics, methods, preprocessCore (>= 1.29.0), RSQLite (>= 1.0.0), splines, stats, stats4, utils, bit LinkingTo: preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg18, hapmap100kxba, pd.hg.u95av2, pd.mapping50k.xba240, pd.huex.1.0.st.v2, pd.hg18.60mer.expr, pd.hugene.1.0.st.v1, maqcExpression4plex, genefilter, limma, RColorBrewer, oligoData, BiocStyle, knitr, RUnit, biomaRt, AnnotationDbi, ACME, RCurl Enhances: doMC, doMPI License: LGPL (>= 2) MD5sum: 506b38fa0aa017df834eea2f8832eefe NeedsCompilation: yes Title: Preprocessing tools for oligonucleotide arrays Description: A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, SNP, DifferentialExpression, ExonArray, GeneExpression, DataImport Author: Benilton Carvalho and Rafael Irizarry Maintainer: Benilton Carvalho URL: https://github.com/benilton/oligo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oligo git_branch: devel git_last_commit: e5c4ecd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/oligo_1.75.0.tar.gz vignettes: vignettes/oligo/inst/doc/oug.pdf vignetteTitles: oligo User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, puma, SCAN.UPC, oligoData, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling, pumadata importsMe: ArrayExpress, cn.farms, frma, ITALICS, mimager suggestsMe: frmaTools, hapmap100khind, hapmap100kxba, hapmap500knsp, hapmap500ksty, hapmapsnp5, hapmapsnp6, maqcExpression4plex, aroma.affymetrix, maGUI, RCPA dependencyCount: 52 Package: oligoClasses Version: 1.73.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.27.1), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>= 1.23.2), foreach, BiocManager, utils, S4Vectors (>= 0.9.25), RSQLite, DBI, ff Suggests: hapmapsnp5, hapmapsnp6, pd.genomewidesnp.6, pd.genomewidesnp.5, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.mapping250k.sty, pd.mapping250k.nsp, genomewidesnp6Crlmm (>= 1.0.7), genomewidesnp5Crlmm (>= 1.0.6), RUnit, human370v1cCrlmm, VanillaICE, crlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: GPL (>= 2) MD5sum: 3bf4a071b70cdb24870302fae5bc9664 NeedsCompilation: no Title: Classes for high-throughput arrays supported by oligo and crlmm Description: This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. biocViews: Infrastructure Author: Benilton Carvalho and Robert Scharpf Maintainer: Benilton Carvalho and Robert Scharpf git_url: https://git.bioconductor.org/packages/oligoClasses git_branch: devel git_last_commit: 924ec0a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/oligoClasses_1.73.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling importsMe: affycoretools, frma, ITALICS, mimager, MinimumDistance, pdInfoBuilder, puma, VanillaICE suggestsMe: hapmapsnp6, aroma.affymetrix, scrime dependencyCount: 48 Package: OLIN Version: 1.89.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: d5bb2bbe14fe372fb910cdcfa5b66371 NeedsCompilation: no Title: Optimized local intensity-dependent normalisation of two-color microarrays Description: Functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLIN git_branch: devel git_last_commit: e409e58 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OLIN_1.89.0.tar.gz vignettes: vignettes/OLIN/inst/doc/OLIN.pdf vignetteTitles: Introduction to OLIN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLIN/inst/doc/OLIN.R dependsOnMe: OLINgui importsMe: OLINgui dependencyCount: 11 Package: OLINgui Version: 1.85.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: fe239fbe8c203b60b1e7d6e834dc40fd NeedsCompilation: no Title: Graphical user interface for OLIN Description: Graphical user interface for the OLIN package biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLINgui git_branch: devel git_last_commit: c20e2c9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OLINgui_1.85.0.tar.gz vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf vignetteTitles: Introduction to OLINgui hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLINgui/inst/doc/OLINgui.R dependencyCount: 17 Package: omada Version: 1.13.0 Depends: pdfCluster (>= 1.0-3), kernlab (>= 0.9-29), R (>= 4.2), fpc (>= 2.2-9), Rcpp (>= 1.0.7), diceR (>= 0.6.0), ggplot2 (>= 3.3.5), reshape (>= 0.8.8), genieclust (>= 1.1.3), clValid (>= 0.7), glmnet (>= 4.1.3), dplyr(>= 1.0.7), stats (>= 4.1.2), clValid(>= 0.7) Suggests: rmarkdown, knitr, testthat License: GPL-3 MD5sum: 86af8d9f7fc31d5db9513063eb8b5b09 NeedsCompilation: no Title: Machine learning tools for automated transcriptome clustering analysis Description: Symptomatic heterogeneity in complex diseases reveals differences in molecular states that need to be investigated. However, selecting the numerous parameters of an exploratory clustering analysis in RNA profiling studies requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent and further gene association analyses need to be performed independently. We have developed a suite of tools to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with four datasets characterised by different expression signal strengths. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Even in datasets with less clear biological distinctions, stable subgroups with different expression profiles and clinical associations were found. biocViews: Software, Clustering, RNASeq, GeneExpression Author: Sokratis Kariotis [aut, cre] (ORCID: ) Maintainer: Sokratis Kariotis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omada git_branch: devel git_last_commit: 4e9162c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/omada_1.13.0.tar.gz vignettes: vignettes/omada/inst/doc/omada-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omada/inst/doc/omada-vignette.R dependencyCount: 138 Package: OmaDB Version: 2.27.0 Depends: R (>= 3.5), httr (>= 1.2.1), plyr(>= 1.8.4) Imports: utils, ape, Biostrings, GenomicRanges, IRanges, methods, topGO, jsonlite Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: a3e2ee71d7116449b15ebbbc2a3f32c8 NeedsCompilation: no Title: R wrapper for the OMA REST API Description: A package for the orthology prediction data download from OMA database. biocViews: Software, ComparativeGenomics, FunctionalGenomics, Genetics, Annotation, GO, FunctionalPrediction Author: Klara Kaleb Maintainer: Klara Kaleb , Adrian Altenhoff URL: https://github.com/DessimozLab/OmaDB VignetteBuilder: knitr BugReports: https://github.com/DessimozLab/OmaDB/issues git_url: https://git.bioconductor.org/packages/OmaDB git_branch: devel git_last_commit: 5040572 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OmaDB_2.27.0.tar.gz vignettes: vignettes/OmaDB/inst/doc/exploring_hogs.html, vignettes/OmaDB/inst/doc/OmaDB.html, vignettes/OmaDB/inst/doc/sequence_mapping.html, vignettes/OmaDB/inst/doc/tree_visualisation.html vignetteTitles: Exploring Hierarchical orthologous groups with OmaDB, Get started with OmaDB, Sequence Mapping with OmaDB, Exploring Taxonomic trees with OmaDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmaDB/inst/doc/exploring_hogs.R, vignettes/OmaDB/inst/doc/OmaDB.R, vignettes/OmaDB/inst/doc/sequence_mapping.R, vignettes/OmaDB/inst/doc/tree_visualisation.R suggestsMe: orthogene, PhyloProfile dependencyCount: 56 Package: omicade4 Version: 1.51.0 Depends: R (>= 3.0.0), ade4 Imports: made4, Biobase Suggests: BiocStyle License: GPL-2 MD5sum: 266c93e253b178f35ae3b94d0789d9ea NeedsCompilation: no Title: Multiple co-inertia analysis of omics datasets Description: This package performes multiple co-inertia analysis of omics datasets. biocViews: Software, Clustering, Classification, MultipleComparison Author: Chen Meng, Aedin Culhane, Amin M. Gholami. Maintainer: Chen Meng git_url: https://git.bioconductor.org/packages/omicade4 git_branch: devel git_last_commit: 13c6995 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/omicade4_1.51.0.tar.gz vignettes: vignettes/omicade4/inst/doc/omicade4.pdf vignetteTitles: Using omicade4 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicade4/inst/doc/omicade4.R importsMe: omicRexposome suggestsMe: biosigner, MultiDataSet, ropls dependencyCount: 39 Package: OmicCircos Version: 1.49.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: 2ffaa207c9f8727c502e35ba76af5f5a NeedsCompilation: no Title: High-quality circular visualization of omics data Description: OmicCircos is an R application and package for generating high-quality circular plots for omics data. biocViews: Visualization,Statistics,Annotation Author: Ying Hu Chunhua Yan Maintainer: Ying Hu git_url: https://git.bioconductor.org/packages/OmicCircos git_branch: devel git_last_commit: 303af10 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OmicCircos_1.49.0.tar.gz vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf vignetteTitles: OmicCircos vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.R dependencyCount: 11 Package: omicplotR Version: 1.31.0 Depends: R (>= 3.6), ALDEx2 (>= 1.18.0) Imports: compositions, DT, grDevices, knitr, jsonlite, matrixStats, rmarkdown, shiny, stats, vegan, zCompositions License: MIT + file LICENSE MD5sum: ecd763943496b4f41e20b253c10b75bb NeedsCompilation: no Title: Visual Exploration of Omic Datasets Using a Shiny App Description: A Shiny app for visual exploration of omic datasets as compositions, and differential abundance analysis using ALDEx2. Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata. biocViews: Software, DifferentialExpression, GeneExpression, GUI, RNASeq, DNASeq, Metagenomics, Transcriptomics, Bayesian, Microbiome, Visualization, Sequencing, ImmunoOncology Author: Daniel Giguere [aut, cre], Jean Macklaim [aut], Greg Gloor [aut] Maintainer: Daniel Giguere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicplotR git_branch: devel git_last_commit: 37d7dc5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/omicplotR_1.31.0.tar.gz vignettes: vignettes/omicplotR/inst/doc/omicplotR.html vignetteTitles: omicplotR: A tool for visualization of omic datasets as compositions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicplotR/inst/doc/omicplotR.R dependencyCount: 106 Package: omicsGMF Version: 1.1.0 Depends: R (>= 4.5.0), sgdGMF, SingleCellExperiment, scuttle, scater Imports: stats, utils, Matrix, S4Vectors, SummarizedExperiment, DelayedArray, MatrixGenerics, BiocSingular, BiocParallel, beachmat, ggplot2, methods, QFeatures Suggests: knitr, dplyr, testthat, BiocGenerics, BiocStyle, graphics, grDevices License: Artistic-2.0 MD5sum: 6f719b7a8b720386183c0067e6d98366 NeedsCompilation: no Title: Dimensionality reduction of (single-cell) omics data in R using omicsGMF Description: omicsGMF is a Bioconductor package that uses the sgdGMF-framework of the \code{sgdGMF} package for highly performant and fast matrix factorization that can be used for dimensionality reduction, visualization and imputation of omics data. It considers data from the general exponential family as input, and therefore suits the use of both RNA-seq (Poisson or Negative Binomial data) and proteomics data (Gaussian data). It does not require prior transformation of counts to the log-scale, because it rather optimizes the deviances from the data family specified. Also, it allows to correct for known sample-level and feature-level covariates, therefore enabling visualization and dimensionality reduction upon batch correction. Last but not least, it deals with missing values, and allows to impute these after matrix factorization, useful for proteomics data. This Bioconductor package allows input of SummarizedExperiment, SingleCellExperiment, and QFeature classes. biocViews: SingleCell, RNASeq, Proteomics, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, DataRepresentation, MassSpectrometry Author: Alexandre Segers [aut, cre, fnd], Cristian Castiglione [ctb], Christophe Vanderaa [ctb], Davide Risso [ctb, fnd], Lieven Clement [ctb, fnd] Maintainer: Alexandre Segers URL: https://github.com/statOmics/omicsGMF VignetteBuilder: knitr BugReports: https://github.com/statOmics/omicsGMF/issues git_url: https://git.bioconductor.org/packages/omicsGMF git_branch: devel git_last_commit: 66f9d2d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/omicsGMF_1.1.0.tar.gz vignettes: vignettes/omicsGMF/inst/doc/Proteomics-vignette.html, vignettes/omicsGMF/inst/doc/RNASeq-vignette.html vignetteTitles: Proteomics-vignette: omicsGMF, RNASeq-vignette: omicsGMF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsGMF/inst/doc/Proteomics-vignette.R, vignettes/omicsGMF/inst/doc/RNASeq-vignette.R dependencyCount: 188 Package: OMICsPCA Version: 1.29.0 Depends: R (>= 3.5.0), OMICsPCAdata Imports: HelloRanges, fpc, stats, MultiAssayExperiment, pdftools, methods, grDevices, utils,clValid, NbClust, cowplot, rmarkdown, kableExtra, rtracklayer, IRanges, Seqinfo, reshape2, ggplot2, factoextra, rgl, corrplot, MASS, graphics, FactoMineR, PerformanceAnalytics, tidyr, data.table, cluster, magick Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: ecf3a5425a178c8c7c89167295a4b4f9 NeedsCompilation: no Title: An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples Description: OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals. biocViews: ImmunoOncology, MultipleComparison, PrincipalComponent, DataRepresentation, Workflow, Visualization, DimensionReduction, Clustering, BiologicalQuestion, EpigeneticsWorkflow, Transcription, GeneticVariability, GUI, BiomedicalInformatics, Epigenetics, FunctionalGenomics, SingleCell Author: Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb] Maintainer: Subhadeep Das VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OMICsPCA git_branch: devel git_last_commit: 3ed273e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OMICsPCA_1.29.0.tar.gz vignettes: vignettes/OMICsPCA/inst/doc/vignettes.html vignetteTitles: OMICsPCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OMICsPCA/inst/doc/vignettes.R dependencyCount: 212 Package: omicsPrint Version: 1.31.0 Depends: R (>= 3.5), MASS Imports: methods, matrixStats, graphics, stats, SummarizedExperiment, MultiAssayExperiment, RaggedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat, GEOquery, VariantAnnotation, Rsamtools, BiocParallel, GenomicRanges, FDb.InfiniumMethylation.hg19, snpStats License: GPL (>= 2) MD5sum: 7683c5ed9bff65d4535546e05b7628a1 NeedsCompilation: no Title: Cross omic genetic fingerprinting Description: omicsPrint provides functionality for cross omic genetic fingerprinting, for example, to verify sample relationships between multiple omics data types, i.e. genomic, transcriptomic and epigenetic (DNA methylation). biocViews: QualityControl, Genetics, Epigenetics, Transcriptomics, DNAMethylation, Transcription, GeneticVariability, ImmunoOncology Author: Maarten van Iterson [aut], Davy Cats [cre] Maintainer: Davy Cats VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicsPrint git_branch: devel git_last_commit: a7fe1fa git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/omicsPrint_1.31.0.tar.gz vignettes: vignettes/omicsPrint/inst/doc/omicsPrint.html vignetteTitles: omicsPrint: detection of data linkage errors in multiple omics studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsPrint/inst/doc/omicsPrint.R dependencyCount: 48 Package: omicsViewer Version: 1.15.4 Depends: R (>= 4.2) Imports: htmltools, shinydashboard, survminer, survival, fastmatch, reshape2, stringr, beeswarm, grDevices, DT, shiny, shinythemes, shinyWidgets, plotly, networkD3, httr, matrixStats, RColorBrewer, Biobase, fgsea, openxlsx, psych, shinybusy, ggseqlogo, htmlwidgets, graphics, grid, stats, utils, methods, shinyjs, curl, flatxml, ggplot2, S4Vectors, SummarizedExperiment, RSQLite, Matrix, shinycssloaders, ROCR, drc Suggests: BiocStyle, knitr, rmarkdown, unittest License: GPL-2 MD5sum: 2ce276d742e87de1d3ad3fe4fd28ac53 NeedsCompilation: no Title: Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer Description: omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisher’s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. Otherwise, a standalone version of omicsViewer together with designated omics data could be easily created by integrating it with portable R, which can be shared with collaborators or submitted as supplementary data together with a manuscript. biocViews: Software, Visualization, GeneSetEnrichment, DifferentialExpression, MotifDiscovery, Network, NetworkEnrichment Author: Chen Meng [aut, cre] Maintainer: Chen Meng URL: https://github.com/mengchen18/omicsViewer VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=0nirB-exquY&list=PLo2m88lJf-RRoLKMY8UEGqCpraKYrX5lk BugReports: https://github.com/mengchen18/omicsViewer git_url: https://git.bioconductor.org/packages/omicsViewer git_branch: devel git_last_commit: bfd7dd7 git_last_commit_date: 2026-03-20 Date/Publication: 2026-04-20 source.ver: src/contrib/omicsViewer_1.15.4.tar.gz vignettes: vignettes/omicsViewer/inst/doc/quickStart.html vignetteTitles: quickStart.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsViewer/inst/doc/quickStart.R dependencyCount: 198 Package: Omixer Version: 1.21.0 Depends: R (>= 4.0.0) Imports: dplyr, ggplot2, forcats, tibble, gridExtra, magrittr, readr, tidyselect, grid, stats, stringr Suggests: knitr, rmarkdown, BiocStyle, magick, testthat License: MIT + file LICENSE MD5sum: 5c1ad77c5a86a3f285e7f2129b6ab989 NeedsCompilation: no Title: Omixer: multivariate and reproducible sample randomization to proactively counter batch effects in omics studies Description: Omixer - an Bioconductor package for multivariate and reproducible sample randomization, which ensures optimal sample distribution across batches with well-documented methods. It outputs lab-friendly sample layouts, reducing the risk of sample mixups when manually pipetting randomized samples. biocViews: DataRepresentation, ExperimentalDesign, QualityControl, Software, Visualization Author: Lucy Sinke [cre, aut] Maintainer: Lucy Sinke VignetteBuilder: knitr BugReports: https://github.com/molepi/Omixer/issues git_url: https://git.bioconductor.org/packages/Omixer git_branch: devel git_last_commit: b40ae0f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Omixer_1.21.0.tar.gz vignettes: vignettes/Omixer/inst/doc/omixer-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Omixer/inst/doc/omixer-vignette.R dependencyCount: 46 Package: ompBAM Version: 1.15.0 Imports: utils, Rcpp Suggests: RcppProgress, knitr, rmarkdown, roxygen2, devtools, usethis, desc, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: ae18cafc5d18535884ecacfa5fe51553 NeedsCompilation: no Title: C++ Library for OpenMP-based multi-threaded sequential profiling of Binary Alignment Map (BAM) files Description: This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged included within ompBAM. biocViews: Alignment, DataImport, RNASeq, Software, Sequencing, Transcriptomics, SingleCell Author: Alex Chit Hei Wong [aut, cre, cph] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/ompBAM VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/ompBAM git_branch: devel git_last_commit: 1bbdce2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ompBAM_1.15.0.tar.gz vignettes: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.html vignetteTitles: ompBAM API Documentation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.R importsMe: SpliceWiz linksToMe: SpliceWiz dependencyCount: 3 Package: oncomix Version: 1.33.0 Depends: R (>= 3.4.0) Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats, SummarizedExperiment Suggests: knitr, rmarkdown, testthat, RMySQL License: GPL-3 MD5sum: 4d037c2d7cf018d09c2707d084aa3caf NeedsCompilation: no Title: Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data Description: This package helps identify mRNAs that are overexpressed in subsets of tumors relative to normal tissue. Ideal inputs would be paired tumor-normal data from the same tissue from many patients (>15 pairs). This unsupervised approach relies on the observation that oncogenes are characteristically overexpressed in only a subset of tumors in the population, and may help identify oncogene candidates purely based on differences in mRNA expression between previously unknown subtypes. biocViews: GeneExpression, Sequencing Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oncomix git_branch: devel git_last_commit: cca44aa git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/oncomix_1.33.0.tar.gz vignettes: vignettes/oncomix/inst/doc/oncomix.html vignetteTitles: OncoMix Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oncomix/inst/doc/oncomix.R dependencyCount: 45 Package: oncoscanR Version: 1.13.0 Depends: R (>= 4.2), IRanges (>= 2.30.0), GenomicRanges (>= 1.48.0), magrittr Imports: readr, S4Vectors, methods, utils Suggests: testthat (>= 3.1.4), jsonlite, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 54639e976000f73485a621872c2c7d9f NeedsCompilation: no Title: Secondary analyses of CNV data (HRD and more) Description: The software uses the copy number segments from a text file and identifies all chromosome arms that are globally altered and computes various genome-wide scores. The following HRD scores (characteristic of BRCA-mutated cancers) are included: LST, HR-LOH, nLST and gLOH. the package is tailored for the ThermoFisher Oncoscan assay analyzed with their Chromosome Alteration Suite (ChAS) but can be adapted to any input. biocViews: CopyNumberVariation, Microarray, Software Author: Yann Christinat [aut, cre], Geneva University Hospitals [aut, cph] Maintainer: Yann Christinat URL: https://github.com/yannchristinat/oncoscanR VignetteBuilder: knitr BugReports: https://github.com/yannchristinat/oncoscanR/issues git_url: https://git.bioconductor.org/packages/oncoscanR git_branch: devel git_last_commit: 1b56ce7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/oncoscanR_1.13.0.tar.gz vignettes: vignettes/oncoscanR/inst/doc/oncoscanR.html vignetteTitles: oncoscanR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/oncoscanR/inst/doc/oncoscanR.R dependencyCount: 36 Package: OncoScore Version: 1.39.1 Depends: R (>= 4.1.0), Imports: biomaRt, grDevices, graphics, utils, methods, Suggests: BiocGenerics, BiocStyle, knitr, testthat, License: file LICENSE MD5sum: 2ec79596fb2e8a1b3e1b8f600306dfcc NeedsCompilation: no Title: A tool to identify potentially oncogenic genes Description: OncoScore is a tool to measure the association of genes to cancer based on citation frequencies in biomedical literature. The score is evaluated from PubMed literature by dynamically updatable web queries. biocViews: BiomedicalInformatics Author: Luca De Sano [cre, aut] (ORCID: ), Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb], Daniele Ramazzotti [aut] (ORCID: ), Roberta Spinelli [ctb] Maintainer: Luca De Sano URL: https://github.com/danro9685/OncoScore VignetteBuilder: knitr BugReports: https://github.com/danro9685/OncoScore git_url: https://git.bioconductor.org/packages/OncoScore git_branch: devel git_last_commit: bcc86a0 git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/OncoScore_1.39.1.tar.gz vignettes: vignettes/OncoScore/inst/doc/v1_introduction.html, vignettes/OncoScore/inst/doc/v2_running_OncoScore.html vignetteTitles: Introduction, Running OncoScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OncoScore/inst/doc/v1_introduction.R, vignettes/OncoScore/inst/doc/v2_running_OncoScore.R dependencyCount: 63 Package: OncoSimulR Version: 4.13.4 Depends: R (>= 3.5.0) Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr, smatr, ggplot2, ggrepel, stringr LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown, pander License: GPL (>= 3) MD5sum: 13e9265cb83b04df7b4f1637b2280de9 NeedsCompilation: yes Title: Forward Genetic Simulation of Cancer Progression with Epistasis Description: Functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Fitness (including just birth, just death, or both birth and death) can also be a function of the relative and absolute frequencies of other genotypes (i.e., frequency-dependent fitness). Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulating multi-species scenarios and therapeutic interventions, including adaptive therapy, is also possible. Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for: simulating random DAGs of the type found in Oncogenetic Trees, Conjunctive Bayesian Networks, and other cancer progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (Rough Mount Fuji, House of Cards, additive, NK, Ising, and Eggbox models) and plotting them. biocViews: BiologicalQuestion, SomaticMutation Author: Ramon Diaz-Uriarte [aut, cre] (ORCID: ), Sergio Sanchez-Carrillo [aut], Juan Antonio Miguel Gonzalez [aut], Mark Taylor [ctb] (plot.stream, plot.stacked), Niklas Endres [ctb] (vignette examples, freq-dep-fitness time), Javier Mu~noz Haro [aut] (interventions), Alberto Gonzalez Klein [aut] (user-specified death rates), Javier Lopez Cano [aut] (user-defined variables), Arash Partow [ctb] (ExprTk), Sophie Brouillet [ctb] (MAGELLAN), Sebastian Matuszewski [ctb] (MAGELLAN), Harry Annoni [ctb] (MAGELLAN), Luca Ferretti [ctb] (MAGELLAN), Guillaume Achaz [ctb] (MAGELLAN), Wolodzko Tymoteusz [ctb] (multivariate hypergeometric), Guillermo Gorines Cordero [ctb] (rfitness), Ivan Lorca Alonso [ctb] (rfitness), Francisco Mu~noz Lopez [ctb] (rfitness), David Roncero Moro~no [ctb] (rfitness), Alvaro Quevedo [ctb] (rfitness), Pablo Perez [ctb] (rfitness), Cristina Devesa [ctb] (rfitness), Alejandro Herrador [ctb] (rfitness), Holger Froehlich [ctb] (simOGraph (transitive closure)), Florian Markowetz [ctb] (simOGraph (transitive closure)), Achim Tresch [ctb] (simOGraph (transitive closure)), Theresa Niederberger [ctb] (simOGraph (transitive closure)), Christian Bender [ctb] (simOGraph (transitive closure)), Matthias Maneck [ctb] (simOGraph (transitive closure)), Claudio Lottaz [ctb] (simOGraph (transitive closure)), Tim Beissbarth [ctb] (simOGraph (transitive closure)), Sara Dorado Alfaro [ctb] (vignette examples), Miguel Hernandez del Valle [ctb] (vignette examples), Alvaro Huertas Garcia [ctb] (vignette examples), Diego Ma~nanes Cayero [ctb] (vignette examples), Alejandro Martin Mu~noz [ctb] (vignette examples), Marta Couce Iglesias [ctb] (vignette examples), Silvia Garcia Cobos [ctb] (vignette examples), Carlos Madariaga Aramendi [ctb] (vignette examples), Ana Rodriguez Ronchel [ctb] (vignette examples), Lucia Sanchez Garcia [ctb] (vignette examples), Yolanda Benitez Quesada [ctb] (vignette examples), Asier Fernandez Pato [ctb] (vignette examples), Esperanza Lopez Lopez [ctb] (vignette examples), Alberto Manuel Parra Perez [ctb] (vignette examples), Jorge Garcia Calleja [ctb] (vignette examples), Ana del Ramo Galian [ctb] (vignette examples), Alejandro de los Reyes Benitez [ctb] (vignette examples), Guillermo Garcia Hoyos [ctb] (vignette examples), Rosalia Palomino Cabrera [ctb] (vignette examples), Rafael Barrero Rodriguez [ctb] (vignette examples), Silvia Talavera Marcos [ctb] (vignette examples) Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/OncoSimul, https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/ VignetteBuilder: knitr BugReports: https://github.com/rdiaz02/OncoSimul/issues git_url: https://git.bioconductor.org/packages/OncoSimulR git_branch: devel git_last_commit: a141959 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/OncoSimulR_4.13.4.tar.gz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.html vignetteTitles: OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R dependencyCount: 87 Package: onlineFDR Version: 2.19.0 Imports: stats, Rcpp, progress LinkingTo: Rcpp, RcppProgress Suggests: knitr, rmarkdown, testthat, covr License: GPL-3 MD5sum: a3f0f7dd90d65247c122b3454ef6276e NeedsCompilation: yes Title: Online error rate control Description: This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online multiple hypothesis testing, where hypotheses arrive in a stream. In this framework, a null hypothesis is rejected based on the evidence against it and on the previous rejection decisions. biocViews: MultipleComparison, Software, StatisticalMethod Author: David S. Robertson [aut, cre], Lathan Liou [aut], Aaditya Ramdas [aut], Adel Javanmard [ctb], Andrea Montanari [ctb], Jinjin Tian [ctb], Tijana Zrnic [ctb], Natasha A. Karp [aut] Maintainer: David S. Robertson URL: https://dsrobertson.github.io/onlineFDR/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/onlineFDR git_branch: devel git_last_commit: b353ec7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/onlineFDR_2.19.0.tar.gz vignettes: vignettes/onlineFDR/inst/doc/advanced-usage.html, vignettes/onlineFDR/inst/doc/onlineFDR.html, vignettes/onlineFDR/inst/doc/theory.html vignetteTitles: Advanced usage of onlineFDR, Using the onlineFDR package, The theory behind onlineFDR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/onlineFDR/inst/doc/advanced-usage.R, vignettes/onlineFDR/inst/doc/onlineFDR.R, vignettes/onlineFDR/inst/doc/theory.R dependencyCount: 17 Package: openCyto Version: 2.23.1 Depends: R (>= 3.5.0) Imports: methods, Biobase, BiocGenerics, flowCore(>= 1.99.17), flowViz, ncdfFlow(>= 2.11.34), flowWorkspace(>= 3.99.1), flowClust(>= 3.11.4), RBGL, graph, data.table, RColorBrewer, grDevices LinkingTo: cpp11, BH(>= 1.62.0-1) Suggests: flowWorkspaceData, knitr, rmarkdown, markdown, testthat, utils, tools, parallel, ggcyto, CytoML, flowStats(>= 4.5.2), MASS License: AGPL-3.0-only MD5sum: 4fc6a74eee3776765ba5b7ec31f1050e NeedsCompilation: yes Title: Hierarchical Gating Pipeline for flow cytometry data Description: This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openCyto git_branch: devel git_last_commit: 6c42736 git_last_commit_date: 2026-03-12 Date/Publication: 2026-04-20 source.ver: src/contrib/openCyto_2.23.1.tar.gz vignettes: vignettes/openCyto/inst/doc/HowToAutoGating.html, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html vignetteTitles: How to use different auto gating functions, How to write a csv gating template, An Introduction to the openCyto package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/openCyto/inst/doc/HowToAutoGating.R, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R, vignettes/openCyto/inst/doc/openCytoVignette.R importsMe: CytoML suggestsMe: CATALYST, flowClust, flowCore, flowStats, flowTime, flowWorkspace, ggcyto, staRgate dependencyCount: 75 Package: openPrimeR Version: 1.33.0 Depends: R (>= 4.0.0) Imports: Biostrings (>= 2.38.4), pwalign, XML (>= 3.98-1.4), scales (>= 0.4.0), reshape2 (>= 1.4.1), seqinr (>= 3.3-3), IRanges (>= 2.4.8), GenomicRanges (>= 1.22.4), ggplot2 (>= 2.1.0), plyr (>= 1.8.4), dplyr (>= 0.5.0), stringdist (>= 0.9.4.1), stringr (>= 1.0.0), RColorBrewer (>= 1.1-2), DECIPHER (>= 1.16.1), lpSolveAPI (>= 5.5.2.0-17), digest (>= 0.6.9), Hmisc (>= 3.17-4), ape (>= 3.5), BiocGenerics (>= 0.16.1), S4Vectors (>= 0.8.11), foreach (>= 1.4.3), magrittr (>= 1.5), uniqtag (>= 1.0), openxlsx (>= 4.0.17), grid (>= 3.1.0), grDevices (>= 3.1.0), stats (>= 3.1.0), utils (>= 3.1.0), methods (>= 3.1.0) Suggests: testthat (>= 1.0.2), knitr (>= 1.13), rmarkdown (>= 1.0), devtools (>= 1.12.0), doParallel (>= 1.0.10), pander (>= 0.6.0), learnr (>= 0.9) License: GPL-2 MD5sum: 476282d281900c97a59c361dc1f5ea67 NeedsCompilation: no Title: Multiplex PCR Primer Design and Analysis Description: An implementation of methods for designing, evaluating, and comparing primer sets for multiplex PCR. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. A Shiny app providing a user interface for the functionalities of this package is provided by the 'openPrimeRui' package. biocViews: Software, Technology, Coverage, MultipleComparison Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring SystemRequirements: MAFFT (>= 7.305), OligoArrayAux (>= 3.8), ViennaRNA (>= 2.4.1), MELTING (>= 5.1.1), Pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeR git_branch: devel git_last_commit: 9ac7cf4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/openPrimeR_1.33.0.tar.gz vignettes: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.html vignetteTitles: openPrimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.R dependencyCount: 104 Package: OpenStats Version: 1.23.0 Depends: nlme Imports: MASS, jsonlite, Hmisc, methods, knitr, AICcmodavg, car, rlist, summarytools, graphics, stats, utils Suggests: rmarkdown License: GPL (>= 2) MD5sum: 587eeb963e7965ad174273c8ba70f146 NeedsCompilation: no Title: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput genotype-phenotype association Description: Package contains several methods for statistical analysis of genotype to phenotype association in high-throughput screening pipelines. biocViews: StatisticalMethod, BatchEffect, Bayesian Author: Hamed Haseli Mashhadi Maintainer: Marina Kan URL: https://git.io/Jv5w0 VignetteBuilder: knitr BugReports: https://git.io/Jv5wg git_url: https://git.bioconductor.org/packages/OpenStats git_branch: devel git_last_commit: cd2f9e9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OpenStats_1.23.0.tar.gz vignettes: vignettes/OpenStats/inst/doc/OpenStats.html vignetteTitles: OpenStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OpenStats/inst/doc/OpenStats.R dependencyCount: 128 Package: oposSOM Version: 2.29.0 Depends: R (>= 4.0.0), igraph (>= 1.0.0) Imports: fastICA, tsne, scatterplot3d, pixmap, fdrtool, ape, biomaRt, Biobase, RcppParallel, Rcpp, methods, graph, XML, png, RCurl LinkingTo: RcppParallel, Rcpp License: GPL (>=2) MD5sum: 5e6656bd7c931937cc3846292cf6b228 NeedsCompilation: yes Title: Comprehensive analysis of transcriptome data Description: This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Visualization Author: Henry Loeffler-Wirth , Hoang Thanh Le and Martin Kalcher Maintainer: Henry Loeffler-Wirth URL: http://som.izbi.uni-leipzig.de git_url: https://git.bioconductor.org/packages/oposSOM git_branch: devel git_last_commit: 5c4952e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/oposSOM_2.29.0.tar.gz vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf vignetteTitles: The oposSOM users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oposSOM/inst/doc/Vignette.R dependencyCount: 82 Package: optimalFlow Version: 1.23.0 Depends: dplyr, optimalFlowData, rlang (>= 0.4.0) Imports: transport, parallel, Rfast, robustbase, dbscan, randomForest, foreach, graphics, doParallel, stats, flowMeans, rgl, ellipse Suggests: knitr, BiocStyle, rmarkdown, magick License: Artistic-2.0 MD5sum: 205c65c86a793c418ecd576f65f50686 NeedsCompilation: no Title: optimalFlow Description: Optimal-transport techniques applied to supervised flow cytometry gating. biocViews: Software, FlowCytometry, Technology Author: Hristo Inouzhe Maintainer: Hristo Inouzhe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/optimalFlow git_branch: devel git_last_commit: 09d158c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/optimalFlow_1.23.0.tar.gz vignettes: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.html vignetteTitles: optimalFlow: optimal-transport approach to Flow Cytometry analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.R dependencyCount: 98 Package: OPWeight Version: 1.33.0 Depends: R (>= 3.4.0), Imports: graphics, qvalue, MASS, tibble, stats, Suggests: airway, BiocStyle, cowplot, DESeq2, devtools, ggplot2, gridExtra, knitr, Matrix, rmarkdown, scales, testthat License: Artistic-2.0 MD5sum: 0ae19c454786d48feeb4fc35f59eaba1 NeedsCompilation: no Title: Optimal p-value weighting with independent information Description: This package perform weighted-pvalue based multiple hypothesis test and provides corresponding information such as ranking probability, weight, significant tests, etc . To conduct this testing procedure, the testing method apply a probabilistic relationship between the test rank and the corresponding test effect size. biocViews: ImmunoOncology, BiomedicalInformatics, MultipleComparison, Regression, RNASeq, SNP Author: Mohamad Hasan [aut, cre], Paul Schliekelman [aut] Maintainer: Mohamad Hasan URL: https://github.com/mshasan/OPWeight VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OPWeight git_branch: devel git_last_commit: 01a9b56 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OPWeight_1.33.0.tar.gz vignettes: vignettes/OPWeight/inst/doc/OPWeight.html vignetteTitles: "Introduction to OPWeight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OPWeight/inst/doc/OPWeight.R dependencyCount: 37 Package: OrderedList Version: 1.83.0 Depends: R (>= 3.6.1), Biobase, twilight Imports: methods License: GPL (>= 2) MD5sum: fc86af0b7487d42c99e953f7f0d873ca NeedsCompilation: no Title: Similarities of Ordered Gene Lists Description: Detection of similarities between ordered lists of genes. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Xinan Yang, Stefanie Scheid, Claudio Lottaz Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/OrderedList.shtml git_url: https://git.bioconductor.org/packages/OrderedList git_branch: devel git_last_commit: 9b40a29 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OrderedList_1.83.0.tar.gz vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf vignetteTitles: Similarities of Ordered Gene Lists hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrderedList/inst/doc/tr_2006_01.R dependencyCount: 10 Package: ORFhunteR Version: 1.19.0 Depends: Biostrings, rtracklayer, Peptides Imports: Rcpp (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg38, data.table, stringr, randomForest, xfun, stats, utils, parallel, graphics LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown License: MIT License MD5sum: 76c591709223ef9623b9c0d6f09778f2 NeedsCompilation: yes Title: Predict open reading frames in nucleotide sequences Description: The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules from the NCBI RefSeq and Ensembl databases. The package can be used in basic and applied biomedical research related to the study of the transcriptome of normal as well as altered (for example, cancer) human cells. biocViews: Technology, StatisticalMethod, Sequencing, RNASeq, Classification, FeatureExtraction Author: Vasily V. Grinev [aut, cre] (ORCID: ), Mikalai M. Yatskou [aut], Victor V. Skakun [aut], Maryna Chepeleva [aut] (ORCID: ), Petr V. Nazarov [aut] (ORCID: ) Maintainer: Vasily V. Grinev VignetteBuilder: knitr BugReports: https://github.com/rfctbio-bsu/ORFhunteR/issues git_url: https://git.bioconductor.org/packages/ORFhunteR git_branch: devel git_last_commit: 8a2dcbf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ORFhunteR_1.19.0.tar.gz vignettes: vignettes/ORFhunteR/inst/doc/ORFhunteR.html vignetteTitles: The ORFhunteR package: User’s manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ORFhunteR/inst/doc/ORFhunteR.R dependencyCount: 74 Package: OrganismDbi Version: 1.53.2 Depends: R (>= 2.14.0), BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.33.15), Seqinfo, GenomicFeatures (>= 1.61.4) Imports: methods, utils, stats, DBI, BiocManager, Biobase, graph, RBGL, S4Vectors, IRanges, GenomicRanges (>= 1.61.1) Suggests: txdbmaker, GenomeInfoDbData, Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, AnnotationHub, FDb.UCSC.tRNAs, rtracklayer, biomaRt, RUnit, RMariaDB, BiocStyle, knitr License: Artistic-2.0 MD5sum: d61d887ee8ffa78ac9529a0d671259ec NeedsCompilation: no Title: Software to enable the smooth interfacing of different database packages Description: The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods. biocViews: Annotation, Infrastructure Author: Marc Carlson [aut], Martin Morgan [aut], Valerie Obenchain [aut], Aliyu Atiku Mustapha [ctb] (Converted 'OrganismDbi' vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OrganismDbi git_branch: devel git_last_commit: a63751b git_last_commit_date: 2025-10-31 Date/Publication: 2026-04-20 source.ver: src/contrib/OrganismDbi_1.53.2.tar.gz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.html vignetteTitles: OrganismDbi: A meta framework for Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R dependsOnMe: Homo.sapiens, Mus.musculus, Rattus.norvegicus importsMe: AnnotationHubData, epivizrData, ggbio, uncoverappLib suggestsMe: ChIPpeakAnno, epivizrStandalone dependencyCount: 78 Package: orthogene Version: 1.17.2 Depends: R (>= 4.5.0) Imports: dplyr, methods, stats, utils, Matrix, jsonlite, homologene, gprofiler2, babelgene, data.table, parallel, ggplot2, ggpubr, patchwork, DelayedArray, repmis, ggtree, tools, magrittr Suggests: remotes, knitr, BiocStyle, markdown, rmarkdown, testthat (>= 3.0.0), piggyback, magick, GenomeInfoDbData, ape, phytools, rphylopic (>= 1.0.0), TreeTools, ggimage, OmaDB License: GPL-3 MD5sum: 48c24039866cbea7f305a38e099d1c1d NeedsCompilation: no Title: Interspecies gene mapping Description: `orthogene` is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across **700+ organisms**. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using **1:1**, **many:1**, **1:many** or **many:many** gene mappings, both within- and between-species. biocViews: Genetics, ComparativeGenomics, Preprocessing, Phylogenetics, Transcriptomics, GeneExpression Author: Brian Schilder [cre, fnd] (ORCID: ) Maintainer: Brian Schilder URL: https://github.com/neurogenomics/orthogene VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/orthogene/issues git_url: https://git.bioconductor.org/packages/orthogene git_branch: devel git_last_commit: 3569148 git_last_commit_date: 2026-01-07 Date/Publication: 2026-04-20 source.ver: src/contrib/orthogene_1.17.2.tar.gz vignettes: vignettes/orthogene/inst/doc/docker.html, vignettes/orthogene/inst/doc/infer_species.html, vignettes/orthogene/inst/doc/orthogene.html vignetteTitles: docker, Infer species, orthogene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/orthogene/inst/doc/docker.R, vignettes/orthogene/inst/doc/infer_species.R, vignettes/orthogene/inst/doc/orthogene.R importsMe: BulkSignalR, EWCE suggestsMe: sparrow dependencyCount: 167 Package: OSAT Version: 1.59.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: bd25d31f3b28c5d6382e66e5caac6f23 NeedsCompilation: no Title: OSAT: Optimal Sample Assignment Tool Description: A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. To minimize batch effects, a careful experiment design should ensure the even distribution of biological groups and confounding factors across batches. OSAT (Optimal Sample Assignment Tool) is developed to facilitate the allocation of collected samples to different batches. With minimum steps, it produces setup that optimizes the even distribution of samples in groups of biological interest into different batches, reducing the confounding or correlation between batches and the biological variables of interest. It can also optimize the even distribution of confounding factors across batches. Our tool can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideal balanced RCBD. OSAT provides a number of predefined layout for some of the most commonly used genomics platform. Related paper can be find at http://www.biomedcentral.com/1471-2164/13/689 . biocViews: DataRepresentation, Visualization, ExperimentalDesign, QualityControl Author: Li Yan Maintainer: Li Yan URL: http://www.biomedcentral.com/1471-2164/13/689 git_url: https://git.bioconductor.org/packages/OSAT git_branch: devel git_last_commit: 3e012f9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OSAT_1.59.0.tar.gz vignettes: vignettes/OSAT/inst/doc/OSAT.pdf vignetteTitles: An introduction to OSAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OSAT/inst/doc/OSAT.R suggestsMe: designit dependencyCount: 2 Package: OTUbase Version: 1.61.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: 4762fa4b28fc27ffb95d3ee5b7992315 NeedsCompilation: no Title: Provides structure and functions for the analysis of OTU data Description: Provides a platform for Operational Taxonomic Unit based analysis biocViews: Sequencing, DataImport Author: Daniel Beck, Matt Settles, and James A. Foster Maintainer: Daniel Beck git_url: https://git.bioconductor.org/packages/OTUbase git_branch: devel git_last_commit: 42c921c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OTUbase_1.61.0.tar.gz vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf vignetteTitles: An introduction to OTUbase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.R dependencyCount: 60 Package: OutSplice Version: 1.11.0 Depends: R(>= 4.3) Imports: AnnotationDbi (>= 1.60.0), GenomicRanges (>= 1.49.0), GenomicFeatures (>= 1.50.2), IRanges (>= 2.32.0), org.Hs.eg.db (>= 3.16.0), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.16.0), S4Vectors (>= 0.36.0) Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 9401fbf48d9cb43e488f6401f4c8f75d NeedsCompilation: no Title: Comparison of Splicing Events between Tumor and Normal Samples Description: An easy to use tool that can compare splicing events in tumor and normal tissue samples using either a user generated matrix, or data from The Cancer Genome Atlas (TCGA). This package generates a matrix of splicing outliers that are significantly over or underexpressed in tumors samples compared to normal denoted by chromosome location. The package also will calculate the splicing burden in each tumor and characterize the types of splicing events that occur. biocViews: AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, RNASeq, Software, VariantAnnotation Author: Joseph Bendik [aut] (ORCID: ), Sandhya Kalavacherla [aut] (ORCID: ), Michael Considine [aut] (ORCID: ), Bahman Afsari [aut] (ORCID: ), Michael F. Ochs [aut], Joseph Califano [aut] (ORCID: ), Daria A. Gaykalova [aut] (ORCID: ), Elana Fertig [aut] (ORCID: ), Theresa Guo [cre, aut] (ORCID: ) Maintainer: Theresa Guo URL: https://github.com/GuoLabUCSD/OutSplice VignetteBuilder: knitr BugReports: https://github.com/GuoLabUCSD/OutSplice/issues git_url: https://git.bioconductor.org/packages/OutSplice git_branch: devel git_last_commit: 14e6259 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OutSplice_1.11.0.tar.gz vignettes: vignettes/OutSplice/inst/doc/OutSplice.html vignetteTitles: Find Splicing Outliers in Tumor Samples with OutSplice hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OutSplice/inst/doc/OutSplice.R dependencyCount: 78 Package: OVESEG Version: 1.27.0 Depends: R (>= 3.6) Imports: stats, utils, methods, BiocParallel, SummarizedExperiment, limma, fdrtool, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, ggplot2, gridExtra, grid, reshape2, scales License: GPL-2 MD5sum: 811beecb6d77b57541237c0542c2e5f5 NeedsCompilation: yes Title: OVESEG-test to detect tissue/cell-specific markers Description: An R package for multiple-group comparison to detect tissue/cell-specific marker genes among subtypes. It provides functions to compute OVESEG-test statistics, derive component weights in the mixture null distribution model and estimate p-values from weightedly aggregated permutations. Obtained posterior probabilities of component null hypotheses can also portrait all kinds of upregulation patterns among subtypes. biocViews: Software, MultipleComparison, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Lululuella/OVESEG git_url: https://git.bioconductor.org/packages/OVESEG git_branch: devel git_last_commit: 08399f1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/OVESEG_1.27.0.tar.gz vignettes: vignettes/OVESEG/inst/doc/OVESEG.html vignetteTitles: OVESEG User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OVESEG/inst/doc/OVESEG.R dependencyCount: 39 Package: PAA Version: 1.45.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.6) Imports: e1071, gplots, gtools, limma, MASS, mRMRe, randomForest, ROCR, sva LinkingTo: Rcpp Suggests: BiocStyle, RUnit, BiocGenerics, vsn License: BSD_3_clause + file LICENSE MD5sum: a473cbeebc67e4e957404eff7cff9cd1 NeedsCompilation: yes Title: PAA (Protein Array Analyzer) Description: PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After preprocessing (background correction, batch filtering, normalization) univariate feature preselection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation. biocViews: Classification, Microarray, OneChannel, Proteomics Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre] Maintainer: Michael Turewicz , Martin Eisenacher URL: http://www.ruhr-uni-bochum.de/mpc/software/PAA/ SystemRequirements: C++ software package Random Jungle git_url: https://git.bioconductor.org/packages/PAA git_branch: devel git_last_commit: dfb636c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PAA_1.45.0.tar.gz vignettes: vignettes/PAA/inst/doc/PAA_1.7.1.pdf, vignettes/PAA/inst/doc/PAA_vignette.pdf vignetteTitles: PAA_1.7.1.pdf, PAA tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R dependencyCount: 84 Package: packFinder Version: 1.23.0 Depends: R (>= 4.1.0) Imports: Biostrings, GenomicRanges, kmer, ape, methods, IRanges, S4Vectors Suggests: biomartr, knitr, rmarkdown, testthat, dendextend, biocViews, BiocCheck, BiocStyle License: GPL-2 MD5sum: e987c41ef12f44a2f9daf9747bfac294 NeedsCompilation: no Title: de novo Annotation of Pack-TYPE Transposable Elements Description: Algorithm and tools for in silico pack-TYPE transposon discovery. Filters a given genome for properties unique to DNA transposons and provides tools for the investigation of returned matches. Sequences are input in DNAString format, and ranges are returned as a dataframe (in the format returned by as.dataframe(GRanges)). biocViews: Genetics, SequenceMatching, Annotation Author: Jack Gisby [aut, cre] (ORCID: ), Marco Catoni [aut] (ORCID: ) Maintainer: Jack Gisby URL: https://github.com/jackgisby/packFinder VignetteBuilder: knitr BugReports: https://github.com/jackgisby/packFinder/issues git_url: https://git.bioconductor.org/packages/packFinder git_branch: devel git_last_commit: a517955 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/packFinder_1.23.0.tar.gz vignettes: vignettes/packFinder/inst/doc/packFinder.html vignetteTitles: packFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/packFinder/inst/doc/packFinder.R dependencyCount: 28 Package: padma Version: 1.21.0 Depends: R (>= 4.1.0), SummarizedExperiment, S4Vectors Imports: FactoMineR, MultiAssayExperiment, methods, graphics, stats, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, KEGGREST, missMDA, ggplot2, ggrepel, car, cowplot, reshape2 License: GPL (>=3) MD5sum: 600df79be4e4e7f907cf8a8993e5e09f NeedsCompilation: no Title: Individualized Multi-Omic Pathway Deviation Scores Using Multiple Factor Analysis Description: Use multiple factor analysis to calculate individualized pathway-centric scores of deviation with respect to the sampled population based on multi-omic assays (e.g., RNA-seq, copy number alterations, methylation, etc). Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles. biocViews: Software, StatisticalMethod, PrincipalComponent, GeneExpression, Pathways, RNASeq, BioCarta, MethylSeq Author: Andrea Rau [cre, aut] (ORCID: ), Regina Manansala [aut], Florence Jaffrézic [ctb], Denis Laloë [aut], Paul Auer [aut] Maintainer: Andrea Rau URL: https://github.com/andreamrau/padma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/padma git_branch: devel git_last_commit: 79cd1b2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/padma_1.21.0.tar.gz vignettes: vignettes/padma/inst/doc/padma.html vignetteTitles: padma package:Quick-start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/padma/inst/doc/padma.R dependencyCount: 133 Package: PADOG Version: 1.53.0 Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db, hgu133a.db, KEGGREST, nlme Suggests: doParallel, parallel License: GPL (>= 2) MD5sum: df817831ab8882de28570f5e98b2f70d NeedsCompilation: no Title: Pathway Analysis with Down-weighting of Overlapping Genes (PADOG) Description: This package implements a general purpose gene set analysis method called PADOG that downplays the importance of genes that apear often accross the sets of genes to be analyzed. The package provides also a benchmark for gene set analysis methods in terms of sensitivity and ranking using 24 public datasets from KEGGdzPathwaysGEO package. biocViews: Microarray, OneChannel, TwoChannel Author: Adi Laurentiu Tarca ; Zhonghui Xu Maintainer: Adi L. Tarca git_url: https://git.bioconductor.org/packages/PADOG git_branch: devel git_last_commit: 4e53350 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PADOG_1.53.0.tar.gz vignettes: vignettes/PADOG/inst/doc/PADOG.pdf vignetteTitles: PADOG hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PADOG/inst/doc/PADOG.R dependsOnMe: BLMA importsMe: EGSEA suggestsMe: ReporterScore dependencyCount: 59 Package: pageRank Version: 1.21.0 Depends: R (>= 4.0) Imports: GenomicRanges, igraph, motifmatchr, stats, utils, grDevices, graphics Suggests: bcellViper, BSgenome.Hsapiens.UCSC.hg19, JASPAR2018, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, TFBSTools, GenomicFeatures, annotate License: GPL-2 MD5sum: 9b0827bd6257b4b56631065537021ea9 NeedsCompilation: no Title: Temporal and Multiplex PageRank for Gene Regulatory Network Analysis Description: Implemented temporal PageRank analysis as defined by Rozenshtein and Gionis. Implemented multiplex PageRank as defined by Halu et al. Applied temporal and multiplex PageRank in gene regulatory network analysis. biocViews: StatisticalMethod, GeneTarget, Network Author: Hongxu Ding [aut, cre, ctb, cph] Maintainer: Hongxu Ding URL: https://github.com/hd2326/pageRank BugReports: https://github.com/hd2326/pageRank/issues git_url: https://git.bioconductor.org/packages/pageRank git_branch: devel git_last_commit: 36f7e99 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pageRank_1.21.0.tar.gz vignettes: vignettes/pageRank/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pageRank/inst/doc/introduction.R dependencyCount: 84 Package: PAIRADISE Version: 1.27.0 Depends: R (>= 3.6), nloptr Imports: SummarizedExperiment, S4Vectors, stats, methods, abind, BiocParallel Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: eab05427092176d2bddab5027ee1a714 NeedsCompilation: no Title: PAIRADISE: Paired analysis of differential isoform expression Description: This package implements the PAIRADISE procedure for detecting differential isoform expression between matched replicates in paired RNA-Seq data. biocViews: RNASeq, DifferentialExpression, AlternativeSplicing, StatisticalMethod, ImmunoOncology Author: Levon Demirdjian, Ying Nian Wu, Yi Xing Maintainer: Qiang Hu , Levon Demirdjian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PAIRADISE git_branch: devel git_last_commit: 02e7d4f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PAIRADISE_1.27.0.tar.gz vignettes: vignettes/PAIRADISE/inst/doc/pairadise.html vignetteTitles: PAIRADISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAIRADISE/inst/doc/pairadise.R dependencyCount: 36 Package: paircompviz Version: 1.49.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: fff45033b0c76afeafa2401101705bcd NeedsCompilation: no Title: Multiple comparison test visualization Description: This package provides visualization of the results from the multiple (i.e. pairwise) comparison tests such as pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test. The groups being compared are visualized as nodes in Hasse diagram. Such approach enables very clear and vivid depiction of which group is significantly greater than which others, especially if comparing a large number of groups. biocViews: GraphAndNetwork Author: Michal Burda Maintainer: Michal Burda git_url: https://git.bioconductor.org/packages/paircompviz git_branch: devel git_last_commit: 597b329 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/paircompviz_1.49.0.tar.gz vignettes: vignettes/paircompviz/inst/doc/vignette.pdf vignetteTitles: Using paircompviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paircompviz/inst/doc/vignette.R dependencyCount: 11 Package: pairedGSEA Version: 1.11.0 Depends: R (>= 4.4.0) Imports: DESeq2, DEXSeq, limma, fgsea, msigdbr, sva, SummarizedExperiment, S4Vectors, BiocParallel, ggplot2, aggregation, stats, utils, methods, showtext Suggests: writexl, readxl, readr, rhdf5, plotly, testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, covr License: MIT + file LICENSE MD5sum: 2e6f521e5dd0ffea3bb447319e8cdb8a NeedsCompilation: no Title: Paired DGE and DGS analysis for gene set enrichment analysis Description: pairedGSEA makes it simple to run a paired Differential Gene Expression (DGE) and Differencital Gene Splicing (DGS) analysis. The package allows you to store intermediate results for further investiation, if desired. pairedGSEA comes with a wrapper function for running an Over-Representation Analysis (ORA) and functionalities for plotting the results. biocViews: DifferentialExpression, AlternativeSplicing, DifferentialSplicing, GeneExpression, ImmunoOncology, GeneSetEnrichment, Pathways, RNASeq, Software, Transcription, Author: Søren Helweg Dam [cre, aut] (ORCID: ), Lars Rønn Olsen [aut] (ORCID: ), Kristoffer Vitting-Seerup [aut] (ORCID: ) Maintainer: Søren Helweg Dam URL: https://github.com/shdam/pairedGSEA VignetteBuilder: knitr BugReports: https://github.com/shdam/pairedGSEA/issues git_url: https://git.bioconductor.org/packages/pairedGSEA git_branch: devel git_last_commit: dcfda05 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pairedGSEA_1.11.0.tar.gz vignettes: vignettes/pairedGSEA/inst/doc/User-Guide.html vignetteTitles: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pairedGSEA/inst/doc/User-Guide.R dependencyCount: 125 Package: pairkat Version: 1.17.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, KEGGREST, igraph, data.table, methods, stats, magrittr, CompQuadForm, tibble Suggests: rmarkdown, knitr, BiocStyle, dplyr License: GPL-3 MD5sum: 95cfd8862937fd0089f45b56d2db81f8 NeedsCompilation: no Title: PaIRKAT Description: PaIRKAT is model framework for assessing statistical relationships between networks of metabolites (pathways) and an outcome of interest (phenotype). PaIRKAT queries the KEGG database to determine interactions between metabolites from which network connectivity is constructed. This model framework improves testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. PaIRKAT uses known biological connections between high dimensional variables by representing them as edges of ‘graphs’ or ‘networks.’ It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or ‘smoothing’ approach for managing this issue. biocViews: Software, Metabolomics, KEGG, Pathways, Network, GraphAndNetwork, Regression Author: Charlie Carpenter [aut], Cameron Severn [aut], Max McGrath [cre, aut] Maintainer: Max McGrath VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/pairkat/issues git_url: https://git.bioconductor.org/packages/pairkat git_branch: devel git_last_commit: 98fe53c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pairkat_1.17.0.tar.gz vignettes: vignettes/pairkat/inst/doc/using-pairkat.html vignetteTitles: using-pairkat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pairkat/inst/doc/using-pairkat.R dependencyCount: 51 Package: pandaR Version: 1.43.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 918b27d036aa7df07f378615ddfbbd87 NeedsCompilation: no Title: PANDA Algorithm Description: Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources. biocViews: StatisticalMethod, GraphAndNetwork, Microarray, GeneRegulation, NetworkInference, GeneExpression, Transcription, Network Author: Dan Schlauch, Joseph N. Paulson, Albert Young, John Quackenbush, Kimberly Glass Maintainer: Joseph N. Paulson , Dan Schlauch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pandaR git_branch: devel git_last_commit: 92841f9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pandaR_1.43.0.tar.gz vignettes: vignettes/pandaR/inst/doc/pandaR.html vignetteTitles: pandaR Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pandaR/inst/doc/pandaR.R dependencyCount: 37 Package: panelcn.mops Version: 1.33.0 Depends: R (>= 3.5), cn.mops, methods, utils, stats, graphics Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, Seqinfo, grDevices Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: LGPL (>= 2.0) MD5sum: 51109cce50203f9f3826422796a5bb02 NeedsCompilation: no Title: CNV detection tool for targeted NGS panel data Description: CNV detection tool for targeted NGS panel data. Extension of the cn.mops package. biocViews: Sequencing, CopyNumberVariation, CellBiology, GenomicVariation, VariantDetection, Genetics Author: Verena Haunschmid [aut], Gundula Povysil [aut, cre] Maintainer: Gundula Povysil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/panelcn.mops git_branch: devel git_last_commit: 5e38d91 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/panelcn.mops_1.33.0.tar.gz vignettes: vignettes/panelcn.mops/inst/doc/panelcn.mops.pdf vignetteTitles: panelcn.mops: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panelcn.mops/inst/doc/panelcn.mops.R suggestsMe: CopyNumberPlots dependencyCount: 31 Package: PanomiR Version: 1.15.0 Depends: R (>= 4.2.0) Imports: clusterProfiler, dplyr, forcats, GSEABase, igraph, limma, metap, org.Hs.eg.db, parallel, preprocessCore, RColorBrewer, rlang, tibble, withr, utils Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 6a71f0b4e531fc5d35de29555cee7a72 NeedsCompilation: no Title: Detection of miRNAs that regulate interacting groups of pathways Description: PanomiR is a package to detect miRNAs that target groups of pathways from gene expression data. This package provides functionality for generating pathway activity profiles, determining differentially activated pathways between user-specified conditions, determining clusters of pathways via the PCxN package, and generating miRNAs targeting clusters of pathways. These function can be used separately or sequentially to analyze RNA-Seq data. biocViews: GeneExpression, GeneSetEnrichment, GeneTarget, miRNA, Pathways Author: Pourya Naderi [aut, cre], Yue Yang (Alan) Teo [aut], Ilya Sytchev [aut], Winston Hide [aut] Maintainer: Pourya Naderi URL: https://github.com/pouryany/PanomiR VignetteBuilder: knitr BugReports: https://github.com/pouryany/PanomiR/issues git_url: https://git.bioconductor.org/packages/PanomiR git_branch: devel git_last_commit: eb18fe4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PanomiR_1.15.0.tar.gz vignettes: vignettes/PanomiR/inst/doc/PanomiR.html vignetteTitles: PanomiR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PanomiR/inst/doc/PanomiR.R dependencyCount: 163 Package: panp Version: 1.81.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 0741ffa82d32a79ab5de78c88a9f1854 NeedsCompilation: no Title: Presence-Absence Calls from Negative Strand Matching Probesets Description: A function to make gene presence/absence calls based on distance from negative strand matching probesets (NSMP) which are derived from Affymetrix annotation. PANP is applied after gene expression values are created, and therefore can be used after any preprocessing method such as MAS5 or GCRMA, or PM-only methods like RMA. NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date. biocViews: Infrastructure Author: Peter Warren Maintainer: Peter Warren git_url: https://git.bioconductor.org/packages/panp git_branch: devel git_last_commit: a363914 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/panp_1.81.0.tar.gz vignettes: vignettes/panp/inst/doc/panp.pdf vignetteTitles: gene presence/absence calls hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panp/inst/doc/panp.R dependencyCount: 12 Package: PANR Version: 1.57.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: ce6382907eca936231464b789ab49086 NeedsCompilation: no Title: Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations Description: This package provides S4 classes and methods for inferring functional gene networks with edges encoding posterior beliefs of gene association types and nodes encoding perturbation effects. biocViews: ImmunoOncology, NetworkInference, Visualization, GraphAndNetwork, Clustering, CellBasedAssays Author: Xin Wang Maintainer: Xin Wang git_url: https://git.bioconductor.org/packages/PANR git_branch: devel git_last_commit: 12acbc0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PANR_1.57.0.tar.gz vignettes: vignettes/PANR/inst/doc/PANR-Vignette.pdf vignetteTitles: Main vignette:Posterior association network and enriched functional gene modules inferred from rich phenotypes of gene perturbations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PANR/inst/doc/PANR-Vignette.R dependencyCount: 26 Package: parati Version: 0.99.8 Imports: data.table, methods, openxlsx, R.utils, vcfR, VariantAnnotation, SummarizedExperiment, BiocGenerics, GenomeInfoDb Suggests: BiocStyle, knitr, optparse, rmarkdown, testthat (>= 3.0.0), waldo License: GPL-3 + file LICENSE MD5sum: 8d6f52afe427c4bc09d60e542a6b0915 NeedsCompilation: no Title: Parental Allele Transmission Inference for Trio Genotype Data Description: Infers maternal and paternal transmitted and non-transmitted alleles from phased trio genotype data. The package supports SNP-level analyses of genetic nurture and transgenerational effects. It interoperates with Bioconductor VCF infrastructure through support for VariantAnnotation::VCF objects and returns R objects for downstream analysis. biocViews: Genetics, SNP, Sequencing, VariantAnnotation, Software Author: Jinyi Che [aut, cre] Maintainer: Jinyi Che URL: https://github.com/newche/parati VignetteBuilder: knitr BugReports: https://github.com/newche/parati/issues git_url: https://git.bioconductor.org/packages/parati git_branch: devel git_last_commit: 9232fff git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/parati_0.99.8.tar.gz vignettes: vignettes/parati/inst/doc/parati-workflow.html vignetteTitles: parati Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/parati/inst/doc/parati-workflow.R dependencyCount: 108 Package: parglms Version: 1.43.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges, gwascat, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: d71c1668575bfe67490a03a9c687751d NeedsCompilation: no Title: support for parallelized estimation of GLMs/GEEs Description: This package provides support for parallelized estimation of GLMs/GEEs, catering for dispersed data. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parglms git_branch: devel git_last_commit: 470abec git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/parglms_1.43.0.tar.gz vignettes: vignettes/parglms/inst/doc/parglms.pdf vignetteTitles: parglms: parallelized GLM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parglms/inst/doc/parglms.R dependencyCount: 37 Package: parody Version: 1.69.0 Depends: R (>= 3.5.0), tools, utils Suggests: knitr, BiocStyle, testthat, rmarkdown License: Artistic-2.0 MD5sum: 156c629db39e0f0247f31b32662eecc4 NeedsCompilation: no Title: Parametric And Resistant Outlier DYtection Description: Provide routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics. biocViews: MultipleComparison Author: Vince Carey [aut, cre] (ORCID: ) Maintainer: Vince Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parody git_branch: devel git_last_commit: d3ae21a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/parody_1.69.0.tar.gz vignettes: vignettes/parody/inst/doc/parody.html vignetteTitles: parody: parametric and resistant outlier dytection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parody/inst/doc/parody.R dependsOnMe: arrayMvout dependencyCount: 2 Package: PAST Version: 1.27.0 Depends: R (>= 4.0) Imports: stats, utils, dplyr, rlang, iterators, parallel, foreach, doParallel, qvalue, rtracklayer, ggplot2, GenomicRanges, S4Vectors Suggests: knitr, rmarkdown License: GPL (>=3) + file LICENSE MD5sum: 7a63e88911e526f09512b953ccc9b334 NeedsCompilation: no Title: Pathway Association Study Tool (PAST) Description: PAST takes GWAS output and assigns SNPs to genes, uses those genes to find pathways associated with the genes, and plots pathways based on significance. Implements methods for reading GWAS input data, finding genes associated with SNPs, calculating enrichment score and significance of pathways, and plotting pathways. biocViews: Pathways, GeneSetEnrichment Author: Thrash Adam [cre, aut], DeOrnellis Mason [aut] Maintainer: Thrash Adam URL: https://github.com/IGBB/past VignetteBuilder: knitr BugReports: https://github.com/IGBB/past/issues git_url: https://git.bioconductor.org/packages/PAST git_branch: devel git_last_commit: a345f7a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PAST_1.27.0.tar.gz vignettes: vignettes/PAST/inst/doc/past.html vignetteTitles: PAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAST/inst/doc/past.R dependencyCount: 89 Package: Path2PPI Version: 1.41.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 36017ab7462ba1d8025bd3d100c094c3 NeedsCompilation: no Title: Prediction of pathway-related protein-protein interaction networks Description: Package to predict protein-protein interaction (PPI) networks in target organisms for which only a view information about PPIs is available. Path2PPI predicts PPI networks based on sets of proteins which can belong to a certain pathway from well-established model organisms. It helps to combine and transfer information of a certain pathway or biological process from several reference organisms to one target organism. Path2PPI only depends on the sequence similarity of the involved proteins. biocViews: NetworkInference, SystemsBiology, Network, Proteomics, Pathways Author: Oliver Philipp [aut, cre], Ina Koch [ctb] Maintainer: Oliver Philipp URL: http://www.bioinformatik.uni-frankfurt.de/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Path2PPI git_branch: devel git_last_commit: 382d4fd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Path2PPI_1.41.0.tar.gz vignettes: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html vignetteTitles: Path2PPI - A brief tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R dependencyCount: 17 Package: pathifier Version: 1.49.0 Imports: R.oo, princurve (>= 2.0.4) License: Artistic-1.0 MD5sum: 32821c86b1993202176e41cec4c4dd0c NeedsCompilation: no Title: Quantify deregulation of pathways in cancer Description: Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. biocViews: Network Author: Yotam Drier Maintainer: Assif Yitzhaky git_url: https://git.bioconductor.org/packages/pathifier git_branch: devel git_last_commit: f57c93e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pathifier_1.49.0.tar.gz vignettes: vignettes/pathifier/inst/doc/Overview.pdf vignetteTitles: Quantify deregulation of pathways in cancer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathifier/inst/doc/Overview.R importsMe: funOmics dependencyCount: 9 Package: pathlinkR Version: 1.7.22 Depends: R (>= 4.5.0) Imports: circlize, clusterProfiler, ComplexHeatmap, dplyr, fgsea, ggplot2, ggraph, ggrepel, grid, igraph, patchwork, purrr, sigora, stringr, tibble, tidygraph, tidyr, vegan, visNetwork Suggests: AnnotationDbi, BiocStyle, biomaRt, covr, DESeq2, jsonlite, knitr, org.Hs.eg.db, rmarkdown, scales, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: 0d91dbb0b5cbc24a176d27e3a80ffc73 NeedsCompilation: no Title: Analyze and interpret RNA-Seq results Description: pathlinkR is an R package designed to facilitate analysis of RNA-Seq results. Specifically, our aim with pathlinkR was to provide a number of tools which take a list of DE genes and perform different analyses on them, aiding with the interpretation of results. Functions are included to perform pathway enrichment, with muliplte databases supported, and tools for visualizing these results. Genes can also be used to create and plot protein-protein interaction networks, all from inside of R. biocViews: GeneSetEnrichment, Network, Pathways, Reactome, RNASeq, NetworkEnrichment Author: Travis Blimkie [cre] (ORCID: ), Andy An [aut] Maintainer: Travis Blimkie URL: https://github.com/hancockinformatics/pathlinkR VignetteBuilder: knitr BugReports: https://github.com/hancockinformatics/pathlinkR/issues git_url: https://git.bioconductor.org/packages/pathlinkR git_branch: devel git_last_commit: 5adccf7 git_last_commit_date: 2026-03-18 Date/Publication: 2026-04-20 source.ver: src/contrib/pathlinkR_1.7.22.tar.gz vignettes: vignettes/pathlinkR/inst/doc/pathlinkR.html vignetteTitles: Analyze and visualize RNA-Seq data with pathlinkR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pathlinkR/inst/doc/pathlinkR.R dependencyCount: 165 Package: PathNet Version: 1.51.0 Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: bd45f9b33eda925379b11f73361fbadd NeedsCompilation: no Title: An R package for pathway analysis using topological information Description: PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10. biocViews: Pathways, DifferentialExpression, MultipleComparison, KEGG, NetworkEnrichment, Network Author: Bhaskar Dutta , Anders Wallqvist , and Jaques Reifman Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/PathNet git_branch: devel git_last_commit: 2a528ed git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PathNet_1.51.0.tar.gz vignettes: vignettes/PathNet/inst/doc/PathNet.pdf vignetteTitles: PathNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathNet/inst/doc/PathNet.R dependencyCount: 0 Package: PathoStat Version: 1.37.0 Depends: R (>= 3.5) Imports: limma, corpcor,matrixStats, reshape2, scales, ggplot2, rentrez, DT, tidyr, plyr, dplyr, phyloseq, shiny, stats, methods, XML, graphics, utils, BiocStyle, edgeR, DESeq2, ComplexHeatmap, plotly, webshot, vegan, shinyjs, glmnet, gmodels, ROCR, RColorBrewer, knitr, devtools, ape Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: 5d4d2882e012ff06e23da338ee64de56 NeedsCompilation: no Title: PathoStat Statistical Microbiome Analysis Package Description: The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis. biocViews: Microbiome, Metagenomics, GraphAndNetwork, Microarray, PatternLogic, PrincipalComponent, Sequencing, Software, Visualization, RNASeq, ImmunoOncology Author: Solaiappan Manimaran , Matthew Bendall , Sandro Valenzuela Diaz , Eduardo Castro , Tyler Faits , Yue Zhao , Anthony Nicholas Federico , W. Evan Johnson Maintainer: Solaiappan Manimaran , Yue Zhao URL: https://github.com/mani2012/PathoStat VignetteBuilder: knitr BugReports: https://github.com/mani2012/PathoStat/issues git_url: https://git.bioconductor.org/packages/PathoStat git_branch: devel git_last_commit: b2188f7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PathoStat_1.37.0.tar.gz vignettes: vignettes/PathoStat/inst/doc/PathoStat-vignette.html vignetteTitles: PathoStat intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathoStat/inst/doc/PathoStat-vignette.R dependencyCount: 202 Package: pathRender Version: 1.79.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: 6ed96b7c8683ff08da9f65477477dc37 NeedsCompilation: no Title: Render molecular pathways Description: build graphs from pathway databases, render them by Rgraphviz. biocViews: GraphAndNetwork, Pathways, Visualization Author: Li Long Maintainer: Vince Carey URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/pathRender git_branch: devel git_last_commit: d982243 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pathRender_1.79.0.tar.gz vignettes: vignettes/pathRender/inst/doc/pathRender.pdf, vignettes/pathRender/inst/doc/plotExG.pdf vignetteTitles: pathRender overview, pathway graphs colored by expression map hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathRender/inst/doc/pathRender.R, vignettes/pathRender/inst/doc/plotExG.R dependencyCount: 47 Package: pathview Version: 1.51.0 Depends: R (>= 3.5.0) Imports: KEGGgraph, XML, Rgraphviz, graph, png, AnnotationDbi, org.Hs.eg.db, KEGGREST, methods, utils Suggests: gage, org.Mm.eg.db, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: 6483d5a7f94ae3b9b12a611e734f0add NeedsCompilation: no Title: a tool set for pathway based data integration and visualization Description: Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis. biocViews: Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/pathview, https://pathview.uncc.edu/ git_url: https://git.bioconductor.org/packages/pathview git_branch: devel git_last_commit: b31fe1e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pathview_1.51.0.tar.gz vignettes: vignettes/pathview/inst/doc/pathview.pdf vignetteTitles: Pathview: pathway based data integration and visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathview/inst/doc/pathview.R dependsOnMe: EGSEA, SBGNview importsMe: debrowser, EnrichmentBrowser, GDCRNATools, lilikoi, SQMtools suggestsMe: gage, TCGAbiolinks, gageData, CAGEWorkflow, ReporterScore dependencyCount: 50 Package: pathwayPCA Version: 1.27.0 Depends: R (>= 3.1) Imports: lars, methods, parallel, stats, survival, utils Suggests: airway, circlize, grDevices, knitr, RCurl, reshape2, rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse License: GPL-3 MD5sum: 139a49b5169c8ec7af8d31928a757d8b NeedsCompilation: no Title: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection Description: pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) ; Chen et al. (2010) ; and Chen (2011) . biocViews: CopyNumberVariation, DNAMethylation, GeneExpression, SNP, Transcription, GenePrediction, GeneSetEnrichment, GeneSignaling, GeneTarget, GenomeWideAssociation, GenomicVariation, CellBiology, Epigenetics, FunctionalGenomics, Genetics, Lipidomics, Metabolomics, Proteomics, SystemsBiology, Transcriptomics, Classification, DimensionReduction, FeatureExtraction, PrincipalComponent, Regression, Survival, MultipleComparison, Pathways Author: Gabriel Odom [aut, cre], James Ban [aut], Lizhong Liu [aut], Lily Wang [aut], Steven Chen [aut] Maintainer: Gabriel Odom URL: VignetteBuilder: knitr BugReports: https://github.com/gabrielodom/pathwayPCA/issues git_url: https://git.bioconductor.org/packages/pathwayPCA git_branch: devel git_last_commit: 9b31249 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pathwayPCA_1.27.0.tar.gz vignettes: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.html, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.html, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.html, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.html, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.html, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.html vignetteTitles: Integrative Pathway Analysis with pathwayPCA, Suppl. 1. Quickstart Guide, Suppl. 2. Importing Data, Suppl. 3. Create Data Objects, Suppl. 4. Test Pathway Significance, Suppl. 5. Visualizing the Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.R, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.R, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.R, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.R, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.R, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.R dependencyCount: 12 Package: pcaMethods Version: 2.3.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) MD5sum: fe994d2396b75b67cd01eec40200b9ed NeedsCompilation: yes Title: A collection of PCA methods Description: Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. biocViews: Bayesian Author: Wolfram Stacklies, Henning Redestig, Kevin Wright Maintainer: Henning Redestig URL: https://github.com/hredestig/pcamethods SystemRequirements: Rcpp BugReports: https://github.com/hredestig/pcamethods/issues git_url: https://git.bioconductor.org/packages/pcaMethods git_branch: devel git_last_commit: 8787174 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pcaMethods_2.3.0.tar.gz vignettes: vignettes/pcaMethods/inst/doc/missingValues.pdf, vignettes/pcaMethods/inst/doc/outliers.pdf, vignettes/pcaMethods/inst/doc/pcaMethods.pdf vignetteTitles: Missing value imputation, Data with outliers, Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R, vignettes/pcaMethods/inst/doc/outliers.R, vignettes/pcaMethods/inst/doc/pcaMethods.R dependsOnMe: crmn, DiffCorr, imputeLCMD importsMe: destiny, FRASER, MAI, MatrixQCvis, MSnbase, MSPrep, MultiBaC, notameViz, OUTRIDER, PhosR, pmp, scde, SomaticSignatures, ADAPTS, geneticae, lfproQC, LOST, MetabolomicsBasics, metamorphr, missCompare, multiDimBio, pmartR, polyRAD, promor, santaR, scMappR suggestsMe: autonomics, cardelino, MsCoreUtils, notame, QFeatures, qmtools, mtbls2, pagoda2, rsvddpd dependencyCount: 10 Package: PCAN Version: 1.39.0 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: b329fc8688d8f1f95b3065b4d081cc22 NeedsCompilation: no Title: Phenotype Consensus ANalysis (PCAN) Description: Phenotypes comparison based on a pathway consensus approach. Assess the relationship between candidate genes and a set of phenotypes based on additional genes related to the candidate (e.g. Pathways or network neighbors). biocViews: Annotation, Sequencing, Genetics, FunctionalPrediction, VariantAnnotation, Pathways, Network Author: Matthew Page and Patrice Godard Maintainer: Matthew Page and Patrice Godard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAN git_branch: devel git_last_commit: 207c5ab git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PCAN_1.39.0.tar.gz vignettes: vignettes/PCAN/inst/doc/PCAN.html vignetteTitles: Assessing gene relevance for a set of phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAN/inst/doc/PCAN.R dependencyCount: 14 Package: PCAtools Version: 2.23.3 Depends: ggplot2, ggrepel Imports: lattice, grDevices, cowplot, methods, reshape2, stats, Matrix, DelayedMatrixStats, DelayedArray, beachmat (>= 2.21.1), BiocSingular, BiocParallel, Rcpp, dqrng LinkingTo: Rcpp, beachmat, assorthead, BH, dqrng Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, hgu133a.db, ggplotify, RMTstat, ggforce, concaveman, DESeq2, airway, org.Hs.eg.db, magrittr, rmarkdown, airway License: GPL-3 MD5sum: af54a4f31c8785879381fd3fbfadcdb6 NeedsCompilation: yes Title: PCAtools: Everything Principal Components Analysis Description: Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data. biocViews: RNASeq, ATACSeq, GeneExpression, Transcription, SingleCell, PrincipalComponent Author: Kevin Blighe [aut], Jared Andrews [aut, cre] (ORCID: ), Anna-Leigh Brown [ctb], Vincent Carey [ctb], Guido Hooiveld [ctb], Aaron Lun [aut, ctb] Maintainer: Jared Andrews URL: https://github.com/kevinblighe/PCAtools SystemRequirements: C++17 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAtools git_branch: devel git_last_commit: 1756510 git_last_commit_date: 2026-01-22 Date/Publication: 2026-04-20 source.ver: src/contrib/PCAtools_2.23.3.tar.gz vignettes: vignettes/PCAtools/inst/doc/PCAtools.html vignetteTitles: PCAtools: everything Principal Component Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAtools/inst/doc/PCAtools.R importsMe: CRISPRball, regionalpcs suggestsMe: RNAseqCovarImpute, scDataviz dependencyCount: 65 Package: PDATK Version: 1.19.0 Depends: R (>= 4.1), SummarizedExperiment Imports: data.table, MultiAssayExperiment, ConsensusClusterPlus, igraph, ggplotify, matrixStats, RColorBrewer, clusterRepro, CoreGx, caret, survminer, methods, S4Vectors, BiocGenerics, survival, stats, plyr, dplyr, MatrixGenerics, BiocParallel, rlang, piano, scales, survcomp, genefu, ggplot2, switchBox, reportROC, pROC, verification, utils Suggests: testthat (>= 3.0.0), msigdbr, BiocStyle, rmarkdown, knitr, HDF5Array License: MIT + file LICENSE MD5sum: 64563fb42b368456dd46a25bf766b2e3 NeedsCompilation: no Title: Pancreatic Ductal Adenocarcinoma Tool-Kit Description: Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification, Survival, Clustering, GenePrediction Author: Vandana Sandhu [aut], Heewon Seo [aut], Christopher Eeles [aut], Neha Rohatgi [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/PDATK/issues git_url: https://git.bioconductor.org/packages/PDATK git_branch: devel git_last_commit: 16dbbfe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PDATK_1.19.0.tar.gz vignettes: vignettes/PDATK/inst/doc/PCOSP_model_analysis.html, vignettes/PDATK/inst/doc/PDATK_introduction.html vignetteTitles: PCOSP: Pancreatic Cancer Overall Survival Predictor, PDATK_introduction.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PDATK/inst/doc/PCOSP_model_analysis.R, vignettes/PDATK/inst/doc/PDATK_introduction.R dependencyCount: 263 Package: pdInfoBuilder Version: 1.75.0 Depends: R (>= 3.2.0), methods, Biobase (>= 2.27.3), RSQLite (>= 1.0.0), affxparser (>= 1.39.4), oligo (>= 1.31.5) Imports: Biostrings (>= 2.35.12), BiocGenerics (>= 0.13.11), DBI (>= 0.3.1), IRanges (>= 2.1.43), oligoClasses (>= 1.29.6), S4Vectors (>= 0.5.22) License: Artistic-2.0 MD5sum: 896b9c5c3f0d19076c99f5cef25cf8da NeedsCompilation: yes Title: Platform Design Information Package Builder Description: Builds platform design information packages. These consist of a SQLite database containing feature-level data such as x, y position on chip and featureSet ID. The database also incorporates featureSet-level annotation data. The products of this packages are used by the oligo pkg. biocViews: Annotation, Infrastructure Author: Seth Falcon, Vince Carey, Matt Settles, Kristof de Beuf, Benilton Carvalho Maintainer: Benilton Carvalho git_url: https://git.bioconductor.org/packages/pdInfoBuilder git_branch: devel git_last_commit: 81fd205 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pdInfoBuilder_1.75.0.tar.gz vignettes: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.pdf, vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.pdf vignetteTitles: Building Annotation Packages with pdInfoBuilder for Use with the oligo Package, PDInfo Package Building Affymetrix Mapping Chips hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R suggestsMe: maqcExpression4plex, aroma.affymetrix, maGUI dependencyCount: 53 Package: PeacoQC Version: 1.21.0 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, flowCore, flowWorkspace, ggplot2, grDevices, grid, gridExtra, methods, plyr, stats, utils Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=3) MD5sum: b25dc904b0e65aa8c6ae1b9e42a0e3c2 NeedsCompilation: no Title: Peak-based selection of high quality cytometry data Description: This is a package that includes pre-processing and quality control functions that can remove margin events, compensate and transform the data and that will use PeacoQCSignalStability for quality control. This last function will first detect peaks in each channel of the flowframe. It will remove anomalies based on the IsolationTree function and the MAD outlier detection method. This package can be used for both flow- and mass cytometry data. biocViews: FlowCytometry, QualityControl, Preprocessing, PeakDetection Author: Annelies Emmaneel [aut, cre] Maintainer: Annelies Emmaneel URL: http://github.com/saeyslab/PeacoQC VignetteBuilder: knitr BugReports: http://github.com/saeyslab/PeacoQC/issues git_url: https://git.bioconductor.org/packages/PeacoQC git_branch: devel git_last_commit: 07d9ad6 git_last_commit_date: 2026-02-10 Date/Publication: 2026-04-20 source.ver: src/contrib/PeacoQC_1.21.0.tar.gz vignettes: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.pdf vignetteTitles: PeacoQC_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.R importsMe: CytoPipeline dependencyCount: 81 Package: peakCombiner Version: 1.1.0 Depends: R (>= 4.5.0) Imports: tidyr, dplyr (>= 1.1.2), IRanges, GenomicRanges, tidyselect, purrr, readr (>= 2.1.2), tibble (>= 3.2.1), rlang, stringr, here, stats, Seqinfo Suggests: testthat (>= 3.0.0), tidyverse, rmarkdown, styler, cli, lintr, rtracklayer, knitr, devtools, ggplot2, BiocStyle, BiocManager, usethis, utils, AnnotationHub, GenomeInfoDb License: MIT + file LICENSE MD5sum: 2284bfbf1b32f06255313d532f1c5f29 NeedsCompilation: no Title: The R package to curate and merge enriched genomic regions into consensus peak sets Description: peakCombiner, a fully R based, user-friendly, transparent, and customizable tool that allows even novice R users to create a high-quality consensus peak list. The modularity of its functions allows an easy way to optimize input and output data. A broad range of accepted input data formats can be used to create a consensus peak set that can be exported to a file or used as the starting point for most downstream peak analyses. biocViews: WorkflowStep, Preprocessing, ChipOnChip Author: Markus Muckenhuber [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Michael Stadler [aut] (ORCID: ), Kathleen Sprouffske [aut] (ORCID: ), Novartis Biomedical Research [cph] Maintainer: Markus Muckenhuber URL: https://github.com/novartis/peakCombiner/, https://bioconductor.org/packages/peakCombiner VignetteBuilder: knitr BugReports: https://github.com/novartis/peakCombiner/issues git_url: https://git.bioconductor.org/packages/peakCombiner git_branch: devel git_last_commit: 266aae6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/peakCombiner_1.1.0.tar.gz vignettes: vignettes/peakCombiner/inst/doc/peakCombiner.html vignetteTitles: peakCombiner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/peakCombiner/inst/doc/peakCombiner.R dependencyCount: 44 Package: PECA Version: 1.47.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: d71b29080f77da3888c765fc7495b7f8 NeedsCompilation: no Title: Probe-level Expression Change Averaging Description: Calculates Probe-level Expression Change Averages (PECA) to identify differential expression in Affymetrix gene expression microarray studies or in proteomic studies using peptide-level mesurements respectively. biocViews: Software, Proteomics, Microarray, DifferentialExpression, GeneExpression, ExonArray, DifferentialSplicing Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/PECA git_branch: devel git_last_commit: ef37402 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PECA_1.47.0.tar.gz vignettes: vignettes/PECA/inst/doc/PECA.pdf vignetteTitles: PECA: Probe-level Expression Change Averaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PECA/inst/doc/PECA.R dependencyCount: 101 Package: peco Version: 1.23.0 Depends: R (>= 3.5.0) Imports: assertthat, circular, conicfit, doParallel, foreach, genlasso (>= 1.4), graphics, methods, parallel, scater, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown License: GPL (>= 3) MD5sum: 24e66c5dc8adfa7b22d7336523985320 NeedsCompilation: no Title: A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data Description: Our approach provides a way to assign continuous cell cycle phase using scRNA-seq data, and consequently, allows to identify cyclic trend of gene expression levels along the cell cycle. This package provides method and training data, which includes scRNA-seq data collected from 6 individual cell lines of induced pluripotent stem cells (iPSCs), and also continuous cell cycle phase derived from FUCCI fluorescence imaging data. biocViews: Sequencing, RNASeq, GeneExpression, Transcriptomics, SingleCell, Software, StatisticalMethod, Classification, Visualization Author: Chiaowen Joyce Hsiao [aut, cre], Matthew Stephens [aut], John Blischak [ctb], Peter Carbonetto [ctb] Maintainer: Chiaowen Joyce Hsiao URL: https://github.com/jhsiao999/peco VignetteBuilder: knitr BugReports: https://github.com/jhsiao999/peco/issues git_url: https://git.bioconductor.org/packages/peco git_branch: devel git_last_commit: 92a92cd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/peco_1.23.0.tar.gz vignettes: vignettes/peco/inst/doc/vignette.html vignetteTitles: An example of predicting cell cycle phase using peco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peco/inst/doc/vignette.R dependencyCount: 102 Package: Pedixplorer Version: 1.7.1 Depends: R (>= 4.4.0) Imports: graphics, stats, methods, ggplot2, utils, grDevices, stringr, plyr, dplyr, tidyr, quadprog, Matrix, S4Vectors, shiny, readxl, DT, igraph, shinycssloaders, shinyhelper, shinyjs, shinyjqui, shinyWidgets, htmlwidgets, plotly, colourpicker, shinytoastr Suggests: diffviewer, gridExtra, testthat (>= 3.0.0), vdiffr, rmarkdown, BiocStyle, knitr, withr, qpdf, shinytest2, devtools, R.devices, usethis, rlang, magick, cowplot License: Artistic-2.0 MD5sum: e35e87f3c704ea1de8c2cc107a58e002 NeedsCompilation: no Title: Pedigree Functions Description: Routines to handle family data with a Pedigree object. The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for Pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the Pedigree object with various criteria, and kinship for the X chromosome. biocViews: Software, DataRepresentation, Genetics, GraphAndNetwork, Visualization Author: Louis Le Nezet [aut, cre, ctb] (ORCID: ), Jason Sinnwell [aut], Terry Therneau [aut], Daniel Schaid [ctb], Elizabeth Atkinson [ctb] Maintainer: Louis Le Nezet URL: https://louislenezet.github.io/Pedixplorer/ VignetteBuilder: knitr BugReports: https://github.com/LouisLeNezet/Pedixplorer/issues git_url: https://git.bioconductor.org/packages/Pedixplorer git_branch: devel git_last_commit: d742c93 git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/Pedixplorer_1.7.1.tar.gz vignettes: vignettes/Pedixplorer/inst/doc/pedigree_alignment.html, vignettes/Pedixplorer/inst/doc/pedigree_kinship.html, vignettes/Pedixplorer/inst/doc/pedigree_object.html, vignettes/Pedixplorer/inst/doc/pedigree_plot.html, vignettes/Pedixplorer/inst/doc/Pedixplorer.html vignetteTitles: Pedigree alignment details, Pedigree kinship() details, Pedigree object, Pedigree plotting details, Pedixplorer tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pedixplorer/inst/doc/pedigree_alignment.R, vignettes/Pedixplorer/inst/doc/pedigree_kinship.R, vignettes/Pedixplorer/inst/doc/pedigree_object.R, vignettes/Pedixplorer/inst/doc/pedigree_plot.R, vignettes/Pedixplorer/inst/doc/Pedixplorer.R dependsOnMe: pedgene dependencyCount: 102 Package: pengls Version: 1.17.0 Depends: R (>= 4.5.0) Imports: glmnet, nlme, stats, BiocParallel Suggests: knitr,rmarkdown,testthat License: GPL-2 MD5sum: 4c3922283055172a2c7bc235105f3c54 NeedsCompilation: no Title: Fit Penalised Generalised Least Squares models Description: Combine generalised least squares methodology from the nlme package for dealing with autocorrelation with penalised least squares methods from the glmnet package to deal with high dimensionality. This pengls packages glues them together through an iterative loop. The resulting method is applicable to high dimensional datasets that exhibit autocorrelation, such as spatial or temporal data. biocViews: Transcriptomics, Regression, TimeCourse, Spatial Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel URL: https://github.com/sthawinke/pengls VignetteBuilder: knitr BugReports: https://github.com/sthawinke/pengls/issues git_url: https://git.bioconductor.org/packages/pengls git_branch: devel git_last_commit: 4915b97 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pengls_1.17.0.tar.gz vignettes: vignettes/pengls/inst/doc/penglsVignette.html vignetteTitles: Vignette of the pengls package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pengls/inst/doc/penglsVignette.R dependencyCount: 27 Package: PepSetTest Version: 1.5.0 Depends: R (>= 4.1.0) Imports: dplyr, limma, lme4, MASS, matrixStats, reshape2, stats, tibble, SummarizedExperiment, methods Suggests: statmod, BiocStyle, knitr, rmarkdown, tidyr License: GPL (>= 3) MD5sum: d44cc5034a4e391c6beee0c7037fb21b NeedsCompilation: no Title: Peptide Set Test Description: Peptide Set Test (PepSetTest) is a peptide-centric strategy to infer differentially expressed proteins in LC-MS/MS proteomics data. This test detects coordinated changes in the expression of peptides originating from the same protein and compares these changes against the rest of the peptidome. Compared to traditional aggregation-based approaches, the peptide set test demonstrates improved statistical power, yet controlling the Type I error rate correctly in most cases. This test can be valuable for discovering novel biomarkers and prioritizing drug targets, especially when the direct application of statistical analysis to protein data fails to provide substantial insights. biocViews: DifferentialExpression, Regression, Proteomics, MassSpectrometry Author: Junmin Wang [aut, cre] Maintainer: Junmin Wang URL: https://github.com/JmWangBio/PepSetTest VignetteBuilder: knitr BugReports: https://github.com/JmWangBio/PepSetTest/issues git_url: https://git.bioconductor.org/packages/PepSetTest git_branch: devel git_last_commit: f2ac665 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PepSetTest_1.5.0.tar.gz vignettes: vignettes/PepSetTest/inst/doc/PepSetTest.html vignetteTitles: A Tutorial for PepSetTest hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PepSetTest/inst/doc/PepSetTest.R dependencyCount: 58 Package: PepsNMR Version: 1.29.0 Depends: R (>= 3.6) Imports: Matrix, ptw, ggplot2, gridExtra, matrixStats, reshape2, methods, graphics, stats Suggests: knitr, markdown, rmarkdown, BiocStyle, PepsNMRData License: GPL-2 | file LICENSE MD5sum: 7ff3040281ac1b624d0d2f71ddac20d7 NeedsCompilation: no Title: Pre-process 1H-NMR FID signals Description: This package provides R functions for common pre-procssing steps that are applied on 1H-NMR data. It also provides a function to read the FID signals directly in the Bruker format. biocViews: Software, Preprocessing, Visualization, Metabolomics, DataImport Author: Manon Martin [aut, cre], Bernadette Govaerts [aut, ths], Benoît Legat [aut], Paul H.C. Eilers [aut], Pascal de Tullio [dtc], Bruno Boulanger [ctb], Julien Vanwinsberghe [ctb] Maintainer: Manon Martin URL: https://github.com/ManonMartin/PepsNMR VignetteBuilder: knitr BugReports: https://github.com/ManonMartin/PepsNMR/issues git_url: https://git.bioconductor.org/packages/PepsNMR git_branch: devel git_last_commit: d78e0f9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PepsNMR_1.29.0.tar.gz vignettes: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.html vignetteTitles: Application of PepsNMR on the Human Serum dataset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.R importsMe: ASICS dependencyCount: 37 Package: pepStat Version: 1.45.0 Depends: R (>= 3.0.0), Biobase, IRanges Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods, data.table Suggests: pepDat, Pviz, knitr, shiny License: Artistic-2.0 MD5sum: 2ed49130ef52b539fa2c35eae12165b4 NeedsCompilation: no Title: Statistical analysis of peptide microarrays Description: Statistical analysis of peptide microarrays biocViews: Microarray, Preprocessing Author: Raphael Gottardo, Gregory C Imholte, Renan Sauteraud, Mike Jiang Maintainer: Gregory C Imholte URL: https://github.com/RGLab/pepStat VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pepStat git_branch: devel git_last_commit: d3092b9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pepStat_1.45.0.tar.gz vignettes: vignettes/pepStat/inst/doc/pepStat.pdf vignetteTitles: Full peptide microarray analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepStat/inst/doc/pepStat.R dependencyCount: 41 Package: pepXMLTab Version: 1.45.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: d0bce390bcfecc6b65841ad33942cd1a NeedsCompilation: no Title: Parsing pepXML files and filter based on peptide FDR. Description: Parsing pepXML files based one XML package. The package tries to handle pepXML files generated from different softwares. The output will be a peptide-spectrum-matching tabular file. The package also provide function to filter the PSMs based on FDR. biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/pepXMLTab git_branch: devel git_last_commit: 2949fd1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pepXMLTab_1.45.0.tar.gz vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf vignetteTitles: Introduction to pepXMLTab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepXMLTab/inst/doc/pepXMLTab.R dependencyCount: 3 Package: periodicDNA Version: 1.21.0 Depends: R (>= 4.0), Biostrings, GenomicRanges, IRanges, BSgenome, BiocParallel Imports: S4Vectors, rtracklayer, stats, Seqinfo, magrittr, zoo, ggplot2, methods, parallel, cowplot Suggests: BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, reticulate, testthat, covr, knitr, rmarkdown, pkgdown License: GPL-3 + file LICENSE MD5sum: 0e195e1512526534e827d4b3f0a6963d NeedsCompilation: no Title: Set of tools to identify periodic occurrences of k-mers in DNA sequences Description: This R package helps the user identify k-mers (e.g. di- or tri-nucleotides) present periodically in a set of genomic loci (typically regulatory elements). The functions of this package provide a straightforward approach to find periodic occurrences of k-mers in DNA sequences, such as regulatory elements. It is not aimed at identifying motifs separated by a conserved distance; for this type of analysis, please visit MEME website. biocViews: SequenceMatching, MotifDiscovery, MotifAnnotation, Sequencing, Coverage, Alignment, DataImport Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/periodicDNA VignetteBuilder: knitr BugReports: https://github.com/js2264/periodicDNA/issues git_url: https://git.bioconductor.org/packages/periodicDNA git_branch: devel git_last_commit: 5bd1078 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/periodicDNA_1.21.0.tar.gz vignettes: vignettes/periodicDNA/inst/doc/periodicDNA.html vignetteTitles: Introduction to periodicDNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/periodicDNA/inst/doc/periodicDNA.R dependencyCount: 76 Package: pfamAnalyzeR Version: 1.11.0 Depends: R (>= 4.3.0), readr, stringr, dplyr Imports: utils, tibble, magrittr Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 0d4c3834bd401e486cb3e81d5e431ddd NeedsCompilation: no Title: Identification of domain isotypes in pfam data Description: Protein domains is one of the most import annoation of proteins we have with the Pfam database/tool being (by far) the most used tool. This R package enables the user to read the pfam prediction from both webserver and stand-alone runs into R. We have recently shown most human protein domains exist as multiple distinct variants termed domain isotypes. Different domain isotypes are used in a cell, tissue, and disease-specific manner. Accordingly, we find that domain isotypes, compared to each other, modulate, or abolish the functionality of a protein domain. This R package enables the identification and classification of such domain isotypes from Pfam data. biocViews: AlternativeSplicing, TranscriptomeVariant, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, Annotation, FunctionalPrediction, GenePrediction, DataImport Author: Kristoffer Vitting-Seerup [cre, aut] (ORCID: ) Maintainer: Kristoffer Vitting-Seerup VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/pfamAnalyzeR/issues git_url: https://git.bioconductor.org/packages/pfamAnalyzeR git_branch: devel git_last_commit: 52a663f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pfamAnalyzeR_1.11.0.tar.gz vignettes: vignettes/pfamAnalyzeR/inst/doc/pfamAnalyzeR.html vignetteTitles: pfamAnalyzeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pfamAnalyzeR/inst/doc/pfamAnalyzeR.R dependsOnMe: IsoformSwitchAnalyzeR dependencyCount: 34 Package: pgca Version: 1.35.0 Imports: utils, stats Suggests: knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: 635bef721a651716e293b2553e705247 NeedsCompilation: no Title: PGCA: An Algorithm to Link Protein Groups Created from MS/MS Data Description: Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data. The algorithm connects two or more groups with overlapping accession numbers. In some cases, pairwise groups are mutually exclusive but they may still be connected by another group (or set of groups) with overlapping accession numbers. Thus, groups created by PGCA from multiple experimental runs (i.e., global groups) are called "connected" groups. These identified global protein groups enable the analysis of quantitative data available for protein groups instead of unique protein identifiers. biocViews: WorkflowStep,AssayDomain,Proteomics,MassSpectrometry,ImmunoOncology Author: Gabriela Cohen-Freue Maintainer: Gabriela Cohen-Freue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pgca git_branch: devel git_last_commit: c628163 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pgca_1.35.0.tar.gz vignettes: vignettes/pgca/inst/doc/intro.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pgca/inst/doc/intro.R dependencyCount: 2 Package: phantasus Version: 1.31.2 Depends: R (>= 4.3) Imports: ggplot2, protolite, Biobase, GEOquery, htmltools, httpuv, jsonlite, limma, edgeR, opencpu, assertthat, methods, httr, rhdf5, utils, parallel, stringr, fgsea (>= 1.9.4), svglite, gtable, stats, Matrix, pheatmap, scales, ccaPP, grid, grDevices, AnnotationDbi, DESeq2, data.table, curl, apeglm, config (>= 0.3.2), rhdf5client (>= 1.25.1), yaml, fs, phantasusLite, XML Suggests: testthat, BiocStyle, knitr, rmarkdown, org.Hs.eg.db, org.Mm.eg.db License: MIT + file LICENSE MD5sum: 8a0a8b75b4914ec8dd96eae0caeaccc1 NeedsCompilation: no Title: Visual and interactive gene expression analysis Description: Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported. biocViews: GeneExpression, GUI, Visualization, DataRepresentation, Transcriptomics, RNASeq, Microarray, Normalization, Clustering, DifferentialExpression, PrincipalComponent, ImmunoOncology Author: Maxim Kleverov [aut], Daria Zenkova [aut], Vladislav Kamenev [aut], Margarita Sablina [ctb], Maxim Artyomov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://alserglab.wustl.edu/phantasus VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasus/issues git_url: https://git.bioconductor.org/packages/phantasus git_branch: devel git_last_commit: c06abfb git_last_commit_date: 2026-04-08 Date/Publication: 2026-04-20 source.ver: src/contrib/phantasus_1.31.2.tar.gz vignettes: vignettes/phantasus/inst/doc/phantasus-tutorial.html vignetteTitles: Using phantasus application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/phantasus/inst/doc/phantasus-tutorial.R dependencyCount: 160 Package: phantasusLite Version: 1.9.0 Depends: R (>= 4.2) Imports: data.table, rhdf5client(>= 1.25.1), httr, stringr, stats, utils, Biobase, methods Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, rhdf5, GEOquery License: MIT + file LICENSE MD5sum: 18fbd54cad6ac28ba14931441d8b9e6c NeedsCompilation: no Title: Loading and annotation RNA-seq counts matrices Description: PhantasusLite – a lightweight package with helper functions of general interest extracted from phantasus package. In parituclar it simplifies working with public RNA-seq datasets from GEO by providing access to the remote HSDS repository with the precomputed gene counts from ARCHS4 and DEE2 projects. biocViews: GeneExpression, Transcriptomics, RNASeq Author: Rita Sablina [aut], Maxim Kleverov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/phantasusLite/ VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasusLite/issues git_url: https://git.bioconductor.org/packages/phantasusLite git_branch: devel git_last_commit: 8ee7242 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/phantasusLite_1.9.0.tar.gz vignettes: vignettes/phantasusLite/inst/doc/phantasusLite-tutorial.html vignetteTitles: phantasusLite tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/phantasusLite/inst/doc/phantasusLite-tutorial.R importsMe: phantasus dependencyCount: 41 Package: PharmacoGx Version: 3.15.0 Depends: R (>= 4.1.0), CoreGx Imports: BiocGenerics, Biobase, S4Vectors, SummarizedExperiment, MultiAssayExperiment, BiocParallel, ggplot2, RColorBrewer, magicaxis, parallel, caTools, methods, downloader, stats, utils, graphics, grDevices, reshape2, jsonlite, data.table, checkmate, boot, coop LinkingTo: Rcpp Suggests: pander, rmarkdown, knitr, knitcitations, crayon, testthat, markdown, BiocStyle, R.utils License: GPL (>= 3) MD5sum: 67a9dd43190177e52699c292520c943c NeedsCompilation: yes Title: Analysis of Large-Scale Pharmacogenomic Data Description: Contains a set of functions to perform large-scale analysis of pharmaco-genomic data. These include the PharmacoSet object for storing the results of pharmacogenomic experiments, as well as a number of functions for computing common summaries of drug-dose response and correlating them with the molecular features in a cancer cell-line. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Petr Smirnov [aut], Christopher Eeles [aut], Jermiah Joseph [aut], Zhaleh Safikhani [aut], Mark Freeman [aut], Feifei Li [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/PharmacoGx/issues git_url: https://git.bioconductor.org/packages/PharmacoGx git_branch: devel git_last_commit: a1fade5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PharmacoGx_3.15.0.tar.gz vignettes: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.html, vignettes/PharmacoGx/inst/doc/DetectingDrugSynergyAndAntagonism.html, vignettes/PharmacoGx/inst/doc/PharmacoGx.html vignetteTitles: Creating a PharmacoSet Object, Detecting Drug Synergy and Antagonism with PharmacoGx 3.0+, PharmacoGx: An R Package for Analysis of Large Pharmacogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.R, vignettes/PharmacoGx/inst/doc/DetectingDrugSynergyAndAntagonism.R, vignettes/PharmacoGx/inst/doc/PharmacoGx.R importsMe: gDRimport, Xeva suggestsMe: ToxicoGx dependencyCount: 143 Package: PhenoGeneRanker Version: 1.19.0 Imports: igraph, Matrix, foreach, doParallel, dplyr, stats, utils, parallel Suggests: knitr, rmarkdown License: Creative Commons Attribution 4.0 International License MD5sum: c0142c2da038a924c66eb2f17e60facc NeedsCompilation: no Title: PhenoGeneRanker: A gene and phenotype prioritization tool Description: This package is a gene/phenotype prioritization tool that utilizes multiplex heterogeneous gene phenotype network. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p-values of gene/phenotype ranking using random stratified sampling of genes/phenotypes based on their connectivity degree in the network. https://dl.acm.org/doi/10.1145/3307339.3342155. biocViews: BiomedicalInformatics, GenePrediction, GraphAndNetwork, Network, NetworkInference, Pathways, Software, SystemsBiology Author: Cagatay Dursun [aut, cre] Maintainer: Cagatay Dursun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhenoGeneRanker git_branch: devel git_last_commit: dcd9177 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PhenoGeneRanker_1.19.0.tar.gz vignettes: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.html vignetteTitles: PhenoGeneRanker hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.R dependencyCount: 30 Package: phenopath Version: 1.35.0 Imports: Rcpp (>= 0.12.8), SummarizedExperiment, methods, stats, dplyr, tibble, ggplot2, tidyr LinkingTo: Rcpp Suggests: knitr, rmarkdown, forcats, testthat, BiocStyle, SingleCellExperiment License: Apache License (== 2.0) MD5sum: 41e44d71e78c3d3a27a20567ab848733 NeedsCompilation: yes Title: Genomic trajectories with heterogeneous genetic and environmental backgrounds Description: PhenoPath infers genomic trajectories (pseudotimes) in the presence of heterogeneous genetic and environmental backgrounds and tests for interactions between them. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell, PrincipalComponent Author: Kieran Campbell Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phenopath git_branch: devel git_last_commit: 4aa1478 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/phenopath_1.35.0.tar.gz vignettes: vignettes/phenopath/inst/doc/introduction_to_phenopath.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenopath/inst/doc/introduction_to_phenopath.R suggestsMe: splatter dependencyCount: 54 Package: philr Version: 1.37.1 Imports: ape, phangorn, tidyr, ggplot2, ggtree, methods Suggests: testthat, knitr, ecodist, rmarkdown, BiocStyle, phyloseq, SummarizedExperiment, TreeSummarizedExperiment, glmnet, dplyr, mia License: GPL-3 MD5sum: 0c80a67cb5e8e6cdbfd40291b7ff9738 NeedsCompilation: no Title: Phylogenetic partitioning based ILR transform for metagenomics data Description: PhILR is short for Phylogenetic Isometric Log-Ratio Transform. This package provides functions for the analysis of compositional data (e.g., data representing proportions of different variables/parts). Specifically this package allows analysis of compositional data where the parts can be related through a phylogenetic tree (as is common in microbiota survey data) and makes available the Isometric Log Ratio transform built from the phylogenetic tree and utilizing a weighted reference measure. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Software Author: Justin Silverman [aut, cre], Leo Lahti [ctb] (ORCID: ) Maintainer: Justin Silverman URL: https://github.com/jsilve24/philr VignetteBuilder: knitr BugReports: https://github.com/jsilve24/philr/issues git_url: https://git.bioconductor.org/packages/philr git_branch: devel git_last_commit: c10d8f4 git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/philr_1.37.1.tar.gz vignettes: vignettes/philr/inst/doc/philr-intro.html vignetteTitles: Introduction to PhILR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/philr/inst/doc/philr-intro.R suggestsMe: mia, miaDash dependencyCount: 85 Package: PhIPData Version: 1.19.0 Depends: R (>= 4.1.0), SummarizedExperiment (>= 1.3.81) Imports: BiocFileCache, BiocGenerics, methods, GenomicRanges, IRanges, S4Vectors, edgeR, cli, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, covr, dplyr, readr, withr License: MIT + file LICENSE MD5sum: 5abd4be5775b782b2e29dbd65ce42a3f NeedsCompilation: no Title: Container for PhIP-Seq Experiments Description: PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters. biocViews: Infrastructure, DataRepresentation, Sequencing, Coverage Author: Athena Chen [aut, cre] (ORCID: ), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen VignetteBuilder: knitr BugReports: https://github.com/athchen/PhIPData/issues git_url: https://git.bioconductor.org/packages/PhIPData git_branch: devel git_last_commit: 82391da git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PhIPData_1.19.0.tar.gz vignettes: vignettes/PhIPData/inst/doc/PhIPData.html vignetteTitles: PhIPData: A Container for PhIP-Seq Experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhIPData/inst/doc/PhIPData.R dependsOnMe: beer dependencyCount: 65 Package: phosphonormalizer Version: 1.35.0 Depends: R (>= 4.0) Imports: plyr, stats, graphics, matrixStats, methods Suggests: knitr, rmarkdown, testthat Enhances: MSnbase License: GPL (>= 2) MD5sum: a56be05fc458cebf76a7429c780d2874 NeedsCompilation: no Title: Compensates for the bias introduced by median normalization in Description: It uses the overlap between enriched and non-enriched datasets to compensate for the bias introduced in global phosphorylation after applying median normalization. biocViews: Software, StatisticalMethod, WorkflowStep, Normalization, Proteomics Author: Sohrab Saraei [aut, cre], Tomi Suomi [ctb], Otto Kauko [ctb], Laura Elo [ths] Maintainer: Sohrab Saraei VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phosphonormalizer git_branch: devel git_last_commit: 40c9d09 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/phosphonormalizer_1.35.0.tar.gz vignettes: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.pdf, vignettes/phosphonormalizer/inst/doc/vignette.html vignetteTitles: phosphonormalizer: Phosphoproteomics Normalization, Pairwise normalization of phosphoproteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.R, vignettes/phosphonormalizer/inst/doc/vignette.R dependencyCount: 7 Package: PhosR Version: 1.21.0 Depends: R (>= 4.2.0) Imports: ruv, e1071, dendextend, limma, pcaMethods, stats, RColorBrewer, circlize, dplyr, igraph, pheatmap, preprocessCore, tidyr, rlang, graphics, grDevices, utils, SummarizedExperiment, methods, S4Vectors, BiocGenerics, ggplot2, GGally, ggdendro, ggpubr, network, reshape2, ggtext, stringi Suggests: testthat, knitr, rgl, sna, ClueR, directPA, rmarkdown, org.Rn.eg.db, org.Mm.eg.db, reactome.db, annotate, BiocStyle, stringr, calibrate License: GPL-3 + file LICENSE MD5sum: 778eba30741386c42169bbd9ec09ed9f NeedsCompilation: no Title: A set of methods and tools for comprehensive analysis of phosphoproteomics data Description: PhosR is a package for the comprenhensive analysis of phosphoproteomic data. There are two major components to PhosR: processing and downstream analysis. PhosR consists of various processing tools for phosphoproteomics data including filtering, imputation, normalisation, and functional analysis for inferring active kinases and signalling pathways. biocViews: Software, ResearchField, Proteomics Author: Pengyi Yang [aut], Di Xiao [aut, cre], Hani Jieun Kim [aut] Maintainer: Di Xiao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhosR git_branch: devel git_last_commit: 244f040 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PhosR_1.21.0.tar.gz vignettes: vignettes/PhosR/inst/doc/PhosR.html vignetteTitles: An introduction to PhosR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhosR/inst/doc/PhosR.R suggestsMe: SmartPhos dependencyCount: 140 Package: PhyloProfile Version: 2.3.5 Depends: R (>= 4.5.0) Imports: ape, bioDist, BiocStyle, Biostrings, bsplus, colourpicker, data.table, dplyr, DT, energy, fastcluster, ggplot2, gridExtra, htmlwidgets, pbapply, plotly, RColorBrewer, RCurl, Rfast, scattermore, shiny, shinycssloaders, shinyFiles, shinyjs, stringr, tsne, svglite, umap, xml2, zoo, yaml Suggests: knitr, rmarkdown, testthat, OmaDB License: MIT + file LICENSE MD5sum: 5a4f836019b82e191781f5fb1b2e8dd9 NeedsCompilation: no Title: PhyloProfile Description: PhyloProfile is a tool for exploring complex phylogenetic profiles. Phylogenetic profiles, presence/absence patterns of genes over a set of species, are commonly used to trace the functional and evolutionary history of genes across species and time. With PhyloProfile we can enrich regular phylogenetic profiles with further data like sequence/structure similarity, to make phylogenetic profiling more meaningful. Besides the interactive visualisation powered by R-Shiny, the package offers a set of further analysis features to gain insights like the gene age estimation or core gene identification. biocViews: Software, Visualization, DataRepresentation, MultipleComparison, FunctionalPrediction, DimensionReduction Author: Vinh Tran [aut, cre] (ORCID: ), Bastian Greshake Tzovaras [aut], Ingo Ebersberger [aut], Carla Mölbert [ctb] Maintainer: Vinh Tran URL: https://github.com/BIONF/PhyloProfile/ VignetteBuilder: knitr BugReports: https://github.com/BIONF/PhyloProfile/issues git_url: https://git.bioconductor.org/packages/PhyloProfile git_branch: devel git_last_commit: d8059e2 git_last_commit_date: 2026-03-11 Date/Publication: 2026-04-20 source.ver: src/contrib/PhyloProfile_2.3.5.tar.gz vignettes: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.html vignetteTitles: PhyloProfile hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.R dependencyCount: 131 Package: phyloseq Version: 1.55.2 Depends: R (>= 3.3.0) Imports: ade4 (>= 1.7-4), ape (>= 5.0), Biobase (>= 2.36.2), BiocGenerics (>= 0.22.0), biomformat (>= 1.0.0), Biostrings (>= 2.40.0), cluster (>= 2.0.4), data.table (>= 1.10.4), foreach (>= 1.4.3), ggplot2 (>= 2.1.0), igraph (>= 1.0.1), methods (>= 3.3.0), multtest (>= 2.28.0), plyr (>= 1.8.3), reshape2 (>= 1.4.1), scales (>= 0.4.0), vegan (>= 2.5) Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58), knitr (>= 1.16), magrittr (>= 1.5), metagenomeSeq (>= 1.14), rmarkdown (>= 1.6), testthat (>= 1.0.2) Enhances: doParallel (>= 1.0.10) License: AGPL-3 MD5sum: 3e49757fec9fc2656a20146bf32b3c88 NeedsCompilation: no Title: Handling and analysis of high-throughput microbiome census data Description: phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Clustering, Classification, MultipleComparison, GeneticVariability Author: Paul J. McMurdie [aut, cre], Susan Holmes [aut], Gregory Jordan [ctb], Scott Chamberlain [ctb] Maintainer: Paul J. McMurdie URL: http://dx.plos.org/10.1371/journal.pone.0061217 VignetteBuilder: knitr BugReports: https://github.com/joey711/phyloseq/issues git_url: https://git.bioconductor.org/packages/phyloseq git_branch: devel git_last_commit: 0187808 git_last_commit_date: 2026-02-27 Date/Publication: 2026-04-20 source.ver: src/contrib/phyloseq_1.55.2.tar.gz vignettes: vignettes/phyloseq/inst/doc/phyloseq-analysis.html, vignettes/phyloseq/inst/doc/phyloseq-basics.html, vignettes/phyloseq/inst/doc/phyloseq-FAQ.html, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html vignetteTitles: analysis vignette, phyloseq basics vignette, phyloseq Frequently Asked Questions (FAQ), phyloseq and DESeq2 on Colorectal Cancer Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phyloseq/inst/doc/phyloseq-analysis.R, vignettes/phyloseq/inst/doc/phyloseq-basics.R, vignettes/phyloseq/inst/doc/phyloseq-FAQ.R, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.R dependsOnMe: microbiome, SIAMCAT, MiscMetabar, phyloseqGraphTest importsMe: ADAPT, benchdamic, combi, dar, DspikeIn, FinfoMDS, MBECS, MeLSI, microbiomeDASim, PathoStat, RCM, reconsi, RPA, SimBu, SpiecEasi, SPsimSeq, zitools, HMP2Data, adaptiveGPCA, breakaway, chem16S, eDNAfuns, FLORAL, holobiont, HybridMicrobiomes, microbial, mixKernel, multimedia, Rsearch, speedytax, structSSI, TaxaNorm, treeDA suggestsMe: ANCOMBC, CrcBiomeScreen, decontam, lefser, MGnifyR, mia, MicrobiotaProcess, MMUPHin, philr, HMP16SData, corncob, demulticoder, FAVA, fido, file2meco, LorMe, metacoder, MIDASim, parafac4microbiome, pctax, phyloregion, radEmu, readyomics, SQMtools dependencyCount: 66 Package: piano Version: 2.27.0 Depends: R (>= 3.5) Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray, fgsea, shiny, DT, htmlwidgets, shinyjs, shinydashboard, visNetwork, scales, grDevices, graphics, stats, utils, methods Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM, gtools, biomaRt, snowfall, AnnotationDbi, knitr, rmarkdown, BiocStyle License: GPL (>=2) MD5sum: c597796bb23e0cebb8e0326065333411 NeedsCompilation: no Title: Platform for integrative analysis of omics data Description: Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses. biocViews: Microarray, Preprocessing, QualityControl, DifferentialExpression, Visualization, GeneExpression, GeneSetEnrichment, Pathways Author: Leif Varemo Wigge and Intawat Nookaew Maintainer: Leif Varemo Wigge URL: http://www.sysbio.se/piano VignetteBuilder: knitr BugReports: https://github.com/varemo/piano/issues git_url: https://git.bioconductor.org/packages/piano git_branch: devel git_last_commit: f61c2c0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/piano_2.27.0.tar.gz vignettes: vignettes/piano/inst/doc/piano-vignette.pdf, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.html vignetteTitles: Piano - Platform for Integrative Analysis of Omics data, Running gene-set anaysis with piano hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/piano/inst/doc/piano-vignette.R, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.R importsMe: CoreGx, PDATK, SmartPhos suggestsMe: BloodCancerMultiOmics2017 dependencyCount: 94 Package: PICB Version: 1.3.0 Imports: utils, Seqinfo, GenomicRanges, GenomicAlignments, Biostrings, Rsamtools, data.table, IRanges, seqinr, stats, openxlsx, dplyr, S4Vectors, methods Suggests: GenomeInfoDb, knitr, rtracklayer, testthat, BiocStyle, prettydoc, BSgenome, BSgenome.Dmelanogaster.UCSC.dm6, BiocManager, rmarkdown, ggplot2 License: CC0 MD5sum: c03c3a1c66da37e3ff6dc2e97c172dd3 NeedsCompilation: no Title: piRNA Cluster Builder Description: piRNAs (short for PIWI-interacting RNAs) and their PIWI protein partners play a key role in fertility and maintaining genome integrity by restricting mobile genetic elements (transposons) in germ cells. piRNAs originate from genomic regions known as piRNA clusters. The piRNA Cluster Builder (PICB) is a versatile toolkit designed to identify genomic regions with a high density of piRNAs. It constructs piRNA clusters through a stepwise integration of unique and multimapping piRNAs and offers wide-ranging parameter settings, supported by an optimization function that allows users to test different parameter combinations to tailor the analysis to their specific piRNA system. The output includes extensive metadata columns, enabling researchers to rank clusters and extract cluster characteristics. biocViews: Genetics, GenomeAnnotation, Sequencing, FunctionalPrediction, Coverage, Transcriptomics Author: Pavol Genzor [aut], Aleksandr Friman [aut], Daniel Stoyko [aut], Parthena Konstantinidou [aut], Franziska Ahrend [aut, cre] (ORCID: ), Zuzana Loubalova [aut], Yuejun Wang [aut], Hernan Lorenzi [aut], Astrid D Haase [aut] Maintainer: Franziska Ahrend URL: https://github.com/HaaseLab/PICB VignetteBuilder: knitr BugReports: https://github.com/HaaseLab/PICB/issues git_url: https://git.bioconductor.org/packages/PICB git_branch: devel git_last_commit: d455820 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PICB_1.3.0.tar.gz vignettes: vignettes/PICB/inst/doc/PICB.html vignetteTitles: Introduction to the piRNA Cluster Builder (PICB) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PICB/inst/doc/PICB.R dependencyCount: 69 Package: pickgene Version: 1.83.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 243d4bf56a42de68cd03d3569df68287 NeedsCompilation: no Title: Adaptive Gene Picking for Microarray Expression Data Analysis Description: Functions to Analyze Microarray (Gene Expression) Data. biocViews: Microarray, DifferentialExpression Author: Brian S. Yandell Maintainer: Brian S. Yandell URL: http://www.stat.wisc.edu/~yandell/statgen git_url: https://git.bioconductor.org/packages/pickgene git_branch: devel git_last_commit: 6cea2ff git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pickgene_1.83.0.tar.gz vignettes: vignettes/pickgene/inst/doc/pickgene.pdf vignetteTitles: Adaptive Gene Picking for Microarray Expression Data Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: pipeComp Version: 1.21.0 Depends: R (>= 4.1) Imports: BiocParallel, S4Vectors, ComplexHeatmap, SingleCellExperiment, SummarizedExperiment, Seurat, matrixStats, Matrix, cluster, aricode, methods, utils, dplyr, grid, scales, scran, viridisLite, clue, randomcoloR, ggplot2, cowplot, intrinsicDimension, scater, knitr, reshape2, stats, Rtsne, uwot, circlize, RColorBrewer Suggests: BiocStyle, rmarkdown License: GPL MD5sum: 1cc1d12e7e1ee11683a8b58e8c5c84d7 NeedsCompilation: no Title: pipeComp pipeline benchmarking framework Description: A simple framework to facilitate the comparison of pipelines involving various steps and parameters. The `pipelineDefinition` class represents pipelines as, minimally, a set of functions consecutively executed on the output of the previous one, and optionally accompanied by step-wise evaluation and aggregation functions. Given such an object, a set of alternative parameters/methods, and benchmark datasets, the `runPipeline` function then proceeds through all combinations arguments, avoiding recomputing the same step twice and compiling evaluations on the fly to avoid storing potentially large intermediate data. biocViews: GeneExpression, Transcriptomics, Clustering, DataRepresentation Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Anthony Sonrel [aut] (ORCID: ), Mark D. Robinson [aut, fnd] (ORCID: ) Maintainer: Pierre-Luc Germain URL: https://doi.org/10.1186/s13059-020-02136-7 VignetteBuilder: knitr BugReports: https://github.com/plger/pipeComp git_url: https://git.bioconductor.org/packages/pipeComp git_branch: devel git_last_commit: 826f159 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pipeComp_1.21.0.tar.gz vignettes: vignettes/pipeComp/inst/doc/pipeComp_dea.html, vignettes/pipeComp/inst/doc/pipeComp_scRNA.html, vignettes/pipeComp/inst/doc/pipeComp.html vignetteTitles: pipeComp_dea, pipeComp_scRNA, pipeComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pipeComp/inst/doc/pipeComp_dea.R, vignettes/pipeComp/inst/doc/pipeComp_scRNA.R, vignettes/pipeComp/inst/doc/pipeComp.R dependencyCount: 214 Package: pipeFrame Version: 1.27.0 Depends: R (>= 4.0.0), Imports: BSgenome, digest, visNetwork, magrittr, methods, Biostrings, Seqinfo, parallel, stats, utils, rmarkdown Suggests: BiocManager, knitr, rtracklayer, testthat, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: dc9b884505cd5319d785e49a11ba39dc NeedsCompilation: no Title: Pipeline framework for bioinformatics in R Description: pipeFrame is an R package for building a componentized bioinformatics pipeline. Each step in this pipeline is wrapped in the framework, so the connection among steps is created seamlessly and automatically. Users could focus more on fine-tuning arguments rather than spending a lot of time on transforming file format, passing task outputs to task inputs or installing the dependencies. Componentized step elements can be customized into other new pipelines flexibly as well. This pipeline can be split into several important functional steps, so it is much easier for users to understand the complex arguments from each step rather than parameter combination from the whole pipeline. At the same time, componentized pipeline can restart at the breakpoint and avoid rerunning the whole pipeline, which may save a lot of time for users on pipeline tuning or such issues as power off or process other interrupts. biocViews: Software, Infrastructure, WorkflowStep Author: Zheng Wei, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/pipeFrame VignetteBuilder: knitr BugReports: https://github.com/wzthu/pipeFrame/issues git_url: https://git.bioconductor.org/packages/pipeFrame git_branch: devel git_last_commit: b7c4419 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pipeFrame_1.27.0.tar.gz vignettes: vignettes/pipeFrame/inst/doc/pipeFrame.html vignetteTitles: An Introduction to pipeFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pipeFrame/inst/doc/pipeFrame.R dependsOnMe: esATAC dependencyCount: 82 Package: PIPETS Version: 1.7.0 Depends: R (>= 4.4.0) Imports: dplyr, utils, stats, GenomicRanges, BiocGenerics, methods Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: d731f2c06a50755d3cba57858960f0ac NeedsCompilation: no Title: Poisson Identification of PEaks from Term-Seq data Description: PIPETS provides statistically robust analysis for 3'-seq/term-seq data. It utilizes a sliding window approach to apply a Poisson Distribution test to identify genomic positions with termination read coverage that is significantly higher than the surrounding signal. PIPETS then condenses proximal signal and produces strand specific results that contain all significant termination peaks. biocViews: Sequencing, Transcription, GeneRegulation, PeakDetection, Genetics, Transcriptomics, Coverage Author: Quinlan Furumo [aut, cre] (ORCID: ) Maintainer: Quinlan Furumo URL: https://github.com/qfurumo/PIPETS VignetteBuilder: knitr BugReports: https://github.com/qfurumo/PIPETS/issues git_url: https://git.bioconductor.org/packages/PIPETS git_branch: devel git_last_commit: 6146b1f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PIPETS_1.7.0.tar.gz vignettes: vignettes/PIPETS/inst/doc/PIPETS.html vignetteTitles: PIPETS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PIPETS/inst/doc/PIPETS.R dependencyCount: 26 Package: PIUMA Version: 1.7.2 Depends: R (>= 4.3) Imports: Hmisc, igraph, patchwork, scales, utils, cluster, umap, tsne, kernlab, vegan, dbscan, grDevices, stats, methods, SummarizedExperiment, ggplot2 Suggests: BiocStyle, testthat, knitr, rmarkdown, Seurat, SingleCellExperiment, aricode, mclust, viridis, magick, ggrepel, dplyr License: GPL-3 + file LICENSE MD5sum: 6b5ba8c38e9514e07ef4da42fd918440 NeedsCompilation: no Title: Phenotypes Identification Using Mapper from topological data Analysis Description: The PIUMA package offers a tidy pipeline of Topological Data Analysis frameworks to identify and characterize communities in high and heterogeneous dimensional data. biocViews: Clustering, GraphAndNetwork, DimensionReduction, Network, Classification Author: Mattia Chiesa [aut, cre] (ORCID: ), Arianna Dagliati [aut] (ORCID: ), Alessia Gerbasi [aut] (ORCID: ), Giuseppe Albi [aut], Laura Ballarini [aut], Luca Piacentini [aut] (ORCID: ), Carlo Leonardi [aut] (ORCID: ) Maintainer: Mattia Chiesa URL: https://github.com/BioinfoMonzino/PIUMA VignetteBuilder: knitr BugReports: https://github.com/BioinfoMonzino/PIUMA/issues git_url: https://git.bioconductor.org/packages/PIUMA git_branch: devel git_last_commit: a2a72a7 git_last_commit_date: 2026-01-02 Date/Publication: 2026-04-20 source.ver: src/contrib/PIUMA_1.7.2.tar.gz vignettes: vignettes/PIUMA/inst/doc/PIUMA_vignette.html, vignettes/PIUMA/inst/doc/VignetteSeurat.html vignetteTitles: PIUMA package, PIUMA package and Seurat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PIUMA/inst/doc/PIUMA_vignette.R, vignettes/PIUMA/inst/doc/VignetteSeurat.R dependencyCount: 104 Package: planet Version: 1.19.0 Depends: R (>= 4.3) Imports: methods, tibble, magrittr, dplyr Suggests: ExperimentHub, mixOmics, ggplot2, testthat, tidyr, scales, minfi, EpiDISH, knitr, rmarkdown License: GPL-2 MD5sum: d52864af489845eae6594e781415408a NeedsCompilation: no Title: Placental DNA methylation analysis tools Description: This package contains R functions to predict biological variables to from placnetal DNA methylation data generated from infinium arrays. This includes inferring ethnicity/ancestry, gestational age, and cell composition from placental DNA methylation array (450k/850k) data. biocViews: Software, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, DNAMethylation, CpGIsland Author: Victor Yuan [aut, cre], Wendy P. Robinson [aut, ctb], Icíar Fernández-Boyano [aut, ctb] Maintainer: Victor Yuan URL: http://github.com/wvictor14/planet, http://victoryuan.com/planet/ VignetteBuilder: knitr BugReports: http://github.com/wvictor14/planet/issues git_url: https://git.bioconductor.org/packages/planet git_branch: devel git_last_commit: 8c6ed45 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/planet_1.19.0.tar.gz vignettes: vignettes/planet/inst/doc/planet.html vignetteTitles: planet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/planet/inst/doc/planet.R importsMe: methylclock suggestsMe: eoPredData dependencyCount: 19 Package: planttfhunter Version: 1.11.0 Depends: R (>= 4.2.0) Imports: Biostrings, SummarizedExperiment, utils, methods Suggests: BiocStyle, covr, sessioninfo, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: d82d80fabc304f0b795c0cf6728f75dd NeedsCompilation: no Title: Identification and classification of plant transcription factors Description: planttfhunter is used to identify plant transcription factors (TFs) from protein sequence data and classify them into families and subfamilies using the classification scheme implemented in PlantTFDB. TFs are identified using pre-built hidden Markov model profiles for DNA-binding domains. Then, auxiliary and forbidden domains are used with DNA-binding domains to classify TFs into families and subfamilies (when applicable). Currently, TFs can be classified in 58 different TF families/subfamilies. biocViews: Software, Transcription, FunctionalPrediction, GenomeAnnotation, FunctionalGenomics, HiddenMarkovModel, Sequencing, Classification Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/planttfhunter SystemRequirements: HMMER VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/planttfhunter git_url: https://git.bioconductor.org/packages/planttfhunter git_branch: devel git_last_commit: 0cf046b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/planttfhunter_1.11.0.tar.gz vignettes: vignettes/planttfhunter/inst/doc/vignette_planttfhunter.html vignetteTitles: Genome-wide identification and classification of transcription factors in plant genomes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/planttfhunter/inst/doc/vignette_planttfhunter.R dependencyCount: 27 Package: plasmut Version: 1.9.0 Depends: R (>= 4.3.0) Imports: tibble, stats, dplyr Suggests: knitr, rmarkdown, tidyverse, ggrepel, magrittr, qpdf, BiocStyle, biocViews, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: f3bf2515fff99ce9985a61b88ec88919 NeedsCompilation: no Title: Stratifying mutations observed in cell-free DNA and white blood cells as germline, hematopoietic, or somatic Description: A Bayesian method for quantifying the liklihood that a given plasma mutation arises from clonal hematopoesis or the underlying tumor. It requires sequencing data of the mutation in plasma and white blood cells with the number of distinct and mutant reads in both tissues. We implement a Monte Carlo importance sampling method to assess the likelihood that a mutation arises from the tumor relative to non-tumor origin. biocViews: Bayesian, SomaticMutation, GermlineMutation, Sequencing Author: Adith Arun [aut, cre], Robert Scharpf [aut] Maintainer: Adith Arun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/plasmut git_branch: devel git_last_commit: e0d320c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/plasmut_1.9.0.tar.gz vignettes: vignettes/plasmut/inst/doc/plasmut.html vignetteTitles: Modeling the origin of mutations in a liquid biopsy: cancer or clonal hematopoiesis? hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plasmut/inst/doc/plasmut.R dependencyCount: 20 Package: plgem Version: 1.83.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS, methods License: GPL-2 MD5sum: a04c0abd5f9a8948598a97e37314e81c NeedsCompilation: no Title: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM) Description: The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets. biocViews: ImmunoOncology, Microarray, DifferentialExpression, Proteomics, GeneExpression, MassSpectrometry Author: Mattia Pelizzola and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it git_url: https://git.bioconductor.org/packages/plgem git_branch: devel git_last_commit: b4e2cd9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/plgem_1.83.0.tar.gz vignettes: vignettes/plgem/inst/doc/plgem.pdf vignetteTitles: An introduction to PLGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plgem/inst/doc/plgem.R importsMe: INSPEcT dependencyCount: 9 Package: plier Version: 1.81.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) MD5sum: 42c78a604a12cf770b589dbe981a8414 NeedsCompilation: yes Title: Implements the Affymetrix PLIER algorithm Description: The PLIER (Probe Logarithmic Error Intensity Estimate) method produces an improved signal by accounting for experimentally observed patterns in probe behavior and handling error at the appropriately at low and high signal values. biocViews: Software Author: Affymetrix Inc., Crispin J Miller, PICR Maintainer: Crispin Miller git_url: https://git.bioconductor.org/packages/plier git_branch: devel git_last_commit: ed680fc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/plier_1.81.0.tar.gz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano dependencyCount: 12 Package: PlinkMatrix Version: 0.99.8 Depends: R (>= 4.1.0), methods, Rcpp, DelayedArray, SummarizedExperiment Imports: BiocFileCache, GenomicRanges, IRanges LinkingTo: Rcpp Suggests: knitr, BiocStyle, testthat, rmarkdown, irlba, GenomeInfoDb License: MIT + file LICENSE MD5sum: 72b16e7958833c5e8e4090d9ee9837a5 NeedsCompilation: yes Title: DelayedArray interface for plink bed files Description: This package provides a DelayedArray interface for plink bed files. There is support for interfacing to plink genotype data via RangedSummarizedExperiment. Example data from the GEUVADIS project (internationalgenome.org) are used for demonstration. biocViews: Infrastructure, Genetics Author: Vince Carey [aut, cre] (ORCID: ), NHGRI U24HG004059 [fnd] Maintainer: Vince Carey URL: https://github.com/vjcitn/PlinkMatrix VignetteBuilder: knitr BugReports: https://github.com/vjcitn/PlinkMatrix/issues git_url: https://git.bioconductor.org/packages/PlinkMatrix git_branch: devel git_last_commit: b2afa0b git_last_commit_date: 2026-04-15 Date/Publication: 2026-04-20 source.ver: src/contrib/PlinkMatrix_0.99.8.tar.gz vignettes: vignettes/PlinkMatrix/inst/doc/PlinkMatrix.html vignetteTitles: PlinkMatrix: DelayedArray interface to plink bed-type files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PlinkMatrix/inst/doc/PlinkMatrix.R dependencyCount: 62 Package: plotgardener Version: 1.17.0 Depends: R (>= 4.2.0) Imports: curl, data.table, dplyr, GenomeInfoDb, GenomicRanges, glue, grDevices, grid, ggplotify, IRanges, methods, plyranges, purrr, Rcpp, RColorBrewer, rhdf5, rlang, stats, strawr, tools, utils, withr LinkingTo: Rcpp Suggests: AnnotationDbi, AnnotationHub, BSgenome, BSgenome.Hsapiens.UCSC.hg19, ComplexHeatmap, GenomicFeatures, ggplot2, InteractionSet, knitr, org.Hs.eg.db, rtracklayer, plotgardenerData, pdftools, png, rmarkdown, scales, showtext, testthat (>= 3.0.0), TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene License: MIT + file LICENSE MD5sum: ffccdb1c33c8b041b46fc48af5b93203 NeedsCompilation: yes Title: Coordinate-Based Genomic Visualization Package for R Description: Coordinate-based genomic visualization package for R. It grants users the ability to programmatically produce complex, multi-paneled figures. Tailored for genomics, plotgardener allows users to visualize large complex genomic datasets and provides exquisite control over how plots are placed and arranged on a page. biocViews: Visualization, GenomeAnnotation, FunctionalGenomics, GenomeAssembly, HiC Author: Nicole Kramer [aut, cre], Eric S. Davis [aut], Craig Wenger [aut], Sarah Parker [ctb], Erika Deoudes [art], Michael Love [ctb], Douglas H. Phanstiel [aut, cre, cph] Maintainer: Nicole Kramer , Douglas Phanstiel URL: https://phanstiellab.github.io/plotgardener, https://github.com/PhanstielLab/plotgardener VignetteBuilder: knitr BugReports: https://github.com/PhanstielLab/plotgardener/issues git_url: https://git.bioconductor.org/packages/plotgardener git_branch: devel git_last_commit: eaa9c96 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/plotgardener_1.17.0.tar.gz vignettes: vignettes/plotgardener/inst/doc/introduction_to_plotgardener.html vignetteTitles: Introduction to plotgardener hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/plotgardener/inst/doc/introduction_to_plotgardener.R importsMe: DegCre, mariner suggestsMe: nullranges, rigvf dependencyCount: 98 Package: plotGrouper Version: 1.29.0 Depends: R (>= 3.5) Imports: ggplot2 (>= 3.0.0), dplyr (>= 0.7.6), tidyr (>= 0.2.0), tibble (>= 1.4.2), stringr (>= 1.3.1), readr (>= 1.1.1), readxl (>= 1.1.0), scales (>= 1.0.0), stats, grid, gridExtra (>= 2.3), egg (>= 0.4.0), gtable (>= 0.2.0), ggpubr (>= 0.1.8), shiny (>= 1.1.0), shinythemes (>= 1.1.1), colourpicker (>= 1.0), magrittr (>= 1.5), Hmisc (>= 4.1.1), rlang (>= 0.2.2) Suggests: knitr, htmltools, BiocStyle, rmarkdown, testthat License: GPL-3 MD5sum: 8a19f8cd5d99692cc215d69ed45bcdf0 NeedsCompilation: no Title: Shiny app GUI wrapper for ggplot with built-in statistical analysis Description: A shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed). biocViews: ImmunoOncology, FlowCytometry, GraphAndNetwork, StatisticalMethod, DataImport, GUI, MultipleComparison Author: John D. Gagnon [aut, cre] Maintainer: John D. Gagnon URL: https://jdgagnon.github.io/plotGrouper/ VignetteBuilder: knitr BugReports: https://github.com/jdgagnon/plotGrouper/issues git_url: https://git.bioconductor.org/packages/plotGrouper git_branch: devel git_last_commit: 026daf9 git_last_commit_date: 2026-03-09 Date/Publication: 2026-04-20 source.ver: src/contrib/plotGrouper_1.29.0.tar.gz vignettes: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.html vignetteTitles: plotGrouper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.R dependencyCount: 142 Package: PLPE Version: 1.71.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: c0b3769670b55c33c132c9ee6926b172 NeedsCompilation: no Title: Local Pooled Error Test for Differential Expression with Paired High-throughput Data Description: This package performs tests for paired high-throughput data. biocViews: Proteomics, Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/PLPE git_branch: devel git_last_commit: d88d290 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PLPE_1.71.0.tar.gz vignettes: vignettes/PLPE/inst/doc/PLPE.pdf vignetteTitles: PLPE Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLPE/inst/doc/PLPE.R dependencyCount: 10 Package: PLSDAbatch Version: 1.99.1 Depends: R (>= 4.5.0) Imports: ggplot2, ggpubr, grid, gridExtra, lmerTest, mixOmics, performance, scales, stats, Rdpack Suggests: SummarizedExperiment, TreeSummarizedExperiment, vegan, knitr, rmarkdown, BiocStyle, testthat, badger, pheatmap, Biobase License: GPL-3 MD5sum: 002ab0508742c735e82a7d1f2484f33a NeedsCompilation: no Title: PLSDA-batch Description: A novel framework to correct for batch effects prior to any downstream analysis in microbiome data based on Projection to Latent Structures Discriminant Analysis. The main method is named “PLSDA-batch”. It first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data whilst preserving biological variation of interest. PLSDA-batch is highly suitable for microbiome data as it is non-parametric, multivariate and allows for ordination and data visualisation. Combined with centered log-ratio transformation for addressing uneven library sizes and compositional structure, PLSDA-batch addresses all characteristics of microbiome data that existing correction methods have ignored so far. Two other variants are proposed for 1/ unbalanced batch x treatment designs that are commonly encountered in studies with small sample sizes, and for 2/ selection of discriminative variables amongst treatment groups to avoid overfitting in classification problems. These two variants have widened the scope of applicability of PLSDA-batch to different data settings. biocViews: StatisticalMethod, DimensionReduction, PrincipalComponent, Classification, Microbiome, BatchEffect, Normalization, Visualization Author: Yiwen (Eva) Wang [aut, cre] (ORCID: ), Kim-Anh Le Cao [aut] Maintainer: Yiwen (Eva) Wang URL: https://github.com/EvaYiwenWang/PLSDAbatch VignetteBuilder: knitr BugReports: https://github.com/EvaYiwenWang/PLSDAbatch/issues/ git_url: https://git.bioconductor.org/packages/PLSDAbatch git_branch: devel git_last_commit: f1c3e0c git_last_commit_date: 2026-01-06 Date/Publication: 2026-04-20 source.ver: src/contrib/PLSDAbatch_1.99.1.tar.gz vignettes: vignettes/PLSDAbatch/inst/doc/brief_vignette.html vignetteTitles: PLSDA-batch Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLSDAbatch/inst/doc/brief_vignette.R dependencyCount: 133 Package: plyinteractions Version: 1.9.3 Depends: R (>= 4.3.0), InteractionSet, plyranges Imports: Seqinfo, BiocGenerics, GenomicRanges, IRanges, S4Vectors, rlang, dplyr, tibble, tidyselect, methods, utils Suggests: tidyverse, BSgenome.Mmusculus.UCSC.mm10, Biostrings, BiocParallel, GenomeInfoDb, scales, HiContactsData, rtracklayer, BiocStyle, covr, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0), RefManageR License: Artistic-2.0 MD5sum: a96da57db5bb91b7be1fc9f1a027a281 NeedsCompilation: no Title: Extending tidy verbs to genomic interactions Description: Operate on `GInteractions` objects as tabular data using `dplyr`-like verbs. The functions and methods in `plyinteractions` provide a grammatical approach to manipulate `GInteractions`, to facilitate their integration in genomic analysis workflows. biocViews: Software, Infrastructure Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://github.com/js2264/plyinteractions VignetteBuilder: knitr BugReports: https://github.com/js2264/plyinteractions/issues git_url: https://git.bioconductor.org/packages/plyinteractions git_branch: devel git_last_commit: 217aa67 git_last_commit_date: 2026-04-19 Date/Publication: 2026-04-20 source.ver: src/contrib/plyinteractions_1.9.3.tar.gz vignettes: vignettes/plyinteractions/inst/doc/plyinteractions.html, vignettes/plyinteractions/inst/doc/process_pairs.html vignetteTitles: plyinteractions, HiCarithmetic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plyinteractions/inst/doc/plyinteractions.R, vignettes/plyinteractions/inst/doc/process_pairs.R importsMe: GenomicCoordinates, OHCA suggestsMe: tidyomics dependencyCount: 73 Package: plyranges Version: 1.31.7 Depends: R (>= 3.5), BiocGenerics, IRanges (>= 2.12.0), GenomicRanges (>= 1.28.4), dplyr Imports: methods, rlang (>= 0.2.0), magrittr, tidyselect (>= 1.0.0), rtracklayer, GenomicAlignments, Seqinfo, Rsamtools, S4Vectors (>= 0.23.10), utils Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 2.1.0), HelloRanges, HelloRangesData, BSgenome.Hsapiens.UCSC.hg19, pasillaBamSubset, covr, ggplot2 License: Artistic-2.0 MD5sum: f3835cf0b9b4914719baec5442609d22 NeedsCompilation: no Title: A fluent interface for manipulating GenomicRanges Description: A dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessiblity for new Bioconductor users is hopefully increased. biocViews: Infrastructure, DataRepresentation, WorkflowStep, Coverage Author: Stuart Lee [aut] (ORCID: ), Michael Lawrence [aut, ctb], Dianne Cook [aut, ctb], Spencer Nystrom [ctb] (ORCID: ), Pierre-Paul Axisa [ctb], Michael Love [ctb, cre] Maintainer: Michael Love URL: https://tidyomics.github.io/plyranges VignetteBuilder: knitr BugReports: https://github.com/tidyomics/plyranges git_url: https://git.bioconductor.org/packages/plyranges git_branch: devel git_last_commit: c2ff9bc git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/plyranges_1.31.7.tar.gz vignettes: vignettes/plyranges/inst/doc/an-introduction.html, vignettes/plyranges/inst/doc/more-examples.html vignetteTitles: Introduction, Additional examples of plyranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plyranges/inst/doc/an-introduction.R, vignettes/plyranges/inst/doc/more-examples.R dependsOnMe: plyinteractions importsMe: BOBaFIT, BUSpaRse, cfDNAPro, Damsel, GenomicCoordinates, GenomicPlot, InPAS, katdetectr, multicrispr, MutSeqR, nearBynding, nullranges, plotgardener, SingleMoleculeFootprinting, tidyomics, fluentGenomics suggestsMe: EpiCompare, extraChIPs, memes, rigvf, svaNUMT, svaRetro, tidyCoverage dependencyCount: 70 Package: plyxp Version: 1.5.0 Depends: R (>= 4.5.0) Imports: dplyr, purrr, rlang, SummarizedExperiment, tidyr, tidyselect, vctrs, tibble, pillar, cli, glue, S7, S4Vectors, utils, methods Suggests: devtools, knitr, rmarkdown, testthat, airway, IRanges, here License: MIT + file LICENSE MD5sum: a46cd509b9ce26ba8efb950a0d86160c NeedsCompilation: no Title: Data masks for SummarizedExperiment enabling dplyr-like manipulation Description: The package provides `rlang` data masks for the SummarizedExperiment class. The enables the evaluation of unquoted expression in different contexts of the SummarizedExperiment object with optional access to other contexts. The goal for `plyxp` is for evaluation to feel like a data.frame object without ever needing to unwind to a rectangular data.frame. biocViews: Annotation, GenomeAnnotation, Transcriptomics Author: Justin Landis [aut, cre] (ORCID: ), Michael Love [aut] (ORCID: ) Maintainer: Justin Landis URL: https://github.com/jtlandis/plyxp, https://jtlandis.github.io/plyxp VignetteBuilder: knitr BugReports: https://www.github.com/jtlandis/plyxp/issues git_url: https://git.bioconductor.org/packages/plyxp git_branch: devel git_last_commit: b0d04f2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/plyxp_1.5.0.tar.gz vignettes: vignettes/plyxp/inst/doc/plyxp.html vignetteTitles: plyxp Usage Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/plyxp/inst/doc/plyxp.R importsMe: tidySummarizedExperiment dependencyCount: 45 Package: pmm Version: 1.43.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: b6d2d3ea5908167390c459edef01a38d NeedsCompilation: no Title: Parallel Mixed Model Description: The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines. biocViews: SystemsBiology, Regression Author: Anna Drewek Maintainer: Anna Drewek git_url: https://git.bioconductor.org/packages/pmm git_branch: devel git_last_commit: 7f644b6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pmm_1.43.0.tar.gz vignettes: vignettes/pmm/inst/doc/pmm-package.pdf vignetteTitles: User manual for R-Package PMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmm/inst/doc/pmm-package.R dependencyCount: 23 Package: pmp Version: 1.23.1 Depends: R (>= 4.0) Imports: stats, impute, pcaMethods, missForest, ggplot2, methods, SummarizedExperiment, S4Vectors, matrixStats, grDevices, reshape2, utils Suggests: testthat, covr, knitr, rmarkdown, BiocStyle, gridExtra, magick License: GPL-3 MD5sum: 43a4b4bed759c38ed9d376d4802b31cf NeedsCompilation: no Title: Peak Matrix Processing and signal batch correction for metabolomics datasets Description: Methods and tools for (pre-)processing of metabolomics datasets (i.e. peak matrices), including filtering, normalisation, missing value imputation, scaling, and signal drift and batch effect correction methods. Filtering methods are based on: the fraction of missing values (across samples or features); Relative Standard Deviation (RSD) calculated from the Quality Control (QC) samples; the blank samples. Normalisation methods include Probabilistic Quotient Normalisation (PQN) and normalisation to total signal intensity. A unified user interface for several commonly used missing value imputation algorithms is also provided. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and a constant small value. The generalised logarithm (glog) transformation algorithm is available to stabilise the variance across low and high intensity mass spectral features. Finally, this package provides an implementation of the Quality Control-Robust Spline Correction (QCRSC) algorithm for signal drift and batch effect correction of mass spectrometry-based datasets. biocViews: MassSpectrometry, Metabolomics, Software, QualityControl, BatchEffect Author: Andris Jankevics [aut], Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pmp git_branch: devel git_last_commit: 0f6f235 git_last_commit_date: 2025-11-06 Date/Publication: 2026-04-20 source.ver: src/contrib/pmp_1.23.1.tar.gz vignettes: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.html, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.html, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.html vignetteTitles: Peak Matrix Processing for metabolomics datasets, Signal drift and batch effect correction and mass spectral quality assessment, Signal drift and batch effect correction for mass spectrometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.R, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.R, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.R suggestsMe: metabolomicsWorkbenchR, structToolbox dependencyCount: 65 Package: PMScanR Version: 1.1.0 Imports: dplyr (>= 1.1.0), shiny, bslib, shinyFiles, plotly, rtracklayer, reshape2, ggseqlogo, ggplot2, seqinr, magrittr, rlang, utils, stringr, BiocFileCache Suggests: BiocStyle, knitr, seqLogo, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 32acfcd02bce5513e569039d42da65a0 NeedsCompilation: no Title: Protein motifs analysis and visualisation Description: Provides tools for large-scale protein motif analysis and visualization in R. PMScanR facilitates the identification of motifs using external tools like PROSITE's ps_scan (handling necessary file downloads and execution) and enables downstream analysis of results. Key features include parsing scan outputs, converting formats (e.g., to GFF-like structures), generating motif occurrence matrices, and creating informative visualizations such as heatmaps, sequence logos (via seqLogo/ggseqlogo). The package also offers an optional Shiny-based graphical user interface for interactive analysis, aiming to streamline the process of exploring motif patterns across multiple protein sequences. biocViews: MotifDiscovery, Visualization Author: Jan Pawel Jastrzebski [aut, cre] (ORCID: ), Monika Gawronska [ctb] (ORCID: ), Wiktor Babis [ctb] (ORCID: ), Miriana Quaranta [ctb] (ORCID: ), Damian Czopek [ctb, aut] (ORCID: ) Maintainer: Jan Pawel Jastrzebski URL: https://github.com/prodakt/PMScanR SystemRequirements: Perl VignetteBuilder: knitr BugReports: https://github.com/prodakt/PMScanR/issues git_url: https://git.bioconductor.org/packages/PMScanR git_branch: devel git_last_commit: 1e526c3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PMScanR_1.1.0.tar.gz vignettes: vignettes/PMScanR/inst/doc/PMScanR.html vignetteTitles: PMScanR: Protein Motif Scanning and Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PMScanR/inst/doc/PMScanR.R dependencyCount: 136 Package: PoDCall Version: 1.19.0 Depends: R (>= 4.5) Imports: ggplot2, gridExtra, mclust, diptest, rlist, shiny, DT, LaplacesDemon, purrr, shinyjs, readr, grDevices, stats, utils Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 72ad524e71f90ca64881e3d4af55a047 NeedsCompilation: no Title: Positive Droplet Calling for DNA Methylation Droplet Digital PCR Description: Reads files exported from 'QX Manager or QuantaSoft' containing amplitude values from a run of ddPCR (96 well plate) and robustly sets thresholds to determine positive droplets for each channel of each individual well. Concentration and normalized concentration in addition to other metrics is then calculated for each well. Results are returned as a table, optionally written to file, as well as optional plots (scatterplot and histogram) for both channels per well written to file. The package includes a shiny application which provides an interactive and user-friendly interface to the full functionality of PoDCall. biocViews: Classification, Epigenetics, ddPCR, DifferentialMethylation, CpGIsland, DNAMethylation, Author: Hans Petter Brodal [aut, cre], Marine Jeanmougin [aut], Guro Elisabeth Lind [aut] Maintainer: Hans Petter Brodal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoDCall git_branch: devel git_last_commit: 9d491e2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PoDCall_1.19.0.tar.gz vignettes: vignettes/PoDCall/inst/doc/PoDCall.html vignetteTitles: PoDCall: Positive Droplet Caller for DNA Methylation ddPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PoDCall/inst/doc/PoDCall.R dependencyCount: 84 Package: podkat Version: 1.43.0 Depends: R (>= 3.5.0), methods, Rsamtools (>= 1.99.1), GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats (>= 4.3.0), graphics, grDevices, utils, Biobase, BiocGenerics, Matrix, Seqinfo, IRanges, Biostrings, BSgenome (>= 1.32.0) LinkingTo: Rcpp, Rhtslib (>= 1.15.3) Suggests: BSgenome.Hsapiens.UCSC.hg38.masked, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24), VariantAnnotation, SummarizedExperiment, knitr License: GPL (>= 2) MD5sum: b8d99b41547009545e73945cd0763f55 NeedsCompilation: yes Title: Position-Dependent Kernel Association Test Description: This package provides an association test that is capable of dealing with very rare and even private variants. This is accomplished by a kernel-based approach that takes the positions of the variants into account. The test can be used for pre-processed matrix data, but also directly for variant data stored in VCF files. Association testing can be performed whole-genome, whole-exome, or restricted to pre-defined regions of interest. The test is complemented by tools for analyzing and visualizing the results. biocViews: Genetics, WholeGenome, Annotation, VariantAnnotation, Sequencing, DataImport Author: Ulrich Bodenhofer [aut, cre] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/podkat SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/podkat git_branch: devel git_last_commit: d216a17 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/podkat_1.43.0.tar.gz vignettes: vignettes/podkat/inst/doc/podkat.pdf vignetteTitles: PODKAT - An R Package for Association Testing Involving Rare and Private Variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/podkat/inst/doc/podkat.R dependencyCount: 59 Package: poem Version: 1.3.0 Depends: R (>= 4.1.0) Imports: aricode, BiocNeighbors, BiocParallel, bluster, clevr, clue, cluster, elsa, fclust, igraph, Matrix, matrixStats, mclustcomp, methods, pdist, sp, spdep, stats, utils, SpatialExperiment, SummarizedExperiment Suggests: testthat (>= 3.0.0), BiocStyle, knitr, DT, dplyr, kableExtra, scico, cowplot, ggnetwork, ggplot2, tidyr, STexampleData License: GPL (>= 3) MD5sum: 3aca722190c45c419f45efae5d6e0874 NeedsCompilation: no Title: POpulation-based Evaluation Metrics Description: This package provides a comprehensive set of external and internal evaluation metrics. It includes metrics for assessing partitions or fuzzy partitions derived from clustering results, as well as for evaluating subpopulation identification results within embeddings or graph representations. Additionally, it provides metrics for comparing spatial domain detection results against ground truth labels, and tools for visualizing spatial errors. biocViews: DimensionReduction, Clustering, GraphAndNetwork, Spatial, ATACSeq, SingleCell, RNASeq, Software, Visualization Author: Siyuan Luo [cre, aut] (ORCID: ), Pierre-Luc Germain [aut, ctb] (ORCID: ) Maintainer: Siyuan Luo URL: https://roseyuan.github.io/poem/ VignetteBuilder: knitr BugReports: https://github.com/RoseYuan/poem/issues git_url: https://git.bioconductor.org/packages/poem git_branch: devel git_last_commit: e74dd69 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/poem_1.3.0.tar.gz vignettes: vignettes/poem/inst/doc/MetricsInPoem.html, vignettes/poem/inst/doc/poem.html, vignettes/poem/inst/doc/PoemOnSpatialExperiment.html vignetteTitles: MetricsInPoem.html, 1_introduction, 3_SpatialExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/poem/inst/doc/MetricsInPoem.R, vignettes/poem/inst/doc/poem.R, vignettes/poem/inst/doc/PoemOnSpatialExperiment.R dependencyCount: 109 Package: PolySTest Version: 1.5.3 Depends: R (>= 4.4.0) Imports: fdrtool (>= 1.2.15), limma (>= 3.61.3), matrixStats (>= 0.57.0), qvalue (>= 2.22.0), shiny (>= 1.5.0), SummarizedExperiment (>= 1.20.0), knitr (>= 1.33), plotly (>= 4.9.4), heatmaply (>= 1.1.1), circlize (>= 0.4.12), UpSetR (>= 1.4.0), gplots (>= 3.1.1), S4Vectors (>= 0.30.0), parallel (>= 4.1.0), grDevices (>= 4.1.0), graphics (>= 4.1.0), stats (>= 4.1.0), utils (>= 4.1.0) Suggests: testthat (>= 3.0.0), BiocStyle License: GPL-2 MD5sum: 0bdcf8243aaaef68c9fe0a1e8d953928 NeedsCompilation: no Title: PolySTest: Detection of differentially regulated features. Combined statistical testing for data with few replicates and missing values Description: The complexity of high-throughput quantitative omics experiments often leads to low replicates numbers and many missing values. We implemented a new test to simultaneously consider missing values and quantitative changes, which we combined with well-performing statistical tests for high confidence detection of differentially regulated features. The package contains functions to run the test and to visualize the results. biocViews: MassSpectrometry, Proteomics, Software, DifferentialExpression Author: Veit Schwämmle [aut, cre] (ORCID: ) Maintainer: Veit Schwämmle URL: https://github.com/computproteomics/PolySTest VignetteBuilder: knitr BugReports: https://github.com/computproteomics/PolySTest/issues git_url: https://git.bioconductor.org/packages/PolySTest git_branch: devel git_last_commit: 7a16c08 git_last_commit_date: 2026-04-20 Date/Publication: 2026-04-20 source.ver: src/contrib/PolySTest_1.5.3.tar.gz vignettes: vignettes/PolySTest/inst/doc/StatisticalTest.html vignetteTitles: PolySTest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PolySTest/inst/doc/StatisticalTest.R dependencyCount: 137 Package: Polytect Version: 1.3.0 Depends: R (>= 4.4.0) Imports: stats, utils, grDevices, mvtnorm, sn, dplyr, flowPeaks, ggplot2, tidyverse, cowplot, mlrMBO, DiceKriging, smoof, ParamHelpers, lhs, rgenoud, BiocManager Suggests: testthat (>= 3.0.0), knitr, rmarkdown, ddPCRclust License: Artistic-2.0 MD5sum: 34863745144aa31deaa52075a4cef9be NeedsCompilation: no Title: An R package for digital data clustering Description: Polytect is an advanced computational tool designed for the analysis of multi-color digital PCR data. It provides automatic clustering and labeling of partitions into distinct groups based on clusters first identified by the flowPeaks algorithm. Polytect is particularly useful for researchers in molecular biology and bioinformatics, enabling them to gain deeper insights into their experimental results through precise partition classification and data visualization. biocViews: ddPCR, Clustering, MultiChannel, Classification Author: Yao Chen [aut, cre] (ORCID: ) Maintainer: Yao Chen URL: https://github.com/emmachenlingo/Polytect VignetteBuilder: knitr BugReports: https://github.com/emmachenlingo/Polytect/issues git_url: https://git.bioconductor.org/packages/Polytect git_branch: devel git_last_commit: cb9d858 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Polytect_1.3.0.tar.gz vignettes: vignettes/Polytect/inst/doc/introduction.pdf vignetteTitles: Polytect Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Polytect/inst/doc/introduction.R dependencyCount: 137 Package: POMA Version: 1.21.0 Depends: R (>= 4.0) Imports: broom, caret, ComplexHeatmap, dbscan, dplyr, DESeq2, fgsea, FSA, ggcorrplot, ggplot2, ggrepel, glmnet, grid, impute, janitor, limma, lme4, magrittr, MASS, mixOmics, multcomp, msigdbr, purrr, randomForest, RankProd (>= 3.14), rlang, SummarizedExperiment, sva, tibble, tidyr, utils, uwot, vegan Suggests: BiocStyle, covr, ggraph, ggtext, knitr, patchwork, plotly, tidyverse, testthat (>= 2.3.2) License: GPL-3 MD5sum: 4ba59fe38af40f6c42e1120c5b4f6b5c NeedsCompilation: no Title: Tools for Omics Data Analysis Description: The POMA package offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness, POMA leverages the standardized SummarizedExperiment class from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, making POMA an essential asset for researchers handling omics datasets. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) for more details. biocViews: BatchEffect, Classification, Clustering, DecisionTree, DimensionReduction, MultidimensionalScaling, Normalization, Preprocessing, PrincipalComponent, Regression, RNASeq, Software, StatisticalMethod, Visualization Author: Pol Castellano-Escuder [aut, cre] (ORCID: ) Maintainer: Pol Castellano-Escuder URL: https://github.com/pcastellanoescuder/POMA VignetteBuilder: knitr BugReports: https://github.com/pcastellanoescuder/POMA/issues git_url: https://git.bioconductor.org/packages/POMA git_branch: devel git_last_commit: e8ab1e6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/POMA_1.21.0.tar.gz vignettes: vignettes/POMA/inst/doc/POMA-normalization.html, vignettes/POMA/inst/doc/POMA-workflow.html vignetteTitles: Normalization Methods, Get Started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POMA/inst/doc/POMA-normalization.R, vignettes/POMA/inst/doc/POMA-workflow.R importsMe: PRONE suggestsMe: fobitools dependencyCount: 232 Package: posDemux Version: 0.99.11 Depends: R (>= 4.6.0) Imports: Biostrings, ggplot2, methods, assertthat, glue, magrittr, dplyr, rlang, ShortRead, readr, shiny, purrr LinkingTo: Rcpp, Biostrings, IRanges, S4Vectors, XVector Suggests: testthat, devtools, DNABarcodes, knitr, rmarkdown, tibble, tidyr, BiocStyle, RefManageR, sessioninfo, DBI, chunked, RSQLite, dbplyr License: AGPL (>= 3) MD5sum: d7205291acb6c70a2c8bd813606e79ba NeedsCompilation: yes Title: Positional combinatorial sequence demultiplexer Description: Demultiplexing and filtering utilities intended for reads with combinatorial barcodes (i.e. PETRI-seq and SPLiT-seq). The demultiplexer algorithm uses the position of the segments to extract and compare the barcodes with the reference (whitelist). A Shiny application is provided to interactively select cutoffs for which barcode combinations to keep. biocViews: SequenceMatching, Sequencing, Software, RNASeq Author: Jakob Peder Pettersen [aut, cre] (ORCID: ), Centre for new antibacterial strategies (CANS) [fnd] Maintainer: Jakob Peder Pettersen URL: https://github.com/yaccos/posDemux, https://yaccos.github.io/posDemux/ VignetteBuilder: knitr BugReports: https://github.com/yaccos/posDemux/issues git_url: https://git.bioconductor.org/packages/posDemux git_branch: devel git_last_commit: 55e19b6 git_last_commit_date: 2026-02-02 Date/Publication: 2026-04-20 source.ver: src/contrib/posDemux_0.99.11.tar.gz vignettes: vignettes/posDemux/inst/doc/posDemux.html, vignettes/posDemux/inst/doc/streaming.html vignetteTitles: Introduction to combinatorial demultiplexing, Demultiplexing with streaming hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/posDemux/inst/doc/posDemux.R, vignettes/posDemux/inst/doc/streaming.R dependencyCount: 109 Package: postNet Version: 0.99.8 Depends: R (>= 4.5.0), Imports: dplyr, plyr, Biostrings, data.table, gridExtra, seqinr, R.utils, reshape2, vioplot, stringr, plotrix, gplots, ggplot2, ggrepel, anota2seq, memes, GenomicRanges, IRanges, WriteXLS, randomForest, igraph, Boruta, ROCR, caret, msigdb, ExperimentHub, AnnotationHub, GSEABase, fgsea, org.Hs.eg.db, org.Mm.eg.db, RColorBrewer, httr2, rvest, umap, clusterProfiler (>= 4.18.4), gage, withr, grDevices, graphics, methods, stats, utils, tools, BiocFileCache, curl LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, BiocStyle, pdftools, magick, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: e880ca2b6c4e70eba2813c7c8f2ba643 NeedsCompilation: yes Title: Post-transcriptional network modeling Description: A tool that enables in silico identification, integration, and modeling of mRNA features that influence post-transcriptional regulation of gene expression at a transcriptome-wide scale. biocViews: GeneExpression, GeneRegulation, Transcriptomics, RiboSeq, RNASeq, Sequencing, Annotation, Network, FeatureExtraction Author: Krzysztof Szkop [aut, cre], Kathleen Watt [aut], Ola Larsson [aut] Maintainer: Krzysztof Szkop URL: https://github.com/kszkop/postNet VignetteBuilder: knitr BugReports: https://github.com/kszkop/postNet/issues git_url: https://git.bioconductor.org/packages/postNet git_branch: devel git_last_commit: 8a26743 git_last_commit_date: 2026-04-05 Date/Publication: 2026-04-20 source.ver: src/contrib/postNet_0.99.8.tar.gz vignettes: vignettes/postNet/inst/doc/postNet.html vignetteTitles: Post-transcriptional network modelling with postNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/postNet/inst/doc/postNet.R dependencyCount: 267 Package: powerTCR Version: 1.31.0 Imports: cubature, doParallel, evmix, foreach, magrittr, methods, parallel, purrr, stats, truncdist, vegan, VGAM Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 234626b49376017964dd649475ff00f1 NeedsCompilation: no Title: Model-Based Comparative Analysis of the TCR Repertoire Description: This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits. biocViews: Software, Clustering, BiomedicalInformatics Author: Hillary Koch Maintainer: Hillary Koch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/powerTCR git_branch: devel git_last_commit: 3cc7131 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/powerTCR_1.31.0.tar.gz vignettes: vignettes/powerTCR/inst/doc/powerTCR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/powerTCR/inst/doc/powerTCR.R dependencyCount: 36 Package: POWSC Version: 1.19.0 Depends: R (>= 4.1), Biobase, SingleCellExperiment, MAST Imports: pheatmap, ggplot2, RColorBrewer, grDevices, SummarizedExperiment, limma Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-2 MD5sum: 5151ea18278671cabc35222fb2beed6b NeedsCompilation: no Title: Simulation, power evaluation, and sample size recommendation for single cell RNA-seq Description: Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship. biocViews: DifferentialExpression, ImmunoOncology, SingleCell, Software Author: Kenong Su [aut, cre], Hao Wu [aut] Maintainer: Kenong Su VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/POWSC git_branch: devel git_last_commit: b092ca1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/POWSC_1.19.0.tar.gz vignettes: vignettes/POWSC/inst/doc/POWSC.html vignetteTitles: The POWSC User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POWSC/inst/doc/POWSC.R dependencyCount: 60 Package: ppcseq Version: 1.19.0 Depends: R (>= 4.1.0), rstan (>= 2.18.1) Imports: benchmarkme, dplyr, edgeR, foreach, ggplot2, graphics, lifecycle, magrittr, methods, parallel, purrr, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rlang, rstantools (>= 2.1.1), stats, tibble, tidybayes, tidyr (>= 0.8.3.9000), utils LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: knitr, testthat, BiocStyle, rmarkdown License: GPL-3 MD5sum: ceea87663fba66c1ca8104c6a369e20c NeedsCompilation: yes Title: Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models Description: Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers. biocViews: RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] (ORCID: ) Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/ppcseq SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/stemangiola/ppcseq/issues git_url: https://git.bioconductor.org/packages/ppcseq git_branch: devel git_last_commit: b907051 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ppcseq_1.19.0.tar.gz vignettes: vignettes/ppcseq/inst/doc/introduction.html vignetteTitles: Overview of the ppcseq package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppcseq/inst/doc/introduction.R dependencyCount: 88 Package: PPInfer Version: 1.37.0 Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb, yeastExpData Imports: httr, grDevices, graphics, stats, utils License: Artistic-2.0 MD5sum: f2fc8d02e41d948467a6e950668a3417 NeedsCompilation: no Title: Inferring functionally related proteins using protein interaction networks Description: Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, NetworkEnrichment, Pathways Author: Dongmin Jung, Xijin Ge Maintainer: Dongmin Jung git_url: https://git.bioconductor.org/packages/PPInfer git_branch: devel git_last_commit: 37e1583 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PPInfer_1.37.0.tar.gz vignettes: vignettes/PPInfer/inst/doc/PPInfer.pdf vignetteTitles: User manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PPInfer/inst/doc/PPInfer.R dependsOnMe: gsean dependencyCount: 106 Package: pqsfinder Version: 2.27.0 Depends: R (>= 3.5.0), Biostrings Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods LinkingTo: Rcpp, BH (>= 1.78.0) Suggests: BiocStyle, knitr, rmarkdown, Gviz, rtracklayer, ggplot2, BSgenome.Hsapiens.UCSC.hg38, testthat, stringr, stringi License: BSD_2_clause + file LICENSE MD5sum: 3cd07f774e78d96cd9edca415e74ae70 NeedsCompilation: yes Title: Identification of potential quadruplex forming sequences Description: Pqsfinder detects DNA and RNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, pqsfinder is able to detect G4s folded from imperfect G-runs containing bulges or mismatches or G4s having long loops. Pqsfinder also assigns an integer score to each hit that was fitted on G4 sequencing data and corresponds to expected stability of the folded G4. biocViews: MotifDiscovery, SequenceMatching, GeneRegulation Author: Jiri Hon, Dominika Labudova, Matej Lexa and Tomas Martinek Maintainer: Jiri Hon URL: https://pqsfinder.fi.muni.cz SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pqsfinder git_branch: devel git_last_commit: 8870134 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pqsfinder_2.27.0.tar.gz vignettes: vignettes/pqsfinder/inst/doc/pqsfinder.html vignetteTitles: pqsfinder: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R dependencyCount: 18 Package: pram Version: 1.27.0 Depends: R (>= 3.6) Imports: methods, BiocParallel, tools, utils, data.table (>= 1.11.8), GenomicAlignments (>= 1.16.0), rtracklayer (>= 1.40.6), BiocGenerics (>= 0.26.0), Seqinfo, GenomicRanges (>= 1.32.0), IRanges (>= 2.14.12), Rsamtools (>= 1.32.3), S4Vectors (>= 0.18.3) Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>= 3) MD5sum: d8e1d488111b644aa1d5e9cfc18a9c31 NeedsCompilation: no Title: Pooling RNA-seq datasets for assembling transcript models Description: Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by large collections of RNA-seq datasets has emerged as one of such analysis. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a new R package named Pooling RNA-seq and Assembling Models (PRAM), which builds transcript models in intergenic regions from pooled RNA-seq datasets. This package includes functions for defining intergenic regions, extracting and pooling related RNA-seq alignments, predicting, selected, and evaluating transcript models. biocViews: Software, Technology, Sequencing, RNASeq, BiologicalQuestion, GenePrediction, GenomeAnnotation, ResearchField, Transcriptomics Author: Peng Liu [aut, cre], Colin N. Dewey [aut], Sündüz Keleş [aut] Maintainer: Peng Liu URL: https://github.com/pliu55/pram VignetteBuilder: knitr BugReports: https://github.com/pliu55/pram/issues git_url: https://git.bioconductor.org/packages/pram git_branch: devel git_last_commit: e034cdc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pram_1.27.0.tar.gz vignettes: vignettes/pram/inst/doc/pram.html vignetteTitles: Pooling RNA-seq and Assembling Models hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pram/inst/doc/pram.R dependencyCount: 58 Package: prebs Version: 1.51.0 Depends: R (>= 2.14.0), GenomicAlignments, affy, RPA Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges, Biobase, Seqinfo, S4Vectors Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe License: Artistic-2.0 MD5sum: 7fd39955c418d437bab3ef8671f65f0c NeedsCompilation: no Title: Probe region expression estimation for RNA-seq data for improved microarray comparability Description: The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using a modified version of RMA algorithm. The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output. biocViews: ImmunoOncology, Microarray, RNASeq, Sequencing, GeneExpression, Preprocessing Author: Karolis Uziela and Antti Honkela Maintainer: Karolis Uziela git_url: https://git.bioconductor.org/packages/prebs git_branch: devel git_last_commit: e4fa52e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/prebs_1.51.0.tar.gz vignettes: vignettes/prebs/inst/doc/prebs.pdf vignetteTitles: prebs User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prebs/inst/doc/prebs.R dependencyCount: 113 Package: preciseTAD Version: 1.21.0 Depends: R (>= 4.1) Imports: S4Vectors, IRanges, GenomicRanges, randomForest, ModelMetrics, e1071, PRROC, pROC, caret, utils, cluster, dbscan, doSNOW, foreach, pbapply, stats, parallel, gtools, rCGH Suggests: knitr, rmarkdown, testthat, BiocCheck, BiocManager, BiocStyle License: MIT + file LICENSE MD5sum: f1cbc263d86afea0e2e99cd050349c13 NeedsCompilation: no Title: preciseTAD: A machine learning framework for precise TAD boundary prediction Description: preciseTAD provides functions to predict the location of boundaries of topologically associated domains (TADs) and chromatin loops at base-level resolution. As an input, it takes BED-formatted genomic coordinates of domain boundaries detected from low-resolution Hi-C data, and coordinates of high-resolution genomic annotations from ENCODE or other consortia. preciseTAD employs several feature engineering strategies and resampling techniques to address class imbalance, and trains an optimized random forest model for predicting low-resolution domain boundaries. Translated on a base-level, preciseTAD predicts the probability for each base to be a boundary. Density-based clustering and scalable partitioning techniques are used to detect precise boundary regions and summit points. Compared with low-resolution boundaries, preciseTAD boundaries are highly enriched for CTCF, RAD21, SMC3, and ZNF143 signal and more conserved across cell lines. The pre-trained model can accurately predict boundaries in another cell line using CTCF, RAD21, SMC3, and ZNF143 annotation data for this cell line. biocViews: Software, HiC, Sequencing, Clustering, Classification, FunctionalGenomics, FeatureExtraction Author: Spiro Stilianoudakis [aut], Mikhail Dozmorov [aut, cre] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/preciseTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/preciseTAD/issues git_url: https://git.bioconductor.org/packages/preciseTAD git_branch: devel git_last_commit: 301ea2e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/preciseTAD_1.21.0.tar.gz vignettes: vignettes/preciseTAD/inst/doc/preciseTAD.html vignetteTitles: preciseTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/preciseTAD/inst/doc/preciseTAD.R suggestsMe: preciseTADhub dependencyCount: 175 Package: PREDA Version: 1.57.0 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: b427103a521c1a7c47fb7d655858d2d5 NeedsCompilation: no Title: Position Related Data Analysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari git_url: https://git.bioconductor.org/packages/PREDA git_branch: devel git_last_commit: 082ec94 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PREDA_1.57.0.tar.gz vignettes: vignettes/PREDA/inst/doc/PREDAclasses.pdf, vignettes/PREDA/inst/doc/PREDAtutorial.pdf vignetteTitles: PREDA S4-classes, PREDA tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PREDA/inst/doc/PREDAtutorial.R dependsOnMe: PREDAsampledata dependencyCount: 54 Package: preprocessCore Version: 1.73.0 Imports: stats License: LGPL (>= 2) MD5sum: 302b5980079f837cb730dbf1cc738d28 NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/preprocessCore git_url: https://git.bioconductor.org/packages/preprocessCore git_branch: devel git_last_commit: bb678ea git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/preprocessCore_1.73.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, crlmm importsMe: affy, BloodGen3Module, bnbc, cn.farms, CPSM, crupR, cypress, EMDomics, ExiMiR, fastLiquidAssociation, frma, frmaTools, hipathia, iCheck, InPAS, lumi, MBCB, MBQN, MEDIPS, methylclock, mimager, minfi, MSPrep, MSstats, NormalyzerDE, oligo, PanomiR, PECA, PhosR, Pigengene, proBatch, PRONE, qPLEXanalyzer, quantiseqr, sesame, yarn, GSE13015, ADAPTS, cinaR, FARDEEP, lilikoi, noise, noisyr, retriever, SMDIC, WGCNA suggestsMe: DAPAR, DspikeIn, MsCoreUtils, multiClust, QFeatures, roastgsa, scp, splatter, tidybulk, wateRmelon, aroma.affymetrix, aroma.core, corrselect, SCdeconR, SigBridgeRUtils, wrMisc, wrTopDownFrag linksToMe: affy, affyPLM, crlmm, oligo dependencyCount: 1 Package: proBatch Version: 1.99.5 Depends: R (>= 4.5.0) Imports: Biobase, QFeatures, SummarizedExperiment, S4Vectors, corrplot, dplyr, data.table, ggfortify, ggplot2, gridExtra, grDevices, lazyeval, lubridate, limma, magrittr, matrixStats, methods, pheatmap, preprocessCore, purrr, pvca, RColorBrewer, reshape2, rlang, scales, stats, sva, tidyr, tibble, tools, utils, viridis, wesanderson, WGCNA Suggests: BiocStyle, cowplot, ggplotify, knitr, rmarkdown, devtools, gtable, roxygen2, testthat (>= 3.0.0), spelling, HDF5Array License: GPL-3 MD5sum: 0e5161e7739542c6e677ffdf01428ea4 NeedsCompilation: no Title: Tools for Diagnostics and Corrections of Batch Effects in Proteomics Description: These tools facilitate batch effects analysis and correction in high-throughput experiments. It was developed primarily for mass-spectrometry proteomics (DIA/SWATH), but could also be applicable to most omic data with minor adaptations. The package contains functions for diagnostics (proteome/genome-wide and feature-level), correction (normalization and batch effects correction) and quality control. Non-linear fitting based approaches were also included to deal with complex, mass spectrometry-specific signal drifts. biocViews: BatchEffect, Normalization, Preprocessing, Software, MassSpectrometry, Proteomics, QualityControl, Visualization Author: Jelena Cuklina [aut], Chloe H. Lee [aut], Patrick Pedrioli [aut], Olga Zolotareva [aut], Yuliya Burankova [cre] Maintainer: Yuliya Burankova URL: https://github.com/Freddsle/proBatch VignetteBuilder: knitr BugReports: https://github.com/Freddsle/proBatch/issues git_url: https://git.bioconductor.org/packages/proBatch git_branch: devel git_last_commit: 18d59ea git_last_commit_date: 2026-03-10 Date/Publication: 2026-04-20 source.ver: src/contrib/proBatch_1.99.5.tar.gz vignettes: vignettes/proBatch/inst/doc/proBatch.html, vignettes/proBatch/inst/doc/proBatchFeatures.html vignetteTitles: proBatch package overview, ProBatchFeatures: QFeatures-based pipelines with operation logging for proBatch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBatch/inst/doc/proBatch.R, vignettes/proBatch/inst/doc/proBatchFeatures.R dependencyCount: 171 Package: PROcess Version: 1.87.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: 041e7d3216558583799f1b48b529b095 NeedsCompilation: no Title: Ciphergen SELDI-TOF Processing Description: A package for processing protein mass spectrometry data. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Xiaochun Li Maintainer: Xiaochun Li git_url: https://git.bioconductor.org/packages/PROcess git_branch: devel git_last_commit: a353f35 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PROcess_1.87.0.tar.gz vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf vignetteTitles: HOWTO PROcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROcess/inst/doc/howtoprocess.R dependencyCount: 11 Package: procoil Version: 2.39.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: e0e8410127f91971c68b574a7df724f7 NeedsCompilation: no Title: Prediction of Oligomerization of Coiled Coil Proteins Description: The package allows for predicting whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. Additionally to the prediction itself, a prediction profile is computed which allows for determining the strengths to which the individual residues are indicative for either class. Prediction profiles can also be visualized as curves or heatmaps. biocViews: Proteomics, Classification, SupportVectorMachine Author: Ulrich Bodenhofer [aut, cre] Maintainer: Ulrich Bodenhofer URL: https://github.com/UBod/procoil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/procoil git_branch: devel git_last_commit: c3f2a65 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/procoil_2.39.0.tar.gz vignettes: vignettes/procoil/inst/doc/procoil.pdf vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting the Oligomerization of Coiled-Coil Proteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/procoil/inst/doc/procoil.R dependencyCount: 27 Package: proDA Version: 1.25.1 Imports: stats, utils, methods, BiocGenerics, SummarizedExperiment, S4Vectors, extraDistr Suggests: testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr, tibble, limma, numDeriv, pheatmap, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: ad7d54f04ea2c0e011cf08a45c2d0c24 NeedsCompilation: no Title: Differential Abundance Analysis of Label-Free Mass Spectrometry Data Description: Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, Bayesian, Regression, Software, Normalization, QualityControl Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ), Simon Anders [ths] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/proDA VignetteBuilder: knitr BugReports: https://github.com/const-ae/proDA/issues git_url: https://git.bioconductor.org/packages/proDA git_branch: devel git_last_commit: abe7b97 git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/proDA_1.25.1.tar.gz vignettes: vignettes/proDA/inst/doc/data-import.html, vignettes/proDA/inst/doc/Introduction.html vignetteTitles: Data Import, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proDA/inst/doc/data-import.R, vignettes/proDA/inst/doc/Introduction.R importsMe: MatrixQCvis, SmartPhos suggestsMe: protti dependencyCount: 28 Package: profileScoreDist Version: 1.39.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE MD5sum: e5cb7175a335f58289937ee17c88b5b0 NeedsCompilation: yes Title: Profile score distributions Description: Regularization and score distributions for position count matrices. biocViews: Software, GeneRegulation, StatisticalMethod Author: Paal O. Westermark Maintainer: Paal O. Westermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileScoreDist git_branch: devel git_last_commit: 6b89885 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/profileScoreDist_1.39.0.tar.gz vignettes: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.pdf vignetteTitles: Using profileScoreDist hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.R dependencyCount: 7 Package: projectR Version: 1.27.0 Depends: R (>= 4.0.0) Imports: SingleCellExperiment, methods, cluster, stats, limma, NMF, ROCR, ggalluvial, RColorBrewer, dplyr, fgsea, reshape2, viridis, scales, Matrix, MatrixModels, msigdbr, ggplot2, cowplot, ggrepel, umap, tsne Suggests: BiocStyle, CoGAPS, gridExtra, grid, testthat, devtools, knitr, rmarkdown, ComplexHeatmap, gplots, SeuratObject License: GPL (==2) MD5sum: 71a33f198bfbd6d7dfca9bf3321e5977 NeedsCompilation: no Title: Functions for the projection of weights from PCA, CoGAPS, NMF, correlation, and clustering Description: Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering. biocViews: FunctionalPrediction, GeneRegulation, BiologicalQuestion, Software Author: Gaurav Sharma, Charles Shin, Jared Slosberg, Loyal Goff, Genevieve Stein-O'Brien Maintainer: Genevieve Stein-O'Brien URL: https://github.com/genesofeve/projectR/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/projectR/ git_url: https://git.bioconductor.org/packages/projectR git_branch: devel git_last_commit: d55d8c4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/projectR_1.27.0.tar.gz vignettes: vignettes/projectR/inst/doc/projectR.html vignetteTitles: projectR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/projectR/inst/doc/projectR.R dependencyCount: 113 Package: PROMISE Version: 1.63.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: 3220d5b9ef78903dd5c178f8514fded3 NeedsCompilation: no Title: PRojection Onto the Most Interesting Statistical Evidence Description: A general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019 biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression Author: Stan Pounds , Xueyuan Cao Maintainer: Stan Pounds , Xueyuan Cao git_url: https://git.bioconductor.org/packages/PROMISE git_branch: devel git_last_commit: d65a441 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PROMISE_1.63.0.tar.gz vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf vignetteTitles: An introduction to PROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROMISE/inst/doc/PROMISE.R dependsOnMe: CCPROMISE dependencyCount: 47 Package: PROPER Version: 1.43.0 Depends: R (>= 3.3) Imports: edgeR Suggests: BiocStyle,DESeq2,DSS,knitr License: GPL MD5sum: ae3183398611ae47234aaf32ea72fa9c NeedsCompilation: no Title: PROspective Power Evaluation for RNAseq Description: This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPER git_branch: devel git_last_commit: 54779f4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PROPER_1.43.0.tar.gz vignettes: vignettes/PROPER/inst/doc/PROPER.pdf vignetteTitles: Power and Sample size analysis for gene expression from RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPER/inst/doc/PROPER.R importsMe: cypress dependencyCount: 11 Package: PROPS Version: 1.33.0 Imports: bnlearn, reshape2, sva, stats, utils, Biobase Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 47ae82ab9f77993fd756ee024b140da9 NeedsCompilation: no Title: PRObabilistic Pathway Score (PROPS) Description: This package calculates probabilistic pathway scores using gene expression data. Gene expression values are aggregated into pathway-based scores using Bayesian network representations of biological pathways. biocViews: Classification, Bayesian, GeneExpression Author: Lichy Han Maintainer: Lichy Han VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPS git_branch: devel git_last_commit: d01cb52 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PROPS_1.33.0.tar.gz vignettes: vignettes/PROPS/inst/doc/props.html vignetteTitles: PRObabilistic Pathway Scores (PROPS) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPS/inst/doc/props.R dependencyCount: 76 Package: proteinProfiles Version: 1.51.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: e5aef9a658ec9a27e938b57961c7fd0e NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/proteinProfiles git_branch: devel git_last_commit: 1c47c2c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/proteinProfiles_1.51.0.tar.gz vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf vignetteTitles: The proteinProfiles package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteinProfiles/inst/doc/proteinProfiles.R dependencyCount: 2 Package: ProteoDisco Version: 1.17.0 Depends: R (>= 4.1.0), Imports: BiocGenerics (>= 0.38.0), BiocParallel (>= 1.26.0), Biostrings (>= 2.60.1), checkmate (>= 2.0.0), cleaver (>= 1.30.0), dplyr (>= 1.0.6), GenomeInfoDb (>= 1.28.0), GenomicFeatures (>= 1.44.0), GenomicRanges (>= 1.44.0), IRanges (>= 2.26.0), methods (>= 4.1.0), ParallelLogger (>= 2.0.1), plyr (>= 1.8.6), rlang (>= 0.4.11), S4Vectors (>= 0.30.0), Seqinfo, tibble (>= 3.1.2), tidyr (>= 1.1.3), VariantAnnotation (>= 1.36.0), XVector (>= 0.32.0), Suggests: AnnotationDbi (>= 1.54.1), BSgenome (>= 1.60.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.3), BiocStyle (>= 2.20.1), DelayedArray (>= 0.18.0), devtools (>= 2.4.2), knitr (>= 1.33), matrixStats (>= 0.59.0), markdown (>= 1.1), org.Hs.eg.db (>= 3.13.0), purrr (>= 0.3.4), RCurl (>= 1.98.1.3), readr (>= 1.4.0), ggplot2 (>= 3.3.5), rmarkdown (>= 2.9), rtracklayer (>= 1.52.0), seqinr (>= 4.2.8), stringr (>= 1.4.0), reshape2 (>= 1.4.4), scales (>= 1.1.1), testthat (>= 3.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2) License: GPL-3 MD5sum: 1bd9a09f1bfde6d6e11e8b6454b775d8 NeedsCompilation: no Title: Generation of customized protein variant databases from genomic variants, splice-junctions and manual sequences Description: ProteoDisco is an R package to facilitate proteogenomics studies. It houses functions to create customized (variant) protein databases based on user-submitted genomic variants, splice-junctions, fusion genes and manual transcript sequences. The flexible workflow can be adopted to suit a myriad of research and experimental settings. biocViews: Software, Proteomics, RNASeq, SNP, Sequencing, VariantAnnotation, DataImport Author: Job van Riet [cre], Wesley van de Geer [aut], Harmen van de Werken [ths] Maintainer: Job van Riet URL: https://github.com/ErasmusMC-CCBC/ProteoDisco VignetteBuilder: knitr BugReports: https://github.com/ErasmusMC-CCBC/ProteoDisco/issues git_url: https://git.bioconductor.org/packages/ProteoDisco git_branch: devel git_last_commit: 583baa5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ProteoDisco_1.17.0.tar.gz vignettes: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.html vignetteTitles: Overview_Proteodisco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.R dependencyCount: 99 Package: ProteoMM Version: 1.29.0 Depends: R (>= 3.5) Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics Suggests: BiocStyle, knitr, rmarkdown License: MIT MD5sum: f09cc716a9d587d14aba8ed0e4e761da NeedsCompilation: no Title: Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform Description: ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009). biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Normalization, DifferentialExpression Author: Yuliya V Karpievitch, Tim Stuart and Sufyaan Mohamed Maintainer: Yuliya V Karpievitch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ProteoMM git_branch: devel git_last_commit: 9f11f1b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ProteoMM_1.29.0.tar.gz vignettes: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.html vignetteTitles: Multi-Dataset Model-based Differential Expression Proteomics Platform hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.R suggestsMe: mi4p dependencyCount: 78 Package: protGear Version: 1.15.0 Depends: R (>= 4.2), dplyr (>= 0.8.0) , limma (>= 3.40.2) ,vsn (>= 3.54.0) Imports: magrittr (>= 1.5) , stats (>= 3.6) , ggplot2 (>= 3.3.0) , tidyr (>= 1.1.3) , data.table (>= 1.14.0), ggpubr (>= 0.4.0), gtools (>= 3.8.2) , tibble (>= 3.1.0) , rmarkdown (>= 2.9) , knitr (>= 1.33), utils (>= 3.6), genefilter (>= 1.74.0), readr (>= 2.0.1) , Biobase (>= 2.52.0), plyr (>= 1.8.6) , Kendall (>= 2.2) , shiny (>= 1.0.0) , purrr (>= 0.3.4), plotly (>= 4.9.0) , MASS (>= 7.3) , htmltools (>= 0.4.0) , flexdashboard (>= 0.5.2) , shinydashboard (>= 0.7.1) , GGally (>= 2.1.2) , pheatmap (>= 1.0.12) , grid(>= 4.1.1), styler (>= 1.6.1) , factoextra (>= 1.0.7) ,FactoMineR (>= 2.4) , rlang (>= 0.4.11), remotes (>= 2.4.0) Suggests: gridExtra (>= 2.3), png (>= 0.1-7) , magick (>= 2.7.3) , ggplotify (>= 0.1.0) , scales (>= 1.1.1) , shinythemes (>= 1.2.0) , shinyjs (>= 2.0.0) , shinyWidgets (>= 0.6.2) , shinycssloaders (>= 1.0.0) , shinyalert (>= 3.0.0) , shinyFiles (>= 0.9.1) , shinyFeedback (>= 0.3.0) License: GPL-3 MD5sum: aee1f95d9413bb78424aa817bd8386c5 NeedsCompilation: no Title: Protein Micro Array Data Management and Interactive Visualization Description: A generic three-step pre-processing package for protein microarray data. This package contains different data pre-processing procedures to allow comparison of their performance.These steps are background correction, the coefficient of variation (CV) based filtering, batch correction and normalization. biocViews: Microarray, OneChannel, Preprocessing , BiomedicalInformatics , Proteomics , BatchEffect, Normalization , Bayesian, Clustering, Regression,SystemsBiology, ImmunoOncology Author: Kennedy Mwai [cre, aut], James Mburu [aut], Jacqueline Waeni [ctb] Maintainer: Kennedy Mwai URL: https://github.com/Keniajin/protGear VignetteBuilder: knitr BugReports: https://github.com/Keniajin/protGear/issues git_url: https://git.bioconductor.org/packages/protGear git_branch: devel git_last_commit: 1c28da5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/protGear_1.15.0.tar.gz vignettes: vignettes/protGear/inst/doc/vignette.html vignetteTitles: protGear hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/protGear/inst/doc/vignette.R dependencyCount: 193 Package: ProtGenerics Version: 1.43.0 Depends: methods Suggests: testthat License: Artistic-2.0 MD5sum: e332bf9790239c063b1e7e0e74c3dbd1 NeedsCompilation: no Title: Generic infrastructure for Bioconductor mass spectrometry packages Description: S4 generic functions and classes needed by Bioconductor proteomics packages. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto , Johannes Rainer Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/ProtGenerics git_url: https://git.bioconductor.org/packages/ProtGenerics git_branch: devel git_last_commit: 52f3a6a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ProtGenerics_1.43.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, Chromatograms, MetaboAnnotatoR, MsExperiment, MSnbase, SpectraQL, topdownr importsMe: CompoundDb, ensembldb, matter, MetaboAnnotation, MsBackendMassbank, MsBackendMetaboLights, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MsFeatures, MSnID, MsQuality, mzID, mzR, PSMatch, QFeatures, Spectra, SpectriPy, xcms dependencyCount: 1 Package: psichomics Version: 1.37.1 Depends: R (>= 4.0), shiny (>= 1.7.0), shinyBS Imports: AnnotationDbi, AnnotationHub, BiocFileCache, cluster, colourpicker, data.table, digest, dplyr, DT (>= 0.2), edgeR, fastICA, fastmatch, ggplot2, ggrepel, graphics, grDevices, highcharter (>= 0.5.0), htmltools, httr, jsonlite, limma, pairsD3, plyr, purrr, Rcpp (>= 0.12.14), recount, Rfast, R.utils, reshape2, shinyjs, stringr, stats, SummarizedExperiment, survival, tools, utils, XML, xtable, methods LinkingTo: Rcpp Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr, car, rstudioapi, spelling License: MIT + file LICENSE MD5sum: 6ce75433534f836625526f0759928d13 NeedsCompilation: yes Title: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation Description: Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, Transcription, GUI, PrincipalComponent, Survival, BiomedicalInformatics, Transcriptomics, ImmunoOncology, Visualization, MultipleComparison, GeneExpression, DifferentialExpression Author: Nuno Saraiva-Agostinho [aut, cre] (ORCID: ), Nuno Luís Barbosa-Morais [aut, led, ths] (ORCID: ), André Falcão [ths], Lina Gallego Paez [ctb], Marie Bordone [ctb], Teresa Maia [ctb], Mariana Ferreira [ctb], Ana Carolina Leote [ctb], Bernardo de Almeida [ctb] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/psichomics/, https://github.com/nuno-agostinho/psichomics/ VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/psichomics/issues git_url: https://git.bioconductor.org/packages/psichomics git_branch: devel git_last_commit: 70c9e2e git_last_commit_date: 2026-01-07 Date/Publication: 2026-04-20 source.ver: src/contrib/psichomics_1.37.1.tar.gz vignettes: vignettes/psichomics/inst/doc/AS_events_preparation.html, vignettes/psichomics/inst/doc/CLI_tutorial.html, vignettes/psichomics/inst/doc/custom_data.html, vignettes/psichomics/inst/doc/GUI_tutorial.html vignetteTitles: Preparing an Alternative Splicing Annotation for psichomics, Case study: command-line interface (CLI) tutorial, Loading user-provided data, Case study: visual interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psichomics/inst/doc/AS_events_preparation.R, vignettes/psichomics/inst/doc/CLI_tutorial.R, vignettes/psichomics/inst/doc/custom_data.R, vignettes/psichomics/inst/doc/GUI_tutorial.R dependencyCount: 205 Package: PSMatch Version: 1.15.3 Depends: S4Vectors, R (>= 4.1.0), PTMods (>= 0.99.4) Imports: utils, stats, igraph, methods, Spectra (>= 1.17.10), Matrix, BiocParallel, BiocGenerics, ProtGenerics (>= 1.27.1), QFeatures, MsCoreUtils Suggests: MsDataHub, rpx, mzID, mzR, SummarizedExperiment, BiocStyle, rmarkdown, knitr, factoextra, vdiffr (>= 1.0.0), testthat License: Artistic-2.0 MD5sum: c87885e2fa654be9db71f69496e94fc5 NeedsCompilation: no Title: Handling and Managing Peptide Spectrum Matches Description: The PSMatch package helps proteomics practitioners to load, handle and manage Peptide Spectrum Matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and make informed decision about shared peptide filtering. The package also provides functions to calculate and visualise MS2 fragment ions. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto [aut, cre] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Samuel Wieczorek [ctb], Thomas Burger [ctb], Guillaume Deflandre [ctb] (ORCID: ) Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/PSM VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/PSM/issues git_url: https://git.bioconductor.org/packages/PSMatch git_branch: devel git_last_commit: 91fe6d8 git_last_commit_date: 2026-04-07 Date/Publication: 2026-04-20 source.ver: src/contrib/PSMatch_1.15.3.tar.gz vignettes: vignettes/PSMatch/inst/doc/AdjacencyMatrix.html, vignettes/PSMatch/inst/doc/Fragments.html, vignettes/PSMatch/inst/doc/PSM.html vignetteTitles: Understanding protein groups with adjacency matrices, MS2 fragment ions, Working with PSM data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSMatch/inst/doc/AdjacencyMatrix.R, vignettes/PSMatch/inst/doc/Fragments.R, vignettes/PSMatch/inst/doc/PSM.R importsMe: MSnbase, topdownr suggestsMe: MsDataHub dependencyCount: 112 Package: PTMods Version: 0.99.6 Depends: R (>= 4.5.0), methods Suggests: xml2, testthat, knitr, BiocStyle, Biostrings Enhances: PSMatch License: GPL-3 MD5sum: 48f253baf43492018f1efbc9df114294 NeedsCompilation: no Title: Managing Post-Translational Modifications in R Description: An interface to the community supported database for amino acid/protein modifications using mass spectrometry. biocViews: Proteomics, MassSpectrometry Author: Laurent Gatto [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Guillaume Deflandre [cre] (ORCID: ) Maintainer: Guillaume Deflandre URL: https://github.com/RforMassSpectrometry/PTMods VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/PTMods/issues git_url: https://git.bioconductor.org/packages/PTMods git_branch: devel git_last_commit: a5738db git_last_commit_date: 2026-04-09 Date/Publication: 2026-04-20 source.ver: src/contrib/PTMods_0.99.6.tar.gz vignettes: vignettes/PTMods/inst/doc/PTMods.html vignetteTitles: The PTMods package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PTMods/inst/doc/PTMods.R dependsOnMe: PSMatch dependencyCount: 1 Package: puma Version: 3.53.0 Depends: R (>= 3.2.0), oligo (>= 1.32.0),graphics,grDevices, methods, stats, utils, mclust, oligoClasses Imports: Biobase (>= 2.5.5), affy (>= 1.46.0), affyio, oligoClasses Suggests: pumadata, affydata, snow, limma, ROCR,annotate License: LGPL MD5sum: 6beb927c9fd6582d6cdf3d4132f715c5 NeedsCompilation: yes Title: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) Description: Most analyses of Affymetrix GeneChip data (including tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. In additon to calculte gene expression from Affymetrix 3' arrays, puma also provides methods to process exon arrays and produces gene and isoform expression for alternative splicing study. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting functions. biocViews: Microarray, OneChannel, Preprocessing, DifferentialExpression, Clustering, ExonArray, GeneExpression, mRNAMicroarray, ChipOnChip, AlternativeSplicing, DifferentialSplicing, Bayesian, TwoChannel, DataImport, HTA2.0 Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo, Neil D. Lawrence, Guido Sanguinetti, Li Zhang Maintainer: Xuejun Liu URL: http://umber.sbs.man.ac.uk/resources/puma git_url: https://git.bioconductor.org/packages/puma git_branch: devel git_last_commit: fdcbc97 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/puma_3.53.0.tar.gz vignettes: vignettes/puma/inst/doc/puma.pdf vignetteTitles: puma User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/puma/inst/doc/puma.R suggestsMe: tigre dependencyCount: 55 Package: PureCN Version: 2.17.1 Depends: R (>= 3.5.0), DNAcopy, VariantAnnotation (>= 1.14.1) Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer, S4Vectors, data.table, grDevices, graphics, stats, utils, SummarizedExperiment, Seqinfo, GenomeInfoDb, GenomicFeatures, Rsamtools, Biobase, Biostrings, BiocGenerics, rtracklayer, ggplot2, gridExtra, futile.logger, VGAM, tools, methods, mclust, rhdf5, Matrix Suggests: BiocParallel, BiocStyle, PSCBS, R.utils, TxDb.Hsapiens.UCSC.hg19.knownGene, covr, knitr, optparse, org.Hs.eg.db, jsonlite, markdown, rmarkdown, testthat Enhances: genomicsdb (>= 0.0.3) License: Artistic-2.0 MD5sum: 87005eadc833ae6ee7473c8e35eed0db NeedsCompilation: no Title: Copy number calling and SNV classification using targeted short read sequencing Description: This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples. biocViews: CopyNumberVariation, Software, Sequencing, VariantAnnotation, VariantDetection, Coverage, ImmunoOncology Author: Markus Riester [aut, cre] (ORCID: ), Angad P. Singh [aut] Maintainer: Markus Riester URL: https://github.com/lima1/PureCN VignetteBuilder: knitr BugReports: https://github.com/lima1/PureCN/issues git_url: https://git.bioconductor.org/packages/PureCN git_branch: devel git_last_commit: 5e801f2 git_last_commit_date: 2026-02-18 Date/Publication: 2026-04-20 source.ver: src/contrib/PureCN_2.17.1.tar.gz vignettes: vignettes/PureCN/inst/doc/PureCN.pdf, vignettes/PureCN/inst/doc/Quick.html vignetteTitles: Overview of the PureCN R package, Best practices,, quick start and command line usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PureCN/inst/doc/PureCN.R, vignettes/PureCN/inst/doc/Quick.R dependencyCount: 101 Package: pvac Version: 1.59.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: 1049827e100d341622b1945464759e25 NeedsCompilation: no Title: PCA-based gene filtering for Affymetrix arrays Description: The package contains the function for filtering genes by the proportion of variation accounted for by the first principal component (PVAC). biocViews: Microarray, OneChannel, QualityControl Author: Jun Lu and Pierre R. Bushel Maintainer: Jun Lu , Pierre R. Bushel git_url: https://git.bioconductor.org/packages/pvac git_branch: devel git_last_commit: 5e7dd13 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pvac_1.59.0.tar.gz vignettes: vignettes/pvac/inst/doc/pvac.pdf vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvac/inst/doc/pvac.R dependencyCount: 12 Package: pvca Version: 1.51.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: 9c1c0f5fc07e553149e8662d9080c237 NeedsCompilation: no Title: Principal Variance Component Analysis (PVCA) Description: This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12. biocViews: Microarray, BatchEffect Author: Pierre Bushel Maintainer: Jianying LI git_url: https://git.bioconductor.org/packages/pvca git_branch: devel git_last_commit: 1b417dc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pvca_1.51.0.tar.gz vignettes: vignettes/pvca/inst/doc/pvca.pdf vignetteTitles: Batch effect estimation in Microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvca/inst/doc/pvca.R importsMe: proBatch, ExpressionNormalizationWorkflow suggestsMe: omicsTools dependencyCount: 49 Package: Pviz Version: 1.45.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 MD5sum: 11ec2b6d6655f6472019f74e99d66dd7 NeedsCompilation: no Title: Peptide Annotation and Data Visualization using Gviz Description: Pviz adapts the Gviz package for protein sequences and data. biocViews: Visualization, Proteomics, Microarray Author: Renan Sauteraud, Mike Jiang, Raphael Gottardo Maintainer: Renan Sauteraud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pviz git_branch: devel git_last_commit: 2605bdc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Pviz_1.45.0.tar.gz vignettes: vignettes/Pviz/inst/doc/Pviz.pdf vignetteTitles: The Pviz users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pviz/inst/doc/Pviz.R suggestsMe: pepStat dependencyCount: 151 Package: pwalign Version: 1.7.0 Depends: BiocGenerics, S4Vectors, IRanges, Biostrings (>= 2.71.5) Imports: methods, utils LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit Enhances: Rmpi License: Artistic-2.0 MD5sum: d38215796702fcfed10b7ea72f99ee6a NeedsCompilation: yes Title: Perform pairwise sequence alignments Description: The two main functions in the package are pairwiseAlignment() and stringDist(). The former solves (Needleman-Wunsch) global alignment, (Smith-Waterman) local alignment, and (ends-free) overlap alignment problems. The latter computes the Levenshtein edit distance or pairwise alignment score matrix for a set of strings. biocViews: Alignment, SequenceMatching, Sequencing, Genetics Author: Patrick Aboyoun [aut], Robert Gentleman [aut], Hervé Pagès [cre] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/pwalign BugReports: https://github.com/Bioconductor/pwalign/issues git_url: https://git.bioconductor.org/packages/pwalign git_branch: devel git_last_commit: a8e536a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/pwalign_1.7.0.tar.gz vignettes: vignettes/pwalign/inst/doc/PairwiseAlignments.pdf vignetteTitles: Pairwise Sequence Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pwalign/inst/doc/PairwiseAlignments.R dependsOnMe: amplican, MethTargetedNGS, QSutils, R453Plus1Toolbox, sangeranalyseR, sangerseqR, CleanBSequences importsMe: ChIPpeakAnno, CNEr, crisprShiny, DominoEffect, enhancerHomologSearch, ggseqalign, GUIDEseq, IMMAN, IsoformSwitchAnalyzeR, methylscaper, MSA2dist, openPrimeR, scanMiR, scifer, ShortRead, SpliceImpactR, SPLINTER, StructuralVariantAnnotation, svaNUMT, TFBSTools, AntibodyForests, BIGr, dowser, longreadvqs, ogrdbstats, PACVr, revert suggestsMe: BiocGenerics, Biostrings, idpr, msa, mutscan, RSVSim, geneviewer, seqtrie dependencyCount: 15 Package: PWMEnrich Version: 4.47.0 Depends: R (>= 3.5.0), methods, BiocGenerics, Biostrings Imports: grid, seqLogo, gdata, evd, S4Vectors Suggests: MotifDb, BSgenome, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 34c1e379b53675c4779f9dc257ab7149 NeedsCompilation: no Title: PWM enrichment analysis Description: A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools. biocViews: MotifAnnotation, SequenceMatching, Software Author: Robert Stojnic, Diego Diez Maintainer: Diego Diez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PWMEnrich git_branch: devel git_last_commit: 3606145 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/PWMEnrich_4.47.0.tar.gz vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf vignetteTitles: Overview of the 'PWMEnrich' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PWMEnrich/inst/doc/PWMEnrich.R dependsOnMe: PWMEnrich.Dmelanogaster.background, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background suggestsMe: rTRM dependencyCount: 20 Package: qcmetrics Version: 1.49.0 Depends: R (>= 3.3) Imports: Biobase, methods, knitr, tools, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, mzR, BiocStyle, rmarkdown, markdown License: GPL-2 MD5sum: dc4092b454a1d5de041b6198bf857599 NeedsCompilation: no Title: A Framework for Quality Control Description: The package provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats. biocViews: ImmunoOncology, Software, QualityControl, Proteomics, Microarray, MassSpectrometry, Visualization, ReportWriting Author: Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: http://lgatto.github.io/qcmetrics/articles/qcmetrics.html VignetteBuilder: knitr BugReports: https://github.com/lgatto/qcmetrics/issues git_url: https://git.bioconductor.org/packages/qcmetrics git_branch: devel git_last_commit: c0694fe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/qcmetrics_1.49.0.tar.gz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.html vignetteTitles: Index file for the qcmetrics package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R importsMe: MSstatsQC dependencyCount: 20 Package: QDNAseq Version: 1.47.0 Depends: R (>= 3.1.0) Imports: graphics, methods, stats, utils, BiocGenerics, Biobase (>= 2.18.0), CGHbase (>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0), Seqinfo, GenomicRanges (>= 1.20), IRanges (>= 2.2), matrixStats (>= 0.60.0), R.utils (>= 2.9.0), Rsamtools (>= 1.20), future.apply (>= 1.8.1) Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>= 0.6.20), GenomeInfoDb (>= 1.6.0), future (>= 1.22.1), parallelly (>= 1.28.1), R.cache (>= 0.13.0), QDNAseq.hg19, QDNAseq.mm10 License: GPL MD5sum: 77ab3b5ab26b84358f77e90e9491228f NeedsCompilation: no Title: Quantitative DNA Sequencing for Chromosomal Aberrations Description: Quantitative DNA sequencing for chromosomal aberrations. The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively. biocViews: CopyNumberVariation, DNASeq, Genetics, GenomeAnnotation, Preprocessing, QualityControl, Sequencing Author: Ilari Scheinin [aut], Daoud Sie [aut, cre], Henrik Bengtsson [aut], Erik van Dijk [ctb] Maintainer: Daoud Sie URL: https://github.com/ccagc/QDNAseq BugReports: https://github.com/ccagc/QDNAseq/issues git_url: https://git.bioconductor.org/packages/QDNAseq git_branch: devel git_last_commit: 2bb3c9e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/QDNAseq_1.47.0.tar.gz vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf vignetteTitles: Introduction to QDNAseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QDNAseq/inst/doc/QDNAseq.R dependsOnMe: GeneBreak, QDNAseq.hg19, QDNAseq.mm10 importsMe: ACE, biscuiteer, cfdnakit dependencyCount: 48 Package: QFeatures Version: 1.21.3 Depends: R (>= 4.1), MultiAssayExperiment (>= 1.33.6) Imports: methods, stats, utils, S4Vectors, IRanges, SummarizedExperiment, BiocGenerics (>= 0.53.4), ProtGenerics (>= 1.35.1), AnnotationFilter, lazyeval, Biobase, MsCoreUtils (>= 1.7.2), igraph, grDevices, plotly, tidyr, tidyselect, reshape2 Suggests: SingleCellExperiment, MsDataHub (>= 1.3.3), Matrix, HDF5Array, msdata, ggplot2, gplots, dplyr, limma, DT, shiny, shinydashboard, testthat, knitr, BiocStyle, rmarkdown, vsn, preprocessCore, matrixStats, imputeLCMD, pcaMethods, impute, norm, ComplexHeatmap License: Artistic-2.0 MD5sum: 377d537ed8a47e0ce44ad3afefc4dd7b NeedsCompilation: no Title: Quantitative features for mass spectrometry data Description: The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format. biocViews: Infrastructure, MassSpectrometry, Proteomics, Metabolomics Author: Laurent Gatto [aut, cre] (ORCID: ), Christophe Vanderaa [aut] (ORCID: ), Karolína Kryštofová [ctb] (ORCID: ), Léopold Guyot [ctb] Maintainer: Laurent Gatto URL: https://rformassspectrometry.github.io/QFeatures VignetteBuilder: knitr BugReports: https://github.com/rformassspectrometry/QFeatures/issues git_url: https://git.bioconductor.org/packages/QFeatures git_branch: devel git_last_commit: a7049ad git_last_commit_date: 2026-04-19 Date/Publication: 2026-04-20 source.ver: src/contrib/QFeatures_1.21.3.tar.gz vignettes: vignettes/QFeatures/inst/doc/Processing.html, vignettes/QFeatures/inst/doc/QFeatures.html, vignettes/QFeatures/inst/doc/read_QFeatures.html, vignettes/QFeatures/inst/doc/Visualization.html vignetteTitles: Processing quantitative proteomics data with QFeatures, Quantitative features for mass spectrometry data, Load data using readQFeatures(), Data visualization from a QFeatures object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QFeatures/inst/doc/Processing.R, vignettes/QFeatures/inst/doc/QFeatures.R, vignettes/QFeatures/inst/doc/read_QFeatures.R, vignettes/QFeatures/inst/doc/Visualization.R dependsOnMe: hdxmsqc, msqrob2, scp, scpdata importsMe: MetaboAnnotation, MsExperiment, mspms, omicsGMF, proBatch, PSMatch suggestsMe: MsDataHub dependencyCount: 99 Package: qmtools Version: 1.15.0 Depends: R (>= 4.2.0), SummarizedExperiment Imports: rlang, ggplot2, patchwork, heatmaply, methods, MsCoreUtils, stats, igraph, VIM, scales, grDevices, graphics, limma Suggests: Rtsne, missForest, vsn, pcaMethods, pls, MsFeatures, impute, imputeLCMD, nlme, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: a7ce735628ebd0ce45a305a773ea189c NeedsCompilation: no Title: Quantitative Metabolomics Data Processing Tools Description: The qmtools (quantitative metabolomics tools) package provides basic tools for processing quantitative metabolomics data with the standard SummarizedExperiment class. This includes functions for imputation, normalization, feature filtering, feature clustering, dimension-reduction, and visualization to help users prepare data for statistical analysis. This package also offers a convenient way to compute empirical Bayes statistics for which metabolic features are different between two sets of study samples. Several functions in this package could also be used in other types of omics data. biocViews: Metabolomics, Preprocessing, Normalization, DimensionReduction, MassSpectrometry Author: Jaehyun Joo [aut, cre], Blanca Himes [aut] Maintainer: Jaehyun Joo URL: https://github.com/HimesGroup/qmtools VignetteBuilder: knitr BugReports: https://github.com/HimesGroup/qmtools/issues git_url: https://git.bioconductor.org/packages/qmtools git_branch: devel git_last_commit: ea14738 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/qmtools_1.15.0.tar.gz vignettes: vignettes/qmtools/inst/doc/qmtools.html vignetteTitles: Quantitative metabolomics data processing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qmtools/inst/doc/qmtools.R dependencyCount: 189 Package: qpcrNorm Version: 1.69.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: fe59b88b825a5953230857f343cd200e NeedsCompilation: no Title: Data-driven normalization strategies for high-throughput qPCR data. Description: The package contains functions to perform normalization of high-throughput qPCR data. Basic functions for processing raw Ct data plus functions to generate diagnostic plots are also available. biocViews: Preprocessing, GeneExpression Author: Jessica Mar Maintainer: Jessica Mar git_url: https://git.bioconductor.org/packages/qpcrNorm git_branch: devel git_last_commit: 9244214 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/qpcrNorm_1.69.0.tar.gz vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf vignetteTitles: qPCR Normalization Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpcrNorm/inst/doc/qpcrNorm.R dependencyCount: 14 Package: qpgraph Version: 2.45.2 Depends: R (>= 3.5) Imports: methods, parallel, Matrix (>= 1.5-0), grid, annotate, graph (>= 1.45.1), Biobase, S4Vectors, BiocParallel, AnnotationDbi, IRanges, Seqinfo, GenomicRanges, GenomicFeatures, mvtnorm, qtl, Rgraphviz Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db, rlecuyer, snow, Category, GOstats License: GPL (>= 2) MD5sum: 6b32dcac10e91203907b1eada75a3e7a NeedsCompilation: yes Title: Estimation of Genetic and Molecular Regulatory Networks from High-Throughput Genomics Data Description: Estimate gene and eQTL networks from high-throughput expression and genotyping assays. biocViews: Microarray, GeneExpression, Transcription, Pathways, NetworkInference, GraphAndNetwork, GeneRegulation, Genetics, GeneticVariability, SNP, Software Author: Robert Castelo [aut, cre], Alberto Roverato [aut] Maintainer: Robert Castelo URL: https://github.com/rcastelo/qpgraph BugReports: https://github.com/rcastelo/qpgraph/issues git_url: https://git.bioconductor.org/packages/qpgraph git_branch: devel git_last_commit: 8666fca git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/qpgraph_2.45.2.tar.gz vignettes: vignettes/qpgraph/inst/doc/BasicUsersGuide.pdf, vignettes/qpgraph/inst/doc/eQTLnetworks.pdf, vignettes/qpgraph/inst/doc/qpgraphSimulate.pdf, vignettes/qpgraph/inst/doc/qpTxRegNet.pdf vignetteTitles: BasicUsersGuide.pdf, Estimate eQTL networks using qpgraph, Simulating molecular regulatory networks using qpgraph, Reverse-engineer transcriptional regulatory networks using qpgraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpgraph/inst/doc/eQTLnetworks.R, vignettes/qpgraph/inst/doc/qpgraphSimulate.R, vignettes/qpgraph/inst/doc/qpTxRegNet.R importsMe: clipper, MOSClip, topologyGSA dependencyCount: 81 Package: QRscore Version: 1.3.0 Depends: R (>= 4.4.0) Imports: MASS, pscl, arrangements, hitandrun, assertthat, dplyr, BiocParallel Suggests: devtools, DESeq2, knitr, rmarkdown, testthat, BiocStyle License: GPL (>= 3) MD5sum: 72db57fc77a657dc6d84d6ded3a06c6f NeedsCompilation: no Title: Quantile Rank Score Description: In genomics, differential analysis enables the discovery of groups of genes implicating important biological processes such as cell differentiation and aging. Non-parametric tests of differential gene expression usually detect shifts in centrality (such as mean or median), and therefore suffer from diminished power against alternative hypotheses characterized by shifts in spread (such as variance). This package provides a flexible family of non-parametric two-sample tests and K-sample tests, which is based on theoretical work around non-parametric tests, spacing statistics and local asymptotic normality (Erdmann-Pham et al., 2022+ [arXiv:2008.06664v2]; Erdmann-Pham, 2023+ [arXiv:2209.14235v2]). biocViews: StatisticalMethod, DifferentialExpression, GeneExpression, StructuralGenomics, GeneTarget Author: Fanding Zhou [cre, aut] (ORCID: ), Alan Aw [aut] (ORCID: ), Dan Erdmann-Pham [aut], Jonathan Fischer [aut] (ORCID: ), Xurui Chen [ctb] Maintainer: Fanding Zhou URL: https://github.com/songlab-cal/QRscore VignetteBuilder: BiocStyle BugReports: https://github.com/songlab-cal/QRscore/issues git_url: https://git.bioconductor.org/packages/QRscore git_branch: devel git_last_commit: 57bfb3d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/QRscore_1.3.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 39 Package: qsea Version: 1.37.0 Depends: R (>= 4.3) Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy, rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges, limma, Seqinfo, BiocGenerics, grDevices, zoo, BiocParallel, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle, knitr, rmarkdown, BiocManager, MASS License: GPL-2 MD5sum: 1502ea67a0e5d292377e3b9e2ee1f63a NeedsCompilation: yes Title: IP-seq data analysis and vizualization Description: qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples. biocViews: Sequencing, DNAMethylation, CpGIsland, ChIPSeq, Preprocessing, Normalization, QualityControl, Visualization, CopyNumberVariation, ChipOnChip, DifferentialMethylation Author: Matthias Lienhard [aut, cre] (ORCID: ), Lukas Chavez [aut] (ORCID: ), Ralf Herwig [aut] (ORCID: ) Maintainer: Matthias Lienhard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsea git_branch: devel git_last_commit: ef750af git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/qsea_1.37.0.tar.gz vignettes: vignettes/qsea/inst/doc/qsea_tutorial.html vignetteTitles: qsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsea/inst/doc/qsea_tutorial.R suggestsMe: MEDIPSData dependencyCount: 64 Package: qsmooth Version: 1.27.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, utils, sva, stats, methods, graphics, Hmisc Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: af104351e2b664cf5ebf83c1d2db572c NeedsCompilation: no Title: Smooth quantile normalization Description: Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing, RNASeq, BatchEffect Author: Stephanie C. Hicks [aut, cre] (ORCID: ), Kwame Okrah [aut], Koen Van den Berge [ctb], Hector Corrada Bravo [aut] (ORCID: ), Rafael Irizarry [aut] (ORCID: ) Maintainer: Stephanie C. Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsmooth git_branch: devel git_last_commit: 427ec51 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/qsmooth_1.27.0.tar.gz vignettes: vignettes/qsmooth/inst/doc/qsmooth.html vignetteTitles: The qsmooth user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsmooth/inst/doc/qsmooth.R importsMe: CleanUpRNAseq suggestsMe: metamorphr dependencyCount: 118 Package: QSutils Version: 1.29.0 Depends: R (>= 3.5), Biostrings, pwalign, BiocGenerics, methods Imports: ape, stats, psych Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: GPL-2 MD5sum: 6d72ec61a84e90d36f65264e320da3d0 NeedsCompilation: no Title: Quasispecies Diversity Description: Set of utility functions for viral quasispecies analysis with NGS data. Most functions are equally useful for metagenomic studies. There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data. biocViews: Software, Genetics, DNASeq, GeneticVariability, Sequencing, Alignment, SequenceMatching, DataImport Author: Mercedes Guerrero-Murillo [cre, aut] (ORCID: ), Josep Gregori i Font [aut] (ORCID: ) Maintainer: Mercedes Guerrero-Murillo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QSutils git_branch: devel git_last_commit: 23b420d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/QSutils_1.29.0.tar.gz vignettes: vignettes/QSutils/inst/doc/QSUtils-Alignment.html, vignettes/QSutils/inst/doc/QSutils-Diversity.html, vignettes/QSutils/inst/doc/QSutils-Simulation.html vignetteTitles: QSUtils-Alignment, QSutils-Diversity, QSutils-Simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QSutils/inst/doc/QSUtils-Alignment.R, vignettes/QSutils/inst/doc/QSutils-Diversity.R, vignettes/QSutils/inst/doc/QSutils-Simulation.R importsMe: longreadvqs dependencyCount: 26 Package: qsvaR Version: 1.15.0 Depends: R (>= 4.2), SummarizedExperiment Imports: dplyr, sva, stats, ggplot2, rlang, methods Suggests: BiocFileCache, BiocStyle, covr, knitr, limma, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: b30ed7d1656d7ff1df5fba95a4c586e0 NeedsCompilation: no Title: Generate Quality Surrogate Variable Analysis for Degradation Correction Description: The qsvaR package contains functions for removing the effect of degration in rna-seq data from postmortem brain tissue. The package is equipped to help users generate principal components associated with degradation. The components can be used in differential expression analysis to remove the effects of degradation. biocViews: Software, WorkflowStep, Normalization, BiologicalQuestion, DifferentialExpression, Sequencing, Coverage Author: Joshua Stolz [aut] (ORCID: ), Hedia Tnani [ctb] (ORCID: ), Leonardo Collado-Torres [ctb] (ORCID: ), Nicholas J. Eagles [aut, cre] (ORCID: ) Maintainer: Nicholas J. Eagles URL: https://github.com/LieberInstitute/qsvaR VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/qsvaR git_url: https://git.bioconductor.org/packages/qsvaR git_branch: devel git_last_commit: 21c225d git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/qsvaR_1.15.0.tar.gz vignettes: vignettes/qsvaR/inst/doc/Intro_qsvaR.html vignetteTitles: Introduction to qsvaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsvaR/inst/doc/Intro_qsvaR.R dependencyCount: 91 Package: QTLExperiment Version: 2.3.1 Depends: SummarizedExperiment Imports: methods, rlang, checkmate, dplyr, collapse, vroom, tidyr, tibble, utils, stats, ashr, S4Vectors, BiocGenerics Suggests: testthat, BiocStyle, knitr, rmarkdown, covr License: GPL-3 MD5sum: 1d3b981d68826f4ece026aa844ddfdf8 NeedsCompilation: no Title: S4 classes for QTL summary statistics and metadata Description: QLTExperiment defines an S4 class for storing and manipulating summary statistics from QTL mapping experiments in one or more states. It is based on the 'SummarizedExperiment' class and contains functions for creating, merging, and subsetting objects. 'QTLExperiment' also stores experiment metadata and has checks in place to ensure that transformations apply correctly. biocViews: FunctionalGenomics, DataImport, DataRepresentation, Infrastructure, Sequencing, SNP, Software Author: Christina Del Azodi [aut], Davis McCarthy [ctb], Amelia Dunstone [cre, aut] (ORCID: ) Maintainer: Amelia Dunstone URL: https://github.com/dunstone-a/QTLExperiment VignetteBuilder: knitr BugReports: https://github.com/dunstone-a/QTLExperiment/issues git_url: https://git.bioconductor.org/packages/QTLExperiment git_branch: devel git_last_commit: 90eb513 git_last_commit_date: 2026-01-29 Date/Publication: 2026-04-20 source.ver: src/contrib/QTLExperiment_2.3.1.tar.gz vignettes: vignettes/QTLExperiment/inst/doc/QTLExperiment.html vignetteTitles: An introduction to the QTLExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QTLExperiment/inst/doc/QTLExperiment.R dependsOnMe: multistateQTL dependencyCount: 64 Package: Qtlizer Version: 1.25.1 Depends: R (>= 3.6.0) Imports: httr, curl, GenomicRanges, stringi Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 77141c4346f7b499b3a00594c18e78f9 NeedsCompilation: no Title: Comprehensive QTL annotation of GWAS results Description: This R package provides access to the Qtlizer web server. Qtlizer annotates lists of common small variants (mainly SNPs) and genes in humans with associated changes in gene expression using the most comprehensive database of published quantitative trait loci (QTLs). biocViews: GenomeWideAssociation, SNP, Genetics, LinkageDisequilibrium Author: Matthias Munz [aut, cre] (ORCID: ), Julia Remes [aut] Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/Qtlizer/issues git_url: https://git.bioconductor.org/packages/Qtlizer git_branch: devel git_last_commit: 57c8167 git_last_commit_date: 2026-03-22 Date/Publication: 2026-04-20 source.ver: src/contrib/Qtlizer_1.25.1.tar.gz vignettes: vignettes/Qtlizer/inst/doc/Qtlizer.html vignetteTitles: Qtlizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Qtlizer/inst/doc/Qtlizer.R dependencyCount: 21 Package: quantiseqr Version: 1.19.0 Depends: R (>= 4.1.0) Imports: Biobase, limSolve, MASS, methods, preprocessCore, stats, SummarizedExperiment, ggplot2, tidyr, rlang, utils Suggests: AnnotationDbi, BiocStyle, dplyr, ExperimentHub, GEOquery, knitr, macrophage, org.Hs.eg.db, reshape2, rmarkdown, testthat, tibble License: GPL-3 MD5sum: a631d483c72e5a50e0d3b133e18d155c NeedsCompilation: no Title: Quantification of the Tumor Immune contexture from RNA-seq data Description: This package provides a streamlined workflow for the quanTIseq method, developed to perform the quantification of the Tumor Immune contexture from RNA-seq data. The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data. biocViews: GeneExpression, Software, Transcription, Transcriptomics, Sequencing, Microarray, Visualization, Annotation, ImmunoOncology, FeatureExtraction, Classification, StatisticalMethod, ExperimentHubSoftware, FlowCytometry Author: Federico Marini [aut, cre] (ORCID: ), Francesca Finotello [aut] (ORCID: ) Maintainer: Federico Marini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantiseqr git_branch: devel git_last_commit: 800b9ff git_last_commit_date: 2025-12-01 Date/Publication: 2026-04-20 source.ver: src/contrib/quantiseqr_1.19.0.tar.gz vignettes: vignettes/quantiseqr/inst/doc/using_quantiseqr.html vignetteTitles: Using quantiseqr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantiseqr/inst/doc/using_quantiseqr.R importsMe: easier dependencyCount: 58 Package: quantsmooth Version: 1.77.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: cf1860081ce8afe0b9f2fc71ea5f5705 NeedsCompilation: no Title: Quantile smoothing and genomic visualization of array data Description: Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. biocViews: Visualization, CopyNumberVariation Author: Jan Oosting, Paul Eilers, Renee Menezes Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/quantsmooth git_branch: devel git_last_commit: d6caae1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/quantsmooth_1.77.0.tar.gz vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf vignetteTitles: quantsmooth hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantsmooth/inst/doc/quantsmooth.R importsMe: GWASTools, SIM suggestsMe: PREDA dependencyCount: 14 Package: QuaternaryProd Version: 1.45.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18) LinkingTo: Rcpp Suggests: knitr License: GPL (>=3) MD5sum: 3e3710d2468b83442a8caaf02b5f286d NeedsCompilation: yes Title: Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs Description: QuaternaryProd is an R package that performs causal reasoning on biological networks, including publicly available networks such as STRINGdb. QuaternaryProd is an open-source alternative to commercial products such as Inginuity Pathway Analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test (Enrichment test). The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. The Vignette provides more details on the Quaternary Statistic and illustrates an example of how to perform causal reasoning using STRINGdb. biocViews: GraphAndNetwork, GeneExpression, Transcription Author: Carl Tony Fakhry [cre, aut], Ping Chen [ths], Kourosh Zarringhalam [aut, ths] Maintainer: Carl Tony Fakhry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuaternaryProd git_branch: devel git_last_commit: 955c5f1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/QuaternaryProd_1.45.0.tar.gz vignettes: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.pdf vignetteTitles: QuaternaryProdVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.R dependencyCount: 21 Package: QUBIC Version: 1.39.2 Depends: R (>= 4.5.0) Imports: Rcpp (>= 0.11.0), methods, Matrix LinkingTo: Rcpp, RcppArmadillo Suggests: QUBICdata, qgraph, fields, knitr, rmarkdown Enhances: RColorBrewer License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: 5b56d1e4be609b5dd9e1bc8476c2abb6 NeedsCompilation: yes Title: An R Package for Qualitative Biclustering in Support of Gene Co-Expression Analyses Description: The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape). biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization, GeneExpression, Network Author: Yu Zhang [aut, cre], Qin Ma [aut] Maintainer: Yu Zhang URL: https://github.com/zy26/QUBIC SystemRequirements: C++11, Rtools (>= 3.1) VignetteBuilder: knitr BugReports: https://github.com/zy26/QUBIC/issues git_url: https://git.bioconductor.org/packages/QUBIC git_branch: devel git_last_commit: 31cfc46 git_last_commit_date: 2026-04-12 Date/Publication: 2026-04-20 source.ver: src/contrib/QUBIC_1.39.2.tar.gz vignettes: vignettes/QUBIC/inst/doc/qubic_vignette.pdf vignetteTitles: QUBIC Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QUBIC/inst/doc/qubic_vignette.R importsMe: mosbi suggestsMe: runibic dependencyCount: 10 Package: qusage Version: 2.45.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, emmeans, fftw License: GPL (>= 2) MD5sum: f78460d806329c48f02eda5b0b7236cd NeedsCompilation: no Title: qusage: Quantitative Set Analysis for Gene Expression Description: This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. The QuSAGE package also includes a mixed effects model implementation, as described in (Turner JA et al, BMC Bioinformatics, 2015), and a meta-analysis framework as described in (Meng H, et al. PLoS Comput Biol. 2019). For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) biocViews: GeneSetEnrichment, Microarray, RNASeq, Software, ImmunoOncology Author: Christopher Bolen and Gur Yaari, with contributions from Juilee Thakar, Hailong Meng, Jacob Turner, Derek Blankenship, and Steven Kleinstein Maintainer: Christopher Bolen URL: http://clip.med.yale.edu/qusage git_url: https://git.bioconductor.org/packages/qusage git_branch: devel git_last_commit: 6db5ad6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/qusage_2.45.0.tar.gz vignettes: vignettes/qusage/inst/doc/qusage.pdf vignetteTitles: Running qusage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qusage/inst/doc/qusage.R importsMe: mExplorer suggestsMe: SigCheck dependencyCount: 19 Package: qvalue Version: 2.43.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL MD5sum: 51f12c499d42ad9d24ae88584732cfba NeedsCompilation: no Title: Q-value estimation for false discovery rate control Description: This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. biocViews: MultipleComparisons Author: John D. Storey [aut, cre], Andrew J. Bass [aut], Alan Dabney [aut], David Robinson [aut], Gregory Warnes [ctb] Maintainer: John D. Storey , Andrew J. Bass URL: http://github.com/jdstorey/qvalue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qvalue git_branch: devel git_last_commit: b7507f6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/qvalue_2.43.0.tar.gz vignettes: vignettes/qvalue/inst/doc/qvalue.pdf vignetteTitles: qvalue Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qvalue/inst/doc/qvalue.R dependsOnMe: anota, DEGseq, DrugVsDisease, r3Cseq, webbioc, BonEV, cp4p, ReAD, STAREG importsMe: Anaquin, anota, clusterProfiler, CTSV, DegCre, derfinder, edge, erccdashboard, EventPointer, FindIT2, fishpond, LimROTS, MetaProViz, metaseqR2, methylKit, MOMA, msmsTests, MWASTools, netresponse, normr, OPWeight, PAST, PolySTest, RiboDiPA, RNAsense, Rnits, RolDE, SDAMS, sights, signatureSearch, SpaceMarkers, subSeq, vsclust, webbioc, IHWpaper, AEenrich, cancerGI, fdrDiscreteNull, glmmSeq, groupedSurv, HDMT, isva, jaccard, medScan, NBPSeq, qch, SeqFeatR, sffdr, shinyExprPortal, ssizeRNA, TFactSR suggestsMe: biobroom, LBE, PREDA, RnBeads, swfdr, BootstrapQTL, dartR, dartR.base, dartR.popgen, DGEobj.utils, easylabel, enrichit, familiar, multiDEGGs, mutoss, readyomics, Rediscover, seqgendiff, volcano3D, wrMisc dependencyCount: 31 Package: r3Cseq Version: 1.57.0 Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue Imports: methods, Seqinfo, IRanges, Biostrings, data.table, sqldf, RColorBrewer Suggests: BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Rnorvegicus.UCSC.rn5.masked License: GPL-3 MD5sum: 9a18575ec928157a96de2598c08d9a80 NeedsCompilation: no Title: Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq) Description: This package is used for the analysis of long-range chromatin interactions from 3C-seq assay. biocViews: Preprocessing, Sequencing Author: Supat Thongjuea, MRC WIMM Centre for Computational Biology, Weatherall Institute of Molecular Medicine, University of Oxford, UK Maintainer: Supat Thongjuea or URL: http://r3cseq.genereg.net,https://github.com/supatt-lab/r3Cseq/ git_url: https://git.bioconductor.org/packages/r3Cseq git_branch: devel git_last_commit: b35813f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/r3Cseq_1.57.0.tar.gz vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf vignetteTitles: r3Cseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/r3Cseq/inst/doc/r3Cseq.R dependencyCount: 95 Package: R453Plus1Toolbox Version: 1.61.1 Depends: R (>= 2.12.0), methods, VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), pwalign, Biobase Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML, TeachingDemos, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector, GenomicRanges (>= 1.31.8), SummarizedExperiment, biomaRt, BSgenome (>= 1.47.3), Rsamtools, ShortRead (>= 1.37.1) Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 MD5sum: 0620687d3b8121860830ec9ab51d7cad NeedsCompilation: yes Title: A package for importing and analyzing data from Roche's Genome Sequencer System Description: The R453Plus1 Toolbox comprises useful functions for the analysis of data generated by Roche's 454 sequencing platform. It adds functions for quality assurance as well as for annotation and visualization of detected variants, complementing the software tools shipped by Roche with their product. Further, a pipeline for the detection of structural variants is provided. biocViews: Sequencing, Infrastructure, DataImport, DataRepresentation, Visualization, QualityControl, ReportWriting Author: Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/R453Plus1Toolbox git_branch: devel git_last_commit: 8022a29 git_last_commit_date: 2026-04-05 Date/Publication: 2026-04-20 source.ver: src/contrib/R453Plus1Toolbox_1.61.1.tar.gz vignettes: vignettes/R453Plus1Toolbox/inst/doc/vignette.pdf vignetteTitles: A package for importing and analyzing data from Roche's Genome Sequencer System hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R453Plus1Toolbox/inst/doc/vignette.R dependencyCount: 112 Package: R4RNA Version: 1.39.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 MD5sum: 05fe10d7c91e7a39bb7ac02465451bc6 NeedsCompilation: no Title: An R package for RNA visualization and analysis Description: A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each. biocViews: Alignment, MultipleSequenceAlignment, Preprocessing, Visualization, DataImport, DataRepresentation, MultipleComparison Author: Daniel Lai, Irmtraud Meyer Maintainer: Daniel Lai URL: http://www.e-rna.org/r-chie/ git_url: https://git.bioconductor.org/packages/R4RNA git_branch: devel git_last_commit: b949aed git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/R4RNA_1.39.0.tar.gz vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R importsMe: ggmsa, rnaCrosslinkOO suggestsMe: rfaRm dependencyCount: 15 Package: RadioGx Version: 2.15.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, BiocGenerics, data.table, S4Vectors, Biobase, parallel, BiocParallel, RColorBrewer, caTools, magicaxis, methods, reshape2, scales, grDevices, graphics, stats, utils, assertthat, matrixStats, downloader Suggests: rmarkdown, BiocStyle, knitr, pander, markdown License: GPL-3 MD5sum: f0ae494a8cbb93a2da8fd3e1cf0595b2 NeedsCompilation: no Title: Analysis of Large-Scale Radio-Genomic Data Description: Computational tool box for radio-genomic analysis which integrates radio-response data, radio-biological modelling and comprehensive cell line annotations for hundreds of cancer cell lines. The 'RadioSet' class enables creation and manipulation of standardized datasets including information about cancer cells lines, radio-response assays and dose-response indicators. Included methods allow fitting and plotting dose-response data using established radio-biological models along with quality control to validate results. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, references, as well as: Manem, V. et al (2018) . biocViews: Software, Pharmacogenetics, QualityControl, Survival, Pharmacogenomics, Classification Author: Venkata Manem [aut], Petr Smirnov [aut], Ian Smith [aut], Meghan Lambie [aut], Christopher Eeles [aut], Scott Bratman [aut], Jermiah Joseph [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RadioGx git_branch: devel git_last_commit: 3cfa0fd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RadioGx_2.15.0.tar.gz vignettes: vignettes/RadioGx/inst/doc/RadioGx.html vignetteTitles: RadioGx: An R Package for Analysis of Large Radiogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RadioGx/inst/doc/RadioGx.R dependencyCount: 142 Package: raer Version: 1.9.0 Imports: stats, methods, GenomicRanges, IRanges, Rsamtools, BSgenome, Biostrings, SummarizedExperiment, SingleCellExperiment, S4Vectors, Seqinfo, GenomeInfoDb, GenomicAlignments, GenomicFeatures, BiocGenerics, BiocParallel, rtracklayer, Matrix, cli LinkingTo: Rhtslib Suggests: testthat (>= 3.0.0), knitr, DESeq2, edgeR, limma, rmarkdown, BiocStyle, ComplexHeatmap, TxDb.Hsapiens.UCSC.hg38.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, BSgenome.Hsapiens.NCBI.GRCh38, scater, scran, scuttle, AnnotationHub, covr, raerdata, txdbmaker License: MIT + file LICENSE MD5sum: 3af91df48250aee384947e3fe356ce5a NeedsCompilation: yes Title: RNA editing tools in R Description: Toolkit for identification and statistical testing of RNA editing signals from within R. Provides support for identifying sites from bulk-RNA and single cell RNA-seq datasets, and general methods for extraction of allelic read counts from alignment files. Facilitates annotation and exploratory analysis of editing signals using Bioconductor packages and resources. biocViews: MultipleComparison, RNASeq, SingleCell, Sequencing, Coverage, Epitranscriptomics, FeatureExtraction, Annotation, Alignment Author: Kent Riemondy [aut, cre] (ORCID: ), Kristen Wells-Wrasman [aut] (ORCID: ), Ryan Sheridan [ctb] (ORCID: ), Jay Hesselberth [ctb] (ORCID: ), RNA Bioscience Initiative [cph, fnd] Maintainer: Kent Riemondy URL: https://rnabioco.github.io/raer, https://github.com/rnabioco/raer SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/rnabioco/raer/issues git_url: https://git.bioconductor.org/packages/raer git_branch: devel git_last_commit: 94ed43d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/raer_1.9.0.tar.gz vignettes: vignettes/raer/inst/doc/raer.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/raer/inst/doc/raer.R dependencyCount: 79 Package: RaggedExperiment Version: 1.35.0 Depends: R (>= 4.5.0), GenomicRanges (>= 1.61.1) Imports: BiocBaseUtils, BiocGenerics, Seqinfo, IRanges, Matrix, MatrixGenerics, methods, S4Vectors, stats, SummarizedExperiment (>= 1.39.1), utils Suggests: BiocStyle, knitr, rmarkdown, testthat, GenomeInfoDb, MultiAssayExperiment License: Artistic-2.0 MD5sum: 083531ce79a605babec02a59c298ff36 NeedsCompilation: no Title: Representation of Sparse Experiments and Assays Across Samples Description: This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset. biocViews: Infrastructure, DataRepresentation Author: Martin Morgan [aut], Marcel Ramos [aut, cre] (ORCID: ), Lydia King [ctb] Maintainer: Marcel Ramos URL: https://bioconductor.github.io/RaggedExperiment, https://bioconductor.org/packages/RaggedExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/RaggedExperiment/issues git_url: https://git.bioconductor.org/packages/RaggedExperiment git_branch: devel git_last_commit: efd60e0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RaggedExperiment_1.35.0.tar.gz vignettes: vignettes/RaggedExperiment/inst/doc/ASCAT_to_RaggedExperiment.html, vignettes/RaggedExperiment/inst/doc/RaggedExperiment.html vignetteTitles: ASCAT to RaggedExperiment, RaggedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RaggedExperiment/inst/doc/ASCAT_to_RaggedExperiment.R, vignettes/RaggedExperiment/inst/doc/RaggedExperiment.R dependsOnMe: CNVRanger, curatedPCaData importsMe: cBioPortalData, omicsPrint, RTCGAToolbox, TCGAutils, terraTCGAdata suggestsMe: maftools, MultiAssayExperiment, MultiDataSet, TENxIO, curatedTCGAData, SingleCellMultiModal dependencyCount: 26 Package: RAIDS Version: 1.9.0 Depends: R (>= 4.2.0), gdsfmt, SNPRelate, stats, utils, GENESIS, dplyr, Rsamtools Imports: S4Vectors, GenomicRanges, ensembldb, BSgenome, AnnotationDbi, methods, class, pROC, IRanges, AnnotationFilter, rlang, VariantAnnotation, MatrixGenerics, ggplot2, stringr Suggests: testthat, knitr, rmarkdown, BiocStyle, withr, Seqinfo, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: Apache License (>= 2) MD5sum: 9321d94f270a040e36367b3d238c68d3 NeedsCompilation: no Title: Robust Ancestry Inference using Data Synthesis Description: This package implements specialized algorithms that enable genetic ancestry inference from various cancer sequences sources (RNA, Exome and Whole-Genome sequences). This package also implements a simulation algorithm that generates synthetic cancer-derived data. This code and analysis pipeline was designed and developed for the following publication: Belleau, P et al. Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms. Cancer Res 1 January 2023; 83 (1): 49–58. biocViews: Genetics, Software, Sequencing, WholeGenome, PrincipalComponent, GeneticVariability, DimensionReduction, BiocViews Author: Pascal Belleau [cre, aut] (ORCID: ), Astrid Deschênes [aut] (ORCID: ), David A. Tuveson [aut] (ORCID: ), Alexander Krasnitz [aut] Maintainer: Pascal Belleau URL: https://krasnitzlab.github.io/RAIDS/ VignetteBuilder: knitr BugReports: https://github.com/KrasnitzLab/RAIDS/issues git_url: https://git.bioconductor.org/packages/RAIDS git_branch: devel git_last_commit: 27cee94 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RAIDS_1.9.0.tar.gz vignettes: vignettes/RAIDS/inst/doc/Create_Reference_GDS_File.html, vignettes/RAIDS/inst/doc/RAIDS.html, vignettes/RAIDS/inst/doc/Wrappers.html vignetteTitles: Population reference dataset GDS files, Robust Ancestry Inference using Data Synthesis, Using wrappper functionss hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RAIDS/inst/doc/Create_Reference_GDS_File.R, vignettes/RAIDS/inst/doc/RAIDS.R, vignettes/RAIDS/inst/doc/Wrappers.R dependencyCount: 167 Package: rain Version: 1.45.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: a9de94c930508455677b59dbe194c087 NeedsCompilation: no Title: Rhythmicity Analysis Incorporating Non-parametric Methods Description: This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements. biocViews: TimeCourse, Genetics, SystemsBiology, Proteomics, Microarray, MultipleComparison Author: Paul F. Thaben, Pål O. Westermark Maintainer: Paul F. Thaben git_url: https://git.bioconductor.org/packages/rain git_branch: devel git_last_commit: 5ed9a65 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rain_1.45.0.tar.gz vignettes: vignettes/rain/inst/doc/rain.pdf vignetteTitles: Rain Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rain/inst/doc/rain.R dependencyCount: 17 Package: ramr Version: 1.19.2 Depends: R (>= 4.1) Imports: methods, data.table, Seqinfo, GenomicRanges, IRanges, BiocGenerics, S4Vectors, Rcpp LinkingTo: Rcpp Suggests: RUnit, knitr, rmarkdown, ggplot2, gridExtra, annotatr, LOLA, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, parallel, doParallel, foreach, doRNG, matrixStats, EnvStats, ExtDist, gamlss, gamlss.dist License: Artistic-2.0 MD5sum: 505c76bcfa8830c28e197b810eb8af41 NeedsCompilation: yes Title: Detection of Rare Aberrantly Methylated Regions in Array and NGS Data Description: ramr is an R package for detection of epimutations (i.e., infrequent aberrant DNA methylation events) in large data sets obtained by methylation profiling using array or high-throughput methylation sequencing. In addition, package provides functions to visualize found aberrantly methylated regions (AMRs), to generate sets of all possible regions to be used as reference sets for enrichment analysis, and to generate biologically relevant test data sets for performance evaluation of AMR/DMR search algorithms. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, MethylationArray, MethylSeq Author: Oleksii Nikolaienko [aut, cre] (ORCID: ) Maintainer: Oleksii Nikolaienko URL: https://github.com/BBCG/ramr SystemRequirements: C++20, GNU make VignetteBuilder: knitr BugReports: https://github.com/BBCG/ramr/issues git_url: https://git.bioconductor.org/packages/ramr git_branch: devel git_last_commit: 4cf1f7d git_last_commit_date: 2026-02-26 Date/Publication: 2026-04-20 source.ver: src/contrib/ramr_1.19.2.tar.gz vignettes: vignettes/ramr/inst/doc/ramr.html vignetteTitles: ramr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramr/inst/doc/ramr.R dependencyCount: 13 Package: ramwas Version: 1.35.0 Depends: R (>= 3.3.0), methods, filematrix Imports: graphics, stats, utils, digest, glmnet, KernSmooth, grDevices, GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings, BiocGenerics Suggests: knitr, rmarkdown, pander, BiocStyle, BSgenome.Ecoli.NCBI.20080805 License: LGPL-3 MD5sum: 044f1d1225ca67bb3616d51dbf5854a1 NeedsCompilation: yes Title: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms Description: A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) . biocViews: DNAMethylation, Sequencing, QualityControl, Coverage, Preprocessing, Normalization, BatchEffect, PrincipalComponent, DifferentialMethylation, Visualization Author: Andrey A Shabalin [aut, cre] (ORCID: ), Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut] Maintainer: Andrey A Shabalin URL: https://bioconductor.org/packages/ramwas/ VignetteBuilder: knitr BugReports: https://github.com/andreyshabalin/ramwas/issues git_url: https://git.bioconductor.org/packages/ramwas git_branch: devel git_last_commit: 4c78862 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ramwas_1.35.0.tar.gz vignettes: vignettes/ramwas/inst/doc/RW1_intro.html, vignettes/ramwas/inst/doc/RW2_CpG_sets.html, vignettes/ramwas/inst/doc/RW3_BAM_QCs.html, vignettes/ramwas/inst/doc/RW4_SNPs.html, vignettes/ramwas/inst/doc/RW5a_matrix.html, vignettes/ramwas/inst/doc/RW5c_matrix.html, vignettes/ramwas/inst/doc/RW6_param.html vignetteTitles: 1. Overview, 2. CpG sets, 3. BAM Quality Control Measures, 4. Joint Analysis of Methylation and Genotype Data, 5.a. Analyzing Illumina Methylation Array Data, 5.c. Analyzing data from other sources, 6. RaMWAS parameters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramwas/inst/doc/RW1_intro.R, vignettes/ramwas/inst/doc/RW2_CpG_sets.R, vignettes/ramwas/inst/doc/RW3_BAM_QCs.R, vignettes/ramwas/inst/doc/RW4_SNPs.R, vignettes/ramwas/inst/doc/RW5a_matrix.R, vignettes/ramwas/inst/doc/RW5c_matrix.R, vignettes/ramwas/inst/doc/RW6_param.R dependencyCount: 99 Package: randPack Version: 1.57.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: 9d012e18d085d4af39815f2de4f616ae NeedsCompilation: no Title: Randomization routines for Clinical Trials Description: A suite of classes and functions for randomizing patients in clinical trials. biocViews: StatisticalMethod Author: Vincent Carey and Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/randPack git_branch: devel git_last_commit: ec45457 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/randPack_1.57.0.tar.gz vignettes: vignettes/randPack/inst/doc/randPack.pdf vignetteTitles: Clinical trial randomization infrastructure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randPack/inst/doc/randPack.R dependencyCount: 7 Package: RankMap Version: 0.99.1 Depends: R (>= 4.6.0) Imports: dplyr, glmnet, graphics, magrittr, Matrix, matrixStats, rlang, Seurat, stats, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, SingleCellExperiment, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: 9484cf291ec1d273ba179d5ae1147c26 NeedsCompilation: no Title: Rank-based reference mapping for fast and robust cell type annotation in spatial and single-cell transcriptomics Description: RankMap is a fast and scalable tool for reference-based cell type annotation of single-cell and spatial transcriptomics data. It uses ranked gene expression and multinomial regression to achieve robust predictions, even with partial gene coverage. Compatible with Seurat, SingleCellExperiment, and SpatialExperiment objects, RankMap offers flexible preprocessing and significantly faster runtime than tools like SingleR, Azimuth, and RCTD. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Annotation, Regression, Preprocessing, Software Author: Jinming Cheng [aut, cre] (ORCID: ) Maintainer: Jinming Cheng URL: https://github.com/jinming-cheng/RankMap VignetteBuilder: knitr BugReports: https://github.com/jinming-cheng/RankMap/issues git_url: https://git.bioconductor.org/packages/RankMap git_branch: devel git_last_commit: 2d05ccc git_last_commit_date: 2026-04-07 Date/Publication: 2026-04-20 source.ver: src/contrib/RankMap_0.99.1.tar.gz vignettes: vignettes/RankMap/inst/doc/RankMap.html vignetteTitles: Getting Started with RankMap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RankMap/inst/doc/RankMap.R dependencyCount: 168 Package: RankProd Version: 3.37.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: 439fa5a41c4a3e112af4bbb44baacb58 NeedsCompilation: no Title: Rank Product method for identifying differentially expressed genes with application in meta-analysis Description: Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. biocViews: DifferentialExpression, StatisticalMethod, Software, ResearchField, Metabolomics, Lipidomics, Proteomics, SystemsBiology, GeneExpression, Microarray, GeneSignaling Author: Francesco Del Carratore , Andris Jankevics Fangxin Hong , Ben Wittner , Rainer Breitling , and Florian Battke Maintainer: Francesco Del Carratore git_url: https://git.bioconductor.org/packages/RankProd git_branch: devel git_last_commit: 78f7615 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RankProd_3.37.0.tar.gz vignettes: vignettes/RankProd/inst/doc/RankProd.pdf vignetteTitles: RankProd Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RankProd/inst/doc/RankProd.R dependsOnMe: tRanslatome importsMe: mslp, POMA, synlet suggestsMe: pepdiff dependencyCount: 6 Package: RAREsim Version: 1.15.0 Depends: R (>= 4.1.0) Imports: nloptr Suggests: markdown, ggplot2, BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: cc056edfe040961f46f55bf2df0eb57a NeedsCompilation: no Title: Simulation of Rare Variant Genetic Data Description: Haplotype simulations of rare variant genetic data that emulates real data can be performed with RAREsim. RAREsim uses the expected number of variants in MAC bins - either as provided by default parameters or estimated from target data - and an abundance of rare variants as simulated HAPGEN2 to probabilistically prune variants. RAREsim produces haplotypes that emulate real sequencing data with respect to the total number of variants, allele frequency spectrum, haplotype structure, and variant annotation. biocViews: Genetics, Software, VariantAnnotation, Sequencing Author: Megan Null [aut], Ryan Barnard [cre] Maintainer: Ryan Barnard URL: https://github.com/meganmichelle/RAREsim VignetteBuilder: knitr BugReports: https://github.com/meganmichelle/RAREsim/issues git_url: https://git.bioconductor.org/packages/RAREsim git_branch: devel git_last_commit: 77919c8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RAREsim_1.15.0.tar.gz vignettes: vignettes/RAREsim/inst/doc/RAREsim_Vignette.html vignetteTitles: RAREsim Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RAREsim/inst/doc/RAREsim_Vignette.R dependencyCount: 1 Package: Rarr Version: 1.11.40 Depends: R (>= 4.1.0) Imports: curl, jsonlite, lifecycle, paws.storage, R.utils, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), withr, ZarrArray License: MIT + file LICENSE MD5sum: f0eabfe1a0ca1d98d307a11cea08a887 NeedsCompilation: yes Title: Read Zarr Files in R Description: The Zarr specification defines a format for chunked, compressed, N-dimensional arrays. It's design allows efficient access to subsets of the stored array, and supports both local and cloud storage systems. Rarr aims to implement this specification in R with minimal reliance on an external tools or libraries. biocViews: DataImport Author: Mike Smith [aut, ccp] (ORCID: , Maintainer from 2022 to 2025.), Hugo Gruson [aut, cre] (ORCID: ), Artür Manukyan [ctb], Sharla Gelfand [ctb], German Network for Bioinformatics Infrastructure - de.NBI [fnd] Maintainer: Hugo Gruson URL: https://huber-group-embl.github.io/Rarr/, https://github.com/Huber-group-EMBL/Rarr SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/Rarr/issues git_url: https://git.bioconductor.org/packages/Rarr git_branch: devel git_last_commit: def970d git_last_commit_date: 2026-04-20 Date/Publication: 2026-04-20 source.ver: src/contrib/Rarr_1.11.40.tar.gz vignettes: vignettes/Rarr/inst/doc/design.html, vignettes/Rarr/inst/doc/features.html, vignettes/Rarr/inst/doc/Rarr.html vignetteTitles: Design principles for the Rarr package, "Supported Zarr features in Rarr", "Working with Zarr arrays in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rarr/inst/doc/features.R, vignettes/Rarr/inst/doc/Rarr.R importsMe: ZarrArray dependencyCount: 30 Package: rawDiag Version: 1.7.0 Depends: R (>= 4.4) Imports: dplyr, ggplot2 (>= 3.4), grDevices, hexbin, htmltools, BiocManager, BiocParallel, rawrr (>= 1.15.5), rlang, reshape2, scales, shiny (>= 1.5), stats, utils Suggests: BiocStyle (>= 2.28), ExperimentHub, tartare, knitr, testthat License: GPL-3 MD5sum: d4b51cc2d8622c31e7da114c5be6e8ec NeedsCompilation: no Title: Brings Orbitrap Mass Spectrometry Data to Life; Fast and Colorful Description: Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) poses a nontrivial challenge. The rawDiag package facilitates rational method optimization by generating MS operator-tailored diagnostic plots of scan-level metadata. The package is designed for use on the R shell or as a Shiny application on the Orbitrap instrument PC. biocViews: MassSpectrometry, Proteomics, Metabolomics, Infrastructure, Software, ShinyApps Author: Christian Panse [aut, cre] (ORCID: ), Christian Trachsel [aut], Tobias Kockmann [aut] Maintainer: Christian Panse URL: https://github.com/fgcz/rawDiag/ VignetteBuilder: knitr BugReports: https://github.com/fgcz/rawDiag/issues git_url: https://git.bioconductor.org/packages/rawDiag git_branch: devel git_last_commit: 92fcac9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rawDiag_1.7.0.tar.gz vignettes: vignettes/rawDiag/inst/doc/rawDiag.html vignetteTitles: Brings Orbitrap Mass Spectrometry Data to Life; Fast and Colorful hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rawDiag/inst/doc/rawDiag.R dependencyCount: 72 Package: rawrr Version: 1.19.0 Depends: R (>= 4.5) Imports: grDevices, graphics, stats, utils Suggests: BiocStyle (>= 2.5), ExperimentHub, knitr, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 MD5sum: 89a8edfee5d4ec294f0a78163a6038e9 NeedsCompilation: no Title: Direct Access to Orbitrap Data and Beyond Description: This package wraps the functionality of the Thermo Fisher Scientic RawFileReader .NET 8.0 assembly. Within the R environment, spectra and chromatograms are represented by S3 objects. The package provides basic functions to download and install the required third-party libraries. The package is developed, tested, and used at the Functional Genomics Center Zurich, Switzerland. biocViews: MassSpectrometry, Proteomics, Metabolomics, Infrastructure, Software Author: Christian Panse [aut, cre] (ORCID: ), Leonardo Schwarz [ctb] (ORCID: ), Tobias Kockmann [aut] (ORCID: ) Maintainer: Christian Panse URL: https://github.com/fgcz/rawrr/ SystemRequirements: .NET 8.0 (optional; required only if you want to compile and link the C# code) VignetteBuilder: knitr BugReports: https://github.com/fgcz/rawrr/issues git_url: https://git.bioconductor.org/packages/rawrr git_branch: devel git_last_commit: 465d5a7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rawrr_1.19.0.tar.gz vignettes: vignettes/rawrr/inst/doc/rawrr.html vignetteTitles: Direct Access to Orbitrap Data and Beyond hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rawrr/inst/doc/rawrr.R importsMe: MsBackendRawFileReader, rawDiag suggestsMe: lcmsPlot dependencyCount: 4 Package: RbcBook1 Version: 1.79.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: 790b6f08bd40c560961c4dce722a9c15 NeedsCompilation: no Title: Support for Springer monograph on Bioconductor Description: tools for building book biocViews: Software Author: Vince Carey and Wolfgang Huber Maintainer: Vince Carey URL: http://www.biostat.harvard.edu/~carey git_url: https://git.bioconductor.org/packages/RbcBook1 git_branch: devel git_last_commit: f5a4884 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RbcBook1_1.79.0.tar.gz vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf vignetteTitles: RbcBook1 Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RbcBook1/inst/doc/RbcBook1.R dependencyCount: 11 Package: Rbec Version: 1.19.0 Imports: Rcpp (>= 1.0.6), dada2, ggplot2, readr, doParallel, foreach, grDevices, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: 47ee6bbbfdff20fe1de49e2d63ce4e9c NeedsCompilation: yes Title: Rbec: a tool for analysis of amplicon sequencing data from synthetic microbial communities Description: Rbec is a adapted version of DADA2 for analyzing amplicon sequencing data from synthetic communities (SynComs), where the reference sequences for each strain exists. Rbec can not only accurately profile the microbial compositions in SynComs, but also predict the contaminants in SynCom samples. biocViews: Sequencing, MicrobialStrain, Microbiome Author: Pengfan Zhang [aut, cre] Maintainer: Pengfan Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbec git_branch: devel git_last_commit: 89d9a5d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Rbec_1.19.0.tar.gz vignettes: vignettes/Rbec/inst/doc/Rbec.html vignetteTitles: Rbec hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbec/inst/doc/Rbec.R dependencyCount: 93 Package: RBGL Version: 1.87.0 Depends: graph, methods Imports: methods LinkingTo: BH Suggests: Rgraphviz, XML, RUnit, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 MD5sum: 3ffaa08e502adc3b8040a306e12adc64 NeedsCompilation: yes Title: An interface to the BOOST graph library Description: A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. biocViews: GraphAndNetwork, Network Author: Vince Carey [aut], Li Long [aut], R. Gentleman [aut], Emmanuel Taiwo [ctb] (Converted RBGL vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RBGL git_branch: devel git_last_commit: 9b0a331 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RBGL_1.87.0.tar.gz vignettes: vignettes/RBGL/inst/doc/RBGL.html vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBGL/inst/doc/RBGL.R dependsOnMe: apComplex, BioNet, CellNOptR, fgga, PerfMeas importsMe: BiocPkgTools, biocViews, CAMERA, Category, ChIPpeakAnno, CHRONOS, CytoML, DEGraph, DEsubs, EventPointer, flowWorkspace, GenomicInteractionNodes, GOstats, NCIgraph, ontoProc, openCyto, OrganismDbi, VariantFiltering, BiDAG, clustNet, eff2, micd, pcalg, rags2ridges, RANKS, SEMgraph, SID suggestsMe: DEGraph, G4SNVHunter, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools, yeastExpData, archeofrag, maGUI dependencyCount: 9 Package: RBioFormats Version: 1.11.0 Imports: EBImage, methods, rJava (>= 0.9-6), S4Vectors, stats Suggests: BiocStyle, knitr, testthat, xml2 License: GPL-3 MD5sum: e54caeefeee3b011cfe948077e009f36 NeedsCompilation: no Title: R interface to Bio-Formats Description: An R package which interfaces the OME Bio-Formats Java library to allow reading of proprietary microscopy image data and metadata. biocViews: DataImport Author: Andrzej Oleś [aut, cre] (ORCID: ), John Lee [ctb] (ORCID: ) Maintainer: Andrzej Oleś URL: https://github.com/aoles/RBioFormats SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr BugReports: https://github.com/aoles/RBioFormats/issues git_url: https://git.bioconductor.org/packages/RBioFormats git_branch: devel git_last_commit: fa3ee7d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RBioFormats_1.11.0.tar.gz vignettes: vignettes/RBioFormats/inst/doc/RBioFormats.html vignetteTitles: RBioFormats: an R interface to the Bio-Formats library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBioFormats/inst/doc/RBioFormats.R importsMe: alabaster.sfe, islify, SpatialOmicsOverlay suggestsMe: SpatialFeatureExperiment, Voyager dependencyCount: 48 Package: rBiopaxParser Version: 2.51.0 Depends: R (>= 4.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, RBGL, igraph License: GPL (>= 2) MD5sum: dd3486f2e7d63773507814c0711bd066 NeedsCompilation: no Title: Parses BioPax files and represents them in R Description: Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported. biocViews: DataRepresentation Author: Frank Kramer Maintainer: Frank Kramer URL: https://github.com/frankkramer-lab/rBiopaxParser git_url: https://git.bioconductor.org/packages/rBiopaxParser git_branch: devel git_last_commit: aed165a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rBiopaxParser_2.51.0.tar.gz vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf vignetteTitles: rBiopaxParser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.R suggestsMe: AnnotationHub, NetPathMiner dependencyCount: 4 Package: rBLAST Version: 1.7.0 Depends: Biostrings (>= 2.26.2) Imports: methods, utils, BiocFileCache Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: a55b466631f1199f4227eabfa536b028 NeedsCompilation: no Title: R Interface for the Basic Local Alignment Search Tool Description: Seamlessly interfaces the Basic Local Alignment Search Tool (BLAST) running locally to search genetic sequence data bases. This work was partially supported by grant no. R21HG005912 from the National Human Genome Research Institute. biocViews: Genetics, Sequencing, SequenceMatching, Alignment, DataImport Author: Michael Hahsler [aut, cre] (ORCID: ), Nagar Anurag [aut] Maintainer: Michael Hahsler URL: https://github.com/mhahsler/rBLAST SystemRequirements: ncbi-blast+ VignetteBuilder: knitr BugReports: https://github.com/mhahsler/rBLAST/issues git_url: https://git.bioconductor.org/packages/rBLAST git_branch: devel git_last_commit: dcc0f85 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rBLAST_1.7.0.tar.gz vignettes: vignettes/rBLAST/inst/doc/blast.html vignetteTitles: rBLAST: R Interface for the Basic Local Alignment Search Tool hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rBLAST/inst/doc/blast.R importsMe: ClustIRR dependencyCount: 51 Package: RBM Version: 1.43.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) MD5sum: 97bbb54a9715700c06c9b721eb9ae888 NeedsCompilation: no Title: RBM: a R package for microarray and RNA-Seq data analysis Description: Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets. biocViews: Microarray, DifferentialExpression Author: Dongmei Li and Chin-Yuan Liang Maintainer: Dongmei Li git_url: https://git.bioconductor.org/packages/RBM git_branch: devel git_last_commit: f326c20 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RBM_1.43.0.tar.gz vignettes: vignettes/RBM/inst/doc/RBM.pdf vignetteTitles: RBM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBM/inst/doc/RBM.R dependencyCount: 8 Package: Rbowtie Version: 1.51.0 Imports: utils Suggests: testthat, parallel, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE Archs: x64 MD5sum: 15597cfc48a52370a463802a417a7745 NeedsCompilation: yes Title: R bowtie wrapper Description: This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly. biocViews: Sequencing, Alignment Author: Florian Hahne [aut], Anita Lerch [aut], Michael Stadler [aut, cre] (ORCID: ) Maintainer: Michael Stadler URL: https://github.com/fmicompbio/Rbowtie SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rbowtie/issues git_url: https://git.bioconductor.org/packages/Rbowtie git_branch: devel git_last_commit: 3dba45f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Rbowtie_1.51.0.tar.gz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.html vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR importsMe: crisprBowtie, multicrispr, seqpac suggestsMe: crisprDesign, eisaR dependencyCount: 1 Package: Rbowtie2 Version: 2.17.0 Depends: R (>= 4.1.0) Imports: magrittr, Rsamtools Suggests: knitr, testthat (>= 3.0.0), rmarkdown License: GPL (>= 3) Archs: x64 MD5sum: 3d8d8734ae0a9211bf4c49c54fe03438 NeedsCompilation: yes Title: An R Wrapper for Bowtie2 and AdapterRemoval Description: This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. The package contains wrapper functions that allow for genome indexing and alignment to those indexes. The package also allows for the creation of .bam files via Rsamtools. biocViews: Sequencing, Alignment, Preprocessing Author: Zheng Wei [aut, cre], Wei Zhang [aut] Maintainer: Zheng Wei SystemRequirements: C++11, GNU make, samtools VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbowtie2 git_branch: devel git_last_commit: 037bb32 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Rbowtie2_2.17.0.tar.gz vignettes: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.html vignetteTitles: An Introduction to Rbowtie2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.R importsMe: CircSeqAlignTk, esATAC, UMI4Cats, MetaScope dependencyCount: 30 Package: rbsurv Version: 2.69.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: 40c4cce985b80c8d7520a81668f1a3ca NeedsCompilation: no Title: Robust likelihood-based survival modeling with microarray data Description: This package selects genes associated with survival. biocViews: Microarray Author: HyungJun Cho , Sukwoo Kim , Soo-heang Eo , Jaewoo Kang Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/rbsurv git_branch: devel git_last_commit: c362232 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rbsurv_2.69.0.tar.gz vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rbsurv/inst/doc/rbsurv.R dependencyCount: 13 Package: Rbwa Version: 1.15.4 Depends: R (>= 4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE OS_type: unix MD5sum: e18573ae5ac14255a4ccd6790c475738 NeedsCompilation: yes Title: R wrapper for BWA-backtrack and BWA-MEM aligners Description: Provides an R wrapper for BWA alignment algorithms. Both BWA-backtrack and BWA-MEM are available. Convenience function to build a BWA index from a reference genome is also provided. Currently not supported for Windows machines. biocViews: Sequencing, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/Rbwa SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/Rbwa/issues git_url: https://git.bioconductor.org/packages/Rbwa git_branch: devel git_last_commit: 5cbabaa git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/Rbwa_1.15.4.tar.gz vignettes: vignettes/Rbwa/inst/doc/Rbwa.html vignetteTitles: An introduction to Rbwa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbwa/inst/doc/Rbwa.R importsMe: crisprBwa suggestsMe: crisprDesign dependencyCount: 0 Package: RCASPAR Version: 1.57.0 License: GPL (>=3) MD5sum: dc7d336f943f671915f76d50e731c117 NeedsCompilation: no Title: A package for survival time prediction based on a piecewise baseline hazard Cox regression model. Description: The package is the R-version of the C-based software \bold{CASPAR} (Kaderali,2006: \url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine. biocViews: aCGH, GeneExpression, Genetics, Proteomics, Visualization Author: Douaa Mugahid, Lars Kaderali Maintainer: Douaa Mugahid , Lars Kaderali git_url: https://git.bioconductor.org/packages/RCASPAR git_branch: devel git_last_commit: ffaea51 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RCASPAR_1.57.0.tar.gz vignettes: vignettes/RCASPAR/inst/doc/RCASPAR.pdf vignetteTitles: RCASPAR: Software for high-dimentional-data driven survival time prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCASPAR/inst/doc/RCASPAR.R dependencyCount: 0 Package: rcellminer Version: 2.33.0 Depends: R (>= 3.2), Biobase, rcellminerData (>= 2.0.0) Imports: stringr, gplots, ggplot2, methods, stats, utils, shiny Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat, BiocStyle, jsonlite, heatmaply, glmnet, foreach, doSNOW, parallel, rmarkdown License: LGPL-3 + file LICENSE MD5sum: 0b700402a1d520a80dde92f7d634df7f NeedsCompilation: no Title: rcellminer: Molecular Profiles, Drug Response, and Chemical Structures for the NCI-60 Cell Lines Description: The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/ cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data. biocViews: aCGH, CellBasedAssays, CopyNumberVariation, GeneExpression, Pharmacogenomics, Pharmacogenetics, miRNA, Cheminformatics, Visualization, Software, SystemsBiology Author: Augustin Luna, Vinodh Rajapakse, Fabricio Sousa Maintainer: Augustin Luna , Vinodh Rajapakse , Fathi Elloumi URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rcellminer git_branch: devel git_last_commit: 271ecd1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rcellminer_2.33.0.tar.gz vignettes: vignettes/rcellminer/inst/doc/rcellminerUsage.html vignetteTitles: Using rcellminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R suggestsMe: rcellminerData dependencyCount: 59 Package: rCGH Version: 1.41.0 Depends: R (>= 3.4),methods,stats,utils,graphics Imports: plyr,DNAcopy,lattice,ggplot2,grid,shiny (>= 0.11.1), limma,affy,mclust,TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db,GenomicFeatures,Seqinfo,GenomicRanges,AnnotationDbi, parallel,IRanges,grDevices,aCGH Suggests: BiocStyle, knitr, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: a33c9a8c091b153c8a86cc18820e449f NeedsCompilation: no Title: Comprehensive Pipeline for Analyzing and Visualizing Array-Based CGH Data Description: A comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through commercial or custom aCGH arrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, Affymetrix SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt files exported from ChAS or Affymetrix Power Tools. rCGH also supports custom arrays, provided data complies with the expected format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to profiles segmentation and gene annotations. This package also provides several visualization functions (static or interactive) which facilitate individual profiles interpretation. Input files can be in compressed format, e.g. .bz2 or .gz. biocViews: aCGH,CopyNumberVariation,Preprocessing,FeatureExtraction Author: Frederic Commo [aut, cre] Maintainer: Frederic Commo URL: https://github.com/fredcommo/rCGH VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rCGH git_branch: devel git_last_commit: 1f54d5a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rCGH_1.41.0.tar.gz vignettes: vignettes/rCGH/inst/doc/rCGH.pdf vignetteTitles: using rCGH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rCGH/inst/doc/rCGH.R importsMe: preciseTAD dependencyCount: 123 Package: RCM Version: 1.27.0 Depends: R (>= 4.5.0) Imports: RColorBrewer, alabama, edgeR, reshape2, tseries, stats, VGAM, ggplot2 (>= 2.2.1.9000), nleqslv, phyloseq, tensor, MASS, grDevices, graphics, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 86cc70782548b4771ce8da316dbe2d2c NeedsCompilation: no Title: Fit row-column association models with the negative binomial distribution for the microbiome Description: Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. In addition, diagnostic plot to detect lack of fit are available. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel URL: https://bioconductor.org/packages/release/bioc/vignettes/RCM/inst/doc/RCMvignette.html/ VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/RCM/issues git_url: https://git.bioconductor.org/packages/RCM git_branch: devel git_last_commit: 3b6b6a8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RCM_1.27.0.tar.gz vignettes: vignettes/RCM/inst/doc/RCMvignette.html vignetteTitles: Manual for the RCM pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCM/inst/doc/RCMvignette.R dependencyCount: 83 Package: Rcollectl Version: 1.11.0 Imports: utils, ggplot2, lubridate, processx Suggests: knitr, BiocStyle, knitcitations, sessioninfo, rmarkdown, testthat, covr License: Artistic-2.0 MD5sum: 658fc83ad9787241020dfeb151f6fcd5 NeedsCompilation: no Title: Help use collectl with R in Linux, to measure resource consumption in R processes Description: Provide functions to obtain instrumentation data on processes in a unix environment. Parse output of a collectl run. Vizualize aspects of system usage over time, with annotation. biocViews: Software, Infrastructure Author: Vincent Carey [aut, cre] (ORCID: ), Yubo Cheng [aut] Maintainer: Vincent Carey URL: https://github.com/vjcitn/Rcollectl SystemRequirements: collectl VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/Rcollectl git_url: https://git.bioconductor.org/packages/Rcollectl git_branch: devel git_last_commit: 4d14b62 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Rcollectl_1.11.0.tar.gz vignettes: vignettes/Rcollectl/inst/doc/Rcollectl.html vignetteTitles: Rcollectl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcollectl/inst/doc/Rcollectl.R dependencyCount: 28 Package: Rcpi Version: 1.47.0 Depends: R (>= 3.0.2) Imports: Biostrings, GOSemSim, curl, doParallel, foreach, httr2, jsonlite, methods, rlang, stats, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 | file LICENSE MD5sum: d1b1a6ff4b722bcaa0e29293d2c5c929 NeedsCompilation: no Title: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery Description: A molecular informatics toolkit with an integration of bioinformatics and chemoinformatics tools for drug discovery. biocViews: Software, DataImport, DataRepresentation, FeatureExtraction, Cheminformatics, BiomedicalInformatics, Proteomics, GO, SystemsBiology Author: Nan Xiao [aut, cre] (ORCID: ), Dong-Sheng Cao [aut], Qing-Song Xu [aut] Maintainer: Nan Xiao URL: https://nanx.me/Rcpi/, https://github.com/nanxstats/Rcpi VignetteBuilder: knitr BugReports: https://github.com/nanxstats/Rcpi/issues git_url: https://git.bioconductor.org/packages/Rcpi git_branch: devel git_last_commit: d010cbe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Rcpi_1.47.0.tar.gz vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.html, vignettes/Rcpi/inst/doc/Rcpi.html vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package as an Integrated Informatics Platform for Drug Discovery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcpi/inst/doc/Rcpi.R dependencyCount: 57 Package: RCSL Version: 1.19.0 Depends: R (>= 4.1) Imports: RcppAnnoy, igraph, NbClust, Rtsne, ggplot2(>= 3.4.0), methods, pracma, umap, grDevices, graphics, stats, Rcpp (>= 0.11.0), MatrixGenerics, SingleCellExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown, mclust, tidyverse, tinytex License: Artistic-2.0 MD5sum: ee2cfaa46373abafd00a4a92eba97bfb NeedsCompilation: no Title: Rank Constrained Similarity Learning for single cell RNA sequencing data Description: A novel clustering algorithm and toolkit RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both lo-cal similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similar-ity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similar-ity of a cell to other cells is a linear combination of its global similarity and local similarity. biocViews: SingleCell, Software, Clustering, DimensionReduction, RNASeq, Visualization, Sequencing Author: Qinglin Mei [cre, aut], Guojun Li [fnd], Zhengchang Su [fnd] Maintainer: Qinglin Mei URL: https://github.com/QinglinMei/RCSL VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCSL git_branch: devel git_last_commit: 01ddc6d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RCSL_1.19.0.tar.gz vignettes: vignettes/RCSL/inst/doc/RCSL.html vignetteTitles: RCSL package manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCSL/inst/doc/RCSL.R dependencyCount: 64 Package: RCX Version: 1.15.2 Depends: R (>= 4.2.0) Imports: jsonlite, plyr, igraph, methods Suggests: BiocStyle, testthat, knitr, rmarkdown, base64enc, graph License: MIT + file LICENSE MD5sum: ecdb43408b63bbb3ab3e8d38ebbfcedb NeedsCompilation: no Title: R package implementing the Cytoscape Exchange (CX) format Description: Create, handle, validate, visualize and convert networks in the Cytoscape exchange (CX) format to standard data types and objects. The package also provides conversion to and from objects of iGraph and graphNEL. The CX format is also used by the NDEx platform, a online commons for biological networks, and the network visualization software Cytocape. biocViews: Pathways, DataImport, Network Author: Florian Auer [aut, cre] (ORCID: ) Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/RCX VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/RCX/issues git_url: https://git.bioconductor.org/packages/RCX git_branch: devel git_last_commit: ee9b861 git_last_commit_date: 2026-03-19 Date/Publication: 2026-04-20 source.ver: src/contrib/RCX_1.15.2.tar.gz vignettes: vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.html, vignettes/RCX/inst/doc/Creating_RCX_from_scratch.html, vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.html, vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.html vignetteTitles: Appendix: The RCX and CX Data Model, 02. Creating RCX from scratch, 03. Extending the RCX Data Model, 01. RCX - an R package implementing the Cytoscape Exchange (CX) format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.R, vignettes/RCX/inst/doc/Creating_RCX_from_scratch.R, vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.R, vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.R dependsOnMe: ndexr dependencyCount: 20 Package: RCy3 Version: 2.31.1 Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, stats, graph, fs, uuid, stringi, glue, RCurl, base64url, base64enc, IRkernel, IRdisplay, RColorBrewer, gplots Suggests: BiocStyle, knitr, rmarkdown, igraph, grDevices License: MIT + file LICENSE MD5sum: 4cce3f8ce8a4a4f357316f83c34a429c NeedsCompilation: no Title: Functions to Access and Control Cytoscape Description: Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network Author: Jing Chen [cre], Alex Pico [aut] (ORCID: ), Tanja Muetze [aut], Paul Shannon [aut], Ruth Isserlin [ctb], Shraddha Pai [ctb], Julia Gustavsen [ctb], Georgi Kolishovski [ctb], Yihang Xin [ctb] Maintainer: Jing Chen URL: https://github.com/cytoscape/RCy3 SystemRequirements: Cytoscape (>= 3.7.1), CyREST (>= 3.8.0) VignetteBuilder: knitr BugReports: https://github.com/cytoscape/RCy3/issues git_url: https://git.bioconductor.org/packages/RCy3 git_branch: devel git_last_commit: 120ba27 git_last_commit_date: 2025-12-02 Date/Publication: 2026-04-20 source.ver: src/contrib/RCy3_2.31.1.tar.gz vignettes: vignettes/RCy3/inst/doc/Cancer-networks-and-data.html, vignettes/RCy3/inst/doc/Custom-Graphics.html, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.html, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.html, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.html, vignettes/RCy3/inst/doc/Filtering-Networks.html, vignettes/RCy3/inst/doc/Group-nodes.html, vignettes/RCy3/inst/doc/Identifier-mapping.html, vignettes/RCy3/inst/doc/Importing-data.html, vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.html, vignettes/RCy3/inst/doc/Network-functions-and-visualization.html, vignettes/RCy3/inst/doc/Overview-of-RCy3.html, vignettes/RCy3/inst/doc/Phylogenetic-trees.html, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.html vignetteTitles: 06. Cancer networks and data ~40 min, 11. Custom Graphics and Labels ~10 min, 03. Cytoscape and graphNEL ~5 min, 02. Cytoscape and igraph ~5 min, 09. Cytoscape and NDEx ~20 min, 12. Filtering Networks ~10 min, 10. Group nodes ~15 min, 07. Identifier mapping ~20 min, 04. Importing data ~5 min, 14. Jupyter Bridge and RCy3 ~10 min, 05. Network functions and visualization ~15 min, 01. Overview of RCy3 ~25 min, 13. Phylogenetic Trees ~3 min, 08. Upgrading existing scripts ~15 min hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCy3/inst/doc/Cancer-networks-and-data.R, vignettes/RCy3/inst/doc/Custom-Graphics.R, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.R, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.R, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.R, vignettes/RCy3/inst/doc/Filtering-Networks.R, vignettes/RCy3/inst/doc/Group-nodes.R, vignettes/RCy3/inst/doc/Identifier-mapping.R, vignettes/RCy3/inst/doc/Importing-data.R, vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.R, vignettes/RCy3/inst/doc/Network-functions-and-visualization.R, vignettes/RCy3/inst/doc/Overview-of-RCy3.R, vignettes/RCy3/inst/doc/Phylogenetic-trees.R, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.R importsMe: categoryCompare, CeTF, enrichViewNet, fedup, GeneNetworkBuilder, MetaPhOR, MOGAMUN, NCIgraph, regutools, transomics2cytoscape, dendroNetwork, lilikoi, netgsa, ScriptMapR suggestsMe: graphite, rScudo, tidysbml, sharp dependencyCount: 49 Package: RCyjs Version: 2.33.0 Depends: R (>= 3.5.0), BrowserViz (>= 2.7.18), graph (>= 1.56.0) Imports: methods, httpuv (>= 1.5.0), BiocGenerics, base64enc, utils Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: dd4fef1f63783794ba1d370fba980275 NeedsCompilation: no Title: Display and manipulate graphs in cytoscape.js Description: Interactive viewing and exploration of graphs, connecting R to Cytoscape.js, using websockets. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCyjs git_branch: devel git_last_commit: 85acbb7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RCyjs_2.33.0.tar.gz vignettes: vignettes/RCyjs/inst/doc/RCyjs.html vignetteTitles: "RCyjs: programmatic access to the web browser-based network viewer,, cytoscape.js" hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R dependencyCount: 22 Package: Rdisop Version: 1.71.1 Depends: R (>= 2.10), Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-2 MD5sum: 34c4f4bc90f9aad291151be504f06688 NeedsCompilation: yes Title: Decomposition of Isotopic Patterns Description: In high resolution mass spectrometry (HR-MS), the measured masses can be decomposed into potential element combinations (chemical sum formulas). Where additional mass/intensity information of respective isotopic peaks is available, decomposition can take this information into account to better rank the potential candidate sum formulas. To compare measured mass/intensity information with the theoretical distribution of candidate sum formulas, the latter needs to be calculated. This package implements fast algorithms to address both tasks, the calculation of isotopic distributions for arbitrary sum formulas (assuming a HR-MS resolution of roughly 30,000), and the ranked list of sum formulas fitting an observed peak or isotopic peak set. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Anton Pervukhin [aut], Steffen Neumann [aut, cre] (ORCID: ), Jan Lisec [ctb] (ORCID: ), Miao Yu [ctb], Roberto Canteri [ctb] Maintainer: Steffen Neumann URL: https://github.com/sneumann/Rdisop SystemRequirements: None VignetteBuilder: knitr BugReports: https://github.com/sneumann/Rdisop/issues/new git_url: https://git.bioconductor.org/packages/Rdisop git_branch: devel git_last_commit: 524de2a git_last_commit_date: 2026-02-23 Date/Publication: 2026-04-20 source.ver: src/contrib/Rdisop_1.71.1.tar.gz vignettes: vignettes/Rdisop/inst/doc/Rdisop.html vignetteTitles: Mass decomposition with the Rdisop package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE importsMe: enviGCMS suggestsMe: adductomicsR, MSnbase, RforProteomics, CorMID, InterpretMSSpectrum dependencyCount: 3 Package: RDRToolbox Version: 1.61.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: 13a9e5eef52b7512f6930ba5c20b6825 NeedsCompilation: no Title: A package for nonlinear dimension reduction with Isomap and LLE. Description: A package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset. biocViews: DimensionReduction, FeatureExtraction, Visualization, Clustering, Microarray Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RDRToolbox git_branch: devel git_last_commit: 6b1f3bb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RDRToolbox_1.61.0.tar.gz vignettes: vignettes/RDRToolbox/inst/doc/vignette.pdf vignetteTitles: A package for nonlinear dimension reduction with Isomap and LLE. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDRToolbox/inst/doc/vignette.R suggestsMe: loon dependencyCount: 35 Package: ReactomePA Version: 1.55.1 Depends: R (>= 3.4.0) Imports: AnnotationDbi, enrichplot, enrichit, ggplot2 (>= 3.3.5), ggraph, reactome.db, igraph, graphite, gson, yulab.utils (>= 0.1.5) Suggests: clusterProfiler, knitr, rmarkdown, org.Hs.eg.db, prettydoc, testthat License: GPL-2 MD5sum: 82f82645025bb2e96af5716f0d22f8ea NeedsCompilation: no Title: Reactome Pathway Analysis Description: This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. This package is not affiliated with the Reactome team. biocViews: Pathways, Visualization, Annotation, MultipleComparison, GeneSetEnrichment, Reactome Author: Guangchuang Yu [aut, cre], Vladislav Petyuk [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ReactomePA/issues git_url: https://git.bioconductor.org/packages/ReactomePA git_branch: devel git_last_commit: cbad461 git_last_commit_date: 2025-12-22 Date/Publication: 2026-04-20 source.ver: src/contrib/ReactomePA_1.55.1.tar.gz vignettes: vignettes/ReactomePA/inst/doc/ReactomePA.html vignetteTitles: An R package for Reactome Pathway Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: bioCancer, famat, miRSM, miRspongeR, mitology, Pigengene, scTensor suggestsMe: CBNplot, ChIPseeker, cola, epiSeeker, GeDi, GRaNIE, scGPS, scGraphVerse dependencyCount: 132 Package: ReadqPCR Version: 1.57.0 Depends: R(>= 2.14.0), Biobase, methods Suggests: qpcR License: LGPL-3 MD5sum: 2bae2fcdb76320687bca86ff446996f0 NeedsCompilation: no Title: Read qPCR data Description: The package provides functions to read raw RT-qPCR data of different platforms. biocViews: DataImport, MicrotitrePlateAssay, GeneExpression, qPCR Author: James Perkins, Matthias Kohl, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html git_url: https://git.bioconductor.org/packages/ReadqPCR git_branch: devel git_last_commit: 8f4627c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ReadqPCR_1.57.0.tar.gz vignettes: vignettes/ReadqPCR/inst/doc/ReadqPCR.pdf vignetteTitles: Functions to load RT-qPCR data into R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReadqPCR/inst/doc/ReadqPCR.R dependsOnMe: NormqPCR importsMe: OAtools dependencyCount: 7 Package: REBET Version: 1.29.0 Depends: ASSET Imports: stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: b2170ee869b546c00074cd7f69ff55d1 NeedsCompilation: yes Title: The subREgion-based BurdEn Test (REBET) Description: There is an increasing focus to investigate the association between rare variants and diseases. The REBET package implements the subREgion-based BurdEn Test which is a powerful burden test that simultaneously identifies susceptibility loci and sub-regions. biocViews: Software, VariantAnnotation, SNP Author: Bill Wheeler [cre], Bin Zhu [aut], Lisa Mirabello [ctb], Nilanjan Chatterjee [ctb] Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/REBET git_branch: devel git_last_commit: 30a4e80 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/REBET_1.29.0.tar.gz vignettes: vignettes/REBET/inst/doc/vignette.pdf vignetteTitles: REBET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REBET/inst/doc/vignette.R dependencyCount: 27 Package: rebook Version: 1.21.0 Imports: utils, methods, knitr (>= 1.32), rmarkdown, CodeDepends, dir.expiry, filelock, BiocStyle Suggests: testthat, igraph, XML, BiocManager, RCurl, bookdown, rappdirs, yaml, BiocParallel, OSCA.intro, OSCA.workflows License: GPL-3 MD5sum: aaf1392e11a7418acddc418f962f9e4a NeedsCompilation: no Title: Re-using Content in Bioconductor Books Description: Provides utilities to re-use content across chapters of a Bioconductor book. This is mostly based on functionality developed while writing the OSCA book, but generalized for potential use in other large books with heavy compute. Also contains some functions to assist book deployment. biocViews: Software, Infrastructure, ReportWriting Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rebook git_branch: devel git_last_commit: e92cb4e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rebook_1.21.0.tar.gz vignettes: vignettes/rebook/inst/doc/userguide.html vignetteTitles: Reusing book content hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rebook/inst/doc/userguide.R dependsOnMe: csawBook, OSCA, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook dependencyCount: 43 Package: reconsi Version: 1.23.0 Imports: phyloseq, ks, reshape2, ggplot2, stats, methods, graphics, grDevices, matrixStats, Matrix Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 87495d2c99d3d8aba12bbf49919429a7 NeedsCompilation: no Title: Resampling Collapsed Null Distributions for Simultaneous Inference Description: Improves simultaneous inference under dependence of tests by estimating a collapsed null distribution through resampling. Accounting for the dependence between tests increases the power while reducing the variability of the false discovery proportion. This dependence is common in genomics applications, e.g. when combining flow cytometry measurements with microbiome sequence counts. biocViews: Metagenomics, Microbiome, MultipleComparison, FlowCytometry Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/reconsi/issues git_url: https://git.bioconductor.org/packages/reconsi git_branch: devel git_last_commit: cd5ced8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/reconsi_1.23.0.tar.gz vignettes: vignettes/reconsi/inst/doc/reconsiVignette.html vignetteTitles: Manual for the RCM pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/reconsi/inst/doc/reconsiVignette.R dependencyCount: 76 Package: recount Version: 1.37.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: BiocParallel, derfinder, downloader, GEOquery, GenomeInfoDb, GenomicRanges, IRanges, methods, RCurl, rentrez, rtracklayer (>= 1.35.3), S4Vectors, stats, utils Suggests: AnnotationDbi, BiocManager, BiocStyle (>= 2.5.19), DESeq2, sessioninfo, EnsDb.Hsapiens.v79, GenomicFeatures, txdbmaker, knitr (>= 1.6), org.Hs.eg.db, RefManageR, regionReport (>= 1.9.4), rmarkdown (>= 0.9.5), testthat (>= 2.1.0), covr, pheatmap, DT, edgeR, ggplot2, RColorBrewer License: Artistic-2.0 MD5sum: 4fc357373effbdf8a3608a99247dd0af NeedsCompilation: no Title: Explore and download data from the recount project Description: Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Abhinav Nellore [ctb], Andrew E. Jaffe [ctb] (ORCID: ), Margaret A. Taub [ctb], Kai Kammers [ctb], Shannon E. Ellis [ctb] (ORCID: ), Kasper Daniel Hansen [ctb] (ORCID: ), Ben Langmead [ctb] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/recount VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recount/ git_url: https://git.bioconductor.org/packages/recount git_branch: devel git_last_commit: ee0517c git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/recount_1.37.0.tar.gz vignettes: vignettes/recount/inst/doc/recount-quickstart.html, vignettes/recount/inst/doc/SRP009615-results.html vignetteTitles: recount quick start guide, Basic DESeq2 results exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount/inst/doc/recount-quickstart.R, vignettes/recount/inst/doc/SRP009615-results.R importsMe: psichomics, RNAAgeCalc, recountWorkflow suggestsMe: recount3 dependencyCount: 162 Package: recount3 Version: 1.21.1 Depends: SummarizedExperiment Imports: BiocFileCache, data.table, GenomicRanges, httr, Matrix, methods, R.utils, rtracklayer, S4Vectors, sessioninfo, tools, utils Suggests: BiocStyle, covr, knitcitations, knitr, lobstr, RefManageR, rmarkdown, testthat, recount License: Artistic-2.0 MD5sum: 795e4e683966a17617cd31aab6a66a7d NeedsCompilation: no Title: Explore and download data from the recount3 project Description: The recount3 package enables access to a large amount of uniformly processed RNA-seq data from human and mouse. You can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level with sample metadata and QC statistics. In addition we provide access to sample coverage BigWig files. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Leonardo Collado-Torres [aut, cre] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/LieberInstitute/recount3 VignetteBuilder: knitr BugReports: https://github.com/LieberInstitute/recount3/issues git_url: https://git.bioconductor.org/packages/recount3 git_branch: devel git_last_commit: 0f6161f git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/recount3_1.21.1.tar.gz vignettes: vignettes/recount3/inst/doc/recount3-quickstart.html vignetteTitles: recount3 quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount3/inst/doc/recount3-quickstart.R suggestsMe: RNAseqQC dependencyCount: 92 Package: RedeR Version: 3.7.2 Depends: R (>= 4.4), methods Imports: scales, igraph Suggests: knitr, rmarkdown, markdown, BiocStyle, TreeAndLeaf License: GPL-3 MD5sum: 3ebb322a1161553f4ccb41eaee1ba386 NeedsCompilation: no Title: Interactive visualization and manipulation of nested networks Description: RedeR combines an R package with a stand-alone Java application for interactive visualization and manipulation of nested networks. Graph, node, and edge attributes can be configured using either graphical or command-line methods, following igraph syntax rules. biocViews: GUI, GraphAndNetwork, Network, NetworkEnrichment, NetworkInference, Software, SystemsBiology Author: Xin Wang [ctb], Florian Markowetz [ctb], Mauro Castro [aut, cre] (ORCID: ) Maintainer: Mauro Castro URL: https://doi.org/10.1186/gb-2012-13-4-r29 SystemRequirements: Java Runtime Environment (Java>= 11) VignetteBuilder: knitr BugReports: https://github.com/sysbiolab/RedeR/issues git_url: https://git.bioconductor.org/packages/RedeR git_branch: devel git_last_commit: 0101566 git_last_commit_date: 2026-02-20 Date/Publication: 2026-04-20 source.ver: src/contrib/RedeR_3.7.2.tar.gz vignettes: vignettes/RedeR/inst/doc/RedeR.html vignetteTitles: "RedeR: nested networks" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R dependsOnMe: Fletcher2013b, dc3net importsMe: PANR, RTN, transcriptogramer, TreeAndLeaf suggestsMe: PathwaySpace dependencyCount: 23 Package: RedisParam Version: 1.13.1 Depends: R (>= 4.2.0), BiocParallel (>= 1.29.12) Imports: methods, redux, withr, logger Suggests: rmarkdown, knitr, testthat, BiocStyle License: Artistic-2.0 MD5sum: c223d84a001f9f688c40b05ec29bec92 NeedsCompilation: no Title: Provide a 'redis' back-end for BiocParallel Description: This package provides a Redis-based back-end for BiocParallel, enabling an alternative mechanism for distributed computation. The The 'manager' distributes tasks to a 'worker' pool through a central Redis server, rather than directly to workers as with other BiocParallel implementations. This means that the worker pool can change dynamically during job evaluation. All features of BiocParallel are supported, including reproducible random number streams, logging to the manager, and alternative 'load balancing' task distributions. biocViews: Infrastructure Author: Martin Morgan [aut, cre] (ORCID: ), Jiefei Wang [aut] Maintainer: Martin Morgan SystemRequirements: hiredis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedisParam git_branch: devel git_last_commit: 45027f9 git_last_commit_date: 2025-12-23 Date/Publication: 2026-04-20 source.ver: src/contrib/RedisParam_1.13.1.tar.gz vignettes: vignettes/RedisParam/inst/doc/RedisParamDeveloperGuide.html, vignettes/RedisParam/inst/doc/RedisParamUserGuide.html vignetteTitles: RedisParam for Developers, Using RedisParam hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/RedisParam/inst/doc/RedisParamDeveloperGuide.R, vignettes/RedisParam/inst/doc/RedisParamUserGuide.R dependencyCount: 21 Package: ReducedExperiment Version: 1.3.0 Depends: R (>= 4.4.0), SummarizedExperiment, methods Imports: WGCNA, ica, moments, clusterProfiler, msigdbr, RColorBrewer, car, lme4, lmerTest, pheatmap, biomaRt, stats, grDevices, BiocParallel, ggplot2, patchwork, BiocGenerics, S4Vectors Suggests: knitr, rmarkdown, testthat, biocViews, BiocCheck, BiocStyle, airway License: GPL (>= 3) MD5sum: 615456635542bcefaa315dfadf38cd4a NeedsCompilation: no Title: Containers and tools for dimensionally-reduced -omics representations Description: Provides SummarizedExperiment-like containers for storing and manipulating dimensionally-reduced assay data. The ReducedExperiment classes allow users to simultaneously manipulate their original dataset and their decomposed data, in addition to other method-specific outputs like feature loadings. Implements utilities and specialised classes for the application of stabilised independent component analysis (sICA) and weighted gene correlation network analysis (WGCNA). biocViews: GeneExpression, Infrastructure, DataRepresentation, Software, DimensionReduction, Network Author: Jack Gisby [aut, cre] (ORCID: ), Michael Barnes [aut] (ORCID: ) Maintainer: Jack Gisby URL: https://github.com/jackgisby/ReducedExperiment VignetteBuilder: knitr BugReports: https://github.com/jackgisby/ReducedExperiment/issues git_url: https://git.bioconductor.org/packages/ReducedExperiment git_branch: devel git_last_commit: 8a1ee25 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ReducedExperiment_1.3.0.tar.gz vignettes: vignettes/ReducedExperiment/inst/doc/ReducedExperiment.html vignetteTitles: ReducedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReducedExperiment/inst/doc/ReducedExperiment.R dependencyCount: 210 Package: Rega Version: 0.99.6 Depends: R (>= 4.6) Imports: askpass, httr2, jsonlite, jsonvalidate, keyring, readxl, rlang, stringr, tibble, tidyr, validate, yaml Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 5cf2e4ac28007531f91ab7c752770554 NeedsCompilation: no Title: R Interface to European Genome-Phenome Archive Description: The European Genome-phenome Archive (EGA) provides long-term storage and controlled sharing of personally identifiable genetic data. The Rega package offers a streamlined and extensible R interface to the EGA API, facilitating the programmatic upload of metadata. GEO-like Excel submission template is provided as a default method of organizing submission metadata. biocViews: Software, Infrastructure, ThirdPartyClient Author: Igor Cervenka [aut, cre] (ORCID: ), Athimed El Taher [aut] (ORCID: ), Robert Ivanek [aut] (ORCID: ) Maintainer: Igor Cervenka URL: https://github.com/ivanek/Rega VignetteBuilder: knitr BugReports: https://github.com/ivanek/Rega/issues git_url: https://git.bioconductor.org/packages/Rega git_branch: devel git_last_commit: 1d3d5c8 git_last_commit_date: 2026-02-03 Date/Publication: 2026-04-20 source.ver: src/contrib/Rega_0.99.6.tar.gz vignettes: vignettes/Rega/inst/doc/Rega.html vignetteTitles: The Rega User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rega/inst/doc/Rega.R dependencyCount: 49 Package: regionalpcs Version: 1.9.0 Depends: R (>= 4.3.0) Imports: dplyr, PCAtools, tibble, GenomicRanges Suggests: knitr, rmarkdown, RMTstat, testthat (>= 3.0.0), BiocStyle, tidyr, minfiData, TxDb.Hsapiens.UCSC.hg19.knownGene, IRanges License: MIT + file LICENSE MD5sum: f8a758f9339e9192dd6a6a9b5a63f57c NeedsCompilation: no Title: Summarizing Regional Methylation with Regional Principal Components Analysis Description: Functions to summarize DNA methylation data using regional principal components. Regional principal components are computed using principal components analysis within genomic regions to summarize the variability in methylation levels across CpGs. The number of principal components is chosen using either the Marcenko-Pasteur or Gavish-Donoho method to identify relevant signal in the data. biocViews: DNAMethylation, DifferentialMethylation, StatisticalMethod, Software, MethylationArray Author: Tiffany Eulalio [aut, cre] (ORCID: ) Maintainer: Tiffany Eulalio URL: https://github.com/tyeulalio/regionalpcs VignetteBuilder: knitr BugReports: https://github.com/tyeulalio/regionalpcs/issues git_url: https://git.bioconductor.org/packages/regionalpcs git_branch: devel git_last_commit: ad2c832 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/regionalpcs_1.9.0.tar.gz vignettes: vignettes/regionalpcs/inst/doc/regionalpcs.html vignetteTitles: regionalpcs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/regionalpcs/inst/doc/regionalpcs.R dependencyCount: 74 Package: RegionalST Version: 1.9.0 Depends: R (>= 4.3.0) Imports: stats, grDevices, utils, ggplot2, dplyr, scater, gridExtra, BiocStyle, BayesSpace, fgsea, magrittr, SingleCellExperiment, RColorBrewer, Seurat, S4Vectors, tibble, TOAST, assertthat, colorspace, shiny, SummarizedExperiment Suggests: knitr, rmarkdown, gplots, testthat (>= 3.0.0) License: GPL-3 MD5sum: cd0cf9f0ea6508b44772b0d7593de5ff NeedsCompilation: no Title: Investigating regions of interest and performing regional cell type-specific analysis with spatial transcriptomics data Description: This package analyze spatial transcriptomics data through cross-regional cell type-specific analysis. It selects regions of interest (ROIs) and identifys cross-regional cell type-specific differential signals. The ROIs can be selected using automatic algorithm or through manual selection. It facilitates manual selection of ROIs using a shiny application. biocViews: Spatial, Transcriptomics, Reactome, KEGG Author: Ziyi Li [aut, cre] Maintainer: Ziyi Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RegionalST git_branch: devel git_last_commit: e3a23f6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RegionalST_1.9.0.tar.gz vignettes: vignettes/RegionalST/inst/doc/RegionalST.html vignetteTitles: RegionalST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RegionalST/inst/doc/RegionalST.R dependencyCount: 257 Package: regioneR Version: 1.43.0 Depends: GenomicRanges Imports: memoise, GenomicRanges, IRanges, BSgenome, Biostrings, rtracklayer, parallel, graphics, stats, utils, methods, Seqinfo, GenomeInfoDb, S4Vectors, tools Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 MD5sum: 400fb787b7c51a4bbdac3060a27952c0 NeedsCompilation: no Title: Association analysis of genomic regions based on permutation tests Description: regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation Author: Anna Diez-Villanueva , Roberto Malinverni and Bernat Gel Maintainer: Bernat Gel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneR git_branch: devel git_last_commit: a9f7f21 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/regioneR_1.43.0.tar.gz vignettes: vignettes/regioneR/inst/doc/regioneR.html vignetteTitles: regioneR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneR/inst/doc/regioneR.R dependsOnMe: karyoploteR, regioneReloaded importsMe: annotatr, ChIPpeakAnno, CNVfilteR, CopyNumberPlots, karyoploteR, UMI4Cats suggestsMe: CNVRanger, EpiMix, UPDhmm, MitoHEAR dependencyCount: 64 Package: regioneReloaded Version: 1.13.0 Depends: R (>= 4.2), regioneR Imports: stats, RColorBrewer, Rtsne, umap, ggplot2, ggrepel, reshape2, methods, scales, cluster, grid, grDevices Suggests: rmarkdown, BiocStyle, GenomeInfoDb, knitr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 6b0c33978ec580be8673850f134f539b NeedsCompilation: no Title: RegioneReloaded: Multiple Association for Genomic Region Sets Description: RegioneReloaded is a package that allows simultaneous analysis of associations between genomic region sets, enabling clustering of data and the creation of ready-to-publish graphs. It takes over and expands on all the features of its predecessor regioneR. It also incorporates a strategy to improve p-value calculations and normalize z-scores coming from multiple analysis to allow for their direct comparison. RegioneReloaded builds upon regioneR by adding new plotting functions for obtaining publication-ready graphs. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation, Clustering, MultipleComparison Author: Roberto Malinverni [aut, cre] (ORCID: ), David Corujo [aut], Bernat Gel [aut] Maintainer: Roberto Malinverni URL: https://github.com/RMalinverni/regioneReload VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneReloaded git_branch: devel git_last_commit: a3fc824 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/regioneReloaded_1.13.0.tar.gz vignettes: vignettes/regioneReloaded/inst/doc/regioneReloaded.html vignetteTitles: regioneReloaded hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneReloaded/inst/doc/regioneReloaded.R dependencyCount: 97 Package: regionReport Version: 1.45.0 Depends: R(>= 3.2) Imports: BiocStyle (>= 2.5.19), derfinder (>= 1.25.3), DEFormats, DESeq2, Seqinfo, GenomeInfoDb, GenomicRanges, knitr (>= 1.6), knitrBootstrap (>= 0.9.0), methods, RefManageR, rmarkdown (>= 0.9.5), S4Vectors, SummarizedExperiment, utils Suggests: BiocManager, biovizBase, bumphunter (>= 1.7.6), derfinderPlot (>= 1.29.1), sessioninfo, DT, edgeR, ggbio (>= 1.35.2), ggplot2, grid, gridExtra, IRanges, mgcv, pasilla, pheatmap, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, whisker License: Artistic-2.0 MD5sum: 301c6204bae598afda4a5e82514d7ea2 NeedsCompilation: no Title: Generate HTML or PDF reports for a set of genomic regions or DESeq2/edgeR results Description: Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, Transcription, Coverage, ReportWriting, DifferentialMethylation, DifferentialPeakCalling, ImmunoOncology, QualityControl Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/regionReport VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regionReport/ git_url: https://git.bioconductor.org/packages/regionReport git_branch: devel git_last_commit: e125ed9 git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/regionReport_1.45.0.tar.gz vignettes: vignettes/regionReport/inst/doc/bumphunterExample.html, vignettes/regionReport/inst/doc/regionReport.html vignetteTitles: Example report using bumphunter results, Introduction to regionReport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regionReport/inst/doc/bumphunterExample.R, vignettes/regionReport/inst/doc/regionReport.R importsMe: recountWorkflow suggestsMe: recount dependencyCount: 154 Package: regsplice Version: 1.37.0 Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats, pbapply, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: ee199f959af8300bbb5144828219babd NeedsCompilation: no Title: L1-regularization based methods for detection of differential splicing Description: Statistical methods for detection of differential splicing (differential exon usage) in RNA-seq and exon microarray data, using L1-regularization (lasso) to improve power. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, RNASeq, Microarray, ExonArray, ExperimentalDesign, Software Author: Lukas M. Weber [aut, cre] Maintainer: Lukas M. Weber URL: https://github.com/lmweber/regsplice VignetteBuilder: knitr BugReports: https://github.com/lmweber/regsplice/issues git_url: https://git.bioconductor.org/packages/regsplice git_branch: devel git_last_commit: e903be0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/regsplice_1.37.0.tar.gz vignettes: vignettes/regsplice/inst/doc/regsplice-workflow.html vignetteTitles: regsplice workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/regsplice/inst/doc/regsplice-workflow.R dependencyCount: 40 Package: regutools Version: 1.23.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationHub, Biostrings, DBI, GenomicRanges, Gviz, IRanges, RCy3, RSQLite, S4Vectors, methods, stats, utils, BiocFileCache Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 2.1.0), covr License: Artistic-2.0 MD5sum: f6e7f96b6a235227dacbcdc411496b17 NeedsCompilation: no Title: regutools: an R package for data extraction from RegulonDB Description: RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools provides researchers with the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. biocViews: GeneRegulation, GeneExpression, SystemsBiology, Network,NetworkInference,Visualization, Transcription Author: Joselyn Chavez [aut, cre] (ORCID: ), Carmina Barberena-Jonas [aut] (ORCID: ), Jesus E. Sotelo-Fonseca [aut] (ORCID: ), Jose Alquicira-Hernandez [ctb] (ORCID: ), Heladia Salgado [ctb] (ORCID: ), Leonardo Collado-Torres [aut] (ORCID: ), Alejandro Reyes [aut] (ORCID: ) Maintainer: Joselyn Chavez URL: https://github.com/ComunidadBioInfo/regutools VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regutools git_url: https://git.bioconductor.org/packages/regutools git_branch: devel git_last_commit: 327f7aa git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/regutools_1.23.0.tar.gz vignettes: vignettes/regutools/inst/doc/regutools.html vignetteTitles: regutools: an R package for data extraction from RegulonDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regutools/inst/doc/regutools.R dependencyCount: 168 Package: RepViz Version: 1.27.0 Depends: R (>= 3.5.1), GenomicRanges (>= 1.30.0), Rsamtools (>= 1.34.1), IRanges (>= 2.14.0), biomaRt (>= 2.36.0), S4Vectors (>= 0.18.0), graphics, grDevices, utils Suggests: rmarkdown, knitr, testthat License: GPL-3 MD5sum: deb725ac43164fac3ba4847e34c703b9 NeedsCompilation: no Title: Replicate oriented Visualization of a genomic region Description: RepViz enables the view of a genomic region in a simple and efficient way. RepViz allows simultaneous viewing of both intra- and intergroup variation in sequencing counts of the studied conditions, as well as their comparison to the output features (e.g. identified peaks) from user selected data analysis methods.The RepViz tool is primarily designed for chromatin data such as ChIP-seq and ATAC-seq, but can also be used with other sequencing data such as RNA-seq, or combinations of different types of genomic data. biocViews: WorkflowStep, Visualization, Sequencing, ChIPSeq, ATACSeq, Software, Coverage, GenomicVariation Author: Thomas Faux, Kalle Rytkönen, Asta Laiho, Laura L. Elo Maintainer: Thomas Faux, Asta Laiho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RepViz git_branch: devel git_last_commit: ca319c2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RepViz_1.27.0.tar.gz vignettes: vignettes/RepViz/inst/doc/RepViz.html vignetteTitles: RepViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RepViz/inst/doc/RepViz.R dependencyCount: 76 Package: ResidualMatrix Version: 1.21.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 MD5sum: 2e9355255cb369afc93b15bd40687c95 NeedsCompilation: no Title: Creating a DelayedMatrix of Regression Residuals Description: Provides delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. Also supports partial computation of residuals where selected factors are to be preserved in the output matrix. Implements a number of efficient methods for operating on the delayed matrix of residuals, most notably matrix multiplication and calculation of row/column sums or means. biocViews: Software, DataRepresentation, Regression, BatchEffect, ExperimentalDesign Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/ResidualMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/ResidualMatrix/issues git_url: https://git.bioconductor.org/packages/ResidualMatrix git_branch: devel git_last_commit: d568a7c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ResidualMatrix_1.21.0.tar.gz vignettes: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.html vignetteTitles: Using the ResidualMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.R importsMe: batchelor suggestsMe: alabaster.matrix, BiocSingular, chihaya, scran dependencyCount: 21 Package: RESOLVE Version: 1.13.1 Depends: R (>= 4.1.0) Imports: Biostrings, BSgenome, BSgenome.Hsapiens.1000genomes.hs37d5, cluster, data.table, GenomeInfoDb, GenomicRanges, glmnet, ggplot2, gridExtra, IRanges, lsa, MutationalPatterns, nnls, parallel, reshape2, S4Vectors, RhpcBLASctl, survival Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: c7f67b60e9c562b4518fe4ffc3ea76b2 NeedsCompilation: no Title: RESOLVE: An R package for the efficient analysis of mutational signatures from cancer genomes Description: Cancer is a genetic disease caused by somatic mutations in genes controlling key biological functions such as cellular growth and division. Such mutations may arise both through cell-intrinsic and exogenous processes, generating characteristic mutational patterns over the genome named mutational signatures. The study of mutational signatures have become a standard component of modern genomics studies, since it can reveal which (environmental and endogenous) mutagenic processes are active in a tumor, and may highlight markers for therapeutic response. Mutational signatures computational analysis presents many pitfalls. First, the task of determining the number of signatures is very complex and depends on heuristics. Second, several signatures have no clear etiology, casting doubt on them being computational artifacts rather than due to mutagenic processes. Last, approaches for signatures assignment are greatly influenced by the set of signatures used for the analysis. To overcome these limitations, we developed RESOLVE (Robust EStimation Of mutationaL signatures Via rEgularization), a framework that allows the efficient extraction and assignment of mutational signatures. RESOLVE implements a novel algorithm that enables (i) the efficient extraction, (ii) exposure estimation, and (iii) confidence assessment during the computational inference of mutational signatures. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Luca De Sano [cre, aut] (ORCID: ) Maintainer: Luca De Sano URL: https://github.com/danro9685/RESOLVE VignetteBuilder: knitr BugReports: https://github.com/danro9685/RESOLVE/issues git_url: https://git.bioconductor.org/packages/RESOLVE git_branch: devel git_last_commit: 4d61d9e git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/RESOLVE_1.13.1.tar.gz vignettes: vignettes/RESOLVE/inst/doc/RESOLVE.html vignetteTitles: RESOLVE.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RESOLVE/inst/doc/RESOLVE.R dependencyCount: 132 Package: retrofit Version: 1.11.0 Depends: R (>= 4.2), Rcpp LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, testthat, DescTools, ggplot2, corrplot, cowplot, grid, colorspace, png, reshape2, pals, RCurl License: GPL-3 MD5sum: 6ae7b1ac89d1821032b41a320fb1afd2 NeedsCompilation: yes Title: RETROFIT: Reference-free deconvolution of cell mixtures in spatial transcriptomics Description: RETROFIT is a Bayesian non-negative matrix factorization framework to decompose cell type mixtures in ST data without using external single-cell expression references. RETROFIT outperforms existing reference-based methods in estimating cell type proportions and reconstructing gene expressions in simulations with varying spot size and sample heterogeneity, irrespective of the quality or availability of the single-cell reference. RETROFIT recapitulates known cell-type localization patterns in a Slide-seq dataset of mouse cerebellum without using any single-cell data. biocViews: Transcriptomics, Visualization, RNASeq, Bayesian, Spatial, Software, GeneExpression, DimensionReduction, FeatureExtraction, SingleCell Author: Adam Park [aut, cre], Roopali Singh [aut] (ORCID: ), Xiang Zhu [aut] (ORCID: ), Qunhua Li [aut] (ORCID: ) Maintainer: Adam Park URL: https://github.com/qunhualilab/retrofit VignetteBuilder: knitr BugReports: https://github.com/qunhualilab/retrofit/issues git_url: https://git.bioconductor.org/packages/retrofit git_branch: devel git_last_commit: 88f4e91 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/retrofit_1.11.0.tar.gz vignettes: vignettes/retrofit/inst/doc/ColonVignette.html, vignettes/retrofit/inst/doc/SimulationVignette.html vignetteTitles: Retrofit Colon Vignette, Retrofit Simulation Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/retrofit/inst/doc/ColonVignette.R, vignettes/retrofit/inst/doc/SimulationVignette.R dependencyCount: 3 Package: Rfastp Version: 1.21.3 Imports: Rcpp, rjson, ggplot2, reshape2 LinkingTo: Rcpp, Rhtslib Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: cbb68b80b8d262f31347afe98fb11afc NeedsCompilation: yes Title: An Ultra-Fast and All-in-One Fastq Preprocessor (Quality Control, Adapter, low quality and polyX trimming) and UMI Sequence Parsing). Description: Rfastp is an R wrapper of fastp developed in c++. fastp performs quality control for fastq files. including low quality bases trimming, polyX trimming, adapter auto-detection and trimming, paired-end reads merging, UMI sequence/id handling. Rfastp can concatenate multiple files into one file (like shell command cat) and accept multiple files as input. biocViews: QualityControl, Sequencing, Preprocessing, Software Author: Wei Wang [cre, aut] (ORCID: ), Ji-Dung Luo [ctb] (ORCID: ), Thomas Carroll [aut] (ORCID: ) Maintainer: Wei Wang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rfastp git_branch: devel git_last_commit: 8883e2a git_last_commit_date: 2026-02-11 Date/Publication: 2026-04-20 source.ver: src/contrib/Rfastp_1.21.3.tar.gz vignettes: vignettes/Rfastp/inst/doc/Rfastp.html vignetteTitles: Rfastp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rfastp/inst/doc/Rfastp.R dependencyCount: 32 Package: RFGeneRank Version: 0.99.4 Depends: R (>= 4.6.0) Imports: ggplot2, limma, methods, pROC, ranger, stats, SummarizedExperiment, sva, AnnotationDbi, umap, scales, utils, S4Vectors, digest, mgcv, Matrix, glmnet, xgboost, patchwork Suggests: GEOquery, Biobase, edgeR, uwot, BiocStyle, knitr, org.Hs.eg.db, rmarkdown, DESeq2, MASS, matrixStats, BiocGenerics, fastshap, caret, testthat (>= 3.0.0), covr License: MIT + file LICENSE MD5sum: c9be263e858bce2e21aa93e665f34b2b NeedsCompilation: no Title: RFGeneRank: Cross-validated Stable Predictive Gene Ranking for Transcriptomics Description: Tools to harmonize bulk RNA-seq matrices, optionally apply batch correction, and train cross-validated classification models using ranger, glmnet, or xgboost. Supports leakage-safe feature selection, permutation importance, SHAP-based interpretability, and calibration methods (Platt or isotonic). Provides stability metrics across folds, embeddings (PCA/UMAP), ROC visualization, SHAP dependence plots, and tidy ranked-gene tables for downstream analysis. biocViews: Transcriptomics, RNASeq, GeneExpression, FeatureExtraction, Classification, Visualization, Software, StatisticalMethod, Alignment Author: Abdulaziz Albeshri [aut, cre] (ORCID: ), Thamer Ahmad Bouback [ctb], Majid Al-Zahrani [ctb], Tasneem Alsahafi [ctb] Maintainer: Abdulaziz Albeshri URL: https://github.com/Abdulaziz-Albeshri/RFGeneRank VignetteBuilder: knitr BugReports: https://github.com/Abdulaziz-Albeshri/RFGeneRank/issues git_url: https://git.bioconductor.org/packages/RFGeneRank git_branch: devel git_last_commit: 7bc34fc git_last_commit_date: 2026-04-06 Date/Publication: 2026-04-20 source.ver: src/contrib/RFGeneRank_0.99.4.tar.gz vignettes: vignettes/RFGeneRank/inst/doc/RFGeneRank.html vignetteTitles: RFGeneRank hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RFGeneRank/inst/doc/RFGeneRank.R dependencyCount: 104 Package: rfPred Version: 1.49.0 Depends: R (>= 3.5.0), methods Imports: utils, Seqinfo, data.table, IRanges, GenomicRanges, parallel, Rsamtools Suggests: BiocStyle License: GPL (>=2 ) MD5sum: b42dc3f5be407a42d4294eb394bba161 NeedsCompilation: yes Title: Assign rfPred functional prediction scores to a missense variants list Description: Based on external numerous data files where rfPred scores are pre-calculated on all genomic positions of the human exome, the package gives rfPred scores to missense variants identified by the chromosome, the position (hg19 version), the referent and alternative nucleotids and the uniprot identifier of the protein. Note that for using the package, the user has to download the TabixFile and index (approximately 3.3 Go). biocViews: Software, Annotation, Classification Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais Maintainer: Hugo Varet git_url: https://git.bioconductor.org/packages/rfPred git_branch: devel git_last_commit: 553494c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rfPred_1.49.0.tar.gz vignettes: vignettes/rfPred/inst/doc/vignette.pdf vignetteTitles: CalculatingrfPredscoreswithpackagerfPred hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfPred/inst/doc/vignette.R dependencyCount: 30 Package: rGenomeTracks Version: 1.17.0 Depends: R (>= 4.1.0), Imports: imager, reticulate, methods, rGenomeTracksData Suggests: rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 0d5dcde222fca661215e285508bc1b4f NeedsCompilation: no Title: Integerated visualization of epigenomic data Description: rGenomeTracks package leverages the power of pyGenomeTracks software with the interactivity of R. pyGenomeTracks is a python software that offers robust method for visualizing epigenetic data files like narrowPeak, Hic matrix, TADs and arcs, however though, here is no way currently to use it within R interactive session. rGenomeTracks wrapped the whole functionality of pyGenomeTracks with additional utilites to make to more pleasant for R users. biocViews: Software, HiC, Visualization Author: Omar Elashkar [aut, cre] (ORCID: ) Maintainer: Omar Elashkar SystemRequirements: pyGenomeTracks (prefered to use install_pyGenomeTracks()) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGenomeTracks git_branch: devel git_last_commit: 5acca55 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rGenomeTracks_1.17.0.tar.gz vignettes: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.html vignetteTitles: rGenomeTracks hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.R dependencyCount: 80 Package: rgoslin Version: 1.15.0 Imports: Rcpp (>= 1.0.3), dplyr LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), BiocStyle, knitr, rmarkdown, kableExtra, BiocManager, stringr, stringi, ggplot2, tibble, lipidr License: MIT + file LICENSE MD5sum: 50d3b7b886f0760b7c75d16dbfdc55a1 NeedsCompilation: yes Title: Lipid Shorthand Name Parsing and Normalization Description: The R implementation for the Grammar of Succint Lipid Nomenclature parses different short hand notation dialects for lipid names. It normalizes them to a standard name. It further provides calculated monoisotopic masses and sum formulas for each successfully parsed lipid name and supplements it with LIPID MAPS Category and Class information. Also, the structural level and further structural details about the head group, fatty acyls and functional groups are returned, where applicable. biocViews: Software, Lipidomics, Metabolomics, Preprocessing, Normalization, MassSpectrometry Author: Nils Hoffmann [aut, cre] (ORCID: ), Dominik Kopczynski [aut] (ORCID: ) Maintainer: Nils Hoffmann URL: https://github.com/lifs-tools/rgoslin VignetteBuilder: knitr BugReports: https://github.com/lifs-tools/rgoslin/issues git_url: https://git.bioconductor.org/packages/rgoslin git_branch: devel git_last_commit: aaf1201 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rgoslin_1.15.0.tar.gz vignettes: vignettes/rgoslin/inst/doc/introduction.html vignetteTitles: Using R Goslin to parse and normalize lipid nomenclature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rgoslin/inst/doc/introduction.R suggestsMe: MetMashR dependencyCount: 20 Package: RGraph2js Version: 1.39.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 MD5sum: abfe11ce7a922a91c905299da12c1a1a NeedsCompilation: no Title: Convert a Graph into a D3js Script Description: Generator of web pages which display interactive network/graph visualizations with D3js, jQuery and Raphael. biocViews: Visualization, Network, GraphAndNetwork, ThirdPartyClient Author: Stephane Cano [aut, cre], Sylvain Gubian [aut], Florian Martin [aut] Maintainer: Stephane Cano SystemRequirements: jQuery, jQueryUI, qTip2, D3js and Raphael are required Javascript libraries made available via the online CDNJS service (http://cdnjs.cloudflare.com). git_url: https://git.bioconductor.org/packages/RGraph2js git_branch: devel git_last_commit: 4327aeb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RGraph2js_1.39.0.tar.gz vignettes: vignettes/RGraph2js/inst/doc/RGraph2js.pdf vignetteTitles: RGraph2js hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGraph2js/inst/doc/RGraph2js.R dependencyCount: 11 Package: Rgraphviz Version: 2.55.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL MD5sum: 749d85bb153123415885d70279f324de NeedsCompilation: yes Title: Provides plotting capabilities for R graph objects Description: Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. biocViews: GraphAndNetwork, Visualization Author: Kasper Daniel Hansen [cre, aut], Jeff Gentry [aut], Li Long [aut], Robert Gentleman [aut], Seth Falcon [aut], Florian Hahne [aut], Deepayan Sarkar [aut] Maintainer: Kasper Daniel Hansen SystemRequirements: optionally Graphviz (>= 2.16), USE_C17 git_url: https://git.bioconductor.org/packages/Rgraphviz git_branch: devel git_last_commit: 508dbe9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Rgraphviz_2.55.0.tar.gz vignettes: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.pdf, vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf vignetteTitles: A New Interface to Plot Graphs Using Rgraphviz, How To Plot A Graph Using Rgraphviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.R, vignettes/Rgraphviz/inst/doc/Rgraphviz.R dependsOnMe: biocGraph, BioMVCClass, CellNOptR, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, dlsem, gridGraphviz importsMe: apComplex, biocGraph, bnem, chimeraviz, CytoML, DEGraph, EnrichDO, EnrichmentBrowser, flowWorkspace, GeneNetworkBuilder, GOstats, hyperdraw, KEGGgraph, mirIntegrator, MIRit, mnem, OncoSimulR, ontoProc, paircompviz, pathview, Pigengene, qpgraph, scConform, TRONCO, abn, agena.ai, BCDAG, BiDAG, bnpa, bnRep, CePa, classGraph, cogmapr, ontologyPlot, SEMgraph, stablespec, WayFindR suggestsMe: a4, altcdfenvs, annotate, Category, CNORfeeder, CNORfuzzy, DEGraph, flowCore, geneplotter, GlobalAncova, globaltest, GSEABase, MLP, NCIgraph, RBGL, rBiopaxParser, safe, SPIA, SRAdb, topGO, ViSEAGO, vtpnet, NCIgraphData, SNAData, arulesViz, BayesNetBP, bivarhr, bnlearn, bnstruct, ChoR, CodeDepends, gbutils, GeneNet, gRain, iTOP, kst, lava, loon, maGUI, micd, multiplex, netmeta, pcalg, PCBN, pchc, pks, psych, rCausalMGM, relations, rEMM, rPref, rSpectral, SCCI, sisal, textplot, tm, topologyGSA, tpc, unifDAG, zenplots dependencyCount: 10 Package: rGREAT Version: 2.13.2 Depends: R (>= 4.0.0), GenomicRanges, IRanges, methods Imports: graphics, rjson, GetoptLong (>= 0.0.9), RCurl, utils, stats, GlobalOptions, shiny, DT, GenomicFeatures, digest, GO.db, progress, circlize, AnnotationDbi, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, RColorBrewer, S4Vectors, GenomeInfoDb, foreach, doParallel, Rcpp LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, rmarkdown, BiocManager, org.Mm.eg.db, msigdbr, KEGGREST, reactome.db Enhances: BioMartGOGeneSets, UniProtKeywords License: MIT + file LICENSE MD5sum: cf1da2b9841f2b92418797b7352a3580 NeedsCompilation: yes Title: GREAT Analysis - Functional Enrichment on Genomic Regions Description: GREAT (Genomic Regions Enrichment of Annotations Tool) is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm (the local GREAT analysis), also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions. biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing, WholeGenome, GenomeAnnotation, Coverage Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/rGREAT, http://great.stanford.edu/public/html/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGREAT git_branch: devel git_last_commit: a1cdd15 git_last_commit_date: 2026-01-30 Date/Publication: 2026-04-20 source.ver: src/contrib/rGREAT_2.13.2.tar.gz vignettes: vignettes/rGREAT/inst/doc/rGREAT.html vignetteTitles: The rGREAT package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 123 Package: RGSEA Version: 1.45.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: 32e3f0a3dacbe49fc43a95a2737e05c4 NeedsCompilation: no Title: Random Gene Set Enrichment Analysis Description: Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements. biocViews: GeneSetEnrichment, StatisticalMethod, Classification Author: Chengcheng Ma Maintainer: Chengcheng Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGSEA git_branch: devel git_last_commit: 525ac54 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RGSEA_1.45.0.tar.gz vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf vignetteTitles: Introduction to RGSEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGSEA/inst/doc/RGSEA.R dependencyCount: 6 Package: rgsepd Version: 1.43.0 Depends: R (>= 4.2.0), DESeq2, goseq (>= 1.28) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, AnnotationDbi Suggests: boot, tools, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: 1aae8b108417bf89e827d6b8f926d62f NeedsCompilation: no Title: Gene Set Enrichment / Projection Displays Description: R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group. biocViews: ImmunoOncology, Software, DifferentialExpression, GeneSetEnrichment, RNASeq Author: Karl Stamm Maintainer: Karl Stamm VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rgsepd git_branch: devel git_last_commit: c93ca40 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rgsepd_1.43.0.tar.gz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R dependencyCount: 124 Package: rhdf5 Version: 2.55.16 Depends: methods, R (>= 4.0.0) Imports: rhdf5filters (>= 1.15.5), Rhdf5lib (>= 1.33.3) LinkingTo: Rhdf5lib Suggests: bench, BiocParallel, BiocStyle, bit64, curl, dplyr, ggplot2, knitr, rmarkdown, testthat, withr License: Artistic-2.0 MD5sum: 9230a46f72ceab86cb2ef3c37c8ed980 NeedsCompilation: yes Title: R Interface to HDF5 Description: This package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM. biocViews: Infrastructure, DataImport Author: Bernd Fischer [aut], Mike Smith [aut] (ORCID: , Maintainer from 2017 to 2025), Gregoire Pau [aut], Martin Morgan [ctb], Daniel van Twisk [ctb], Hugo Gruson [cre] (ORCID: ), German Network for Bioinformatics Infrastructure - de.NBI [fnd] Maintainer: Hugo Gruson URL: https://huber-group-embl.github.io/rhdf5/, https://github.com/Huber-group-EMBL/rhdf5 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/rhdf5/issues git_url: https://git.bioconductor.org/packages/rhdf5 git_branch: devel git_last_commit: 34ead97 git_last_commit_date: 2026-03-14 Date/Publication: 2026-04-20 source.ver: src/contrib/rhdf5_2.55.16.tar.gz vignettes: vignettes/rhdf5/inst/doc/practical_tips.html, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.html, vignettes/rhdf5/inst/doc/rhdf5.html vignetteTitles: rhdf5 Practical Tips, Reading HDF5 Files In The Cloud, rhdf5 - HDF5 interface for R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/practical_tips.R, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.R, vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GSCA, h5mread, HiCBricks, LoomExperiment, MuData, octad importsMe: alabaster.base, alabaster.bumpy, alabaster.mae, alabaster.matrix, alabaster.ranges, alabaster.spatial, BayesSpace, BgeeCall, bnbc, bsseq, chihaya, CiteFuse, cmapR, CoGAPS, CopyNumberPlots, cTRAP, cytomapper, diffHic, DropletUtils, epigraHMM, EventPointer, FRASER, GenomicScores, gep2pep, h5vc, HDF5Array, HicAggR, HiCcompare, HiCExperiment, HiCPotts, IONiseR, mariner, methodical, MOFA2, MoleculeExperiment, phantasus, plotgardener, ptairMS, PureCN, RBedMethyl, recountmethylation, ribor, scafari, scCB2, scMitoMut, scone, scRNAseqApp, signatureSearch, SpaceTrooper, SpliceWiz, SpotClean, SurfR, TENxIO, trackViewer, MafH5.gnomAD.v4.0.GRCh38, MethylSeqData, ptairData, scMultiome, signatureSearchData, TumourMethData, bioRad, ebvcube, file2meco, karyotapR, LOMAR, OmicFlow, rDataPipeline suggestsMe: anndataR, beachmat.hdf5, biomformat, edgeR, HiCDOC, HiCParser, imageFeatureTCGA, imageTCGAutils, mia, MicrobiotaProcess, pairedGSEA, phantasusLite, rhdf5filters, SCArray, scviR, slalom, SpatialFeatureExperiment, spatialHeatmap, Spectra, SummarizedExperiment, tximport, Voyager, xcms, zellkonverter, ClustAssess, conos, CRMetrics, getRad, io, MplusAutomation, neonstore, neonUtilities, SignacX dependencyCount: 8 Package: rhdf5client Version: 1.33.0 Depends: R (>= 3.6), methods, DelayedArray Imports: httr, rjson, utils, data.table Suggests: knitr, testthat, BiocStyle, DT, rmarkdown License: Artistic-2.0 MD5sum: 544b49f5202a74ea0039a949122cceb0 NeedsCompilation: yes Title: Access HDF5 content from HDF Scalable Data Service Description: This package provides functionality for reading data from HDF Scalable Data Service from within R. The HSDSArray function bridges from HSDS to the user via the DelayedArray interface. Bioconductor manages an open HSDS instance graciously provided by John Readey of the HDF Group. biocViews: DataImport, Software, Infrastructure Author: Samuela Pollack [aut], Shweta Gopaulakrishnan [aut], BJ Stubbs [aut], Alexey Sergushichev [aut, cre], Vincent Carey [aut] Maintainer: Alexey Sergushichev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rhdf5client git_branch: devel git_last_commit: d37daa8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rhdf5client_1.33.0.tar.gz vignettes: vignettes/rhdf5client/inst/doc/delayed-array.html vignetteTitles: HSDSArray DelayedArray backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5client/inst/doc/delayed-array.R importsMe: phantasus, phantasusLite dependencyCount: 31 Package: rhdf5filters Version: 1.23.3 LinkingTo: Rhdf5lib Suggests: BiocStyle, knitr, rmarkdown, testthat, rhdf5 (>= 2.47.7) License: BSD_2_clause + file LICENSE MD5sum: 89b08883107d2867fb731d0e59a8d635 NeedsCompilation: yes Title: HDF5 Compression Filters Description: Provides a collection of additional compression filters for HDF5 datasets. The package is intended to provide seamless integration with rhdf5, however the compiled filters can also be used with external applications. biocViews: Infrastructure, DataImport Author: Mike Smith [aut, ccp] (ORCID: ), Hugo Gruson [cre] (ORCID: ) Maintainer: Hugo Gruson URL: https://github.com/Huber-group-EMBL/rhdf5filters SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/rhdf5filters/issues git_url: https://git.bioconductor.org/packages/rhdf5filters git_branch: devel git_last_commit: 98bdffb git_last_commit_date: 2025-12-09 Date/Publication: 2026-04-20 source.ver: src/contrib/rhdf5filters_1.23.3.tar.gz vignettes: vignettes/rhdf5filters/inst/doc/rhdf5filters.html vignetteTitles: HDF5 Compression Filters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rhdf5filters/inst/doc/rhdf5filters.R importsMe: h5mread, rhdf5 dependencyCount: 6 Package: Rhdf5lib Version: 1.33.6 Depends: R (>= 4.2.0) LinkingTo: biocmake Suggests: BiocStyle, knitr, rmarkdown, tinytest, mockery License: Artistic-2.0 MD5sum: 049c0092adb1a227c9752370fc0c8427 NeedsCompilation: yes Title: hdf5 library as an R package Description: Provides C and C++ hdf5 libraries. biocViews: Infrastructure Author: Mike Smith [ctb] (ORCID: ), Hugo Gruson [cre] (ORCID: ), The HDF Group [cph] Maintainer: Hugo Gruson URL: https://github.com/Huber-group-EMBL/Rhdf5lib VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/Rhdf5lib/issues git_url: https://git.bioconductor.org/packages/Rhdf5lib git_branch: devel git_last_commit: c0f2cf6 git_last_commit_date: 2026-03-16 Date/Publication: 2026-04-20 source.ver: src/contrib/Rhdf5lib_1.33.6.tar.gz vignettes: vignettes/Rhdf5lib/inst/doc/Rhdf5lib.html vignetteTitles: Linking to Rhdf5lib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhdf5lib/inst/doc/Rhdf5lib.R importsMe: epigraHMM, rhdf5 suggestsMe: mbkmeans linksToMe: alabaster.base, beachmat.hdf5, chihaya, CytoML, DropletUtils, epigraHMM, h5mread, mbkmeans, mzR, ncdfFlow, rhdf5, rhdf5filters, stPipe, smer dependencyCount: 5 Package: rhinotypeR Version: 1.5.0 Depends: R (>= 4.5.0) Imports: Biostrings, methods, MSA2dist, msa Suggests: knitr, rmarkdown, BiocManager, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: d17649fb05a5b61409d0daa74d24619c NeedsCompilation: no Title: Rhinovirus genotyping Description: "rhinotypeR" is designed to automate the comparison of sequence data against prototype strains, streamlining the genotype assignment process. By implementing predefined pairwise distance thresholds, this package makes genotype assignment accessible to researchers and public health professionals. This tool enhances our epidemiological toolkit by enabling more efficient surveillance and analysis of rhinoviruses (RVs) and other viral pathogens with complex genomic landscapes. Additionally, "rhinotypeR" supports comprehensive visualization and analysis of single nucleotide polymorphisms (SNPs) and amino acid substitutions, facilitating in-depth genetic and evolutionary studies. biocViews: Sequencing, Genetics, Phylogenetics, Visualization, MultipleSequenceAlignment, MultipleComparison Author: Martha Luka [aut, cre] (ORCID: ), Ruth Nanjala [aut], Winfred Gatua [aut], Wafaa M. Rashed [aut], Olaitan Awe [aut] Maintainer: Martha Luka URL: https://github.com/omicscodeathon/rhinotypeR VignetteBuilder: knitr BugReports: https://github.com/omicscodeathon/rhinotypeR/issues git_url: https://git.bioconductor.org/packages/rhinotypeR git_branch: devel git_last_commit: cf7ced9 git_last_commit_date: 2025-11-24 Date/Publication: 2026-04-20 source.ver: src/contrib/rhinotypeR_1.5.0.tar.gz vignettes: vignettes/rhinotypeR/inst/doc/rhinotypeR.html vignetteTitles: Introduction to rhinotypeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rhinotypeR/inst/doc/rhinotypeR.R dependencyCount: 57 Package: Rhtslib Version: 3.7.0 Imports: tools Suggests: knitr, rmarkdown, BiocStyle License: LGPL (>= 2) MD5sum: 46074d444a510724eebc75c3ebacf66a NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.18 of the 'HTSlib' C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package="Rhtslib", "Rhtslib"). biocViews: DataImport, Sequencing Author: Nathaniel Hayden [led, aut], Martin Morgan [aut], Hervé Pagès [aut, cre], Tomas Kalibera [ctb], Jeroen Ooms [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/Rhtslib, http://www.htslib.org/ SystemRequirements: libbz2 & liblzma & libcurl (with header files), GNU make VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Rhtslib/issues git_url: https://git.bioconductor.org/packages/Rhtslib git_branch: devel git_last_commit: 0dcad2a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Rhtslib_3.7.0.tar.gz vignettes: vignettes/Rhtslib/inst/doc/Rhtslib.html vignetteTitles: Motivation and use of Rhtslib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R importsMe: deepSNV, diffHic, maftools, mitoClone2, scPipe, stPipe linksToMe: bamsignals, csaw, deepSNV, diffHic, epialleleR, FLAMES, h5vc, iscream, maftools, methylKit, mitoClone2, podkat, QuasR, raer, Rfastp, Rsamtools, scPipe, ShortRead, stPipe, VariantAnnotation, jackalope dependencyCount: 1 Package: ribor Version: 1.23.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, hash, methods, rhdf5, rlang, stats, S4Vectors, tidyr, tools, yaml Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 8707cf49f5c8e60e83b35536dac517ff NeedsCompilation: no Title: An R Interface for Ribo Files Description: The ribor package provides an R Interface for .ribo files. It provides functionality to read the .ribo file, which is of HDF5 format, and performs common analyses on its contents. biocViews: Software, Infrastructure Author: Michael Geng [cre, aut], Hakan Ozadam [aut], Can Cenik [aut] Maintainer: Michael Geng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribor git_branch: devel git_last_commit: 9decab8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ribor_1.23.0.tar.gz vignettes: vignettes/ribor/inst/doc/ribor.html vignetteTitles: A Walkthrough of RiboR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ribor/inst/doc/ribor.R dependencyCount: 47 Package: riboSeqR Version: 1.45.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, S4Vectors, baySeq, Seqinfo, seqLogo Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: 72c64c58830e51dd96fc113ca52e0cd6 NeedsCompilation: no Title: Analysis of sequencing data from ribosome profiling experiments Description: Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments. biocViews: Sequencing,Genetics,Visualization,RiboSeq Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/riboSeqR BugReports: https://github.com/samgg/riboSeqR/issues git_url: https://git.bioconductor.org/packages/riboSeqR git_branch: devel git_last_commit: 77f80b5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/riboSeqR_1.45.0.tar.gz vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf vignetteTitles: riboSeqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/riboSeqR/inst/doc/riboSeqR.R dependencyCount: 38 Package: rifi Version: 1.15.0 Depends: R (>= 4.2) Imports: car, cowplot, doMC, parallel, dplyr, egg, foreach, ggplot2, graphics, grDevices, grid, methods, nls2, nnet, rlang, S4Vectors, scales, stats, stringr, SummarizedExperiment, tibble, rtracklayer, reshape2, utils Suggests: DescTools, devtools, knitr, rmarkdown, BiocStyle License: GPL-3 + file LICENSE MD5sum: abac128279df80980b9fc4fe2ffadafb NeedsCompilation: no Title: 'rifi' analyses data from rifampicin time series created by microarray or RNAseq Description: 'rifi' analyses data from rifampicin time series created by microarray or RNAseq. 'rifi' is a transcriptome data analysis tool for the holistic identification of transcription and decay associated processes. The decay constants and the delay of the onset of decay is fitted for each probe/bin. Subsequently, probes/bins of equal properties are combined into segments by dynamic programming, independent of a existing genome annotation. This allows to detect transcript segments of different stability or transcriptional events within one annotated gene. In addition to the classic decay constant/half-life analysis, 'rifi' detects processing sites, transcription pausing sites, internal transcription start sites in operons, sites of partial transcription termination in operons, identifies areas of likely transcriptional interference by the collision mechanism and gives an estimate of the transcription velocity. All data are integrated to give an estimate of continous transcriptional units, i.e. operons. Comprehensive output tables and visualizations of the full genome result and the individual fits for all probes/bins are produced. biocViews: RNASeq, DifferentialExpression, GeneRegulation, Transcriptomics, Regression, Microarray, Software Author: Loubna Youssar [aut, ctb], Walja Wanney [aut, ctb], Jens Georg [aut, cre] Maintainer: Jens Georg VignetteBuilder: knitr BugReports: https://github.com/CyanolabFreiburg/rifi git_url: https://git.bioconductor.org/packages/rifi git_branch: devel git_last_commit: 16a7acf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rifi_1.15.0.tar.gz vignettes: vignettes/rifi/inst/doc/vignette.html vignetteTitles: Rifi for decay estimation,, based on high resolution microarray or RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rifi/inst/doc/vignette.R dependencyCount: 130 Package: rifiComparative Version: 1.11.1 Depends: R (>= 4.2) Imports: cowplot, doMC, parallel, dplyr, egg, foreach, ggplot2, ggrepel, graphics, grDevices, grid, methods, nnet, rlang, S4Vectors, scales, stats, stringr, tibble, rtracklayer, utils, writexl, DTA, LSD, reshape2, devtools, SummarizedExperiment Suggests: DescTools, knitr, rmarkdown, BiocStyle License: GPL-3 + file LICENSE MD5sum: 53b5d0afeb0ee1881d75698cb2daa0c3 NeedsCompilation: no Title: 'rifiComparative' compares the output of rifi from two different conditions. Description: 'rifiComparative' is a continuation of rifi package. It compares two conditions output of rifi using half-life and mRNA at time 0 segments. As an input for the segmentation, the difference between half-life of both condtions and log2FC of the mRNA at time 0 are used. The package provides segmentation, statistics, summary table, fragments visualization and some additional useful plots for further anaylsis. biocViews: RNASeq, DifferentialExpression, GeneRegulation, Transcriptomics, Microarray, Software Author: Loubna Youssar [aut, cre], Jens cre Georg [aut] Maintainer: Loubna Youssar VignetteBuilder: knitr BugReports: https://github.com/CyanolabFreiburg/rifiComparative git_url: https://git.bioconductor.org/packages/rifiComparative git_branch: devel git_last_commit: f4b9e26 git_last_commit_date: 2026-04-19 Date/Publication: 2026-04-20 source.ver: src/contrib/rifiComparative_1.11.1.tar.gz vignettes: vignettes/rifiComparative/inst/doc/rifiComparative.html vignetteTitles: rifiComparative hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rifiComparative/inst/doc/rifiComparative.R dependencyCount: 167 Package: Rigraphlib Version: 1.3.2 LinkingTo: biocmake Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 90244e9f18244ce8efd099c9771f83d0 NeedsCompilation: yes Title: igraph library as an R package Description: Vendors the igraph C source code and builds it into a static library. Other Bioconductor packages can link to libigraph.a in their own C/C++ code. This is intended for packages wrapping C/C++ libraries that depend on the igraph C library and cannot be easily adapted to use the igraph R package. biocViews: Clustering, GraphAndNetwork Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun URL: https://github.com/libscran/Rigraphlib VignetteBuilder: knitr BugReports: https://github.com/libscran/Rigraphlib/issues git_url: https://git.bioconductor.org/packages/Rigraphlib git_branch: devel git_last_commit: 7cfc7f9 git_last_commit_date: 2025-12-07 Date/Publication: 2026-04-20 source.ver: src/contrib/Rigraphlib_1.3.2.tar.gz vignettes: vignettes/Rigraphlib/inst/doc/userguide.html vignetteTitles: Using the igraph C library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rigraphlib/inst/doc/userguide.R linksToMe: scrapper dependencyCount: 5 Package: rigvf Version: 1.3.7 Depends: R (>= 4.1.0) Imports: methods, httr2, rjsoncons, dplyr, tidyr, rlang, memoise, cachem, whisker, jsonlite, GenomicRanges, IRanges, Seqinfo Suggests: knitr, rmarkdown, testthat (>= 3.0.0), plyranges, plotgardener, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, tibble License: MIT + file LICENSE MD5sum: 00dd9f964043f204f0ca5bbe532a75dc NeedsCompilation: no Title: R interface to the IGVF Catalog Description: The IGVF Catalog provides data on the impact of genomic variants on function. The `rigvf` package provides an interface to the IGVF Catalog, allowing easy integration with Bioconductor resources. biocViews: ThirdPartyClient, Annotation, VariantAnnotation, FunctionalGenomics, GeneRegulation, GenomicVariation, GeneTarget Author: Martin Morgan [aut] (ORCID: ), Michael Love [aut, cre] (ORCID: ), NIH NHGRI UM1HG012003 [fnd] Maintainer: Michael Love URL: https://IGVF.github.io/rigvf VignetteBuilder: knitr BugReports: https://github.com/IGVF/rigvf/issues git_url: https://git.bioconductor.org/packages/rigvf git_branch: devel git_last_commit: 54c4737 git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/rigvf_1.3.7.tar.gz vignettes: vignettes/rigvf/inst/doc/rigvf.html vignetteTitles: Accessing data from the IGVF Catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rigvf/inst/doc/rigvf.R dependencyCount: 44 Package: RImmPort Version: 1.39.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 MD5sum: 22a269e2acff14280fdafd2b8f55af5d NeedsCompilation: no Title: RImmPort: Enabling Ready-for-analysis Immunology Research Data Description: The RImmPort package simplifies access to ImmPort data for analysis in the R environment. It provides a standards-based interface to the ImmPort study data that is in a proprietary format. biocViews: BiomedicalInformatics, DataImport, DataRepresentation Author: Ravi Shankar Maintainer: Zicheng Hu , Ravi Shankar URL: http://bioconductor.org/packages/RImmPort/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RImmPort git_branch: devel git_last_commit: 61765d4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RImmPort_1.39.0.tar.gz vignettes: vignettes/RImmPort/inst/doc/RImmPort_Article.pdf, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.pdf vignetteTitles: RImmPort: Enabling ready-for-analysis immunology research data, RImmPort: Quick Start Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RImmPort/inst/doc/RImmPort_Article.R, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.R dependencyCount: 40 Package: RIVER Version: 1.35.0 Depends: R (>= 3.3.2) Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools License: GPL (>= 2) MD5sum: 77c4e7bad7fb9cb84dc88c776f9f2141 NeedsCompilation: no Title: R package for RIVER (RNA-Informed Variant Effect on Regulation) Description: An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples. biocViews: GeneExpression, GeneticVariability, SNP, Transcription, FunctionalPrediction, GeneRegulation, GenomicVariation, BiomedicalInformatics, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Bayesian, Clustering, TranscriptomeVariant, Regression Author: Yungil Kim [aut, cre], Alexis Battle [aut] Maintainer: Yungil Kim URL: https://github.com/ipw012/RIVER VignetteBuilder: knitr BugReports: https://github.com/ipw012/RIVER/issues git_url: https://git.bioconductor.org/packages/RIVER git_branch: devel git_last_commit: 24ebc88 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RIVER_1.35.0.tar.gz vignettes: vignettes/RIVER/inst/doc/RIVER.html vignetteTitles: RIVER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIVER/inst/doc/RIVER.R dependencyCount: 38 Package: RJMCMCNucleosomes Version: 1.35.0 Depends: R (>= 3.5), IRanges, GenomicRanges Imports: Rcpp (>= 0.12.5), consensusSeekeR, BiocGenerics, Seqinfo, S4Vectors (>= 0.23.10), BiocParallel, stats, graphics, methods, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, nucleoSim, RUnit License: Artistic-2.0 MD5sum: 41a597f1c0eabb4891bbd732b0470d8a NeedsCompilation: yes Title: Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq) Description: This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling. biocViews: BiologicalQuestion, ChIPSeq, NucleosomePositioning, Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Pascal Belleau [aut], Rawane Samb [aut], Astrid Deschênes [cre, aut], Khader Khadraoui [aut], Lajmi Lakhal-Chaieb [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes SystemRequirements: Rcpp VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues git_url: https://git.bioconductor.org/packages/RJMCMCNucleosomes git_branch: devel git_last_commit: e3d8d0f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RJMCMCNucleosomes_1.35.0.tar.gz vignettes: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.html vignetteTitles: Nucleosome Positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.R dependencyCount: 67 Package: RLassoCox Version: 1.19.0 Depends: R (>= 4.1), glmnet Imports: Matrix, igraph, survival, stats Suggests: knitr License: Artistic-2.0 MD5sum: 68fda6d7da187aeba2474cfa8d517169 NeedsCompilation: no Title: A reweighted Lasso-Cox by integrating gene interaction information Description: RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types. biocViews: Survival, Regression, GeneExpression, GenePrediction, Network Author: Wei Liu [cre, aut] (ORCID: ) Maintainer: Wei Liu VignetteBuilder: knitr BugReports: https://github.com/weiliu123/RLassoCox/issues git_url: https://git.bioconductor.org/packages/RLassoCox git_branch: devel git_last_commit: 3312604 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RLassoCox_1.19.0.tar.gz vignettes: vignettes/RLassoCox/inst/doc/RLassoCox.pdf vignetteTitles: RLassoCox hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLassoCox/inst/doc/RLassoCox.R dependencyCount: 26 Package: RLMM Version: 1.73.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: 9711b2728bff509f477ffd24d095d2bf NeedsCompilation: no Title: A Genotype Calling Algorithm for Affymetrix SNP Arrays Description: A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now. biocViews: Microarray, OneChannel, SNP, GeneticVariability Author: Nusrat Rabbee , Gary Wong Maintainer: Nusrat Rabbee URL: http://www.stat.berkeley.edu/users/nrabbee/RLMM SystemRequirements: Internal files Xba.CQV, Xba.regions (or other regions file) git_url: https://git.bioconductor.org/packages/RLMM git_branch: devel git_last_commit: b928d91 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RLMM_1.73.0.tar.gz vignettes: vignettes/RLMM/inst/doc/RLMM.pdf vignetteTitles: RLMM Doc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLMM/inst/doc/RLMM.R dependencyCount: 6 Package: Rmagpie Version: 1.67.0 Depends: R (>= 2.6.1), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), e1071, graphics, grDevices, kernlab, methods, pamr, stats, utils Suggests: xtable License: GPL (>= 3) MD5sum: 9e0525c9bcf24e1f17439ce85ecfd1c3 NeedsCompilation: no Title: MicroArray Gene-expression-based Program In Error rate estimation Description: Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes. biocViews: Microarray, Classification Author: Camille Maumet , with contributions from C. Ambroise J. Zhu Maintainer: Camille Maumet URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rmagpie git_branch: devel git_last_commit: 531a8ca git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Rmagpie_1.67.0.tar.gz vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf vignetteTitles: Rmagpie Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmagpie/inst/doc/Magpie_examples.R dependencyCount: 20 Package: rmelting Version: 1.27.0 Depends: R (>= 3.6) Imports: Rdpack, rJava (>= 0.9-8) Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat License: GPL-2 | GPL-3 MD5sum: bfff80294e0341c69b47681ed6dc0b51 NeedsCompilation: no Title: R Interface to MELTING 5 Description: R interface to the MELTING 5 program (https://www.ebi.ac.uk/biomodels/tools/melting/) to compute melting temperatures of nucleic acid duplexes along with other thermodynamic parameters. biocViews: BiomedicalInformatics, Cheminformatics, Author: J. Aravind [aut, cre] (ORCID: ), G. K. Krishna [aut], Bob Rudis [ctb] (melting5jars), Nicolas Le Novère [ctb] (MELTING 5 Java Library), Marine Dumousseau [ctb] (MELTING 5 Java Library), William John Gowers [ctb] (MELTING 5 Java Library) Maintainer: J. Aravind URL: https://github.com/aravind-j/rmelting, https://aravind-j.github.io/rmelting/ SystemRequirements: Java VignetteBuilder: knitr BugReports: https://github.com/aravind-j/rmelting/issues git_url: https://git.bioconductor.org/packages/rmelting git_branch: devel git_last_commit: 9b3c944 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rmelting_1.27.0.tar.gz vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: Rmmquant Version: 1.29.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, S4Vectors, GenomicRanges, SummarizedExperiment, devtools, TBX20BamSubset, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, DESeq2, apeglm, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 19cc26f6a6bd052106ada7be6674ec2e NeedsCompilation: yes Title: RNA-Seq multi-mapping Reads Quantification Tool Description: RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way. biocViews: GeneExpression, Transcription Author: Zytnicki Matthias [aut, cre] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rmmquant git_branch: devel git_last_commit: b7f3f59 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Rmmquant_1.29.0.tar.gz vignettes: vignettes/Rmmquant/inst/doc/Rmmquant.html vignetteTitles: The Rmmquant package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmmquant/inst/doc/Rmmquant.R dependencyCount: 180 Package: rmspc Version: 1.17.0 Imports: processx, BiocManager, rtracklayer, stats, tools, methods, GenomicRanges, stringr Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: 27377f75285e4f243af6d8bcdd19b0e7 NeedsCompilation: no Title: Multiple Sample Peak Calling Description: The rmspc package runs MSPC (Multiple Sample Peak Calling) software using R. The analysis of ChIP-seq samples outputs a number of enriched regions (commonly known as "peaks"), each indicating a protein-DNA interaction or a specific chromatin modification. When replicate samples are analyzed, overlapping peaks are expected. This repeated evidence can therefore be used to locally lower the minimum significance required to accept a peak. MSPC uses combined evidence from replicated experiments to evaluate peak calling output, rescuing peaks, and reduce false positives. It takes any number of replicates as input and improves sensitivity and specificity of peak calling on each, and identifies consensus regions between the input samples. biocViews: ChIPSeq, Sequencing, ChipOnChip, DataImport, RNASeq Author: Vahid Jalili [aut], Marzia Angela Cremona [aut], Fernando Palluzzi [aut], Meriem Bahda [aut, cre] Maintainer: Meriem Bahda URL: https://genometric.github.io/MSPC/ SystemRequirements: .NET 9.0 VignetteBuilder: knitr BugReports: https://github.com/Genometric/MSPC/issues git_url: https://git.bioconductor.org/packages/rmspc git_branch: devel git_last_commit: 2f7604a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rmspc_1.17.0.tar.gz vignettes: vignettes/rmspc/inst/doc/rmpsc.html vignetteTitles: User guide to the rmspc package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rmspc/inst/doc/rmpsc.R dependencyCount: 68 Package: RNAAgeCalc Version: 1.23.0 Depends: R (>= 3.6) Imports: ggplot2, recount, impute, AnnotationDbi, org.Hs.eg.db, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: b4535ce1827c87058afacff83b8f582c NeedsCompilation: no Title: A multi-tissue transcriptional age calculator Description: It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data. biocViews: RNASeq,GeneExpression Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/RNAAgeCalc VignetteBuilder: knitr BugReports: https://github.com/reese3928/RNAAgeCalc/issues git_url: https://git.bioconductor.org/packages/RNAAgeCalc git_branch: devel git_last_commit: bb08720 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RNAAgeCalc_1.23.0.tar.gz vignettes: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.html vignetteTitles: RNAAgeCalc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.R dependencyCount: 165 Package: RNAdecay Version: 1.31.0 Depends: R (>= 4.3) Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr, scales Suggests: parallel, knitr, reshape2, rmarkdown License: GPL-2 MD5sum: 99f89ad69fa34e4305bb3a02338dbb8a NeedsCompilation: yes Title: Maximum Likelihood Decay Modeling of RNA Degradation Data Description: RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions. biocViews: ImmunoOncology, Software, GeneExpression, GeneRegulation, DifferentialExpression, Transcription, Transcriptomics, TimeCourse, Regression, RNASeq, Normalization, WorkflowStep Author: Reed Sorenson [aut, cre] (ORCID: ), Katrina Johnson [aut], Frederick Adler [aut], Leslie Sieburth [aut] (ORCID: ) Maintainer: Reed Sorenson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAdecay git_branch: devel git_last_commit: 5f4a6cd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RNAdecay_1.31.0.tar.gz vignettes: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.html vignetteTitles: RNAdecay Vignette: Normalization,, Modeling,, and Visualization of RNA Decay Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.R dependencyCount: 34 Package: rnaEditr Version: 1.21.0 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, BiocGenerics, Seqinfo, bumphunter, S4Vectors, stats, survival, logistf, plyr, corrplot Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 7c35a28777bddc24782e8b8564f53d1c NeedsCompilation: no Title: Statistical analysis of RNA editing sites and hyper-editing regions Description: RNAeditr analyzes site-specific RNA editing events, as well as hyper-editing regions. The editing frequencies can be tested against binary, continuous or survival outcomes. Multiple covariate variables as well as interaction effects can also be incorporated in the statistical models. biocViews: GeneTarget, Epigenetics, DimensionReduction, FeatureExtraction, Regression, Survival, RNASeq Author: Lanyu Zhang [aut, cre], Gabriel Odom [aut], Tiago Silva [aut], Lissette Gomez [aut], Lily Wang [aut] Maintainer: Lanyu Zhang URL: https://github.com/TransBioInfoLab/rnaEditr VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/rnaEditr/issues git_url: https://git.bioconductor.org/packages/rnaEditr git_branch: devel git_last_commit: 6261cfb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rnaEditr_1.21.0.tar.gz vignettes: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.html vignetteTitles: Introduction to rnaEditr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.R dependencyCount: 136 Package: RNAsense Version: 1.25.0 Depends: R (>= 3.6) Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment, stats, utils, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 24c8f7c0eba6584cf01f239a3b906364 NeedsCompilation: no Title: Analysis of Time-Resolved RNA-Seq Data Description: RNA-sense tool compares RNA-seq time curves in two experimental conditions, i.e. wild-type and mutant, and works in three steps. At Step 1, it builds expression profile for each transcript in one condition (i.e. wild-type) and tests if the transcript abundance grows or decays significantly. Dynamic transcripts are then sorted to non-overlapping groups (time profiles) by the time point of switch up or down. At Step 2, RNA-sense outputs the groups of differentially expressed transcripts, which are up- or downregulated in the mutant compared to the wild-type at each time point. At Step 3, Correlations (Fisher's exact test) between the outputs of Step 1 (switch up- and switch down- time profile groups) and the outputs of Step2 (differentially expressed transcript groups) are calculated. The results of the correlation analysis are printed as two-dimensional color plot, with time profiles and differential expression groups at y- and x-axis, respectively, and facilitates the biological interpretation of the data. biocViews: RNASeq, GeneExpression, DifferentialExpression Author: Marcus Rosenblatt [cre], Gao Meijang [aut], Helge Hass [aut], Daria Onichtchouk [aut] Maintainer: Marcus Rosenblatt VignetteBuilder: knitr BugReports: https://github.com/marcusrosenblatt/RNAsense git_url: https://git.bioconductor.org/packages/RNAsense git_branch: devel git_last_commit: 3880afe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RNAsense_1.25.0.tar.gz vignettes: vignettes/RNAsense/inst/doc/example.html vignetteTitles: Put the title of your vignette here hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAsense/inst/doc/example.R dependencyCount: 52 Package: rnaseqcomp Version: 1.41.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 5fe85b86daba06e8182d18af0dbabdd6 NeedsCompilation: no Title: Benchmarks for RNA-seq Quantification Pipelines Description: Several quantitative and visualized benchmarks for RNA-seq quantification pipelines. Two-condition quantifications for genes, transcripts, junctions or exons by each pipeline with necessary meta information should be organized into numeric matrices in order to proceed the evaluation. biocViews: RNASeq, Visualization, QualityControl Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/rnaseqcomp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqcomp git_branch: devel git_last_commit: 088baa1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rnaseqcomp_1.41.0.tar.gz vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.html vignetteTitles: The rnaseqcomp user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.R dependencyCount: 2 Package: RNAseqCovarImpute Version: 1.9.0 Depends: R (>= 4.3.0) Imports: Biobase, BiocGenerics, BiocParallel, stats, limma, dplyr, magrittr, rlang, edgeR, foreach, mice Suggests: BiocStyle, knitr, PCAtools, rmarkdown, tidyr, stringr, testthat (>= 3.0.0) License: GPL-3 MD5sum: a06a9bff5b22c747727baea12e55b310 NeedsCompilation: no Title: Impute Covariate Data in RNA Sequencing Studies Description: The RNAseqCovarImpute package makes linear model analysis for RNA sequencing read counts compatible with multiple imputation (MI) of missing covariates. A major problem with implementing MI in RNA sequencing studies is that the outcome data must be included in the imputation prediction models to avoid bias. This is difficult in omics studies with high-dimensional data. The first method we developed in the RNAseqCovarImpute package surmounts the problem of high-dimensional outcome data by binning genes into smaller groups to analyze pseudo-independently. This method implements covariate MI in gene expression studies by 1) randomly binning genes into smaller groups, 2) creating M imputed datasets separately within each bin, where the imputation predictor matrix includes all covariates and the log counts per million (CPM) for the genes within each bin, 3) estimating gene expression changes using `limma::voom` followed by `limma::lmFit` functions, separately on each M imputed dataset within each gene bin, 4) un-binning the gene sets and stacking the M sets of model results before applying the `limma::squeezeVar` function to apply a variance shrinking Bayesian procedure to each M set of model results, 5) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 6) adjusting P-values for multiplicity to account for false discovery rate (FDR). A faster method uses principal component analysis (PCA) to avoid binning genes while still retaining outcome information in the MI models. Binning genes into smaller groups requires that the MI and limma-voom analysis is run many times (typically hundreds). The more computationally efficient MI PCA method implements covariate MI in gene expression studies by 1) performing PCA on the log CPM values for all genes using the Bioconductor `PCAtools` package, 2) creating M imputed datasets where the imputation predictor matrix includes all covariates and the optimum number of PCs to retain (e.g., based on Horn’s parallel analysis or the number of PCs that account for >80% explained variation), 3) conducting the standard limma-voom pipeline with the `voom` followed by `lmFit` followed by `eBayes` functions on each M imputed dataset, 4) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 5) adjusting P-values for multiplicity to account for false discovery rate (FDR). biocViews: RNASeq, GeneExpression, DifferentialExpression, Sequencing Author: Brennan Baker [aut, cre] (ORCID: ), Sheela Sathyanarayana [aut], Adam Szpiro [aut], James MacDonald [aut], Alison Paquette [aut] Maintainer: Brennan Baker URL: https://github.com/brennanhilton/RNAseqCovarImpute VignetteBuilder: knitr BugReports: https://github.com/brennanhilton/RNAseqCovarImpute/issues git_url: https://git.bioconductor.org/packages/RNAseqCovarImpute git_branch: devel git_last_commit: c21a243 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RNAseqCovarImpute_1.9.0.tar.gz vignettes: vignettes/RNAseqCovarImpute/inst/doc/Example_Data_for_RNAseqCovarImpute.html, vignettes/RNAseqCovarImpute/inst/doc/Impute_Covariate_Data_in_RNA_sequencing_Studies.html vignetteTitles: Example Data for RNAseqCovarImpute, Impute Covariate Data in RNA-sequencing Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAseqCovarImpute/inst/doc/Example_Data_for_RNAseqCovarImpute.R, vignettes/RNAseqCovarImpute/inst/doc/Impute_Covariate_Data_in_RNA_sequencing_Studies.R dependencyCount: 84 Package: RNASeqPower Version: 1.51.0 License: LGPL (>=2) MD5sum: 1ce3b26be7f71b7de3bc3bd6b72b6194 NeedsCompilation: no Title: Sample size for RNAseq studies Description: RNA-seq, sample size biocViews: ImmunoOncology, RNASeq Author: Terry M Therneau [aut, cre], Hart Stephen [ctb] Maintainer: Terry M Therneau git_url: https://git.bioconductor.org/packages/RNASeqPower git_branch: devel git_last_commit: 4968608 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RNASeqPower_1.51.0.tar.gz vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf vignetteTitles: RNAseq samplesize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqPower/inst/doc/samplesize.R suggestsMe: DGEobj.utils dependencyCount: 0 Package: RNAshapeQC Version: 0.99.10 Depends: R (>= 4.4) Imports: GenomicRanges, IRanges, Rsamtools, foreach, zoo, ggplot2, dplyr, magrittr, tidyr, ComplexHeatmap, circlize, dendextend, DescTools, ggpubr, MASS, SummarizedExperiment, BiocParallel Suggests: doParallel, knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), PupillometryR, matrixStats, RColorBrewer, scales, tibble, patchwork, ConsensusClusterPlus License: MIT + file LICENSE MD5sum: a594c37ea984cd1c69ab61ee91d32f9b NeedsCompilation: no Title: RNA Coverage-Shape-Based Quality Control Metrics Description: RNAshapeQC provides coverage-shape-based quality control (QC) metrics for mRNA-seq and total RNA-seq data. It supports per-gene pileup construction from BAM files as well as toy datasets for quick-start examples. The package implements protocol-specific metrics, including decay rate (DR), degradation score (DS), mean coverage depth (MCD), window coefficient of variation (wCV), area under the curve (AUC), and shape-based sample-level indices. RNAshapeQC also includes HPC-friendly functions for per-gene batch processing and cross-study pileup generation. This package enables interpretable, protocol-specific QC assessments for diverse RNA-seq workflows. biocViews: RNASeq, QualityControl, Coverage, Transcriptomics, Sequencing Author: Miyeon Yeon [aut, cre, cph] (ORCID: ), Won-Young Choi [aut, cph] (ORCID: ), Jin Young Lee [ctb] (ORCID: ), Katherine A. Hoadley [aut] (ORCID: ), D. Neil Hayes [aut, fnd, cph] (ORCID: ), Hyo Young Choi [aut, cph] (ORCID: ) Maintainer: Miyeon Yeon URL: https://github.com/hyochoi/RNAshapeQC VignetteBuilder: knitr BugReports: https://github.com/hyochoi/RNAshapeQC/issues git_url: https://git.bioconductor.org/packages/RNAshapeQC git_branch: devel git_last_commit: fefca97 git_last_commit_date: 2026-03-25 Date/Publication: 2026-04-20 source.ver: src/contrib/RNAshapeQC_0.99.10.tar.gz vignettes: vignettes/RNAshapeQC/inst/doc/RNAshapeQC_intro.html vignetteTitles: RNAshapeQC: Quick Start Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RNAshapeQC/inst/doc/RNAshapeQC_intro.R dependencyCount: 164 Package: Rnits Version: 1.45.0 Depends: R (>= 3.6.0), Biobase, ggplot2, limma, methods Imports: affy, boot, impute, splines, graphics, qvalue, reshape2 Suggests: BiocStyle, knitr, GEOquery, stringr License: GPL-3 MD5sum: cca3bd7212b08dffa4fa6fef45329d50 NeedsCompilation: no Title: R Normalization and Inference of Time Series data Description: R/Bioconductor package for normalization, curve registration and inference in time course gene expression data. biocViews: GeneExpression, Microarray, TimeCourse, DifferentialExpression, Normalization Author: Dipen P. Sangurdekar Maintainer: Dipen P. Sangurdekar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rnits git_branch: devel git_last_commit: d587459 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Rnits_1.45.0.tar.gz vignettes: vignettes/Rnits/inst/doc/Rnits-vignette.pdf vignetteTitles: R/Bioconductor package for normalization and differential expression inference in time series gene expression microarray data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rnits/inst/doc/Rnits-vignette.R dependencyCount: 43 Package: roar Version: 1.47.0 Depends: R (>= 3.0.1) Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, GenomicAlignments (>= 0.99.4), rtracklayer, GenomeInfoDb Suggests: RNAseqData.HNRNPC.bam.chr14, testthat License: GPL-3 MD5sum: 480ad6d7c4f2e999a27f728a06f602e5 NeedsCompilation: no Title: Identify differential APA usage from RNA-seq alignments Description: Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments. biocViews: Sequencing, HighThroughputSequencing, RNAseq, Transcription Author: Elena Grassi Maintainer: Elena Grassi URL: https://github.com/vodkatad/roar/ git_url: https://git.bioconductor.org/packages/roar git_branch: devel git_last_commit: a867789 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/roar_1.47.0.tar.gz vignettes: vignettes/roar/inst/doc/roar.pdf vignetteTitles: Identify differential APA usage from RNA-seq alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roar/inst/doc/roar.R dependencyCount: 59 Package: roastgsa Version: 1.9.0 Depends: R (>= 4.3.0) Imports: parallel, grDevices, graphics, utils, stats, methods, grid, RColorBrewer, gplots, ggplot2, limma, Biobase Suggests: BiocStyle, knitr, rmarkdown, GSEABenchmarkeR, EnrichmentBrowser, preprocessCore, DESeq2 License: GPL-3 MD5sum: 8ed17ffdc39c57212d052c52a0a895ce NeedsCompilation: no Title: Rotation based gene set analysis Description: This package implements a variety of functions useful for gene set analysis using rotations to approximate the null distribution. It contributes with the implementation of seven test statistic scores that can be used with different goals and interpretations. Several functions are available to complement the statistical results with graphical representations. biocViews: Microarray, Preprocessing, Normalization, GeneExpression, Survival, Transcription, Sequencing, Transcriptomics, Bayesian, Clustering, Regression, RNASeq, MicroRNAArray, mRNAMicroarray, FunctionalGenomics, SystemsBiology, ImmunoOncology, DifferentialExpression, GeneSetEnrichment, BatchEffect, MultipleComparison, QualityControl, TimeCourse, Metabolomics, Proteomics, Epigenetics, Cheminformatics, ExonArray, OneChannel, TwoChannel, ProprietaryPlatforms, CellBiology, BiomedicalInformatics, AlternativeSplicing, DifferentialSplicing, DataImport, Pathways Author: Adria Caballe [aut, cre] (ORCID: ) Maintainer: Adria Caballe VignetteBuilder: knitr BugReports: https://github.com/adricaba/roastgsa/issues git_url: https://git.bioconductor.org/packages/roastgsa git_branch: devel git_last_commit: 02f08d7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/roastgsa_1.9.0.tar.gz vignettes: vignettes/roastgsa/inst/doc/roastgsaExample_genesetcollections.html, vignettes/roastgsa/inst/doc/roastgsaExample_main.html, vignettes/roastgsa/inst/doc/roastgsaExample_RNAseq.html vignetteTitles: roastgsa vignette (gene set collections), roastgsa vignette (main), roastgsa vignette (RNAseq) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roastgsa/inst/doc/roastgsaExample_genesetcollections.R, vignettes/roastgsa/inst/doc/roastgsaExample_main.R, vignettes/roastgsa/inst/doc/roastgsaExample_RNAseq.R dependencyCount: 34 Package: ROC Version: 1.87.2 Depends: R (>= 1.9.0), utils, methods Imports: knitr Suggests: rmarkdown, Biobase, BiocStyle License: Artistic-2.0 MD5sum: b0e9c18acf2b9e9c792c14976b0437e6 NeedsCompilation: yes Title: utilities for ROC, with microarray focus Description: Provide utilities for ROC, with microarray focus. biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: https://github.com/vjcitn/ROC VignetteBuilder: knitr BugReports: https://github.com/vjcitn/ROC/issues git_url: https://git.bioconductor.org/packages/ROC git_branch: devel git_last_commit: 993d4a1 git_last_commit_date: 2026-03-11 Date/Publication: 2026-04-20 source.ver: src/contrib/ROC_1.87.2.tar.gz vignettes: vignettes/ROC/inst/doc/ROCnotes.html vignetteTitles: Notes on ROC package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: TCC, wateRmelon importsMe: clst suggestsMe: genefilter dependencyCount: 10 Package: ROCpAI Version: 1.23.0 Depends: boot, SummarizedExperiment, fission, knitr, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 917ff76a5e0e0e1480d73836ce0cb265 NeedsCompilation: no Title: Receiver Operating Characteristic Partial Area Indexes for evaluating classifiers Description: The package analyzes the Curve ROC, identificates it among different types of Curve ROC and calculates the area under de curve through the method that is most accuracy. This package is able to standarizate proper and improper pAUC. biocViews: Software, StatisticalMethod, Classification Author: Juan-Pedro Garcia [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut] Maintainer: Juan-Pedro Garcia VignetteBuilder: knitr BugReports: https://github.com/juanpegarcia/ROCpAI/tree/master/issues git_url: https://git.bioconductor.org/packages/ROCpAI git_branch: devel git_last_commit: c0493ad git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ROCpAI_1.23.0.tar.gz vignettes: vignettes/ROCpAI/inst/doc/vignettes.html vignetteTitles: ROC Partial Area Indexes for evaluating classifiers hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROCpAI/inst/doc/vignettes.R dependencyCount: 32 Package: RolDE Version: 1.15.0 Depends: R (>= 4.2.0) Imports: stats, methods, ROTS, matrixStats, foreach, parallel, doParallel, doRNG, rngtools, SummarizedExperiment, nlme, qvalue, grDevices, graphics, utils Suggests: knitr, printr, rmarkdown, testthat License: GPL-3 MD5sum: 38a61fec4087541d085d98901830ac64 NeedsCompilation: no Title: RolDE: Robust longitudinal Differential Expression Description: RolDE detects longitudinal differential expression between two conditions in noisy high-troughput data. Suitable even for data with a moderate amount of missing values.RolDE is a composite method, consisting of three independent modules with different approaches to detecting longitudinal differential expression. The combination of these diverse modules allows RolDE to robustly detect varying differences in longitudinal trends and expression levels in diverse data types and experimental settings. biocViews: StatisticalMethod, Software, TimeCourse, Regression, Proteomics, DifferentialExpression Author: Tommi Valikangas [aut], Medical Bioinformatics Centre [cre] Maintainer: Medical Bioinformatics Centre URL: https://github.com/elolab/RolDE VignetteBuilder: knitr BugReports: https://github.com/elolab/RolDE/issues git_url: https://git.bioconductor.org/packages/RolDE git_branch: devel git_last_commit: 30dfeba git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RolDE_1.15.0.tar.gz vignettes: vignettes/RolDE/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RolDE/inst/doc/Introduction.R dependencyCount: 77 Package: ROntoTools Version: 2.39.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: c8eb7657441330fc17cfefad2230e468 NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis. biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sahar Ansari and Sorin Draghici Maintainer: Sorin Draghici git_url: https://git.bioconductor.org/packages/ROntoTools git_branch: devel git_last_commit: 5a6e829 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ROntoTools_2.39.0.tar.gz vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf vignetteTitles: ROntoTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROntoTools/inst/doc/rontotools.R dependsOnMe: BLMA suggestsMe: RCPA dependencyCount: 33 Package: ROSeq Version: 1.23.0 Depends: R (>= 4.0) Imports: pbmcapply, edgeR, limma Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics License: GPL-3 MD5sum: c482e234d17808325281866794961a86 NeedsCompilation: no Title: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data Description: ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. ROSeq takes filtered and normalized read matrix and cell-annotation/condition as input and determines the differentially expressed genes between the contrasting groups of single cells. One of the input parameters is the number of cores to be used. biocViews: GeneExpression, DifferentialExpression, SingleCell Author: Krishan Gupta [aut, cre], Manan Lalit [aut], Aditya Biswas [aut], Abhik Ghosh [aut], Debarka Sengupta [aut] Maintainer: Krishan Gupta URL: https://github.com/krishan57gupta/ROSeq VignetteBuilder: knitr BugReports: https://github.com/krishan57gupta/ROSeq/issues git_url: https://git.bioconductor.org/packages/ROSeq git_branch: devel git_last_commit: da1505d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ROSeq_1.23.0.tar.gz vignettes: vignettes/ROSeq/inst/doc/ROSeq.html vignetteTitles: ROSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROSeq/inst/doc/ROSeq.R dependencyCount: 13 Package: ROTS Version: 2.3.8 Depends: R (>= 3.6) Imports: Rcpp, stats, Biobase, methods, BiocParallel, lme4, survival LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) MD5sum: 1995f30f7483178447c51474a75a308e NeedsCompilation: yes Title: Reproducibility-Optimized Test Statistic Description: Calculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics, ImmunoOncology Author: Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/ROTS git_branch: devel git_last_commit: 777a322 git_last_commit_date: 2026-02-02 Date/Publication: 2026-04-20 source.ver: src/contrib/ROTS_2.3.8.tar.gz vignettes: vignettes/ROTS/inst/doc/ROTS.pdf vignetteTitles: ROTS: Reproducibility Optimized Test Statistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROTS/inst/doc/ROTS.R importsMe: PECA, PRONE, RolDE suggestsMe: LimROTS, wrProteo dependencyCount: 36 Package: RPA Version: 1.67.0 Depends: R (>= 3.1.1), affy, BiocGenerics, BiocStyle, methods, rmarkdown Imports: phyloseq Suggests: knitr, parallel License: BSD_2_clause + file LICENSE MD5sum: 250545c046f5dc119e16fa7e40ede866 NeedsCompilation: no Title: RPA: Robust Probabilistic Averaging for probe-level analysis Description: Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti [aut, cre] (ORCID: ) Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA VignetteBuilder: knitr BugReports: https://github.com/antagomir/RPA git_url: https://git.bioconductor.org/packages/RPA git_branch: devel git_last_commit: ae57fb9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RPA_1.67.0.tar.gz vignettes: vignettes/RPA/inst/doc/RPA.html vignetteTitles: RPA R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs dependencyCount: 92 Package: rprimer Version: 1.15.0 Depends: R (>= 4.1) Imports: Biostrings, bslib, DT, ggplot2, IRanges, mathjaxr, methods, patchwork, reshape2, S4Vectors, shiny, shinycssloaders, shinyFeedback Suggests: BiocStyle, covr, kableExtra, knitr, rmarkdown, styler, testthat (>= 3.0.0) License: GPL-3 MD5sum: 2479dfc6eb3a684c31e9af72277781bc NeedsCompilation: no Title: Design Degenerate Oligos from a Multiple DNA Sequence Alignment Description: Functions, workflow, and a Shiny application for visualizing sequence conservation and designing degenerate primers, probes, and (RT)-(q/d)PCR assays from a multiple DNA sequence alignment. The results can be presented in data frame format and visualized as dashboard-like plots. For more information, please see the package vignette. biocViews: Alignment, ddPCR, Coverage, MultipleSequenceAlignment, SequenceMatching, qPCR Author: Sofia Persson [aut, cre] (ORCID: ) Maintainer: Sofia Persson URL: https://github.com/sofpn/rprimer VignetteBuilder: knitr BugReports: https://github.com/sofpn/rprimer/issues git_url: https://git.bioconductor.org/packages/rprimer git_branch: devel git_last_commit: bc508a0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rprimer_1.15.0.tar.gz vignettes: vignettes/rprimer/inst/doc/getting-started-with-rprimer.html vignetteTitles: Instructions for use hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rprimer/inst/doc/getting-started-with-rprimer.R dependencyCount: 76 Package: RProtoBufLib Version: 2.23.0 Suggests: knitr, rmarkdown License: BSD_3_clause MD5sum: 3bfa90aa6c0d71fea7fa203578fb6dc0 NeedsCompilation: yes Title: C++ headers and static libraries of Protocol buffers Description: This package provides the headers and static library of Protocol buffers for other R packages to compile and link against. biocViews: Infrastructure Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RProtoBufLib git_branch: devel git_last_commit: d280051 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RProtoBufLib_2.23.0.tar.gz vignettes: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.html vignetteTitles: Using RProtoBufLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.R importsMe: cytolib, flowWorkspace linksToMe: cytolib, CytoML, flowCore, flowWorkspace dependencyCount: 0 Package: rpx Version: 2.19.0 Depends: R (>= 3.5.0), methods Imports: BiocFileCache, jsonlite, xml2, RCurl, curl, utils Suggests: Biostrings, BiocStyle, testthat, knitr, tibble, rmarkdown License: GPL-2 MD5sum: 0f668fa7106681b9841bd8ffe5c7e782 NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DataImport, ThirdPartyClient Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/rpx VignetteBuilder: knitr BugReports: https://github.com/lgatto/rpx/issues git_url: https://git.bioconductor.org/packages/rpx git_branch: devel git_last_commit: 70deff4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rpx_2.19.0.tar.gz vignettes: vignettes/rpx/inst/doc/rpx.html vignetteTitles: An R interface to the ProteomeXchange repository hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R suggestsMe: MsExperiment, MSnbase, PSMatch, RforProteomics dependencyCount: 47 Package: Rqc Version: 1.45.0 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics (>= 0.25.1), Biostrings, IRanges, methods, S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid, reshape2, Rcpp (>= 0.11.6), biovizBase, shiny, Rsamtools, GenomicAlignments, GenomicFiles LinkingTo: Rcpp Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: 556724cad1952665cc4d6339a944bbd8 NeedsCompilation: yes Title: Quality Control Tool for High-Throughput Sequencing Data Description: Rqc is an optimised tool designed for quality control and assessment of high-throughput sequencing data. It performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics. biocViews: Sequencing, QualityControl, DataImport Author: Welliton Souza, Benilton Carvalho Maintainer: Welliton Souza URL: https://github.com/labbcb/Rqc VignetteBuilder: knitr BugReports: https://github.com/labbcb/Rqc/issues git_url: https://git.bioconductor.org/packages/Rqc git_branch: devel git_last_commit: 81cf3fd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Rqc_1.45.0.tar.gz vignettes: vignettes/Rqc/inst/doc/Rqc.html vignetteTitles: Using Rqc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R dependencyCount: 154 Package: RRHO Version: 1.51.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: 3a1e0c11862951f4be95c95053024430 NeedsCompilation: no Title: Inference on agreement between ordered lists Description: The package is aimed at inference on the amount of agreement in two sorted lists using the Rank-Rank Hypergeometric Overlap test. biocViews: Genetics, SequenceMatching, Microarray, Transcription Author: Jonathan Rosenblatt and Jason Stein Maintainer: Jonathan Rosenblatt git_url: https://git.bioconductor.org/packages/RRHO git_branch: devel git_last_commit: d4e5577 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RRHO_1.51.0.tar.gz vignettes: vignettes/RRHO/inst/doc/RRHO.pdf vignetteTitles: RRHO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RRHO/inst/doc/RRHO.R dependencyCount: 8 Package: rrvgo Version: 1.23.0 Imports: GOSemSim, AnnotationDbi, GO.db, pheatmap, ggplot2, ggrepel, treemap, tm, wordcloud, shiny, grDevices, grid, stats, methods, umap Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), shinydashboard, DT, plotly, heatmaply, magrittr, utils, clusterProfiler, DOSE, slam, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db License: GPL-3 MD5sum: 0974e60b498fec7d54244a0d0dc45e41 NeedsCompilation: no Title: Reduce + Visualize GO Description: Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity. biocViews: Annotation, Clustering, GO, Network, Pathways, Software Author: Sergi Sayols [aut, cre], Sara Elmeligy [ctb] Maintainer: Sergi Sayols URL: https://www.bioconductor.org/packages/rrvgo, https://ssayols.github.io/rrvgo/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rrvgo git_branch: devel git_last_commit: a44717f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rrvgo_1.23.0.tar.gz vignettes: vignettes/rrvgo/inst/doc/rrvgo.html vignetteTitles: Using rrvgo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rrvgo/inst/doc/rrvgo.R suggestsMe: genekitr, scDiffCom dependencyCount: 98 Package: Rsamtools Version: 2.27.2 Depends: R (>= 3.5.0), methods, Seqinfo, GenomicRanges (>= 1.61.1), Biostrings (>= 2.77.2) Imports: utils, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector (>= 0.19.7), bitops, BiocParallel, stats LinkingTo: Rhtslib (>= 3.3.1), S4Vectors, IRanges, XVector, Biostrings Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures, VariantAnnotation, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocStyle, knitr License: Artistic-2.0 | file LICENSE MD5sum: e16c7a1ffef6a9dfb6d8e03c1e8a575a NeedsCompilation: yes Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import Description: This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files. biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl Author: Martin Morgan [aut], Hervé Pagès [aut], Valerie Obenchain [aut], Nathaniel Hayden [aut], Busayo Samuel [ctb] (Converted Rsamtools vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Rsamtools SystemRequirements: GNU make VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q BugReports: https://github.com/Bioconductor/Rsamtools/issues git_url: https://git.bioconductor.org/packages/Rsamtools git_branch: devel git_last_commit: ee01a1f git_last_commit_date: 2026-04-08 Date/Publication: 2026-04-20 source.ver: src/contrib/Rsamtools_2.27.2.tar.gz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.html vignetteTitles: An Introduction to Rsamtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R dependsOnMe: CODEX, CoverageView, esATAC, FRASER, GenomicAlignments, GenomicFiles, gmapR, HelloRanges, IntEREst, MEDIPS, methylPipe, MMDiff2, podkat, r3Cseq, RAIDS, RepViz, RiboDiPA, SCOPE, SGSeq, ShortRead, SICtools, SNPhood, spiky, ssviz, strandCheckR, systemPipeR, TEQC, VariantAnnotation, wavClusteR, leeBamViews, TBX20BamSubset, sequencing, csawBook importsMe: alabaster.files, alabaster.vcf, AllelicImbalance, annmap, AnnotationHubData, APAlyzer, appreci8R, ASpli, ATACseqQC, ATACseqTFEA, atena, BadRegionFinder, bambu, BBCAnalyzer, Bioc.gff, biovizBase, biscuiteer, breakpointR, BSgenome, CAGEr, casper, CellBarcode, cellbaseR, CexoR, cfdnakit, cfDNAPro, chimeraviz, ChIPexoQual, ChIPpeakAnno, ChromSCape, CircSeqAlignTk, CleanUpRNAseq, cn.mops, CNVfilteR, CNVPanelizer, CNVrd2, compEpiTools, CopyNumberPlots, CrispRVariants, crupR, csaw, CSSQ, customProDB, DAMEfinder, Damsel, DegNorm, derfinder, DEXSeq, diffHic, DMRcaller, DNAfusion, DOTSeq, easyRNASeq, EDASeq, ensembldb, epigenomix, epigraHMM, eudysbiome, EventPointer, extraChIPs, FilterFFPE, FLAMES, fRagmentomics, gcapc, gDNAx, genomation, GenomicAlignments, GenomicInteractions, GenomicPlot, GenVisR, ggbio, gmoviz, GOTHiC, GreyListChIP, GUIDEseq, Gviz, h5vc, icetea, INSPEcT, karyoploteR, magpie, MDTS, metagene2, metaseqR2, methylKit, mosaics, motifmatchr, MotifPeeker, msgbsR, NADfinder, NanoMethViz, nearBynding, nucleR, ORFik, panelcn.mops, PICB, plyranges, pram, PureCN, QDNAseq, qsea, QuasR, R453Plus1Toolbox, raer, ramwas, Rbowtie2, recoup, rfPred, riboSeqR, ribosomeProfilingQC, RNAmodR, RNAshapeQC, Rqc, rtracklayer, scDblFinder, scPipe, scRNAseqApp, scruff, segmentSeq, seqsetvis, SimFFPE, sitadela, SplicingGraphs, srnadiff, tadar, TCseq, TFutils, tracktables, trackViewer, transcriptR, TRESS, tRNAscanImport, TVTB, UMI4Cats, uncoverappLib, VariantFiltering, VariantTools, VaSP, VCFArray, VplotR, ZygosityPredictor, chipseqDBData, gDNAinRNAseqData, LungCancerLines, MetaScope, raerdata, BIGr, GenoPop, iimi, NIPTeR, noisyr, PlasmaMutationDetector, revert, scPloidy, Signac, umiAnalyzer, VALERIE suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocGenerics, BiocParallel, biomvRCNS, BSgenomeForge, Chicago, cigarillo, ELViS, epivizrChart, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, gwascat, HIBAG, igvShiny, IRanges, iscream, ldblock, MOSim, MungeSumstats, omicsPrint, RNAmodR.ML, SeqArray, similaRpeak, TENxIO, GeuvadisTranscriptExpr, NanoporeRNASeq, systemPipeRdata, chipseqDB, futurize, gaawr2, inDAGO, karyotapR, MoBPS, polyRAD, seqmagick dependencyCount: 28 Package: rsbml Version: 2.69.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 MD5sum: b891f7cf6a94ef1de689ede71ebf33c6 NeedsCompilation: yes Title: R support for SBML, using libsbml Description: Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models. biocViews: GraphAndNetwork, Pathways, Network Author: Michael Lawrence Maintainer: Michael Lawrence URL: http://www.sbml.org SystemRequirements: libsbml (==5.10.2) git_url: https://git.bioconductor.org/packages/rsbml git_branch: devel git_last_commit: 7c17b73 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rsbml_2.69.0.tar.gz vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rsbml/inst/doc/quick-start.R suggestsMe: piano, SBMLR dependencyCount: 8 Package: rScudo Version: 1.27.0 Depends: R (>= 3.6) Imports: methods, stats, igraph, stringr, grDevices, Biobase, S4Vectors, SummarizedExperiment, BiocGenerics Suggests: testthat, BiocStyle, knitr, rmarkdown, ALL, RCy3, caret, e1071, parallel, doParallel License: GPL-3 MD5sum: 85e2e2048221caaf10ede764e42b99c6 NeedsCompilation: no Title: Signature-based Clustering for Diagnostic Purposes Description: SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two samples is computed using a method similar to GSEA. The graph of samples can then be used to perform community clustering or to perform supervised classification of samples in a testing set. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Clustering, GraphAndNetwork, Network, Proteomics, Transcriptomics, SystemsBiology, FeatureExtraction Author: Matteo Ciciani [aut, cre], Thomas Cantore [aut], Enrica Colasurdo [ctb], Mario Lauria [ctb] Maintainer: Matteo Ciciani URL: https://github.com/Matteo-Ciciani/scudo VignetteBuilder: knitr BugReports: https://github.com/Matteo-Ciciani/scudo/issues git_url: https://git.bioconductor.org/packages/rScudo git_branch: devel git_last_commit: 29e385c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rScudo_1.27.0.tar.gz vignettes: vignettes/rScudo/inst/doc/rScudo-vignette.html vignetteTitles: Signature-based Clustering for Diagnostic Purposes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rScudo/inst/doc/rScudo-vignette.R dependencyCount: 36 Package: rsemmed Version: 1.21.0 Depends: R (>= 4.0), igraph Imports: methods, magrittr, stringr, dplyr Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 2e7ccd1abc3d8b5074a20deeac7ff441 NeedsCompilation: no Title: An interface to the Semantic MEDLINE database Description: A programmatic interface to the Semantic MEDLINE database. It provides functions for searching the database for concepts and finding paths between concepts. Path searching can also be tailored to user specifications, such as placing restrictions on concept types and the type of link between concepts. It also provides functions for summarizing and visualizing those paths. biocViews: Software, Annotation, Pathways, SystemsBiology Author: Leslie Myint [aut, cre] (ORCID: ) Maintainer: Leslie Myint URL: https://github.com/lmyint/rsemmed VignetteBuilder: knitr BugReports: https://github.com/lmyint/rsemmed/issues git_url: https://git.bioconductor.org/packages/rsemmed git_branch: devel git_last_commit: a6f800c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rsemmed_1.21.0.tar.gz vignettes: vignettes/rsemmed/inst/doc/rsemmed_user_guide.html vignetteTitles: rsemmed User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rsemmed/inst/doc/rsemmed_user_guide.R dependencyCount: 28 Package: RSeqAn Version: 1.31.0 Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: 22f9c936a6ca6a1e98bb821c958456ba NeedsCompilation: yes Title: R SeqAn Description: Headers and some wrapper functions from the SeqAn C++ library for ease of usage in R. biocViews: Infrastructure, Software Author: August Guang [aut, cre] Maintainer: August Guang VignetteBuilder: knitr BugReports: https://github.com/compbiocore/RSeqAn/issues git_url: https://git.bioconductor.org/packages/RSeqAn git_branch: devel git_last_commit: f1d8c09 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RSeqAn_1.31.0.tar.gz vignettes: vignettes/RSeqAn/inst/doc/first_example.html vignetteTitles: Introduction to Using RSeqAn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RSeqAn/inst/doc/first_example.R dependencyCount: 3 Package: Rsubread Version: 2.25.2 Imports: grDevices, stats, utils, Matrix License: GPL (>=3) MD5sum: 3d3cdc4c1ca7a827e2571edad05f892a NeedsCompilation: yes Title: Mapping, quantification and variant analysis of sequencing data Description: Alignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads. biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq, SingleCell, GeneExpression, GeneRegulation, Genetics, ImmunoOncology, SNP, GeneticVariability, Preprocessing, QualityControl, GenomeAnnotation, GeneFusionDetection, IndelDetection, VariantAnnotation, VariantDetection, MultipleSequenceAlignment Author: Wei Shi, Yang Liao and Gordon K Smyth with contributions from Jenny Dai Maintainer: Wei Shi , Yang Liao and Gordon K Smyth URL: http://bioconductor.org/packages/Rsubread git_url: https://git.bioconductor.org/packages/Rsubread git_branch: devel git_last_commit: d66685a git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/Rsubread_2.25.2.tar.gz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf, vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf vignetteTitles: Rsubread Vignette, SubreadUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R dependsOnMe: ExCluster importsMe: APAlyzer, CleanUpRNAseq, Damsel, diffUTR, dupRadar, FRASER, ribosomeProfilingQC, scPipe, scruff, stPipe suggestsMe: autonomics, icetea, singleCellTK, SpliceWiz, tidybulk, MetaScope, inDAGO dependencyCount: 8 Package: RSVSim Version: 1.51.0 Depends: R (>= 3.5.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer, pwalign License: LGPL-3 MD5sum: 8a5b5b02b14c26eb2718a582720b73d6 NeedsCompilation: no Title: RSVSim: an R/Bioconductor package for the simulation of structural variations Description: RSVSim is a package for the simulation of deletions, insertions, inversion, tandem-duplications and translocations of various sizes in any genome available as FASTA-file or BSgenome data package. SV breakpoints can be placed uniformly accross the whole genome, with a bias towards repeat regions and regions of high homology (for hg19) or at user-supplied coordinates. biocViews: Sequencing Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RSVSim git_branch: devel git_last_commit: d131b77 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RSVSim_1.51.0.tar.gz vignettes: vignettes/RSVSim/inst/doc/vignette.pdf vignetteTitles: RSVSim: an R/Bioconductor package for the simulation of structural variations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RSVSim/inst/doc/vignette.R dependencyCount: 54 Package: rSWeeP Version: 1.23.0 Depends: foreach, doParallel, parallel, Biostrings, methods, utils Imports: tools, stringi, Suggests: Rtsne, ape, Seurat, knitr, rmarkdown, tictoc, BiocStyle, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: 1446268250fac60608b742de3d15ee0a NeedsCompilation: no Title: Spaced Words Projection (SWeeP) Description: "Spaced Words Projection (SWeeP)" is a method for representing biological sequences using vectors preserving inter-sequence comparability. Author: Camila Pereira Perico [com, cre, aut, cph] (ORCID: ), Danrley Rafael Fernandes [aut], Mariane Gonçalves Kulik [aut] (ORCID: ), Júlia Formighieri Varaschin [aut], Camilla Reginatto de Pierri [aut] (ORCID: ), Ricardo Assunção Vialle [aut] (ORCID: ), Roberto Tadeu Raittz [aut, cph] (ORCID: ) Maintainer: Camila P Perico VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rSWeeP git_branch: devel git_last_commit: 3b61954 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rSWeeP_1.23.0.tar.gz vignettes: vignettes/rSWeeP/inst/doc/rSWeeP.html vignetteTitles: rSWeeP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rSWeeP/inst/doc/rSWeeP.R dependencyCount: 21 Package: RTCA Version: 1.63.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: ddde9aab31a83b101de8f975b43f8ac1 NeedsCompilation: no Title: Open-source toolkit to analyse data from xCELLigence System (RTCA) Description: Import, analyze and visualize data from Roche(R) xCELLigence RTCA systems. The package imports real-time cell electrical impedance data into R. As an alternative to commercial software shipped along the system, the Bioconductor package RTCA provides several unique transformation (normalization) strategies and various visualization tools. biocViews: ImmunoOncology, CellBasedAssays, Infrastructure, Visualization, TimeCourse Author: Jitao David Zhang Maintainer: Jitao David Zhang URL: http://code.google.com/p/xcelligence/,http://www.xcelligence.roche.com/,http://www.nextbiomotif.com/Home/scientific-programming git_url: https://git.bioconductor.org/packages/RTCA git_branch: devel git_last_commit: d713410 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RTCA_1.63.0.tar.gz vignettes: vignettes/RTCA/inst/doc/aboutRTCA.pdf, vignettes/RTCA/inst/doc/RTCAtransformation.pdf vignetteTitles: Introduction to Data Analysis of the Roche xCELLigence System with RTCA Package, RTCAtransformation: Discussion of transformation methods of RTCA data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R dependencyCount: 9 Package: RTN Version: 2.35.1 Depends: R (>= 3.6.3), methods, Imports: RedeR, minet, viper, mixtools, snow, stats, limma, data.table, IRanges, igraph, S4Vectors, SummarizedExperiment, car, pwr, pheatmap, grDevices, graphics, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 0cd55f54bde403562e1b9065ff15a744 NeedsCompilation: no Title: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons Description: A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons. biocViews: Transcription, Network, NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork, GeneSetEnrichment, GeneticVariability Author: Clarice Groeneveld [ctb], Gordon Robertson [ctb], Xin Wang [aut], Michael Fletcher [aut], Florian Markowetz [aut], Kerstin Meyer [aut], and Mauro Castro [aut] Maintainer: Mauro Castro URL: http://dx.doi.org/10.1038/ncomms3464 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTN git_branch: devel git_last_commit: 78f4b0e git_last_commit_date: 2026-02-17 Date/Publication: 2026-04-20 source.ver: src/contrib/RTN_2.35.1.tar.gz vignettes: vignettes/RTN/inst/doc/RTN.html vignetteTitles: "RTN: reconstruction of transcriptional regulatory networks and analysis of regulons."" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R dependsOnMe: RTNduals, RTNsurvival, Fletcher2013b suggestsMe: geneplast dependencyCount: 143 Package: RTNduals Version: 1.35.1 Depends: R(>= 4.4.0), RTN(>= 2.32), methods Imports: graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: fdce2d884832b7f27cfeb169c52b5e96 NeedsCompilation: no Title: Analysis of co-regulation and inference of 'dual regulons' Description: RTNduals identifies co-regulatory loops between pairs of regulons inferred by the RTN package by evaluating their shared target genes. It infers dual regulons and tests whether regulator pairs exhibit cooperative or competitive influences on common targets. biocViews: GeneRegulation, GeneExpression, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Vinicius S. Chagas, Clarice S. Groeneveld, Gordon Robertson, Kerstin B. Meyer, Mauro A. A. Castro Maintainer: Mauro Castro , Clarice Groeneveld VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNduals git_branch: devel git_last_commit: 47ce907 git_last_commit_date: 2026-02-17 Date/Publication: 2026-04-20 source.ver: src/contrib/RTNduals_1.35.1.tar.gz vignettes: vignettes/RTNduals/inst/doc/RTNduals.html vignetteTitles: "RTNduals: analysis of co-regulation and inference of dual regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNduals/inst/doc/RTNduals.R dependsOnMe: RTNsurvival dependencyCount: 144 Package: RTNsurvival Version: 1.35.1 Depends: R(>= 4.4.0), RTN(>= 2.32), RTNduals(>= 1.32), methods Imports: survival, RColorBrewer, grDevices, graphics, stats, utils, scales, data.table, egg, ggplot2, pheatmap, dunn.test Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: e4ce03b18bc745fdf76cc5097e9921ee NeedsCompilation: no Title: Survival analysis using transcriptional networks inferred by the RTN package Description: RTNsurvival integrates regulons inferred by the RTN package with survival data. For each regulon, a two-tailed GSEA framework computes a differential Enrichment Score (dES) at the individual-sample level. The resulting dES distribution across samples is then used to evaluate survival associations within the cohort. Two primary workflows are supported: (i) Cox proportional hazards models, in which regulon activities are treated as predictors of survival time, and (ii) Kaplan–Meier analyses assessing cohort stratification based on regulon activity. All graphical outputs are customizable according to user specifications. biocViews: NetworkEnrichment, Survival, GeneRegulation, GeneSetEnrichment, NetworkInference, GraphAndNetwork Author: Clarice S. Groeneveld, Vinicius S. Chagas, Mauro A. A. Castro Maintainer: Clarice Groeneveld , Mauro A. A. Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNsurvival git_branch: devel git_last_commit: 9f7c312 git_last_commit_date: 2026-02-17 Date/Publication: 2026-04-20 source.ver: src/contrib/RTNsurvival_1.35.1.tar.gz vignettes: vignettes/RTNsurvival/inst/doc/RTNsurvival.html vignetteTitles: "RTNsurvival: multivariate survival analysis using transcriptional networks and regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNsurvival/inst/doc/RTNsurvival.R dependencyCount: 148 Package: RTopper Version: 1.57.0 Depends: R (>= 2.12.0), Biobase Imports: limma, multtest Suggests: org.Hs.eg.db, KEGGREST, GO.db License: GPL (>= 3) + file LICENSE MD5sum: 6a79f716a8901f620c07604f9086ee04 NeedsCompilation: no Title: This package is designed to perform Gene Set Analysis across multiple genomic platforms Description: the RTopper package is designed to perform and integrate gene set enrichment results across multiple genomic platforms. biocViews: Microarray Author: Luigi Marchionni , Svitlana Tyekucheva Maintainer: Luigi Marchionni git_url: https://git.bioconductor.org/packages/RTopper git_branch: devel git_last_commit: cedb1d4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RTopper_1.57.0.tar.gz vignettes: vignettes/RTopper/inst/doc/RTopper.pdf vignetteTitles: RTopper user's manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTopper/inst/doc/RTopper.R dependencyCount: 18 Package: rtracklayer Version: 1.71.3 Depends: R (>= 3.5), methods, GenomicRanges (>= 1.37.2) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.35.3), S4Vectors (>= 0.23.18), IRanges (>= 2.13.13), XVector (>= 0.19.7), Seqinfo, Biostrings (>= 2.77.2), curl, httr, Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6), BiocIO, tools, restfulr (>= 0.0.13) LinkingTo: S4Vectors, IRanges, XVector Suggests: GenomeInfoDb, BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit License: Artistic-2.0 + file LICENSE MD5sum: ebabca310dfefe8cd21a8214bdb782b0 NeedsCompilation: yes Title: R interface to genome annotation files and the UCSC genome browser Description: Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport. biocViews: Annotation,Visualization,DataImport Author: Michael Lawrence, Vince Carey, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/rtracklayer git_branch: devel git_last_commit: d9b23f9 git_last_commit_date: 2026-03-15 Date/Publication: 2026-04-20 source.ver: src/contrib/rtracklayer_1.71.3.tar.gz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BSgenome, CAGEfightR, CoverageView, CSSQ, ExCluster, geneXtendeR, GenomicFiles, groHMM, HelloRanges, IdeoViz, MethylSeekR, ORFhunteR, r3Cseq, StructuralVariantAnnotation, svaNUMT, svaRetro, EatonEtAlChIPseq, liftOver, sequencing, csawBook, OSCA.intro importsMe: AnnotationHubData, annotatr, APAlyzer, ATACseqQC, ATACseqTFEA, ballgown, bedbaser, BgeeCall, BindingSiteFinder, biscuiteer, BiSeq, branchpointer, BSgenomeForge, CAGEr, casper, CexoR, chipenrich, ChIPpeakAnno, ChIPseeker, ChromHeatMap, ChromSCape, circRNAprofiler, cliProfiler, CNEr, consensusSeekeR, conumee, crisprDesign, crupR, customProDB, damidBind, derfinder, DEScan2, diffHic, diffUTR, DMCFB, DMCHMM, dmrseq, DOTSeq, DuplexDiscovereR, easylift, ELMER, enhancerHomologSearch, ensembldb, EpiCompare, epidecodeR, epigraHMM, epimutacions, epiSeeker, esATAC, extraChIPs, factR, fcScan, FindIT2, FLAMES, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, GenomicPlot, ggbio, gmapR, gmoviz, goseq, GOTHiC, GreyListChIP, gVenn, Gviz, HicAggR, HiCPotts, hicVennDiagram, HiTC, icetea, igvR, INSPEcT, IsoformSwitchAnalyzeR, karyoploteR, m6Aboost, magpie, maser, MEDIPS, metagene2, metaseqR2, methodical, methrix, methylKit, mist, mobileRNA, Moonlight2R, MotifDb, MotifPeeker, multicrispr, MungeSumstats, NADfinder, nearBynding, normr, OGRE, OMICsPCA, ORFik, PAST, periodicDNA, plyranges, PMScanR, pram, primirTSS, proBAMr, PureCN, qsea, QuasR, raer, RCAS, recount, recount3, recoup, regioneR, REMP, RiboCrypt, ribosomeProfilingQC, rifi, rifiComparative, rmspc, RNAmodR, roar, scanMiRApp, scDblFinder, scPipe, scRNAseqApp, scruff, seqCAT, seqsetvis, sevenC, SGSeq, shinyepico, signeR, SigsPack, sitadela, SMTrackR, SOMNiBUS, SpliceImpactR, SpliceWiz, srnadiff, STADyUM, TEKRABber, TENET, TFBSTools, tidyCoverage, trackViewer, transcriptR, TRESS, tRNAscanImport, txcutr, txdbmaker, VariantAnnotation, VariantTools, wavClusteR, wiggleplotr, GenomicState, chipenrich.data, DMRcatedata, geneLenDataBase, NxtIRFdata, raerdata, spatialLIBD, seqpac, OSTA, crispRdesignR, GALLO, GencoDymo2, locuszoomr, ocrRBBR, PlasmaMutationDetector, PopPsiSeqR, tepr suggestsMe: alabaster.files, annoLinker, AnnotationHub, autonomics, BiocFileCache, biovizBase, BREW3R.r, bsseq, cicero, compEpiTools, CrispRVariants, crisprViz, DAMEfinder, DMRcaller, eisaR, epistack, epivizrChart, epivizrData, FRASER, G4SNVHunter, geneXtendeR, GenomicAlignments, GenomicDistributions, GenomicInteractionNodes, gwascat, HiCExperiment, HiContacts, igvShiny, InPAS, megadepth, methylumi, miRBaseConverter, motifTestR, MutationalPatterns, NanoMethViz, OrganismDbi, peakCombiner, PICB, pipeFrame, plotgardener, plyinteractions, pqsfinder, ProteoDisco, R453Plus1Toolbox, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, RSVSim, similaRpeak, syntenet, systemPipeR, TAPseq, TCGAutils, transmogR, triplex, tRNAdbImport, TVTB, xcore, EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3, excluderanges, FDb.FANTOM4.promoters.hg19, fourDNData, GeuvadisTranscriptExpr, nanotubes, PasillaTranscriptExpr, systemPipeRdata, chipseqDB, gkmSVM, inDAGO, Rgff, Seurat, Signac dependencyCount: 56 Package: rTRM Version: 1.49.0 Depends: R (>= 2.10), igraph (>= 1.0) Imports: methods, AnnotationDbi, DBI, RSQLite Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db, org.Mm.eg.db, ggplot2, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 64e2a9f6b20762fa39d90ec03f9f441e NeedsCompilation: no Title: Identification of Transcriptional Regulatory Modules from Protein-Protein Interaction Networks Description: rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRM VignetteBuilder: knitr BugReports: https://github.com/ddiez/rTRM/issues git_url: https://git.bioconductor.org/packages/rTRM git_branch: devel git_last_commit: deb123f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rTRM_1.49.0.tar.gz vignettes: vignettes/rTRM/inst/doc/Introduction.html vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRM/inst/doc/Introduction.R importsMe: rTRMui dependencyCount: 47 Package: rTRMui Version: 1.49.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: a897004418705695d02a3bdf81bdf1f9 NeedsCompilation: no Title: A shiny user interface for rTRM Description: This package provides a web interface to compute transcriptional regulatory modules with rTRM. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork, GUI Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRMui BugReports: https://github.com/ddiez/rTRMui/issues git_url: https://git.bioconductor.org/packages/rTRMui git_branch: devel git_last_commit: 20ed697 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rTRMui_1.49.0.tar.gz vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf vignetteTitles: Introduction to rTRMui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRMui/inst/doc/rTRMui.R dependencyCount: 101 Package: RUCova Version: 1.3.0 Depends: R (>= 4.4.0) Imports: dplyr, fastDummies, ggplot2, stringr, tibble, Matrix, ComplexHeatmap, grid, circlize, SingleCellExperiment, SummarizedExperiment, tidyverse, tidyr, magrittr, S4Vectors Suggests: knitr, rmarkdown, BiocManager, BiocStyle, remotes, ggpubr, ggcorrplot, ggh4x, testthat (>= 3.0.0) License: GPL-3 MD5sum: 5aba52967228e64534b1989bca364156 NeedsCompilation: no Title: Removes unwanted covariance from mass cytometry data Description: Mass cytometry enables the simultaneous measurement of dozens of protein markers at the single-cell level, producing high dimensional datasets that provide deep insights into cellular heterogeneity and function. However, these datasets often contain unwanted covariance introduced by technical variations, such as differences in cell size, staining efficiency, and instrument-specific artifacts, which can obscure biological signals and complicate downstream analysis. This package addresses this challenge by implementing a robust framework of linear models designed to identify and remove these sources of unwanted covariance. By systematically modeling and correcting for technical noise, the package enhances the quality and interpretability of mass cytometry data, enabling researchers to focus on biologically relevant signals. biocViews: Software, SingleCell Author: Rosario Astaburuaga-García [aut, cre] (ORCID: ) Maintainer: Rosario Astaburuaga-García URL: https://github.com/molsysbio/RUCova SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/molsysbio/RUCova/issues git_url: https://git.bioconductor.org/packages/RUCova git_branch: devel git_last_commit: cdd0481 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RUCova_1.3.0.tar.gz vignettes: vignettes/RUCova/inst/doc/RUCova.html vignetteTitles: Removing Unwanted Covariance in mass cytometry data with RUCova hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RUCova/inst/doc/RUCova.R dependencyCount: 140 Package: RUVcorr Version: 1.43.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2, graphics Suggests: knitr, hgu133a2.db, rmarkdown License: GPL-2 MD5sum: 6da19be1b773ef79dac9a8ddb290cf19 NeedsCompilation: no Title: Removal of unwanted variation for gene-gene correlations and related analysis Description: RUVcorr allows to apply global removal of unwanted variation (ridged version of RUV) to real and simulated gene expression data. biocViews: GeneExpression, Normalization Author: Saskia Freytag Maintainer: Saskia Freytag VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RUVcorr git_branch: devel git_last_commit: 9b61201 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RUVcorr_1.43.0.tar.gz vignettes: vignettes/RUVcorr/inst/doc/Vignette.html vignetteTitles: Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVcorr/inst/doc/Vignette.R dependencyCount: 42 Package: RUVnormalize Version: 1.45.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: 38982343848dcb23b7a9340883fb1a1e NeedsCompilation: no Title: RUV for normalization of expression array data Description: RUVnormalize is meant to remove unwanted variation from gene expression data when the factor of interest is not defined, e.g., to clean up a dataset for general use or to do any kind of unsupervised analysis. biocViews: StatisticalMethod, Normalization Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/RUVnormalize git_branch: devel git_last_commit: 0a376e3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RUVnormalize_1.45.0.tar.gz vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf vignetteTitles: RUVnormalize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVnormalize/inst/doc/RUVnormalize.R dependencyCount: 8 Package: RUVSeq Version: 1.45.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 MD5sum: c6096f6e034271b727aba30802daf6f8 NeedsCompilation: no Title: Remove Unwanted Variation from RNA-Seq Data Description: This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, Software Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Lorena Pantano [ctb], Kamil Slowikowski [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/RUVSeq VignetteBuilder: knitr BugReports: https://github.com/drisso/RUVSeq/issues git_url: https://git.bioconductor.org/packages/RUVSeq git_branch: devel git_last_commit: 7e08fb9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RUVSeq_1.45.0.tar.gz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.html vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R dependsOnMe: octad, rnaseqGene importsMe: ribosomeProfilingQC, scone, standR suggestsMe: DEScan2, NanoTube, notame dependencyCount: 117 Package: Rvisdiff Version: 1.9.1 Depends: R (>= 4.5.0) Imports: edgeR, utils Suggests: knitr, rmarkdown, DESeq2, limma, SummarizedExperiment, airway, BiocStyle, matrixTests, BiocManager License: GPL-2 | GPL-3 MD5sum: 6c501dad9438bf36d2fd52c8adabf081 NeedsCompilation: no Title: Interactive Graphs for Differential Expression Description: Creates a muti-graph web page which allows the interactive exploration of differential analysis tests. The graphical web interface presents results as a table which is integrated with five interactive graphs: MA-plot, volcano plot, box plot, lines plot and cluster heatmap. Graphical aspect and information represented in the graphs can be customized by means of user controls. Final graphics can be exported as PNG format. biocViews: Software, Visualization, RNASeq, DataRepresentation, DifferentialExpression Author: Carlos Prieto [aut] (ORCID: ), David Barrios [cre, aut] (ORCID: ) Maintainer: David Barrios URL: https://github.com/BioinfoUSAL/Rvisdiff/ VignetteBuilder: knitr BugReports: https://github.com/BioinfoUSAL/Rvisdiff/issues/ git_url: https://git.bioconductor.org/packages/Rvisdiff git_branch: devel git_last_commit: 4b215d8 git_last_commit_date: 2025-12-19 Date/Publication: 2026-04-20 source.ver: src/contrib/Rvisdiff_1.9.1.tar.gz vignettes: vignettes/Rvisdiff/inst/doc/Rvisdiff.html vignetteTitles: Visualize Differential Expression results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rvisdiff/inst/doc/Rvisdiff.R dependencyCount: 11 Package: RVS Version: 1.33.0 Depends: R (>= 3.5.0) Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils, R.utils Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation License: GPL-2 MD5sum: 4bd0d7b30e49dbb012f1431a5f0b0898 NeedsCompilation: no Title: Computes estimates of the probability of related individuals sharing a rare variant Description: Rare Variant Sharing (RVS) implements tests of association and linkage between rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status, in family samples. The tests are based on probabilities of rare variant sharing by relatives under the null hypothesis of absence of linkage and association between the rare variants and the phenotype and apply to single variants or multiple variants in a region (e.g. gene-based test). biocViews: ImmunoOncology, Genetics, GenomeWideAssociation, VariantDetection, ExomeSeq, WholeGenome Author: Alexandre Bureau, Ingo Ruczinski, Samuel Younkin, Thomas Sherman Maintainer: Alexandre Bureau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RVS git_branch: devel git_last_commit: 67a6d75 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/RVS_1.33.0.tar.gz vignettes: vignettes/RVS/inst/doc/RVS.html vignetteTitles: The RVS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RVS/inst/doc/RVS.R dependencyCount: 60 Package: rWikiPathways Version: 1.31.0 Imports: httr, utils, XML, rjson, data.table, RCurl, dplyr, tidyr, readr, stringr, purrr, lubridate Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 5ae88f2e8dbd3ea1d9fd2d03f8d640b6 NeedsCompilation: no Title: rWikiPathways - R client library for the WikiPathways API Description: Use this package to interface with the WikiPathways API. It provides programmatic access to WikiPathways content in multiple data and image formats, including official monthly release files and convenient GMT read/write functions. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network, Metabolomics Author: Egon Willighagen [aut, cre] (ORCID: ), Alex Pico [aut] (ORCID: ) Maintainer: Egon Willighagen URL: https://github.com/wikipathways/rWikiPathways VignetteBuilder: knitr BugReports: https://github.com/wikipathways/rWikiPathways/issues git_url: https://git.bioconductor.org/packages/rWikiPathways git_branch: devel git_last_commit: edca798 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/rWikiPathways_1.31.0.tar.gz vignettes: vignettes/rWikiPathways/inst/doc/Overview.html, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.html vignetteTitles: 1. Overview, 4. Pathway Analysis, 2. rWikiPathways and BridgeDbR, 3. rWikiPathways and RCy3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rWikiPathways/inst/doc/Overview.R, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.R importsMe: famat suggestsMe: TRONCO dependencyCount: 50 Package: S4Arrays Version: 1.11.1 Depends: R (>= 4.3.0), methods, Matrix, abind, BiocGenerics (>= 0.45.2), S4Vectors (>= 0.47.6), IRanges Imports: stats LinkingTo: S4Vectors Suggests: BiocParallel, SparseArray (>= 0.0.4), DelayedArray, HDF5Array, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: f2cff431e17746db4aa03f96f8e39ccc NeedsCompilation: yes Title: Foundation of array-like containers in Bioconductor Description: The S4Arrays package defines the Array virtual class to be extended by other S4 classes that wish to implement a container with an array-like semantic. 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Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. 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vignetteTitles: Rle Tips and Tricks, A quick overview of the S4 class system, An Overview of the S4Vectors package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R, vignettes/S4Vectors/inst/doc/S4QuickOverview.R, vignettes/S4Vectors/inst/doc/S4VectorsOverview.R dependsOnMe: altcdfenvs, AnnotationHubData, ATACseqQC, bambu, bandle, betaHMM, Biostrings, BiSeq, BSgenome, bumphunter, Cardinal, CellMapper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, ChIPseqR, cigarillo, ClassifyR, cliProfiler, CODEX, CompoundDb, coseq, CSAR, CSSQ, DelayedArray, DelayedDataFrame, DESeq2, DEXSeq, DirichletMultinomial, DMCFB, DMCHMM, DMRcaller, epigenomix, ExperimentHubData, ExpressionAtlas, fCCAC, GA4GHclient, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicScores, GenomicTuples, GeomxTools, groHMM, Gviz, hdxmsqc, HelloRanges, HERON, InTAD, IntEREst, IRanges, LoomExperiment, m6Aboost, MetNet, MotifDb, MSnbase, MuData, 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TCGAbiolinks, TCGAutils, TENET, TENxIO, TEQC, terraTCGAdata, TFBSTools, TFHAZ, tidybulk, tidyCoverage, tidyexposomics, tidyprint, tidySingleCellExperiment, tidySpatialExperiment, tidySummarizedExperiment, TileDBArray, TnT, toppgene, ToxicoGx, trackViewer, tradeSeq, TrajectoryUtils, transcriptR, transmogR, treeclimbR, Trendy, tricycle, tRNA, tRNAdbImport, tRNAscanImport, TSCAN, TVTB, twoddpcr, txcutr, tximeta, UCSC.utils, UMI4Cats, universalmotif, UPDhmm, VanillaICE, VariantAnnotation, VariantFiltering, VaSP, VCFArray, VDJdive, velociraptor, VisiumIO, visiumStitched, VISTA, vmrseq, Voyager, VplotR, wavClusteR, weitrix, wiggleplotr, xcms, xcore, XeniumIO, xenLite, XVector, yamss, ZarrArray, zellkonverter, BioMartGOGeneSets, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, bugphyzz, celldex, chipenrich.data, chipseqDBData, curatedMetagenomicData, curatedTCGAData, DNAZooData, DoReMiTra, DropletTestFiles, FlowSorted.Blood.EPIC, fourDNData, HCATonsilData, HighlyReplicatedRNASeq, HMP16SData, HMP2Data, imcdatasets, leeBamViews, LegATo, MerfishData, MetaGxPancreas, MetaScope, MethylSeqData, MicrobiomeBenchmarkData, MouseGastrulationData, MouseThymusAgeing, pd.atdschip.tiling, scMultiome, scpdata, scRNAseq, sesameData, SimBenchData, SingleCellMultiModal, SomaticCancerAlterations, spatialLIBD, TransOmicsData, tuberculosis, GeoMxWorkflows, seqpac, ActiveDriverWGS, crispRdesignR, DESNP, DR.SC, driveR, genBaRcode, geno2proteo, hicream, imcExperiment, karyotapR, lisat, LoopRig, MetAlyzer, microbial, mikropml, multimedia, NIPTeR, ocrRBBR, PlasmaMutationDetector, PopPsiSeqR, restfulr, revert, rliger, rnaCrosslinkOO, rsolr, scROSHI, Signac, TaxaNorm, TmCalculator, toxpiR suggestsMe: AlphaMissenseR, AlpsNMR, ANCOMBC, anndataR, BiocGenerics, biomformat, CCAFE, chihaya, ClusterGVis, COTAN, dearseq, edgeR, epiregulon.extra, epivizrChart, GeoTcgaData, globalSeq, GWASTools, GWENA, gypsum, iscream, koinar, maftools, martini, MicrobiotaProcess, MsQuality, MungeSumstats, RTCGA, scFeatures, scToppR, SpectraQL, SPOTlight, TFEA.ChIP, TFutils, XAItest, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, curatedAdipoChIP, curatedAdipoRNA, ObMiTi, xcoredata, dependentsimr, fioRa, gkmSVM, grandR, inDAGO, LorMe, pmartR, polyRAD, pQTLdata, RCPA, Rgff, Seurat, SNPassoc, updog, valr linksToMe: Bioc.gff, Biostrings, cigarillo, CNEr, DECIPHER, GenomicAlignments, GenomicFeatures, h5mread, IRanges, kebabs, MatrixRider, posDemux, pwalign, Rsamtools, rtracklayer, S4Arrays, ShortRead, SparseArray, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 7 Package: sagenhaft Version: 1.81.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, stats, utils License: GPL (>= 2) MD5sum: 226e64008c6a442975d32d74dc60259a NeedsCompilation: no Title: Collection of functions for reading and comparing SAGE libraries Description: This package implements several functions useful for analysis of gene expression data by sequencing tags as done in SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction of a SAGE library from sequence files, sequence error correction, library comparison. Sequencing error correction is implementing using an Expectation Maximization Algorithm based on a Mixture Model of tag counts. biocViews: SAGE Author: Tim Beissbarth , with contributions from Gordon Smyth Maintainer: Tim Beissbarth URL: http://www.bioinf.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/sagenhaft git_branch: devel git_last_commit: a13ddc0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sagenhaft_1.81.0.tar.gz vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf vignetteTitles: SAGEnhaft hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sagenhaft/inst/doc/SAGEnhaft.R dependencyCount: 5 Package: SAIGEgds Version: 2.11.4 Depends: R (>= 4.0.0), gdsfmt (>= 1.28.0), SeqArray (>= 1.50.2), Rcpp Imports: methods, stats, utils, Matrix, RcppParallel, SKAT, CompQuadForm, survey LinkingTo: Rcpp, RcppArmadillo, RcppParallel (>= 5.0.0) Suggests: parallel, markdown, rmarkdown, crayon, SNPRelate, RUnit, knitr, ggmanh, BiocGenerics License: GPL-3 MD5sum: e63aedfc81343c10f03e66827ad1f851 NeedsCompilation: yes Title: Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association Studies Description: Scalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests and set-based aggregate tests in large-scale Phenome-wide Association Studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the SAIGE R package (v0.45, Zhou et al. 2018 and Zhou et al. 2020), and it is extended to include the state-of-the-art ACAT-O set-based tests. Benchmarks show that SAIGEgds is significantly faster than the SAIGE R package. biocViews: Software, Genetics, StatisticalMethod, GenomeWideAssociation Author: Xiuwen Zheng [aut, cre] (ORCID: ), Wei Zhou [ctb] (the original author of the SAIGE R package), J. Wade Davis [ctb] Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SAIGEgds SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SAIGEgds git_branch: devel git_last_commit: dadbd38 git_last_commit_date: 2026-04-15 Date/Publication: 2026-04-20 source.ver: src/contrib/SAIGEgds_2.11.4.tar.gz vignettes: vignettes/SAIGEgds/inst/doc/SAIGEgds.html vignetteTitles: SAIGEgds Tutorial (single variant tests) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAIGEgds/inst/doc/SAIGEgds.R dependencyCount: 38 Package: SamSPECTRAL Version: 1.65.0 Depends: R (>= 3.3.3) Imports: methods License: GPL (>= 2) MD5sum: c77c37ee96d5d74a311115edf617920d NeedsCompilation: yes Title: Identifies cell population in flow cytometry data Description: Samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting "connected components" estimate biological cell populations in the data. See the vignette for more details on how to use this package, some illustrations, and simple examples. biocViews: FlowCytometry, CellBiology, Clustering, Cancer, FlowCytometry, StemCells, HIV, ImmunoOncology Author: Habil Zare and Parisa Shooshtari Maintainer: Habil git_url: https://git.bioconductor.org/packages/SamSPECTRAL git_branch: devel git_last_commit: 604c1d8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SamSPECTRAL_1.65.0.tar.gz vignettes: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.pdf vignetteTitles: A modified spectral clustering method for clustering Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.R importsMe: ddPCRclust dependencyCount: 1 Package: sangeranalyseR Version: 1.21.1 Depends: R (>= 4.0.0), stringr, ape, Biostrings, pwalign, DECIPHER, parallel, reshape2, sangerseqR, gridExtra, shiny, shinydashboard, shinyjs, data.table, plotly, DT, zeallot, excelR, shinycssloaders, ggdendro, shinyWidgets, openxlsx, tools, rmarkdown (>= 2.9), knitr (>= 1.33), seqinr, BiocStyle, logger Suggests: testthat (>= 2.1.0) License: GPL-2 MD5sum: 5617d20a1a304768b9ad43d22e5dc42f NeedsCompilation: no Title: sangeranalyseR: a suite of functions for the analysis of Sanger sequence data in R Description: This package builds on sangerseqR to allow users to create contigs from collections of Sanger sequencing reads. It provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. There is extensive online documentation, and the package can outputs detailed HTML reports, including chromatograms. biocViews: Genetics, Alignment, Sequencing, SangerSeq, Preprocessing, QualityControl, Visualization, GUI Author: Rob Lanfear , Kuan-Hao Chao Maintainer: Kuan-Hao Chao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangeranalyseR git_branch: devel git_last_commit: a8787bd git_last_commit_date: 2025-11-18 Date/Publication: 2026-04-20 source.ver: src/contrib/sangeranalyseR_1.21.1.tar.gz vignettes: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.html vignetteTitles: sangeranalyseR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.R dependencyCount: 116 Package: sangerseqR Version: 1.47.0 Depends: R (>= 3.5.0), Biostrings, pwalign, stringr Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: ec1fa56e4833898cd09580b97a1bcc18 NeedsCompilation: no Title: Tools for Sanger Sequencing Data in R Description: This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms. biocViews: Sequencing, SNP, Visualization Author: Jonathon T. Hill, Bradley Demarest Maintainer: Jonathon Hill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangerseqR git_branch: devel git_last_commit: fda56d9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sangerseqR_1.47.0.tar.gz vignettes: vignettes/sangerseqR/inst/doc/sangerseqRWalkthrough.html vignetteTitles: Using the sangerseqR package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sangerseqR/inst/doc/sangerseqRWalkthrough.R dependsOnMe: sangeranalyseR importsMe: scifer suggestsMe: CrispRVariants dependencyCount: 49 Package: SanityR Version: 1.1.0 Imports: Rcpp, BiocGenerics, BiocParallel, MatrixGenerics, methods, S4Vectors, scuttle, SingleCellExperiment, SummarizedExperiment LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), scater, Rtsne License: GPL (>= 3) MD5sum: 9b79439980f6ec31d232727978aac219 NeedsCompilation: yes Title: R/Bioconductor interface to the Sanity model gene expression analysis Description: a Bayesian normalization procedure derived from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. biocViews: Software, GeneExpression, SingleCell, Normalization, Bayesian Author: Teo Sakel [aut, cre] (ORCID: ), MCIU/AEI [fnd] (ROR: , DOI: 10.13039/501100011033) Maintainer: Teo Sakel URL: https://github.com/TeoSakel/SanityR VignetteBuilder: knitr BugReports: https://github.com/TeoSakel/SanityR/issues git_url: https://git.bioconductor.org/packages/SanityR git_branch: devel git_last_commit: 65e0964 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SanityR_1.1.0.tar.gz vignettes: vignettes/SanityR/inst/doc/SanityR.html vignetteTitles: Normalizing scRNA-seq data with Sanity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SanityR/inst/doc/SanityR.R dependencyCount: 40 Package: SANTA Version: 2.47.0 Depends: R (>= 4.1), igraph Imports: graphics, Matrix, methods, stats Suggests: BiocGenerics, BioNet, formatR, knitr, msm, org.Sc.sgd.db, markdown, rmarkdown, RUnit License: GPL (>= 2) MD5sum: 357ab1d454761c0d0bda621cea926008 NeedsCompilation: yes Title: Spatial Analysis of Network Associations Description: This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode). biocViews: Network, NetworkEnrichment, Clustering Author: Alex Cornish [cre, aut] Maintainer: Alex Cornish VignetteBuilder: knitr BugReports: https://github.com/alexjcornish/SANTA git_url: https://git.bioconductor.org/packages/SANTA git_branch: devel git_last_commit: 83eab2a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SANTA_2.47.0.tar.gz vignettes: vignettes/SANTA/inst/doc/SANTA-vignette.html vignetteTitles: Introduction to SANTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R dependencyCount: 17 Package: sarks Version: 1.23.0 Depends: R (>= 4.0) Imports: rJava, Biostrings, IRanges, utils, stats, cluster, binom Suggests: RUnit, BiocGenerics, ggplot2 License: BSD_3_clause + file LICENSE MD5sum: f25a2301cba8750decbadfae4b0fdcca NeedsCompilation: no Title: Suffix Array Kernel Smoothing for discovery of correlative sequence motifs and multi-motif domains Description: Suffix Array Kernel Smoothing (see https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797), or SArKS, identifies sequence motifs whose presence correlates with numeric scores (such as differential expression statistics) assigned to the sequences (such as gene promoters). SArKS smooths over sequence similarity, quantified by location within a suffix array based on the full set of input sequences. A second round of smoothing over spatial proximity within sequences reveals multi-motif domains. Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing. biocViews: MotifDiscovery, GeneRegulation, GeneExpression, Transcriptomics, RNASeq, DifferentialExpression, FeatureExtraction Author: Dennis Wylie [aut, cre] (ORCID: ) Maintainer: Dennis Wylie URL: https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797, https://github.com/denniscwylie/sarks SystemRequirements: Java (>= 1.8) BugReports: https://github.com/denniscwylie/sarks/issues git_url: https://git.bioconductor.org/packages/sarks git_branch: devel git_last_commit: 5f25697 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sarks_1.23.0.tar.gz vignettes: vignettes/sarks/inst/doc/sarks-vignette.pdf vignetteTitles: sarks-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sarks/inst/doc/sarks-vignette.R dependencyCount: 18 Package: satuRn Version: 1.19.0 Depends: R (>= 4.1) Imports: locfdr, SummarizedExperiment, BiocParallel, limma, pbapply, ggplot2, boot, Matrix, stats, methods, graphics Suggests: knitr, rmarkdown, testthat, covr, BiocStyle, AnnotationHub, ensembldb, edgeR, DEXSeq, stageR, DelayedArray License: Artistic-2.0 MD5sum: 9242ff9599acf6928051dd789e3a3846 NeedsCompilation: no Title: Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications Description: satuRn provides a higly performant and scalable framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest. biocViews: Regression, ExperimentalDesign, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Jeroen Gilis [aut, cre], Kristoffer Vitting-Seerup [ctb], Koen Van den Berge [ctb], Lieven Clement [ctb] Maintainer: Jeroen Gilis URL: https://github.com/statOmics/satuRn VignetteBuilder: knitr BugReports: https://github.com/statOmics/satuRn/issues git_url: https://git.bioconductor.org/packages/satuRn git_branch: devel git_last_commit: f956e7e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/satuRn_1.19.0.tar.gz vignettes: vignettes/satuRn/inst/doc/Vignette.html vignetteTitles: satuRn - vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/satuRn/inst/doc/Vignette.R dependsOnMe: IsoformSwitchAnalyzeR dependencyCount: 57 Package: SBGNview Version: 1.25.0 Depends: R (>= 3.6), pathview, SBGNview.data Imports: Rdpack, grDevices, methods, stats, utils, xml2, rsvg, igraph, rmarkdown, knitr, SummarizedExperiment, AnnotationDbi, httr, KEGGREST, bookdown Suggests: testthat, gage License: AGPL-3 MD5sum: da02c40ab3e2a4a20ae68df29ba55b70 NeedsCompilation: no Title: "SBGNview: Data Analysis, Integration and Visualization on SBGN Pathways" Description: SBGNview is a tool set for pathway based data visalization, integration and analysis. SBGNview is similar and complementary to the widely used Pathview, with the following key features: 1. Pathway definition by the widely adopted Systems Biology Graphical Notation (SBGN); 2. Supports multiple major pathway databases beyond KEGG (Reactome, MetaCyc, SMPDB, PANTHER, METACROP) and user defined pathways; 3. Covers 5,200 reference pathways and over 3,000 species by default; 4. Extensive graphics controls, including glyph and edge attributes, graph layout and sub-pathway highlight; 5. SBGN pathway data manipulation, processing, extraction and analysis. biocViews: GeneTarget, Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing, GeneTarget Author: Xiaoxi Dong*, Kovidh Vegesna*, Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/SBGNview VignetteBuilder: knitr BugReports: https://github.com/datapplab/SBGNview/issues git_url: https://git.bioconductor.org/packages/SBGNview git_branch: devel git_last_commit: 146fd47 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SBGNview_1.25.0.tar.gz vignettes: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.html, vignettes/SBGNview/inst/doc/SBGNview.quick.start.html, vignettes/SBGNview/inst/doc/SBGNview.Vignette.html vignetteTitles: Pathway analysis using SBGNview gene set, Quick start SBGNview, SBGNview functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.R, vignettes/SBGNview/inst/doc/SBGNview.quick.start.R, vignettes/SBGNview/inst/doc/SBGNview.Vignette.R dependencyCount: 85 Package: SBMLR Version: 2.7.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: 44d70309edeb04c097ddf2cf86328c90 NeedsCompilation: no Title: SBML-R Interface and Analysis Tools Description: This package contains a systems biology markup language (SBML) interface to R. biocViews: GraphAndNetwork, Pathways, Network Author: Tomas Radivoyevitch, Vishak Venkateswaran Maintainer: Tomas Radivoyevitch URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html git_url: https://git.bioconductor.org/packages/SBMLR git_branch: devel git_last_commit: 47fc532 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SBMLR_2.7.0.tar.gz vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf vignetteTitles: Quick intro to SBMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBMLR/inst/doc/quick-start.R dependencyCount: 7 Package: SC3 Version: 1.39.0 Depends: R(>= 3.3) Imports: graphics, stats, utils, methods, e1071, parallel, foreach, doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8), ROCR, robustbase, rrcov, cluster, WriteXLS, Rcpp (>= 0.11.1), SummarizedExperiment, SingleCellExperiment, BiocGenerics, S4Vectors LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, mclust, scater, BiocStyle License: GPL-3 MD5sum: 288e9cde769d8ecbe205ee7494ceec9f NeedsCompilation: yes Title: Single-Cell Consensus Clustering Description: A tool for unsupervised clustering and analysis of single cell RNA-Seq data. biocViews: ImmunoOncology, SingleCell, Software, Classification, Clustering, DimensionReduction, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, GUI, DifferentialExpression, Transcription Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/SC3 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/sc3/ git_url: https://git.bioconductor.org/packages/SC3 git_branch: devel git_last_commit: 177cc94 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SC3_1.39.0.tar.gz vignettes: vignettes/SC3/inst/doc/SC3.html vignetteTitles: SC3 package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SC3/inst/doc/SC3.R importsMe: FEAST suggestsMe: InteractiveComplexHeatmap, scTreeViz, VAExprs dependencyCount: 93 Package: Scale4C Version: 1.33.0 Depends: R (>= 3.5.0), smoothie, GenomicRanges, IRanges, SummarizedExperiment Imports: methods, grDevices, graphics, utils License: LGPL-3 MD5sum: 18fae32a78449dcd5a302d8e249380d7 NeedsCompilation: no Title: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data Description: Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples. biocViews: Visualization, QualityControl, DataImport, Sequencing, Coverage Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Scale4C git_branch: devel git_last_commit: 7236205 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Scale4C_1.33.0.tar.gz vignettes: vignettes/Scale4C/inst/doc/vignette.pdf vignetteTitles: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Scale4C/inst/doc/vignette.R dependencyCount: 26 Package: ScaledMatrix Version: 1.19.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular, DelayedMatrixStats License: GPL-3 MD5sum: 69729298f86ae4772a2387921cb49147 NeedsCompilation: no Title: Creating a DelayedMatrix of Scaled and Centered Values Description: Provides delayed computation of a matrix of scaled and centered values. The result is equivalent to using the scale() function but avoids explicit realization of a dense matrix during block processing. This permits greater efficiency in common operations, most notably matrix multiplication. biocViews: Software, DataRepresentation Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/ScaledMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/ScaledMatrix/issues git_url: https://git.bioconductor.org/packages/ScaledMatrix git_branch: devel git_last_commit: 439f56b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ScaledMatrix_1.19.0.tar.gz vignettes: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.html vignetteTitles: Using the ScaledMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.R importsMe: batchelor, BiocSingular, mumosa, scPCA suggestsMe: scran dependencyCount: 21 Package: SCAN.UPC Version: 2.53.0 Depends: R (>= 2.14.0), Biobase (>= 2.6.0), oligo, Biostrings, GEOquery, affy, affyio, foreach, sva Imports: utils, methods, MASS, tools, IRanges Suggests: pd.hg.u95a License: MIT MD5sum: 97a20574c8e54d62af5588941c2c0a75 NeedsCompilation: no Title: Single-channel array normalization (SCAN) and Universal exPression Codes (UPC) Description: SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration. biocViews: ImmunoOncology, Software, Microarray, Preprocessing, RNASeq, TwoChannel, OneChannel Author: Stephen R. Piccolo and Andrea H. Bild and W. Evan Johnson Maintainer: Stephen R. Piccolo URL: http://bioconductor.org, http://jlab.bu.edu/software/scan-upc git_url: https://git.bioconductor.org/packages/SCAN.UPC git_branch: devel git_last_commit: 89643cf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SCAN.UPC_2.53.0.tar.gz vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.R dependencyCount: 115 Package: scanMiR Version: 1.17.0 Depends: R (>= 4.0) Imports: Biostrings, pwalign, GenomicRanges, IRanges, data.table, BiocParallel, methods, Seqinfo, S4Vectors, ggplot2, stats, stringi, utils, graphics, grid, seqLogo, cowplot Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: a54c7d334eee8a2239564feb7a3edd27 NeedsCompilation: no Title: scanMiR Description: A set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3' alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment). biocViews: miRNA, SequenceMatching, Alignment Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Michael Soutschek [aut], Fridolin Gross [aut] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiR git_branch: devel git_last_commit: 6328f02 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scanMiR_1.17.0.tar.gz vignettes: vignettes/scanMiR/inst/doc/Kdmodels.html, vignettes/scanMiR/inst/doc/scanning.html vignetteTitles: 2_Kdmodels, 1_scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scanMiR/inst/doc/Kdmodels.R, vignettes/scanMiR/inst/doc/scanning.R dependsOnMe: scanMiRApp importsMe: scanMiRData dependencyCount: 48 Package: scAnnotatR Version: 1.17.1 Depends: R (>= 4.1), Seurat, SingleCellExperiment, SummarizedExperiment Imports: dplyr, ggplot2, caret, ROCR, pROC, data.tree, methods, stats, e1071, ape, kernlab, AnnotationHub, utils Suggests: knitr, rmarkdown, scRNAseq, testthat License: MIT + file LICENSE MD5sum: 9fb694dfc6a8ab86a829f4b1116e43d0 NeedsCompilation: no Title: Pretrained learning models for cell type prediction on single cell RNA-sequencing data Description: The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs. biocViews: SingleCell, Transcriptomics, GeneExpression, SupportVectorMachine, Classification, Software Author: Vy Nguyen [aut] (ORCID: ), Johannes Griss [cre] (ORCID: ) Maintainer: Johannes Griss URL: https://github.com/grisslab/scAnnotatR VignetteBuilder: knitr BugReports: https://github.com/grisslab/scAnnotatR/issues/new git_url: https://git.bioconductor.org/packages/scAnnotatR git_branch: devel git_last_commit: fa58911 git_last_commit_date: 2026-01-16 Date/Publication: 2026-04-20 source.ver: src/contrib/scAnnotatR_1.17.1.tar.gz vignettes: vignettes/scAnnotatR/inst/doc/classifying-cells.html, vignettes/scAnnotatR/inst/doc/training-basic-model.html, vignettes/scAnnotatR/inst/doc/training-child-model.html vignetteTitles: 1. Introduction to scAnnotatR, 2. Training basic model, 3. Training child model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scAnnotatR/inst/doc/classifying-cells.R, vignettes/scAnnotatR/inst/doc/training-basic-model.R, vignettes/scAnnotatR/inst/doc/training-child-model.R suggestsMe: scAnnotatR.models dependencyCount: 211 Package: SCANVIS Version: 1.25.0 Depends: R (>= 3.6) Imports: IRanges,plotrix,RCurl,rtracklayer Suggests: knitr, rmarkdown License: file LICENSE MD5sum: 1e6397a2bed320c6ce24fa4244f388e7 NeedsCompilation: no Title: SCANVIS - a tool for SCoring, ANnotating and VISualizing splice junctions Description: SCANVIS is a set of annotation-dependent tools for analyzing splice junctions and their read support as predetermined by an alignment tool of choice (for example, STAR aligner). SCANVIS assesses each junction's relative read support (RRS) by relating to the context of local split reads aligning to annotated transcripts. SCANVIS also annotates each splice junction by indicating whether the junction is supported by annotation or not, and if not, what type of junction it is (e.g. exon skipping, alternative 5' or 3' events, Novel Exons). Unannotated junctions are also futher annotated by indicating whether it induces a frame shift or not. SCANVIS includes a visualization function to generate static sashimi-style plots depicting relative read support and number of split reads using arc thickness and arc heights, making it easy for users to spot well-supported junctions. These plots also clearly delineate unannotated junctions from annotated ones using designated color schemes, and users can also highlight splice junctions of choice. Variants and/or a read profile are also incoroporated into the plot if the user supplies variants in bed format and/or the BAM file. One further feature of the visualization function is that users can submit multiple samples of a certain disease or cohort to generate a single plot - this occurs via a "merge" function wherein junction details over multiple samples are merged to generate a single sashimi plot, which is useful when contrasting cohorots (eg. disease vs control). biocViews: Software,ResearchField,Transcriptomics,WorkflowStep,Annotation,Visualization Author: Phaedra Agius Maintainer: Phaedra Agius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCANVIS git_branch: devel git_last_commit: fa77b41 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SCANVIS_1.25.0.tar.gz vignettes: vignettes/SCANVIS/inst/doc/runningSCANVIS.pdf vignetteTitles: SCANVIS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SCANVIS/inst/doc/runningSCANVIS.R dependencyCount: 58 Package: SCArray Version: 1.19.1 Depends: R (>= 3.5.0), gdsfmt (>= 1.36.0), methods, DelayedArray (>= 0.31.5) Imports: S4Vectors, utils, Matrix, SparseArray (>= 1.5.13), BiocParallel, DelayedMatrixStats, SummarizedExperiment, SingleCellExperiment, BiocSingular Suggests: BiocGenerics, scater, scuttle, uwot, RUnit, knitr, markdown, rmarkdown, rhdf5, HDF5Array License: GPL-3 MD5sum: 7edfb7f574b9ecb033daab1bfc9dc23e NeedsCompilation: yes Title: Large-scale single-cell omics data manipulation with GDS files Description: Provides large-scale single-cell omics data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language. biocViews: Infrastructure, DataRepresentation, DataImport, SingleCell, RNASeq Author: Xiuwen Zheng [aut, cre] (ORCID: ) Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SCArray VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCArray git_branch: devel git_last_commit: b480c17 git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/SCArray_1.19.1.tar.gz vignettes: vignettes/SCArray/inst/doc/Overview.html, vignettes/SCArray/inst/doc/SCArray.html vignetteTitles: Overview, Single-cell RNA-seq data manipulation using GDS files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCArray/inst/doc/SCArray.R dependsOnMe: SCArray.sat dependencyCount: 46 Package: SCArray.sat Version: 1.11.2 Depends: methods, SCArray (>= 1.13.1), SeuratObject (>= 5.0), Seurat (>= 5.0) Imports: S4Vectors, utils, stats, BiocGenerics, BiocParallel, gdsfmt, DelayedArray, BiocSingular, SummarizedExperiment, Matrix Suggests: future, RUnit, knitr, markdown, rmarkdown, BiocStyle License: GPL-3 MD5sum: d6d9185c2f622a61725e5bc66c013dc5 NeedsCompilation: no Title: Large-scale single-cell RNA-seq data analysis using GDS files and Seurat Description: Extends the Seurat classes and functions to support Genomic Data Structure (GDS) files as a DelayedArray backend for data representation. It relies on the implementation of GDS-based DelayedMatrix in the SCArray package to represent single cell RNA-seq data. The common optimized algorithms leveraging GDS-based and single cell-specific DelayedMatrix (SC_GDSMatrix) are implemented in the SCArray package. SCArray.sat introduces a new SCArrayAssay class (derived from the Seurat Assay), which wraps raw counts, normalized expressions and scaled data matrix based on GDS-specific DelayedMatrix. It is designed to integrate seamlessly with the Seurat package to provide common data analysis in the SeuratObject-based workflow. Compared with Seurat, SCArray.sat significantly reduces the memory usage without downsampling and can be applied to very large datasets. biocViews: DataRepresentation, DataImport, SingleCell, RNASeq Author: Xiuwen Zheng [aut, cre] (ORCID: ), Seurat contributors [ctb] (for the classes and methods defined in Seurat) Maintainer: Xiuwen Zheng VignetteBuilder: knitr BugReports: https://github.com/AbbVie-ComputationalGenomics/SCArray/issues git_url: https://git.bioconductor.org/packages/SCArray.sat git_branch: devel git_last_commit: c6a8d42 git_last_commit_date: 2026-02-24 Date/Publication: 2026-04-20 source.ver: src/contrib/SCArray.sat_1.11.2.tar.gz vignettes: vignettes/SCArray.sat/inst/doc/SCArray.sat.html vignetteTitles: scRNA-seq data analysis with GDS files and Seurat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCArray.sat/inst/doc/SCArray.sat.R dependencyCount: 180 Package: scater Version: 1.39.4 Depends: SingleCellExperiment, scuttle, ggplot2 Imports: stats, utils, methods, Matrix, BiocGenerics, S4Vectors, SummarizedExperiment, MatrixGenerics, SparseArray, DelayedArray, beachmat, BiocNeighbors, BiocSingular, BiocParallel, rlang, ggbeeswarm, viridis, Rtsne, RColorBrewer, RcppML, uwot, pheatmap, ggrepel, ggrastr Suggests: BiocStyle, DelayedMatrixStats, snifter, densvis, cowplot, biomaRt, knitr, scRNAseq, robustbase, rmarkdown, testthat, Biobase, scattermore License: GPL-3 MD5sum: efcfbddf991fd79541bdccb924bc0586 NeedsCompilation: no Title: Single-Cell Analysis Toolkit for Gene Expression Data in R Description: A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage Author: Davis McCarthy [aut], Kieran Campbell [aut], Aaron Lun [aut, ctb], Quin Wills [aut], Vladimir Kiselev [ctb], Felix G.M. Ernst [ctb], Alan O'Callaghan [ctb, cre], Yun Peng [ctb], Leo Lahti [ctb] (ORCID: ), Tuomas Borman [ctb] (ORCID: ) Maintainer: Alan O'Callaghan URL: http://bioconductor.org/packages/scater/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/scater git_branch: devel git_last_commit: f23aaa1 git_last_commit_date: 2026-04-06 Date/Publication: 2026-04-20 source.ver: src/contrib/scater_1.39.4.tar.gz vignettes: vignettes/scater/inst/doc/overview.html vignetteTitles: Overview of scater functionality hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scater/inst/doc/overview.R dependsOnMe: chevreulProcess, netSmooth, omicsGMF, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, scrapbook, SingleRBook importsMe: airpart, BayesSpace, blase, CATALYST, celda, CelliD, CellMixS, chevreulPlot, ChromSCape, clustSIGNAL, decontX, distinct, epiregulon.extra, FLAMES, M3Drop, MEB, mia, miaDash, miaViz, muscat, peco, pipeComp, RegionalST, scDblFinder, scDotPlot, scMerge, scTreeViz, scviR, shinyDSP, singleCellTK, SpaceTrooper, Spaniel, tricycle, VAExprs, DoReMiTra, spatialLIBD, OSTA, CAESAR.Suite, PRECAST suggestsMe: alabaster.sfe, anglemania, APL, Banksy, batchelor, bluster, ccImpute, CellMentor, CellTrails, Cepo, CiteFuse, concordexR, Coralysis, corral, crumblr, dandelionR, DeeDeeExperiment, dittoSeq, DOtools, dreamlet, epiregulon, escheR, ExperimentSubset, ggsc, ggspavis, Glimma, GSABenchmark, hammers, HoloFoodR, HVP, Ibex, InteractiveComplexHeatmap, iSEE, iSEEfier, iSEEhex, iSEEpathways, iSEEtree, iSEEu, jazzPanda, MAST, mbkmeans, MGnifyR, miaTime, miloR, miQC, monocle, MOSim, msqrob2, MuData, mumosa, Nebulosa, raer, ReactomeGSA, SanityR, SC3, SCArray, scDiagnostics, scds, scGraphVerse, schex, scHOT, scLANE, scLang, scone, scp, scPipe, scran, scrapper, scRepertoire, Seqtometry, simPIC, SingleCellAlleleExperiment, sketchR, slalom, smartid, smoothclust, SpaNorm, SpatialFeatureExperiment, spatialHeatmap, speckle, splatter, SPOTlight, StabMap, standR, SuperCellCyto, SVP, tidySingleCellExperiment, tidySpatialExperiment, UCell, velociraptor, Voyager, waddR, curatedMetagenomicData, DuoClustering2018, HCAData, HCATonsilData, MerfishData, MouseAgingData, muscData, SingleCellMultiModal, TabulaMurisData, tuberculosis, simpleSingleCell, Canek, coFAST, futurize, ProFAST, SCdeconR, scellpam, SuperCell dependencyCount: 87 Package: scatterHatch Version: 1.17.0 Depends: R (>= 4.1) Imports: grid, ggplot2, plyr, spatstat.geom, stats, grDevices Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 109a49705e362a4f61953093e45042eb NeedsCompilation: no Title: Creates hatched patterns for scatterplots Description: The objective of this package is to efficiently create scatterplots where groups can be distinguished by color and texture. Visualizations in computational biology tend to have many groups making it difficult to distinguish between groups solely on color. Thus, this package is useful for increasing the accessibility of scatterplot visualizations to those with visual impairments such as color blindness. biocViews: Visualization, SingleCell, CellBiology, Software, Spatial Author: Atul Deshpande [aut, cre] (ORCID: ) Maintainer: Atul Deshpande URL: https://github.com/FertigLab/scatterHatch VignetteBuilder: knitr BugReports: https://github.com/FertigLab/scatterHatch/issues git_url: https://git.bioconductor.org/packages/scatterHatch git_branch: devel git_last_commit: be1b788 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scatterHatch_1.17.0.tar.gz vignettes: vignettes/scatterHatch/inst/doc/vignette.html vignetteTitles: Creating a Scatterplot with Texture hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scatterHatch/inst/doc/vignette.R dependencyCount: 33 Package: scBFA Version: 1.25.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, Seurat, MASS, zinbwave, stats, copula, ggplot2, DESeq2, utils, grid, methods, Matrix Suggests: knitr, rmarkdown, testthat, Rtsne License: GPL-3 + file LICENSE MD5sum: 6584143a4eea0da069f3d2fd6afa2ec6 NeedsCompilation: no Title: A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq Description: This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis. biocViews: SingleCell, Transcriptomics, DimensionReduction,GeneExpression, ATACSeq, BatchEffect, KEGG, QualityControl Author: Ruoxin Li [aut, cre], Gerald Quon [aut] Maintainer: Ruoxin Li URL: https://github.com/ucdavis/quon-titative-biology/BFA VignetteBuilder: knitr BugReports: https://github.com/ucdavis/quon-titative-biology/BFA/issues git_url: https://git.bioconductor.org/packages/scBFA git_branch: devel git_last_commit: 128392e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scBFA_1.25.0.tar.gz vignettes: vignettes/scBFA/inst/doc/vignette.html vignetteTitles: Gene Detection Analysis for scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scBFA/inst/doc/vignette.R dependencyCount: 199 Package: SCBN Version: 1.29.0 Depends: R (>= 3.5.0) Imports: stats Suggests: knitr,rmarkdown,BiocStyle,BiocManager License: GPL-2 MD5sum: 622b0fda02b11c5f4aeef2ce15212bd9 NeedsCompilation: no Title: A statistical normalization method and differential expression analysis for RNA-seq data between different species Description: This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article 'A statistical normalization method and differential expression analysis for RNA-seq data between different species' by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication). biocViews: DifferentialExpression, GeneExpression, Normalization Author: Yan Zhou Maintainer: Yan Zhou <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCBN git_branch: devel git_last_commit: 5b54dae git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SCBN_1.29.0.tar.gz vignettes: vignettes/SCBN/inst/doc/SCBN.html vignetteTitles: SCBN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCBN/inst/doc/SCBN.R importsMe: TEKRABber dependencyCount: 1 Package: scBubbletree Version: 1.13.0 Depends: R (>= 4.2.0) Imports: reshape2, BiocParallel, ape, scales, Seurat, ggplot2, ggtree, patchwork, proxy, methods, stats, base, utils, dplyr Suggests: BiocStyle, knitr, testthat, cluster, SingleCellExperiment License: GPL-3 + file LICENSE MD5sum: afdeb4003cd6d168cc4e6b1c731b30a1 NeedsCompilation: no Title: Quantitative visual exploration of scRNA-seq data Description: scBubbletree is a quantitative method for the visual exploration of scRNA-seq data, preserving key biological properties such as local and global cell distances and cell density distributions across samples. It effectively resolves overplotting and enables the visualization of diverse cell attributes from multiomic single-cell experiments. Additionally, scBubbletree is user-friendly and integrates seamlessly with popular scRNA-seq analysis tools, facilitating comprehensive and intuitive data interpretation. biocViews: Visualization,Clustering, SingleCell,Transcriptomics,RNASeq Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski URL: https://github.com/snaketron/scBubbletree SystemRequirements: Python (>= 3.6), leidenalg (>= 0.8.2) VignetteBuilder: knitr BugReports: https://github.com/snaketron/scBubbletree/issues git_url: https://git.bioconductor.org/packages/scBubbletree git_branch: devel git_last_commit: 7ad7e1e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scBubbletree_1.13.0.tar.gz vignettes: vignettes/scBubbletree/inst/doc/User_manual.html vignetteTitles: User Manual: scBubbletree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scBubbletree/inst/doc/User_manual.R dependencyCount: 174 Package: sccomp Version: 2.3.0 Depends: R (>= 4.3.0), instantiate (>= 0.2.3) Imports: stats, boot, utils, scales, lifecycle, rlang, tidyselect, magrittr, crayon, cli, fansi, dplyr, tidyr, purrr, tibble, ggplot2, ggrepel, patchwork, forcats, readr, stringr, glue, SingleCellExperiment Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), markdown, loo, prettydoc, SeuratObject, tidyseurat, tidySingleCellExperiment, bayesplot, posterior License: GPL-3 MD5sum: 08831bf1de6e21e55e78a8fb31930ca3 NeedsCompilation: no Title: Differential Composition and Variability Analysis for Single-Cell Data Description: Comprehensive R package for differential composition and variability analysis in single-cell RNA sequencing, CyTOF, and microbiome data. Provides robust Bayesian modeling with outlier detection, random effects, and advanced statistical methods for cell type proportion analysis. Features include probabilistic outlier identification, mixed-effect modeling, differential variability testing, and comprehensive visualization tools. Perfect for cancer research, immunology, developmental biology, and single-cell genomics applications. biocViews: Bayesian, Regression, DifferentialExpression, SingleCell, Metagenomics, FlowCytometry, Spatial Author: Stefano Mangiola [aut, cre], Alexandra J. Roth-Schulze [aut], Marie Trussart [aut], Enrique Zozaya-Valdés [aut], Mengyao Ma [aut], Zijie Gao [aut], Alan F. Rubin [aut], Terence P. Speed [aut], Heejung Shim [aut], Anthony T. Papenfuss [aut] Maintainer: Stefano Mangiola URL: https://github.com/MangiolaLaboratory/sccomp SystemRequirements: CmdStan (https://mc-stan.org/users/interfaces/cmdstan), C++14 VignetteBuilder: knitr BugReports: https://github.com/MangiolaLaboratory/sccomp/issues git_url: https://git.bioconductor.org/packages/sccomp git_branch: devel git_last_commit: ea50568 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sccomp_2.3.0.tar.gz vignettes: vignettes/sccomp/inst/doc/introduction.html vignetteTitles: sccomp: Differential Composition and Variability Analysis for Single-Cell Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sccomp/inst/doc/introduction.R dependencyCount: 75 Package: scConform Version: 0.99.4 Depends: R (>= 4.6.0) Imports: igraph, stats, SummarizedExperiment, BiocParallel, Rgraphviz Suggests: knitr, Matrix, rmarkdown, BiocStyle, VGAM, ontoProc, MerfishData, doParallel, scuttle, SingleCellExperiment, scran, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 500d8952ef7bc1cf3c18f54ef9571b69 NeedsCompilation: no Title: Conformal Inference for Cell Type Annotation Description: Builds prediction interval for cell type annotation using conformal inference and conformal risk control. It provides two main methods. The first one gives prediction intervals with coverage guarantees based on standard conformal inference. The second one instead gives hierarchical prediction intervals that are consistent with the cell ontology. biocViews: Software, Classification, SingleCell, Annotation Author: Daniela Corbetta [aut, cre] (ORCID: ), Tram Nguyen [ctb], Nitesh Turaga [ctb], Ludwig Geistlinger [ctb] Maintainer: Daniela Corbetta URL: https://github.com/ccb-hms/scConform VignetteBuilder: knitr BugReports: https://github.com/ccb-hms/scConform/issues git_url: https://git.bioconductor.org/packages/scConform git_branch: devel git_last_commit: db853e5 git_last_commit_date: 2026-03-20 Date/Publication: 2026-04-20 source.ver: src/contrib/scConform_0.99.4.tar.gz vignettes: vignettes/scConform/inst/doc/scConform.html vignetteTitles: Conformal Prediction for cell type annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scConform/inst/doc/scConform.R dependencyCount: 45 Package: scDataviz Version: 1.21.0 Depends: R (>= 4.0), S4Vectors, SingleCellExperiment, Imports: ggplot2, ggrepel, flowCore, umap, Seurat, reshape2, scales, RColorBrewer, corrplot, stats, grDevices, graphics, utils, MASS, matrixStats, methods Suggests: PCAtools, cowplot, BiocGenerics, RUnit, knitr, kableExtra, rmarkdown License: GPL-3 MD5sum: 4093f5bf196ab6c3e9569ea82185c793 NeedsCompilation: no Title: scDataviz: single cell dataviz and downstream analyses Description: In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a 'plug and play' feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can 'add on' features to these with ease. biocViews: SingleCell, ImmunoOncology, RNASeq, GeneExpression, Transcription, FlowCytometry, MassSpectrometry, DataImport Author: Kevin Blighe [aut, cre] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/scDataviz VignetteBuilder: knitr BugReports: https://github.com/kevinblighe/scDataviz/issues git_url: https://git.bioconductor.org/packages/scDataviz git_branch: devel git_last_commit: 77d65c4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scDataviz_1.21.0.tar.gz vignettes: vignettes/scDataviz/inst/doc/scDataviz.html vignetteTitles: scDataviz: single cell dataviz and downstream analyses hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDataviz/inst/doc/scDataviz.R dependencyCount: 174 Package: scDblFinder Version: 1.25.4 Depends: R (>= 4.0), SingleCellExperiment Imports: igraph, Matrix, BiocGenerics, BiocParallel, BiocNeighbors, BiocSingular, S4Vectors, SummarizedExperiment, scran, scater, scuttle, bluster, methods, DelayedArray, xgboost (>= 3.1), stats, utils, MASS, IRanges, GenomicRanges, GenomeInfoDb, Rsamtools, rtracklayer Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, circlize, ComplexHeatmap, ggplot2, dplyr, viridisLite, mbkmeans License: GPL-3 + file LICENSE MD5sum: 0e36dfda1d4e0ea88086391dc04e360a NeedsCompilation: no Title: scDblFinder Description: The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, the new fast and comprehensive scDblFinder method, and a reimplementation of the Amulet detection method for single-cell ATAC-seq. biocViews: Preprocessing, SingleCell, RNASeq, ATACSeq Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Aaron Lun [ctb] Maintainer: Pierre-Luc Germain URL: https://github.com/plger/scDblFinder, https://plger.github.io/scDblFinder/ VignetteBuilder: knitr BugReports: https://github.com/plger/scDblFinder/issues git_url: https://git.bioconductor.org/packages/scDblFinder git_branch: devel git_last_commit: 4d32241 git_last_commit_date: 2026-02-11 Date/Publication: 2026-04-20 source.ver: src/contrib/scDblFinder_1.25.4.tar.gz vignettes: vignettes/scDblFinder/inst/doc/computeDoubletDensity.html, vignettes/scDblFinder/inst/doc/findDoubletClusters.html, vignettes/scDblFinder/inst/doc/introduction.html, vignettes/scDblFinder/inst/doc/recoverDoublets.html, vignettes/scDblFinder/inst/doc/scATAC.html, vignettes/scDblFinder/inst/doc/scDblFinder.html vignetteTitles: 4_computeDoubletDensity, 3_findDoubletClusters, 1_introduction, 5_recoverDoublets, 6_scATAC, 2_scDblFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scDblFinder/inst/doc/computeDoubletDensity.R, vignettes/scDblFinder/inst/doc/findDoubletClusters.R, vignettes/scDblFinder/inst/doc/introduction.R, vignettes/scDblFinder/inst/doc/recoverDoublets.R, vignettes/scDblFinder/inst/doc/scATAC.R, vignettes/scDblFinder/inst/doc/scDblFinder.R importsMe: DOtools, singleCellTK dependencyCount: 124 Package: scde Version: 2.39.0 Depends: R (>= 3.0.0), flexmix Imports: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook, rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods, nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, cba, fastcluster, WGCNA, GO.db, org.Hs.eg.db, rmarkdown License: GPL-2 MD5sum: e88f98c2ba916028e1ba9091d74ad670 NeedsCompilation: yes Title: Single Cell Differential Expression Description: The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734). biocViews: ImmunoOncology, RNASeq, StatisticalMethod, DifferentialExpression, Bayesian, Transcription, Software Author: Peter Kharchenko [aut, cre], Jean Fan [aut], Evan Biederstedt [aut] Maintainer: Evan Biederstedt URL: http://pklab.med.harvard.edu/scde VignetteBuilder: knitr BugReports: https://github.com/hms-dbmi/scde/issues git_url: https://git.bioconductor.org/packages/scde git_branch: devel git_last_commit: 013e4aa git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scde_2.39.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: pagoda2 dependencyCount: 50 Package: scDesign3 Version: 1.9.0 Depends: R (>= 4.3.0) Imports: dplyr, tibble, stats, methods, mgcv, gamlss, gamlss.dist, SummarizedExperiment, SingleCellExperiment, mclust, mvtnorm, parallel, pbmcapply, umap, ggplot2, irlba, viridis, BiocParallel, matrixStats, Matrix, sparseMVN, coop Suggests: mvnfast, igraph, rvinecopulib, knitr, rmarkdown, testthat (>= 3.0.0), RefManageR, sessioninfo, BiocStyle License: MIT + file LICENSE MD5sum: c17b641bf0da1522bf5b879ad8e747d8 NeedsCompilation: no Title: A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics Description: We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools. biocViews: Software, SingleCell, Sequencing, GeneExpression, Spatial Author: Dongyuan Song [aut, cre] (ORCID: ), Qingyang Wang [aut] (ORCID: ), Chenxin Jiang [aut] (ORCID: ) Maintainer: Dongyuan Song URL: https://github.com/SONGDONGYUAN1994/scDesign3 VignetteBuilder: knitr BugReports: https://github.com/SONGDONGYUAN1994/scDesign3/issues git_url: https://git.bioconductor.org/packages/scDesign3 git_branch: devel git_last_commit: f87307f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scDesign3_1.9.0.tar.gz vignettes: vignettes/scDesign3/inst/doc/scDesign3.html vignetteTitles: scDesign3-quickstart-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scDesign3/inst/doc/scDesign3.R dependencyCount: 89 Package: scDiagnostics Version: 1.5.1 Depends: R (>= 4.4.0) Imports: SingleCellExperiment, methods, isotree, FNN, igraph, ggplot2, GGally, ggridges, SummarizedExperiment, ranger, transport, cramer, rlang, bluster, scales, MASS, stringr, Matrix, grDevices Suggests: AUCell, BiocStyle, knitr, rmarkdown, scran, scRNAseq, SingleR, celldex, scuttle, scater, dplyr, ComplexHeatmap, grid, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 9b7ca2f2dcd3cbb18b6aa310dfa9f1d5 NeedsCompilation: no Title: Cell type annotation diagnostics Description: The scDiagnostics package provides diagnostic plots to assess the quality of cell type assignments from single cell gene expression profiles. The implemented functionality allows to assess the reliability of cell type annotations, investigate gene expression patterns, and explore relationships between different cell types in query and reference datasets allowing users to detect potential misalignments between reference and query datasets. The package also provides visualization capabilities for diagnostics purposes. biocViews: Annotation, Classification, Clustering, GeneExpression, RNASeq, SingleCell, Software, Transcriptomics Author: Anthony Christidis [aut, cre] (ORCID: ), Andrew Ghazi [aut], Smriti Chawla [aut], Nitesh Turaga [ctb], Ludwig Geistlinger [aut], Robert Gentleman [aut] Maintainer: Anthony Christidis URL: https://github.com/ccb-hms/scDiagnostics VignetteBuilder: knitr BugReports: https://github.com/ccb-hms/scDiagnostics/issues git_url: https://git.bioconductor.org/packages/scDiagnostics git_branch: devel git_last_commit: 9ec62cd git_last_commit_date: 2026-01-25 Date/Publication: 2026-04-20 source.ver: src/contrib/scDiagnostics_1.5.1.tar.gz vignettes: vignettes/scDiagnostics/inst/doc/AnnotationAnomalies.html, vignettes/scDiagnostics/inst/doc/DatasetMarkerGeneAlignment.html, vignettes/scDiagnostics/inst/doc/scDiagnostics.html, vignettes/scDiagnostics/inst/doc/VisualizationTools.html vignetteTitles: 4. Detection and Analysis of Annotation Anomalies, 3. Evaluation of Dataset and Marker Gene Alignment, 1. Getting Started with scDiagnostics, 2. Visualization of Cell Type Annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDiagnostics/inst/doc/AnnotationAnomalies.R, vignettes/scDiagnostics/inst/doc/DatasetMarkerGeneAlignment.R, vignettes/scDiagnostics/inst/doc/scDiagnostics.R, vignettes/scDiagnostics/inst/doc/VisualizationTools.R dependencyCount: 90 Package: scDotPlot Version: 1.5.0 Depends: R (>= 4.4.0) Imports: aplot, BiocGenerics, cli, dplyr, ggplot2, ggsci, ggtree, grDevices, magrittr, purrr, rlang, scales, scater, Seurat, SingleCellExperiment, stats, stringr, tibble, tidyr Suggests: AnnotationDbi, BiocStyle, knitr, rmarkdown, scran, scRNAseq, scuttle, SeuratObject, testthat, vdiffr License: Artistic-2.0 MD5sum: 7dbabf73178c19c5a02650c2a7b38d0d NeedsCompilation: no Title: Cluster a Single-cell RNA-seq Dot Plot Description: Dot plots of single-cell RNA-seq data allow for an examination of the relationships between cell groupings (e.g. clusters) and marker gene expression. The scDotPlot package offers a unified approach to perform a hierarchical clustering analysis and add annotations to the columns and/or rows of a scRNA-seq dot plot. It works with SingleCellExperiment and Seurat objects as well as data frames. biocViews: Software, Visualization, DifferentialExpression, GeneExpression, Transcription, RNASeq, SingleCell, Sequencing, Clustering Author: Benjamin I Laufer [aut, cre], Brad A Friedman [aut] Maintainer: Benjamin I Laufer URL: https://github.com/ben-laufer/scDotPlot VignetteBuilder: knitr BugReports: https://github.com/ben-laufer/scDotPlot/issues git_url: https://git.bioconductor.org/packages/scDotPlot git_branch: devel git_last_commit: 060123e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scDotPlot_1.5.0.tar.gz vignettes: vignettes/scDotPlot/inst/doc/scDotPlot.html vignetteTitles: scDotPlot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDotPlot/inst/doc/scDotPlot.R dependencyCount: 205 Package: scds Version: 1.99.0 Depends: R (>= 3.6.0) Imports: Matrix, S4Vectors, SingleCellExperiment, SummarizedExperiment, xgboost, methods, stats, dplyr, pROC Suggests: BiocStyle, knitr, rsvd, Rtsne, scater, cowplot, rmarkdown License: MIT + file LICENSE MD5sum: ccbfab6e2301462e98391bbdc9d11ed0 NeedsCompilation: no Title: In-Silico Annotation of Doublets for Single Cell RNA Sequencing Data Description: In single cell RNA sequencing (scRNA-seq) data combinations of cells are sometimes considered a single cell (doublets). The scds package provides methods to annotate doublets in scRNA-seq data computationally. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Transcriptomics, GeneExpression, Sequencing, Software, Classification Author: Dennis Kostka [aut, cre], Bais Abha [aut] Maintainer: Dennis Kostka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scds git_branch: devel git_last_commit: 3f70cad git_last_commit_date: 2026-01-07 Date/Publication: 2026-04-20 source.ver: src/contrib/scds_1.99.0.tar.gz vignettes: vignettes/scds/inst/doc/scds.html vignetteTitles: Introduction to the scds package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scds/inst/doc/scds.R importsMe: singleCellTK suggestsMe: ExperimentSubset, muscData dependencyCount: 45 Package: scECODA Version: 0.99.9 Depends: R (>= 4.6.0) Imports: BiocGenerics, cluster, corrplot, DESeq2, dplyr, factoextra (>= 2.0.0), ggplot2, ggpubr, ggrepel, gtools, Matrix, mclust, methods, pheatmap, plotly, rlang, rstatix, S4Vectors, stringr, SummarizedExperiment (>= 1.34.0), tidyr, vegan Suggests: Seurat (>= 5.0.0), igraph, knitr, rmarkdown, BiocStyle, testthat, scRNAseq License: GPL-3 + file LICENSE MD5sum: c8085e444c1c135332b6b5bfa37b054b NeedsCompilation: no Title: Single-Cell Exploratory Compositional Data Analysis Description: The scECODA R package provides a complete workflow for the analysis and visualization of compositional data, primarily focusing on cell type proportions derived from single-cell data. It implements specialized methods, such as the Centered Log-Ratio (CLR) transformation, to properly analyze proportional data while avoiding the biases introduced by the compositional constraint. The package encapsulates data management, transformation, and analysis into a single SummarizedExperiment object, offering downstream tools for dimensionality reduction via PCA, calculating critical metrics like the Adjusted Rand Index (ARI) and Modularity to quantify sample grouping quality, and generating high-quality visualizations like heatmaps and scatter plots. biocViews: Software, SingleCell, Transcriptomics, CellBasedAssays, Normalization, Preprocessing, Visualization, Clustering, DimensionReduction, FeatureExtraction, PrincipalComponent Author: Christian Halter [aut, cre] (ORCID: ), Massimo Andreatta [aut] (ORCID: ), Santiago Carmona [aut] (ORCID: ), Swiss Cancer Research Foundation [fnd] Maintainer: Christian Halter URL: https://github.com/carmonalab/scECODA VignetteBuilder: knitr BugReports: https://github.com/carmonalab/scECODA/issues git_url: https://git.bioconductor.org/packages/scECODA git_branch: devel git_last_commit: f1182a2 git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/scECODA_0.99.9.tar.gz vignettes: vignettes/scECODA/inst/doc/scECODA.html vignetteTitles: scECODA.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scECODA/inst/doc/scECODA.R dependencyCount: 164 Package: SCFA Version: 1.21.0 Depends: R (>= 4.0) Imports: matrixStats, BiocParallel, torch (>= 0.3.0), coro, igraph, Matrix, cluster, psych, glmnet, RhpcBLASctl, stats, utils, methods, survival Suggests: knitr, rmarkdown, BiocStyle License: LGPL MD5sum: b57f3e41501d5199373f3c30fbd08e5d NeedsCompilation: no Title: SCFA: Subtyping via Consensus Factor Analysis Description: Subtyping via Consensus Factor Analysis (SCFA) can efficiently remove noisy signals from consistent molecular patterns in multi-omics data. SCFA first uses an autoencoder to select only important features and then repeatedly performs factor analysis to represent the data with different numbers of factors. Using these representations, it can reliably identify cancer subtypes and accurately predict risk scores of patients. biocViews: Survival, Clustering, Classification Author: Duc Tran [aut, cre], Hung Nguyen [aut], Tin Nguyen [fnd] Maintainer: Duc Tran URL: https://github.com/duct317/SCFA VignetteBuilder: knitr BugReports: https://github.com/duct317/SCFA/issues git_url: https://git.bioconductor.org/packages/SCFA git_branch: devel git_last_commit: 4eb486e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SCFA_1.21.0.tar.gz vignettes: vignettes/SCFA/inst/doc/Example.html vignetteTitles: SCFA package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCFA/inst/doc/Example.R dependencyCount: 59 Package: scFeatureFilter Version: 1.31.1 Depends: R (>= 4.5.0) Imports: dplyr (>= 0.7.3), ggplot2 (>= 2.1.0), magrittr (>= 1.5), rlang (>= 0.1.2), tibble (>= 1.3.4), stats, methods Suggests: testthat, knitr, rmarkdown, BiocStyle, MASS, SingleCellExperiment, SummarizedExperiment, scRNAseq, cowplot License: MIT + file LICENSE MD5sum: 0540aa97fe6f5bbd3025c12c97958af3 NeedsCompilation: no Title: A correlation-based method for quality filtering of single-cell RNAseq data Description: An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise. biocViews: ImmunoOncology, SingleCell, RNASeq, Preprocessing, GeneExpression Author: Angeles Arzalluz-Luque [aut], Guillaume Devailly [aut, cre] (ORCID: ), Anagha Joshi [aut] Maintainer: Guillaume Devailly URL: https://bioconductor.org/packages/scFeatureFilter/ VignetteBuilder: knitr BugReports: https://github.com/gdevailly/scFeatureFilter/issues git_url: https://git.bioconductor.org/packages/scFeatureFilter git_branch: devel git_last_commit: 1142aeb git_last_commit_date: 2026-02-23 Date/Publication: 2026-04-20 source.ver: src/contrib/scFeatureFilter_1.31.1.tar.gz vignettes: vignettes/scFeatureFilter/inst/doc/Introduction.html vignetteTitles: Introduction to scFeatureFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scFeatureFilter/inst/doc/Introduction.R dependencyCount: 31 Package: scGPS Version: 1.25.0 Depends: R (>= 3.6), SummarizedExperiment, dynamicTreeCut, SingleCellExperiment Imports: glmnet (> 2.0), caret (>= 6.0), ggplot2 (>= 2.2.1), fastcluster, dplyr, Rcpp, RcppArmadillo, RcppParallel, grDevices, graphics, stats, utils, DESeq2, locfit LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: Matrix (>= 1.2), testthat, knitr, parallel, rmarkdown, RColorBrewer, ReactomePA, clusterProfiler, cowplot, org.Hs.eg.db, reshape2, xlsx, dendextend, networkD3, Rtsne, BiocParallel, e1071, WGCNA, devtools, DOSE License: GPL-3 MD5sum: 97d61c55a9c4fd896c075996c0793ece NeedsCompilation: yes Title: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation) Description: The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations. biocViews: SingleCell, Clustering, DataImport, Sequencing, Coverage Author: Quan Nguyen [aut, cre], Michael Thompson [aut], Anne Senabouth [aut] Maintainer: Quan Nguyen SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/IMB-Computational-Genomics-Lab/scGPS/issues git_url: https://git.bioconductor.org/packages/scGPS git_branch: devel git_last_commit: 73072f6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scGPS_1.25.0.tar.gz vignettes: vignettes/scGPS/inst/doc/vignette.html vignetteTitles: single cell Global fate Potential of Subpopulations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scGPS/inst/doc/vignette.R dependencyCount: 113 Package: scGraphVerse Version: 1.1.0 Depends: R (>= 4.5.0) Imports: BiocBaseUtils, BiocParallel (>= 1.30.0), doParallel, doRNG, GENIE3, Matrix, MultiAssayExperiment, SingleCellExperiment, SummarizedExperiment, distributions3, dplyr, grDevices, graphics, httr, igraph, jsonlite, methods, parallel, reticulate, tidyr, glmnet, MASS, utils, stats, S4Vectors, graph, mpath Suggests: AnnotationDbi, BiocStyle, clusterProfiler, DOSE, enrichplot, fmsb, ggplot2, ggraph, gridExtra, INetTool, org.Hs.eg.db, org.Mm.eg.db, patchwork, pROC, RColorBrewer, ReactomePA, rentrez, robin, scales, Seurat, STRINGdb, testthat (>= 3.0.0), knitr, rmarkdown, tidyverse, magick, celldex, SingleR, TENxPBMCData, scater, GenomeInfoDb, GenomicRanges, License: GPL-3 + file LICENSE MD5sum: 0b57e179232e4ba5d13eed2a99aa31d3 NeedsCompilation: yes Title: scGraphVerse: A Gene Network Analysis Package Description: A package for inferring, comparing, and visualizing gene networks from single-cell RNA sequencing data. It integrates multiple methods (GENIE3, GRNBoost2, ZILGM, PCzinb, and JRF) for robust network inference, supports consensus building across methods or datasets, and provides tools for evaluating regulatory structure and community similarity. GRNBoost2 requires Python package 'arboreto' which can be installed using init_py(install_missing = TRUE). This package includes adapted functions from ZILGM (Park et al., 2021), JRF (Petralia et al., 2015), and learn2count (Nguyen et al. 2023) packages with proper attribution under GPL-2 license. biocViews: GeneRegulation, NetworkInference, SingleCell, RNASeq, Visualization, Software, GraphAndNetwork, GeneSetEnrichment, NetworkEnrichment, Pathways, Sequencing, Reactome, Network, KEGG Author: Francesco Cecere [aut, cre] (ORCID: ), Annamaria Carissimo [aut], Daniela De Canditiis [aut], Claudia Angelini [aut, fnd] Maintainer: Francesco Cecere URL: https://ngsFC.github.io/scGraphVerse SystemRequirements: Python (>= 3.6) and arboreto Python package for GRNBoost2 method. Use init_py(install_missing = TRUE) for automated installation. VignetteBuilder: knitr BugReports: https://github.com/ngsFC/scGraphVerse/issues git_url: https://git.bioconductor.org/packages/scGraphVerse git_branch: devel git_last_commit: 1e63abe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scGraphVerse_1.1.0.tar.gz vignettes: vignettes/scGraphVerse/inst/doc/case_study.html, vignettes/scGraphVerse/inst/doc/simulation_study.html vignetteTitles: scGraphVerse Case Study: B-cell GRN Reconstruction, scGraphVerse Simulation Study: Sim & GRN Reconstruction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scGraphVerse/inst/doc/case_study.R, vignettes/scGraphVerse/inst/doc/simulation_study.R dependencyCount: 105 Package: schex Version: 1.25.0 Depends: SingleCellExperiment (>= 1.7.4), ggplot2 (>= 3.2.1) Imports: hexbin, stats, methods, cluster, dplyr, entropy, ggforce, grid, rlang, concaveman Suggests: ggrepel, knitr, rmarkdown, testthat (>= 2.1.0), covr, TENxPBMCData, scater, Seurat, shinydashboard, iSEE, igraph, scran, tibble, scuttle License: GPL-3 MD5sum: 1c46a4bc51bd3b215d9d5b7f14cb801f NeedsCompilation: no Title: Hexbin plots for single cell omics data Description: Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley. biocViews: Software, Sequencing, SingleCell, DimensionReduction, Visualization, ImmunoOncology, DataImport Author: Saskia Freytag [aut, cre], Wancheng Tang [ctb], Zimo Peng [ctb], Jingxiu Huang [ctb] Maintainer: Saskia Freytag URL: https://github.com/SaskiaFreytag/schex VignetteBuilder: knitr BugReports: https://github.com/SaskiaFreytag/schex/issues git_url: https://git.bioconductor.org/packages/schex git_branch: devel git_last_commit: 34f1fa2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/schex_1.25.0.tar.gz vignettes: vignettes/schex/inst/doc/Seurat_to_SCE.html, vignettes/schex/inst/doc/using_schex.html vignetteTitles: Seurat_to_SCE, using_schex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/schex/inst/doc/Seurat_to_SCE.R, vignettes/schex/inst/doc/using_schex.R importsMe: scTensor, scTGIF dependencyCount: 74 Package: scHiCcompare Version: 1.3.0 Depends: R (>= 4.5.0) Imports: grDevices, graphics, stats, utils, dplyr, ggplot2, gtools, HiCcompare, lattice, mclust, mice, miceadds, ranger, rstatix, tidyr, rlang, data.table, BiocParallel Suggests: knitr, rmarkdown, testthat, BiocStyle, DT, gridExtra License: MIT + file LICENSE MD5sum: ba1d78a64bc23fd27c6419ba0da6aca5 NeedsCompilation: no Title: Differential Analysis of Single-cell Hi-C Data Description: This package provides functions for differential chromatin interaction analysis between two single-cell Hi-C data groups. It includes tools for imputation, normalization, and differential analysis of chromatin interactions. The package implements pooling techniques for imputation and offers methods to normalize and test for differential interactions across single-cell Hi-C datasets. biocViews: Software, SingleCell, HiC, Sequencing, Normalization Author: My Nguyen [aut, cre] (ORCID: ), Mikhail Dozmorov [aut] (ORCID: ) Maintainer: My Nguyen URL: https://github.com/dozmorovlab/ScHiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/ScHiCcompare/issues git_url: https://git.bioconductor.org/packages/scHiCcompare git_branch: devel git_last_commit: 5da8e20 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scHiCcompare_1.3.0.tar.gz vignettes: vignettes/scHiCcompare/inst/doc/ScHiCcompare.html vignetteTitles: Chromatin Differential Analysis of scHiC -scHiCcompare Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scHiCcompare/inst/doc/ScHiCcompare.R dependencyCount: 143 Package: scHOT Version: 1.23.0 Depends: R (>= 4.0) Imports: S4Vectors (>= 0.24.3), SingleCellExperiment, Matrix, SummarizedExperiment, IRanges, methods, stats, BiocParallel, reshape, ggplot2, igraph, grDevices, ggforce, graphics Suggests: knitr, markdown, rmarkdown, scater, scattermore, scales, matrixStats, deldir License: GPL-3 MD5sum: 89bfc5f3e7278905d9aaa0c79d951fac NeedsCompilation: no Title: single-cell higher order testing Description: Single cell Higher Order Testing (scHOT) is an R package that facilitates testing changes in higher order structure of gene expression along either a developmental trajectory or across space. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data. biocViews: GeneExpression, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Shila Ghazanfar [aut, cre], Yingxin Lin [aut] Maintainer: Shila Ghazanfar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scHOT git_branch: devel git_last_commit: 70965ea git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scHOT_1.23.0.tar.gz vignettes: vignettes/scHOT/inst/doc/scHOT.html vignetteTitles: Getting started: scHOT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scHOT/inst/doc/scHOT.R dependencyCount: 66 Package: scider Version: 1.9.0 Depends: R (>= 4.3) Imports: SpatialExperiment, SummarizedExperiment, spatstat.geom, spatstat.explore, sf, lwgeom, SpatialPack, ggplot2, stats, pheatmap, plotly, shiny, igraph, janitor, knitr, methods, utils, isoband, S4Vectors, grDevices, dbscan, hexDensity, hexbin, uwot, SingleCellExperiment, BiocNeighbors, irlba Suggests: edgeR, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: 6bb39dedac38d295820426fbd516a13a NeedsCompilation: yes Title: Spatial cell-type inter-correlation by density in R Description: scider is an user-friendly R package providing functions to model the global density of cells in a slide of spatial transcriptomics data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. After modelling density, the package allows for serveral downstream analysis, including colocalization analysis, boundary detection analysis and differential density analysis. biocViews: Spatial, Transcriptomics Author: Mengbo Li [aut] (ORCID: ), Ning Liu [aut] (ORCID: ), Quoc Hoang Nguyen [aut] (ORCID: ), Yunshun Chen [aut, cre] (ORCID: ) Maintainer: Yunshun Chen URL: https://github.com/ChenLaboratory/scider, https://chenlaboratory.github.io/scider/ VignetteBuilder: knitr BugReports: https://github.com/ChenLaboratory/scider/issues git_url: https://git.bioconductor.org/packages/scider git_branch: devel git_last_commit: e0a2a6b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scider_1.9.0.tar.gz vignettes: vignettes/scider/inst/doc/scider_userGuide.html vignetteTitles: scider_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scider/inst/doc/scider_userGuide.R importsMe: OSTA dependencyCount: 154 Package: scLANE Version: 1.1.3 Depends: glm2, magrittr, R (>= 4.5.0) Imports: geeM, MASS, mpath, dplyr, stats, utils, withr, purrr, tidyr, furrr, doSNOW, gamlss, scales, future, Matrix, ggplot2, splines, foreach, glmmTMB, parallel, RcppEigen, bigstatsr, tidyselect, broom.mixed, Rcpp LinkingTo: Rcpp, RcppEigen Suggests: covr, grid, coop, uwot, scran, ggh4x, knitr, UCell, irlba, rlang, magick, igraph, scater, gtable, ggpubr, viridis, bluster, cluster, circlize, speedglm, rmarkdown, gridExtra, BiocStyle, slingshot, gprofiler2, GenomeInfoDb, BiocParallel, BiocGenerics, BiocNeighbors, ComplexHeatmap, Seurat (>= 5.0.0), testthat (>= 3.0.0), SingleCellExperiment, SummarizedExperiment License: MIT + file LICENSE MD5sum: 3c94004ed205f34479bbed2052aea9fb NeedsCompilation: yes Title: Model Gene Expression Dynamics with Spline-Based NB GLMs, GEEs, & GLMMs Description: Our scLANE model uses truncated power basis spline models to build flexible, interpretable models of single cell gene expression over pseudotime or latent time. The modeling architectures currently supported are Negative-binomial GLMs, GEEs, & GLMMs. Downstream analysis functionalities include model comparison, dynamic gene clustering, smoothed counts generation, gene set enrichment testing, & visualization. biocViews: RNASeq, Software, Clustering, TimeCourse, Sequencing, Regression, SingleCell, Visualization, GeneExpression, Transcriptomics, GeneSetEnrichment, DifferentialExpression Author: Jack R. Leary [aut, cre] (ORCID: ), Rhonda Bacher [ctb, fnd] (ORCID: ) Maintainer: Jack R. Leary URL: https://github.com/jr-leary7/scLANE VignetteBuilder: knitr BugReports: https://github.com/jr-leary7/scLANE/issues git_url: https://git.bioconductor.org/packages/scLANE git_branch: devel git_last_commit: 538e73c git_last_commit_date: 2026-03-03 Date/Publication: 2026-04-20 source.ver: src/contrib/scLANE_1.1.3.tar.gz vignettes: vignettes/scLANE/inst/doc/scLANE.html vignetteTitles: Interpretable Trajectory DE Testing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scLANE/inst/doc/scLANE.R dependencyCount: 113 Package: scLang Version: 0.99.3 Imports: dplyr, ggplot2, henna, methods, paletteer, rlang, S4Vectors, SeuratObject, SingleCellExperiment, stats, SummarizedExperiment Suggests: BiocStyle, knitr, qs2, rmarkdown, scater, scRNAseq, Seurat, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 306dadf4241f003d395a5f0c64ed2ae7 NeedsCompilation: no Title: A unified language for interacting with Seurat and SingleCellExperiment Description: scLang is a suite for package development for scRNA-seq analysis. It offers functions that can operate on both Seurat and SingleCellExperiment objects. These functions are primarily aimed to help developers build tools compatible with both types of input. biocViews: Software, SingleCell, GeneExpression, Visualization Author: Andrei-Florian Stoica [aut, cre] (ORCID: ) Maintainer: Andrei-Florian Stoica URL: https://github.com/andrei-stoica26/scLang VignetteBuilder: knitr BugReports: https://github.com/andrei-stoica26/scLang/issues git_url: https://git.bioconductor.org/packages/scLang git_branch: devel git_last_commit: f4362b8 git_last_commit_date: 2026-03-03 Date/Publication: 2026-04-20 source.ver: src/contrib/scLang_0.99.3.tar.gz vignettes: vignettes/scLang/inst/doc/scLang.html vignetteTitles: scLang hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scLang/inst/doc/scLang.R importsMe: GSABenchmark, hammers dependencyCount: 103 Package: scmap Version: 1.33.0 Depends: R(>= 3.4) Imports: Biobase, SingleCellExperiment, SummarizedExperiment, BiocGenerics, S4Vectors, dplyr, reshape2, matrixStats, proxy, utils, googleVis, ggplot2, methods, stats, e1071, randomForest, Rcpp (>= 0.12.12) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 47ff92b0388559864ce30554e269e09b NeedsCompilation: yes Title: A tool for unsupervised projection of single cell RNA-seq data Description: Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment. biocViews: ImmunoOncology, SingleCell, Software, Classification, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, Transcription, Sequencing, Preprocessing, GeneExpression, DataImport Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/scmap VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/scmap/ git_url: https://git.bioconductor.org/packages/scmap git_branch: devel git_last_commit: 4fdff8f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scmap_1.33.0.tar.gz vignettes: vignettes/scmap/inst/doc/scmap.html vignetteTitles: `scmap` package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scmap/inst/doc/scmap.R dependencyCount: 63 Package: scMerge Version: 1.27.0 Depends: R (>= 3.6.0) Imports: BiocParallel, BiocSingular, BiocNeighbors, cluster, DelayedArray, DelayedMatrixStats, distr, igraph, M3Drop (>= 1.9.4), proxyC, ruv, cvTools, scater, batchelor, scran, methods, S4Vectors (>= 0.23.19), SingleCellExperiment (>= 1.7.3), SummarizedExperiment Suggests: BiocStyle, covr, HDF5Array, knitr, Matrix, rmarkdown, scales, proxy, testthat, badger License: GPL-3 MD5sum: c1714101c300f20ac2640fa15c3db355 NeedsCompilation: no Title: scMerge: Merging multiple batches of scRNA-seq data Description: Like all gene expression data, single-cell data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of single-cell data. biocViews: BatchEffect, GeneExpression, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Yingxin Lin [aut, cre], Kevin Wang [aut], Sydney Bioinformatics and Biometrics Group [fnd] Maintainer: Yingxin Lin URL: https://github.com/SydneyBioX/scMerge VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scMerge/issues git_url: https://git.bioconductor.org/packages/scMerge git_branch: devel git_last_commit: 1741f7e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scMerge_1.27.0.tar.gz vignettes: vignettes/scMerge/inst/doc/scMerge.html, vignettes/scMerge/inst/doc/scMerge2.html vignetteTitles: scMerge, scMerge2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMerge/inst/doc/scMerge.R, vignettes/scMerge/inst/doc/scMerge2.R importsMe: singleCellTK suggestsMe: Cepo dependencyCount: 178 Package: scMET Version: 1.13.0 Depends: R (>= 4.2.0) Imports: methods, Rcpp (>= 1.0.0), RcppParallel (>= 5.0.1), rstan (>= 2.21.3), rstantools (>= 2.1.0), VGAM, data.table, MASS, logitnorm, ggplot2, matrixStats, assertthat, viridis, coda, BiocStyle, cowplot, stats, SummarizedExperiment, SingleCellExperiment, Matrix, dplyr, S4Vectors LinkingTo: BH (>= 1.66.0), Rcpp (>= 1.0.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.21.3), StanHeaders (>= 2.21.0.7) Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: d8433096ad549ef0d67ee17deab54299 NeedsCompilation: yes Title: Bayesian modelling of cell-to-cell DNA methylation heterogeneity Description: High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation, DifferentialExpression, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, Bayesian, Sequencing, Coverage, SingleCell Author: Andreas C. Kapourani [aut, cre] (ORCID: ), John Riddell [ctb] Maintainer: Andreas C. Kapourani SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/andreaskapou/scMET/issues git_url: https://git.bioconductor.org/packages/scMET git_branch: devel git_last_commit: 5af0e5e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scMET_1.13.0.tar.gz vignettes: vignettes/scMET/inst/doc/scMET_vignette.html vignetteTitles: scMET analysis using synthetic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMET/inst/doc/scMET_vignette.R dependencyCount: 106 Package: scMultiSim Version: 1.7.0 Depends: R (>= 4.4.0) Imports: foreach, rlang, dplyr, ggplot2, Rtsne, ape, MASS, matrixStats, phytools, KernelKnn, gplots, zeallot, crayon, assertthat, igraph, methods, grDevices, graphics, stats, utils, markdown, SummarizedExperiment, BiocParallel Suggests: knitr, rmarkdown, roxygen2, shiny, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 3e6b8ff41387414de096ff0756b4404f NeedsCompilation: no Title: Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions Description: scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments. biocViews: SingleCell, Transcriptomics, GeneExpression, Sequencing, ExperimentalDesign Author: Hechen Li [aut, cre] (ORCID: ), Xiuwei Zhang [aut], Ziqi Zhang [aut], Michael Squires [aut] Maintainer: Hechen Li URL: https://zhanglabgt.github.io/scMultiSim/ VignetteBuilder: knitr BugReports: https://github.com/ZhangLabGT/scMultiSim/issues git_url: https://git.bioconductor.org/packages/scMultiSim git_branch: devel git_last_commit: 206826c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scMultiSim_1.7.0.tar.gz vignettes: vignettes/scMultiSim/inst/doc/basics.html, vignettes/scMultiSim/inst/doc/options.html, vignettes/scMultiSim/inst/doc/spatialCCI.html, vignettes/scMultiSim/inst/doc/workflow.html vignetteTitles: 2. Simulating Multimodal Single-cell Datasets, 4. Parameter Guide, 3. Simulating Spatial Cell-Cell Interactions, 1. Getting Started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMultiSim/inst/doc/basics.R, vignettes/scMultiSim/inst/doc/options.R, vignettes/scMultiSim/inst/doc/spatialCCI.R, vignettes/scMultiSim/inst/doc/workflow.R dependencyCount: 96 Package: SCnorm Version: 1.33.0 Depends: R (>= 3.4.0), Imports: SingleCellExperiment, SummarizedExperiment, stats, methods, graphics, grDevices, parallel, quantreg, cluster, moments, data.table, BiocParallel, S4Vectors, ggplot2, forcats, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL (>= 2) MD5sum: 65a5ae54b1a0862f29428b6f0046911b NeedsCompilation: no Title: Normalization of single cell RNA-seq data Description: This package implements SCnorm — a method to normalize single-cell RNA-seq data. biocViews: Normalization, RNASeq, SingleCell, ImmunoOncology Author: Rhonda Bacher Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/SCnorm VignetteBuilder: knitr BugReports: https://github.com/rhondabacher/SCnorm/issues git_url: https://git.bioconductor.org/packages/SCnorm git_branch: devel git_last_commit: 8cf7fc8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SCnorm_1.33.0.tar.gz vignettes: vignettes/SCnorm/inst/doc/SCnorm.pdf vignetteTitles: SCnorm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCnorm/inst/doc/SCnorm.R dependencyCount: 67 Package: scone Version: 1.35.0 Depends: R (>= 3.4), methods, SummarizedExperiment Imports: graphics, stats, utils, aroma.light, BiocParallel, class, cluster, compositions, diptest, edgeR, fpc, gplots, grDevices, hexbin, limma, matrixStats, mixtools, RColorBrewer, boot, rhdf5, RUVSeq, rARPACK, MatrixGenerics, SingleCellExperiment, DelayedMatrixStats, sparseMatrixStats, SparseArray (>= 1.7.6) Suggests: BiocStyle, DT, ggplot2, knitr, miniUI, NMF, plotly, reshape2, rmarkdown, scran, scRNAseq, shiny, testthat, DelayedArray, visNetwork, doParallel, batchtools, splatter, scater, kableExtra, mclust, TENxPBMCData License: Artistic-2.0 MD5sum: 0124fd6ee4bee35d24145cd6e63bef59 NeedsCompilation: no Title: Single Cell Overview of Normalized Expression data Description: SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses. biocViews: ImmunoOncology, Normalization, Preprocessing, QualityControl, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell, Coverage Author: Michael Cole [aut, cph], Davide Risso [aut, cre, cph], Matteo Borella [ctb], Chiara Romualdi [ctb] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/YosefLab/scone/issues git_url: https://git.bioconductor.org/packages/scone git_branch: devel git_last_commit: 60f7f37 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scone_1.35.0.tar.gz vignettes: vignettes/scone/inst/doc/PsiNorm.html, vignettes/scone/inst/doc/sconeTutorial.html vignetteTitles: PsiNorm normalization, Introduction to SCONE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scone/inst/doc/PsiNorm.R, vignettes/scone/inst/doc/sconeTutorial.R dependencyCount: 185 Package: Sconify Version: 1.31.0 Depends: R (>= 3.5) Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils, stats, readr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 58404b0e6dca99f2c54fb9f98d0e57ae NeedsCompilation: no Title: A toolkit for performing KNN-based statistics for flow and mass cytometry data Description: This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold. biocViews: ImmunoOncology, SingleCell, FlowCytometry, Software, MultipleComparison, Visualization Author: Tyler J Burns Maintainer: Tyler J Burns VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Sconify git_branch: devel git_last_commit: 5f12fd9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Sconify_1.31.0.tar.gz vignettes: vignettes/Sconify/inst/doc/DataQuality.html, vignettes/Sconify/inst/doc/FindingIdealK.html, vignettes/Sconify/inst/doc/Step1.PreProcessing.html, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.html, vignettes/Sconify/inst/doc/Step3.PostProcessing.html vignetteTitles: Data Quality, Finding Ideal K, How to process FCS files for downstream use in R, General Scone Analysis, Final Post-Processing Steps for Scone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sconify/inst/doc/DataQuality.R, vignettes/Sconify/inst/doc/FindingIdealK.R, vignettes/Sconify/inst/doc/Step1.PreProcessing.R, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.R, vignettes/Sconify/inst/doc/Step3.PostProcessing.R dependencyCount: 58 Package: SCOPE Version: 1.23.0 Depends: R (>= 3.6.0), GenomicRanges, IRanges, Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19 Imports: stats, grDevices, graphics, utils, DescTools, RColorBrewer, gplots, foreach, parallel, doParallel, DNAcopy, BSgenome, Biostrings, BiocGenerics, S4Vectors Suggests: knitr, rmarkdown, WGSmapp, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, testthat (>= 2.1.0) License: GPL-2 MD5sum: 58bdb6d06c26830afb27dfbe8c8e9af1 NeedsCompilation: no Title: A normalization and copy number estimation method for single-cell DNA sequencing Description: Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background. biocViews: SingleCell, Normalization, CopyNumberVariation, Sequencing, WholeGenome, Coverage, Alignment, QualityControl, DataImport, DNASeq Author: Rujin Wang, Danyu Lin, Yuchao Jiang Maintainer: Rujin Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCOPE git_branch: devel git_last_commit: ba69d58 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SCOPE_1.23.0.tar.gz vignettes: vignettes/SCOPE/inst/doc/SCOPE_vignette.html vignetteTitles: SCOPE: Single-cell Copy Number Estimation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCOPE/inst/doc/SCOPE_vignette.R dependencyCount: 113 Package: scoreInvHap Version: 1.33.0 Depends: R (>= 3.6.0) Imports: Biostrings, methods, snpStats, VariantAnnotation, GenomicRanges, BiocParallel, graphics, SummarizedExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown License: file LICENSE MD5sum: 68b46f74a40693f91c6dccc244fea013 NeedsCompilation: no Title: Get inversion status in predefined regions Description: scoreInvHap can get the samples' inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for 21 inversions. biocViews: SNP, Genetics, GenomicVariation Author: Carlos Ruiz [aut], Dolors Pelegrí [aut], Juan R. Gonzalez [aut, cre] Maintainer: Dolors Pelegri-Siso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scoreInvHap git_branch: devel git_last_commit: e410f84 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scoreInvHap_1.33.0.tar.gz vignettes: vignettes/scoreInvHap/inst/doc/scoreInvHap.html vignetteTitles: Inversion genotyping with scoreInvHap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scoreInvHap/inst/doc/scoreInvHap.R dependencyCount: 80 Package: scoup Version: 1.5.5 Depends: R (>= 4.4), Matrix Imports: Biostrings, methods Suggests: BiocManager, BiocStyle, bookdown, htmltools, knitr, testthat (>= 3.0.0), yaml License: GPL (>= 2) MD5sum: a2ea660a4b8332de111d641efcf8230f NeedsCompilation: no Title: Simulate Codons with Darwinian Selection Modelled as an OU Process Description: An elaborate molecular evolutionary framework that facilitates straightforward simulation of codon genetic sequences subjected to different degrees and/or patterns of Darwinian selection. The model is built upon the fitness landscape paradigm of Sewall Wright, as popularised by the mutation-selection model of Halpern and Bruno. This enables realistic evolutionary process of living organisms to be reproducible seamlessly. For example, an Ornstein-Uhlenbeck fitness update algorithm is incorporated herein. Consequently, otherwise complex biological processes, such as the effect of the interplay between genetic drift and fitness landscape fluctuations on the inference of diversifying selection, may now be investigated with minimal effort. Frequency-dependent and stochastic fitness landscape update techniques are available. biocViews: Alignment, Classification, ComparativeGenomics, DataImport, Genetics, MathematicalBiology, ResearchField, Sequencing, SequenceMatching, Software, StatisticalMethod, WorkflowStep Author: Hassan Sadiq [aut, cre, cph] (ORCID: ) Maintainer: Hassan Sadiq URL: https://github.com/thsadiq/scoup VignetteBuilder: knitr BugReports: https://github.com/thsadiq/scoup/issues git_url: https://git.bioconductor.org/packages/scoup git_branch: devel git_last_commit: cc2e075 git_last_commit_date: 2026-03-17 Date/Publication: 2026-04-20 source.ver: src/contrib/scoup_1.5.5.tar.gz vignettes: vignettes/scoup/inst/doc/scoup.html vignetteTitles: scoup Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scoup/inst/doc/scoup.R dependencyCount: 18 Package: scp Version: 1.21.1 Depends: R (>= 4.3.0), QFeatures (>= 1.19.1) Imports: IHW, ggplot2, ggrepel, matrixStats, metapod, methods, MsCoreUtils, MultiAssayExperiment, nipals, RColorBrewer, S4Vectors, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, MsDataHub (>= 1.3.3), impute, knitr, patchwork, preprocessCore, rmarkdown, scater, scpdata, sva, testthat, vdiffr, vsn, uwot License: Artistic-2.0 MD5sum: 2d32b9f4845cb4e9c56b51d7babdeb7a NeedsCompilation: no Title: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis Description: Utility functions for manipulating, processing, and analyzing mass spectrometry-based single-cell proteomics data. The package is an extension to the 'QFeatures' package and relies on 'SingleCellExpirement' to enable single-cell proteomics analyses. The package offers the user the functionality to process quantitative table (as generated by MaxQuant, Proteome Discoverer, and more) into data tables ready for downstream analysis and data visualization. biocViews: GeneExpression, Proteomics, SingleCell, MassSpectrometry, Preprocessing, CellBasedAssays Author: Christophe Vanderaa [aut, cre] (ORCID: ), Laurent Gatto [aut] (ORCID: ), Léopold Guyot [ctb] Maintainer: Christophe Vanderaa URL: https://UCLouvain-CBIO.github.io/scp VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/scp/issues git_url: https://git.bioconductor.org/packages/scp git_branch: devel git_last_commit: 41ea768 git_last_commit_date: 2026-04-06 Date/Publication: 2026-04-20 source.ver: src/contrib/scp_1.21.1.tar.gz vignettes: vignettes/scp/inst/doc/advanced.html, vignettes/scp/inst/doc/QFeatures_nutshell.html, vignettes/scp/inst/doc/read_scp.html, vignettes/scp/inst/doc/reporting_missing_values.html, vignettes/scp/inst/doc/scp_data_modelling.html, vignettes/scp/inst/doc/scp.html vignetteTitles: Advanced usage of `scp`, QFeatures in a nutshell, Load data using readSCP, Reporting missing values, Single Cell Proteomics data modelling, Single Cell Proteomics data processing and analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scp/inst/doc/advanced.R, vignettes/scp/inst/doc/QFeatures_nutshell.R, vignettes/scp/inst/doc/read_scp.R, vignettes/scp/inst/doc/reporting_missing_values.R, vignettes/scp/inst/doc/scp_data_modelling.R, vignettes/scp/inst/doc/scp.R suggestsMe: scpdata dependencyCount: 108 Package: scPassport Version: 0.99.2 Depends: R (>= 4.3) Imports: shiny, miniUI, Rcpp, S4Vectors LinkingTo: Rcpp Suggests: Seurat, SingleCellExperiment, SummarizedExperiment, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: a69f95a29ea3deebb1b499d88833a852 NeedsCompilation: yes Title: Passport System for Single-Cell Objects Description: Stamps Seurat, SingleCellExperiment, and SummarizedExperiment objects with a persistent metadata passport. For Seurat objects the passport is stored in the misc slot; for SingleCellExperiment and SummarizedExperiment objects it is stored in the metadata slot. Tracks animal info, experiment details, lineage (parent/child relationships), RDS registry numbers, processing logs, and custom fields. Includes an interactive Shiny gadget to fill and update the passport, and a read mode to print the full passport to console. The passport persists inside the RDS file with no external files needed. biocViews: SingleCell, DataImport, Visualization, Infrastructure Author: Sedat Kacar [aut, cre] (ORCID: ) Maintainer: Sedat Kacar URL: https://github.com/sedatkacar56/scPassport VignetteBuilder: knitr BugReports: https://github.com/sedatkacar56/scPassport/issues git_url: https://git.bioconductor.org/packages/scPassport git_branch: devel git_last_commit: 2297a74 git_last_commit_date: 2026-03-30 Date/Publication: 2026-04-20 source.ver: src/contrib/scPassport_0.99.2.tar.gz vignettes: vignettes/scPassport/inst/doc/scPassport.html vignetteTitles: Getting Started with scPassport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scPassport/inst/doc/scPassport.R dependencyCount: 41 Package: scPCA Version: 1.25.0 Depends: R (>= 4.0.0) Imports: stats, methods, assertthat, tibble, dplyr, purrr, stringr, Rdpack, matrixStats, BiocParallel, elasticnet, sparsepca, cluster, kernlab, origami, RSpectra, coop, Matrix, DelayedArray, ScaledMatrix, MatrixGenerics Suggests: DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter, SingleCellExperiment, microbenchmark License: MIT + file LICENSE MD5sum: d15e9b7de3a76b1eaeca018ebc2ce524 NeedsCompilation: no Title: Sparse Contrastive Principal Component Analysis Description: A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA. biocViews: PrincipalComponent, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq Author: Philippe Boileau [aut, cre, cph] (ORCID: ), Nima Hejazi [aut] (ORCID: ), Sandrine Dudoit [ctb, ths] (ORCID: ) Maintainer: Philippe Boileau URL: https://github.com/PhilBoileau/scPCA VignetteBuilder: knitr BugReports: https://github.com/PhilBoileau/scPCA/issues git_url: https://git.bioconductor.org/packages/scPCA git_branch: devel git_last_commit: 93c5905 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scPCA_1.25.0.tar.gz vignettes: vignettes/scPCA/inst/doc/scpca_intro.html vignetteTitles: Sparse contrastive principal component analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scPCA/inst/doc/scpca_intro.R dependsOnMe: OSCA.workflows dependencyCount: 70 Package: scran Version: 1.39.2 Depends: SingleCellExperiment, scuttle Imports: SummarizedExperiment, S4Vectors, BiocGenerics, BiocParallel, Rcpp, stats, methods, utils, Matrix, edgeR, limma, igraph, statmod, MatrixGenerics, S4Arrays, DelayedArray, BiocSingular, bluster, metapod, dqrng, beachmat LinkingTo: Rcpp, beachmat, BH, dqrng, scuttle Suggests: testthat, BiocStyle, knitr, rmarkdown, DelayedMatrixStats, HDF5Array, scRNAseq, dynamicTreeCut, ResidualMatrix, ScaledMatrix, DESeq2, pheatmap, scater, scrapper License: GPL-3 MD5sum: ab324550da5cee3919f63f69dede4bea NeedsCompilation: yes Title: Methods for Single-Cell RNA-Seq Data Analysis Description: Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb] Maintainer: Aaron Lun URL: https://github.com/MarioniLab/scran/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/MarioniLab/scran/issues git_url: https://git.bioconductor.org/packages/scran git_branch: devel git_last_commit: 83da947 git_last_commit_date: 2026-04-06 Date/Publication: 2026-04-20 source.ver: src/contrib/scran_1.39.2.tar.gz vignettes: vignettes/scran/inst/doc/scran.html vignetteTitles: Using scran to analyze scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scran/inst/doc/scran.R dependsOnMe: OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: BASiCS, BASiCStan, BatchQC, BayesSpace, BioTIP, celda, chevreulPlot, chevreulProcess, ChromSCape, CiteFuse, Coralysis, DeconvoBuddies, Dino, epiregulon, epiregulon.extra, FLAMES, MOSim, MPAC, msImpute, mumosa, pipeComp, scDblFinder, scDD, scMerge, scTreeViz, scTypeEval, singIST, singleCellTK, Spaniel, SpaNorm, StatescopeR, OSTA, mixhvg suggestsMe: anglemania, APL, Banksy, batchelor, blase, bluster, CellTrails, clusterExperiment, decontX, destiny, dittoSeq, DOtools, escape, escheR, ExperimentSubset, ggsc, ggspavis, Glimma, glmGamPoi, iSEEu, jazzPanda, miloR, Nebulosa, nnSVG, PCAtools, raer, ReactomeGSA, scConform, scDiagnostics, scDotPlot, schex, scLANE, scone, scuttle, simPIC, SingleCellAlleleExperiment, sketchR, smoothclust, spatialHeatmap, splatter, SPOTlight, StabMap, SuperCellCyto, SVP, tidySingleCellExperiment, tpSVG, transformGamPoi, TSCAN, velociraptor, Voyager, HCAData, SingleCellMultiModal, TabulaMurisData, simpleSingleCell, Canek, SCdeconR dependencyCount: 62 Package: scrapper Version: 1.5.18 Imports: methods, Rcpp, beachmat (>= 2.25.1), S4Vectors, SparseArray, DelayedArray, BiocNeighbors (>= 1.99.0), parallel LinkingTo: Rcpp, assorthead (>= 1.5.16), beachmat, BiocNeighbors, Rigraphlib Suggests: testthat, knitr, rmarkdown, BiocStyle, Matrix, IRanges, SummarizedExperiment, SingleCellExperiment, scRNAseq, org.Mm.eg.db, scater, igraph License: MIT + file LICENSE MD5sum: e6e84e8a2a94ac38cb29d085815e8780 NeedsCompilation: yes Title: Bindings to C++ Libraries for Single-Cell Analysis Description: Implements R bindings to C++ code for analyzing single-cell (expression) data, mostly from various libscran libraries. Each function performs an individual step in the single-cell analysis workflow, ranging from quality control to clustering and marker detection. Additional wrappers are provided for easy construction of end-to-end workflows involving Bioconductor objects like SingleCellExperiments. biocViews: Normalization, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, QualityControl, DifferentialExpression, FeatureExtraction, PrincipalComponent, Clustering Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun URL: https://github.com/libscran/scrapper SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/libscran/scrapper/issues git_url: https://git.bioconductor.org/packages/scrapper git_branch: devel git_last_commit: 8a77890 git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/scrapper_1.5.18.tar.gz vignettes: vignettes/scrapper/inst/doc/userguide.html vignetteTitles: Using scrapper to analyze single-cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scrapper/inst/doc/userguide.R dependsOnMe: OSCA.basic, scrapbook, SingleRBook importsMe: epiregulon, FLAMES, splatter suggestsMe: Coralysis, GSVA, scran, SingleR, OSTA dependencyCount: 30 Package: scReClassify Version: 1.17.0 Depends: R (>= 4.1) Imports: randomForest, e1071, stats, SummarizedExperiment, SingleCellExperiment, methods Suggests: testthat, knitr, BiocStyle, rmarkdown, DT, mclust, dplyr License: GPL-3 + file LICENSE MD5sum: 3a62b8af64409a569fae80e1f5295855 NeedsCompilation: no Title: scReClassify: post hoc cell type classification of single-cell RNA-seq data Description: A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier. biocViews: Software, Transcriptomics, SingleCell, Classification, SupportVectorMachine Author: Pengyi Yang [aut] (ORCID: ), Taiyun Kim [aut, cre] (ORCID: ) Maintainer: Taiyun Kim URL: https://github.com/SydneyBioX/scReClassify, http://www.bioconductor.org/packages/release/bioc/html/scReClassify.html VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scReClassify/issues git_url: https://git.bioconductor.org/packages/scReClassify git_branch: devel git_last_commit: e44cea1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scReClassify_1.17.0.tar.gz vignettes: vignettes/scReClassify/inst/doc/scReClassify.html vignetteTitles: An introduction to scReClassify package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scReClassify/inst/doc/scReClassify.R dependencyCount: 31 Package: scRecover Version: 1.27.0 Depends: R (>= 3.4.0) Imports: stats, utils, methods, graphics, doParallel, foreach, parallel, penalized, kernlab, rsvd, Matrix (>= 1.2-14), MASS (>= 7.3-45), pscl (>= 1.4.9), bbmle (>= 1.0.18), gamlss (>= 4.4-0), preseqR (>= 4.0.0), SAVER (>= 1.1.1), BiocParallel (>= 1.12.0) Suggests: knitr, rmarkdown, SingleCellExperiment, testthat License: GPL MD5sum: 0f404a9ef7b484193c8f055b18c9e0bb NeedsCompilation: no Title: scRecover for imputation of single-cell RNA-seq data Description: scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged, since there are both dropout zeros and real zeros in scRNA-seq data. By combination with scImpute, SAVER and MAGIC, scRecover not only detects dropout and real zeros at higher accuracy, but also improve the downstream clustering and visualization results. biocViews: GeneExpression, SingleCell, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao, Xuegong Zhang Maintainer: Zhun Miao URL: https://miaozhun.github.io/scRecover VignetteBuilder: knitr BugReports: https://github.com/miaozhun/scRecover/issues git_url: https://git.bioconductor.org/packages/scRecover git_branch: devel git_last_commit: 0b2febe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scRecover_1.27.0.tar.gz vignettes: vignettes/scRecover/inst/doc/scRecover.html vignetteTitles: scRecover hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRecover/inst/doc/scRecover.R dependencyCount: 46 Package: screenCounter Version: 1.11.0 Depends: S4Vectors, SummarizedExperiment Imports: Rcpp, BiocParallel LinkingTo: Rcpp Suggests: BiocGenerics, Biostrings, BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 089d0d244f26fa9380cd576add3d4860 NeedsCompilation: yes Title: Counting Reads in High-Throughput Sequencing Screens Description: Provides functions for counting reads from high-throughput sequencing screen data (e.g., CRISPR, shRNA) to quantify barcode abundance. Currently supports single barcodes in single- or paired-end data, and combinatorial barcodes in paired-end data. biocViews: CRISPR, Alignment, FunctionalGenomics, FunctionalPrediction Author: Aaron Lun [aut, cre] (ORCID: ) Maintainer: Aaron Lun URL: https://github.com/crisprVerse/screenCounter SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/screenCounter/issues git_url: https://git.bioconductor.org/packages/screenCounter git_branch: devel git_last_commit: 57d0e9c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/screenCounter_1.11.0.tar.gz vignettes: vignettes/screenCounter/inst/doc/counting.html vignetteTitles: Counting barcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/screenCounter/inst/doc/counting.R dependencyCount: 36 Package: ScreenR Version: 1.13.1 Depends: R (>= 4.3) Imports: methods (>= 4.0), rlang (>= 0.4), stringr (>= 1.4), limma (>= 3.46), patchwork (>= 1.1), tibble (>= 3.1.6), scales (>= 1.1.1), ggvenn (>= 0.1.9), purrr (>= 0.3.4), ggplot2 (>= 3.3), stats, tidyr (>= 1.2), magrittr (>= 1.0), dplyr (>= 1.0), edgeR (>= 3.32), tidyselect (>= 1.1.2) Suggests: rmarkdown (>= 2.11), markdown, knitr (>= 1.37), testthat (>= 3.0.0), BiocStyle (>= 2.22.0), covr (>= 3.5) License: MIT + file LICENSE MD5sum: d375979e97436c7a96d388591cf5abe0 NeedsCompilation: no Title: Package to Perform High Throughput Biological Screening Description: ScreenR is a package suitable to perform hit identification in loss of function High Throughput Biological Screenings performed using barcoded shRNA-based libraries. ScreenR combines the computing power of software such as edgeR with the simplicity of use of the Tidyverse metapackage. ScreenR executes a pipeline able to find candidate hits from barcode counts, and integrates a wide range of visualization modes for each step of the analysis. biocViews: Software, AssayDomain, GeneExpression Author: Emanuel Michele Soda [aut, cre] (ORICD: 0000-0002-2301-6465), Elena Ceccacci [aut] (ORICD: 0000-0002-2285-8994), Saverio Minucci [fnd, ths] (ORICD: 0000-0001-5678-536X) Maintainer: Emanuel Michele Soda URL: https://emanuelsoda.github.io/ScreenR/ VignetteBuilder: knitr BugReports: https://github.com/EmanuelSoda/ScreenR/issues git_url: https://git.bioconductor.org/packages/ScreenR git_branch: devel git_last_commit: d7f3153 git_last_commit_date: 2026-02-08 Date/Publication: 2026-04-20 source.ver: src/contrib/ScreenR_1.13.1.tar.gz vignettes: vignettes/ScreenR/inst/doc/Analysis_Example.html vignetteTitles: ScreenR Example Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ScreenR/inst/doc/Analysis_Example.R dependencyCount: 43 Package: scRepertoire Version: 2.7.3 Depends: ggplot2, R (>= 4.0) Imports: dplyr, evmix, ggalluvial, ggdendro, ggraph, grDevices, igraph, immApex, iNEXT, Matrix, quantreg, Rcpp, rjson, rlang, S4Vectors, SeuratObject, SingleCellExperiment, SummarizedExperiment, tidygraph, purrr, lifecycle, methods LinkingTo: Rcpp Suggests: BiocManager, BiocStyle, circlize, knitr, Peptides, rmarkdown, scales, scater, Seurat, spelling, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 2ad8a4412c9bc8e660f91a9329cd7b76 NeedsCompilation: yes Title: A toolkit for single-cell immune receptor profiling Description: scRepertoire is a toolkit for processing and analyzing single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R single-cell workflows. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing Author: Nick Borcherding [aut, cre], Qile Yang [aut], Ksenia Safina [aut], Justin Reimertz [ctb] Maintainer: Nick Borcherding URL: https://www.borch.dev/uploads/scRepertoire/ VignetteBuilder: knitr BugReports: https://github.com/BorchLab/scRepertoire/issues git_url: https://git.bioconductor.org/packages/scRepertoire git_branch: devel git_last_commit: a7f3953 git_last_commit_date: 2026-04-03 Date/Publication: 2026-04-20 source.ver: src/contrib/scRepertoire_2.7.3.tar.gz vignettes: vignettes/scRepertoire/inst/doc/vignette.html vignetteTitles: Using scRepertoire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scRepertoire/inst/doc/vignette.R importsMe: Ibex suggestsMe: dandelionR, immApex dependencyCount: 115 Package: scRNAseqApp Version: 1.11.26 Depends: R (>= 4.3.0) Imports: bibtex, bslib, circlize, ComplexHeatmap, colourpicker, data.table, desc, DBI, DT, fs, GenomicRanges, GenomeInfoDb, ggdendro, ggforce, ggnewscale, ggplot2, ggrepel, ggridges, grDevices, grid, gridExtra, htmltools, IRanges, jsonlite, Matrix, magrittr, methods, patchwork, plotly, RColorBrewer, RefManageR, reshape2, rhdf5, Rsamtools, RSQLite, rtracklayer, S4Vectors, scales, scrypt, Seurat, SeuratObject, shiny, shinyhelper, shinymanager, slingshot, SingleCellExperiment, sortable, stats, tools, xfun, xml2, utils Suggests: rmarkdown, knitr, testthat, BiocStyle, shinytest2 Enhances: celldex, future, SingleR, SummarizedExperiment, tricycle, terra License: GPL-3 MD5sum: 1622ed6cfcaeb0a97bca1029866c7979 NeedsCompilation: no Title: A single-cell RNAseq Shiny app-package Description: The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously, facilitating comprehensive analysis. Additionally, it includes a user management system to regulate database accessibility for different users. biocViews: Visualization, SingleCell, RNASeq Author: Jianhong Ou [aut, cre] (ORCID: ) Maintainer: Jianhong Ou URL: https://github.com/jianhong/scRNAseqApp VignetteBuilder: knitr BugReports: https://github.com/jianhong/scRNAseqApp/issues git_url: https://git.bioconductor.org/packages/scRNAseqApp git_branch: devel git_last_commit: 0f153a2 git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/scRNAseqApp_1.11.26.tar.gz vignettes: vignettes/scRNAseqApp/inst/doc/scRNAseqApp.html vignetteTitles: scRNAseqApp Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRNAseqApp/inst/doc/scRNAseqApp.R dependencyCount: 238 Package: scry Version: 1.23.0 Depends: R (>= 4.0), stats, methods Imports: DelayedArray, glmpca (>= 0.2.0), Matrix, SingleCellExperiment, SummarizedExperiment, BiocSingular Suggests: BiocGenerics, covr, DuoClustering2018, ggplot2, HDF5Array, knitr, markdown, rmarkdown, TENxPBMCData, testthat License: Artistic-2.0 MD5sum: 9d5f4836cf7a4b31ea57efc74edd91f2 NeedsCompilation: no Title: Small-Count Analysis Methods for High-Dimensional Data Description: Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq. biocViews: DimensionReduction, GeneExpression, Normalization, PrincipalComponent, RNASeq, Software, Sequencing, SingleCell, Transcriptomics Author: Kelly Street [aut, cre], F. William Townes [aut, cph], Davide Risso [aut], Stephanie Hicks [aut] Maintainer: Kelly Street URL: https://bioconductor.org/packages/scry.html VignetteBuilder: knitr BugReports: https://github.com/kstreet13/scry/issues git_url: https://git.bioconductor.org/packages/scry git_branch: devel git_last_commit: 0eeb235 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scry_1.23.0.tar.gz vignettes: vignettes/scry/inst/doc/bigdata.html, vignettes/scry/inst/doc/scry.html vignetteTitles: Scry Methods For Larger Datasets, Overview of Scry Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scry/inst/doc/bigdata.R, vignettes/scry/inst/doc/scry.R importsMe: BatchSVG dependencyCount: 45 Package: scShapes Version: 1.17.0 Depends: R (>= 4.1) Imports: Matrix, stats, methods, pscl, VGAM, dgof, BiocParallel, MASS, emdbook, magrittr, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 0019ba4f81f2b49dc0259f59fa6bfcc4 NeedsCompilation: yes Title: A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data Description: We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic. biocViews: RNASeq, SingleCell, MultipleComparison, GeneExpression Author: Malindrie Dharmaratne [cre, aut] (ORCID: ) Maintainer: Malindrie Dharmaratne URL: https://github.com/Malindrie/scShapes VignetteBuilder: knitr BugReports: https://github.com/Malindrie/scShapes/issues git_url: https://git.bioconductor.org/packages/scShapes git_branch: devel git_last_commit: 437ea16 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scShapes_1.17.0.tar.gz vignettes: vignettes/scShapes/inst/doc/vignette_scShapes.html vignetteTitles: The vignette for running scShapes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scShapes/inst/doc/vignette_scShapes.R dependencyCount: 34 Package: scTGIF Version: 1.25.0 Depends: R (>= 3.6.0) Imports: GSEABase, Biobase, SingleCellExperiment, BiocStyle, plotly, tagcloud, rmarkdown, Rcpp, grDevices, graphics, utils, knitr, S4Vectors, SummarizedExperiment, RColorBrewer, nnTensor, methods, scales, msigdbr, schex, tibble, ggplot2, igraph Suggests: testthat License: Artistic-2.0 MD5sum: e6072ec5dd73a1d8b67570601e4d0b6f NeedsCompilation: no Title: Cell type annotation for unannotated single-cell RNA-Seq data Description: scTGIF connects the cells and the related gene functions without cell type label. biocViews: DimensionReduction, QualityControl, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTGIF git_branch: devel git_last_commit: f7ab3ab git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scTGIF_1.25.0.tar.gz vignettes: vignettes/scTGIF/inst/doc/scTGIF.html vignetteTitles: scTGIF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTGIF/inst/doc/scTGIF.R suggestsMe: scTensor dependencyCount: 141 Package: scTHI Version: 1.23.0 Depends: R (>= 4.0) Imports: BiocParallel, Rtsne, grDevices, graphics, stats Suggests: scTHI.data, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: e0e8c34aeb05cb41c2e7831082c7b6ee NeedsCompilation: no Title: Indentification of significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data Description: scTHI is an R package to identify active pairs of ligand-receptors from single cells in order to study,among others, tumor-host interactions. scTHI contains a set of signatures to classify cells from the tumor microenvironment. biocViews: Software,SingleCell Author: Francesca Pia Caruso [aut], Michele Ceccarelli [aut, cre] Maintainer: Michele Ceccarelli VignetteBuilder: knitr BugReports: https://github.com/miccec/scTHI/issues git_url: https://git.bioconductor.org/packages/scTHI git_branch: devel git_last_commit: 5cede23 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scTHI_1.23.0.tar.gz vignettes: vignettes/scTHI/inst/doc/vignette.html vignetteTitles: Using scTHI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTHI/inst/doc/vignette.R dependencyCount: 17 Package: scToppR Version: 0.99.10 Depends: R (>= 4.5.0) Imports: dplyr, forcats, ggplot2, stringr, openxlsx, viridis, patchwork, utils, httr2 Suggests: airway, BiocStyle, curl, DESeq2, knitr, rmarkdown, S4Vectors, SingleCellExperiment, SummarizedExperiment, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 818c453ce70d5574b0721a922b97ed3f NeedsCompilation: no Title: API Wrapper for ToppGene Description: scToppR provides an easy-to-use API wrapper for the ToppGene web platform, used for gene ontology and functional enrichment research. The package also integrates visualization tools, making it a convenient tool directly connecting ToppGene to code-based workflows in R. The tool can also easily save results into different formats. biocViews: Pathways, SingleCell Author: Bryan Granger [aut, cre] (ORCID: ) Maintainer: Bryan Granger URL: https://github.com/BioinformaticsMUSC/scToppR VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsMUSC/scToppR git_url: https://git.bioconductor.org/packages/scToppR git_branch: devel git_last_commit: b89b560 git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/scToppR_0.99.10.tar.gz vignettes: vignettes/scToppR/inst/doc/differential_expression_airway.html, vignettes/scToppR/inst/doc/differential_expression_seurat.html, vignettes/scToppR/inst/doc/introduction.html vignetteTitles: 2. Introduction to scToppR using the Airway dataset, 3. Introduction to scToppr with differential expression using Seurat object data, 1. Introduction to scToppR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scToppR/inst/doc/differential_expression_airway.R, vignettes/scToppR/inst/doc/differential_expression_seurat.R, vignettes/scToppR/inst/doc/introduction.R dependencyCount: 47 Package: scTreeViz Version: 1.17.0 Depends: R (>= 4.0), methods, epivizr, SummarizedExperiment Imports: data.table, S4Vectors, digest, Matrix, Rtsne, httr, igraph, clustree, scran, sys, epivizrData, epivizrServer, ggraph, scater, Seurat, SingleCellExperiment, ggplot2, stats, utils Suggests: knitr, BiocStyle, testthat, SC3, scRNAseq, rmarkdown, msd16s, metagenomeSeq, epivizrStandalone, GenomeInfoDb License: Artistic-2.0 MD5sum: 1c5886d0623c6c7d7ea984521ed443c5 NeedsCompilation: no Title: R/Bioconductor package to interactively explore and visualize single cell RNA-seq datasets with hierarhical annotations Description: scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters. biocViews: Visualization, Infrastructure, GUI, SingleCell Author: Jayaram Kancherla [aut, cre], Hector Corrada Bravo [aut], Kazi Tasnim Zinat [aut], Stephanie Hicks [aut] Maintainer: Jayaram Kancherla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTreeViz git_branch: devel git_last_commit: 8bb53d8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/scTreeViz_1.17.0.tar.gz vignettes: vignettes/scTreeViz/inst/doc/ExploreTreeViz.html vignetteTitles: Explore Data using scTreeViz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTreeViz/inst/doc/ExploreTreeViz.R dependencyCount: 242 Package: scTypeEval Version: 0.99.31 Depends: R (>= 4.6.0) Imports: Matrix (>= 1.6-5), BiocParallel (>= 1.34.2), dplyr (>= 1.1.4), tidyr (>= 1.3.1), scran (>= 1.30.2), bluster (>= 1.12.0), ggplot2 (>= 3.5.1), ggrepel (>= 0.9.6), cluster (>= 2.1.4), SingleR (>= 2.4.1), irlba (>= 2.3.5.1), transport (>= 0.15-4), grDevices, methods, stats, utils Suggests: testthat (>= 3.0.0), transformGamPoi (>= 1.8.0), glmGamPoi (>= 1.14.3), anndata (>= 0.8.0), SummarizedExperiment (>= 1.32.0), igraph (>= 2.1.1), Seurat, SingleCellExperiment, knitr, rmarkdown, BiocStyle, BiocManager, SeuratObject, ggpubr, rlang, stringr, tibble License: GPL-3 + file LICENSE MD5sum: 7a681a70389dd6dff809ad825a9d941d NeedsCompilation: no Title: Evaluation of cell type classifications in single-cell transcriptomics Description: scTypeEval provides tools to evaluate and validate cell type classifications in single-cell transcriptomics when ground truth labels are limited or unavailable. Results are organized in an S4 object that integrates processed data, dimensional reductions, dissimilarity assays, and consistency metrics computed across samples. The workflow includes preprocessing and feature selection, principal component analysis, computation of dissimilarity matrices, internal validation metrics (for example, silhouette-based summaries), and visualization utilities to inspect heatmaps and PCA plots. Functions support common single-cell containers and enable comparison of clustering and labeling strategies across datasets. biocViews: SingleCell, Transcriptomics, GeneExpression, CellBasedAssays, DimensionReduction, Preprocessing, PrincipalComponent Author: Josep Garnica [aut, cre] (ORCID: ), Massimo Andreatta [aut] (ORCID: ), Santiago Carmona [aut] (ORCID: ) Maintainer: Josep Garnica URL: https://github.com/carmonalab/scTypeEval VignetteBuilder: knitr BugReports: https://github.com/carmonalab/scTypeEval/issues git_url: https://git.bioconductor.org/packages/scTypeEval git_branch: devel git_last_commit: 8d66e4d git_last_commit_date: 2026-04-15 Date/Publication: 2026-04-20 source.ver: src/contrib/scTypeEval_0.99.31.tar.gz vignettes: vignettes/scTypeEval/inst/doc/quick-start.html, vignettes/scTypeEval/inst/doc/scTypeEval.html vignetteTitles: Quick Start Guide, scTypeEval: Evaluating Cell Type Classifications hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scTypeEval/inst/doc/quick-start.R, vignettes/scTypeEval/inst/doc/scTypeEval.R dependencyCount: 88 Package: scuttle Version: 1.21.6 Depends: SingleCellExperiment Imports: methods, utils, stats, Matrix, Rcpp, BiocGenerics, S4Vectors, BiocParallel, GenomicRanges, SummarizedExperiment, S4Arrays, MatrixGenerics, SparseArray, DelayedArray, beachmat LinkingTo: Rcpp, beachmat, assorthead Suggests: BiocStyle, knitr, scRNAseq, rmarkdown, testthat, sparseMatrixStats, DelayedMatrixStats, scran License: GPL-3 MD5sum: 7c35e83ef3e8bb0058a7438e047913f0 NeedsCompilation: yes Title: Legacy Utilities for Single-Cell RNA-Seq Analysis Description: Provides some legacy utility functions for performing single-cell analyses. Most of these functions are deprecated in favor of newer, more performant alternatives. We just keep this package around for back-compatibility and to point to the replacement functions. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport Author: Aaron Lun [aut, cre], Davis McCarthy [aut] Maintainer: Aaron Lun SystemRequirements: C++17 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scuttle git_branch: devel git_last_commit: 28c380d git_last_commit_date: 2026-04-11 Date/Publication: 2026-04-20 source.ver: src/contrib/scuttle_1.21.6.tar.gz vignettes: vignettes/scuttle/inst/doc/userguide.html vignetteTitles: Single-cell utilities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scuttle/inst/doc/userguide.R dependsOnMe: omicsGMF, scater, scran, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: BASiCS, BASiCStan, batchelor, chevreulPlot, chevreulProcess, ClusterGVis, DESpace, DropletUtils, epiregulon, FLAMES, imcRtools, miaDash, mumosa, muscat, SanityR, scDblFinder, simPIC, singIST, singleCellTK, SpaceTrooper, SpatialArtifacts, splatter, SplineDV, spoon, velociraptor, spatialLIBD, OSTA, mixhvg suggestsMe: Banksy, bluster, CSOA, dreamlet, epiregulon.extra, escheR, ggsc, GSABenchmark, hammers, iSEEde, iSEEfier, iSEEpathways, mastR, mia, miloR, raer, ReactomeGSA, SCArray, scConform, scDiagnostics, scDotPlot, schex, Seqtometry, SingleCellAlleleExperiment, sketchR, smoothclust, spatialHeatmap, SpotSweeper, StatescopeR, SVP, tpSVG, TSCAN, HCAData, MouseThymusAgeing, futurize, LISTO, scCustomize linksToMe: DropletUtils, scran dependencyCount: 39 Package: SDAMS Version: 1.31.0 Depends: R(>= 3.5), SummarizedExperiment Imports: trust, qvalue, methods, stats, utils Suggests: testthat License: GPL MD5sum: 5072dc3fe59cf6ca50d4cd47a0f006e6 NeedsCompilation: no Title: Differential Abundant/Expression Analysis for Metabolomics, Proteomics and single-cell RNA sequencing Data Description: This Package utilizes a Semi-parametric Differential Abundance/expression analysis (SDA) method for metabolomics and proteomics data from mass spectrometry as well as single-cell RNA sequencing data. SDA is able to robustly handle non-normally distributed data and provides a clear quantification of the effect size. biocViews: ImmunoOncology, DifferentialExpression, Metabolomics, Proteomics, MassSpectrometry, SingleCell Author: Yuntong Li , Chi Wang , Li Chen Maintainer: Yuntong Li git_url: https://git.bioconductor.org/packages/SDAMS git_branch: devel git_last_commit: b82dfb1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SDAMS_1.31.0.tar.gz vignettes: vignettes/SDAMS/inst/doc/SDAMS.pdf vignetteTitles: SDAMS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SDAMS/inst/doc/SDAMS.R dependencyCount: 51 Package: seahtrue Version: 1.5.0 Depends: R (>= 4.2.0) Imports: dplyr (>= 1.1.2), readxl (>= 1.4.1), logger (>= 0.2.2), tidyxl (>= 1.0.8), purrr (>= 0.3.5), tidyr (>= 1.3.0), lubridate (>= 1.8.0), stringr (>= 1.4.1), tibble (>= 3.1.8), validate (>= 1.1.1), rlang (>= 1.0.0), glue (>= 1.6.2), cli (>= 3.4.1), janitor (>= 2.2.0), ggplot2 (>= 3.5.0), RColorBrewer (>= 1.1.3), colorspace (>= 2.1.0), forcats (>= 1.0.0), ggridges (>= 0.5.6), readr (>= 2.1.5), scales (>= 1.3.0) Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle License: Artistic-2.0 MD5sum: e7a74c492fba65b15c6efc6413826f93 NeedsCompilation: no Title: Seahtrue revives XF data for structured data analysis Description: Seahtrue organizes oxygen consumption and extracellular acidification analysis data from experiments performed on an XF analyzer into structured nested tibbles.This allows for detailed processing of raw data and advanced data visualization and statistics. Seahtrue introduces an open and reproducible way to analyze these XF experiments. It uses file paths to .xlsx files. These .xlsx files are supplied by the userand are generated by the user in the Wave software from Agilent from the assay result files (.asyr). The .xlsx file contains different sheets of important data for the experiment; 1. Assay Information - Details about how the experiment was set up. 2. Rate Data - Information about the OCR and ECAR rates. 3. Raw Data - The original raw data collected during the experiment. 4. Calibration Data - Data related to calibrating the instrument. Seahtrue focuses on getting the specific data needed for analysis. Once this data is extracted, it is prepared for calculations through preprocessing. To make sure everything is accurate, both the initial data and the preprocessed data go through thorough checks. biocViews: CellBasedAssays, FunctionalPrediction, DataRepresentation, DataImport, CellBiology, Cheminformatics, Metabolomics, MicrotitrePlateAssay, Visualization, QualityControl, BatchEffect, ExperimentalDesign, Preprocessing, GO Author: Vincent de Boer [cre, aut] (ORCID: ), Gerwin Smits [aut], Xiang Zhang [aut] Maintainer: Vincent de Boer URL: https://vcjdeboer.github.io/seahtrue/ VignetteBuilder: knitr BugReports: https://vcjdeboer.github.io/seahtrue/issues git_url: https://git.bioconductor.org/packages/seahtrue git_branch: devel git_last_commit: a315884 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/seahtrue_1.5.0.tar.gz vignettes: vignettes/seahtrue/inst/doc/seahtrue.html vignetteTitles: Introduction to Seahtrue hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seahtrue/inst/doc/seahtrue.R dependencyCount: 63 Package: sechm Version: 1.19.0 Depends: R (>= 4.0), SummarizedExperiment, ComplexHeatmap Imports: S4Vectors, seriation, circlize, methods, randomcoloR, stats, grid, grDevices, matrixStats Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 3fa24631eb2ef999c297171f0838c196 NeedsCompilation: no Title: sechm: Complex Heatmaps from a SummarizedExperiment Description: sechm provides a simple interface between SummarizedExperiment objects and the ComplexHeatmap package. It enables plotting annotated heatmaps from SE objects, with easy access to rowData and colData columns, and implements a number of features to make the generation of heatmaps easier and more flexible. These functionalities used to be part of the SEtools package. biocViews: GeneExpression, Visualization Author: Pierre-Luc Germain [cre, aut] (ORCID: ) Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/sechm git_url: https://git.bioconductor.org/packages/sechm git_branch: devel git_last_commit: 80bb3d7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sechm_1.19.0.tar.gz vignettes: vignettes/sechm/inst/doc/sechm.html vignetteTitles: sechm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sechm/inst/doc/sechm.R dependsOnMe: SEtools importsMe: broadSeq dependencyCount: 74 Package: segmentSeq Version: 2.45.0 Depends: R (>= 3.5.0), methods, baySeq (>= 2.9.0), S4Vectors, parallel, GenomicRanges, ShortRead, stats Imports: Rsamtools, IRanges, Seqinfo, graphics, grDevices, utils, abind Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown License: GPL-3 MD5sum: 3d99f99d3ab5aa26df959d06e07d8a01 NeedsCompilation: no Title: Methods for identifying small RNA loci from high-throughput sequencing data Description: High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery. biocViews: MultipleComparison, Sequencing, Alignment, DifferentialExpression, QualityControl, DataImport Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID: ) Maintainer: Samuel Granjeaud URL: https://github.com/samgg/segmentSeq VignetteBuilder: knitr BugReports: https://github.com/samgg/segmentSeq/issues git_url: https://git.bioconductor.org/packages/segmentSeq git_branch: devel git_last_commit: a33a34a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/segmentSeq_2.45.0.tar.gz vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.html, vignettes/segmentSeq/inst/doc/segmentSeq.html vignetteTitles: segmentsSeq: Methylation locus identification, segmentSeq: small RNA locus detection hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmentSeq/inst/doc/methylationAnalysis.R, vignettes/segmentSeq/inst/doc/segmentSeq.R dependencyCount: 59 Package: selectKSigs Version: 1.23.0 Depends: R(>= 3.6) Imports: HiLDA, magrittr, gtools, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, dplyr, tidyr License: GPL-3 MD5sum: 8372fa470e6dc7e4114abb8da5b4ad30 NeedsCompilation: yes Title: Selecting the number of mutational signatures using a perplexity-based measure and cross-validation Description: A package to suggest the number of mutational signatures in a collection of somatic mutations using calculating the cross-validated perplexity score. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Clustering Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/selectKSigs VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/selectKSigs git_url: https://git.bioconductor.org/packages/selectKSigs git_branch: devel git_last_commit: 3098545 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/selectKSigs_1.23.0.tar.gz vignettes: vignettes/selectKSigs/inst/doc/selectKSigs.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/selectKSigs/inst/doc/selectKSigs.R dependencyCount: 108 Package: SELEX Version: 1.43.0 Depends: rJava (>= 0.5-0), Biostrings (>= 2.26.0) Imports: stats, utils License: GPL (>=2) MD5sum: 8d23730585d901f266e9b21550b96348 NeedsCompilation: no Title: Functions for analyzing SELEX-seq data Description: Tools for quantifying DNA binding specificities based on SELEX-seq data. biocViews: Software, MotifDiscovery, MotifAnnotation, GeneRegulation, Transcription Author: Chaitanya Rastogi, Dahong Liu, Lucas Melo, and Harmen J. Bussemaker Maintainer: Harmen J. Bussemaker URL: https://bussemakerlab.org/site/software/ SystemRequirements: Java (>= 1.5) git_url: https://git.bioconductor.org/packages/SELEX git_branch: devel git_last_commit: a65d5a7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SELEX_1.43.0.tar.gz vignettes: vignettes/SELEX/inst/doc/SELEX.pdf vignetteTitles: Motif Discovery with SELEX-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SELEX/inst/doc/SELEX.R dependencyCount: 16 Package: SemDist Version: 1.45.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: 06174973833592ba57746e30487d670f NeedsCompilation: no Title: Information Accretion-based Function Predictor Evaluation Description: This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor. biocViews: Classification, Annotation, GO, Software Author: Ian Gonzalez and Wyatt Clark Maintainer: Ian Gonzalez URL: http://github.com/iangonzalez/SemDist git_url: https://git.bioconductor.org/packages/SemDist git_branch: devel git_last_commit: f13588e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SemDist_1.45.0.tar.gz vignettes: vignettes/SemDist/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SemDist/inst/doc/introduction.R dependencyCount: 46 Package: semisup Version: 1.35.0 Depends: R (>= 3.0.0) Imports: VGAM Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: 9a3d4a0de1c552423cd4df13f22af1f1 NeedsCompilation: no Title: Semi-Supervised Mixture Model Description: Implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis. biocViews: SNP, GenomicVariation, SomaticMutation, Genetics, Classification, Clustering, DNASeq, Microarray, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/semisup VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/semisup/issues git_url: https://git.bioconductor.org/packages/semisup git_branch: devel git_last_commit: ecec762 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/semisup_1.35.0.tar.gz vignettes: vignettes/semisup/inst/doc/semisup.pdf, vignettes/semisup/inst/doc/article.html, vignettes/semisup/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/semisup/inst/doc/semisup.R dependencyCount: 5 Package: SEMPLR Version: 0.99.2 Depends: R (>= 4.1.0) Imports: BiocGenerics, Biostrings, GenomeInfoDb, AnnotationDbi, ggplot2, ggrepel, VariantAnnotation, GenomicRanges, GenomicFeatures, data.table, methods, scales, S4Vectors, stats, rlang, stringi, universalmotif, Rcpp, ggtree LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, devtools, testthat (>= 3.0.0), IRanges, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db License: MIT + file LICENSE MD5sum: b45aa075cde363722555af53e159897b NeedsCompilation: yes Title: SNP Effect Matrix Pipeline in R Description: SEMPLR computes transcription factor binding affinity scores for genomic positions and genetic variants. Scores are computed from SNP Effect Matrices (SEMs) produced by SEMpl. 223 pre-computed SEMs are included with the package or custom sets can be provided. Enrichment can be tested among sets of genomic positions to determine if transcription factor binding events occur more often than expected. Comparing binding affinity scores between alleles can reveal differences in transcription factor binding with genetic variation. This package also includes several visualization functions to view scores both on the motif and variant/position level. biocViews: MotifAnnotation, Transcription, SNP, GenomicVariation Author: Grace Kenney [aut, cre] (ORCID: ), Douglas Phanstiel [aut], NIH NIGMS [fnd], NSF GRFP [fnd] Maintainer: Grace Kenney URL: https://github.com/grkenney/SEMPLR, https://grkenney.github.io/SEMPLR VignetteBuilder: knitr BugReports: https://www.github.com/grkenney/SEMPLR/issues git_url: https://git.bioconductor.org/packages/SEMPLR git_branch: devel git_last_commit: 5d0ce71 git_last_commit_date: 2026-03-09 Date/Publication: 2026-04-20 source.ver: src/contrib/SEMPLR_0.99.2.tar.gz vignettes: vignettes/SEMPLR/inst/doc/SEMPLR.html vignetteTitles: SEMPLR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SEMPLR/inst/doc/SEMPLR.R dependencyCount: 139 Package: seq.hotSPOT Version: 1.11.0 Depends: R (>= 3.5.0) Imports: R.utils, hash, stats, base, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: c5ab11b186dfe22874102dab75a8c4de NeedsCompilation: no Title: Targeted sequencing panel design based on mutation hotspots Description: seq.hotSPOT provides a resource for designing effective sequencing panels to help improve mutation capture efficacy for ultradeep sequencing projects. Using SNV datasets, this package designs custom panels for any tissue of interest and identify the genomic regions likely to contain the most mutations. Establishing efficient targeted sequencing panels can allow researchers to study mutation burden in tissues at high depth without the economic burden of whole-exome or whole-genome sequencing. This tool was developed to make high-depth sequencing panels to study low-frequency clonal mutations in clinically normal and cancerous tissues. biocViews: Software, Technology, Sequencing, DNASeq, WholeGenome Author: Sydney Grant [aut, cre], Lei Wei [aut], Gyorgy Paragh [aut] Maintainer: Sydney Grant URL: https://github.com/sydney-grant/seq.hotSPOT VignetteBuilder: knitr BugReports: https://github.com/sydney-grant/seq.hotSPOT/issues git_url: https://git.bioconductor.org/packages/seq.hotSPOT git_branch: devel git_last_commit: b122765 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/seq.hotSPOT_1.11.0.tar.gz vignettes: vignettes/seq.hotSPOT/inst/doc/hotSPOT-vignette.html vignetteTitles: hotSPOT-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seq.hotSPOT/inst/doc/hotSPOT-vignette.R dependencyCount: 9 Package: SeqArray Version: 1.51.9 Depends: R (>= 3.5.0), gdsfmt (>= 1.31.1) Imports: methods, parallel, digest, S4Vectors, IRanges, GenomicRanges, Seqinfo, Biostrings LinkingTo: gdsfmt Suggests: Biobase, BiocGenerics, BiocParallel, RUnit, Rcpp, SNPRelate, crayon, knitr, markdown, rmarkdown, Rsamtools, VariantAnnotation License: GPL-3 MD5sum: 58b2dae086c76f6c155deb9990c728ba NeedsCompilation: yes Title: Data management of large-scale whole-genome sequence variant calls using GDS files Description: Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. biocViews: Infrastructure, DataRepresentation, Sequencing, Genetics Author: Xiuwen Zheng [aut, cre] (ORCID: ), Stephanie Gogarten [aut], David Levine [ctb], Cathy Laurie [ctb] Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/SeqArray VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/SeqArray/issues git_url: https://git.bioconductor.org/packages/SeqArray git_branch: devel git_last_commit: 661a910 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/SeqArray_1.51.9.tar.gz vignettes: vignettes/SeqArray/inst/doc/OverviewSlides.html, vignettes/SeqArray/inst/doc/SeqArray.html, vignettes/SeqArray/inst/doc/SeqArrayTutorial.html vignetteTitles: SeqArray Overview, R Integration, SeqArray Data Format and Access hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/SeqArray.R, vignettes/SeqArray/inst/doc/SeqArrayTutorial.R dependsOnMe: GBScleanR, SAIGEgds, SeqVarTools importsMe: GDSArray, GENESIS, ggmanh, VariantExperiment suggestsMe: DelayedDataFrame, HIBAG, VCFArray, GMMAT, MAGEE dependencyCount: 19 Package: seqCAT Version: 1.33.0 Depends: R (>= 3.6), GenomicRanges (>= 1.26.4), VariantAnnotation(>= 1.20.3) Imports: dplyr (>= 0.5.0), GenomeInfoDb (>= 1.13.4), ggplot2 (>= 2.2.1), grid (>= 3.5.0), IRanges (>= 2.8.2), methods, rtracklayer, rlang, scales (>= 0.4.1), S4Vectors (>= 0.12.2), stats, SummarizedExperiment (>= 1.4.0), tidyr (>= 0.6.1), utils Suggests: knitr, BiocStyle, rmarkdown, testthat, BiocManager License: MIT + file LICENCE MD5sum: 38e62a496ef24b966feda362f4eff6fd NeedsCompilation: no Title: High Throughput Sequencing Cell Authentication Toolkit Description: The seqCAT package uses variant calling data (in the form of VCF files) from high throughput sequencing technologies to authenticate and validate the source, function and characteristics of biological samples used in scientific endeavours. biocViews: Coverage, GenomicVariation, Sequencing, VariantAnnotation Author: Erik Fasterius [aut, cre] Maintainer: Erik Fasterius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqCAT git_branch: devel git_last_commit: 52e812d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/seqCAT_1.33.0.tar.gz vignettes: vignettes/seqCAT/inst/doc/seqCAT.html vignetteTitles: seqCAT: The High Throughput Sequencing Cell Authentication Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCAT/inst/doc/seqCAT.R dependencyCount: 99 Package: seqcombo Version: 1.33.0 Depends: R (>= 3.4.0) Imports: ggplot2, grid, igraph, utils, yulab.utils Suggests: emojifont, knitr, rmarkdown, prettydoc, tibble License: Artistic-2.0 MD5sum: 3054ee933c13e7fa327e232383a8fb67 NeedsCompilation: no Title: Visualization Tool for Genetic Reassortment Description: Provides useful functions for visualizing virus reassortment events. biocViews: Alignment, Software, Visualization Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/seqcombo/issues git_url: https://git.bioconductor.org/packages/seqcombo git_branch: devel git_last_commit: 29bf96e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/seqcombo_1.33.0.tar.gz vignettes: vignettes/seqcombo/inst/doc/seqcombo.html vignetteTitles: Reassortment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqcombo/inst/doc/seqcombo.R dependencyCount: 33 Package: SeqGate Version: 1.21.0 Depends: S4Vectors, SummarizedExperiment, GenomicRanges Imports: stats, methods, BiocManager Suggests: testthat (>= 3.0.0), edgeR, BiocStyle, knitr, rmarkdown License: GPL (>= 2.0) MD5sum: f91118a536d01a3aae3873c954a22e08 NeedsCompilation: no Title: Filtering of Lowly Expressed Features Description: Filtering of lowly expressed features (e.g. genes) is a common step before performing statistical analysis, but an arbitrary threshold is generally chosen. SeqGate implements a method that rationalize this step by the analysis of the distibution of counts in replicate samples. The gate is the threshold above which sequenced features can be considered as confidently quantified. biocViews: DifferentialExpression, GeneExpression, Transcriptomics, Sequencing, RNASeq Author: Christelle Reynès [aut], Stéphanie Rialle [aut, cre] Maintainer: Stéphanie Rialle VignetteBuilder: knitr BugReports: https://github.com/srialle/SeqGate/issues git_url: https://git.bioconductor.org/packages/SeqGate git_branch: devel git_last_commit: ac612ff git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SeqGate_1.21.0.tar.gz vignettes: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.html vignetteTitles: SeqGate: Filter lowly expressed features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.R dependencyCount: 26 Package: SeqGSEA Version: 1.51.0 Depends: Biobase, doParallel, DESeq2 Imports: methods, biomaRt Suggests: GenomicRanges License: GPL (>= 3) MD5sum: a9fc591959abdaba271ed4e662dbf2dc NeedsCompilation: no Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing Description: The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively. biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression, DifferentialExpression, DifferentialSplicing, ImmunoOncology Author: Xi Wang Maintainer: Xi Wang git_url: https://git.bioconductor.org/packages/SeqGSEA git_branch: devel git_last_commit: d9a1c2a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SeqGSEA_1.51.0.tar.gz vignettes: vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf vignetteTitles: Gene set enrichment analysis of RNA-Seq data with the SeqGSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGSEA/inst/doc/SeqGSEA.R dependencyCount: 99 Package: Seqinfo Version: 1.1.0 Depends: methods, BiocGenerics Imports: stats, S4Vectors (>= 0.47.6), IRanges Suggests: GenomeInfoDb, GenomicRanges, BSgenome, GenomicFeatures, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Celegans.UCSC.ce2, RUnit, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 0d9c553c3274573d1b92a33474945765 NeedsCompilation: no Title: A simple S4 class for storing basic information about a collection of genomic sequences Description: The Seqinfo class stores the names, lengths, circularity flags, and genomes for a particular collection of sequences. These sequences are typically the chromosomes and/or scaffolds of a specific genome assembly of a given organism. Seqinfo objects are rarely used as standalone objects. Instead, they are used as part of higher-level objects to represent their seqinfo() component. Examples of such higher-level objects are GRanges, RangedSummarizedExperiment, VCF, GAlignments, etc... defined in other Bioconductor infrastructure packages. biocViews: Infrastructure, DataRepresentation, GenomeAssembly, Annotation, GenomeAnnotation Author: Hervé Pagès [aut, cre] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/Seqinfo VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Seqinfo/issues git_url: https://git.bioconductor.org/packages/Seqinfo git_branch: devel git_last_commit: 17fc74a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Seqinfo_1.1.0.tar.gz vignettes: vignettes/Seqinfo/inst/doc/Seqinfo.html vignetteTitles: The Seqinfo package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Seqinfo/inst/doc/Seqinfo.R dependsOnMe: Biostrings, BSgenome, BSgenomeForge, bumphunter, CSAR, extraChIPs, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, groHMM, HelloRanges, OrganismDbi, Rsamtools, txdbmaker, VariantAnnotation importsMe: alabaster.ranges, amplican, annoLinker, AnnotationHubData, annotatr, ATACseqTFEA, atena, ballgown, Bioc.gff, biovizBase, BiSeq, bnbc, branchpointer, bsseq, CAGEfightR, CAGEr, casper, cBioPortalData, CexoR, chipenrich, ChIPexoQual, cleanUpdTSeq, CleanUpRNAseq, cn.mops, CNEr, Cogito, compEpiTools, consensusSeekeR, conumee, crisprBowtie, crisprBwa, crisprDesign, CRISPRseek, crisprShiny, crisprViz, crupR, csaw, DAMEfinder, decompTumor2Sig, DegCre, demuxSNP, derfinder, derfinderPlot, DEScan2, DEWSeq, DMRcaller, DMRcate, DMRScan, dmrseq, DominoEffect, easyRNASeq, ELMER, enhancerHomologSearch, ensembldb, EpiCompare, epigraHMM, EpiMix, EpiTxDb, epivizrData, epivizrStandalone, esATAC, FindIT2, FLAMES, G4SNVHunter, GA4GHclient, GA4GHshiny, gcapc, gDNAx, geneAttribution, genomation, GenomAutomorphism, genomeIntervals, GenomicCoordinates, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicOZone, GenomicPlot, GenomicScores, GenVisR, geomeTriD, ggbio, gmoviz, goseq, GOTHiC, GreyListChIP, Gviz, gwascat, heatmaps, HicAggR, HiCBricks, HiCDOC, HiCExperiment, HiCParser, hicVennDiagram, HiTC, InPAS, INSPEcT, InteractionSet, IsoformSwitchAnalyzeR, IVAS, karyoploteR, ldblock, maser, metaseqR2, methInheritSim, methylKit, methylPipe, methylSig, minfi, MinimumDistance, monaLisa, mosaics, motifmatchr, MotifPeeker, motifTestR, MouseFM, msgbsR, multicrispr, MutationalPatterns, MutSeqR, myvariant, nearBynding, nucleR, nullranges, OGRE, OMICsPCA, panelcn.mops, peakCombiner, periodicDNA, PICB, pipeFrame, plyinteractions, plyranges, podkat, pram, prebs, ProteoDisco, PureCN, QDNAseq, qpgraph, qsea, QuasR, r3Cseq, raer, RaggedExperiment, ramr, recoup, regioneR, regionReport, REMP, rfPred, RiboCrypt, riboSeqR, ribosomeProfilingQC, rigvf, RJMCMCNucleosomes, rnaEditr, RNAmodR, RTCGAToolbox, rtracklayer, scanMiR, scmeth, segmentSeq, SeqArray, seqsetvis, sesame, sevenC, SGSeq, ShortRead, SingleMoleculeFootprinting, sitadela, SomaticSignatures, SplicingGraphs, SPLINTER, srnadiff, strandCheckR, SummarizedExperiment, svaNUMT, svaRetro, tadar, TCGAutils, TEKRABber, TENxIO, TEQC, TFBSTools, trackViewer, transmogR, tRNAscanImport, TVTB, tximeta, VariantFiltering, VplotR, YAPSA, GenomicState, grasp2db, sesameData suggestsMe: AlphaMissenseR, AnnotationHub, RAIDS, RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, TFEA.ChIP, TFutils dependencyCount: 9 Package: seqLogo Version: 1.77.0 Depends: R (>= 4.2), methods, grid Imports: stats4, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat License: LGPL (>= 2) MD5sum: fe2f96cf23cbdcd7ab6eb25d11451fcb NeedsCompilation: no Title: Sequence logos for DNA sequence alignments Description: seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990). biocViews: SequenceMatching Author: Oliver Bembom [aut], Robert Ivanek [aut, cre] (ORCID: ) Maintainer: Robert Ivanek VignetteBuilder: knitr BugReports: https://github.com/ivanek/seqLogo/issues git_url: https://git.bioconductor.org/packages/seqLogo git_branch: devel git_last_commit: f1113d7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/seqLogo_1.77.0.tar.gz vignettes: vignettes/seqLogo/inst/doc/seqLogo.html vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R dependsOnMe: generegulation importsMe: IntEREst, PWMEnrich, RCAS, riboSeqR, scanMiR, SPLINTER, TENET, TFBSTools, kmeRtone suggestsMe: BCRANK, DiffLogo, igvR, MAGAR, motifcounter, MotifDb, PMScanR, universalmotif dependencyCount: 4 Package: seqPattern Version: 1.43.0 Depends: methods, R (>= 2.15.0) Imports: Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix Suggests: BSgenome.Drerio.UCSC.danRer7, CAGEr, RUnit, BiocGenerics, BiocStyle Enhances: parallel License: GPL-3 MD5sum: 7e14a2f06152a5578a3d351d096a6315 NeedsCompilation: no Title: Visualising oligonucleotide patterns and motif occurrences across a set of sorted sequences Description: Visualising oligonucleotide patterns and sequence motifs occurrences across a large set of sequences centred at a common reference point and sorted by a user defined feature. biocViews: Visualization, SequenceMatching Author: Vanja Haberle Maintainer: Vanja Haberle git_url: https://git.bioconductor.org/packages/seqPattern git_branch: devel git_last_commit: 4b1e21f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/seqPattern_1.43.0.tar.gz vignettes: vignettes/seqPattern/inst/doc/seqPattern.pdf vignetteTitles: seqPattern hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqPattern/inst/doc/seqPattern.R importsMe: genomation dependencyCount: 18 Package: seqsetvis Version: 1.31.2 Depends: R (>= 4.3), ggplot2 Imports: cowplot, data.table, eulerr, Seqinfo, GenomicAlignments, GenomicRanges, ggplotify, grDevices, grid, IRanges, limma, methods, pbapply, pbmcapply, png, RColorBrewer, Rsamtools, rtracklayer, S4Vectors, scales, stats, UpSetR Suggests: BiocFileCache, BiocManager, BiocStyle, ChIPpeakAnno, GenomeInfoDb, covr, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 7642b0bb918f93d5833f0202482c8e25 NeedsCompilation: no Title: Set Based Visualizations for Next-Gen Sequencing Data Description: seqsetvis enables the visualization and analysis of sets of genomic sites in next gen sequencing data. Although seqsetvis was designed for the comparison of mulitple ChIP-seq samples, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files pileups from bam file). seqsetvis has multiple functions for fetching data from regions into a tidy format for analysis in data.table or tidyverse and visualization via ggplot2. biocViews: Software, ChIPSeq, MultipleComparison, Sequencing, Visualization Author: Joseph R Boyd [aut, cre] (ORCID: ) Maintainer: Joseph R Boyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqsetvis git_branch: devel git_last_commit: b035d64 git_last_commit_date: 2026-02-23 Date/Publication: 2026-04-20 source.ver: src/contrib/seqsetvis_1.31.2.tar.gz vignettes: vignettes/seqsetvis/inst/doc/seqsetvis_overview.html vignetteTitles: Overview and Use Cases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/seqsetvis/inst/doc/seqsetvis_overview.R dependencyCount: 94 Package: SeqSQC Version: 1.33.0 Depends: R (>= 3.4), ExperimentHub (>= 1.3.7), SNPRelate (>= 1.10.2) Imports: e1071, GenomicRanges, gdsfmt, ggplot2, GGally, IRanges, methods, plotly, RColorBrewer, reshape2, rmarkdown, S4Vectors, stats, utils Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: e045a13925cf96b9a6f231d69a6874a1 NeedsCompilation: no Title: A bioconductor package for sample quality check with next generation sequencing data Description: The SeqSQC is designed to identify problematic samples in NGS data, including samples with gender mismatch, contamination, cryptic relatedness, and population outlier. biocViews: Experiment Data, Homo_sapiens_Data, Sequencing Data, Project1000genomes, Genome Author: Qian Liu [aut, cre] Maintainer: Qian Liu URL: https://github.com/Liubuntu/SeqSQC VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/SeqSQC/issues git_url: https://git.bioconductor.org/packages/SeqSQC git_branch: devel git_last_commit: 1bf7b5e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SeqSQC_1.33.0.tar.gz vignettes: vignettes/SeqSQC/inst/doc/vignette.html vignetteTitles: Sample Quality Check for Next-Generation Sequencing Data with SeqSQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqSQC/inst/doc/vignette.R dependencyCount: 114 Package: Seqtometry Version: 0.99.3 Depends: R (>= 4.5.0) Imports: BiocSingular, checkmate, data.table, future.apply, Matrix, MatrixGenerics, purrr, Rcpp, RcppHNSW, RSpectra, zeallot LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, box, dplyr, future, ggplot2, harmony, knitr, MASS, patchwork, rmarkdown, scater, scuttle, SingleCellExperiment, sparseMatrixStats, stringr, TENxPBMCData, testthat (>= 3.0.0), tibble License: MIT + file LICENSE MD5sum: 6a120033562c3adb6f73d211e1eb0f9f NeedsCompilation: yes Title: Signature scoring for single cell analysis Description: This package provides functions used in Seqtometry (Kousnetsov et al. 2024), a method for analyzing single cell (scRNA-seq or scATAC-seq) data via signature (gene set) enrichment scores. The Seqtometry scores may be useful for annotating or characterizing cells, either in a flow cytometry like workflow (where scores are standalone features used for progressive partitoning as described in the Seqtometry publication) or in a cluster-based workflow (as features of clusters). The exported impute function (a port of Python's MAGIC-impute, van Dijk et al. 2018), may also be useful for single cell analysis on its own. biocViews: SingleCell, GeneSetEnrichment, GeneExpression Author: Robert Kousnetsov [aut, cre], Daniel Hawiger [cph, fnd] Maintainer: Robert Kousnetsov URL: https://github.com/HawigerLab/Seqtometry VignetteBuilder: knitr BugReports: https://github.com/HawigerLab/Seqtometry/issues git_url: https://git.bioconductor.org/packages/Seqtometry git_branch: devel git_last_commit: b541506 git_last_commit_date: 2026-03-25 Date/Publication: 2026-04-20 source.ver: src/contrib/Seqtometry_0.99.3.tar.gz vignettes: vignettes/Seqtometry/inst/doc/Seqtometry.html vignetteTitles: Seqtometry vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Seqtometry/inst/doc/Seqtometry.R dependencyCount: 59 Package: SeqVarTools Version: 1.49.0 Depends: SeqArray Imports: grDevices, graphics, stats, methods, Biobase, BiocGenerics, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, logistf, Matrix, data.table, Suggests: BiocStyle, RUnit, stringr License: GPL-3 MD5sum: 88029290398f24d632dd4ecffb2fd34e NeedsCompilation: no Title: Tools for variant data Description: An interface to the fast-access storage format for VCF data provided in SeqArray, with tools for common operations and analysis. biocViews: SNP, GeneticVariability, Sequencing, Genetics Author: Stephanie M. Gogarten, Xiuwen Zheng, Adrienne Stilp Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/SeqVarTools git_url: https://git.bioconductor.org/packages/SeqVarTools git_branch: devel git_last_commit: 2d4221b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SeqVarTools_1.49.0.tar.gz vignettes: vignettes/SeqVarTools/inst/doc/Iterators.pdf, vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Iterators in SeqVarTools, Introduction to SeqVarTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqVarTools/inst/doc/Iterators.R, vignettes/SeqVarTools/inst/doc/SeqVarTools.R importsMe: GENESIS suggestsMe: GMMAT, MAGEE dependencyCount: 89 Package: SEraster Version: 1.3.0 Depends: R (>= 4.5.0) Imports: BiocParallel, ggplot2, Matrix, methods, rearrr, sf, SpatialExperiment, SummarizedExperiment Suggests: CooccurrenceAffinity, nnSVG, testthat (>= 3.0.0), knitr, rmarkdown, BiocManager, remotes License: GPL-3 MD5sum: a962f56c0f23717f69b16416978488b5 NeedsCompilation: no Title: Rasterization Preprocessing Framework for Scalable Spatial Omics Data Analysis Description: SEraster is a rasterization preprocessing framework that aggregates cellular information into spatial pixels to reduce resource requirements for spatial omics data analysis. SEraster reduces the number of spatial points in spatial omics datasets for downstream analysis through a process of rasterization where single cells’ gene expression or cell-type labels are aggregated into equally sized pixels based on a user-defined resolution. SEraster is built on an R/Bioconductor S4 class called SpatialExperiment. SEraster can be incorporated with other packages to conduct downstream analyses for spatial omics datasets, such as detecting spatially variable genes. biocViews: Software, Spatial, GeneExpression, Transcriptomics, SingleCell, Preprocessing Author: Gohta Aihara [aut, cre] (ORCID: ), Mayling Chen [aut] (ORCID: ), Lyla Atta [aut] (ORCID: ), Jean Fan [aut, rev] (ORCID: ) Maintainer: Gohta Aihara URL: https://github.com/JEFworks-Lab/SEraster VignetteBuilder: knitr BugReports: https://github.com/JEFworks-Lab/SEraster/issues git_url: https://git.bioconductor.org/packages/SEraster git_branch: devel git_last_commit: 0d1b44c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SEraster_1.3.0.tar.gz vignettes: vignettes/SEraster/inst/doc/getting-started-with-SEraster.html vignetteTitles: Getting Started With SEraster hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEraster/inst/doc/getting-started-with-SEraster.R dependencyCount: 98 Package: sesame Version: 1.29.5 Depends: R (>= 4.5.0), sesameData Imports: graphics, BiocParallel, utils, methods, stringr, readr, tibble, MASS, wheatmap (>= 0.2.0), GenomicRanges (>= 1.61.1), IRanges, grid, preprocessCore, S4Vectors, ggplot2, BiocFileCache, Seqinfo, stats, SummarizedExperiment (>= 1.39.1), dplyr, reshape2 Suggests: scales, BiocManager, GenomeInfoDb, knitr, DNAcopy, e1071, randomForest, RPMM, rmarkdown, testthat, tidyr, BiocStyle, ggrepel, grDevices, KernSmooth, pals License: MIT + file LICENSE MD5sum: e526f96c9a9c9e2e2a89e239459cef0f NeedsCompilation: no Title: SEnsible Step-wise Analysis of DNA MEthylation BeadChips Description: Tools For analyzing Illumina Infinium DNA methylation arrays. SeSAMe provides utilities to support analyses of multiple generations of Infinium DNA methylation BeadChips, including preprocessing, quality control, visualization and inference. SeSAMe features accurate detection calling, intelligent inference of ethnicity, sex and advanced quality control routines. biocViews: DNAMethylation, MethylationArray, Preprocessing, QualityControl Author: Wanding Zhou [aut, cre, fnd] (ORCID: ), Wubin Ding [ctb], David Goldberg [ctb], Ethan Moyer [ctb], Bret Barnes [ctb], Timothy Triche [ctb], Hui Shen [aut, fnd] Maintainer: Wanding Zhou URL: https://github.com/zwdzwd/sesame VignetteBuilder: knitr BugReports: https://github.com/zwdzwd/sesame/issues git_url: https://git.bioconductor.org/packages/sesame git_branch: devel git_last_commit: 0af1c1b git_last_commit_date: 2026-02-06 Date/Publication: 2026-04-20 source.ver: src/contrib/sesame_1.29.5.tar.gz vignettes: vignettes/sesame/inst/doc/inferences.html, vignettes/sesame/inst/doc/modeling.html, vignettes/sesame/inst/doc/nonhuman.html, vignettes/sesame/inst/doc/QC.html, vignettes/sesame/inst/doc/sesame.html vignetteTitles: "4. Data Inference", 3. Modeling, 2. Non-human Array, 1. Quality Control, "0. Basic Usage" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sesame/inst/doc/inferences.R, vignettes/sesame/inst/doc/modeling.R, vignettes/sesame/inst/doc/nonhuman.R, vignettes/sesame/inst/doc/QC.R, vignettes/sesame/inst/doc/sesame.R importsMe: MethReg, TENET, CytoMethIC suggestsMe: knowYourCG, RnBeads, TCGAbiolinks, sesameData dependencyCount: 108 Package: SETA Version: 1.1.0 Depends: R (>= 4.5.0) Imports: dplyr, MASS, Matrix, SingleCellExperiment (>= 1.30.1), stats, tidygraph, rlang, utils Suggests: BiocStyle, caret, glmnet, corrplot, ggplot2, ggraph, knitr, methods, patchwork, reshape2, rmarkdown, SeuratObject, Seurat, SummarizedExperiment, TabulaMurisSenisData, tidyr, tidytext, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 9afbbec94418ed1ff9b4932660cb26f1 NeedsCompilation: no Title: Single Cell Ecological Taxonomic Analysis Description: Tools for compositional and other sample-level ecological analyses and visualizations tailored for single-cell RNA-seq data. SETA includes functions for taxonomizing celltypes, normalizing data, performing statistical tests, and visualizing results. Several tutorials are included to guide users and introduce them to key concepts. SETA is meant to teach users about statistical concepts underlying ecological analysis methods so they can apply them to their own single-cell data. biocViews: SingleCell, Transcriptomics, RNASeq, GeneExpression, StatisticalMethod, DimensionReduction, Visualization, Normalization, DataRepresentation, SystemsBiology Author: Kyle Kimler [aut, cre] (ORCID: ), Marc Elosua-Bayes [aut] Maintainer: Kyle Kimler URL: https://github.com/kkimler/SETA VignetteBuilder: knitr BugReports: https://github.com/kkimler/SETA/issues git_url: https://git.bioconductor.org/packages/SETA git_branch: devel git_last_commit: 4f2c997 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SETA_1.1.0.tar.gz vignettes: vignettes/SETA/inst/doc/comparing_samples.html, vignettes/SETA/inst/doc/introductory_vignette.html, vignettes/SETA/inst/doc/reference_frames.html vignetteTitles: Comparing samples with SETA, Introduction to SETA ecological transforms and sample-level latent spaces, Multi-Resolution Compositional Analysis in scRNA-seq: Reference Frames with SETA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SETA/inst/doc/comparing_samples.R, vignettes/SETA/inst/doc/introductory_vignette.R, vignettes/SETA/inst/doc/reference_frames.R dependencyCount: 48 Package: SEtools Version: 1.25.0 Depends: R (>= 4.0), SummarizedExperiment, sechm Imports: BiocParallel, Matrix, DESeq2, S4Vectors, data.table, edgeR, openxlsx, pheatmap, stats, circlize, methods, sva Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: GPL MD5sum: 09f9901bc75de7d0edd3c1eab512ffbc NeedsCompilation: no Title: SEtools: tools for working with SummarizedExperiment Description: This includes a set of convenience functions for working with the SummarizedExperiment class. Note that plotting functions historically in this package have been moved to the sechm package (see vignette for details). biocViews: GeneExpression Author: Pierre-Luc Germain [cre, aut] (ORCID: ) Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/SEtools git_url: https://git.bioconductor.org/packages/SEtools git_branch: devel git_last_commit: beb739e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SEtools_1.25.0.tar.gz vignettes: vignettes/SEtools/inst/doc/SEtools.html vignetteTitles: SEtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEtools/inst/doc/SEtools.R dependencyCount: 121 Package: sevenbridges Version: 1.41.0 Depends: methods, utils, stats Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors, docopt, curl, uuid, data.table Suggests: knitr, rmarkdown, testthat, readr License: Apache License 2.0 | file LICENSE MD5sum: 42e23875c98e3bf0d38d8b1747a0ddba NeedsCompilation: no Title: Seven Bridges Platform API Client and Common Workflow Language Tool Builder in R Description: R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms. biocViews: Software, DataImport, ThirdPartyClient Author: Phil Webster [aut, cre], Soner Koc [aut] (ORCID: ), Nan Xiao [aut], Tengfei Yin [aut], Dusan Randjelovic [ctb], Emile Young [ctb], Velsera [cph, fnd] Maintainer: Phil Webster URL: https://www.sevenbridges.com, https://sbg.github.io/sevenbridges-r/, https://github.com/sbg/sevenbridges-r VignetteBuilder: knitr BugReports: https://github.com/sbg/sevenbridges-r/issues git_url: https://git.bioconductor.org/packages/sevenbridges git_branch: devel git_last_commit: bf30988 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sevenbridges_1.41.0.tar.gz vignettes: vignettes/sevenbridges/inst/doc/api.html, vignettes/sevenbridges/inst/doc/apps.html, vignettes/sevenbridges/inst/doc/bioc-workflow.html, vignettes/sevenbridges/inst/doc/cgc-datasets.html, vignettes/sevenbridges/inst/doc/docker.html, vignettes/sevenbridges/inst/doc/rstudio.html vignetteTitles: Complete Guide for Seven Bridges API R Client, Describe and Execute CWL Tools/Workflows in R, Master Tutorial: Use R for Cancer Genomics Cloud, Find Data on CGC via Data Browser and Datasets API, Creating Your Docker Container and Command Line Interface (with docopt), IDE Container: RStudio Server,, Shiny Server,, and More hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sevenbridges/inst/doc/api.R, vignettes/sevenbridges/inst/doc/apps.R, vignettes/sevenbridges/inst/doc/bioc-workflow.R, vignettes/sevenbridges/inst/doc/cgc-datasets.R, vignettes/sevenbridges/inst/doc/docker.R, vignettes/sevenbridges/inst/doc/rstudio.R dependencyCount: 31 Package: sevenC Version: 1.31.0 Depends: R (>= 3.5), InteractionSet (>= 1.2.0) Imports: rtracklayer (>= 1.34.1), BiocGenerics (>= 0.22.0), Seqinfo, GenomicRanges (>= 1.28.5), IRanges (>= 2.10.3), S4Vectors (>= 0.14.4), readr (>= 1.1.0), purrr (>= 0.2.2), data.table (>= 1.10.4), boot (>= 1.3-20), methods (>= 3.4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicInteractions, covr License: GPL-3 MD5sum: 48117ad690cb632ea8c3ca5792b9eae6 NeedsCompilation: no Title: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs Description: Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes. biocViews: DNA3DStructure, ChIPchip, Coverage, DataImport, Epigenetics, FunctionalGenomics, Classification, Regression, ChIPSeq, HiC, Annotation Author: Jonas Ibn-Salem [aut, cre] Maintainer: Jonas Ibn-Salem URL: https://github.com/ibn-salem/sevenC VignetteBuilder: knitr BugReports: https://github.com/ibn-salem/sevenC/issues git_url: https://git.bioconductor.org/packages/sevenC git_branch: devel git_last_commit: d1682a2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sevenC_1.31.0.tar.gz vignettes: vignettes/sevenC/inst/doc/sevenC.html vignetteTitles: Introduction to sevenC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sevenC/inst/doc/sevenC.R dependencyCount: 83 Package: sfi Version: 0.99.5 Depends: R (>= 4.5.0) Imports: Rcpp, enviGCMS, stats, mzR, rmarkdown, methods, SummarizedExperiment, S4Vectors LinkingTo: Rcpp Suggests: knitr, data.table, BiocStyle, ggplot2, MsCoreUtils, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 4b5ed98b8d39f768ab9f5520cb0b944d NeedsCompilation: yes Title: Data analysis for Single File Injections (SFIs) mode LC-MS analysis Description: Data analysis for Single File Injections(SFIs) mode LC-MS analysis. In SFIs mode, pooled samples are initially injected to serve as reference peaks for subsequent analyses. Repeated injections of individual samples are then performed at fixed time intervals using isocratic elution. This package provides the functions to analyze data from SFIs mode including peak picking and peak reassignment. biocViews: MassSpectrometry, Metabolomics, FeatureExtraction Author: Miao YU [aut, cre] (ORCID: ) Maintainer: Miao YU URL: https://github.com/yufree/sfi VignetteBuilder: knitr BugReports: https://github.com/yufree/sfi/issues/new git_url: https://git.bioconductor.org/packages/sfi git_branch: devel git_last_commit: e0897f4 git_last_commit_date: 2026-03-18 Date/Publication: 2026-04-20 source.ver: src/contrib/sfi_0.99.5.tar.gz vignettes: vignettes/sfi/inst/doc/workflow.html vignetteTitles: sfi workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sfi/inst/doc/workflow.R dependencyCount: 116 Package: SGSeq Version: 1.45.0 Depends: R (>= 4.0), IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), Rsamtools (>= 1.31.2), SummarizedExperiment, methods Imports: AnnotationDbi, BiocGenerics (>= 0.31.5), Biostrings (>= 2.47.6), GenomicAlignments (>= 1.15.7), GenomicFeatures (>= 1.31.5), GenomeInfoDb, RUnit, S4Vectors (>= 0.23.19), Seqinfo, grDevices, graphics, igraph, parallel, rtracklayer (>= 1.39.7), stats Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown License: Artistic-2.0 MD5sum: f4e6064fbd8706c239f0601666e6e9ce NeedsCompilation: no Title: Splice event prediction and quantification from RNA-seq data Description: SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. biocViews: AlternativeSplicing, ImmunoOncology, RNASeq, Transcription Author: Leonard Goldstein [cre, aut] Maintainer: Leonard Goldstein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SGSeq git_branch: devel git_last_commit: fef2917 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SGSeq_1.45.0.tar.gz vignettes: vignettes/SGSeq/inst/doc/SGSeq.html vignetteTitles: SGSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R dependsOnMe: EventPointer importsMe: Rhisat2 suggestsMe: FRASER dependencyCount: 80 Package: shinybiocloader Version: 1.1.0 Depends: htmltools Imports: shiny Suggests: shinydashboard, tinytest, quarto License: Artistic-2.0 MD5sum: c1c6475346bbb20a792d137b671ff8c8 NeedsCompilation: no Title: Use a Shiny Bioconductor CSS loader Description: Add a Bioconductor themed CSS loader to your shiny app. It is based on the shinycustomloader R package. Use a spinning Bioconductor note loader to enhance your shiny app loading screen. This package is intended for developer use. biocViews: Software, Infrastructure, GUI Author: Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/shinybiocloader SystemRequirements: quarto VignetteBuilder: quarto BugReports: https://github.com/Bioconductor/shinybiocloader/issues git_url: https://git.bioconductor.org/packages/shinybiocloader git_branch: devel git_last_commit: 4ca9aed git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/shinybiocloader_1.1.0.tar.gz vignettes: vignettes/shinybiocloader/inst/doc/shinybiocloader.html vignetteTitles: shinybiocloader.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinybiocloader/inst/doc/shinybiocloader.R importsMe: BiocHubsShiny, BiocPkgDash dependencyCount: 36 Package: shinyDSP Version: 1.3.0 Depends: R (>= 4.5) Imports: AnnotationHub, BiocGenerics, bsicons, bslib, circlize, ComplexHeatmap, cowplot, dplyr, DT, edgeR, ExperimentHub, ggplot2, ggpubr, ggrepel, grDevices, grid, htmltools, limma, magrittr, pals, readr, S4Vectors, scales, scater, shiny, shinycssloaders, shinyjs, shinyvalidate, shinyWidgets, SingleCellExperiment, standR, stats, stringr, SummarizedExperiment, tibble, tidyr, utils, withr Suggests: BiocStyle, knitr, rmarkdown, shinytest2, spelling, svglite, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 5933f0f73f5f9bc680165336ee3cc28c NeedsCompilation: no Title: A Shiny App For Visualizing Nanostring GeoMx DSP Data Description: This package is a Shiny app for interactively analyzing and visualizing Nanostring GeoMX Whole Transcriptome Atlas data. Users have the option of exploring a sample data to explore this app's functionality. Regions of interest (ROIs) can be filtered based on any user-provided metadata. Upon taking two or more groups of interest, all pairwise and ANOVA-like testing are automatically performed. Available ouputs include PCA, Volcano plots, tables and heatmaps. Aesthetics of each output are highly customizable. biocViews: DifferentialExpression, GeneExpression, ShinyApps, Spatial, Transcriptomics Author: Seung J. Kim [aut, cre] (ORCID: ), Marco Mura [aut, fnd] Maintainer: Seung J. Kim URL: https://github.com/kimsjune/shinyDSP, http://joonkim.ca/shinyDSP/ VignetteBuilder: knitr BugReports: https://github.com/kimsjune/shinyDSP/issues git_url: https://git.bioconductor.org/packages/shinyDSP git_branch: devel git_last_commit: cdbba28 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/shinyDSP_1.3.0.tar.gz vignettes: vignettes/shinyDSP/inst/doc/shinyDSP_secondary.html, vignettes/shinyDSP/inst/doc/shinyDSP.html vignetteTitles: shinyDSP_secondary, shinyDSP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/shinyDSP/inst/doc/shinyDSP_secondary.R, vignettes/shinyDSP/inst/doc/shinyDSP.R dependencyCount: 258 Package: ShortRead Version: 1.69.4 Depends: BiocGenerics (>= 0.23.3), BiocParallel, Biostrings (>= 2.47.6), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6) Imports: Biobase, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Seqinfo, GenomicRanges (>= 1.31.8), pwalign, hwriter, methods, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi, knitr License: Artistic-2.0 MD5sum: e6e8f826bfd39c9f39ade75519ccdf17 NeedsCompilation: yes Title: FASTQ input and manipulation Description: This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. biocViews: DataImport, Sequencing, QualityControl Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Michael Lawrence [ctb], Simon Anders [ctb], Rohit Satyam [ctb] (Converted Overview.Rnw vignette from Sweave to RMarkdown / HTML.), J Wokaty [ctb] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/ShortRead, https://github.com/Bioconductor/ShortRead, https://support.bioconductor.org/tag/ShortRead VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ShortRead/issues git_url: https://git.bioconductor.org/packages/ShortRead git_branch: devel git_last_commit: 43f8fc8 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/ShortRead_1.69.4.tar.gz vignettes: vignettes/ShortRead/inst/doc/Overview.html vignetteTitles: An introduction to ShortRead hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ShortRead/inst/doc/Overview.R dependsOnMe: chipseq, EDASeq, esATAC, OTUbase, Rqc, segmentSeq, systemPipeR, EatonEtAlChIPseq, NestLink, sequencing importsMe: amplican, basecallQC, BEAT, CellBarcode, chipseq, ChIPseqR, ChIPsim, CircSeqAlignTk, dada2, easyRNASeq, FastqCleaner, FLAMES, fraq, GOTHiC, icetea, IONiseR, nucleR, posDemux, QuasR, R453Plus1Toolbox, RSVSim, scruff, UMI4Cats, seqpac, DBTC, genBaRcode, rsahmi, tidyGenR suggestsMe: BiocParallel, CSAR, DspikeIn, GenomicAlignments, Rsamtools, S4Vectors, HiCDataLymphoblast, systemPipeRdata, yeastRNASeq, demulticoder, inDAGO dependencyCount: 53 Package: SIAMCAT Version: 2.15.0 Depends: R (>= 4.2.0), mlr3, phyloseq Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase, gridExtra, LiblineaR, matrixStats, methods, pROC, PRROC, RColorBrewer, scales, stats, stringr, utils, infotheo, progress, corrplot, lmerTest, mlr3learners, mlr3tuning, paradox, lgr Suggests: BiocStyle, testthat, knitr, rmarkdown, tidyverse, ggpubr License: GPL-3 MD5sum: 4779efdbbed3c4a793a2c94b5560aaa5 NeedsCompilation: no Title: Statistical Inference of Associations between Microbial Communities And host phenoTypes Description: Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots). biocViews: ImmunoOncology, Metagenomics, Classification, Microbiome, Sequencing, Preprocessing, Clustering, FeatureExtraction, GeneticVariability, MultipleComparison,Regression Author: Konrad Zych [aut] (ORCID: ), Jakob Wirbel [aut, cre] (ORCID: ), Georg Zeller [aut] (ORCID: ), Morgan Essex [ctb], Nicolai Karcher [ctb], Kersten Breuer [ctb] Maintainer: Jakob Wirbel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIAMCAT git_branch: devel git_last_commit: 19b3733 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SIAMCAT_2.15.0.tar.gz vignettes: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.html vignetteTitles: SIAMCAT confounder example, SIAMCAT holdout testing, SIAMCAT meta-analysis, SIAMCAT ML pitfalls, SIAMCAT input, SIAMCAT basic vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.R dependencyCount: 112 Package: SICtools Version: 1.41.0 Depends: R (>= 3.0.0), methods, Rsamtools (>= 1.18.1), doParallel (>= 1.0.8), Biostrings (>= 2.32.1), stringr (>= 0.6.2), matrixStats (>= 0.10.0), plyr (>= 1.8.3), GenomicRanges (>= 1.22.4), IRanges (>= 2.4.8) Suggests: knitr, RUnit, BiocGenerics License: GPL (>=2) MD5sum: 4355869ee03f60488159ea73419bf251 NeedsCompilation: yes Title: Find SNV/Indel differences between two bam files with near relationship Description: This package is to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison thourgh each base position across the genome region of interest. The difference is inferred by fisher test and euclidean distance, the input of which is the base count (A,T,G,C) in a given position and read counts for indels that span no less than 2bp on both sides of indel region. biocViews: Alignment, Sequencing, Coverage, SequenceMatching, QualityControl, DataImport, Software, SNP, VariantDetection Author: Xiaobin Xing, Wu Wei Maintainer: Xiaobin Xing VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SICtools git_branch: devel git_last_commit: d34246d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SICtools_1.41.0.tar.gz vignettes: vignettes/SICtools/inst/doc/SICtools.pdf vignetteTitles: Using SICtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SICtools/inst/doc/SICtools.R dependencyCount: 43 Package: SigCheck Version: 2.43.0 Depends: R (>= 4.0.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: bcd1c8d2cc93c9347dd007b8bfddfa17 NeedsCompilation: no Title: Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata Description: While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata. biocViews: GeneExpression, Classification, GeneSetEnrichment Author: Rory Stark and Justin Norden Maintainer: Rory Stark git_url: https://git.bioconductor.org/packages/SigCheck git_branch: devel git_last_commit: 57c2e1c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SigCheck_2.43.0.tar.gz vignettes: vignettes/SigCheck/inst/doc/SigCheck.pdf vignetteTitles: Checking gene expression signatures against random and known signatures with SigCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigCheck/inst/doc/SigCheck.R dependencyCount: 131 Package: sigFeature Version: 1.29.0 Depends: R (>= 3.5.0) Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL (>= 2) MD5sum: 09d0a81a5fc8c159b5779a85e7798b36 NeedsCompilation: no Title: sigFeature: Significant feature selection using SVM-RFE & t-statistic Description: This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy. biocViews: FeatureExtraction, GeneExpression, Microarray, Transcription, mRNAMicroarray, GenePrediction, Normalization, Classification, SupportVectorMachine Author: Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb] Maintainer: Pijush Das Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sigFeature git_branch: devel git_last_commit: 0877319 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sigFeature_1.29.0.tar.gz vignettes: vignettes/sigFeature/inst/doc/vignettes.html vignetteTitles: sigFeature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigFeature/inst/doc/vignettes.R dependencyCount: 65 Package: siggenes Version: 1.85.0 Depends: Biobase, multtest, splines, methods Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5) Suggests: affy, annotate, genefilter, KernSmooth License: LGPL (>= 2) MD5sum: 99762cb34e56e86f9e88afc8dc6ad461 NeedsCompilation: no Title: Multiple Testing using SAM and Efron's Empirical Bayes Approaches Description: Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). biocViews: MultipleComparison, Microarray, GeneExpression, SNP, ExonArray, DifferentialExpression Author: Holger Schwender Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/siggenes git_branch: devel git_last_commit: d3b5d6f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/siggenes_1.85.0.tar.gz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf, vignettes/siggenes/inst/doc/siggenesRnews.pdf, vignettes/siggenes/inst/doc/identify.sam.html, vignettes/siggenes/inst/doc/plot.ebam.html, vignettes/siggenes/inst/doc/plot.finda0.html, vignettes/siggenes/inst/doc/plot.sam.html, vignettes/siggenes/inst/doc/print.ebam.html, vignettes/siggenes/inst/doc/print.finda0.html, vignettes/siggenes/inst/doc/print.sam.html, vignettes/siggenes/inst/doc/summary.ebam.html, vignettes/siggenes/inst/doc/summary.sam.html vignetteTitles: siggenes Manual, siggenesRnews.pdf, identify.sam.html, plot.ebam.html, plot.finda0.html, plot.sam.html, print.ebam.html, print.finda0.html, print.sam.html, summary.ebam.html, summary.sam.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/siggenes/inst/doc/siggenes.R dependsOnMe: KCsmart importsMe: asuri, minfi, trio, XDE, DeSousa2013, NPFD suggestsMe: logicFS dependencyCount: 17 Package: sights Version: 1.37.0 Depends: R(>= 3.3) Imports: MASS(>= 7.3), qvalue(>= 2.2), ggplot2(>= 2.0), reshape2(>= 1.4), lattice(>= 0.2), stats(>= 3.3) Suggests: testthat, knitr, rmarkdown, ggthemes, gridExtra, xlsx License: GPL-3 | file LICENSE MD5sum: 09c8cac864c02d782885831be824f481 NeedsCompilation: no Title: Statistics and dIagnostic Graphs for HTS Description: SIGHTS is a suite of normalization methods, statistical tests, and diagnostic graphical tools for high throughput screening (HTS) assays. HTS assays use microtitre plates to screen large libraries of compounds for their biological, chemical, or biochemical activity. biocViews: ImmunoOncology, CellBasedAssays, MicrotitrePlateAssay, Normalization, MultipleComparison, Preprocessing, QualityControl, BatchEffect, Visualization Author: Elika Garg [aut, cre], Carl Murie [aut], Heydar Ensha [ctb], Robert Nadon [aut] Maintainer: Elika Garg URL: https://eg-r.github.io/sights/ VignetteBuilder: knitr BugReports: https://github.com/eg-r/sights/issues git_url: https://git.bioconductor.org/packages/sights git_branch: devel git_last_commit: 36d6919 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sights_1.37.0.tar.gz vignettes: vignettes/sights/inst/doc/sights.html vignetteTitles: Using **SIGHTS** R-package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sights/inst/doc/sights.R dependencyCount: 34 Package: signeR Version: 2.13.0 Depends: R (>= 4.1.0), NMF Imports: BiocGenerics, Biostrings, class, grDevices, GenomeInfoDb, GenomicRanges, IRanges, nloptr, methods, stats, utils, PMCMRplus, parallel, pvclust, ppclust, clue, survival, maxstat, future, VGAM, MASS, kknn, glmnet, e1071, randomForest, ada, future.apply, ggplot2, pROC, pheatmap, RColorBrewer, listenv, reshape2, scales, survminer, dplyr, ggpubr, cowplot, tibble, readr, shiny, shinydashboard, shinycssloaders, shinyWidgets, bsplus, DT, magrittr, tidyr, BiocFileCache, proxy, rtracklayer, BSgenome, broom, VariantAnnotation LinkingTo: Rcpp, RcppArmadillo (>= 0.7.100) Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, rmarkdown License: GPL-3 MD5sum: a914dc85c381fcf3737b2b4be70d4af1 NeedsCompilation: yes Title: Empirical Bayesian approach to mutational signature discovery Description: The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variation (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided. biocViews: GenomicVariation, SomaticMutation, StatisticalMethod, Visualization Author: Rafael Rosales, Rodrigo Drummond, Renan Valieris, Alexandre Defelicibus, Israel Tojal da Silva Maintainer: Renan Valieris URL: https://github.com/TojalLab/signeR SystemRequirements: C++14 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/signeR git_branch: devel git_last_commit: 54b8b4d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/signeR_2.13.0.tar.gz vignettes: vignettes/signeR/inst/doc/signeR-vignette.html, vignettes/signeR/inst/doc/signeRFlow.html vignetteTitles: signeR, signeRFlow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R, vignettes/signeR/inst/doc/signeRFlow.R dependencyCount: 241 Package: SigsPack Version: 1.25.0 Depends: R (>= 3.6) Imports: quadprog (>= 1.5-5), methods, Biobase, BSgenome (>= 1.46.0), VariantAnnotation (>= 1.24.5), Biostrings, GenomeInfoDb, GenomicRanges, rtracklayer, SummarizedExperiment, graphics, stats, utils Suggests: IRanges, BSgenome.Hsapiens.UCSC.hg19, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: c72d84e0da6d3e55933c8bc92883a124 NeedsCompilation: no Title: Mutational Signature Estimation for Single Samples Description: Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues. biocViews: SomaticMutation, SNP, VariantAnnotation, BiomedicalInformatics, DNASeq Author: Franziska Schumann Maintainer: Franziska Schumann URL: https://github.com/bihealth/SigsPack VignetteBuilder: knitr BugReports: https://github.com/bihealth/SigsPack/issues git_url: https://git.bioconductor.org/packages/SigsPack git_branch: devel git_last_commit: 76511e2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SigsPack_1.25.0.tar.gz vignettes: vignettes/SigsPack/inst/doc/SigsPack.html vignetteTitles: Introduction to SigsPack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigsPack/inst/doc/SigsPack.R dependencyCount: 80 Package: sigsquared Version: 1.43.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: c30b564d800e2e86996633cd7136a010 NeedsCompilation: no Title: Gene signature generation for functionally validated signaling pathways Description: By leveraging statistical properties (log-rank test for survival) of patient cohorts defined by binary thresholds, poor-prognosis patients are identified by the sigsquared package via optimization over a cost function reducing type I and II error. Author: UnJin Lee Maintainer: UnJin Lee git_url: https://git.bioconductor.org/packages/sigsquared git_branch: devel git_last_commit: 6b1120b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sigsquared_1.43.0.tar.gz vignettes: vignettes/sigsquared/inst/doc/sigsquared.pdf vignetteTitles: SigSquared hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigsquared/inst/doc/sigsquared.R dependencyCount: 13 Package: SIM Version: 1.81.0 Depends: R (>= 3.5), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) MD5sum: 6c446d1ade9f7a4fca04a5b4cff71e54 NeedsCompilation: yes Title: Integrated Analysis on two human genomic datasets Description: Finds associations between two human genomic datasets. biocViews: Microarray, Visualization Author: Renee X. de Menezes and Judith M. Boer Maintainer: Renee X. de Menezes git_url: https://git.bioconductor.org/packages/SIM git_branch: devel git_last_commit: ca66dc5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SIM_1.81.0.tar.gz vignettes: vignettes/SIM/inst/doc/SIM.pdf vignetteTitles: SIM vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIM/inst/doc/SIM.R dependencyCount: 56 Package: SIMAT Version: 1.43.0 Depends: R (>= 3.5.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: eeca847fb4397566cf04da35bac60e13 NeedsCompilation: no Title: GC-SIM-MS data processing and alaysis tool Description: This package provides a pipeline for analysis of GC-MS data acquired in selected ion monitoring (SIM) mode. The tool also provides a guidance in choosing appropriate fragments for the targets of interest by using an optimization algorithm. This is done by considering overlapping peaks from a provided library by the user. biocViews: ImmunoOncology, Software, Metabolomics, MassSpectrometry Author: M. R. Nezami Ranjbar Maintainer: M. R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html git_url: https://git.bioconductor.org/packages/SIMAT git_branch: devel git_last_commit: 042d9a6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SIMAT_1.43.0.tar.gz vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf vignetteTitles: SIMAT Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMAT/inst/doc/SIMAT-vignette.R dependencyCount: 40 Package: SIMD Version: 1.29.0 Depends: R (>= 3.5.0) Imports: edgeR, statmod, methylMnM, stats, utils Suggests: BiocStyle, knitr,rmarkdown License: GPL-3 MD5sum: 17c6c1aa504473cebe9dde204e8b1a9b NeedsCompilation: yes Title: Statistical Inferences with MeDIP-seq Data (SIMD) to infer the methylation level for each CpG site Description: This package provides a inferential analysis method for detecting differentially expressed CpG sites in MeDIP-seq data. It uses statistical framework and EM algorithm, to identify differentially expressed CpG sites. The methods on this package are described in the article 'Methylation-level Inferences and Detection of Differential Methylation with Medip-seq Data' by Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang and Xiyan Yang (2018, pending publication). biocViews: ImmunoOncology, DifferentialMethylation,SingleCell, DifferentialExpression Author: Yan Zhou Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIMD git_branch: devel git_last_commit: cf6185c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SIMD_1.29.0.tar.gz vignettes: vignettes/SIMD/inst/doc/SIMD.html vignetteTitles: SIMD Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMD/inst/doc/SIMD.R dependencyCount: 12 Package: SimFFPE Version: 1.23.0 Depends: Biostrings Imports: dplyr, foreach, doParallel, truncnorm, GenomicRanges, IRanges, Rsamtools, parallel, graphics, stats, utils, methods Suggests: BiocStyle License: LGPL-3 MD5sum: 84406d2db6e4e5cb26b0c6adf37b9c22 NeedsCompilation: no Title: NGS Read Simulator for FFPE Tissue Description: The NGS (Next-Generation Sequencing) reads from FFPE (Formalin-Fixed Paraffin-Embedded) samples contain numerous artifact chimeric reads (ACRS), which can lead to false positive structural variant calls. These ACRs are derived from the combination of two single-stranded DNA (ss-DNA) fragments with short reverse complementary regions (SRCRs). This package simulates these artifact chimeric reads as well as normal reads for FFPE samples on the whole genome / several chromosomes / large regions. biocViews: Sequencing, Alignment, MultipleComparison, SequenceMatching, DataImport Author: Lanying Wei [aut, cre] (ORCID: ) Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/SimFFPE git_branch: devel git_last_commit: daeddda git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SimFFPE_1.23.0.tar.gz vignettes: vignettes/SimFFPE/inst/doc/SimFFPE.pdf vignetteTitles: An introduction to SimFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimFFPE/inst/doc/SimFFPE.R dependencyCount: 47 Package: similaRpeak Version: 1.43.0 Depends: R6 (>= 2.0) Imports: stats Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments, rtracklayer, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 5a1d78a0d6b85301f0134a24a6d8ab9a NeedsCompilation: no Title: Metrics to estimate a level of similarity between two ChIP-Seq profiles Description: This package calculates metrics which quantify the level of similarity between ChIP-Seq profiles. More specifically, the package implements six pseudometrics specialized in pattern similarity detection in ChIP-Seq profiles. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, DifferentialExpression Author: Astrid Deschênes [cre, aut], Elsa Bernatchez [aut], Charles Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb [aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/similaRpeak VignetteBuilder: knitr BugReports: https://github.com/adeschen/similaRpeak/issues git_url: https://git.bioconductor.org/packages/similaRpeak git_branch: devel git_last_commit: 5ee6d71 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/similaRpeak_1.43.0.tar.gz vignettes: vignettes/similaRpeak/inst/doc/similaRpeak.html vignetteTitles: Similarity between two ChIP-Seq profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R dependencyCount: 2 Package: SIMLR Version: 1.37.1 Depends: R (>= 4.1.0), Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra LinkingTo: Rcpp Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph License: file LICENSE MD5sum: 1ed3d7ae5bbf68474c2cef1a61828746 NeedsCompilation: yes Title: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR) Description: Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. biocViews: ImmunoOncology, Clustering, GeneExpression, Sequencing, SingleCell Author: Daniele Ramazzotti [aut] (ORCID: ), Bo Wang [aut], Luca De Sano [cre, aut] (ORCID: ), Serafim Batzoglou [ctb] Maintainer: Luca De Sano URL: https://github.com/BatzoglouLabSU/SIMLR VignetteBuilder: knitr BugReports: https://github.com/BatzoglouLabSU/SIMLR git_url: https://git.bioconductor.org/packages/SIMLR git_branch: devel git_last_commit: b7cc0ad git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/SIMLR_1.37.1.tar.gz vignettes: vignettes/SIMLR/inst/doc/v1_introduction.html, vignettes/SIMLR/inst/doc/v2_running_SIMLR.html vignetteTitles: Introduction, Running SIMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SIMLR/inst/doc/v1_introduction.R, vignettes/SIMLR/inst/doc/v2_running_SIMLR.R dependencyCount: 14 Package: simona Version: 1.9.1 Depends: R (>= 4.1.0) Imports: methods, Rcpp, matrixStats, GetoptLong, grid, GlobalOptions, igraph, Polychrome, S4Vectors, xml2 (>= 1.3.3), circlize, ComplexHeatmap, grDevices, stats, utils, shiny, fastmatch LinkingTo: Rcpp Suggests: knitr, testthat, BiocManager, GO.db, org.Hs.eg.db, proxyC, AnnotationDbi, Matrix, DiagrammeR, ragg, png, InteractiveComplexHeatmap, UniProtKeywords, simplifyEnrichment, AnnotationHub, jsonlite License: MIT + file LICENSE MD5sum: 954785b998e5569842204ad3bc38e110 NeedsCompilation: yes Title: Semantic Similarity on Bio-Ontologies Description: This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis. biocViews: Software, Annotation, GO, BiomedicalInformatics Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/simona SystemRequirements: Perl, Java VignetteBuilder: knitr BugReports: https://github.com/jokergoo/simona/issues git_url: https://git.bioconductor.org/packages/simona git_branch: devel git_last_commit: ed86d29 git_last_commit_date: 2026-01-30 Date/Publication: 2026-04-20 source.ver: src/contrib/simona_1.9.1.tar.gz vignettes: vignettes/simona/inst/doc/simona.html vignetteTitles: The simona package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: GeDi, simplifyEnrichment dependencyCount: 70 Package: simPIC Version: 1.7.0 Depends: R (>= 4.5.0), SingleCellExperiment Imports: BiocGenerics, checkmate (>= 2.0.0), fitdistrplus, matrixStats, actuar, Matrix, stats, SummarizedExperiment, rlang, S4Vectors, methods, scales, scuttle, edgeR, withr Suggests: ggplot2 (>= 3.4.0), knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), scater, scran, magick License: GPL-3 MD5sum: 1eff204bfe85224cc161ca954452cb40 NeedsCompilation: no Title: Flexible simulation of paired-insertion counts for single-cell ATAC-sequencing data Description: simPIC is a package for simulating single-cell ATAC-seq count data. It provides a user-friendly, well documented interface for data simulation. Functions are provided for parameter estimation, realistic scATAC-seq data simulation, and comparing real and simulated datasets. biocViews: SingleCell, ATACSeq, Software, Sequencing, ImmunoOncology, DataImport Author: Sagrika Chugh [aut, cre] (ORCID: ), Heejung Shim [aut], Davis McCarthy [aut] Maintainer: Sagrika Chugh URL: https://github.com/sagrikachugh/simPIC VignetteBuilder: knitr BugReports: https://github.com/sagrikachugh/simPIC/issues git_url: https://git.bioconductor.org/packages/simPIC git_branch: devel git_last_commit: 8f16458 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/simPIC_1.7.0.tar.gz vignettes: vignettes/simPIC/inst/doc/vignette.html vignetteTitles: simPIC: simulating single-cell ATAC-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simPIC/inst/doc/vignette.R dependencyCount: 63 Package: simplifyEnrichment Version: 2.5.1 Depends: R (>= 4.1.0) Imports: simona, ComplexHeatmap (>= 2.7.4), grid, circlize, GetoptLong, digest, tm, GO.db, AnnotationDbi, slam, methods, clue, grDevices, stats, utils, cluster (>= 1.14.2), colorspace, GlobalOptions (>= 0.1.0) Suggests: knitr, ggplot2, cowplot, mclust, apcluster, MCL, dbscan, igraph, gridExtra, dynamicTreeCut, testthat, gridGraphics, flexclust, BiocManager, InteractiveComplexHeatmap (>= 0.99.11), shiny, shinydashboard, cola, hu6800.db, rmarkdown, genefilter, gridtext, fpc License: MIT + file LICENSE MD5sum: 70c4da6354a8686383c61444155b5611 NeedsCompilation: no Title: Simplify Functional Enrichment Results Description: A new clustering algorithm, "binary cut", for clustering similarity matrices of functional terms is implemeted in this package. It also provides functions for visualizing, summarizing and comparing the clusterings. biocViews: Software, Visualization, GO, Clustering, GeneSetEnrichment Author: Zuguang Gu [aut, cre] (ORCID: ) Maintainer: Zuguang Gu URL: https://github.com/jokergoo/simplifyEnrichment, https://simplifyEnrichment.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/simplifyEnrichment git_branch: devel git_last_commit: 9ef3f70 git_last_commit_date: 2026-01-30 Date/Publication: 2026-04-20 source.ver: src/contrib/simplifyEnrichment_2.5.1.tar.gz vignettes: vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.html vignetteTitles: The simplifyEnrichment package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: cola, InteractiveComplexHeatmap, simona, scITD dependencyCount: 92 Package: SingleCellAlleleExperiment Version: 1.7.0 Depends: R (>= 4.4.0), SingleCellExperiment Imports: SummarizedExperiment, BiocParallel, DelayedArray, methods, utils, Matrix, S4Vectors, stats Suggests: scaeData, knitr, rmarkdown, BiocStyle, scran, scater, scuttle, ggplot2, patchwork, org.Hs.eg.db, AnnotationDbi, DropletUtils, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: e9086f7ef7748e8b552dd428e446cd25 NeedsCompilation: no Title: S4 Class for Single Cell Data with Allele and Functional Levels for Immune Genes Description: Defines a S4 class that is based on SingleCellExperiment. In addition to the usual gene layer the object can also store data for immune genes such as HLAs, Igs and KIRs at allele and functional level. The package is part of a workflow named single-cell ImmunoGenomic Diversity (scIGD), that firstly incorporates allele-aware quantification data for immune genes. This new data can then be used with the here implemented data structure and functionalities for further data handling and data analysis. biocViews: DataRepresentation, Infrastructure, SingleCell, Transcriptomics, GeneExpression, Genetics, ImmunoOncology, DataImport Author: Jonas Schuck [aut, cre] (ORCID: ), Ahmad Al Ajami [aut] (ORCID: ), Federico Marini [aut] (ORCID: ), Katharina Imkeller [aut] (ORCID: ) Maintainer: Jonas Schuck URL: https://github.com/AGImkeller/SingleCellAlleleExperiment VignetteBuilder: knitr BugReports: https://github.com/AGImkeller/SingleCellAlleleExperiment/issues git_url: https://git.bioconductor.org/packages/SingleCellAlleleExperiment git_branch: devel git_last_commit: 51ef84b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SingleCellAlleleExperiment_1.7.0.tar.gz vignettes: vignettes/SingleCellAlleleExperiment/inst/doc/scae_intro.html vignetteTitles: An introduction to the SingleCellAlleleExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SingleCellAlleleExperiment/inst/doc/scae_intro.R suggestsMe: scaeData dependencyCount: 36 Package: SingleCellExperiment Version: 1.33.2 Depends: SummarizedExperiment Imports: methods, utils, stats, S4Vectors, BiocGenerics, GenomicRanges, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, scRNAseq (>= 2.9.1), Rtsne License: GPL-3 MD5sum: ebe3dd0485103369d5010211a56fdb0a NeedsCompilation: no Title: S4 Classes for Single Cell Data Description: Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Aaron Lun [aut, cph], Davide Risso [aut, cre, cph], Keegan Korthauer [ctb], Kevin Rue-Albrecht [ctb], Luke Zappia [ctb] (ORCID: , github: lazappi) Maintainer: Davide Risso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellExperiment git_branch: devel git_last_commit: f781efa git_last_commit_date: 2026-03-24 Date/Publication: 2026-04-20 source.ver: src/contrib/SingleCellExperiment_1.33.2.tar.gz vignettes: vignettes/SingleCellExperiment/inst/doc/apply.html, vignettes/SingleCellExperiment/inst/doc/devel.html, vignettes/SingleCellExperiment/inst/doc/intro.html vignetteTitles: 2. Applying over a SingleCellExperiment object, 3. Developing around the SingleCellExperiment class, 1. An introduction to the SingleCellExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellExperiment/inst/doc/apply.R, vignettes/SingleCellExperiment/inst/doc/devel.R, vignettes/SingleCellExperiment/inst/doc/intro.R dependsOnMe: alabaster.sce, BASiCS, batchelor, BayesSpace, CATALYST, celda, CellBench, CelliD, CellTrails, CHETAH, chevreulPlot, chevreulProcess, chevreulShiny, clusterExperiment, cydar, cytomapper, DeeDeeExperiment, demuxSNP, dreamlet, DropletUtils, epiregulon, epiregulon.extra, ExperimentSubset, iSEE, iSEEhub, iSEEindex, LoomExperiment, MAST, mia, mumosa, omicsGMF, POWSC, scAnnotatR, scater, scDblFinder, scGPS, schex, scPipe, scran, scuttle, scviR, simPIC, SingleCellAlleleExperiment, singleCellTK, SiPSiC, SpatialExperiment, splatter, switchde, TENxIO, tidySingleCellExperiment, TrajectoryUtils, TreeSummarizedExperiment, tricycle, TSCAN, zinbwave, HCAData, imcdatasets, MouseAgingData, MouseGastrulationData, MouseThymusAgeing, muscData, scATAC.Explorer, scMultiome, scRNAseq, STexampleData, TENxBrainData, TENxPBMCData, TMExplorer, WeberDivechaLCdata, OSCA.intro, DIscBIO, imcExperiment, karyotapR importsMe: ADImpute, aggregateBioVar, airpart, alabaster.sfe, anansi, anglemania, APL, ASURAT, Banksy, BASiCStan, bayNorm, blase, BUSseq, CARDspa, CatsCradle, ccfindR, ccImpute, CDI, CellMentor, CellMixS, Cepo, ChromSCape, CiteFuse, ClusterFoldSimilarity, ClusterGVis, clustifyr, clustSIGNAL, CoGAPS, concordexR, condiments, Coralysis, corral, COTAN, crumblr, CTexploreR, CuratedAtlasQueryR, cytofQC, cytoviewer, dandelionR, decontX, DeconvoBuddies, destiny, DifferentialRegulation, Dino, distinct, dittoSeq, DOtools, escheR, EWCE, FEAST, fishpond, FLAMES, ggsc, ggspavis, glmGamPoi, GloScope, GSVA, HIPPO, Ibex, ILoReg, imageFeatureTCGA, imcRtools, immApex, infercnv, iSEEfier, iSEEtree, iSEEu, lemur, lisaClust, looking4clusters, mastR, mbkmeans, MEB, miaDash, miaTime, miaViz, miloR, miQC, mist, MPAC, MuData, muscat, Nebulosa, netSmooth, NewWave, nnSVG, partCNV, peco, pipeComp, projectR, raer, RCSL, RegionalST, RUCova, SanityR, SC3, scafari, SCArray, scBFA, scCB2, sccomp, scDD, scDDboost, scDesign3, scDiagnostics, scDotPlot, scds, scGraphVerse, scHOT, scider, scLang, scmap, scMerge, scMET, SCnorm, scone, scp, scQTLtools, scReClassify, scRepertoire, scRNAseqApp, scruff, scry, scTensor, scTGIF, scTreeViz, SETA, shinyDSP, singIST, slalom, slingshot, sosta, Spaniel, SpaNorm, SpatialExperimentIO, SpatialFeatureExperiment, spatialHeatmap, speckle, spicyR, SplineDV, SpNeigh, SPOTlight, SpotSweeper, SPsimSeq, standR, StatescopeR, Statial, stPipe, SVP, tidySpatialExperiment, tpSVG, tradeSeq, treekoR, UCell, VAExprs, VDJdive, velociraptor, VisiumIO, visiumStitched, Voyager, waddR, xCell2, XeniumIO, xenLite, zellkonverter, EMTscoreData, HCATonsilData, MerfishData, raerdata, scpdata, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, mikropml, mixhvg, nebula, scROSHI suggestsMe: ANCOMBC, anndataR, cellxgenedp, CTdata, DEsingle, dominoSignal, escape, FuseSOM, genomicInstability, GEOquery, HDF5Array, HVP, InteractiveComplexHeatmap, jazzPanda, M3Drop, MOFA2, MOSim, ontoProc, phenopath, PIUMA, progeny, QFeatures, RankMap, ReactomeGSA, scBubbletree, scConform, scFeatureFilter, scLANE, scPassport, scPCA, scrapper, scRecover, scToppR, scTypeEval, Seqtometry, SingleR, sketchR, SummarizedExperiment, SuperCellCyto, TREG, updateObject, dorothea, DuoClustering2018, GSE103322, microbiomeDataSets, TabulaMurisData, simpleSingleCell, Canek, clustree, CytoSimplex, dyngen, futurize, harmony, RaceID, radEmu, rliger, SCdeconR, Seurat, singleCellHaystack, SuperCell, SVG, tidydr dependencyCount: 25 Package: SingleCellSignalR Version: 2.1.2 Depends: R (>= 4.5) Imports: stats, utils, methods, ggplot2, matrixTests, matrixStats, foreach, BulkSignalR, grid, ComplexHeatmap, circlize Suggests: knitr, markdown, rmarkdown License: CeCILL | file LICENSE MD5sum: 842258dcd7324403df455903dfcb684f NeedsCompilation: no Title: Cell Signalling Using Single-Cell RNA-seq or Proteomics Data Description: Inference of ligand-receptor (L-R) interactions from single-cell expression (transcriptomics/proteomics) data. SingleCellSignalR v2 inferences rely on the statistical model we introduced in the BulkSignalR package as well as the original SingleCellSignalR LR-score (both are available). SingleCellSignalR v2 can be regarded as a wrapper to BulkSignalR fundamental classes. This also enables v2 users to work with any species, whereas only Mus musculus & Homo sapiens were available before in SingleCellSignalR v1. biocViews: Network, RNASeq, Software, Proteomics, Transcriptomics, SingleCell, NetworkInference Author: Jacques Colinge [aut] (ORCID: ), Jean-Philippe Villemin [cre] (ORCID: ) Maintainer: Jean-Philippe Villemin URL: https://github.com/jcolinge/SingleCellSignalR VignetteBuilder: knitr BugReports: https://github.com/jcolinge/SingleCellSignalR/issues git_url: https://git.bioconductor.org/packages/SingleCellSignalR git_branch: devel git_last_commit: 2f421d2 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/SingleCellSignalR_2.1.2.tar.gz vignettes: vignettes/SingleCellSignalR/inst/doc/SingleCellSignalR-Main.html vignetteTitles: SingleCellSignalR-Main hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SingleCellSignalR/inst/doc/SingleCellSignalR-Main.R suggestsMe: tidySingleCellExperiment dependencyCount: 204 Package: SingleR Version: 2.13.5 Depends: SummarizedExperiment Imports: methods, Matrix, BiocGenerics, S4Vectors, DelayedArray, stats, utils, Rcpp, beachmat (>= 2.27.3) LinkingTo: Rcpp, beachmat, assorthead (>= 1.3.5) Suggests: testthat, knitr, rmarkdown, BiocStyle, BiocParallel, SingleCellExperiment, scrapper (>= 1.5.16), scRNAseq, ggplot2, pheatmap, grDevices, gridExtra, viridis, celldex License: GPL-3 MD5sum: cc8bb3ef83b31d25bdedfa85597a11a2 NeedsCompilation: yes Title: Reference-Based Single-Cell RNA-Seq Annotation Description: Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. biocViews: Software, SingleCell, GeneExpression, Transcriptomics, Classification, Clustering, Annotation Author: Dvir Aran [aut, cph], Aaron Lun [ctb, cre], Daniel Bunis [ctb], Jared Andrews [ctb], Friederike Dündar [ctb] Maintainer: Aaron Lun URL: https://github.com/SingleR-inc/SingleR SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/SingleR-inc/SingleR/issues git_url: https://git.bioconductor.org/packages/SingleR git_branch: devel git_last_commit: 7ace1ab git_last_commit_date: 2026-04-08 Date/Publication: 2026-04-20 source.ver: src/contrib/SingleR_2.13.5.tar.gz vignettes: vignettes/SingleR/inst/doc/SingleR.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleR/inst/doc/SingleR.R dependsOnMe: OSCA.basic, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: CellMentor, scTypeEval, singleCellTK, OSTA suggestsMe: Coralysis, scDiagnostics, scGraphVerse, sketchR, tidySingleCellExperiment dependencyCount: 28 Package: singscore Version: 1.31.0 Depends: R (>= 3.6) Imports: methods, stats, graphics, ggplot2, grDevices, ggrepel, GSEABase, plotly, tidyr, plyr, magrittr, reshape, edgeR, RColorBrewer, Biobase, BiocParallel, SummarizedExperiment, matrixStats, reshape2, S4Vectors Suggests: pkgdown, BiocStyle, hexbin, knitr, rmarkdown, testthat, covr License: GPL-3 MD5sum: a0447e7d9c8a61a4ba5adf1763bbc1c5 NeedsCompilation: no Title: Rank-based single-sample gene set scoring method Description: A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Dharmesh D. Bhuva [aut] (ORCID: ), Ruqian Lyu [aut, ctb], Momeneh Foroutan [aut, ctb] (ORCID: ), Malvika Kharbanda [aut, cre] (ORCID: ) Maintainer: Malvika Kharbanda URL: https://davislaboratory.github.io/singscore VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/singscore/issues git_url: https://git.bioconductor.org/packages/singscore git_branch: devel git_last_commit: c86aa46 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/singscore_1.31.0.tar.gz vignettes: vignettes/singscore/inst/doc/singscore.html vignetteTitles: Single sample scoring hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/singscore/inst/doc/singscore.R importsMe: GSABenchmark, pathMED, TBSignatureProfiler, xCell2, clustermole suggestsMe: mastR, vissE, msigdb dependencyCount: 120 Package: SiPSiC Version: 1.11.0 Depends: Matrix, SingleCellExperiment Suggests: knitr, rmarkdown, BiocStyle License: file LICENSE MD5sum: 751a23d68feb5ed46abf58088313f245 NeedsCompilation: no Title: Calculate Pathway Scores for Each Cell in scRNA-Seq Data Description: Infer biological pathway activity of cells from single-cell RNA-sequencing data by calculating a pathway score for each cell (pathway genes are specified by the user). It is recommended to have the data in Transcripts-Per-Million (TPM) or Counts-Per-Million (CPM) units for best results. Scores may change when adding cells to or removing cells off the data. SiPSiC stands for Single Pathway analysis in Single Cells. biocViews: Software, DifferentialExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, Transcriptomics, RNASeq, SingleCell, Transcription, Sequencing, ImmunoOncology, DataImport Author: Daniel Davis [aut, cre] (ORCID: ), Yotam Drier [aut] Maintainer: Daniel Davis URL: https://www.genome.org/cgi/doi/10.1101/gr.278431.123 VignetteBuilder: knitr BugReports: https://github.com/DanielDavis12/SiPSiC/issues git_url: https://git.bioconductor.org/packages/SiPSiC git_branch: devel git_last_commit: 45e6d3f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SiPSiC_1.11.0.tar.gz vignettes: vignettes/SiPSiC/inst/doc/SiPSiC.html vignetteTitles: Infer Biological Pathway Activity from Single-Cell RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SiPSiC/inst/doc/SiPSiC.R importsMe: GSABenchmark dependencyCount: 26 Package: Site2Target Version: 1.3.0 Depends: R (>= 4.4) Imports: S4Vectors, stats, utils, BiocGenerics, GenomeInfoDb, MASS, IRanges, GenomicRanges Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-2 MD5sum: ec811eacb27102ef830a364490c09eb3 NeedsCompilation: no Title: An R package to associate peaks and target genes Description: Statistics implemented for both peak-wise and gene-wise associations. In peak-wise associations, the p-value of the target genes of a given set of peaks are calculated. Negative binomial or Poisson distributions can be used for modeling the unweighted peaks targets and log-nromal can be used to model the weighted peaks. In gene-wise associations a table consisting of a set of genes, mapped to specific peaks, is generated using the given rules. biocViews: Annotation, ChIPSeq, Software, Epigenetics, GeneExpression, GeneTarget Author: Peyman Zarrineh [cre, aut] (ORCID: ) Maintainer: Peyman Zarrineh VignetteBuilder: knitr BugReports: https://github.com/fls-bioinformatics-core/Site2Target/issues git_url: https://git.bioconductor.org/packages/Site2Target git_branch: devel git_last_commit: a1401da git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Site2Target_1.3.0.tar.gz vignettes: vignettes/Site2Target/inst/doc/Site2Target.html vignetteTitles: Site2Target hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Site2Target/inst/doc/Site2Target.R dependencyCount: 24 Package: sitePath Version: 1.27.0 Depends: R (>= 4.2) Imports: RColorBrewer, Rcpp, ape, aplot, ggplot2, ggrepel, ggtree, graphics, grDevices, gridExtra, methods, parallel, seqinr, stats, tidytree, utils LinkingTo: Rcpp Suggests: BiocStyle, devtools, knitr, magick, rmarkdown, testthat License: MIT + file LICENSE MD5sum: df009167c3ec28453717f4a307e2f16c NeedsCompilation: yes Title: Phylogeny-based sequence clustering with site polymorphism Description: Using site polymorphism is one of the ways to cluster DNA/protein sequences but it is possible for the sequences with the same polymorphism on a single site to be genetically distant. This package is aimed at clustering sequences using site polymorphism and their corresponding phylogenetic trees. By considering their location on the tree, only the structurally adjacent sequences will be clustered. However, the adjacent sequences may not necessarily have the same polymorphism. So a branch-and-bound like algorithm is used to minimize the entropy representing the purity of site polymorphism of each cluster. biocViews: Alignment, MultipleSequenceAlignment, Phylogenetics, SNP, Software Author: Chengyang Ji [aut, cre, cph] (ORCID: ), Hangyu Zhou [ths], Aiping Wu [ths] Maintainer: Chengyang Ji URL: https://wuaipinglab.github.io/sitePath/ VignetteBuilder: knitr BugReports: https://github.com/wuaipinglab/sitePath/issues git_url: https://git.bioconductor.org/packages/sitePath git_branch: devel git_last_commit: 26cac60 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sitePath_1.27.0.tar.gz vignettes: vignettes/sitePath/inst/doc/sitePath.html vignetteTitles: An introduction to sitePath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sitePath/inst/doc/sitePath.R dependencyCount: 88 Package: sizepower Version: 1.81.0 Depends: stats License: LGPL MD5sum: d476c70f24babf29c3c977d33fed3ebe NeedsCompilation: no Title: Sample Size and Power Calculation in Micorarray Studies Description: This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice. biocViews: Microarray Author: Weiliang Qiu and Mei-Ling Ting Lee and George Alex Whitmore Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/sizepower git_branch: devel git_last_commit: e4dd327 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sizepower_1.81.0.tar.gz vignettes: vignettes/sizepower/inst/doc/sizepower.pdf vignetteTitles: Sample Size and Power Calculation in Microarray Studies Using the \Rpackage{sizepower} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sizepower/inst/doc/sizepower.R dependencyCount: 1 Package: slalom Version: 1.33.0 Depends: R (>= 4.0) Imports: Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase, methods, rsvd, SingleCellExperiment, SummarizedExperiment, stats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: BiocStyle, knitr, rhdf5, rmarkdown, scater, testthat License: GPL-2 MD5sum: 3eb4634a21e795135639953862779b88 NeedsCompilation: yes Title: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data Description: slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. The method uses Bayesian factor analysis with a latent variable model to identify active pathways (selected by the user, e.g. KEGG pathways) that explain variation in a single-cell RNA-seq dataset. This an R/C++ implementation of the f-scLVM Python package. See the publication describing the method at https://doi.org/10.1186/s13059-017-1334-8. biocViews: ImmunoOncology, SingleCell, RNASeq, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, Reactome, KEGG Author: Florian Buettner [aut], Naruemon Pratanwanich [aut], Davis McCarthy [aut, cre], John Marioni [aut], Oliver Stegle [aut] Maintainer: Davis McCarthy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/slalom git_branch: devel git_last_commit: 42126fd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/slalom_1.33.0.tar.gz vignettes: vignettes/slalom/inst/doc/vignette.html vignetteTitles: Introduction to slalom hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slalom/inst/doc/vignette.R dependencyCount: 73 Package: slingshot Version: 2.19.0 Depends: R (>= 4.0), princurve (>= 2.0.4), stats, TrajectoryUtils Imports: graphics, grDevices, igraph, matrixStats, methods, S4Vectors, SingleCellExperiment, SummarizedExperiment Suggests: BiocGenerics, BiocStyle, clusterExperiment, DelayedMatrixStats, knitr, mclust, mgcv, RColorBrewer, rgl, rmarkdown, testthat, uwot, covr License: Artistic-2.0 MD5sum: 1004803ab4283a39d3a5541533010750 NeedsCompilation: no Title: Tools for ordering single-cell sequencing Description: Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction. biocViews: Clustering, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, Sequencing, SingleCell, Transcriptomics, Visualization Author: Kelly Street [aut, cre, cph], Davide Risso [aut], Diya Das [aut], Sandrine Dudoit [ths], Koen Van den Berge [ctb], Robrecht Cannoodt [ctb] (ORCID: , github: rcannood) Maintainer: Kelly Street VignetteBuilder: knitr BugReports: https://github.com/kstreet13/slingshot/issues git_url: https://git.bioconductor.org/packages/slingshot git_branch: devel git_last_commit: 800ceb7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/slingshot_2.19.0.tar.gz vignettes: vignettes/slingshot/inst/doc/conditionsVignette.html, vignettes/slingshot/inst/doc/vignette.html vignetteTitles: Differential Topology: Comparing Conditions along a Trajectory, Slingshot: Trajectory Inference for Single-Cell Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slingshot/inst/doc/conditionsVignette.R, vignettes/slingshot/inst/doc/vignette.R importsMe: condiments, scRNAseqApp, tradeSeq suggestsMe: blase, dandelionR, scLANE, RaceID dependencyCount: 38 Package: SLqPCR Version: 1.77.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: 97d63bf71af9437259cf06544281033f NeedsCompilation: no Title: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH Description: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH biocViews: MicrotitrePlateAssay, qPCR Author: Matthias Kohl Maintainer: Matthias Kohl git_url: https://git.bioconductor.org/packages/SLqPCR git_branch: devel git_last_commit: 1186231 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SLqPCR_1.77.0.tar.gz vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf vignetteTitles: SLqPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLqPCR/inst/doc/SLqPCR.R dependencyCount: 1 Package: SMAD Version: 1.27.6 Depends: R (>= 4.5.0), RcppAlgos Imports: magrittr (>= 1.5), dplyr, stats, tidyr, utils, data.table, Rcpp (>= 1.0.0) LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: b46bf5f577547808baa9e56c2dc0cfc1 NeedsCompilation: yes Title: Statistical Modelling of AP-MS Data (SMAD) Description: Assigning probability scores to protein interactions captured in affinity purification mass spectrometry (AP-MS) expriments to infer protein-protein interactions. The output would facilitate non-specific background removal as contaminants are commonly found in AP-MS data. biocViews: MassSpectrometry, Proteomics, Software, Network Author: Qingzhou Zhang [aut, cre] (ORCID: ) Maintainer: Qingzhou Zhang URL: https://github.com/zqzneptune/SMAD VignetteBuilder: knitr BugReports: https://github.com/zqzneptune/SMAD/issues git_url: https://git.bioconductor.org/packages/SMAD git_branch: devel git_last_commit: 8f18864 git_last_commit_date: 2026-04-19 Date/Publication: 2026-04-20 source.ver: src/contrib/SMAD_1.27.6.tar.gz vignettes: vignettes/SMAD/inst/doc/quickstart.html, vignettes/SMAD/inst/doc/scoring_functions.html vignetteTitles: 1. Introduction to SMAD, 2. Scoring Functions in SMAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SMAD/inst/doc/quickstart.R, vignettes/SMAD/inst/doc/scoring_functions.R dependencyCount: 31 Package: smartid Version: 1.7.2 Depends: R (>= 4.4) Imports: dplyr, ggplot2, graphics, Matrix, mclust, methods, mixtools, sparseMatrixStats, stats, SummarizedExperiment, tidyr, utils Suggests: BiocStyle, dbscan, ggpubr, knitr, rmarkdown, scater, splatter, testthat (>= 3.0.0), tibble, tidytext, UpSetR License: MIT + file LICENSE MD5sum: 9ce9cab6b3a3b94f089a7795c0dc961a NeedsCompilation: no Title: Scoring and Marker Selection Method Based on Modified TF-IDF Description: This package enables automated selection of group specific signature, especially for rare population. The package is developed for generating specifc lists of signature genes based on Term Frequency-Inverse Document Frequency (TF-IDF) modified methods. It can also be used as a new gene-set scoring method or data transformation method. Multiple visualization functions are implemented in this package. biocViews: Software, GeneExpression, Transcriptomics Author: Jinjin Chen [aut, cre] (ORCID: ) Maintainer: Jinjin Chen URL: https://davislaboratory.github.io/smartid VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/smartid/issues git_url: https://git.bioconductor.org/packages/smartid git_branch: devel git_last_commit: 9f60653 git_last_commit_date: 2026-03-12 Date/Publication: 2026-04-20 source.ver: src/contrib/smartid_1.7.2.tar.gz vignettes: vignettes/smartid/inst/doc/smartid_Demo.html vignetteTitles: smartid: Scoring and MARker selection method based on modified Tf-IDf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/smartid/inst/doc/smartid_Demo.R dependencyCount: 97 Package: SMITE Version: 1.39.0 Depends: R (>= 3.5), GenomicRanges Imports: scales, plyr, Hmisc, AnnotationDbi, org.Hs.eg.db, ggplot2, reactome.db, KEGGREST, BioNet, goseq, methods, IRanges, igraph, Biobase,tools, S4Vectors, geneLenDataBase, grDevices, graphics, stats, utils Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: ac5170f09f45e1072f48076f5ab4c16f NeedsCompilation: no Title: Significance-based Modules Integrating the Transcriptome and Epigenome Description: This package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules. biocViews: ImmunoOncology, DifferentialMethylation, DifferentialExpression, SystemsBiology, NetworkEnrichment,GenomeAnnotation,Network, Sequencing, RNASeq, Coverage Author: Neil Ari Wijetunga, Andrew Damon Johnston, John Murray Greally Maintainer: Neil Ari Wijetunga , Andrew Damon Johnston URL: https://github.com/GreallyLab/SMITE VignetteBuilder: knitr BugReports: https://github.com/GreallyLab/SMITE/issues git_url: https://git.bioconductor.org/packages/SMITE git_branch: devel git_last_commit: cf2f9ae git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SMITE_1.39.0.tar.gz vignettes: vignettes/SMITE/inst/doc/SMITE.pdf vignetteTitles: SMITE Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMITE/inst/doc/SMITE.R dependencyCount: 151 Package: smoothclust Version: 1.7.0 Depends: R (>= 4.4.0) Imports: SpatialExperiment, SummarizedExperiment, BiocNeighbors, Matrix, methods, utils Suggests: BiocStyle, knitr, STexampleData, scuttle, scran, scater, ggspavis, testthat License: MIT + file LICENSE MD5sum: 5143af48c4a318af26384f23176b1b19 NeedsCompilation: no Title: smoothclust Description: Method for identification of spatial domains and spatially-aware clustering in spatial transcriptomics data. The method generates spatial domains with smooth boundaries by smoothing gene expression profiles across neighboring spatial locations, followed by unsupervised clustering. Spatial domains consisting of consistent mixtures of cell types may then be further investigated by applying cell type compositional analyses or differential analyses. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Clustering Author: Lukas M. Weber [aut, cre] (ORCID: ) Maintainer: Lukas M. Weber URL: https://github.com/lmweber/smoothclust VignetteBuilder: knitr BugReports: https://github.com/lmweber/smoothclust/issues git_url: https://git.bioconductor.org/packages/smoothclust git_branch: devel git_last_commit: 1246c80 git_last_commit_date: 2025-10-31 Date/Publication: 2026-04-20 source.ver: src/contrib/smoothclust_1.7.0.tar.gz vignettes: vignettes/smoothclust/inst/doc/smoothclust.html vignetteTitles: Smoothclust Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/smoothclust/inst/doc/smoothclust.R dependencyCount: 69 Package: smoppix Version: 1.3.4 Depends: R (>= 4.5.0) Imports: spatstat.geom(>= 3.2.0),spatstat.random,methods,BiocParallel,SummarizedExperiment,SpatialExperiment,Rdpack,stats,utils,lmerTest,lme4,ggplot2,graphics,grDevices,Rcpp (>= 1.0.11),spatstat.model,openxlsx,Rfast,reformulas,mgcv LinkingTo: Rcpp Suggests: testthat,rmarkdown,knitr,DropletUtils,polyCub,RImageJROI,sp,ape,htmltools,funkycells,glmnet,doParallel License: GPL-2 MD5sum: cb7405563be7cedf5799c2999f453cb8 NeedsCompilation: yes Title: Analyze Single Molecule Spatial Omics Data Using the Probabilistic Index Description: Test for univariate and bivariate spatial patterns in spatial omics data with single-molecule resolution. The tests implemented allow for analysis of nested designs and are automatically calibrated to different biological specimens. Tests for aggregation, colocalization, gradients and vicinity to cell edge or centroid are provided. biocViews: Transcriptomics, Spatial, SingleCell Author: Stijn Hawinkel [cre, aut] (ORCID: ) Maintainer: Stijn Hawinkel URL: https://github.com/sthawinke/smoppix VignetteBuilder: knitr BugReports: https://github.com/sthawinke/smoppix/issues git_url: https://git.bioconductor.org/packages/smoppix git_branch: devel git_last_commit: dabed52 git_last_commit_date: 2026-02-26 Date/Publication: 2026-04-20 source.ver: src/contrib/smoppix_1.3.4.tar.gz vignettes: vignettes/smoppix/inst/doc/smoppixVignette.html vignetteTitles: Vignette of the smoppix package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/smoppix/inst/doc/smoppixVignette.R dependencyCount: 117 Package: SMTrackR Version: 0.99.6 Depends: R (>= 4.5) Imports: jsonlite, GenomicRanges, rtracklayer, stringr, BiocFileCache, S4Vectors Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 10bf2a690c62a4abdbbfcf6ceb3a031c NeedsCompilation: no Title: SMTrackR: a R/Bioconductor package for mapping protein binding at individual DNA molecules Description: The package uses exogenous enzyme imprinted information to map protein-DNA binding on individual sequenced DNA molecules. For example, GpC methyltransferase, CpG methyltransferase, and Adenine methyltransferases. Public datasets from such assays are compiled into tracks, and hosted at public servers like Galaxy for their seamless access by this package. biocViews: NucleosomePositioning, Visualization, GeneTarget, GenomeAssembly Author: Aashna Bansal [aut, ctb], Himani Barmola [aut, ctb], Shivam K Yadav [aut, ctb], Satyanarayan Rao [aut, cre] (ORCID: ) Maintainer: Satyanarayan Rao URL: https://www.raolab.in VignetteBuilder: knitr BugReports: https://www.raolab.in git_url: https://git.bioconductor.org/packages/SMTrackR git_branch: devel git_last_commit: 6533c78 git_last_commit_date: 2026-03-19 Date/Publication: 2026-04-20 source.ver: src/contrib/SMTrackR_0.99.6.tar.gz vignettes: vignettes/SMTrackR/inst/doc/reference_manual.html vignetteTitles: reference_manual.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SMTrackR/inst/doc/reference_manual.R dependencyCount: 87 Package: SNAGEE Version: 1.51.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: 73a4d6c6b31e5a41bc99f1bc834038ac NeedsCompilation: no Title: Signal-to-Noise applied to Gene Expression Experiments Description: Signal-to-Noise applied to Gene Expression Experiments. Signal-to-noise ratios can be used as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set. biocViews: Microarray, OneChannel, TwoChannel, QualityControl Author: David Venet Maintainer: David Venet URL: http://bioconductor.org/ git_url: https://git.bioconductor.org/packages/SNAGEE git_branch: devel git_last_commit: 8c01875 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SNAGEE_1.51.0.tar.gz vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf vignetteTitles: SNAGEE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNAGEE/inst/doc/SNAGEE.R suggestsMe: SNAGEEdata dependencyCount: 1 Package: snapcount Version: 1.23.0 Depends: R (>= 4.0.0) Imports: R6, httr, rlang, purrr, jsonlite, assertthat, data.table, Matrix, magrittr, methods, stringr, stats, IRanges, GenomicRanges, SummarizedExperiment Suggests: BiocManager, bit64, covr, knitcitations, knitr (>= 1.6), devtools, BiocStyle (>= 2.5.19), rmarkdown (>= 0.9.5), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: f20d53722e2a15877bf4c8ab024a6e0e NeedsCompilation: no Title: R/Bioconductor Package for interfacing with Snaptron for rapid querying of expression counts Description: snapcount is a client interface to the Snaptron webservices which support querying by gene name or genomic region. Results include raw expression counts derived from alignment of RNA-seq samples and/or various summarized measures of expression across one or more regions/genes per-sample (e.g. percent spliced in). biocViews: Coverage, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Rone Charles [aut, cre] Maintainer: Rone Charles URL: https://github.com/langmead-lab/snapcount VignetteBuilder: knitr BugReports: https://github.com/langmead-lab/snapcount/issues git_url: https://git.bioconductor.org/packages/snapcount git_branch: devel git_last_commit: 1af1119 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/snapcount_1.23.0.tar.gz vignettes: vignettes/snapcount/inst/doc/snapcount_vignette.html vignetteTitles: snapcount quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/snapcount/inst/doc/snapcount_vignette.R dependencyCount: 44 Package: snm Version: 1.59.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: 42aad71799521d9581cade64e23d282e NeedsCompilation: no Title: Supervised Normalization of Microarrays Description: SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest. biocViews: Microarray, OneChannel, TwoChannel, MultiChannel, DifferentialExpression, ExonArray, GeneExpression, Transcription, MultipleComparison, Preprocessing, QualityControl Author: Brig Mecham and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/snm git_branch: devel git_last_commit: 20b98fe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/snm_1.59.0.tar.gz vignettes: vignettes/snm/inst/doc/snm.pdf vignetteTitles: snm Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snm/inst/doc/snm.R importsMe: ExpressionNormalizationWorkflow dependencyCount: 24 Package: SNPediaR Version: 1.37.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: bfc67b0ad0fead4632893bd019354af7 NeedsCompilation: no Title: Query data from SNPedia Description: SNPediaR provides some tools for downloading and parsing data from the SNPedia web site . The implemented functions allow users to import the wiki text available in SNPedia pages and to extract the most relevant information out of them. If some information in the downloaded pages is not automatically processed by the library functions, users can easily implement their own parsers to access it in an efficient way. biocViews: SNP, VariantAnnotation Author: David Montaner [aut, cre] Maintainer: David Montaner URL: https://github.com/genometra/SNPediaR VignetteBuilder: knitr BugReports: https://github.com/genometra/SNPediaR/issues git_url: https://git.bioconductor.org/packages/SNPediaR git_branch: devel git_last_commit: aead6b1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SNPediaR_1.37.0.tar.gz vignettes: vignettes/SNPediaR/inst/doc/SNPediaR.html vignetteTitles: SNPediaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPediaR/inst/doc/SNPediaR.R dependencyCount: 4 Package: SNPRelate Version: 1.45.2 Depends: R (>= 2.15), gdsfmt (>= 1.8.3) Imports: methods, RhpcBLASctl LinkingTo: gdsfmt Suggests: parallel, Matrix, RUnit, knitr, markdown, rmarkdown, MASS, BiocGenerics Enhances: SeqArray (>= 1.12.0) License: GPL-3 MD5sum: 5e85050359efe419e81872c784cf092c NeedsCompilation: yes Title: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data Description: Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data. biocViews: Infrastructure, Genetics, StatisticalMethod, PrincipalComponent Author: Xiuwen Zheng [aut, cre, cph] (ORCID: ), Stephanie Gogarten [ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] (ORCID: ) Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/SNPRelate VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/SNPRelate/issues git_url: https://git.bioconductor.org/packages/SNPRelate git_branch: devel git_last_commit: ca5d9bc git_last_commit_date: 2026-04-14 Date/Publication: 2026-04-20 source.ver: src/contrib/SNPRelate_1.45.2.tar.gz vignettes: vignettes/SNPRelate/inst/doc/SNPRelate.html vignetteTitles: SNPRelate Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelate.R dependsOnMe: RAIDS, SeqSQC importsMe: CNVRanger, GDSArray, GENESIS, gwasurvivr, VariantExperiment, EthSEQ, gwid, simplePHENOTYPES, snplinkage suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray dependencyCount: 3 Package: snpStats Version: 1.61.2 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics Suggests: hexbin License: GPL-3 MD5sum: f89eb752f6ce8ad8ad5888ff184ac0d3 NeedsCompilation: yes Title: SnpMatrix and XSnpMatrix classes and methods Description: Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. biocViews: Microarray, SNP, GeneticVariability Author: David Clayton Maintainer: David Clayton git_url: https://git.bioconductor.org/packages/snpStats git_branch: devel git_last_commit: 0ad6fb0 git_last_commit_date: 2026-03-15 Date/Publication: 2026-04-20 source.ver: src/contrib/snpStats_1.61.2.tar.gz vignettes: vignettes/snpStats/inst/doc/data-input-vignette.pdf, vignettes/snpStats/inst/doc/differences.pdf, vignettes/snpStats/inst/doc/Fst-vignette.pdf, vignettes/snpStats/inst/doc/imputation-vignette.pdf, vignettes/snpStats/inst/doc/ld-vignette.pdf, vignettes/snpStats/inst/doc/pca-vignette.pdf, vignettes/snpStats/inst/doc/snpStats-vignette.pdf, vignettes/snpStats/inst/doc/tdt-vignette.pdf vignetteTitles: Data input, snpMatrix-differences, Fst, Imputation and meta-analysis, LD statistics, Principal components analysis, snpStats introduction, TDT tests hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snpStats/inst/doc/data-input-vignette.R, vignettes/snpStats/inst/doc/Fst-vignette.R, vignettes/snpStats/inst/doc/imputation-vignette.R, vignettes/snpStats/inst/doc/ld-vignette.R, vignettes/snpStats/inst/doc/pca-vignette.R, vignettes/snpStats/inst/doc/snpStats-vignette.R, vignettes/snpStats/inst/doc/tdt-vignette.R dependsOnMe: MAGAR importsMe: cardelino, DExMA, gwascat, martini, RVS, scoreInvHap, GenomicTools.fileHandler, gpcp, GWASbyCluster, TriadSim suggestsMe: crlmm, GenomicFiles, GWASTools, ldblock, omicRexposome, omicsPrint, VariantAnnotation, adjclust, dartR, dartR.popgen, genio, pegas, RcppDPR, statgenGWAS dependencyCount: 12 Package: sosta Version: 1.3.4 Depends: R (>= 4.4.0) Imports: terra, sf, smoothr, spatstat.explore, spatstat.geom, SpatialExperiment, SingleCellExperiment, dplyr, ggplot2, patchwork, SummarizedExperiment, stats, rlang, parallel, EBImage, spatstat.random, S4Vectors Suggests: knitr, rmarkdown, BiocStyle, ExperimentHub, lme4, lmerTest, ggfortify, tidyr, testthat (>= 3.0.0) License: GPL (>= 3) + file LICENSE MD5sum: b133cee72bc783fdc4e0f7a4df5e006c NeedsCompilation: no Title: A package for the analysis of anatomical tissue structures in spatial omics data Description: sosta (Spatial Omics STructure Analysis) is a package for analyzing spatial omics data to explore tissue organization at the anatomical structure level. It reconstructs anatomically relevant structures based on molecular features or cell types. It further calculates a range of metrics at the structure level to quantitatively describe tissue architecture. The package is designed to integrate with other packages for the analysis of spatial omics data. biocViews: Software, Spatial, Transcriptomics, Visualization Author: Samuel Gunz [aut, cre] (ORCID: ), Mark D. Robinson [aut, fnd] Maintainer: Samuel Gunz URL: https://github.com/sgunz/sosta, https://sgunz.github.io/sosta/ VignetteBuilder: knitr BugReports: https://github.com/sgunz/sosta/issues git_url: https://git.bioconductor.org/packages/sosta git_branch: devel git_last_commit: 196c827 git_last_commit_date: 2026-03-26 Date/Publication: 2026-04-20 source.ver: src/contrib/sosta_1.3.4.tar.gz vignettes: vignettes/sosta/inst/doc/ImcDiabetesIsletsVignette.html, vignettes/sosta/inst/doc/StructureReconstructionVignette.html vignetteTitles: Reconstruction and analysis of pancreatic islets from IMC data, Overview of sosta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sosta/inst/doc/ImcDiabetesIsletsVignette.R, vignettes/sosta/inst/doc/StructureReconstructionVignette.R importsMe: OSTA dependencyCount: 127 Package: SpaceMarkers Version: 2.1.0 Depends: R (>= 4.4.0) Imports: matrixStats, matrixTests, rstatix, spatstat.explore, spatstat.geom, ape, hdf5r, nanoparquet, jsonlite, Matrix, qvalue, stats, utils, methods, ggplot2, reshape2, RColorBrewer, circlize, mixtools, dplyr, readbitmap, rlang, effsize, viridis Suggests: data.table, devtools, knitr, cowplot, rjson, rmarkdown, BiocStyle, testthat (>= 3.0.0), CoGAPS, ComplexHeatmap Enhances: BiocParallel License: MIT + file LICENSE MD5sum: adcbf6b5142dfca880892a94e1d84daf NeedsCompilation: no Title: Spatial Interaction Markers Description: Spatial transcriptomic technologies have helped to resolve the connection between gene expression and the 2D orientation of tissues relative to each other. However, the limited single-cell resolution makes it difficult to highlight the most important molecular interactions in these tissues. SpaceMarkers, R/Bioconductor software, can help to find molecular interactions, by identifying genes associated with latent space interactions in spatial transcriptomics. biocViews: SingleCell, GeneExpression, Software, Spatial, Transcriptomics Author: Atul Deshpande [aut, cre] (ORCID: ), Ludmila Danilova [ctb], Dmitrijs Lvovs [ctb] (ORCID: ) Maintainer: Atul Deshpande URL: https://github.com/DeshpandeLab/SpaceMarkers VignetteBuilder: knitr BugReports: https://github.com/DeshpandeLab/SpaceMarkers/issues git_url: https://git.bioconductor.org/packages/SpaceMarkers git_branch: devel git_last_commit: 2b359d8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SpaceMarkers_2.1.0.tar.gz vignettes: vignettes/SpaceMarkers/inst/doc/SpaceMarkers_vignette.html vignetteTitles: Inferring Immune Interactions in Breast Cancer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpaceMarkers/inst/doc/SpaceMarkers_vignette.R dependencyCount: 149 Package: spacexr Version: 1.3.0 Depends: R (>= 4.5.0) Imports: ggplot2, Matrix, parallel, quadprog, httr, methods, memoise, BiocParallel, BiocFileCache, SummarizedExperiment, scatterpie, SpatialExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 3) MD5sum: 0341c553edfc90222f6a9f2a8cae493a NeedsCompilation: no Title: SpatialeXpressionR: Cell Type Identification in Spatial Transcriptomics Description: Spatial-eXpression-R (spacexr) is a package for analyzing cell types in spatial transcriptomics data. This implementation is a fork of the spacexr GitHub repo (https://github.com/dmcable/spacexr), adapted to work with Bioconductor objects. The original package implements two statistical methods: RCTD for learning cell types and CSIDE for inferring cell type-specific differential expression. Currently, this fork only implements RCTD, which learns cell type profiles from annotated RNA sequencing (RNA-seq) reference data and uses these profiles to identify cell types in spatial transcriptomic pixels while accounting for platform-specific effects. Future releases will include an implementation of CSIDE. biocViews: GeneExpression, DifferentialExpression, SingleCell, RNASeq, Software, Spatial, Transcriptomics Author: Dylan Cable [aut], Rafael Irizarry [aut] (ORCID: ), Gabriel Grajeda [cre] (ORCID: ), Fannie and John Hertz Foundation [fnd] Maintainer: Gabriel Grajeda URL: https://github.com/ggrajeda/spacexr VignetteBuilder: knitr BugReports: https://github.com/ggrajeda/spacexr/issues git_url: https://git.bioconductor.org/packages/spacexr git_branch: devel git_last_commit: ea2a493 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/spacexr_1.3.0.tar.gz vignettes: vignettes/spacexr/inst/doc/rctd-tutorial.html vignetteTitles: rctd-tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spacexr/inst/doc/rctd-tutorial.R importsMe: OSTA dependencyCount: 99 Package: SpaNorm Version: 1.5.2 Depends: R (>= 4.4) Imports: edgeR, ggplot2, Matrix, matrixStats, methods, rlang, scran, SeuratObject, SingleCellExperiment, SpatialExperiment, stats, SummarizedExperiment, S4Vectors, utils, BiocParallel, BiocSingular Suggests: testthat (>= 3.0.0), knitr, rmarkdown, prettydoc, pkgdown, covr, BiocStyle, scater, Seurat (>= 5.0.0), patchwork, ggforce, ggnewscale, tensorflow License: GPL (>= 3) MD5sum: 09f9323e03f5eb9a0aa1c6f8f824da23 NeedsCompilation: no Title: Spatially-aware normalisation for spatial transcriptomics data Description: This package implements the spatially aware library size normalisation algorithm, SpaNorm. SpaNorm normalises out library size effects while retaining biology through the modelling of smooth functions for each effect. Normalisation is performed in a gene- and cell-/spot- specific manner, yielding library size adjusted data. biocViews: Software, GeneExpression, Transcriptomics, Spatial, CellBiology Author: Dharmesh D. Bhuva [aut, cre] (ORCID: ), Agus Salim [aut] (ORCID: ), Ahmed Mohamed [aut] (ORCID: ) Maintainer: Dharmesh D. Bhuva URL: https://bhuvad.github.io/SpaNorm VignetteBuilder: knitr BugReports: https://github.com/bhuvad/SpaNorm/issues git_url: https://git.bioconductor.org/packages/SpaNorm git_branch: devel git_last_commit: 81ee46f git_last_commit_date: 2026-01-21 Date/Publication: 2026-04-20 source.ver: src/contrib/SpaNorm_1.5.2.tar.gz vignettes: vignettes/SpaNorm/inst/doc/SpaNorm.html vignetteTitles: SpaNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpaNorm/inst/doc/SpaNorm.R dependencyCount: 115 Package: spARI Version: 1.1.0 Depends: R (>= 4.1.0) Imports: Rcpp, stats, Matrix, SpatialExperiment, SummarizedExperiment, BiocParallel (>= 1.0) LinkingTo: Rcpp Suggests: FNN, knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: 2ca6a41b75c69d4946cccabcffb00de4 NeedsCompilation: yes Title: Spatially Aware Adjusted Rand Index for Evaluating Spatial Transcritpomics Clustering Description: The R package used in the manuscript "Spatially Aware Adjusted Rand Index for Evaluating Spatial Transcritpomics Clustering". biocViews: Clustering, DataImport, GeneExpression, Transcriptomics, Spatial, Software Author: Yinqiao Yan [aut, cre], Xiangnan Feng [aut, fnd], Xiangyu Luo [aut, fnd] Maintainer: Yinqiao Yan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/spARI git_branch: devel git_last_commit: 537c914 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/spARI_1.1.0.tar.gz vignettes: vignettes/spARI/inst/doc/spARI.html vignetteTitles: An Introduction to spARI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spARI/inst/doc/spARI.R dependencyCount: 75 Package: SparseArray Version: 1.11.13 Depends: R (>= 4.3.0), methods, Matrix, BiocGenerics (>= 0.43.1), MatrixGenerics (>= 1.11.1), S4Vectors (>= 0.43.2), S4Arrays (>= 1.11.1) Imports: utils, stats, matrixStats, IRanges, XVector LinkingTo: S4Vectors, IRanges, XVector Suggests: HDF5Array, ExperimentHub, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: abbac9786027ad7e81acdeb43a8c7e4a NeedsCompilation: yes Title: High-performance sparse data representation and manipulation in R Description: The SparseArray package provides array-like containers for efficient in-memory representation of multidimensional sparse data in R (arrays and matrices). The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data: the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they suppport most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre] (ORCID: ), Vince Carey [fnd] (ORCID: ), Rafael A. Irizarry [fnd] (ORCID: ), Jacques Serizay [ctb] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/SparseArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SparseArray/issues git_url: https://git.bioconductor.org/packages/SparseArray git_branch: devel git_last_commit: 020902a git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/SparseArray_1.11.13.tar.gz vignettes: vignettes/SparseArray/inst/doc/SparseArray_objects.html vignetteTitles: SparseArray objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SparseArray/inst/doc/SparseArray_objects.R dependsOnMe: DelayedArray, DelayedRandomArray, h5mread, HDF5Array, TileDBArray, ZarrArray importsMe: alabaster.matrix, batchelor, beachmat, DelayedMatrixStats, DelayedTensor, dreamlet, DropletUtils, glmGamPoi, GSVA, SCArray, scater, scone, scrapper, scuttle, TSCAN, zellkonverter, scRNAseq, IDLFM suggestsMe: BiocGenerics, MatrixGenerics, metagenomeSeq, S4Arrays, SummarizedExperiment dependencyCount: 19 Package: sparseMatrixStats Version: 1.23.0 Depends: MatrixGenerics (>= 1.5.3) Imports: Rcpp, Matrix, matrixStats (>= 0.60.0), methods LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), knitr, bench, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 0adf1f29aea4a574447f4e671c69894d NeedsCompilation: yes Title: Summary Statistics for Rows and Columns of Sparse Matrices Description: High performance functions for row and column operations on sparse matrices. For example: col / rowMeans2, col / rowMedians, col / rowVars etc. Currently, the optimizations are limited to data in the column sparse format. This package is inspired by the matrixStats package by Henrik Bengtsson. biocViews: Infrastructure, Software, DataRepresentation Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/sparseMatrixStats SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/const-ae/sparseMatrixStats/issues git_url: https://git.bioconductor.org/packages/sparseMatrixStats git_branch: devel git_last_commit: 1592487 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sparseMatrixStats_1.23.0.tar.gz vignettes: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.html vignetteTitles: sparseMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.R importsMe: atena, ccImpute, concordexR, Coralysis, DelayedMatrixStats, DenoIST, dreamlet, GSVA, scone, SimBu, smartid, SplineDV, SPOTlight, adjclust, CRMetrics, GrabSVG, modelSelection, mombf, scBSP, Signac suggestsMe: APL, blase, MatrixGenerics, miloR, plaid, scPCA, scuttle, Seqtometry, SpatialFeatureExperiment, StabMap, zinbwave, SigBridgeRUtils, singleCellHaystack dependencyCount: 11 Package: sparsenetgls Version: 1.29.0 Depends: R (>= 4.0.0), Matrix, MASS Imports: methods, glmnet, huge, stats, graphics, utils Suggests: testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>= 5.0.0) License: GPL-3 MD5sum: 64de673c3f080d9e0409eaf3c2bb37c4 NeedsCompilation: no Title: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression Description: The package provides methods of combining the graph structure learning and generalized least squares regression to improve the regression estimation. The main function sparsenetgls() provides solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the sandwich estimators in generalized least squares (gls) regression. This package also provides functions for assessing a Gaussian graphical model which uses the penalized approach. It uses Receiver Operative Characteristics curve as a visualization tool in the assessment. biocViews: ImmunoOncology, GraphAndNetwork,Regression,Metabolomics,CopyNumberVariation,MassSpectrometry,Proteomics,Software,Visualization Author: Irene Zeng [aut, cre], Thomas Lumley [ctb] Maintainer: Irene Zeng SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparsenetgls git_branch: devel git_last_commit: 92f4ff1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sparsenetgls_1.29.0.tar.gz vignettes: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.html vignetteTitles: Introduction to sparsenetgls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.R dependencyCount: 28 Package: SparseSignatures Version: 2.21.1 Depends: R (>= 4.1.0), NMF Imports: nnlasso, nnls, parallel, data.table, Biostrings, GenomicRanges, IRanges, BSgenome, GenomeInfoDb, ggplot2, gridExtra, reshape2, RhpcBLASctl Suggests: BiocGenerics, BSgenome.Hsapiens.1000genomes.hs37d5, BiocStyle, testthat, knitr, License: file LICENSE MD5sum: 771f03eaedcba6ea2377f42e489d0fa6 NeedsCompilation: no Title: SparseSignatures Description: Point mutations occurring in a genome can be divided into 96 categories based on the base being mutated, the base it is mutated into and its two flanking bases. Therefore, for any patient, it is possible to represent all the point mutations occurring in that patient's tumor as a vector of length 96, where each element represents the count of mutations for a given category in the patient. A mutational signature represents the pattern of mutations produced by a mutagen or mutagenic process inside the cell. Each signature can also be represented by a vector of length 96, where each element represents the probability that this particular mutagenic process generates a mutation of the 96 above mentioned categories. In this R package, we provide a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Avantika Lal [aut], Keli Liu [ctb], Luca De Sano [cre, aut] (ORCID: ), Robert Tibshirani [ctb], Arend Sidow [aut] Maintainer: Luca De Sano URL: https://github.com/danro9685/SparseSignatures VignetteBuilder: knitr BugReports: https://github.com/danro9685/SparseSignatures git_url: https://git.bioconductor.org/packages/SparseSignatures git_branch: devel git_last_commit: cc8943c git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/SparseSignatures_2.21.1.tar.gz vignettes: vignettes/SparseSignatures/inst/doc/v1_introduction.html, vignettes/SparseSignatures/inst/doc/v2_using_the_package.html vignetteTitles: v1_introduction.html, v2_using_the_package.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SparseSignatures/inst/doc/v2_using_the_package.R dependencyCount: 97 Package: spaSim Version: 1.13.0 Depends: R (>= 4.2.0) Imports: ggplot2, methods, stats, dplyr, spatstat.geom, spatstat.random, SpatialExperiment, SummarizedExperiment, RANN Suggests: RefManageR, BiocStyle, knitr, testthat (>= 3.0.0), sessioninfo, rmarkdown, markdown License: Artistic-2.0 MD5sum: f5a660ec5a8f9d951ae9e41a07d38df6 NeedsCompilation: no Title: Spatial point data simulator for tissue images Description: A suite of functions for simulating spatial patterns of cells in tissue images. Output images are multitype point data in SingleCellExperiment format. Each point represents a cell, with its 2D locations and cell type. Potential cell patterns include background cells, tumour/immune cell clusters, immune rings, and blood/lymphatic vessels. biocViews: StatisticalMethod, Spatial, BiomedicalInformatics Author: Yuzhou Feng [aut, cre] (ORCID: ), Anna Trigos [aut] (ORCID: ) Maintainer: Yuzhou Feng URL: https://trigosteam.github.io/spaSim/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/spaSim git_url: https://git.bioconductor.org/packages/spaSim git_branch: devel git_last_commit: 3d330bf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/spaSim_1.13.0.tar.gz vignettes: vignettes/spaSim/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spaSim/inst/doc/vignette.R dependencyCount: 83 Package: SpatialArtifacts Version: 0.99.10 Depends: R (>= 4.4.0) Imports: SpatialExperiment, SummarizedExperiment, S4Vectors, scuttle, dplyr, terra, stats, methods Suggests: BiocStyle, knitr, rmarkdown, BiocCheck, ggplot2, patchwork, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 7a8b88fd40dc01ca50d91952f6afa15b NeedsCompilation: no Title: Identification and Classification of Spatial Artifacts in Visium and Visium HD Data Description: SpatialArtifacts provides a data-driven two-step workflow to identify, classify, and handle spatial artifacts in spatial transcriptomics data. The package combines median absolute deviation (MAD)-based outlier detection with morphological image processing (fill, outline, and star patterns) to detect edge and interior artifacts. It supports multiple platforms including 10x Genomics Visium (standard and HD), allowing for consistent quality control across different spatial resolutions. biocViews: Software, Spatial, Transcriptomics, QualityControl, DataImport, WorkflowStep, Classification Author: Harriet Jiali He [aut, cre] (ORCID: ), Jacqueline R. Thompson [aut], Michael Totty [aut], Stephanie C. Hicks [aut, fnd] (ORCID: ) Maintainer: Harriet Jiali He URL: https://github.com/CambridgeCat13/SpatialArtifacts SystemRequirements: quarto, GDAL (>= 2.0.1), GEOS (>= 3.4.0), PROJ (>= 4.8.0) VignetteBuilder: knitr BugReports: https://github.com/CambridgeCat13/SpatialArtifacts/issues git_url: https://git.bioconductor.org/packages/SpatialArtifacts git_branch: devel git_last_commit: a41ea39 git_last_commit_date: 2026-04-19 Date/Publication: 2026-04-20 source.ver: src/contrib/SpatialArtifacts_0.99.10.tar.gz vignettes: vignettes/SpatialArtifacts/inst/doc/hippocampus-edge-detection.html vignetteTitles: SpatialArtifacts Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialArtifacts/inst/doc/hippocampus-edge-detection.R dependencyCount: 79 Package: SpatialCPie Version: 1.27.0 Depends: R (>= 3.6) Imports: colorspace (>= 1.3-2), data.table (>= 1.12.2), digest (>= 0.6.21), dplyr (>= 0.7.6), ggforce (>= 0.3.0), ggiraph (>= 0.5.0), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), grid (>= 3.5.1), igraph (>= 1.2.2), lpSolve (>= 5.6.13), methods (>= 3.5.0), purrr (>= 0.2.5), readr (>= 1.1.1), rlang (>= 0.2.2), shiny (>= 1.1.0), shinycssloaders (>= 0.2.0), shinyjs (>= 1.0), shinyWidgets (>= 0.4.8), stats (>= 3.6.0), SummarizedExperiment (>= 1.10.1), tibble (>= 1.4.2), tidyr (>= 0.8.1), tidyselect (>= 0.2.4), tools (>= 3.6.0), utils (>= 3.5.0), zeallot (>= 0.1.0) Suggests: BiocStyle (>= 2.8.2), jpeg (>= 0.1-8), knitr (>= 1.20), rmarkdown (>= 1.10), testthat (>= 2.0.0) License: MIT + file LICENSE MD5sum: 3201bc64becbe5c74eaa8b02b0c496d4 NeedsCompilation: no Title: Cluster analysis of Spatial Transcriptomics data Description: SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues. biocViews: Transcriptomics, Clustering, RNASeq, Software Author: Joseph Bergenstraahle [aut, cre] Maintainer: Joseph Bergenstraahle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialCPie git_branch: devel git_last_commit: 6712818 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SpatialCPie_1.27.0.tar.gz vignettes: vignettes/SpatialCPie/inst/doc/SpatialCPie.html vignetteTitles: SpatialCPie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialCPie/inst/doc/SpatialCPie.R dependencyCount: 113 Package: SpatialDecon Version: 1.21.1 Depends: R (>= 4.0.0) Imports: grDevices, stats, utils, graphics, SeuratObject, Biobase, GeomxTools, repmis, methods, Matrix, logNormReg (>= 0.4) Suggests: testthat, knitr, rmarkdown, qpdf, Seurat License: MIT + file LICENSE MD5sum: 13af20edf83b1d5bb32f859744de6fcb NeedsCompilation: no Title: Deconvolution of mixed cells from spatial and/or bulk gene expression data Description: Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data. biocViews: ImmunoOncology, FeatureExtraction, GeneExpression, Transcriptomics, Spatial Author: Maddy Griswold [cre, aut], Patrick Danaher [aut] Maintainer: Maddy Griswold VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/SpatialDecon/issues git_url: https://git.bioconductor.org/packages/SpatialDecon git_branch: devel git_last_commit: 733ae7a git_last_commit_date: 2026-02-05 Date/Publication: 2026-04-20 source.ver: src/contrib/SpatialDecon_1.21.1.tar.gz vignettes: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.html, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.html vignetteTitles: Use of SpatialDecon in a large GeoMx dataset with GeomxTools, Use of SpatialDecon in a small GeoMx dataet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.R, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.R suggestsMe: GeomxTools dependencyCount: 135 Package: SpatialExperiment Version: 1.21.0 Depends: R (>= 4.1.0), methods, SingleCellExperiment Imports: rjson, grDevices, magick, utils, S4Vectors, SummarizedExperiment, BiocGenerics, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle, BumpyMatrix, DropletUtils, VisiumIO License: GPL-3 MD5sum: fba179329207b01d578260ad9b68ea98 NeedsCompilation: no Title: S4 Class for Spatially Resolved -omics Data Description: Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform. biocViews: DataRepresentation, DataImport, Infrastructure, ImmunoOncology, GeneExpression, Transcriptomics, SingleCell, Spatial Author: Dario Righelli [aut, cre] (ORCID: ), Davide Risso [aut] (ORCID: ), Helena L. Crowell [aut] (ORCID: ), Lukas M. Weber [aut] (ORCID: ), Nicholas J. Eagles [ctb] Maintainer: Dario Righelli URL: https://github.com/drighelli/SpatialExperiment VignetteBuilder: knitr BugReports: https://github.com/drighelli/SpatialExperiment/issues git_url: https://git.bioconductor.org/packages/SpatialExperiment git_branch: devel git_last_commit: 0414e3b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SpatialExperiment_1.21.0.tar.gz vignettes: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html vignetteTitles: Introduction to the SpatialExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.R dependsOnMe: alabaster.spatial, clustSIGNAL, ExperimentSubset, imcRtools, SpaceTrooper, SPIAT, tidySpatialExperiment, visiumStitched, imcdatasets, MerfishData, MouseGastrulationData, spatialLIBD, STexampleData, TENxVisiumData, VectraPolarisData, WeberDivechaLCdata importsMe: Banksy, BulkSignalR, CARDspa, CatsCradle, concordexR, CTSV, cytomapper, DenoIST, DESpace, escheR, FLAMES, ggspavis, GSVA, HistoImagePlot, hoodscanR, imageFeatureTCGA, imageTCGAutils, lisaClust, MoleculeExperiment, nnSVG, poem, scider, SEraster, signifinder, smoothclust, sosta, spacexr, SpaNorm, spARI, spaSim, SpatialArtifacts, spatialDE, SpatialExperimentIO, spatialFDA, SpatialFeatureExperiment, spatialSimGP, spicyR, SpNeigh, spoon, SpotClean, SpotSweeper, standR, Statial, stJoincount, stPipe, SVP, tpSVG, VisiumIO, Voyager, XeniumIO, xenLite, HCATonsilData, SingleCellMultiModal, SubcellularSpatialData, TENxXeniumData, OSTA suggestsMe: Battlefield, GeomxTools, ggsc, SPOTlight, zellkonverter, muSpaData, SVG dependencyCount: 65 Package: spatialFDA Version: 1.3.3 Depends: R (>= 4.3.0) Imports: dplyr, ggplot2, parallel, patchwork, purrr, refund, SpatialExperiment, spatstat.explore, spatstat.geom, SummarizedExperiment, methods, stats, fda, tidyr, graphics, ExperimentHub, scales, S4Vectors, mgcv Suggests: stringr, knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL (>= 3) + file LICENSE MD5sum: a68fb2815ab3c50e896f27aedf1c7b49 NeedsCompilation: no Title: A Tool for Spatial Multi-sample Comparisons Description: spatialFDA is a package to calculate spatial statistics metrics. The package takes a SpatialExperiment object and calculates spatial statistics metrics using the package spatstat. Then it compares the resulting functions across samples/conditions using functional additive models as implemented in the package refund. Furthermore, it provides exploratory visualisations using functional principal component analysis, as well implemented in refund. biocViews: Software, Spatial, Transcriptomics Author: Martin Emons [aut, cre] (ORCID: ), Samuel Gunz [aut] (ORCID: ), Fabian Scheipl [aut] (ORCID: ), Elizabeth Purdom [aut] (ORCID: ), Mark D. Robinson [aut, fnd] (ORCID: ) Maintainer: Martin Emons URL: https://github.com/mjemons/spatialFDA VignetteBuilder: knitr BugReports: https://github.com/mjemons/spatialFDA/issues git_url: https://git.bioconductor.org/packages/spatialFDA git_branch: devel git_last_commit: 72ba4df git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/spatialFDA_1.3.3.tar.gz vignettes: vignettes/spatialFDA/inst/doc/DiabetesIsletExample.html, vignettes/spatialFDA/inst/doc/DiabetesIsletExampleBrief.html vignetteTitles: Functional Data Analysis of Spatial Metrics, Overview Functional Data Analysis of Spatial Metrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spatialFDA/inst/doc/DiabetesIsletExample.R, vignettes/spatialFDA/inst/doc/DiabetesIsletExampleBrief.R importsMe: OSTA dependencyCount: 140 Package: SpatialOmicsOverlay Version: 1.11.0 Depends: R (>= 4.1.0) Imports: S4Vectors, Biobase, base64enc, EBImage, ggplot2, XML, scattermore, dplyr, pbapply, data.table, readxl, magick, grDevices, stringr, plotrix, GeomxTools, BiocFileCache, stats, utils, methods, ggtext, tools, RBioFormats Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), stringi, qpdf, pheatmap, viridis, cowplot, vdiffr, sf License: MIT MD5sum: 36d6925f800ddf7410cc6f6f4a76ce2a NeedsCompilation: no Title: Spatial Overlay for Omic Data from Nanostring GeoMx Data Description: Tools for NanoString Technologies GeoMx Technology. Package to easily graph on top of an OME-TIFF image. Plotting annotations can range from tissue segment to gene expression. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, ProprietaryPlatforms, RNASeq, Spatial, DataRepresentation, Visualization Author: Maddy Griswold [cre, aut], Megan Vandenberg [ctb], Stephanie Zimmerman [ctb] Maintainer: Maddy Griswold VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialOmicsOverlay git_branch: devel git_last_commit: 5b92d66 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SpatialOmicsOverlay_1.11.0.tar.gz vignettes: vignettes/SpatialOmicsOverlay/inst/doc/SpatialOmicsOverlay.html vignetteTitles: Introduction to SpatialOmicsOverlay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialOmicsOverlay/inst/doc/SpatialOmicsOverlay.R dependencyCount: 159 Package: spatialSimGP Version: 1.5.0 Depends: R (>= 4.4) Imports: SpatialExperiment, MASS, SummarizedExperiment Suggests: testthat (>= 3.0.0), STexampleData, ggplot2, knitr License: MIT + file LICENSE MD5sum: 839acc9fa34c51eba7643d84dd1e41c0 NeedsCompilation: no Title: Simulate Spatial Transcriptomics Data with the Mean-variance Relationship Description: This packages simulates spatial transcriptomics data with the mean- variance relationship using a Gaussian Process model per gene. biocViews: Spatial, Transcriptomics, GeneExpression Author: Kinnary Shah [aut, cre] (ORCID: ), Boyi Guo [aut] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Kinnary Shah URL: https://github.com/kinnaryshah/spatialSimGP VignetteBuilder: knitr BugReports: https://github.com/kinnaryshah/spatialSimGP/issues git_url: https://git.bioconductor.org/packages/spatialSimGP git_branch: devel git_last_commit: de108a0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/spatialSimGP_1.5.0.tar.gz vignettes: vignettes/spatialSimGP/inst/doc/spatialSimGP.html vignetteTitles: spatialSimGP Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spatialSimGP/inst/doc/spatialSimGP.R dependencyCount: 67 Package: speckle Version: 1.11.0 Depends: R (>= 4.2.0) Imports: limma, edgeR, SingleCellExperiment, Seurat, ggplot2, methods, stats, grDevices, graphics Suggests: BiocStyle, knitr, rmarkdown, statmod, CellBench, scater, patchwork, jsonlite, vdiffr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 48f12abafa6824333622ee620d68bc50 NeedsCompilation: no Title: Statistical methods for analysing single cell RNA-seq data Description: The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions. biocViews: SingleCell, RNASeq, Regression, GeneExpression Author: Belinda Phipson [aut, cre] Maintainer: Belinda Phipson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/speckle git_branch: devel git_last_commit: 36f86ad git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/speckle_1.11.0.tar.gz vignettes: vignettes/speckle/inst/doc/speckle.html vignetteTitles: speckle: statistical methods for analysing single cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/speckle/inst/doc/speckle.R dependencyCount: 169 Package: specL Version: 1.45.0 Depends: R (>= 4.1), DBI (>= 0.5), methods (>= 3.3), protViz (>= 0.7), RSQLite (>= 1.1), seqinr (>= 3.3) Suggests: BiocGenerics, BiocStyle (>= 2.2), knitr (>= 1.15), rmarkdown, RUnit (>= 0.4) License: GPL-3 MD5sum: 2c96442d9ad9904781f799ef3a91d651 NeedsCompilation: no Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: provides a functions for generating spectra libraries that can be used for MRM SRM MS workflows in proteomics. The package provides a BiblioSpec reader, a function which can add the protein information using a FASTA formatted amino acid file, and an export method for using the created library in the Spectronaut software. The package is developed, tested and used at the Functional Genomics Center Zurich . biocViews: MassSpectrometry, Proteomics Author: Christian Panse [aut, cre] (ORCID: ), Jonas Grossmann [aut] (ORCID: ), Christian Trachsel [aut], Witold E. Wolski [ctb] Maintainer: Christian Panse URL: http://bioconductor.org/packages/specL/ VignetteBuilder: knitr BugReports: https://github.com/fgcz/specL/issues git_url: https://git.bioconductor.org/packages/specL git_branch: devel git_last_commit: fcfdc28 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/specL_1.45.0.tar.gz vignettes: vignettes/specL/inst/doc/report.html, vignettes/specL/inst/doc/specL.html vignetteTitles: Automatic specL Workflow, Introduction to specL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/report.R, vignettes/specL/inst/doc/specL.R suggestsMe: msqc1, NestLink dependencyCount: 32 Package: SpeCond Version: 1.65.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: 1c275772a3cc3833e794a61fdf068d45 NeedsCompilation: no Title: Condition specific detection from expression data Description: This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, ReportWriting Author: Florence Cavalli Maintainer: Florence Cavalli git_url: https://git.bioconductor.org/packages/SpeCond git_branch: devel git_last_commit: 3518186 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SpeCond_1.65.0.tar.gz vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf vignetteTitles: SpeCond hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpeCond/inst/doc/SpeCond.R dependencyCount: 18 Package: Spectra Version: 1.21.7 Depends: R (>= 4.1.0), S4Vectors, BiocParallel Imports: ProtGenerics (>= 1.39.2), methods, IRanges, MsCoreUtils (>= 1.23.6), graphics, grDevices, stats, tools, utils, fs, BiocGenerics, MetaboCoreUtils, data.table Suggests: testthat, knitr (>= 1.1.0), MsDataHub, roxygen2, BiocStyle (>= 2.5.19), mzR (>= 2.19.6), rhdf5 (>= 2.32.0), rmarkdown, vdiffr (>= 1.0.0), msentropy, patrick License: Artistic-2.0 MD5sum: fe6ecdd4ba67f1d65900972cb1b44903 NeedsCompilation: no Title: Spectra Infrastructure for Mass Spectrometry Data Description: The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (ORCID: ), Johannes Rainer [aut] (ORCID: ), Sebastian Gibb [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Jan Stanstrup [ctb] (ORCID: ), Nir Shahaf [ctb], Mar Garcia-Aloy [ctb] (ORCID: ), Guillaume Deflandre [ctb] (ORCID: ), Ahlam Mentag [ctb] (ORCID: ) Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/Spectra VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/Spectra/issues git_url: https://git.bioconductor.org/packages/Spectra git_branch: devel git_last_commit: 254af3b git_last_commit_date: 2026-04-08 Date/Publication: 2026-04-20 source.ver: src/contrib/Spectra_1.21.7.tar.gz vignettes: vignettes/Spectra/inst/doc/MsBackend.html, vignettes/Spectra/inst/doc/Spectra-large-scale.html, vignettes/Spectra/inst/doc/Spectra.html vignetteTitles: Creating new `MsBackend` class, Large-scale data handling and processing with Spectra, Description and usage of Spectra object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Spectra/inst/doc/MsBackend.R, vignettes/Spectra/inst/doc/Spectra-large-scale.R, vignettes/Spectra/inst/doc/Spectra.R dependsOnMe: hdxmsqc, MetCirc, MsBackendMassbank, MsBackendMetaboLights, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql importsMe: Chromatograms, CompoundDb, lcmsPlot, MetaboAnnotation, MsExperiment, MsQuality, PSMatch, SpectraQL, SpectriPy, xcms suggestsMe: koinar, MetNet, MsDataHub, MSnbase, fioRa, RaMS dependencyCount: 30 Package: SpectralTAD Version: 1.27.0 Depends: R (>= 3.6) Imports: dplyr, PRIMME, cluster, Matrix, parallel, BiocParallel, magrittr, HiCcompare, GenomicRanges, utils Suggests: BiocCheck, BiocManager, BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr License: MIT + file LICENSE MD5sum: caad05a2c6c8cffeda0d9ba068411f9f NeedsCompilation: no Title: SpectralTAD: Hierarchical TAD detection using spectral clustering Description: SpectralTAD is an R package designed to identify Topologically Associated Domains (TADs) from Hi-C contact matrices. It uses a modified version of spectral clustering that uses a sliding window to quickly detect TADs. The function works on a range of different formats of contact matrices and returns a bed file of TAD coordinates. The method does not require users to adjust any parameters to work and gives them control over the number of hierarchical levels to be returned. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Mikhail Dozmorov [aut, cre] (ORCID: ), Kellen Cresswell [aut], John Stansfield [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/SpectralTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/SpectralTAD/issues git_url: https://git.bioconductor.org/packages/SpectralTAD git_branch: devel git_last_commit: 8bab84c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SpectralTAD_1.27.0.tar.gz vignettes: vignettes/SpectralTAD/inst/doc/SpectralTAD.html vignetteTitles: SpectralTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpectralTAD/inst/doc/SpectralTAD.R suggestsMe: TADCompare dependencyCount: 77 Package: SpectraQL Version: 1.5.1 Depends: R (>= 4.4.0), ProtGenerics (>= 1.25.1) Imports: Spectra (>= 1.5.6), MsCoreUtils, methods Suggests: testthat, MsDataHub, roxygen2, rmarkdown, knitr, S4Vectors, BiocStyle, mzR License: Artistic-2.0 MD5sum: 8be61560aaa32837f15d14c55db62ba4 NeedsCompilation: no Title: MassQL support for Spectra Description: The Mass Spec Query Language (MassQL) is a domain-specific language enabling to express a query and retrieve mass spectrometry (MS) data in a more natural and understandable way for MS users. It is inspired by SQL and is by design programming language agnostic. The SpectraQL package adds support for the MassQL query language to R, in particular to MS data represented by Spectra objects. Users can thus apply MassQL expressions to analyze and retrieve specific data from Spectra objects. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: Johannes Rainer [aut, cre] (ORCID: ), Andrea Vicini [aut], Sebastian Gibb [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/SpectraQL VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/SpectraQL/issues git_url: https://git.bioconductor.org/packages/SpectraQL git_branch: devel git_last_commit: 8bf5d82 git_last_commit_date: 2026-02-06 Date/Publication: 2026-04-20 source.ver: src/contrib/SpectraQL_1.5.1.tar.gz vignettes: vignettes/SpectraQL/inst/doc/SpectraQL.html vignetteTitles: Mass Spec Query Language Support to the Spectra Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpectraQL/inst/doc/SpectraQL.R dependencyCount: 31 Package: SpectriPy Version: 1.1.7 Depends: R (>= 4.4.0), reticulate (>= 1.42.0) Imports: Spectra (>= 1.19.9), IRanges, S4Vectors, MsCoreUtils, ProtGenerics, methods, data.table, snakecase Suggests: testthat, quarto, MsBackendMgf, MsDataHub, mzR, knitr, BiocStyle License: Artistic-2.0 MD5sum: 4deada724a660cf1695083c554c566f4 NeedsCompilation: no Title: Enhancing Cross-Language Mass Spectrometry Data Analysis with R and Python Description: The SpectriPy package allows integration of Python-based MS analysis code with the Spectra package. Spectra objects can be converted into Python MS data structures. In addition, SpectriPy integrates and wraps the similarity scoring and processing/filtering functions from the Python matchms package into R. biocViews: Infrastructure, Metabolomics, MassSpectrometry, Proteomics Author: Michael Witting [aut] (ORCID: ), Johannes Rainer [aut, cre] (ORCID: ), Carolin Huber [aut] (ORCID: ), Helge Hecht [ctb] (ORCID: ), Marilyn De Graeve [aut] (ORCID: ), Wout Bittremieux [aut] (ORCID: ), Thomas Naake [aut] (ORCID: ), Victor Chrone [ctb] (ORCID: ), Matthias Anagho-Mattanovich [ctb] (ORCID: ), Pierre Marchal [ctb] (ORCID: ), Philippine Louail [ctb] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/SpectriPy SystemRequirements: python (>= 3.12), pandoc, quarto VignetteBuilder: quarto BugReports: https://github.com/RforMassSpectrometry/SpectriPy/issues git_url: https://git.bioconductor.org/packages/SpectriPy git_branch: devel git_last_commit: 17b98de git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/SpectriPy_1.1.7.tar.gz vignettes: vignettes/SpectriPy/inst/doc/detailed-installation-configuration.html, vignettes/SpectriPy/inst/doc/SpectriPy.html vignetteTitles: detailed-installation-configuration.html, SpectriPy.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpectriPy/inst/doc/detailed-installation-configuration.R, vignettes/SpectriPy/inst/doc/SpectriPy.R dependencyCount: 51 Package: SPEM Version: 1.51.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 552c4f38b519608dcf807af436d57cb3 NeedsCompilation: no Title: S-system parameter estimation method Description: This package can optimize the parameter in S-system models given time series data biocViews: Network, NetworkInference, Software Author: Xinyi YANG Developer, Jennifer E. DENT Developer and Christine NARDINI Supervisor Maintainer: Xinyi YANG git_url: https://git.bioconductor.org/packages/SPEM git_branch: devel git_last_commit: 6218bd4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SPEM_1.51.0.tar.gz vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf vignetteTitles: Vignette for SPEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPEM/inst/doc/SPEM-package.R importsMe: TMixClust dependencyCount: 21 Package: SPIA Version: 2.63.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: 10e250b4395a4b1ff6104b10bcdf3cb4 NeedsCompilation: no Title: Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations Description: This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. biocViews: Microarray, GraphAndNetwork Author: Adi Laurentiu Tarca , Purvesh Kathri and Sorin Draghici Maintainer: Adi Laurentiu Tarca URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1 git_url: https://git.bioconductor.org/packages/SPIA git_branch: devel git_last_commit: 42fe419 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SPIA_2.63.0.tar.gz vignettes: vignettes/SPIA/inst/doc/SPIA.pdf vignetteTitles: SPIA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SPIA/inst/doc/SPIA.R importsMe: EnrichmentBrowser suggestsMe: graphite, KEGGgraph dependencyCount: 15 Package: SPIAT Version: 1.13.1 Depends: R (>= 4.2.0), SpatialExperiment (>= 1.8.0) Imports: apcluster (>= 1.4.7), ggplot2 (>= 3.2.1), gridExtra (>= 2.3), gtools (>= 3.8.1), reshape2 (>= 1.4.3), dplyr (>= 0.8.3), RANN (>= 2.6.1), pracma (>= 2.2.5), dbscan (>= 1.1-5), mmand (>= 1.5.4), tibble (>= 2.1.3), grDevices, stats, utils, vroom, dittoSeq, spatstat.geom, methods, spatstat.explore, raster, sp, SummarizedExperiment, rlang Suggests: BiocStyle, plotly (>= 4.9.0), knitr, rmarkdown, pkgdown, testthat, graphics, alphahull, Rtsne, umap, ComplexHeatmap, elsa License: Artistic-2.0 + file LICENSE MD5sum: c615ec569a96c6499d835540e67d655f NeedsCompilation: no Title: Spatial Image Analysis of Tissues Description: SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues) is an R package with a suite of data processing, quality control, visualization and data analysis tools. SPIAT is compatible with data generated from single-cell spatial proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler). SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes. SPIAT includes six analysis modules that allow visualization, calculation of cell colocalization, categorization of the immune microenvironment relative to tumor areas, analysis of cellular neighborhoods, and the quantification of spatial heterogeneity, providing a comprehensive toolkit for spatial data analysis. biocViews: BiomedicalInformatics, CellBiology, Spatial, Clustering, DataImport, ImmunoOncology, QualityControl, SingleCell, Software, Visualization Author: Anna Trigos [aut] (ORCID: ), Yuzhou Feng [aut, cre] (ORCID: ), Tianpei Yang [aut], Mabel Li [aut], John Zhu [aut], Volkan Ozcoban [aut], Maria Doyle [aut] Maintainer: Yuzhou Feng URL: https://trigosteam.github.io/SPIAT/ VignetteBuilder: knitr BugReports: https://github.com/trigosteam/SPIAT/issues git_url: https://git.bioconductor.org/packages/SPIAT git_branch: devel git_last_commit: 0737e63 git_last_commit_date: 2026-01-31 Date/Publication: 2026-04-20 source.ver: src/contrib/SPIAT_1.13.1.tar.gz vignettes: vignettes/SPIAT/inst/doc/basic_analysis.html, vignettes/SPIAT/inst/doc/cell-colocalisation.html, vignettes/SPIAT/inst/doc/data_reading-formatting.html, vignettes/SPIAT/inst/doc/neighborhood.html, vignettes/SPIAT/inst/doc/quality-control_visualisation.html, vignettes/SPIAT/inst/doc/spatial-heterogeneity.html, vignettes/SPIAT/inst/doc/SPIAT.html, vignettes/SPIAT/inst/doc/tissue-structure.html vignetteTitles: Basic analyses with SPIAT, Quantifying cell colocalisation with SPIAT, Reading in data and data formatting in SPIAT, Identifying cellular neighborhood with SPIAT, Quality control and visualisation with SPIAT, Spatial heterogeneity with SPIAT, Overview of the SPIAT package, Characterising tissue structure with SPIAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SPIAT/inst/doc/basic_analysis.R, vignettes/SPIAT/inst/doc/cell-colocalisation.R, vignettes/SPIAT/inst/doc/data_reading-formatting.R, vignettes/SPIAT/inst/doc/neighborhood.R, vignettes/SPIAT/inst/doc/quality-control_visualisation.R, vignettes/SPIAT/inst/doc/spatial-heterogeneity.R, vignettes/SPIAT/inst/doc/SPIAT.R, vignettes/SPIAT/inst/doc/tissue-structure.R dependencyCount: 111 Package: SPICEY Version: 1.1.0 Depends: R (>= 4.5.0), utils, stats, grDevices Imports: GenomicRanges, GenomicFeatures, AnnotationDbi, S4Vectors, ggplot2, dplyr, tidyr, tibble, GenomeInfoDb, scales, cowplot Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 4926ebcc39ba131512f310bcc3fcbc6c NeedsCompilation: no Title: Calculates cell type specificity from single cell data Description: SPICEY (SPecificity Index for Coding and Epigenetic activitY) is an R package designed to quantify cell-type specificity in single-cell transcriptomic and epigenomic data, particularly scRNA-seq and scATAC-seq. It introduces two complementary indices: the Gene Expression Tissue Specificity Index (GETSI) and the Regulatory Element Tissue Specificity Index (RETSI), both based on entropy to provide continuous, interpretable measures of specificity. By integrating gene expression and chromatin accessibility, SPICEY enables standardized analysis of cell-type-specific regulatory programs across diverse tissues and conditions. biocViews: Transcriptomics, Epigenetics, SingleCell, DifferentialExpression, DifferentialPeakCalling, GeneRegulation, GeneTarget, GeneExpression, Transcription Author: Georgina Fuentes-Páez [aut, cre] (ORCID: ), Nacho Molina [aut], Mireia Ramos-Rodriguez [aut], Lorenzo Pasquali [aut], Ministerio de Ciencia e Innovación Spain [fnd] (program: FPI Fellowship) Maintainer: Georgina Fuentes-Páez URL: https://georginafp.github.io/SPICEY VignetteBuilder: knitr BugReports: https://github.com/georginafp/SPICEY/issues git_url: https://git.bioconductor.org/packages/SPICEY git_branch: devel git_last_commit: a5c4c20 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SPICEY_1.1.0.tar.gz vignettes: vignettes/SPICEY/inst/doc/SPICEY.html vignetteTitles: Measuring tissue specificity from single cell data with SPICEY hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPICEY/inst/doc/SPICEY.R dependencyCount: 98 Package: SpiecEasi Version: 1.99.5 Depends: R (>= 4.5.0), Imports: stats, methods, graphics, grDevices, huge (>= 1.3.2), pulsar (>= 0.3.11), MASS, VGAM, Matrix (>= 1.5), glmnet, phyloseq LinkingTo: Rcpp, RcppArmadillo Suggests: parallel, boot, igraph, batchtools, testthat, covr, knitr, BiocStyle, rmarkdown, RefManageR, sessioninfo, magick License: GPL (>= 3) MD5sum: fe8c15b8fdaa0cda0dc57fa4886004be NeedsCompilation: yes Title: Sparse Inverse Covariance for Ecological Statistical Inference Description: Estimate networks from the precision matrix of compositional microbial abundance data. biocViews: Software, Microbiome, Metagenomics, GraphAndNetwork, NetworkInference Author: Zachary Kurtz [aut, cre], Christian Mueller [aut], Emily Miraldi [aut], Richard Bonneau [aut], Laura Tipton [ctb] Maintainer: Zachary Kurtz URL: https://github.com/zdk123/SpiecEasi VignetteBuilder: knitr BugReports: https://github.com/zdk123/SpiecEasi/issues git_url: https://git.bioconductor.org/packages/SpiecEasi git_branch: devel git_last_commit: 54bbc2c git_last_commit_date: 2026-04-06 Date/Publication: 2026-04-20 source.ver: src/contrib/SpiecEasi_1.99.5.tar.gz vignettes: vignettes/SpiecEasi/inst/doc/cross-domain-interactions.html, vignettes/SpiecEasi/inst/doc/latent-variable-models.html, vignettes/SpiecEasi/inst/doc/phyloseq-integration.html, vignettes/SpiecEasi/inst/doc/pulsar-parallel.html, vignettes/SpiecEasi/inst/doc/SpiecEasi.html, vignettes/SpiecEasi/inst/doc/troubleshooting.html vignetteTitles: Cross Domain SPIEC-EASI, Learning latent variable graphical models, Working with phyloseq, pulsar: parallel utilities for model selection, Introduction to SpiecEasi, Troubleshooting hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpiecEasi/inst/doc/cross-domain-interactions.R, vignettes/SpiecEasi/inst/doc/latent-variable-models.R, vignettes/SpiecEasi/inst/doc/phyloseq-integration.R, vignettes/SpiecEasi/inst/doc/pulsar-parallel.R, vignettes/SpiecEasi/inst/doc/SpiecEasi.R, vignettes/SpiecEasi/inst/doc/troubleshooting.R dependencyCount: 73 Package: spikeLI Version: 2.71.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: 34b4d135fcb73c0a7f9ad6cc77b9afd7 NeedsCompilation: no Title: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool Description: SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006). biocViews: Microarray, QualityControl Author: Delphine Baillon, Paul Leclercq , Sarah Ternisien, Thomas Heim, Enrico Carlon Maintainer: Enrico Carlon git_url: https://git.bioconductor.org/packages/spikeLI git_branch: devel git_last_commit: 37b2573 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/spikeLI_2.71.0.tar.gz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: spiky Version: 1.17.0 Depends: Rsamtools, GenomicRanges, R (>= 3.6.0) Imports: stats, scales, bamlss, methods, tools, IRanges, Biostrings, GenomicAlignments, BlandAltmanLeh, GenomeInfoDb, BSgenome, S4Vectors, graphics, ggplot2, utils Suggests: covr, testthat, rmarkdown, markdown, knitr, devtools, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg38.masked, BiocManager License: GPL-2 MD5sum: 3a56dfab5a33ab8bf8fd78f5e3869248 NeedsCompilation: no Title: Spike-in calibration for cell-free MeDIP Description: spiky implements methods and model generation for cfMeDIP (cell-free methylated DNA immunoprecipitation) with spike-in controls. CfMeDIP is an enrichment protocol which avoids destructive conversion of scarce template, making it ideal as a "liquid biopsy," but creating certain challenges in comparing results across specimens, subjects, and experiments. The use of synthetic spike-in standard oligos allows diagnostics performed with cfMeDIP to quantitatively compare samples across subjects, experiments, and time points in both relative and absolute terms. biocViews: DifferentialMethylation, DNAMethylation, Normalization, Preprocessing, QualityControl, Sequencing Author: Samantha Wilson [aut], Lauren Harmon [aut], Tim Triche [aut, cre] Maintainer: Tim Triche URL: https://github.com/trichelab/spiky VignetteBuilder: knitr BugReports: https://github.com/trichelab/spiky/issues git_url: https://git.bioconductor.org/packages/spiky git_branch: devel git_last_commit: 5e5bb23 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/spiky_1.17.0.tar.gz vignettes: vignettes/spiky/inst/doc/spiky_vignette.html vignetteTitles: Spiky: Analysing cfMeDIP-seq data with spike-in controls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spiky/inst/doc/spiky_vignette.R dependencyCount: 88 Package: spillR Version: 1.7.0 Depends: R (>= 4.3.0), SummarizedExperiment, CATALYST Imports: dplyr, tibble, tidyselect, stats, ggplot2, tidyr, spatstat.univar, S4Vectors, parallel Suggests: knitr, rmarkdown, cowplot, testthat (>= 3.0.0), BiocStyle, hexbin License: LGPL-3 MD5sum: 2e534e34454105de412cbea0709b99a2 NeedsCompilation: no Title: Spillover Compensation in Mass Cytometry Data Description: Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. We implement our method using expectation-maximization to fit the mixture model. biocViews: FlowCytometry, ImmunoOncology, MassSpectrometry, Preprocessing, SingleCell, Software, StatisticalMethod, Visualization, Regression Author: Marco Guazzini [aut, cre] (ORCID: ), Alexander G. Reisach [aut] (ORCID: ), Sebastian Weichwald [aut] (ORCID: ), Christof Seiler [aut] (ORCID: ) Maintainer: Marco Guazzini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/spillR git_branch: devel git_last_commit: c3abfa4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/spillR_1.7.0.tar.gz vignettes: vignettes/spillR/inst/doc/spillR-vignette.html vignetteTitles: spillR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spillR/inst/doc/spillR-vignette.R dependencyCount: 184 Package: spkTools Version: 1.67.0 Depends: R (>= 2.7.0), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), graphics, grDevices, gtools, methods, RColorBrewer, stats, utils Suggests: xtable License: GPL (>= 2) MD5sum: 20edddc61fb4db10bdecee9ac3309bdc NeedsCompilation: no Title: Methods for Spike-in Arrays Description: The package contains functions that can be used to compare expression measures on different array platforms. biocViews: Software, Technology, Microarray Author: Matthew N McCall , Rafael A Irizarry Maintainer: Matthew N McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/spkTools git_branch: devel git_last_commit: ff503f5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/spkTools_1.67.0.tar.gz vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf vignetteTitles: spkTools: Spike-in Data Analysis and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spkTools/inst/doc/spkDoc.R dependencyCount: 10 Package: SpliceImpactR Version: 0.99.4 Depends: R (>= 3.5.0) Imports: data.table, BiocFileCache, BiocParallel, Biostrings, GenomicRanges, SummarizedExperiment, biomaRt, IRanges, PFAM.db, dplyr, ggplot2, ggpubr, patchwork, pwalign, rtracklayer, scales, stats, tidyr, tools, utils, magrittr, methods, S4Vectors Suggests: devtools, testthat (>= 3.0.0), knitr, rmarkdown, cowplot, stringr, readr, tibble, BiocStyle, clusterProfiler, AnnotationDbi, msigdbr, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: ecfb5c06039dfdd9b657018971818747 NeedsCompilation: no Title: An R package to identify functional impacts due to alternative RNA processing events Description: Works by taking in processed data from the HIT Index and/or rMATS and identifying how differentially used alternative RNA processing events lead to changes in protein function through various means. Primarily this is done through protein similarity, functional protein domain analysis, and domain-domain interaction changes. Notably, we both identify alterantive RNA processing event 'swaps' across condition and are able to perform holistic analyses regarding the impact of different RNA processing events. biocViews: AlternativeSplicing, DifferentialSplicing, StatisticalMethod, Alignment Author: Zachary Wakefield [cre, aut] (ORCID: ), Ana Fiszbein [aut] Maintainer: Zachary Wakefield VignetteBuilder: knitr BugReports: https://github.com/fiszbein-lab/SpliceImpactR/issues git_url: https://git.bioconductor.org/packages/SpliceImpactR git_branch: devel git_last_commit: 25ae9d1 git_last_commit_date: 2026-03-23 Date/Publication: 2026-04-20 source.ver: src/contrib/SpliceImpactR_0.99.4.tar.gz vignettes: vignettes/SpliceImpactR/inst/doc/SpliceImpactR.html vignetteTitles: SpliceImpactR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpliceImpactR/inst/doc/SpliceImpactR.R dependencyCount: 154 Package: SplicingFactory Version: 1.19.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, methods, stats Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr License: GPL-3 + file LICENSE MD5sum: 8665f79c6388fd00dad1c2535e0ccb05 NeedsCompilation: no Title: Splicing Diversity Analysis for Transcriptome Data Description: The SplicingFactory R package uses transcript-level expression values to analyze splicing diversity based on various statistical measures, like Shannon entropy or the Gini index. These measures can quantify transcript isoform diversity within samples or between conditions. Additionally, the package analyzes the isoform diversity data, looking for significant changes between conditions. biocViews: Transcriptomics, RNASeq, DifferentialSplicing, AlternativeSplicing, TranscriptomeVariant Author: Peter A. Szikora [aut], Tamas Por [aut], Endre Sebestyen [aut, cre] (ORCID: ) Maintainer: Endre Sebestyen URL: https://github.com/esebesty/SplicingFactory VignetteBuilder: knitr BugReports: https://github.com/esebesty/SplicingFactory/issues git_url: https://git.bioconductor.org/packages/SplicingFactory git_branch: devel git_last_commit: e9a3630 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SplicingFactory_1.19.0.tar.gz vignettes: vignettes/SplicingFactory/inst/doc/SplicingFactory.html vignetteTitles: SplicingFactory hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SplicingFactory/inst/doc/SplicingFactory.R dependencyCount: 25 Package: SplicingGraphs Version: 1.51.1 Depends: R (>= 3.5.0), GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, graphics, igraph, BiocGenerics, S4Vectors (>= 0.17.5), BiocParallel, IRanges (>= 2.21.2), Seqinfo, GenomicRanges (>= 1.23.21), Rsamtools, graph Suggests: igraph, Gviz, txdbmaker, TxDb.Hsapiens.UCSC.hg19.refGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: d1f84eedbb91b7d72e2ef4af98fd2282 NeedsCompilation: no Title: Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them Description: This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways. biocViews: Genetics, Annotation, DataRepresentation, Visualization, Sequencing, RNASeq, GeneExpression, AlternativeSplicing, Transcription, ImmunoOncology Author: D. Bindreither, M. Carlson, M. Morgan, H. Pagès Maintainer: H. Pagès URL: https://bioconductor.org/packages/SplicingGraphs BugReports: https://github.com/Bioconductor/SplicingGraphs/issues git_url: https://git.bioconductor.org/packages/SplicingGraphs git_branch: devel git_last_commit: 8abbef9 git_last_commit_date: 2025-11-02 Date/Publication: 2026-04-20 source.ver: src/contrib/SplicingGraphs_1.51.1.tar.gz vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf vignetteTitles: Splicing graphs and RNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.R dependencyCount: 79 Package: SplineDV Version: 1.3.0 Depends: R (>= 3.5.0) Imports: plotly, dplyr, scuttle, methods, Biobase, BiocGenerics, S4Vectors, sparseMatrixStats, SingleCellExperiment, SummarizedExperiment, Matrix (>= 1.6.4), utils Suggests: knitr, DelayedMatrixStats, rmarkdown, BiocStyle, ggplot2, ggpubr, MASS, scales, scRNAseq, testthat (>= 3.0.0) License: GPL-2 MD5sum: 311ad6acfdbe0c2ea786284d3f1b6613 NeedsCompilation: no Title: Differential Variability (DV) analysis for single-cell RNA sequencing data. (e.g. Identify Differentially Variable Genes across two experimental conditions) Description: A spline based scRNA-seq method for identifying differentially variable (DV) genes across two experimental conditions. Spline-DV constructs a 3D spline from 3 key gene statistics: mean expression, coefficient of variance, and dropout rate. This is done for both conditions. The 3D spline provides the “expected” behavior of genes in each condition. The distance of the observed mean, CV and dropout rate of each gene from the expected 3D spline is used to measure variability. As the final step, the spline-DV method compares the variabilities of each condition to identify differentially variable (DV) genes. biocViews: Software, SingleCell, Sequencing, DifferentialExpression, RNASeq, GeneExpression, Transcriptomics, FeatureExtraction Author: Shreyan Gupta [aut, cre] (ORCID: ), James Cai [aut] (ORCID: ) Maintainer: Shreyan Gupta URL: https://github.com/Xenon8778/SplineDV VignetteBuilder: knitr BugReports: https://github.com/Xenon8778/SplineDV/issues git_url: https://git.bioconductor.org/packages/SplineDV git_branch: devel git_last_commit: 0bab677 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SplineDV_1.3.0.tar.gz vignettes: vignettes/SplineDV/inst/doc/SplineDV.html vignetteTitles: Introduction to Spline-DV hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SplineDV/inst/doc/SplineDV.R dependencyCount: 102 Package: splineTimeR Version: 1.39.0 Depends: R (>= 3.3), Biobase, igraph, limma, GSEABase, gtools, splines, GeneNet (>= 1.2.13), longitudinal (>= 1.1.12), FIs Suggests: knitr License: GPL-3 MD5sum: e82a7a3d013413710a73a58249d38ed8 NeedsCompilation: no Title: Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction Description: This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks. biocViews: GeneExpression, DifferentialExpression, TimeCourse, Regression, GeneSetEnrichment, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Agata Michna Maintainer: Herbert Braselmann , Martin Selmansberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splineTimeR git_branch: devel git_last_commit: 2658c19 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/splineTimeR_1.39.0.tar.gz vignettes: vignettes/splineTimeR/inst/doc/splineTimeR.pdf vignetteTitles: splineTimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splineTimeR/inst/doc/splineTimeR.R dependencyCount: 61 Package: SPLINTER Version: 1.37.0 Depends: R (>= 3.6.0), grDevices, stats Imports: graphics, ggplot2, seqLogo, Biostrings, pwalign, biomaRt, GenomicAlignments, GenomicRanges, GenomicFeatures, Gviz, IRanges, S4Vectors, Seqinfo, utils, plyr,stringr, methods, BSgenome.Mmusculus.UCSC.mm9, googleVis Suggests: txdbmaker, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 2b193019d5019ecd8698ecad38db251d NeedsCompilation: no Title: Splice Interpreter of Transcripts Description: Provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments. biocViews: ImmunoOncology, GeneExpression, RNASeq, Visualization, AlternativeSplicing Author: Diana Low [aut, cre] Maintainer: Diana Low URL: https://github.com/dianalow/SPLINTER/ VignetteBuilder: knitr BugReports: https://github.com/dianalow/SPLINTER/issues git_url: https://git.bioconductor.org/packages/SPLINTER git_branch: devel git_last_commit: 2ff2d3c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SPLINTER_1.37.0.tar.gz vignettes: vignettes/SPLINTER/inst/doc/vignette.pdf vignetteTitles: SPLINTER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPLINTER/inst/doc/vignette.R dependencyCount: 156 Package: splots Version: 1.77.0 Imports: grid, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown, assertthat, HD2013SGI, dplyr, ggplot2 License: LGPL MD5sum: 69d2cb5598178d40c8cb5f84a7b34653 NeedsCompilation: no Title: Visualization of high-throughput assays in microtitre plate or slide format Description: This package is here to support legacy usages of it, but it should not be used for new code development. It provides a single function, plotScreen, for visualising data in microtitre plate or slide format. As a better alternative for such functionality, please consider the platetools package on CRAN (https://cran.r-project.org/package=platetools and https://github.com/Swarchal/platetools), or ggplot2 (geom_raster, facet_wrap) as exemplified in the vignette of this package. biocViews: Visualization, Sequencing, MicrotitrePlateAssay Author: Wolfgang Huber, Oleg Sklyar Maintainer: Wolfgang Huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splots git_branch: devel git_last_commit: 355c4b6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/splots_1.77.0.tar.gz vignettes: vignettes/splots/inst/doc/splots.html vignetteTitles: splots: visualization of data from assays in microtitre plate or slide format hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splots/inst/doc/splots.R dependsOnMe: HD2013SGI dependencyCount: 2 Package: SpNeigh Version: 0.99.43 Depends: R (>= 4.4.0) Imports: concaveman, dbscan, dplyr, FNN, ggplot2, limma, magrittr, Matrix, methods, patchwork, purrr, rlang, scales, Seurat, sf, SingleCellExperiment, SpatialExperiment, splines, stringr, SummarizedExperiment, tibble, tidyr Suggests: BiocStyle, knitr, rmarkdown, SeuratObject, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: 275975f790dc2d5dfd105920b3f37b7a NeedsCompilation: no Title: Spatial Neighborhood Modeling and Differential Expression Analysis for Transcriptomics Description: SpNeigh provides methods for neighborhood-aware analysis of spatial transcriptomics data. It supports boundary detection, spatial weighting (centroid- and boundary-based), spatially informed differential expression using spline-based models, and spatial enrichment analysis via the Spatial Enrichment Index (SEI). Designed for compatibility with Seurat objects, SpatialExperiment objects and spatial data frames, SpNeigh enables interpretable, publication-ready analysis of spatial gene expression patterns. biocViews: Spatial, SingleCell, GeneExpression, DifferentialExpression, Transcriptomics, Software Author: Jinming Cheng [aut, cre] (ORCID: ) Maintainer: Jinming Cheng URL: https://github.com/jinming-cheng/SpNeigh VignetteBuilder: knitr BugReports: https://github.com/jinming-cheng/SpNeigh/issues git_url: https://git.bioconductor.org/packages/SpNeigh git_branch: devel git_last_commit: 510beba git_last_commit_date: 2026-04-09 Date/Publication: 2026-04-20 source.ver: src/contrib/SpNeigh_0.99.43.tar.gz vignettes: vignettes/SpNeigh/inst/doc/SpNeigh.html vignetteTitles: Getting Started with SpNeigh hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpNeigh/inst/doc/SpNeigh.R dependencyCount: 190 Package: spoon Version: 1.7.1 Depends: R (>= 4.4) Imports: SpatialExperiment, BRISC, nnSVG, BiocParallel, Matrix, methods, SummarizedExperiment, stats, utils, scuttle Suggests: testthat, STexampleData, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: b525bc0000054cd907319cc91f940e8b NeedsCompilation: no Title: Address the Mean-variance Relationship in Spatial Transcriptomics Data Description: This package addresses the mean-variance relationship in spatially resolved transcriptomics data. Precision weights are generated for individual observations using Empirical Bayes techniques. These weights are used to rescale the data and covariates, which are then used as input in spatially variable gene detection tools. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Preprocessing Author: Kinnary Shah [aut, cre] (ORCID: ), Boyi Guo [aut] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Kinnary Shah URL: https://github.com/kinnaryshah/spoon VignetteBuilder: knitr BugReports: https://github.com/kinnaryshah/spoon/issues git_url: https://git.bioconductor.org/packages/spoon git_branch: devel git_last_commit: ba475a1 git_last_commit_date: 2026-03-07 Date/Publication: 2026-04-20 source.ver: src/contrib/spoon_1.7.1.tar.gz vignettes: vignettes/spoon/inst/doc/spoon.html vignetteTitles: spoon Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spoon/inst/doc/spoon.R dependencyCount: 84 Package: SpotClean Version: 1.13.1 Depends: R (>= 4.2.0), Imports: stats, methods, utils, dplyr, S4Vectors, SummarizedExperiment, SpatialExperiment, Matrix, rhdf5, ggplot2, grid, readbitmap, rjson, tibble, viridis, grDevices, RColorBrewer, Seurat, rlang Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, R.utils, spelling License: GPL-3 MD5sum: 78c4bb88e29c6a09620457b46cb7000d NeedsCompilation: yes Title: SpotClean adjusts for spot swapping in spatial transcriptomics data Description: SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses. biocViews: DataImport, RNASeq, Sequencing, GeneExpression, Spatial, SingleCell, Transcriptomics, Preprocessing Author: Zijian Ni [aut, cre] (ORCID: ), Christina Kendziorski [ctb] Maintainer: Zijian Ni URL: https://github.com/zijianni/SpotClean VignetteBuilder: knitr BugReports: https://github.com/zijianni/SpotClean/issues git_url: https://git.bioconductor.org/packages/SpotClean git_branch: devel git_last_commit: adfff15 git_last_commit_date: 2026-01-13 Date/Publication: 2026-04-20 source.ver: src/contrib/SpotClean_1.13.1.tar.gz vignettes: vignettes/SpotClean/inst/doc/SpotClean.html vignetteTitles: SpotClean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpotClean/inst/doc/SpotClean.R dependencyCount: 187 Package: SPOTlight Version: 1.15.0 Depends: R (>= 4.5.0) Imports: ggplot2, Matrix, SingleCellExperiment, sparseMatrixStats, stats LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, colorBlindness, DelayedArray, DropletUtils, ExperimentHub, ggcorrplot, grDevices, grid, igraph, jpeg, knitr, methods, png, rmarkdown, scater, scatterpie, scran, SpatialExperiment, SummarizedExperiment, S4Vectors, TabulaMurisSenisData, TENxVisiumData, testthat License: GPL-3 MD5sum: a067925a552b9b43417f456c5be07c69 NeedsCompilation: yes Title: `SPOTlight`: Spatial Transcriptomics Deconvolution Description: `SPOTlight` provides a method to deconvolute spatial transcriptomics spots using a seeded NMF approach along with visualization tools to assess the results. Spatially resolved gene expression profiles are key to understand tissue organization and function. However, novel spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). biocViews: SingleCell, Spatial, StatisticalMethod Author: Marc Elosua-Bayes [aut, cre], Zachary DeBruine [aut], Helena L. Crowell [aut] Maintainer: Marc Elosua-Bayes URL: https://github.com/MarcElosua/SPOTlight VignetteBuilder: knitr BugReports: https://github.com/MarcElosua/SPOTlight/issues git_url: https://git.bioconductor.org/packages/SPOTlight git_branch: devel git_last_commit: b112ee3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SPOTlight_1.15.0.tar.gz vignettes: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.html vignetteTitles: "SPOTlight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.R dependencyCount: 46 Package: SpotSweeper Version: 1.7.0 Depends: R (>= 4.4.0) Imports: SpatialExperiment, SummarizedExperiment, BiocNeighbors, SingleCellExperiment, stats, escheR, MASS, ggplot2, spatialEco, grDevices, BiocParallel Suggests: knitr, BiocStyle, rmarkdown, scuttle, STexampleData, ggpubr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 634534b58dbe651571af2072004d9bc1 NeedsCompilation: no Title: Spatially-aware quality control for spatial transcriptomics Description: Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts. biocViews: Software, Spatial, Transcriptomics, QualityControl, GeneExpression, Author: Michael Totty [aut, cre] (ORCID: ), Stephanie Hicks [aut] (ORCID: ), Boyi Guo [aut] (ORCID: ) Maintainer: Michael Totty URL: https://github.com/MicTott/SpotSweeper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/SpotSweeper git_url: https://git.bioconductor.org/packages/SpotSweeper git_branch: devel git_last_commit: 61632f2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SpotSweeper_1.7.0.tar.gz vignettes: vignettes/SpotSweeper/inst/doc/getting_started.html vignetteTitles: Getting Started with `SpotSweeper` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpotSweeper/inst/doc/getting_started.R importsMe: OSTA dependencyCount: 100 Package: spqn Version: 1.23.0 Depends: R (>= 4.0), ggplot2, ggridges, SummarizedExperiment, BiocGenerics Imports: graphics, stats, utils, matrixStats Suggests: BiocStyle, knitr, rmarkdown, tools, spqnData (>= 0.99.3), RUnit License: Artistic-2.0 MD5sum: 025d0abaa15658ff5b3e0744867f971f NeedsCompilation: no Title: Spatial quantile normalization Description: The spqn package implements spatial quantile normalization (SpQN). This method was developed to remove a mean-correlation relationship in correlation matrices built from gene expression data. It can serve as pre-processing step prior to a co-expression analysis. biocViews: NetworkInference, GraphAndNetwork, Normalization Author: Yi Wang [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Yi Wang URL: https://github.com/hansenlab/spqn VignetteBuilder: knitr BugReports: https://github.com/hansenlab/spqn/issues git_url: https://git.bioconductor.org/packages/spqn git_branch: devel git_last_commit: 86a8920 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/spqn_1.23.0.tar.gz vignettes: vignettes/spqn/inst/doc/spqn.html vignetteTitles: spqn User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spqn/inst/doc/spqn.R dependencyCount: 43 Package: SPsimSeq Version: 1.21.0 Depends: R (>= 4.0) Imports: stats, methods, SingleCellExperiment, fitdistrplus, graphics, edgeR, Hmisc, WGCNA, limma, mvtnorm, phyloseq, utils Suggests: knitr, rmarkdown, LSD, testthat, BiocStyle License: GPL-2 MD5sum: 685211ce40266aab7436b3b164afb848 NeedsCompilation: no Title: Semi-parametric simulation tool for bulk and single-cell RNA sequencing data Description: SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size. biocViews: GeneExpression, RNASeq, SingleCell, Sequencing, DNASeq Author: Alemu Takele Assefa [aut], Olivier Thas [ths], Joris Meys [cre], Stijn Hawinkel [aut] Maintainer: Joris Meys URL: https://github.com/CenterForStatistics-UGent/SPsimSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPsimSeq git_branch: devel git_last_commit: c584581 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SPsimSeq_1.21.0.tar.gz vignettes: vignettes/SPsimSeq/inst/doc/SPsimSeq.html vignetteTitles: Manual for the SPsimSeq package: semi-parametric simulation for bulk and single cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPsimSeq/inst/doc/SPsimSeq.R importsMe: SurfR suggestsMe: benchdamic dependencyCount: 119 Package: sRACIPE Version: 2.3.2 Depends: R (>= 3.6.0),SummarizedExperiment, methods, Rcpp Imports: ggplot2, reshape2, MASS, RColorBrewer, gridExtra,visNetwork, gplots, umap, htmlwidgets, S4Vectors, BiocGenerics, grDevices, stats, utils, graphics, doFuture, doRNG, future, foreach LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, tinytest License: MIT + file LICENSE MD5sum: babc27873f0bacfe84b16679569c0527 NeedsCompilation: yes Title: Systems biology tool to simulate gene regulatory circuits Description: sRACIPE implements a randomization-based method for gene circuit modeling. It allows us to study the effect of both the gene expression noise and the parametric variation on any gene regulatory circuit (GRC) using only its topology, and simulates an ensemble of models with random kinetic parameters at multiple noise levels. Statistical analysis of the generated gene expressions reveals the basin of attraction and stability of various phenotypic states and their changes associated with intrinsic and extrinsic noises. sRACIPE provides a holistic picture to evaluate the effects of both the stochastic nature of cellular processes and the parametric variation. biocViews: ResearchField, SystemsBiology, MathematicalBiology, GeneExpression, GeneRegulation, GeneTarget Author: Mingyang Lu [aut, cre] (ORCID: ), Vivek Kohar [aut], Aidan Tillman [aut], Daniel Ramirez [aut] Maintainer: Mingyang Lu URL: https://github.com/lusystemsbio/sRACIPE, https://geneex.jax.org/, https://vivekkohar.github.io/sRACIPE/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sRACIPE git_branch: devel git_last_commit: d27cbad git_last_commit_date: 2026-02-14 Date/Publication: 2026-04-20 source.ver: src/contrib/sRACIPE_2.3.2.tar.gz vignettes: vignettes/sRACIPE/inst/doc/sRACIPE.html vignetteTitles: A systems biology tool for gene regulatory circuit simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sRACIPE/inst/doc/sRACIPE.R dependencyCount: 101 Package: SRAdb Version: 1.73.0 Depends: RSQLite, graph, RCurl Imports: R.utils Suggests: Rgraphviz License: Artistic-2.0 MD5sum: b4e065e603ed523f75728e88f63333d1 NeedsCompilation: no Title: A compilation of metadata from NCBI SRA and tools Description: The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. biocViews: Infrastructure, Sequencing, DataImport Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu BugReports: https://github.com/zhujack/SRAdb/issues/new git_url: https://git.bioconductor.org/packages/SRAdb git_branch: devel git_last_commit: 0d39542 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SRAdb_1.73.0.tar.gz vignettes: vignettes/SRAdb/inst/doc/SRAdb.pdf vignetteTitles: Using SRAdb to Query the Sequence Read Archive hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SRAdb/inst/doc/SRAdb.R dependencyCount: 29 Package: srnadiff Version: 1.31.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), stats, methods, S4Vectors, Seqinfo, rtracklayer, SummarizedExperiment, IRanges, GenomicRanges, DESeq2, edgeR, Rsamtools, GenomicFeatures, GenomicAlignments, grDevices, Gviz, BiocParallel, BiocManager, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocManager, BiocStyle License: GPL-3 MD5sum: ad037e263ce201c5bcb2bfe1a7a8af11 NeedsCompilation: yes Title: Finding differentially expressed unannotated genomic regions from RNA-seq data Description: srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. It reads BAM files as input, and outputs a list differentially regions, together with the adjusted p-values. biocViews: ImmunoOncology, GeneExpression, Coverage, SmallRNA, Epigenetics, StatisticalMethod, Preprocessing, DifferentialExpression Author: Zytnicki Matthias [aut, cre], Gonzalez Ignacio [aut] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/srnadiff git_branch: devel git_last_commit: 680956d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/srnadiff_1.31.0.tar.gz vignettes: vignettes/srnadiff/inst/doc/srnadiff.html vignetteTitles: The srnadiff package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/srnadiff/inst/doc/srnadiff.R dependencyCount: 160 Package: sscu Version: 2.41.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: e6a3614b16b07fad51af51c13e12b8a6 NeedsCompilation: no Title: Strength of Selected Codon Usage Description: The package calculates the indexes for selective stength in codon usage in bacteria species. (1) The package can calculate the strength of selected codon usage bias (sscu, also named as s_index) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on four pairs of codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. (2) The package can detect the strength of translational accuracy selection by Akashi's test. The test tabulating all codons into four categories with the feature as conserved/variable amino acids and optimal/non-optimal codons. (3) Optimal codon lists (selected codons) can be calculated by either op_highly function (by using the highly expressed genes compared with all genes to identify optimal codons), or op_corre_CodonW/op_corre_NCprime function (by correlative method developed by Hershberg & Petrov). Users will have a list of optimal codons for further analysis, such as input to the Akashi's test. (4) The detailed codon usage information, such as RSCU value, number of optimal codons in the highly/all gene set, as well as the genomic gc3 value, can be calculate by the optimal_codon_statistics and genomic_gc3 function. (5) Furthermore, we added one test function low_frequency_op in the package. The function try to find the low frequency optimal codons, among all the optimal codons identified by the op_highly function. biocViews: Genetics, GeneExpression, WholeGenome Author: Yu Sun Maintainer: Yu Sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sscu git_branch: devel git_last_commit: 2db5d27 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sscu_2.41.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 26 Package: sSeq Version: 1.49.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: a4fe3a299629be1fc8fbb17eeafe2d71 NeedsCompilation: no Title: Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size Description: The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution. biocViews: ImmunoOncology, RNASeq Author: Danni Yu , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu git_url: https://git.bioconductor.org/packages/sSeq git_branch: devel git_last_commit: 0e7ac26 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sSeq_1.49.0.tar.gz vignettes: vignettes/sSeq/inst/doc/sSeq.pdf vignetteTitles: sSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSeq/inst/doc/sSeq.R importsMe: MLSeq suggestsMe: NBLDA dependencyCount: 3 Package: ssize Version: 1.85.0 Depends: gdata, xtable License: LGPL MD5sum: be80ae66fba2131a23d9b0397e933bd3 NeedsCompilation: no Title: Estimate Microarray Sample Size Description: Functions for computing and displaying sample size information for gene expression arrays. biocViews: Microarray, DifferentialExpression Author: Gregory R. Warnes, Peng Liu, and Fasheng Li Maintainer: Gregory R. Warnes git_url: https://git.bioconductor.org/packages/ssize git_branch: devel git_last_commit: 0dfd5e9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ssize_1.85.0.tar.gz vignettes: vignettes/ssize/inst/doc/ssize.pdf vignetteTitles: Sample Size Estimation for Microarray Experiments Using the \code{ssize} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssize/inst/doc/ssize.R suggestsMe: maGUI dependencyCount: 6 Package: sSNAPPY Version: 1.15.0 Depends: R (>= 4.3.0), ggplot2 Imports: dplyr (>= 1.1), magrittr, rlang, stats, graphite, tibble, ggraph, igraph, reshape2, org.Hs.eg.db, SummarizedExperiment, edgeR, methods, ggforce, pheatmap, utils, stringr, gtools, tidyr Suggests: BiocManager, BiocStyle, colorspace, cowplot, DT, htmltools, knitr, pander, patchwork, rmarkdown, spelling, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: 10d958504c8a5582f8bd8eb9f17ba274 NeedsCompilation: no Title: Single Sample directioNAl Pathway Perturbation analYsis Description: A single sample pathway perturbation testing method for RNA-seq data. The method propagates changes in gene expression down gene-set topologies to compute single-sample directional pathway perturbation scores that reflect potential direction of change. Perturbation scores can be used to test significance of pathway perturbation at both individual-sample and treatment levels. biocViews: Software, GeneExpression, GeneSetEnrichment, GeneSignaling Author: Wenjun Liu [aut, cre] (ORCID: ), Stephen Pederson [aut] (ORCID: ) Maintainer: Wenjun Liu URL: https://wenjun-liu.github.io/sSNAPPY/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Wenjun-Liu/sSNAPPY/issues git_url: https://git.bioconductor.org/packages/sSNAPPY git_branch: devel git_last_commit: a86fed1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sSNAPPY_1.15.0.tar.gz vignettes: vignettes/sSNAPPY/inst/doc/sSNAPPY.html vignetteTitles: Single Sample Directional Pathway Perturbation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSNAPPY/inst/doc/sSNAPPY.R dependencyCount: 102 Package: ssPATHS Version: 1.25.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: ROCR, dml, MESS Suggests: ggplot2, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 241252fade6534539fe9a4debafcd751 NeedsCompilation: no Title: ssPATHS: Single Sample PATHway Score Description: This package generates pathway scores from expression data for single samples after training on a reference cohort. The score is generated by taking the expression of a gene set (pathway) from a reference cohort and performing linear discriminant analysis to distinguish samples in the cohort that have the pathway augmented and not. The separating hyperplane is then used to score new samples. biocViews: Software, GeneExpression, BiomedicalInformatics, RNASeq, Pathways, Transcriptomics, DimensionReduction, Classification Author: Natalie R. Davidson Maintainer: Natalie R. Davidson git_url: https://git.bioconductor.org/packages/ssPATHS git_branch: devel git_last_commit: 279a5e8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ssPATHS_1.25.0.tar.gz vignettes: vignettes/ssPATHS/inst/doc/ssPATHS.pdf vignetteTitles: Using ssPATHS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ssPATHS/inst/doc/ssPATHS.R dependencyCount: 129 Package: ssrch Version: 1.27.0 Depends: R (>= 3.6), methods Imports: shiny, DT, utils Suggests: knitr, testthat, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 35f381f88b60bccf77490a8ffb18c43a NeedsCompilation: no Title: a simple search engine Description: Demonstrate tokenization and a search gadget for collections of CSV files. biocViews: Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssrch git_branch: devel git_last_commit: 1426291 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ssrch_1.27.0.tar.gz vignettes: vignettes/ssrch/inst/doc/ssrch.html vignetteTitles: ssrch: small search engine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssrch/inst/doc/ssrch.R dependencyCount: 47 Package: ssviz Version: 1.45.0 Depends: R (>= 3.5.0), methods, Rsamtools, Biostrings, reshape, ggplot2, RColorBrewer, stats Suggests: knitr License: GPL-2 MD5sum: 6ec2a678e24d93d6e277baadad5d959f NeedsCompilation: no Title: A small RNA-seq visualizer and analysis toolkit Description: Small RNA sequencing viewer biocViews: ImmunoOncology, Sequencing,RNASeq,Visualization,MultipleComparison,Genetics Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssviz git_branch: devel git_last_commit: ba9f121 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ssviz_1.45.0.tar.gz vignettes: vignettes/ssviz/inst/doc/ssviz.pdf vignetteTitles: ssviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssviz/inst/doc/ssviz.R dependencyCount: 49 Package: StabMap Version: 1.5.0 Depends: R (>= 4.4.0), Imports: igraph, slam, BiocNeighbors, Matrix, MASS, abind, SummarizedExperiment, methods, MatrixGenerics, BiocGenerics, BiocSingular, BiocParallel Suggests: scran, scater, knitr, UpSetR, gridExtra, SingleCellMultiModal, BiocStyle, magrittr, testthat (>= 3.0.0), purrr, sparseMatrixStats License: GPL-2 MD5sum: 82e7145ce6391a05d54cde7e3008f1ff NeedsCompilation: no Title: Stabilised mosaic single cell data integration using unshared features Description: StabMap performs single cell mosaic data integration by first building a mosaic data topology, and for each reference dataset, traverses the topology to project and predict data onto a common embedding. Mosaic data should be provided in a list format, with all relevant features included in the data matrices within each list object. The output of stabMap is a joint low-dimensional embedding taking into account all available relevant features. Expression imputation can also be performed using the StabMap embedding and any of the original data matrices for given reference and query cell lists. biocViews: SingleCell, DimensionReduction, Software Author: Shila Ghazanfar [aut, cre, ctb], Aiden Jin [ctb], Nicholas Robertson [ctb] Maintainer: Shila Ghazanfar URL: https://sydneybiox.github.io/StabMap, https://sydneybiox.github.io/StabMap/ VignetteBuilder: knitr BugReports: https://github.com/sydneybiox/StabMap/issues git_url: https://git.bioconductor.org/packages/StabMap git_branch: devel git_last_commit: ce4dd56 git_last_commit_date: 2025-12-03 Date/Publication: 2026-04-20 source.ver: src/contrib/StabMap_1.5.0.tar.gz vignettes: vignettes/StabMap/inst/doc/stabMap_PBMC_Multiome.html vignetteTitles: Mosaic single cell data integration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StabMap/inst/doc/stabMap_PBMC_Multiome.R dependencyCount: 53 Package: STADyUM Version: 1.1.3 Depends: R (>= 4.5.0) Imports: GenomicRanges, IRanges, S4Vectors, methods, tibble, dplyr, ggplot2, progress, GenomeInfoDb, Rcpp, data.table, purrr, rtracklayer, tidyr, rlang, MASS LinkingTo: Rcpp Suggests: testthat (>= 3.0.0), knitr, rmarkdown, devtools License: MIT + file LICENSE MD5sum: ad041ab1f476e722a413fc11d48cd3d3 NeedsCompilation: yes Title: Statistical Transcriptome Analysis under a Dynamic Unified Model Description: STADyUM is a package with functionality for analyzing nascent RNA read counts to infer transcription rates. This includes utilities for processing experimental nascent RNA read counts as well as for simulating PRO-seq data. Rates such as initiation, pause release and landing pad occupancy are estimated from either synthetic or experimental data. There are also options for varying pause sites and including steric hindrance of initiation in the model. biocViews: StatisticalMethod, Transcriptomics, Transcription, Sequencing Author: Yixin Zhao [aut] (ORCID: ), Lingjie Liu [aut] (ORCID: ), Rebecca Hassett [aut, cre] (ORCID: ) Maintainer: Rebecca Hassett URL: https://github.com/rhassett-cshl/STADyUM VignetteBuilder: knitr BugReports: https://github.com/rhassett-cshl/STADyUM git_url: https://git.bioconductor.org/packages/STADyUM git_branch: devel git_last_commit: c0a84f5 git_last_commit_date: 2025-12-04 Date/Publication: 2026-04-20 source.ver: src/contrib/STADyUM_1.1.3.tar.gz vignettes: vignettes/STADyUM/inst/doc/STADyUM.html vignetteTitles: STADyUM: Simulating and Analyzing Transcription Dynamics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/STADyUM/inst/doc/STADyUM.R dependencyCount: 91 Package: stageR Version: 1.33.0 Depends: R (>= 3.4), SummarizedExperiment Imports: methods, stats Suggests: knitr, rmarkdown, BiocStyle, methods, Biobase, edgeR, limma, DEXSeq, testthat License: GNU General Public License version 3 MD5sum: 6d47b82faebb7015d8d370d35a32d170 NeedsCompilation: no Title: stageR: stage-wise analysis of high throughput gene expression data in R Description: The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0 biocViews: Software, StatisticalMethod Author: Koen Van den Berge and Lieven Clement Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stageR git_branch: devel git_last_commit: d375191 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/stageR_1.33.0.tar.gz vignettes: vignettes/stageR/inst/doc/stageRVignette.html vignetteTitles: stageR: stage-wise analysis of high-throughput gene expression data in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stageR/inst/doc/stageRVignette.R dependsOnMe: rnaseqDTU suggestsMe: MethReg, muscat, satuRn dependencyCount: 25 Package: standR Version: 1.15.0 Depends: R (>= 4.1) Imports: dplyr, SpatialExperiment (>= 1.5.2), SummarizedExperiment, SingleCellExperiment, edgeR, rlang, readr, tibble, ggplot2, tidyr, ruv, limma, patchwork, S4Vectors, Biobase, BiocGenerics, grDevices, stats, methods, ggalluvial, mclustcomp, RUVSeq Suggests: knitr, ExperimentHub, rmarkdown, scater, uwot, ggpubr, ggrepel, cluster, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 8ede9e381b7fc97ce9ef80775fb98b9a NeedsCompilation: no Title: Spatial transcriptome analyses of Nanostring's DSP data in R Description: standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations. biocViews: Spatial, Transcriptomics, GeneExpression, DifferentialExpression, QualityControl, Normalization, ExperimentHubSoftware Author: Ning Liu [aut, cre] (ORCID: ), Dharmesh D Bhuva [aut] (ORCID: ), Ahmed Mohamed [aut] Maintainer: Ning Liu URL: https://github.com/DavisLaboratory/standR VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/standR/issues git_url: https://git.bioconductor.org/packages/standR git_branch: devel git_last_commit: 9a79065 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/standR_1.15.0.tar.gz vignettes: vignettes/standR/inst/doc/Quick_start.html vignetteTitles: standR_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/standR/inst/doc/Quick_start.R importsMe: shinyDSP dependencyCount: 142 Package: staRgate Version: 0.99.8 Depends: R (>= 4.3.0) Imports: dplyr, janitor, purrr, rlang, stringr, tidyr, flowCore, flowWorkspace, glue, tibble Suggests: flowAI, ggplot2, gt, knitr, openCyto, ggcyto, rmarkdown, data.table, here, testthat (>= 3.0.0), BiocStyle License: MIT + file LICENSE MD5sum: f6d6c8c3dbd5504722d8ebdd01dd5fc4 NeedsCompilation: no Title: Automated gating pipeline for flow cytometry analysis to characterize the lineage, differentiation, and functional states of T-cells Description: An R-based automated gating pipeline for flow cytometry data designed to mimic the manual gating strategy of defining flow biomarker positive populations relative to a unimodal background population to include cells with varying intensities of marker expression. The pipeline’s main feature is a flexible density-based gating strategy capable of capturing varying scenarios based on marker expression patterns to analyze a 29-marker flow panel that characterizes T-cell lineage, differentiation, and functional states. biocViews: FlowCytometry, Preprocessing, ImmunoOncology Author: Jasme Lee [aut, cre] (ORCID: ), Matthew Adamow [aut], Colleen Maher [aut], Xiyu Peng [aut], Phillip Wong [aut], Fiona Ehrich [aut], Michael A Postow [aut], Margaret K Callahan [aut], Ronglai Shen [aut], Katherine S Panageas [aut], V foundation [fnd], MSK-MIND [fnd], NIH R01CA276286 [fnd], NIH P30CA008748 [fnd] Maintainer: Jasme Lee URL: https://bioconductor.org/packages/staRgate, https://leejasme.github.io/staRgate VignetteBuilder: knitr BugReports: https://github.com/leejasme/staRgate/issues git_url: https://git.bioconductor.org/packages/staRgate git_branch: devel git_last_commit: f007905 git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/staRgate_0.99.8.tar.gz vignettes: vignettes/staRgate/inst/doc/vignette_run_pipeline.html vignetteTitles: Tutorial: Running the pipeline hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/staRgate/inst/doc/vignette_run_pipeline.R dependencyCount: 71 Package: STATegRa Version: 1.47.0 Depends: R (>= 2.10) Imports: Biobase, gridExtra, ggplot2, methods, stats, grid, MASS, calibrate, gplots, edgeR, limma, foreach, affy Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>= 1.3), roxygen2, doSNOW License: GPL-2 MD5sum: 21cb6056831bf7d56efeccbaea1f4ce7 NeedsCompilation: no Title: Classes and methods for multi-omics data integration Description: Classes and tools for multi-omics data integration. biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: STATegra Consortia Maintainer: David Gomez-Cabrero , Núria Planell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STATegRa git_branch: devel git_last_commit: 1932640 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/STATegRa_1.47.0.tar.gz vignettes: vignettes/STATegRa/inst/doc/STATegRa.html vignetteTitles: STATegRa User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R dependencyCount: 46 Package: Statial Version: 1.13.0 Depends: R (>= 4.1.0) Imports: BiocParallel, spatstat.geom, concaveman, data.table, spatstat.explore, dplyr, tidyr, SingleCellExperiment, tibble, stringr, tidyselect, ggplot2, methods, stats, SummarizedExperiment, S4Vectors, plotly, purrr, ranger, magrittr, limma, SpatialExperiment, cluster, treekoR, edgeR Suggests: BiocStyle, knitr, testthat (>= 3.0.0), ClassifyR, spicyR, ggsurvfit, lisaClust, survival License: GPL-3 MD5sum: eaf1f4aff30741f4c9316454ae744ed4 NeedsCompilation: no Title: A package to identify changes in cell state relative to spatial associations Description: Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with. biocViews: SingleCell, Spatial, Classification Author: Farhan Ameen [aut, cre], Sourish Iyengar [aut], Alex Qin [aut], Shila Ghazanfar [aut], Ellis Patrick [aut] Maintainer: Farhan Ameen URL: https://sydneybiox.github.io/Statial https://github.com/SydneyBioX/Statial/issues VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/Statial/issues git_url: https://git.bioconductor.org/packages/Statial git_branch: devel git_last_commit: 2a5a1ee git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Statial_1.13.0.tar.gz vignettes: vignettes/Statial/inst/doc/Statial.html vignetteTitles: "Introduction to Statial" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Statial/inst/doc/Statial.R suggestsMe: OSTA dependencyCount: 235 Package: statTarget Version: 1.41.0 Depends: R (>= 3.6.0) Imports: randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats, pls,impute Suggests: testthat, BiocStyle, knitr, rmarkdown License: LGPL (>= 3) MD5sum: 7169898f7b2aea3987ac945010f7b2c1 NeedsCompilation: no Title: Statistical Analysis of Molecular Profiles Description: A streamlined tool provides a graphical user interface for quality control based signal drift correction (QC-RFSC), integration of data from multi-batch MS-based experiments, and the comprehensive statistical analysis in metabolomics and proteomics. biocViews: ImmunoOncology, Metabolomics, Proteomics, Machine Learning, Lipidomics, MassSpectrometry, QualityControl, Normalization, QC-RFSC, ComBat, DifferentialExpression, BatchEffect, Visualization, MultipleComparison,Preprocessing, Software Author: Hemi Luan Maintainer: Hemi Luan URL: https://stattarget.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/statTarget git_branch: devel git_last_commit: 47c118f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/statTarget_1.41.0.tar.gz vignettes: vignettes/statTarget/inst/doc/Combat.html, vignettes/statTarget/inst/doc/pathway_analysis.html, vignettes/statTarget/inst/doc/statTarget.html vignetteTitles: QC_free approach with Combat method, statTarget2 for pathway analysis, statTarget2 On using the Graphical User Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/statTarget/inst/doc/Combat.R, vignettes/statTarget/inst/doc/pathway_analysis.R, vignettes/statTarget/inst/doc/statTarget.R dependencyCount: 26 Package: stepNorm Version: 1.83.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: 2eae0babc98569c2600b2845b7b72ace NeedsCompilation: no Title: Stepwise normalization functions for cDNA microarrays Description: Stepwise normalization functions for cDNA microarray data. biocViews: Microarray, TwoChannel, Preprocessing Author: Yuanyuan Xiao , Yee Hwa (Jean) Yang Maintainer: Yuanyuan Xiao URL: http://www.biostat.ucsf.edu/jean/ git_url: https://git.bioconductor.org/packages/stepNorm git_branch: devel git_last_commit: efe8204 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/stepNorm_1.83.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 9 Package: strandCheckR Version: 1.29.0 Depends: ggplot2 (>= 4.0.0), Rsamtools, S4Vectors Imports: BiocGenerics, dplyr, Seqinfo, GenomicAlignments, GenomicRanges, gridExtra, IRanges, grid, methods, reshape2, rlang, stats, stringr, TxDb.Hsapiens.UCSC.hg38.knownGene, tidyselect Suggests: BiocStyle, knitr, magrittr, rmarkdown, testthat License: GPL (>= 2) MD5sum: 1d15d838256a84cda442c9d2ab902aa0 NeedsCompilation: no Title: Calculate strandness information of a bam file Description: This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering. biocViews: RNASeq, Alignment, QualityControl, Coverage, ImmunoOncology Author: Thu-Hien To [aut, cre], Stevie Pederson [aut] (ORCID: ) Maintainer: Thu-Hien To URL: https://github.com/UofABioinformaticsHub/strandCheckR VignetteBuilder: knitr BugReports: https://github.com/UofABioinformaticsHub/strandCheckR/issues git_url: https://git.bioconductor.org/packages/strandCheckR git_branch: devel git_last_commit: b8130fd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/strandCheckR_1.29.0.tar.gz vignettes: vignettes/strandCheckR/inst/doc/strandCheckR.html vignetteTitles: An Introduction To strandCheckR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/strandCheckR/inst/doc/strandCheckR.R dependencyCount: 98 Package: STRINGdb Version: 2.23.0 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, httr, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: c252b196233355b66df09c1cf19be2a5 NeedsCompilation: no Title: STRINGdb - Protein-Protein Interaction Networks and Functional Enrichment Analysis Description: The STRINGdb package provides an R interface to STRING, a protein-protein interaction database and functional enrichment analysis tool (https://string-db.org). biocViews: Network Author: Andrea Franceschini Maintainer: Damian Szklarczyk git_url: https://git.bioconductor.org/packages/STRINGdb git_branch: devel git_last_commit: 071ea9e git_last_commit_date: 2026-04-13 Date/Publication: 2026-04-20 source.ver: src/contrib/STRINGdb_2.23.0.tar.gz vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf vignetteTitles: STRINGdb Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STRINGdb/inst/doc/STRINGdb.R dependsOnMe: PPInfer importsMe: GeDi, IMMAN, RITAN, TDbasedUFEadv, XINA, DeSciDe suggestsMe: epiNEM, GeneNetworkBuilder, martini, netSmooth, PCAN, scGraphVerse, protti dependencyCount: 49 Package: struct Version: 1.23.2 Depends: R (>= 4.0) Imports: methods, datasets, graphics, stats, utils, knitr, SummarizedExperiment, S4Vectors, httr2, jsonlite Suggests: testthat, rstudioapi, rmarkdown, covr, BiocStyle, openxlsx, ggplot2, magick License: GPL-3 MD5sum: fd5a355c338cd80dca5d63ca48eb3954 NeedsCompilation: no Title: Statistics in R Using Class-based Templates Description: Defines and includes a set of class-based templates for developing and implementing data processing and analysis workflows, with a strong emphasis on statistics and machine learning. The templates can be used and where needed extended to 'wrap' tools and methods from other packages into a common standardised structure to allow for effective and fast integration. Model objects can be combined into sequences, and sequences nested in iterators using overloaded operators to simplify and improve readability of the code. Ontology lookup has been integrated and implemented to provide standardised definitions for methods, inputs and outputs wrapped using the class-based templates. biocViews: WorkflowStep Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/struct git_branch: devel git_last_commit: 72c97d4 git_last_commit_date: 2026-04-13 Date/Publication: 2026-04-20 source.ver: src/contrib/struct_1.23.2.tar.gz vignettes: vignettes/struct/inst/doc/struct_templates_and_helper_functions.html vignetteTitles: Introduction to STRUCT - STatistics in R using Class-based Templates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/struct/inst/doc/struct_templates_and_helper_functions.R dependsOnMe: MetMashR, structToolbox importsMe: metabolomicsWorkbenchR dependencyCount: 46 Package: Structstrings Version: 1.27.0 Depends: R (>= 4.0), S4Vectors (>= 0.47.2), IRanges (>= 2.23.9), Biostrings (>= 2.57.2) Imports: methods, BiocGenerics, XVector, stringr, stringi, crayon, grDevices LinkingTo: IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, tRNAscanImport, BiocStyle License: Artistic-2.0 MD5sum: 4fd198c3db6cbaa6a82d532b298da6d4 NeedsCompilation: yes Title: Implementation of the dot bracket annotations with Biostrings Description: The Structstrings package implements the widely used dot bracket annotation for storing base pairing information in structured RNA. Structstrings uses the infrastructure provided by the Biostrings package and derives the DotBracketString and related classes from the BString class. From these, base pair tables can be produced for in depth analysis. In addition, the loop indices of the base pairs can be retrieved as well. For better efficiency, information conversion is implemented in C, inspired to a large extend by the ViennaRNA package. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software, Alignment, SequenceMatching Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/Structstrings VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Structstrings/issues git_url: https://git.bioconductor.org/packages/Structstrings git_branch: devel git_last_commit: 983c850 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Structstrings_1.27.0.tar.gz vignettes: vignettes/Structstrings/inst/doc/Structstrings.html vignetteTitles: Structstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Structstrings/inst/doc/Structstrings.R dependsOnMe: tRNA, tRNAdbImport importsMe: tRNAscanImport dependencyCount: 23 Package: structToolbox Version: 1.23.2 Depends: R (>= 4.0), struct (>= 1.5.1) Imports: ggplot2, ggthemes, grid, gridExtra, httr, jsonlite, methods, scales, sp, stats, limma Suggests: agricolae, BiocFileCache, BiocStyle, car, covr, cowplot, e1071, emmeans, ggdendro, knitr, magick, nlme, openxlsx, pls, pmp, reshape2, ropls, rmarkdown, Rtsne, testthat, rappdirs License: GPL-3 MD5sum: 2044cfaca3a1fecd85b16e257e91188a NeedsCompilation: no Title: Data processing & analysis tools for Metabolomics and other omics Description: An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). Ontology terms have been integrated to provide standardised definitions for the different methods, inputs and outputs. biocViews: WorkflowStep, Metabolomics Author: Gavin Rhys Lloyd [aut, cre] (ORCID: ), Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd URL: https://github.com/computational-metabolomics/structToolbox, https://computational-metabolomics.github.io/structToolbox/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/structToolbox git_branch: devel git_last_commit: b88a25e git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/structToolbox_1.23.2.tar.gz vignettes: vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.html vignetteTitles: Data analysis of metabolomics and other omics datasets using the structToolbox hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.R suggestsMe: metabolomicsWorkbenchR, MetMashR dependencyCount: 71 Package: StructuralVariantAnnotation Version: 1.27.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, BiocGenerics, R (>= 4.1.0) Imports: assertthat, Biostrings, pwalign, stringr, dplyr, methods, rlang, GenomicFeatures, IRanges, S4Vectors, SummarizedExperiment, GenomeInfoDb, Suggests: ggplot2, devtools, testthat (>= 2.1.0), roxygen2, rmarkdown, tidyverse, knitr, ggbio, biovizBase, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, License: GPL-3 + file LICENSE MD5sum: 3a7787677c51f936a128f7dbcf9a68b6 NeedsCompilation: no Title: Variant annotations for structural variants Description: StructuralVariantAnnotation provides a framework for analysis of structural variants within the Bioconductor ecosystem. This package contains contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Daniel Cameron [aut, cre] (ORCID: ), Ruining Dong [aut] (ORCID: ) Maintainer: Daniel Cameron VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/StructuralVariantAnnotation git_branch: devel git_last_commit: 72cd374 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/StructuralVariantAnnotation_1.27.0.tar.gz vignettes: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.html vignetteTitles: Structural Variant Annotation Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.R dependsOnMe: svaNUMT, svaRetro dependencyCount: 90 Package: SubCellBarCode Version: 1.27.0 Depends: R (>= 3.6) Imports: Rtsne, scatterplot3d, caret, e1071, ggplot2, gridExtra, networkD3, ggrepel, graphics, stats, org.Hs.eg.db, AnnotationDbi Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 45c8a945bda3617121d45bff4164ac3c NeedsCompilation: no Title: SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome Description: Mass-Spectrometry based spatial proteomics have enabled the proteome-wide mapping of protein subcellular localization (Orre et al. 2019, Molecular Cell). SubCellBarCode R package robustly classifies proteins into corresponding subcellular localization. biocViews: Proteomics, MassSpectrometry, Classification Author: Taner Arslan Maintainer: Taner Arslan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SubCellBarCode git_branch: devel git_last_commit: 945ded8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SubCellBarCode_1.27.0.tar.gz vignettes: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.html vignetteTitles: SubCellBarCode R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.R dependencyCount: 133 Package: subSeq Version: 1.41.0 Depends: R (>= 3.2) Imports: data.table, dplyr, tidyr, ggplot2, magrittr, qvalue (>= 1.99), digest, Biobase Suggests: limma, edgeR, DESeq2, DEXSeq (>= 1.9.7), testthat, knitr License: MIT + file LICENSE MD5sum: 49fc76ee0cc9d9117d00cebed5b4629f NeedsCompilation: no Title: Subsampling of high-throughput sequencing count data Description: Subsampling of high throughput sequencing count data for use in experiment design and analysis. biocViews: ImmunoOncology, Sequencing, Transcription, RNASeq, GeneExpression, DifferentialExpression Author: David Robinson, John D. Storey, with contributions from Andrew J. Bass Maintainer: Andrew J. Bass , John D. Storey URL: http://github.com/StoreyLab/subSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/subSeq git_branch: devel git_last_commit: 133fac9 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/subSeq_1.41.0.tar.gz vignettes: vignettes/subSeq/inst/doc/subSeq.pdf vignetteTitles: subSeq Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/subSeq/inst/doc/subSeq.R dependencyCount: 45 Package: SUITOR Version: 1.13.0 Depends: R (>= 4.2.0) Imports: stats, utils, graphics, ggplot2, BiocParallel Suggests: devtools, MutationalPatterns, RUnit, BiocManager, BiocGenerics, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: d8f88905f4bd67f8308e98e7d3689580 NeedsCompilation: yes Title: Selecting the number of mutational signatures through cross-validation Description: An unsupervised cross-validation method to select the optimal number of mutational signatures. A data set of mutational counts is split into training and validation data.Signatures are estimated in the training data and then used to predict the mutations in the validation data. biocViews: Genetics, Software, SomaticMutation Author: DongHyuk Lee [aut], Bin Zhu [aut], Bill Wheeler [cre] Maintainer: Bill Wheeler VignetteBuilder: knitr BugReports: https://github.com/wheelerb/SUITOR/issues git_url: https://git.bioconductor.org/packages/SUITOR git_branch: devel git_last_commit: c762b8f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SUITOR_1.13.0.tar.gz vignettes: vignettes/SUITOR/inst/doc/vignette.pdf vignetteTitles: SUITOR: selecting the number of mutational signatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SUITOR/inst/doc/vignette.R dependencyCount: 32 Package: SummarizedExperiment Version: 1.41.1 Depends: R (>= 4.0.0), methods, MatrixGenerics (>= 1.1.3), GenomicRanges (>= 1.61.4), Biobase Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.51.3), S4Vectors (>= 0.33.7), IRanges (>= 2.23.9), Seqinfo, S4Arrays (>= 1.1.1), DelayedArray (>= 0.31.12) Suggests: GenomeInfoDb (>= 1.45.5), rhdf5, HDF5Array (>= 1.7.5), annotate, AnnotationDbi, GenomicFeatures, SparseArray, SingleCellExperiment, TxDb.Hsapiens.UCSC.hg19.knownGene, hgu95av2.db, airway (>= 1.15.1), BiocStyle, knitr, rmarkdown, RUnit, testthat, digest License: Artistic-2.0 MD5sum: 3151ebb13324a29dabb898df835879b5 NeedsCompilation: no Title: A container (S4 class) for matrix-like assays Description: The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: Martin Morgan [aut], Valerie Obenchain [aut], Jim Hester [aut], Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/SummarizedExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SummarizedExperiment/issues git_url: https://git.bioconductor.org/packages/SummarizedExperiment git_branch: devel git_last_commit: 66ad59a git_last_commit_date: 2026-02-05 Date/Publication: 2026-04-20 source.ver: src/contrib/SummarizedExperiment_1.41.1.tar.gz vignettes: vignettes/SummarizedExperiment/inst/doc/Extensions.html, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html vignetteTitles: 2. Extending the SummarizedExperiment class, 1. SummarizedExperiment for Coordinating Experimental Assays,, Samples,, and Regions of Interest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedExperiment/inst/doc/Extensions.R, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.R dependsOnMe: AffiXcan, alabaster.se, AllelicImbalance, atena, bambu, batchCorr, betaHMM, betterChromVAR, BiocSklearn, BiSeq, bnbc, broadSeq, bsseq, CAGEfightR, celaref, clusterExperiment, CoreGx, coseq, csaw, CSSQ, DaMiRseq, deepSNV, DeMixT, DESeq2, DEXSeq, diffcoexp, diffHic, dinoR, divergence, DMCFB, DMCHMM, EnrichmentBrowser, epigenomix, evaluomeR, EventPointer, ExperimentSubset, ExpressionAtlas, extraChIPs, FEAST, FRASER, GenomicAlignments, GenomicFiles, GenomicSuperSignature, GRmetrics, GSEABenchmarkeR, HelloRanges, hermes, HERON, HiCDOC, hipathia, InteractionSet, IntEREst, iSEE, iSEEhex, iSEEhub, iSEEindex, ISLET, isomiRs, ivygapSE, lefser, LimROTS, lipidr, LoomExperiment, Macarron, made4, MatrixQCvis, MBASED, methodical, methrix, methylPipe, MetNet, MGnifyR, mia, miaViz, MICSQTL, minfi, moanin, mpra, MultiAssayExperiment, multistateQTL, NADfinder, NBAMSeq, NewWave, notame, notameViz, orthos, OUTRIDER, padma, PDATK, PhIPData, PlinkMatrix, PRONE, qmtools, qsvaR, QTLExperiment, recount, recount3, ReducedExperiment, RegEnrich, REMP, RFLOMICS, ROCpAI, rqt, runibic, Scale4C, scAnnotatR, scGPS, scone, screenCounter, scTreeViz, SDAMS, sechm, SeqGate, SEtools, SGSeq, signatureSearch, SingleCellExperiment, singleCellTK, SingleR, spillR, spqn, ssPATHS, stageR, survtype, TENxIO, tidyCoverage, tidySummarizedExperiment, TissueEnrich, TREG, UMI4Cats, VanillaICE, VariantAnnotation, VariantExperiment, velociraptor, weitrix, yamss, zinbwave, airway, BioPlex, bodymapRat, celldex, curatedAdipoChIP, curatedAdipoRNA, curatedMetagenomicData, fission, GSVAdata, HDCytoData, HighlyReplicatedRNASeq, HMP16SData, HumanRetinaLRSData, MetaGxOvarian, MetaGxPancreas, MethylSeqData, MicrobiomeBenchmarkData, microbiomeDataSets, microRNAome, MouseGastrulationData, MouseThymusAgeing, ObMiTi, sampleClassifierData, scMultiome, spatialDmelxsim, spqnData, timecoursedata, tuberculosis, TumourMethData, DRomics, OncoSubtype, ordinalbayes importsMe: ADAM, ADImpute, aggregateBioVar, airpart, alabaster.sfe, ALDEx2, anansi, anglemania, animalcules, anota2seq, APAlyzer, apeglm, APL, appreci8R, ASICS, ASURAT, asuri, ATACseqTFEA, AUCell, autonomics, awst, Banksy, barbieQ, BASiCS, BASiCStan, batchelor, BatchQC, BatchSVG, Battlefield, BayesSpace, bayNorm, BBCAnalyzer, beer, benchdamic, BERT, bettr, BioGA, BioNERO, biosigner, biotmle, biovizBase, biscuiteer, BiSeq, blacksheepr, blase, BloodGen3Module, BreastSubtypeR, BulkSignalR, BUMHMM, BUScorrect, BUSseq, CaDrA, CAGEr, CalibraCurve, CARDspa, carnation, CATALYST, CatsCradle, cBioPortalData, ccfindR, ccImpute, CDI, celda, CelliD, CellMixS, CellTrails, censcyt, Cepo, CeTF, CHETAH, ChIPpeakAnno, ChromSCape, CiteFuse, CleanUpRNAseq, ClusterGVis, clustifyr, clustSIGNAL, cmapR, CNVfilteR, CNVRanger, CoGAPS, comapr, combi, concordexR, condiments, consICA, CopyNumberPlots, Coralysis, corral, COTAN, countsimQC, CPSM, CrcBiomeScreen, crupR, CSOA, CTexploreR, CTSV, CuratedAtlasQueryR, cydar, cypress, CyTOFpower, cytofQC, cytoKernel, cytomapper, cytoviewer, DAMEfinder, dandelionR, debrowser, decemedip, decompTumor2Sig, decontX, DeconvoBuddies, DeeDeeExperiment, DEFormats, DEGreport, DELocal, deltaCaptureC, demuxSNP, DenoIST, DEScan2, DESpace, destiny, DEWSeq, diffcyt, DifferentialRegulation, diffUTR, Dino, DiscoRhythm, distinct, dittoSeq, DMRcate, DNEA, dominatR, DominoEffect, doppelgangR, doseR, DOTSeq, dreamlet, DropletUtils, DspikeIn, Dune, easyRNASeq, eisaR, ELMER, epigraHMM, EpiMix, epimutacions, epiregulon, epiregulon.extra, epiSeeker, epistack, epivizrData, escape, escheR, EWCE, ExpoRiskR, fcScan, FeatSeekR, findIPs, FindIT2, fishpond, FLAMES, FuseSOM, G4SNVHunter, GARS, gCrisprTools, gDNAx, gDRcore, gDRimport, gDRutils, gemma.R, GeneTonic, genomicInstability, GEOquery, GeoTcgaData, getDEE2, geyser, ggbio, ggsc, ggspavis, Glimma, glmGamPoi, glmSparseNet, glycoTraitR, GRaNIE, GreyListChIP, gscreend, GSVA, gwasurvivr, GWENA, HarmonizR, HiContacts, HiCParser, HistoImagePlot, HoloFoodR, hoodscanR, hummingbird, HybridExpress, iasva, Ibex, icetea, ideal, IFAA, IgGeneUsage, ILoReg, imageFeatureTCGA, imageTCGAutils, imcRtools, iModMix, iNETgrate, infercnv, INSPEcT, iSEEde, iSEEfier, iSEEpathways, iSEEtree, iSEEu, IsoBayes, IsoformSwitchAnalyzeR, kmcut, LACE, leapR, lemur, limpca, lineagespot, lionessR, LipidTrend, lisaClust, looking4clusters, LRDE, maaslin3, MAI, mariner, marr, MAST, mastR, mbkmeans, MBQN, mCSEA, MEAL, MEAT, MEB, MetaboAnnotation, MetaboDynamics, metabolomicsWorkbenchR, MetaProViz, metaseqR2, MethReg, MethylAid, methyLImp2, methylscaper, methylumi, miaDash, miaSim, miaTime, MicrobiotaProcess, midasHLA, miloR, MinimumDistance, miRSM, missMethyl, mist, MLInterfaces, MLSeq, mobileRNA, monaLisa, MoonlightR, mosdef, motifmatchr, MotifPeeker, MPAC, MPRAnalyze, MsExperiment, MsFeatures, msgbsR, mspms, MSPrep, msqrob2, MuData, MultiDataSet, MultiRNAflow, multiWGCNA, mumosa, muscat, musicatk, mutscan, MutSeqR, MWASTools, NanoMethViz, Nebulosa, NetActivity, netSmooth, nipalsMCIA, nnSVG, NormalyzerDE, OAtools, oligoClasses, omicRexposome, omicsGMF, omicsPrint, omicsViewer, oncomix, ontoProc, ORFik, OVESEG, PAIRADISE, pairedGSEA, pairkat, parati, pcaExplorer, peco, PepSetTest, pgxRpi, PharmacoGx, phenopath, PhosR, pipeComp, Pirat, PIUMA, plaid, planttfhunter, plyxp, pmp, poem, PolySTest, POMA, POWSC, proActiv, proBatch, proDA, psichomics, PureCN, QFeatures, qsmooth, quantiseqr, R453Plus1Toolbox, RadioGx, raer, RaggedExperiment, RankMap, RareVariantVis, RBedMethyl, ReactomeGSA, RegionalST, regionReport, regsplice, RFGeneRank, rgsepd, rifi, rifiComparative, Rmmquant, RNAAgeCalc, RNAsense, RNAshapeQC, roar, RolDE, ropls, rScudo, RTCGAToolbox, RTN, RUCova, SanityR, saseR, satuRn, SBGNview, SC3, scafari, SCArray, SCArray.sat, scater, scBFA, scCB2, scConform, scDblFinder, scDD, scDDboost, scDesign3, scDiagnostics, scds, scECODA, scGraphVerse, scHOT, scider, scLang, scmap, scMerge, scMET, scmeth, scMultiSim, SCnorm, scoreInvHap, scp, scPipe, scQTLtools, scran, scReClassify, scRepertoire, scruff, scry, scTensor, scTGIF, scuttle, scviR, segmenter, seqCAT, SEraster, sesame, sfi, shinyDSP, sigFeature, signifinder, SigsPack, SimBu, simPIC, simpleSeg, singIST, SingleCellAlleleExperiment, singscore, slalom, slingshot, smartid, SmartPhos, smoothclust, snapcount, SNPhood, sosta, SpaceTrooper, spacexr, Spaniel, SpaNorm, spARI, spaSim, SpatialArtifacts, SpatialCPie, spatialDE, SpatialExperiment, spatialFDA, SpatialFeatureExperiment, spatialHeatmap, spatialSimGP, SPIAT, spicyR, splatter, SpliceImpactR, SpliceWiz, SplicingFactory, SplineDV, SpNeigh, spoon, SpotClean, SpotSweeper, srnadiff, sSNAPPY, StabMap, standR, StatescopeR, Statial, stJoincount, stPipe, struct, StructuralVariantAnnotation, supersigs, SurfR, SVMDO, SVP, switchde, systemPipeR, systemPipeTools, TBSignatureProfiler, TCGAbiolinks, TCGAutils, TCseq, TENET, tenXplore, TFutils, tidybulk, tidyexposomics, tidyprint, tidySingleCellExperiment, tidySpatialExperiment, TOAST, tomoda, ToxicoGx, tpSVG, tradeSeq, TrajectoryUtils, transformGamPoi, transmogR, treeclimbR, TreeSummarizedExperiment, Trendy, tricycle, TSCAN, TTMap, TVTB, tximeta, UCell, UPDhmm, VAExprs, VariantFiltering, VDJdive, vidger, VisiumIO, visiumStitched, VISTA, vmrseq, Voyager, wpm, XAItest, xCell2, xcms, XeniumIO, xenLite, zellkonverter, zFPKM, zitools, BloodCancerMultiOmics2017, brgedata, CLLmethylation, COSMIC.67, curatedTCGAData, DoReMiTra, easierData, emtdata, EMTscoreData, FieldEffectCrc, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, GSE13015, HCATonsilData, HiBED, HMP2Data, IHWpaper, LegATo, MerfishData, MetaGxBreast, MetaScope, orthosData, scRNAseq, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TCGAWorkflowData, TENET.ExperimentHub, TENxXeniumData, fluentGenomics, autoGO, bioLeak, DWLS, hicream, imcExperiment, karyotapR, MetAlyzer, microbial, mikropml, multimedia, PlasmaMutationDetector, RCPA, RNAseqQC, ROCnGO, scROSHI, treediff, VSOLassoBag suggestsMe: alabaster.mae, AlpsNMR, ANCOMBC, anndataR, AnnotationHub, BindingSiteFinder, biobroom, BiocPkgTools, biomformat, cageminer, CCAFE, CTdata, dar, dcanr, dearseq, decoupleR, DelayedArray, DOtools, easier, edgeR, EnMCB, epialleleR, epivizr, epivizrChart, esetVis, fobitools, funOmics, gDR, GENIE3, GenomicRanges, globalSeq, gsean, HDF5Array, HPiP, HVP, Informeasure, InteractiveComplexHeatmap, iscream, knowYourCG, MatrixGenerics, mitology, MOFA2, MSnbase, pathMED, pathwayPCA, philr, PLSDAbatch, podkat, PSMatch, Rvisdiff, S4Vectors, scFeatureFilter, scLANE, scPassport, scrapper, scToppR, scTypeEval, semisup, SETA, sketchR, sparrow, SPOTlight, svaNUMT, svaRetro, systemPipeShiny, TaxSEA, updateObject, biotmleData, curatedAdipoArray, curatedTBData, dorothea, DuoClustering2018, gDRtestData, GSE103322, multiWGCNAdata, pRolocdata, RforProteomics, SBGNview.data, tissueTreg, CAGEWorkflow, Canek, CimpleG, clustree, conos, CytoSimplex, dependentsimr, dyngen, file2meco, ggpicrust2, lfc, methFuse, MiscMetabar, parafac4microbiome, polyRAD, RaceID, radEmu, rliger, scStability, seqgendiff, Seurat, Signac, singleCellHaystack, speakeasyR, SuperCell, SVG, teal.slice, tidydr, volcano3D dependencyCount: 24 Package: Summix Version: 2.17.0 Depends: R (>= 4.3) Imports: dplyr, nloptr, magrittr, methods, tibble, tidyselect, BEDASSLE, scales, visNetwork, randomcoloR Suggests: rmarkdown, markdown, knitr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 6539605d212960ca96888e1a6d84d0ce NeedsCompilation: no Title: Summix2: A suite of methods to estimate, adjust, and leverage substructure in genetic summary data Description: This package contains the Summix2 method for estimating and adjusting for substructure in genetic summary allele frequency data. The function summix() estimates reference group proportions using a mixture model. The adjAF() function produces adjusted allele frequencies for an observed group with reference group proportions matching a target individual or sample. The summix_local() function estimates local ancestry mixture proportions and performs selection scans in genetic summary data. biocViews: StatisticalMethod, WholeGenome, Genetics Author: Audrey Hendricks [cre], Price Adelle [aut], Stoneman Haley [aut] Maintainer: Audrey Hendricks VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Summix/issues git_url: https://git.bioconductor.org/packages/Summix git_branch: devel git_last_commit: 82731de git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Summix_2.17.0.tar.gz vignettes: vignettes/Summix/inst/doc/Summix.html vignetteTitles: Summix.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Summix/inst/doc/Summix.R dependencyCount: 73 Package: SuperCellCyto Version: 1.1.0 Imports: SuperCell, data.table, Matrix, BiocParallel Suggests: flowCore, knitr, rmarkdown, usethis, testthat (>= 3.0.0), BiocSingular, bluster, scater, scran, Seurat, SingleCellExperiment, BiocStyle, magick, qs2 License: GPL-3 + file LICENSE MD5sum: 7fbc6643528aa23ee235b246b5d75643 NeedsCompilation: no Title: SuperCell For Cytometry Data Description: SuperCellCyto provides the ability to summarise cytometry data into supercells by merging together cells that are similar in their marker expressions using the SuperCell package. biocViews: CellBiology, FlowCytometry, Software, SingleCell Author: Givanna Putri [aut, cre] (ORCID: ), George Howitt [aut], Felix Marsh-Wakefield [aut], Thomas Ashhurst [aut], Belinda Phipson [aut] Maintainer: Givanna Putri URL: https://phipsonlab.github.io/SuperCellCyto/ VignetteBuilder: knitr BugReports: https://github.com/phipsonlab/SuperCellCyto/issues git_url: https://git.bioconductor.org/packages/SuperCellCyto git_branch: devel git_last_commit: 0098ab2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SuperCellCyto_1.1.0.tar.gz vignettes: vignettes/SuperCellCyto/inst/doc/how_to_prepare_data.html, vignettes/SuperCellCyto/inst/doc/interoperability_with_sce.html, vignettes/SuperCellCyto/inst/doc/interoperability_with_seurat.html, vignettes/SuperCellCyto/inst/doc/SuperCellCyto.html, vignettes/SuperCellCyto/inst/doc/using_supercellcyto_for_stratified_summarising.html vignetteTitles: how_to_prepare_data, Using SuperCellCyto with Single-Cell Based Objects, interoperability_with_seurat, How to create supercells, using-runsupercellcyto-for-stratified-summarising hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SuperCellCyto/inst/doc/how_to_prepare_data.R, vignettes/SuperCellCyto/inst/doc/interoperability_with_sce.R, vignettes/SuperCellCyto/inst/doc/interoperability_with_seurat.R, vignettes/SuperCellCyto/inst/doc/SuperCellCyto.R, vignettes/SuperCellCyto/inst/doc/using_supercellcyto_for_stratified_summarising.R dependencyCount: 145 Package: surfaltr Version: 1.17.0 Depends: R (>= 4.0) Imports: dplyr (>= 1.0.6), biomaRt (>= 2.46.0), protr (>= 1.6-2), seqinr (>= 4.2-5), ggplot2 (>= 3.3.2), utils (>= 2.10.1), stringr (>= 1.4.0), Biostrings (>= 2.58.0),readr (>= 1.4.0), httr (>= 1.4.2), testthat(>= 3.0.0), xml2(>= 1.3.2), msa (>= 1.22.0), methods (>= 4.0.3) Suggests: knitr, rmarkdown, devtools, kableExtra License: MIT + file LICENSE MD5sum: 8a86568aef7a2f053acfa843e395be37 NeedsCompilation: no Title: Rapid Comparison of Surface Protein Isoform Membrane Topologies Through surfaltr Description: Cell surface proteins form a major fraction of the druggable proteome and can be used for tissue-specific delivery of oligonucleotide/cell-based therapeutics. Alternatively spliced surface protein isoforms have been shown to differ in their subcellular localization and/or their transmembrane (TM) topology. Surface proteins are hydrophobic and remain difficult to study thereby necessitating the use of TM topology prediction methods such as TMHMM and Phobius. However, there exists a need for bioinformatic approaches to streamline batch processing of isoforms for comparing and visualizing topologies. To address this gap, we have developed an R package, surfaltr. It pairs inputted isoforms, either known alternatively spliced or novel, with their APPRIS annotated principal counterparts, predicts their TM topologies using TMHMM or Phobius, and generates a customizable graphical output. Further, surfaltr facilitates the prioritization of biologically diverse isoform pairs through the incorporation of three different ranking metrics and through protein alignment functions. Citations for programs mentioned here can be found in the vignette. biocViews: Software, Visualization, DataRepresentation, SplicedAlignment, Alignment, MultipleSequenceAlignment, MultipleComparison Author: Pooja Gangras [aut, cre] (ORCID: ), Aditi Merchant [aut] Maintainer: Pooja Gangras VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/surfaltr git_branch: devel git_last_commit: 3063fd2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/surfaltr_1.17.0.tar.gz vignettes: vignettes/surfaltr/inst/doc/surfaltr_vignette.html vignetteTitles: surfaltr_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/surfaltr/inst/doc/surfaltr_vignette.R dependencyCount: 103 Package: SurfR Version: 1.7.0 Depends: R (>= 4.4.0) Imports: httr, BiocFileCache, SPsimSeq, DESeq2, edgeR, openxlsx, stringr, rhdf5, ggplot2, ggrepel, stats, magrittr, assertr, tidyr, dplyr, TCGAbiolinks, biomaRt, metaRNASeq, scales, venn, gridExtra, SummarizedExperiment, knitr, rjson, grDevices, graphics, curl, utils Suggests: BiocStyle, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: 29459d90f07d5dca29ff642a2303bd5e NeedsCompilation: no Title: Surface Protein Prediction and Identification Description: Identify Surface Protein coding genes from a list of candidates. Systematically download data from GEO and TCGA or use your own data. Perform DGE on bulk RNAseq data. Perform Meta-analysis. Descriptive enrichment analysis and plots. biocViews: Software, Sequencing, RNASeq, GeneExpression, Transcription, DifferentialExpression, PrincipalComponent, GeneSetEnrichment, Pathways, BatchEffect, FunctionalGenomics, Visualization, DataImport, FunctionalPrediction, GenePrediction, GO Author: Aurora Maurizio [aut, cre] (ORCID: ), Anna Sofia Tascini [aut, ctb] (ORCID: ) Maintainer: Aurora Maurizio URL: https://github.com/auroramaurizio/SurfR VignetteBuilder: knitr BugReports: https://github.com/auroramaurizio/SurfR/issues git_url: https://git.bioconductor.org/packages/SurfR git_branch: devel git_last_commit: 90bbf06 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SurfR_1.7.0.tar.gz vignettes: vignettes/SurfR/inst/doc/Intro_to_SurfR.html vignetteTitles: Introduction to SurfR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SurfR/inst/doc/Intro_to_SurfR.R dependencyCount: 183 Package: survClust Version: 1.5.0 Depends: R (>= 3.5.0) Imports: Rcpp, MultiAssayExperiment, pdist, survival LinkingTo: Rcpp Suggests: knitr, testthat (>= 3.0.0), gplots, htmltools, BiocParallel License: MIT + file LICENSE MD5sum: 4e47f1e28de23c2a02752fd47c0f3680 NeedsCompilation: yes Title: Identification Of Clinically Relevant Genomic Subtypes Using Outcome Weighted Learning Description: survClust is an outcome weighted integrative clustering algorithm used to classify multi-omic samples on their available time to event information. The resulting clusters are cross-validated to avoid over overfitting and output classification of samples that are molecularly distinct and clinically meaningful. It takes in binary (mutation) as well as continuous data (other omic types). biocViews: Software, Clustering, Survival, Classification Author: Arshi Arora [aut, cre] (ORCID: ) Maintainer: Arshi Arora URL: https://github.com/arorarshi/survClust VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/survClust git_url: https://git.bioconductor.org/packages/survClust git_branch: devel git_last_commit: eb66f82 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/survClust_1.5.0.tar.gz vignettes: vignettes/survClust/inst/doc/survClust_vignette.html vignetteTitles: An introduction to survClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/survClust/inst/doc/survClust_vignette.R dependencyCount: 50 Package: survcomp Version: 1.61.1 Depends: survival, prodlim, R (>= 3.4) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta, stats, graphics Suggests: Hmisc, clinfun, xtable, Biobase, BiocManager License: Artistic-2.0 MD5sum: 010b945fadfd6a029bc54088bf7aa94e NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: Assessment and Comparison for Performance of Risk Prediction (Survival) Models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains [aut, cre], Markus Schroeder [aut], Catharina Olsen [aut], Christos Sotiriou [aut], Gianluca Bontempi [aut], John Quackenbush [aut], Samuel Branders [aut], Zhaleh Safikhani [aut] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/ git_url: https://git.bioconductor.org/packages/survcomp git_branch: devel git_last_commit: e7ba2a5 git_last_commit_date: 2026-03-06 Date/Publication: 2026-04-20 source.ver: src/contrib/survcomp_1.61.1.tar.gz vignettes: vignettes/survcomp/inst/doc/survcomp.pdf vignetteTitles: SurvComp: a package for performance assessment and comparison for survival analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survcomp/inst/doc/survcomp.R dependsOnMe: genefu importsMe: asuri, metaseqR2, PDATK, bigPLScox, Coxmos, pencal, plsRcox, SIGN suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 55 Package: survtype Version: 1.27.0 Depends: SummarizedExperiment, pheatmap, survival, survminer, clustvarsel, stats, utils Suggests: maftools, scales, knitr, rmarkdown License: Artistic-2.0 MD5sum: ad287b3c4981b346809e1c9444bf2646 NeedsCompilation: no Title: Subtype Identification with Survival Data Description: Subtypes are defined as groups of samples that have distinct molecular and clinical features. Genomic data can be analyzed for discovering patient subtypes, associated with clinical data, especially for survival information. This package is aimed to identify subtypes that are both clinically relevant and biologically meaningful. biocViews: Software, StatisticalMethod, GeneExpression, Survival, Clustering, Sequencing, Coverage Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/survtype git_branch: devel git_last_commit: 2853971 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/survtype_1.27.0.tar.gz vignettes: vignettes/survtype/inst/doc/survtype.html vignetteTitles: survtype hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survtype/inst/doc/survtype.R dependencyCount: 127 Package: sva Version: 3.59.0 Depends: R (>= 3.2), mgcv, genefilter, BiocParallel Imports: matrixStats, stats, graphics, utils, limma, edgeR Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 MD5sum: c11a6790eda92f1d84860b8185bac980 NeedsCompilation: yes Title: Surrogate Variable Analysis Description: The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics). biocViews: ImmunoOncology, Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , Yuqing Zhang , John D. Storey , Leonardo Collado Torres Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson git_url: https://git.bioconductor.org/packages/sva git_branch: devel git_last_commit: fefe806 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/sva_3.59.0.tar.gz vignettes: vignettes/sva/inst/doc/sva.pdf vignetteTitles: sva tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sva/inst/doc/sva.R dependsOnMe: IsoformSwitchAnalyzeR, SCAN.UPC, rnaseqGene, bapred, leapp, SmartSVA importsMe: ASSIGN, ballgown, BatchQC, BERT, BioNERO, bnbc, bnem, DaMiRseq, debrowser, DeMixT, DExMA, doppelgangR, edge, HarmonizR, KnowSeq, MatrixQCvis, MBECS, MSPrep, omicRexposome, PAA, pairedGSEA, POMA, proBatch, PROPS, qsmooth, qsvaR, RFGeneRank, SEtools, singleCellTK, DeSousa2013, ExpressionNormalizationWorkflow, causalBatch, cinaR, dSVA, scITD, seqgendiff, TransProR suggestsMe: compcodeR, GSVA, Harman, iasva, randRotation, RnBeads, scp, SomaticSignatures, TBSignatureProfiler, TCGAbiolinks, tidybulk, curatedBladderData, curatedCRCData, curatedOvarianData, curatedTBData, FieldEffectCrc, CAGEWorkflow, DGEobj.utils, DRomics, easyEWAS, futurize, SuperLearner dependencyCount: 68 Package: svaNUMT Version: 1.17.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, StructuralVariantAnnotation, BiocGenerics, Biostrings, R (>= 4.0) Imports: assertthat, stringr, dplyr, methods, rlang, S4Vectors, Seqinfo, GenomeInfoDb, GenomicFeatures, pwalign Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, ggplot2, devtools, testthat (>= 2.1.0), roxygen2, knitr, readr, plyranges, circlize, IRanges, SummarizedExperiment, rmarkdown License: GPL-3 + file LICENSE MD5sum: e867d47cfba9b84fd1a50e0685ad6ea2 NeedsCompilation: no Title: NUMT detection from structural variant calls Description: svaNUMT contains functions for detecting NUMT events from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies NUMTs by nuclear-mitochondrial breakend junctions. The main function reports candidate NUMTs if there is a pair of valid insertion sites found on the nuclear genome within a certain distance threshold. The candidate NUMTs are reported by events. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Ruining Dong [aut, cre] (ORCID: ) Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaNUMT/issues git_url: https://git.bioconductor.org/packages/svaNUMT git_branch: devel git_last_commit: 798135e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/svaNUMT_1.17.0.tar.gz vignettes: vignettes/svaNUMT/inst/doc/svaNUMT.html vignetteTitles: svaNUMT Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/svaNUMT/inst/doc/svaNUMT.R dependencyCount: 91 Package: svaRetro Version: 1.17.5 Depends: GenomicRanges, rtracklayer, BiocGenerics, StructuralVariantAnnotation, R (>= 4.0) Imports: VariantAnnotation, AnnotationDbi, assertthat, Biostrings, stringr, dplyr, methods, rlang, S4Vectors, Seqinfo, GenomeInfoDb, GenomicFeatures, utils Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, ggplot2, devtools, testthat (>= 2.1.0), roxygen2, knitr, BiocStyle, plyranges, circlize, tictoc, IRanges, stats, SummarizedExperiment, rmarkdown License: GPL-3 + file LICENSE MD5sum: 82fe5bf67b6c2c9178837d03a15d095f NeedsCompilation: no Title: Retrotransposed transcript detection from structural variants Description: svaRetro contains functions for detecting retrotransposed transcripts (RTs) from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies RTs by exon-exon junctions and insertion sites. The candidate RTs are reported by events and annotated with information of the inserted transcripts. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation, Coverage, VariantDetection Author: Ruining Dong [aut, cre] (ORCID: ) Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaRetro/issues git_url: https://git.bioconductor.org/packages/svaRetro git_branch: devel git_last_commit: 01d5f3a git_last_commit_date: 2026-02-17 Date/Publication: 2026-04-20 source.ver: src/contrib/svaRetro_1.17.5.tar.gz vignettes: vignettes/svaRetro/inst/doc/svaRetro.html vignetteTitles: svaRetro Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/svaRetro/inst/doc/svaRetro.R dependencyCount: 91 Package: SVP Version: 1.3.1 Depends: R (>= 4.1.0) Imports: Rcpp, RcppParallel, methods, cli, dplyr, rlang, S4Vectors, SummarizedExperiment, SingleCellExperiment, SpatialExperiment, BiocGenerics, BiocParallel, fastmatch, pracma, stats, withr, Matrix, DelayedMatrixStats, deldir, utils, BiocNeighbors, ggplot2, ggstar, ggtree, ggfun LinkingTo: Rcpp, RcppArmadillo (>= 14.0), RcppParallel, RcppEigen, dqrng Suggests: rmarkdown, prettydoc, broman, RSpectra, BiasedUrn, knitr, ks, igraph, testthat (>= 3.0.0), scuttle, magrittr, DropletUtils, tibble, tidyr, harmony, aplot, scales, ggsc, scatterpie, scran, scater, STexampleData, ape License: GPL-3 MD5sum: 386343f8c9b7b28afaab9adcc8f1af00 NeedsCompilation: yes Title: Predicting cell states and their variability in single-cell or spatial omics data Description: SVP uses the distance between cells and cells, features and features, cells and features in the space of MCA to build nearest neighbor graph, then uses random walk with restart algorithm to calculate the activity score of gene sets (such as cell marker genes, kegg pathway, go ontology, gene modules, transcription factor or miRNA target sets, reactome pathway, ...), which is then further weighted using the hypergeometric test results from the original expression matrix. To detect the spatially or single cell variable gene sets or (other features) and the spatial colocalization between the features accurately, SVP provides some global and local spatial autocorrelation method to identify the spatial variable features. SVP is developed based on SingleCellExperiment class, which can be interoperable with the existing computing ecosystem. biocViews: SingleCell, Software, Spatial, Transcriptomics, GeneTarget, GeneExpression, GeneSetEnrichment, Transcription, GO, KEGG Author: Shuangbin Xu [aut, cre] (ORCID: ), Guangchuang Yu [aut, ctb] (ORCID: ) Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/SVP SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/SVP/issues git_url: https://git.bioconductor.org/packages/SVP git_branch: devel git_last_commit: e864351 git_last_commit_date: 2025-11-03 Date/Publication: 2026-04-20 source.ver: src/contrib/SVP_1.3.1.tar.gz vignettes: vignettes/SVP/inst/doc/SVP.html vignetteTitles: SVP Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SVP/inst/doc/SVP.R dependencyCount: 136 Package: SWATH2stats Version: 1.41.1 Depends: R(>= 2.10.0) Imports: data.table, reshape2, ggplot2, stats, grDevices, graphics, utils, biomaRt, methods Suggests: testthat, knitr, rmarkdown Enhances: MSstats, PECA, aLFQ License: GPL-3 MD5sum: 19a5e5c9a80276402bcdf976508b9a36 NeedsCompilation: no Title: Transform and Filter SWATH Data for Statistical Packages Description: This package is intended to transform SWATH data from the OpenSWATH software into a format readable by other statistics packages while performing filtering, annotation and FDR estimation. biocViews: Proteomics, Annotation, ExperimentalDesign, Preprocessing, MassSpectrometry, ImmunoOncology Author: Peter Blattmann [aut, cre] Moritz Heusel [aut] Ruedi Aebersold [aut] Maintainer: Peter Blattmann URL: https://peterblattmann.github.io/SWATH2stats/ VignetteBuilder: knitr BugReports: https://github.com/peterblattmann/SWATH2stats git_url: https://git.bioconductor.org/packages/SWATH2stats git_branch: devel git_last_commit: e7338e2 git_last_commit_date: 2025-12-16 Date/Publication: 2026-04-20 source.ver: src/contrib/SWATH2stats_1.41.1.tar.gz vignettes: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.pdf, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.pdf vignetteTitles: SWATH2stats example script, SWATH2stats package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.R, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.R dependencyCount: 77 Package: SwathXtend Version: 2.33.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: e295e4441d17484cc53867f12844b389 NeedsCompilation: no Title: SWATH extended library generation and statistical data analysis Description: Contains utility functions for integrating spectral libraries for SWATH and statistical data analysis for SWATH generated data. biocViews: Software Author: J WU and D Pascovici Maintainer: Jemma Wu git_url: https://git.bioconductor.org/packages/SwathXtend git_branch: devel git_last_commit: e37d319 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SwathXtend_2.33.0.tar.gz vignettes: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.pdf vignetteTitles: SwathXtend hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.R dependencyCount: 21 Package: swfdr Version: 1.37.0 Depends: R (>= 3.4) Imports: methods, splines, stats4, stats Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat License: GPL (>= 3) MD5sum: 8fdc7373f419f61cba4a8d3f152a6872 NeedsCompilation: no Title: Estimation of the science-wise false discovery rate and the false discovery rate conditional on covariates Description: This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the false discovery rate conditional on covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ. biocViews: MultipleComparison, StatisticalMethod, Software Author: Jeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka Maintainer: Simina M. Boca , Jeffrey T. Leek URL: https://github.com/leekgroup/swfdr VignetteBuilder: knitr BugReports: https://github.com/leekgroup/swfdr/issues git_url: https://git.bioconductor.org/packages/swfdr git_branch: devel git_last_commit: a5b2ff1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/swfdr_1.37.0.tar.gz vignettes: vignettes/swfdr/inst/doc/swfdrQ.pdf, vignettes/swfdr/inst/doc/swfdrTutorial.pdf vignetteTitles: Computing covariate-adjusted q-values, Tutorial for swfdr package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/swfdr/inst/doc/swfdrQ.R, vignettes/swfdr/inst/doc/swfdrTutorial.R dependencyCount: 4 Package: switchBox Version: 1.47.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 MD5sum: 38b3f4019b40b537be62accd86eaf9ca NeedsCompilation: yes Title: Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm Description: The package offer different classifiers based on comparisons of pair of features (TSP), using various decision rules (e.g., majority wins principle). biocViews: Software, StatisticalMethod, Classification Author: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara Maintainer: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara git_url: https://git.bioconductor.org/packages/switchBox git_branch: devel git_last_commit: e2f9399 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/switchBox_1.47.0.tar.gz vignettes: vignettes/switchBox/inst/doc/switchBox.pdf vignetteTitles: Working with the switchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchBox/inst/doc/switchBox.R importsMe: PDATK suggestsMe: multiclassPairs dependencyCount: 10 Package: switchde Version: 1.37.0 Depends: R (>= 3.4), SingleCellExperiment Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr License: GPL (>= 2) MD5sum: 98dd7b55a268aae2dd8cfa19bbf1d39d NeedsCompilation: no Title: Switch-like differential expression across single-cell trajectories Description: Inference and detection of switch-like differential expression across single-cell RNA-seq trajectories. biocViews: ImmunoOncology, Software, Transcriptomics, GeneExpression, RNASeq, Regression, DifferentialExpression, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell URL: https://github.com/kieranrcampbell/switchde VignetteBuilder: knitr BugReports: https://github.com/kieranrcampbell/switchde git_url: https://git.bioconductor.org/packages/switchde git_branch: devel git_last_commit: 5b6f873 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/switchde_1.37.0.tar.gz vignettes: vignettes/switchde/inst/doc/switchde_vignette.html vignetteTitles: An overview of the switchde package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchde/inst/doc/switchde_vignette.R dependencyCount: 50 Package: synapsis Version: 1.17.0 Depends: R (>= 4.1) Imports: EBImage, stats, utils, graphics Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2, tidyverse, BiocStyle License: MIT + file LICENSE MD5sum: b251d8e9311a49c56cb8c85d773045e5 NeedsCompilation: no Title: An R package to automate the analysis of double-strand break repair during meiosis Description: Synapsis is a Bioconductor software package for automated (unbiased and reproducible) analysis of meiotic immunofluorescence datasets. The primary functions of the software can i) identify cells in meiotic prophase that are labelled by a synaptonemal complex axis or central element protein, ii) isolate individual synaptonemal complexes and measure their physical length, iii) quantify foci and co-localise them with synaptonemal complexes, iv) measure interference between synaptonemal complex-associated foci. The software has applications that extend to multiple species and to the analysis of other proteins that label meiotic prophase chromosomes. The software converts meiotic immunofluorescence images into R data frames that are compatible with machine learning methods. Given a set of microscopy images of meiotic spread slides, synapsis crops images around individual single cells, counts colocalising foci on strands on a per cell basis, and measures the distance between foci on any given strand. biocViews: Software, SingleCell Author: Lucy McNeill [aut, cre, cph] (ORCID: ), Wayne Crismani [rev, ctb] (ORCID: ) Maintainer: Lucy McNeill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapsis git_branch: devel git_last_commit: cc1375e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/synapsis_1.17.0.tar.gz vignettes: vignettes/synapsis/inst/doc/synapsis_tutorial.html vignetteTitles: Using-synapsis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/synapsis/inst/doc/synapsis_tutorial.R dependencyCount: 45 Package: synergyfinder Version: 3.19.0 Depends: R (>= 4.0.0) Imports: drc (>= 3.0-1), reshape2 (>= 1.4.4), tidyverse (>= 1.3.0), dplyr (>= 1.0.3), tidyr (>= 1.1.2), purrr (>= 0.3.4), furrr (>= 0.2.2), ggplot2 (>= 3.3.3), ggforce (>= 0.3.2), grid (>= 4.0.2), vegan (>= 2.5-7), gstat (>= 2.0-6), sp (>= 1.4-5), methods (>= 4.0.2), SpatialExtremes (>= 2.0-9), ggrepel (>= 0.9.1), kriging (>= 1.1), plotly (>= 4.9.3), stringr (>= 1.4.0), future (>= 1.21.0), mice (>= 3.13.0), lattice (>= 0.20-41), nleqslv (>= 3.3.2), stats (>= 4.0.2), graphics (>= 4.0.2), grDevices (>= 4.0.2), magrittr (>= 2.0.1), pbapply (>= 1.4-3), metR (>= 0.9.1) Suggests: knitr, rmarkdown License: Mozilla Public License 2.0 MD5sum: 21397389f7195055eb3dc936ef395035 NeedsCompilation: no Title: Calculate and Visualize Synergy Scores for Drug Combinations Description: Efficient implementations for analyzing pre-clinical multiple drug combination datasets. It provides efficient implementations for 1.the popular synergy scoring models, including HSA, Loewe, Bliss, and ZIP to quantify the degree of drug combination synergy; 2. higher order drug combination data analysis and synergy landscape visualization for unlimited number of drugs in a combination; 3. statistical analysis of drug combination synergy and sensitivity with confidence intervals and p-values; 4. synergy barometer for harmonizing multiple synergy scoring methods to provide a consensus metric of synergy; 5. evaluation of synergy and sensitivity simultaneously to provide an unbiased interpretation of the clinical potential of the drug combinations. Based on this package, we also provide a web application (http://www.synergyfinder.org) for users who prefer graphical user interface. biocViews: Software, StatisticalMethod Author: Shuyu Zheng [aut, cre], Jing Tang [aut] Maintainer: Shuyu Zheng URL: http://www.synergyfinder.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synergyfinder git_branch: devel git_last_commit: 65b3086 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/synergyfinder_3.19.0.tar.gz vignettes: vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.html vignetteTitles: User tutorial of the SynergyFinder Plus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.R dependencyCount: 214 Package: SynExtend Version: 1.23.0 Depends: R (>= 4.5.0), DECIPHER (>= 2.28.0) Imports: methods, Biostrings, S4Vectors, IRanges, utils, stats, parallel, graphics, grDevices, RSQLite, DBI Suggests: BiocStyle, knitr, igraph, markdown, rmarkdown License: GPL-3 MD5sum: 5f9d51dc25ee6b969f82b44388ed9a17 NeedsCompilation: yes Title: Tools for Comparative Genomics Description: A multitude of tools for comparative genomics, focused on large-scale analyses of biological data. SynExtend includes tools for working with syntenic data, clustering massive network structures, and estimating functional relationships among genes. biocViews: Genetics, Clustering, ComparativeGenomics, DataImport Author: Nicholas Cooley [aut, cre] (ORCID: ), Aidan Lakshman [aut, ctb] (ORCID: ), Adelle Fernando [ctb], Erik Wright [aut] Maintainer: Nicholas Cooley URL: https://github.com/npcooley/SynExtend VignetteBuilder: knitr BugReports: https://github.com/npcooley/SynExtend/issues/new/ git_url: https://git.bioconductor.org/packages/SynExtend git_branch: devel git_last_commit: 9315c75 git_last_commit_date: 2026-01-21 Date/Publication: 2026-04-20 source.ver: src/contrib/SynExtend_1.23.0.tar.gz vignettes: vignettes/SynExtend/inst/doc/UsingSynExtend.html vignetteTitles: UsingSynExtend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynExtend/inst/doc/UsingSynExtend.R dependencyCount: 32 Package: synlet Version: 2.11.0 Depends: R (>= 3.5.0) Imports: data.table, ggplot2, grDevices, magrittr, methods, patchwork, RankProd, RColorBrewer, stats, utils Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-3 MD5sum: 81bced349d109e2e5f6152d094a6fb00 NeedsCompilation: no Title: Hits Selection for Synthetic Lethal RNAi Screen Data Description: Select hits from synthetic lethal RNAi screen data. For example, there are two identical celllines except one gene is knocked-down in one cellline. The interest is to find genes that lead to stronger lethal effect when they are knocked-down further by siRNA. Quality control and various visualisation tools are implemented. Four different algorithms could be used to pick up the interesting hits. This package is designed based on 384 wells plates, but may apply to other platforms with proper configuration. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization, FeatureExtraction Author: Chunxuan Shao [aut, cre] Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synlet git_branch: devel git_last_commit: 6bb754e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/synlet_2.11.0.tar.gz vignettes: vignettes/synlet/inst/doc/synlet-vignette.html vignetteTitles: A working Demo for synlet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R dependencyCount: 29 Package: SynMut Version: 1.27.0 Imports: seqinr, methods, Biostrings, stringr, BiocGenerics Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc, glue License: GPL-2 MD5sum: 9ad79e080e7982531badf6066b054b58 NeedsCompilation: no Title: SynMut: Designing Synonymously Mutated Sequences with Different Genomic Signatures Description: There are increasing demands on designing virus mutants with specific dinucleotide or codon composition. This tool can take both dinucleotide preference and/or codon usage bias into account while designing mutants. It is a powerful tool for in silico designs of DNA sequence mutants. biocViews: SequenceMatching, ExperimentalDesign, Preprocessing Author: Haogao Gu [aut, cre], Leo L.M. Poon [led] Maintainer: Haogao Gu URL: https://github.com/Koohoko/SynMut VignetteBuilder: knitr BugReports: https://github.com/Koohoko/SynMut/issues git_url: https://git.bioconductor.org/packages/SynMut git_branch: devel git_last_commit: 0bb1b93 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/SynMut_1.27.0.tar.gz vignettes: vignettes/SynMut/inst/doc/SynMut.html vignetteTitles: SynMut: Designing Synonymous Mutants for DNA Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynMut/inst/doc/SynMut.R dependencyCount: 34 Package: syntenet Version: 1.13.0 Depends: R (>= 4.2) Imports: Rcpp (>= 1.0.8), BiocParallel, GenomicRanges, rlang, Biostrings, utils, methods, igraph, stats, grDevices, RColorBrewer, pheatmap, ggplot2, ggnetwork, intergraph LinkingTo: Rcpp, testthat Suggests: rtracklayer, BiocStyle, ggtree, labdsv, covr, knitr, rmarkdown, testthat (>= 3.0.0), xml2, networkD3 License: GPL-3 MD5sum: e4a787daf7f27de216f407b8b5b2405d NeedsCompilation: yes Title: Inference And Analysis Of Synteny Networks Description: syntenet can be used to infer synteny networks from whole-genome protein sequences and analyze them. Anchor pairs are detected with the MCScanX algorithm, which was ported to this package with the Rcpp framework for R and C++ integration. Anchor pairs from synteny analyses are treated as an undirected unweighted graph (i.e., a synteny network), and users can perform: i. network clustering; ii. phylogenomic profiling (by identifying which species contain which clusters) and; iii. microsynteny-based phylogeny reconstruction with maximum likelihood. biocViews: Software, NetworkInference, FunctionalGenomics, ComparativeGenomics, Phylogenetics, SystemsBiology, GraphAndNetwork, WholeGenome, Network Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Tao Zhao [aut] (ORCID: ), Kristian K Ullrich [aut] (ORCID: ), Yves Van de Peer [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/syntenet VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/syntenet git_url: https://git.bioconductor.org/packages/syntenet git_branch: devel git_last_commit: da094a2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/syntenet_1.13.0.tar.gz vignettes: vignettes/syntenet/inst/doc/vignette_01_inference_and_analysis_of_synteny_networks.html, vignettes/syntenet/inst/doc/vignette_02_synteny_detection_with_syntenet.html vignetteTitles: Inference and analysis of synteny networks, syntenet as a synteny detection tool hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/syntenet/inst/doc/vignette_01_inference_and_analysis_of_synteny_networks.R, vignettes/syntenet/inst/doc/vignette_02_synteny_detection_with_syntenet.R importsMe: doubletrouble dependencyCount: 75 Package: systemPipeR Version: 2.17.2 Depends: R (>= 4.1.0), Rsamtools (>= 1.31.2), Biostrings, ShortRead (>= 1.37.1), methods Imports: GenomicRanges, SummarizedExperiment, ggplot2, yaml, stringr, magrittr, S4Vectors, crayon, BiocGenerics, htmlwidgets Suggests: BiocStyle, knitr, rmarkdown, systemPipeRdata, GenomicAlignments, grid, dplyr, testthat, rjson, annotate, AnnotationDbi, kableExtra, GO.db, GenomeInfoDb, DT, rtracklayer, limma, edgeR, DESeq2, IRanges, batchtools, GenomicFeatures, txdbmaker, GenomeInfoDbData, VariantAnnotation (>= 1.25.11) License: Artistic-2.0 MD5sum: 75c989ab0436653a18e380ddd90ad636 NeedsCompilation: no Title: systemPipeR: A Multipurpose Workflow Management System for Reproducible Data Analysis Description: systemPipeR is a workflow management environment for reproducible data analysis that integrates R with command-line software. It enables researchers to design, execute, and report complex workflows on local machines and HPC systems. The framework combines R-based analysis with external tools through a Common Workflow Language (CWL) interface, manages workflow dependencies and restart capabilities, and automatically generates reproducible scientific analysis reports. The companion package systemPipeRdata provides ready-to-use workflow templates that simplify workflow setup and customization. Alternatively, workflow templates can be loaded from dedicated GitHub repositories. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage, GeneSetEnrichment, Alignment, QualityControl, ImmunoOncology, ReportWriting, WorkflowStep, WorkflowManagement Author: Thomas Girke Maintainer: Thomas Girke URL: https://github.com/tgirke/systemPipeR SystemRequirements: systemPipeR can be used to run external command-line software (e.g. short read aligners), but the corresponding tool needs to be installed on a system. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeR git_branch: devel git_last_commit: e354aff git_last_commit_date: 2026-03-05 Date/Publication: 2026-04-20 source.ver: src/contrib/systemPipeR_2.17.2.tar.gz vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html, vignettes/systemPipeR/inst/doc/systemPipeR.html vignetteTitles: systemPipeR: Workflow Templates, Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.R, vignettes/systemPipeR/inst/doc/systemPipeR.R suggestsMe: systemPipeShiny, systemPipeTools, systemPipeRdata dependencyCount: 94 Package: systemPipeShiny Version: 1.21.0 Depends: R (>= 4.0.0), shiny (>= 1.6.0), spsUtil (>= 0.2.2), spsComps (>= 0.3.3), drawer (>= 0.2) Imports: DT, assertthat, bsplus, crayon, dplyr, ggplot2, htmltools, glue, magrittr, methods, plotly, rlang, rstudioapi, shinyAce, shinyFiles, shinyWidgets, shinydashboard, shinydashboardPlus (>= 2.0.0), shinyjqui, shinyjs, shinytoastr, stringr, stats, styler, tibble, utils, vroom (>= 1.3.1), yaml, R6, RSQLite, openssl Suggests: testthat, BiocStyle, knitr, rmarkdown, systemPipeR (>= 2.12.0), systemPipeRdata (>= 2.10.0), rhandsontable, zip, callr, pushbar, fs, readr, R.utils, DESeq2, SummarizedExperiment, glmpca, pheatmap, grid, ape, Rtsne, UpSetR, tidyr, esquisse (>= 1.1.0), cicerone License: GPL (>= 3) MD5sum: a4f168830c71fd451e3ed74ca808aada NeedsCompilation: no Title: systemPipeShiny: An Interactive Framework for Workflow Management and Visualization Description: systemPipeShiny (SPS) extends the widely used systemPipeR (SPR) workflow environment with a versatile graphical user interface provided by a Shiny App. This allows non-R users, such as experimentalists, to run many systemPipeR’s workflow designs, control, and visualization functionalities interactively without requiring knowledge of R. Most importantly, SPS has been designed as a general purpose framework for interacting with other R packages in an intuitive manner. Like most Shiny Apps, SPS can be used on both local computers as well as centralized server-based deployments that can be accessed remotely as a public web service for using SPR’s functionalities with community and/or private data. The framework can integrate many core packages from the R/Bioconductor ecosystem. Examples of SPS’ current functionalities include: (a) interactive creation of experimental designs and metadata using an easy to use tabular editor or file uploader; (b) visualization of workflow topologies combined with auto-generation of R Markdown preview for interactively designed workflows; (d) access to a wide range of data processing routines; (e) and an extendable set of visualization functionalities. Complex visual results can be managed on a 'Canvas Workbench’ allowing users to organize and to compare plots in an efficient manner combined with a session snapshot feature to continue work at a later time. The present suite of pre-configured visualization examples. The modular design of SPR makes it easy to design custom functions without any knowledge of Shiny, as well as extending the environment in the future with contributions from the community. biocViews: ShinyApps, Infrastructure, DataImport, Sequencing, QualityControl, ReportWriting, ExperimentalDesign, Clustering Author: Le Zhang [aut, cre], Daniela Cassol [aut], Ponmathi Ramasamy [aut], Jianhai Zhang [aut], Gordon Mosher [aut], Thomas Girke [aut] Maintainer: Le Zhang URL: https://systempipe.org/sps, https://github.com/systemPipeR/systemPipeShiny VignetteBuilder: knitr BugReports: https://github.com/systemPipeR/systemPipeShiny/issues git_url: https://git.bioconductor.org/packages/systemPipeShiny git_branch: devel git_last_commit: 7829fef git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/systemPipeShiny_1.21.0.tar.gz vignettes: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.html vignetteTitles: systemPipeShiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.R dependencyCount: 112 Package: systemPipeTools Version: 1.19.0 Imports: DESeq2, GGally, Rtsne, SummarizedExperiment, ape, dplyr, ggplot2, ggrepel, ggtree, glmpca, pheatmap, plotly, tibble, magrittr, DT, stats Suggests: systemPipeR, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), BiocGenerics, Biostrings, methods License: Artistic-2.0 MD5sum: fe4ec41afe04685f982c4f49ef873fd2 NeedsCompilation: no Title: Tools for data visualization Description: systemPipeTools package extends the widely used systemPipeR (SPR) workflow environment with an enhanced toolkit for data visualization, including utilities to automate the data visualizaton for analysis of differentially expressed genes (DEGs). systemPipeTools provides data transformation and data exploration functions via scatterplots, hierarchical clustering heatMaps, principal component analysis, multidimensional scaling, generalized principal components, t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and volcano plots. All these utilities can be integrated with the modular design of the systemPipeR environment that allows users to easily substitute any of these features and/or custom with alternatives. biocViews: Infrastructure, DataImport, Sequencing, QualityControl, ReportWriting, ExperimentalDesign, Clustering, DifferentialExpression, MultidimensionalScaling, PrincipalComponent Author: Daniela Cassol [aut, cre], Ponmathi Ramasamy [aut], Le Zhang [aut], Thomas Girke [aut] Maintainer: Daniela Cassol VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeTools git_branch: devel git_last_commit: 5e1379b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/systemPipeTools_1.19.0.tar.gz vignettes: vignettes/systemPipeTools/inst/doc/systemPipeTools.html vignetteTitles: systemPipeTools: Data Visualizations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeTools/inst/doc/systemPipeTools.R dependencyCount: 130 Package: tadar Version: 1.9.0 Depends: GenomicRanges, ggplot2, R (>= 4.4.0) Imports: BiocGenerics, Seqinfo, Gviz, IRanges, lifecycle, MatrixGenerics, methods, rlang, Rsamtools, S4Vectors, stats, VariantAnnotation Suggests: BiocStyle, covr, knitr, limma, rmarkdown, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: 458317776deb37c91e8542b9eecb84e7 NeedsCompilation: no Title: Transcriptome Analysis of Differential Allelic Representation Description: This package provides functions to standardise the analysis of Differential Allelic Representation (DAR). DAR compromises the integrity of Differential Expression analysis results as it can bias expression, influencing the classification of genes (or transcripts) as being differentially expressed. DAR analysis results in an easy-to-interpret value between 0 and 1 for each genetic feature of interest, where 0 represents identical allelic representation and 1 represents complete diversity. This metric can be used to identify features prone to false-positive calls in Differential Expression analysis, and can be leveraged with statistical methods to alleviate the impact of such artefacts on RNA-seq data. biocViews: Sequencing, RNASeq, SNP, GenomicVariation, VariantAnnotation, DifferentialExpression Author: Lachlan Baer [aut, cre] (ORCID: ), Stevie Pederson [aut] (ORCID: ) Maintainer: Lachlan Baer URL: https://github.com/baerlachlan/tadar VignetteBuilder: knitr BugReports: https://github.com/baerlachlan/tadar/issues git_url: https://git.bioconductor.org/packages/tadar git_branch: devel git_last_commit: 7b05448 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tadar_1.9.0.tar.gz vignettes: vignettes/tadar/inst/doc/dar.html vignetteTitles: DAR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tadar/inst/doc/dar.R dependencyCount: 151 Package: TADCompare Version: 1.21.0 Depends: R (>= 4.0) Imports: dplyr, PRIMME, cluster, Matrix, magrittr, HiCcompare, ggplot2, tidyr, ggpubr, RColorBrewer, reshape2, cowplot Suggests: BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr, pheatmap, SpectralTAD, magick, qpdf License: MIT + file LICENSE MD5sum: aa077b8816b2d0d2dab85156aadfa642 NeedsCompilation: no Title: TADCompare: Identification and characterization of differential TADs Description: TADCompare is an R package designed to identify and characterize differential Topologically Associated Domains (TADs) between multiple Hi-C contact matrices. It contains functions for finding differential TADs between two datasets, finding differential TADs over time and identifying consensus TADs across multiple matrices. It takes all of the main types of HiC input and returns simple, comprehensive, easy to analyze results. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Mikhail Dozmorov [aut, cre] (ORCID: ), Kellen Cresswell [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/TADCompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/TADCompare/issues git_url: https://git.bioconductor.org/packages/TADCompare git_branch: devel git_last_commit: 44ca011 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TADCompare_1.21.0.tar.gz vignettes: vignettes/TADCompare/inst/doc/Input_Data.html, vignettes/TADCompare/inst/doc/Ontology_Analysis.html, vignettes/TADCompare/inst/doc/TADCompare.html vignetteTitles: Input data formats, Gene Ontology Enrichment Analysis, TAD comparison between two conditions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TADCompare/inst/doc/Input_Data.R, vignettes/TADCompare/inst/doc/Ontology_Analysis.R, vignettes/TADCompare/inst/doc/TADCompare.R dependencyCount: 124 Package: tanggle Version: 1.17.0 Depends: R (>= 4.1), ggplot2 (>= 3.0.0), ggtree Imports: ape (>= 5.0), phangorn (>= 2.12), rlang, utils, methods, dplyr Suggests: tinytest, BiocStyle, ggimage, knitr, rmarkdown License: Artistic-2.0 MD5sum: 7ccd6aa62dce29a8997fd75b8a9c9370 NeedsCompilation: no Title: Visualization of Phylogenetic Networks Description: Offers functions for plotting split (or implicit) networks (unrooted, undirected) and explicit networks (rooted, directed) with reticulations extending. 'ggtree' and using functions from 'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017] to allow the visualization of phylogenetic networks using the 'ggplot2' syntax. It offers an alternative to the plot functions already available in 'ape' Paradis and Schliep (2019) and 'phangorn' Schliep (2011) . biocViews: Software, Visualization, Phylogenetics, Alignment, Clustering, MultipleSequenceAlignment, DataImport Author: Klaus Schliep [aut, cre] (ORCID: ), Marta Vidal-Garcia [aut], Claudia Solis-Lemus [aut] (ORCID: ), Leann Biancani [aut], Eren Ada [aut], L. Francisco Henao Diaz [aut], Guangchuang Yu [ctb], Joshua Justison [aut] Maintainer: Klaus Schliep URL: https://klausvigo.github.io/tanggle/, https://github.com/KlausVigo/tanggle VignetteBuilder: knitr BugReports: https://github.com/KlausVigo/tanggle/issues git_url: https://git.bioconductor.org/packages/tanggle git_branch: devel git_last_commit: a5309c6 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tanggle_1.17.0.tar.gz vignettes: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.html, vignettes/tanggle/inst/doc/tanggle_vignette.html vignetteTitles: ***tanggle***: Visualización de redes filogenéticas con *ggplot2*, ***tanggle***: Visualization of phylogenetic networks in a *ggplot2* framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.R, vignettes/tanggle/inst/doc/tanggle_vignette.R dependencyCount: 85 Package: TAPseq Version: 1.23.1 Depends: R (>= 4.0.0) Imports: methods, GenomicAlignments, GenomicRanges, IRanges, BiocGenerics, S4Vectors (>= 0.20.1), GenomeInfoDb, BSgenome, GenomicFeatures, Biostrings, dplyr, tidyr, BiocParallel Suggests: testthat, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, ggplot2, Seurat, glmnet, cowplot, Matrix, rtracklayer, BiocStyle License: MIT + file LICENSE MD5sum: d7a95455488afc60257a14310e80e3f7 NeedsCompilation: no Title: Targeted scRNA-seq primer design for TAP-seq Description: Design primers for targeted single-cell RNA-seq used by TAP-seq. Create sequence templates for target gene panels and design gene-specific primers using Primer3. Potential off-targets can be estimated with BLAST. Requires working installations of Primer3 and BLASTn. biocViews: SingleCell, Sequencing, Technology, CRISPR, PooledScreens Author: Andreas R. Gschwind [aut, cre] (ORCID: ), Lars Velten [aut] (ORCID: ), Lars M. Steinmetz [aut] Maintainer: Andreas R. Gschwind URL: https://github.com/argschwind/TAPseq SystemRequirements: Primer3 (>= 2.5.0), BLAST+ (>=2.6.0) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TAPseq git_branch: devel git_last_commit: ec17e14 git_last_commit_date: 2026-01-12 Date/Publication: 2026-04-20 source.ver: src/contrib/TAPseq_1.23.1.tar.gz vignettes: vignettes/TAPseq/inst/doc/tapseq_primer_design.html, vignettes/TAPseq/inst/doc/tapseq_target_genes.html vignetteTitles: TAP-seq primer design workflow, Select target genes for TAP-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TAPseq/inst/doc/tapseq_primer_design.R, vignettes/TAPseq/inst/doc/tapseq_target_genes.R dependencyCount: 89 Package: target Version: 1.25.0 Depends: R (>= 3.6) Imports: BiocGenerics, GenomicRanges, IRanges, matrixStats, methods, stats, graphics, shiny Suggests: testthat (>= 2.1.0), knitr, rmarkdown, shinytest, shinyBS, covr License: GPL-3 MD5sum: d6cfa3eb3bc44c37676285f0b82efb7c NeedsCompilation: no Title: Predict Combined Function of Transcription Factors Description: Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) . Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data. biocViews: Software, StatisticalMethod, Transcription Author: Mahmoud Ahmed [aut, cre] Maintainer: Mahmoud Ahmed URL: https://github.com/MahShaaban/target VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/target/issues git_url: https://git.bioconductor.org/packages/target git_branch: devel git_last_commit: fd1fceb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/target_1.25.0.tar.gz vignettes: vignettes/target/inst/doc/extend-target.html, vignettes/target/inst/doc/target.html vignetteTitles: Using target to predict combined binding, Using the target package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/target/inst/doc/extend-target.R, vignettes/target/inst/doc/target.R dependencyCount: 44 Package: TargetDecoy Version: 1.17.0 Depends: R (>= 4.1) Imports: ggplot2, ggpubr, methods, miniUI, mzID, mzR, shiny, stats Suggests: BiocStyle, knitr, msdata, sessioninfo, rmarkdown, gridExtra, testthat (>= 3.0.0), covr License: Artistic-2.0 MD5sum: aa280313743e691b6bc550b1b438aa9f NeedsCompilation: no Title: Diagnostic Plots to Evaluate the Target Decoy Approach Description: A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum is then assigned to the theoretical peptide with the best score, which is also referred to as the peptide to spectrum match (PSM). It is of course crucial for the downstream analysis to evaluate the quality of these matches. Therefore False Discovery Rate (FDR) control is used to return a reliable list PSMs. The FDR, however, requires a good characterisation of the score distribution of PSMs that are matched to the wrong peptide (bad target hits). In proteomics, the target decoy approach (TDA) is typically used for this purpose. The TDA method matches the spectra to a database of real (targets) and nonsense peptides (decoys). A popular approach to generate these decoys is to reverse the target database. Hence, all the PSMs that match to a decoy are known to be bad hits and the distribution of their scores are used to estimate the distribution of the bad scoring target PSMs. A crucial assumption of the TDA is that the decoy PSM hits have similar properties as bad target hits so that the decoy PSM scores are a good simulation of the target PSM scores. Users, however, typically do not evaluate these assumptions. To this end we developed TargetDecoy to generate diagnostic plots to evaluate the quality of the target decoy method. biocViews: MassSpectrometry, Proteomics, QualityControl, Software, Visualization Author: Elke Debrie [aut, cre], Lieven Clement [aut] (ORCID: ), Milan Malfait [aut] (ORCID: ) Maintainer: Elke Debrie URL: https://www.bioconductor.org/packages/TargetDecoy, https://statomics.github.io/TargetDecoy/, https://github.com/statOmics/TargetDecoy/ VignetteBuilder: knitr BugReports: https://github.com/statOmics/TargetDecoy/issues git_url: https://git.bioconductor.org/packages/TargetDecoy git_branch: devel git_last_commit: ab5d4fd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TargetDecoy_1.17.0.tar.gz vignettes: vignettes/TargetDecoy/inst/doc/TargetDecoy.html vignetteTitles: Introduction to TargetDecoy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetDecoy/inst/doc/TargetDecoy.R dependencyCount: 125 Package: TargetScore Version: 1.49.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 1e62e9e617539197514605598ff1470f NeedsCompilation: no Title: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information Description: Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html git_url: https://git.bioconductor.org/packages/TargetScore git_branch: devel git_last_commit: 063ca5d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TargetScore_1.49.0.tar.gz vignettes: vignettes/TargetScore/inst/doc/TargetScore.pdf vignetteTitles: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetScore/inst/doc/TargetScore.R suggestsMe: TargetScoreData dependencyCount: 9 Package: TargetSearch Version: 2.13.0 Imports: graphics, grDevices, methods, ncdf4, stats, utils, assertthat Suggests: TargetSearchData, BiocStyle, knitr, tinytest License: GPL (>= 2) MD5sum: 7f725e89fd5ce9d8cefbc3f84e9e7a8a NeedsCompilation: yes Title: A package for the analysis of GC-MS metabolite profiling data Description: This packages provides a flexible, fast and accurate method for targeted pre-processing of GC-MS data. The user provides a (often very large) set of GC chromatograms and a metabolite library of targets. The package will automatically search those targets in the chromatograms resulting in a data matrix that can be used for further data analysis. biocViews: MassSpectrometry, Preprocessing, DecisionTree, ImmunoOncology Author: Alvaro Cuadros-Inostroza [aut, cre], Jan Lisec [aut], Henning Redestig [aut], Matt Hannah [aut] Maintainer: Alvaro Cuadros-Inostroza URL: https://github.com/acinostroza/TargetSearch VignetteBuilder: knitr BugReports: https://github.com/acinostroza/TargetSearch/issues git_url: https://git.bioconductor.org/packages/TargetSearch git_branch: devel git_last_commit: ea1ec73 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TargetSearch_2.13.0.tar.gz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction extra, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RetentionIndexCorrection.R, vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R dependencyCount: 8 Package: TaxSEA Version: 1.3.3 Depends: R (>= 4.5.0) Imports: stats, utils Suggests: BiocStyle, bugsigdbr, fgsea, knitr, mia, rmarkdown, SummarizedExperiment, testthat License: GPL-3 MD5sum: ad00db2f74f03a90221fad062997bd9e NeedsCompilation: no Title: Taxon Set Enrichment Analysis Description: TaxSEA is an R package for Taxon Set Enrichment Analysis, which utilises a Kolmogorov-Smirnov test analyses to investigate differential abundance analysis output for whether there are alternations in a-priori defined sets of taxa from public databases (BugSigDB, MiMeDB, GutMGene, mBodyMap, BacDive and GMRepoV2) and collated from the literature. TaxSEA takes as input a list of taxonomic identifiers (e.g. species names, NCBI IDs etc.) and a rank (E.g. fold change, correlation coefficient). TaxSEA be applied to any microbiota taxonomic profiling technology (array-based, 16S rRNA gene sequencing, shotgun metagenomics & metatranscriptomics etc.) and enables researchers to rapidly contextualize their findings within the broader literature to accelerate interpretation of results. biocViews: Microbiome, Metagenomics, Sequencing, GeneSetEnrichment, RNASeq Author: Feargal Ryan [aut, cre, fnd] (ORCID: , funding: Supported by NHMRC Investigator Grant) Maintainer: Feargal Ryan URL: https://github.com/feargalr/taxsea, https://feargalr.github.io/TaxSEA/ VignetteBuilder: knitr BugReports: https://github.com/feargalr/taxsea/issues git_url: https://git.bioconductor.org/packages/TaxSEA git_branch: devel git_last_commit: e315498 git_last_commit_date: 2026-02-11 Date/Publication: 2026-04-20 source.ver: src/contrib/TaxSEA_1.3.3.tar.gz vignettes: vignettes/TaxSEA/inst/doc/FAQs.html, vignettes/TaxSEA/inst/doc/ORA_vs_ES.html, vignettes/TaxSEA/inst/doc/single-sample-enrichment.html, vignettes/TaxSEA/inst/doc/Taxonomic-aggregation.html, vignettes/TaxSEA/inst/doc/TaxSEA.html, vignettes/TaxSEA/inst/doc/why-taxon-set-enrichment.html vignetteTitles: frequently asked questions, analysis_types, single_sample_enrichment, taxonomic_aggregation, TaxSEA, Why use taxon set enrichment analysis? hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TaxSEA/inst/doc/TaxSEA.R dependencyCount: 2 Package: TCC Version: 1.51.0 Depends: R (>= 3.0), methods, DESeq2, edgeR, ROC Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 0464925e9d72c02a763a88770ecbfdf2 NeedsCompilation: no Title: TCC: Differential expression analysis for tag count data with robust normalization strategies Description: This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages. biocViews: ImmunoOncology, Sequencing, DifferentialExpression, RNASeq Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer: Jianqiang Sun , Tomoaki Nishiyama git_url: https://git.bioconductor.org/packages/TCC git_branch: devel git_last_commit: 0eb2b06 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TCC_1.51.0.tar.gz vignettes: vignettes/TCC/inst/doc/TCC.pdf vignetteTitles: TCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCC/inst/doc/TCC.R suggestsMe: compcodeR dependencyCount: 64 Package: TCGAbiolinks Version: 2.39.0 Depends: R (>= 4.1.0) Imports: downloader (>= 0.4), grDevices, biomaRt, dplyr, graphics, tibble, GenomicRanges, XML (>= 3.98.0), data.table, jsonlite (>= 1.0.0), plyr, knitr, methods, ggplot2, stringr (>= 1.0.0), IRanges, rvest (>= 0.3.0), stats, utils, S4Vectors, R.utils, SummarizedExperiment (>= 1.4.0), TCGAbiolinksGUI.data (>= 1.15.1), readr, tools, tidyr, purrr, xml2, httr (>= 1.2.1) Suggests: jpeg, png, BiocStyle, rmarkdown, devtools, maftools, parmigene, c3net, minet, Biobase, affy, testthat, sesame, AnnotationHub, ExperimentHub, pathview, clusterProfiler, Seurat, ComplexHeatmap, circlize, ConsensusClusterPlus, igraph, limma, edgeR, sva, EDASeq, survminer, genefilter, gridExtra, survival, doParallel, parallel, ggrepel (>= 0.6.3), scales, grid, DT License: GPL (>= 3) MD5sum: 91d54b660f4a4842fe1281ea01426797 NeedsCompilation: no Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data Description: The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Sequencing, Survival, Software Author: Antonio Colaprico, Tiago Chedraoui Silva, Catharina Olsen, Luciano Garofano, Davide Garolini, Claudia Cava, Thais Sabedot, Tathiane Malta, Stefano M. Pagnotta, Isabella Castiglioni, Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr Maintainer: Tiago Chedraoui Silva , Antonio Colaprico URL: https://github.com/BioinformaticsFMRP/TCGAbiolinks VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues git_url: https://git.bioconductor.org/packages/TCGAbiolinks git_branch: devel git_last_commit: 2072af1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TCGAbiolinks_2.39.0.tar.gz vignettes: vignettes/TCGAbiolinks/inst/doc/analysis.html, vignettes/TCGAbiolinks/inst/doc/casestudy.html, vignettes/TCGAbiolinks/inst/doc/classifiers.html, vignettes/TCGAbiolinks/inst/doc/clinical.html, vignettes/TCGAbiolinks/inst/doc/download_prepare.html, vignettes/TCGAbiolinks/inst/doc/extension.html, vignettes/TCGAbiolinks/inst/doc/index.html, vignettes/TCGAbiolinks/inst/doc/mutation.html, vignettes/TCGAbiolinks/inst/doc/query.html, vignettes/TCGAbiolinks/inst/doc/stemness_score.html, vignettes/TCGAbiolinks/inst/doc/subtypes.html vignetteTitles: 7. Analyzing and visualizing TCGA data, 8. Case Studies, 10. Classifiers, "4. Clinical data", "3. Downloading and preparing files for analysis", "10. TCGAbiolinks_Extension", "1. Introduction", "5. Mutation data", "2. Searching GDC database", 11. Stemness score, 6. Compilation of TCGA molecular subtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinks/inst/doc/analysis.R, vignettes/TCGAbiolinks/inst/doc/casestudy.R, vignettes/TCGAbiolinks/inst/doc/classifiers.R, vignettes/TCGAbiolinks/inst/doc/clinical.R, vignettes/TCGAbiolinks/inst/doc/download_prepare.R, vignettes/TCGAbiolinks/inst/doc/extension.R, vignettes/TCGAbiolinks/inst/doc/index.R, vignettes/TCGAbiolinks/inst/doc/mutation.R, vignettes/TCGAbiolinks/inst/doc/query.R, vignettes/TCGAbiolinks/inst/doc/stemness_score.R, vignettes/TCGAbiolinks/inst/doc/subtypes.R importsMe: CBN2Path, ELMER, MoonlightR, SurfR, TENET suggestsMe: GeoTcgaData, iNETgrate, musicatk dependencyCount: 104 Package: TCseq Version: 1.35.0 Depends: R (>= 3.4) Imports: edgeR, BiocGenerics, reshape2, GenomicRanges, IRanges, SummarizedExperiment, GenomicAlignments, Rsamtools, e1071, cluster, ggplot2, grid, grDevices, stats, utils, methods, locfit Suggests: testthat License: GPL (>= 2) MD5sum: a6182c59c0c5190a09cd59f3151906d6 NeedsCompilation: no Title: Time course sequencing data analysis Description: Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of the temporal patterns of time course data. biocViews: Epigenetics, TimeCourse, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, Clustering, Visualization Author: Mengjun Wu , Lei Gu Maintainer: Mengjun Wu git_url: https://git.bioconductor.org/packages/TCseq git_branch: devel git_last_commit: 049f241 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TCseq_1.35.0.tar.gz vignettes: vignettes/TCseq/inst/doc/TCseq.pdf vignetteTitles: TCseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCseq/inst/doc/TCseq.R suggestsMe: ClusterGVis dependencyCount: 73 Package: TDbasedUFE Version: 1.11.0 Imports: GenomicRanges, rTensor, readr, methods, MOFAdata, tximport, tximportData, graphics, stats, utils, shiny Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 59fa0967099b83f02eb27b6dd8e98365 NeedsCompilation: no Title: Tensor Decomposition Based Unsupervised Feature Extraction Description: This is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. It can perform unsupervised feature extraction. It uses tensor decomposition. It is applicable to gene expression, DNA methylation, and histone modification etc. It can perform multiomics analysis. It is also potentially applicable to single cell omics data sets. biocViews: GeneExpression, FeatureExtraction, MethylationArray, SingleCell Author: Y-h. Taguchi [aut, cre] (ORCID: ) Maintainer: Y-h. Taguchi URL: https://github.com/tagtag/TDbasedUFE VignetteBuilder: knitr BugReports: https://github.com/tagtag/TDbasedUFE/issues git_url: https://git.bioconductor.org/packages/TDbasedUFE git_branch: devel git_last_commit: 00400bb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TDbasedUFE_1.11.0.tar.gz vignettes: vignettes/TDbasedUFE/inst/doc/QuickStart.html, vignettes/TDbasedUFE/inst/doc/TDbasedUFE.html vignetteTitles: QuickStart, TDbasedUFE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDbasedUFE/inst/doc/QuickStart.R, vignettes/TDbasedUFE/inst/doc/TDbasedUFE.R importsMe: TDbasedUFEadv dependencyCount: 64 Package: TEKRABber Version: 1.15.0 Depends: R (>= 4.3) Imports: AnnotationHub, apeglm, biomaRt, dplyr, doParallel, DESeq2, foreach, magrittr, Rcpp (>= 1.0.7), rtracklayer, SCBN, stats, utils LinkingTo: Rcpp Suggests: BiocStyle, GenomeInfoDb, bslib, ggplot2, ggpubr, plotly, rmarkdown, shiny, knitr, testthat (>= 3.0.0) License: LGPL (>=3) MD5sum: ec1b58a823a3ab38709c58d2b38950f0 NeedsCompilation: yes Title: An R package estimates the correlations of orthologs and transposable elements between two species Description: TEKRABber is made to provide a user-friendly pipeline for comparing orthologs and transposable elements (TEs) between two species. It considers the orthology confidence between two species from BioMart to normalize expression counts and detect differentially expressed orthologs/TEs. Then it provides one to one correlation analysis for desired orthologs and TEs. There is also an app function to have a first insight on the result. Users can prepare orthologs/TEs RNA-seq expression data by their own preference to run TEKRABber following the data structure mentioned in the vignettes. biocViews: DifferentialExpression, Normalization, Transcription, GeneExpression Author: Yao-Chung Chen [aut, cre] (ORCID: ), Katja Nowick [aut] (ORCID: ) Maintainer: Yao-Chung Chen URL: https://github.com/ferygood/TEKRABber VignetteBuilder: knitr BugReports: https://github.com/ferygood/TEKRABber/issues git_url: https://git.bioconductor.org/packages/TEKRABber git_branch: devel git_last_commit: 29653a7 git_last_commit_date: 2026-01-10 Date/Publication: 2026-04-20 source.ver: src/contrib/TEKRABber_1.15.0.tar.gz vignettes: vignettes/TEKRABber/inst/doc/TEKRABber.html vignetteTitles: TEKRABber hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEKRABber/inst/doc/TEKRABber.R dependencyCount: 123 Package: TENET Version: 1.3.0 Depends: R (>= 4.5) Imports: graphics, grDevices, stats, utils, tools, S4Vectors, GenomicRanges, IRanges, parallel, pastecs, ggplot2 (>= 4.0), RCircos, survival, BSgenome.Hsapiens.UCSC.hg38, seqLogo, Biostrings, matlab, TCGAbiolinks, methods, R.utils, MultiAssayExperiment, SummarizedExperiment, sesame, sesameData, AnnotationHub, ExperimentHub, TENET.ExperimentHub, rtracklayer, MotifDb, BAMMtools, survminer Suggests: TENET.AnnotationHub, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: a02b77c1bb39e64b6c0ca0eb89fb9f28 NeedsCompilation: no Title: R package for TENET (Tracing regulatory Element Networks using Epigenetic Traits) to identify key transcription factors Description: TENET identifies key transcription factors (TFs) and regulatory elements (REs) linked to a specific cell type by finding significantly correlated differences in gene expression and RE DNA methylation between case and control input datasets, and identifying the top genes by number of significant RE DNA methylation site links. It also includes many tools for visualization and analysis of the results, including plots displaying and comparing methylation and expression data and methylation site link counts, survival analysis, TF motif searching in the vicinity of linked RE DNA methylation sites, custom TAD and peak overlap analysis, and UCSC Genome Browser track file generation. A utility function is also provided to download methylation, expression, and patient survival data from The Cancer Genome Atlas (TCGA) for use in TENET or other analyses. biocViews: Software, BiomedicalInformatics, CellBiology, Genetics, Epigenetics, MultipleComparison, GeneExpression, DifferentialExpression, DNAMethylation, DifferentialMethylation, MethylationArray, Sequencing, MethylSeq, RNASeq, FunctionalGenomics, GeneRegulation, GeneTarget, HistoneModification, Transcription, Transcriptomics, Survival, Visualization Author: Rhie Lab at the University of Southern California [cre], Daniel Mullen [aut] (ORCID: ), Zexun Wu [aut] (ORCID: ), Ethan Nelson-Moore [aut] (ORCID: ), Suhn Rhie [aut] (ORCID: ) Maintainer: Rhie Lab at the University of Southern California URL: https://github.com/rhielab/TENET VignetteBuilder: knitr BugReports: https://github.com/rhielab/TENET/issues git_url: https://git.bioconductor.org/packages/TENET git_branch: devel git_last_commit: 135855c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TENET_1.3.0.tar.gz vignettes: vignettes/TENET/inst/doc/TENET_vignette.html vignetteTitles: Using TENET hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TENET/inst/doc/TENET_vignette.R suggestsMe: TENET.AnnotationHub, TENET.ExperimentHub dependencyCount: 207 Package: TEQC Version: 4.33.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: S4Vectors, Seqinfo, GenomicRanges, Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: d6774749767046f1b214753f70e68606 NeedsCompilation: no Title: Quality control for target capture experiments Description: Target capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on. biocViews: QualityControl, Microarray, Sequencing, Genetics Author: M. Hummel, S. Bonnin, E. Lowy, G. Roma Maintainer: Sarah Bonnin git_url: https://git.bioconductor.org/packages/TEQC git_branch: devel git_last_commit: bd3d4b2 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TEQC_4.33.0.tar.gz vignettes: vignettes/TEQC/inst/doc/TEQC.pdf vignetteTitles: TEQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEQC/inst/doc/TEQC.R dependencyCount: 31 Package: terapadog Version: 1.3.0 Imports: DESeq2, KEGGREST, stats, utils, dplyr, plotly, htmlwidgets, biomaRt, methods Suggests: apeglm, BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 8bd1859e311d0f54a7317ece47c32dc6 NeedsCompilation: no Title: Translational Efficiency Regulation Analysis using the PADOG Method Description: This package performs a Gene Set Analysis with the approach adopted by PADOG on the genes that are reported as translationally regulated (ie. exhibit a significant change in TE) by the DeltaTE package. It can be used on its own to see the impact of translation regulation on gene sets, but it is also integrated as an additional analysis method within ReactomeGSA, where results are further contextualised in terms of pathways and directionality of the change. biocViews: RiboSeq, Transcriptomics, GeneSetEnrichment, GeneRegulation, Reactome, Software Author: Gionmattia Carancini [cre, aut] (ORCID: ) Maintainer: Gionmattia Carancini URL: https://github.com/Gionmattia/terapadog VignetteBuilder: knitr BugReports: https://github.com/Gionmattia/terapadog/issues git_url: https://git.bioconductor.org/packages/terapadog git_branch: devel git_last_commit: 48dbe0f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/terapadog_1.3.0.tar.gz vignettes: vignettes/terapadog/inst/doc/terapadog_vignette.html vignetteTitles: terapadog: Translational Efficiency Regulation Analysis & Pathway Analysis with Down-weighting of Overlapping Genes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/terapadog/inst/doc/terapadog_vignette.R dependencyCount: 119 Package: ternarynet Version: 1.55.0 Depends: R (>= 4.0) Imports: utils, igraph, methods, graphics, stats, BiocParallel Suggests: testthat Enhances: Rmpi, snow License: GPL (>= 2) MD5sum: 57f593338bae403e9e73ebc0d61b1e9e NeedsCompilation: yes Title: Ternary Network Estimation Description: Gene-regulatory network (GRN) modeling seeks to infer dependencies between genes and thereby provide insight into the regulatory relationships that exist within a cell. This package provides a computational Bayesian approach to GRN estimation from perturbation experiments using a ternary network model, in which gene expression is discretized into one of 3 states: up, unchanged, or down). The ternarynet package includes a parallel implementation of the replica exchange Monte Carlo algorithm for fitting network models, using MPI. biocViews: Software, CellBiology, GraphAndNetwork, Network, Bayesian Author: Matthew N. McCall , Anthony Almudevar , David Burton , Harry Stern Maintainer: McCall N. Matthew git_url: https://git.bioconductor.org/packages/ternarynet git_branch: devel git_last_commit: 4041542 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ternarynet_1.55.0.tar.gz vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf vignetteTitles: ternarynet: A Computational Bayesian Approach to Ternary Network Estimation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R dependencyCount: 26 Package: TFARM Version: 1.33.0 Depends: R (>= 3.5.0) Imports: arules, fields, GenomicRanges, graphics, stringr, methods, stats, gplots Suggests: BiocStyle, knitr, plyr License: Artistic-2.0 MD5sum: e6f1b70004e5a0bdebcd028ac11d2151 NeedsCompilation: no Title: Transcription Factors Association Rules Miner Description: It searches for relevant associations of transcription factors with a transcription factor target, in specific genomic regions. It also allows to evaluate the Importance Index distribution of transcription factors (and combinations of transcription factors) in association rules. biocViews: BiologicalQuestion, Infrastructure, StatisticalMethod, Transcription Author: Liuba Nausicaa Martino, Alice Parodi, Gaia Ceddia, Piercesare Secchi, Stefano Campaner, Marco Masseroli Maintainer: Liuba Nausicaa Martino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFARM git_branch: devel git_last_commit: cbabd4a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TFARM_1.33.0.tar.gz vignettes: vignettes/TFARM/inst/doc/TFARM.pdf vignetteTitles: Transcription Factor Association Rule Miner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFARM/inst/doc/TFARM.R dependencyCount: 37 Package: TFBSTools Version: 1.49.0 Depends: R (>= 3.2.2) Imports: Biobase(>= 2.28), Biostrings(>= 2.36.4), pwalign, BiocGenerics(>= 0.14.0), BiocParallel(>= 1.2.21), BSgenome(>= 1.36.3), caTools(>= 1.17.1), DirichletMultinomial(>= 1.10.0), Seqinfo, GenomicRanges(>= 1.20.6), gtools(>= 3.5.0), grid, IRanges(>= 2.2.7), methods, DBI (>= 0.6), RSQLite(>= 1.0.0), rtracklayer(>= 1.28.10), seqLogo(>= 1.34.0), S4Vectors(>= 0.9.25), TFMPvalue(>= 0.0.5), XML(>= 3.98-1.3), XVector(>= 0.8.0), parallel Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11), testthat, JASPAR2016(>= 1.0.0), JASPAR2018(>= 1.0.0), rmarkdown License: GPL-2 MD5sum: f0337babf357f753415a13d745b27b99 NeedsCompilation: yes Title: Software Package for Transcription Factor Binding Site (TFBS) Analysis Description: TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software. biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery, Transcription, Alignment Author: Ge Tan [aut, cre] Maintainer: Ge Tan URL: https://github.com/ge11232002/TFBSTools VignetteBuilder: knitr BugReports: https://github.com/ge11232002/TFBSTools/issues git_url: https://git.bioconductor.org/packages/TFBSTools git_branch: devel git_last_commit: d5d1319 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TFBSTools_1.49.0.tar.gz vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.html vignetteTitles: Transcription factor binding site (TFBS) analysis with the "TFBSTools" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R importsMe: ATACseqTFEA, esATAC, MatrixRider, MethReg, monaLisa, motifmatchr, motifStack, primirTSS suggestsMe: enhancerHomologSearch, epiSeeker, GRaNIE, MAGAR, pageRank, universalmotif, JASPAR2018, JASPAR2020, JASPAR2022, CAGEWorkflow, Signac dependencyCount: 79 Package: TFEA.ChIP Version: 1.31.0 Depends: R (>= 4.2.0) Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, GenomicRanges, grDevices, dplyr, stats, utils, R.utils, methods, org.Hs.eg.db, org.Mm.eg.db, rlang, ExperimentHub Suggests: knitr, rmarkdown, BiocStyle, S4Vectors, Seqinfo, meta, plotly, scales, tidyr, purrr, tibble, ggplot2, DESeq2, edgeR, limma, babelgene, BiocGenerics, ggrepel, rcompanion, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi, RColorBrewer, RUnit, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 0f1cbc0f9b0099ab33a4e35b976b0af8 NeedsCompilation: no Title: TFEA.ChIP, a Tool Kit for Transcription Factor Enrichment Description: Package to analyze transcription factor enrichment in a gene set using data from ChIP-Seq experiments. biocViews: Transcription, GeneRegulation, GeneSetEnrichment, Transcriptomics, Sequencing, ChIPSeq, RNASeq, ImmunoOncology, GeneExpression, ChipOnChip Author: Yosra Berrouayel [aut, cre] (ORCID: ), Laura Puente-Santamaria [aut], Luis del Peso [aut] (ORCID: ) Maintainer: Yosra Berrouayel URL: https://github.com/yberda/TFEA.ChIP VignetteBuilder: knitr, BiocStyle BugReports: https://github.com/yberda/TFEA.ChIP/issues git_url: https://git.bioconductor.org/packages/TFEA.ChIP git_branch: devel git_last_commit: b0fbb73 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TFEA.ChIP_1.31.0.tar.gz vignettes: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.html vignetteTitles: TFEA.ChIP: a tool kit for transcription factor enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.R suggestsMe: ChIPDBData dependencyCount: 106 Package: TFutils Version: 1.31.0 Depends: R (>= 4.1.0) Imports: methods, dplyr, magrittr, miniUI, shiny, Rsamtools, GSEABase, rjson, BiocFileCache, DT, httr, readxl, AnnotationDbi, org.Hs.eg.db, utils, GenomicFiles, SummarizedExperiment Suggests: knitr, data.table, testthat, AnnotationFilter, Biobase, GenomicFeatures, GenomicRanges, Gviz, IRanges, S4Vectors, EnsDb.Hsapiens.v75, BiocParallel, BiocStyle, GO.db, Seqinfo, UpSetR, ggplot2, png, gwascat, MotifDb, motifStack, RColorBrewer, rmarkdown License: Artistic-2.0 MD5sum: 58a5cc6c5295c26e7485574be3294be3 NeedsCompilation: no Title: TFutils Description: This package helps users to work with TF metadata from various sources. Significant catalogs of TFs and classifications thereof are made available. Tools for working with motif scans are also provided. biocViews: Transcriptomics Author: Vincent Carey [aut, cre], Shweta Gopaulakrishnan [aut] Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFutils git_branch: devel git_last_commit: 775ebfc git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TFutils_1.31.0.tar.gz vignettes: vignettes/TFutils/inst/doc/fimo16.html, vignettes/TFutils/inst/doc/TFutils.html vignetteTitles: A note on fimo16, TFutils -- representing TFBS and TF target sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFutils/inst/doc/fimo16.R, vignettes/TFutils/inst/doc/TFutils.R dependencyCount: 135 Package: tidybulk Version: 2.1.2 Depends: R (>= 4.4.0), ttservice (>= 0.3.6) Imports: tibble, dplyr (>= 1.1.0), magrittr, tidyr, stringr, rlang, purrr, tidyselect, stats, parallel, utils, lifecycle, scales, ggplot2, SummarizedExperiment, GenomicRanges, methods, S4Vectors, crayon, Matrix Suggests: BiocStyle, testthat, vctrs, AnnotationDbi, BiocManager, Rsubread, e1071, edgeR, limma, org.Hs.eg.db, org.Mm.eg.db, sva, GGally, knitr, qpdf, covr, Seurat, KernSmooth, Rtsne, widyr, clusterProfiler, msigdbr, DESeq2, broom, survival, boot, betareg, tidyHeatmap, pasilla, ggrepel, devtools, fastmatch, functional, survminer, tidySummarizedExperiment, markdown, uwot, matrixStats, preprocessCore, igraph, EGSEA, IRanges, here, glmmSeq, pbapply, pbmcapply, lme4, glmmTMB, MASS, pkgconfig, enrichplot, patchwork, airway License: GPL-3 MD5sum: d0d115f0126822b50ca9e9f838803177 NeedsCompilation: no Title: Brings transcriptomics to the tidyverse Description: This is a collection of utility functions that allow to perform exploration of and calculations to RNA sequencing data, in a modular, pipe-friendly and tidy fashion. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre], Maria Doyle [ctb] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidybulk VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidybulk/issues git_url: https://git.bioconductor.org/packages/tidybulk git_branch: devel git_last_commit: 264714c git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/tidybulk_2.1.2.tar.gz vignettes: vignettes/tidybulk/inst/doc/comparison_coding.html, vignettes/tidybulk/inst/doc/introduction.html vignetteTitles: Side-by-side comparison with standard interfaces, Overview of the tidybulk package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidybulk/inst/doc/comparison_coding.R, vignettes/tidybulk/inst/doc/introduction.R importsMe: tidyexposomics suggestsMe: tidyomics dependencyCount: 91 Package: tidyCoverage Version: 1.7.1 Depends: R (>= 4.3.0), SummarizedExperiment Imports: S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, BiocParallel, BiocIO, rtracklayer, methods, tidyr, tibble, ggplot2, ggrastr, dplyr, fansi, pillar, rlang, scales, cli, purrr, vctrs, stats Suggests: tidySummarizedExperiment, plyranges, TxDb.Mmusculus.UCSC.mm10.knownGene, AnnotationHub, GenomicFeatures, BiocStyle, hues, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 93095cc0212f005aea2a8eb123ce9a43 NeedsCompilation: no Title: Extract and aggregate genomic coverage over features of interest Description: `tidyCoverage` framework enables tidy manipulation of collections of genomic tracks and features using `tidySummarizedExperiment` methods. It facilitates the extraction, aggregation and visualization of genomic coverage over individual or thousands of genomic loci, relying on `CoverageExperiment` and `AggregatedCoverage` classes. This accelerates the integration of genomic track data in genomic analysis workflows. biocViews: Software, Sequencing, Coverage, Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://github.com/js2264/tidyCoverage VignetteBuilder: knitr BugReports: https://github.com/js2264/tidyCoverage/issues git_url: https://git.bioconductor.org/packages/tidyCoverage git_branch: devel git_last_commit: 8a0985f git_last_commit_date: 2026-01-06 Date/Publication: 2026-04-20 source.ver: src/contrib/tidyCoverage_1.7.1.tar.gz vignettes: vignettes/tidyCoverage/inst/doc/tidyCoverage.html vignetteTitles: Introduction to tidyCoverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidyCoverage/inst/doc/tidyCoverage.R dependencyCount: 96 Package: tidyexposomics Version: 0.99.15 Depends: R (>= 4.5.0), MultiAssayExperiment Imports: BiocFileCache, broom, cluster, dplyr, DT, factoextra, fenr, ggplot2 (>= 3.4.0), ggpubr, ggrepel, Hmisc, httr, igraph, jsonlite, limma, MASS, methods, mixOmics, naniar, purrr, readr, RGCCA, rlang, S4Vectors, scales, shiny, stats, stringr, SummarizedExperiment, tibble, tidybulk, tidyr, utils Suggests: BiocStyle, circlize, curl, densityClust, DiagrammeR, dynamicTreeCut, edgeR, forcats, ggh4x, ggnewscale, ggraph, ggridges, ggsci, ggvenn, grid, gridExtra, impute, janitor, knitr, matrixStats, mice, mirt, missForest, MOFA2, nipalsMCIA, openxlsx, patchwork, reticulate, rmarkdown, testthat (>= 3.0.0), tidygraph, tidyHeatmap, tidytext, tidyverse License: MIT + file LICENSE MD5sum: ff334fcf02056321865bceda07f854c7 NeedsCompilation: no Title: Integrated Exposure-Omics Analysis Powered by Tidy Principles Description: The tidyexposomics package is designed to facilitate the integration of exposure and omics data to identify exposure-omics associations. We structure our commands to fit into the tidyverse framework, where commands are designed to be simplified and intuitive. Here we provide functionality to perform quality control, sample and exposure association analysis, differential abundance analysis, multi-omics integration, and functional enrichment analysis. biocViews: Software, Transcriptomics, GeneExpression, Epigenetics, Proteomics, DifferentialExpression, DifferentialMethylation, QualityControl, GraphAndNetwork, MultipleComparison, Regression, StatisticalMethod, Visualization, WorkflowStep Author: Jason Laird [aut, cre] (ORCID: ), Thomas Hartung [ctb] (ORCID: ), Fenna Sillé [ctb] (ORCID: ), Alexandra Maertens [ctb] (ORCID: ), JHU Discovery Award [fnd] Maintainer: Jason Laird URL: https://bionomad.github.io/tidyexposomics/ VignetteBuilder: knitr BugReports: https://github.com/BioNomad/tidyexposomics/issues git_url: https://git.bioconductor.org/packages/tidyexposomics git_branch: devel git_last_commit: 592d112 git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/tidyexposomics_0.99.15.tar.gz vignettes: vignettes/tidyexposomics/inst/doc/tidyexposomics.html vignetteTitles: tidyexposomics: integrated exposure-omics analysis powered by tidy principles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidyexposomics/inst/doc/tidyexposomics.R dependencyCount: 241 Package: tidyFlowCore Version: 1.5.0 Depends: R (>= 4.3) Imports: Biobase, dplyr, flowCore, ggplot2, methods, purrr, rlang, stats, stringr, tibble, tidyr Suggests: BiocStyle, HDCytoData, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 82cb6935662940767f5d67ee7d92f654 NeedsCompilation: no Title: tidyFlowCore: Bringing flowCore to the tidyverse Description: tidyFlowCore bridges the gap between flow cytometry analysis using the flowCore Bioconductor package and the tidy data principles advocated by the tidyverse. It provides a suite of dplyr-, ggplot2-, and tidyr-like verbs specifically designed for working with flowFrame and flowSet objects as if they were tibbles; however, your data remain flowCore data structures under this layer of abstraction. tidyFlowCore enables intuitive and streamlined analysis workflows that can leverage both the Bioconductor and tidyverse ecosystems for cytometry data. biocViews: SingleCell, FlowCytometry, Infrastructure Author: Timothy Keyes [cre] (ORCID: ), Kara Davis [rth, own], Garry Nolan [rth, own] Maintainer: Timothy Keyes URL: https://github.com/keyes-timothy/tidyFlowCore, https://keyes-timothy.github.io/tidyFlowCore/ VignetteBuilder: knitr BugReports: https://github.com/keyes-timothy/tidyFlowCore/issues git_url: https://git.bioconductor.org/packages/tidyFlowCore git_branch: devel git_last_commit: 1848897 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tidyFlowCore_1.5.0.tar.gz vignettes: vignettes/tidyFlowCore/inst/doc/tidyFlowCore.html vignetteTitles: tidyFlowCore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidyFlowCore/inst/doc/tidyFlowCore.R dependencyCount: 50 Package: tidyomics Version: 1.7.0 Depends: R (>= 4.2) Imports: tidySummarizedExperiment, tidySingleCellExperiment, tidySpatialExperiment, tidyseurat, plyranges, purrr, rlang, stringr, cli, Suggests: utils, tidyr, dplyr, tibble, ggplot2, mockr (>= 0.2.0), knitr (>= 1.41), rmarkdown (>= 2.20), testthat (>= 3.1.6), nullranges, tidybulk, plyinteractions License: MIT + file LICENSE MD5sum: f6fb55c3be7583fabb6873458ffc99ba NeedsCompilation: no Title: Easily install and load the tidyomics ecosystem Description: The tidyomics ecosystem is a set of packages for ’omic data analysis that work together in harmony; they share common data representations and API design, consistent with the tidyverse ecosystem. The tidyomics package is designed to make it easy to install and load core packages from the tidyomics ecosystem with a single command. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] (ORCID: ), Michael Love [aut] (ORCID: ), William Hutchison [aut] (ORCID: ) Maintainer: Stefano Mangiola URL: https://github.com/tidyomics/tidyomics VignetteBuilder: knitr BugReports: https://github.com/tidyomics/tidyomics/issues git_url: https://git.bioconductor.org/packages/tidyomics git_branch: devel git_last_commit: 4528d62 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tidyomics_1.7.0.tar.gz vignettes: vignettes/tidyomics/inst/doc/loading-tidyomics.html vignetteTitles: Loading the tidyomics ecosystem hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidyomics/inst/doc/loading-tidyomics.R dependencyCount: 205 Package: tidyprint Version: 0.99.10 Depends: R (>= 4.4.0) Imports: magrittr, tidyr, tibble, dplyr, purrr, pillar, S4Vectors, stringr, cli, vctrs, SummarizedExperiment, methods, rlang, fansi, pkgconfig Suggests: knitr, rmarkdown, BiocStyle, airway, pasilla, testthat (>= 3.0.0) License: GPL-3 MD5sum: cd4726c7a8c75ea9f2edecf71c9dea9f NeedsCompilation: no Title: Custom Print Methods for SummarizedExperiment Description: Provides customized print methods for 'SummarizedExperiment' objects to enhance readability and usability within a tidy workflow. It offers consistent, tidyverse-aligned console displays, including alternative tibble abstractions for large genomic data to improve discoverability and interpretation. The package also includes unified, contextual messaging utilities intended for the 'tidyomics' ecosystem. biocViews: Software, Visualization, Infrastructure Author: Chen Zhan [aut, cre] (ORCID: ) Maintainer: Chen Zhan URL: https://github.com/tidyomics/tidyprint VignetteBuilder: knitr BugReports: https://github.com/tidyomics/tidyprint/issues git_url: https://git.bioconductor.org/packages/tidyprint git_branch: devel git_last_commit: 704be89 git_last_commit_date: 2026-03-15 Date/Publication: 2026-04-20 source.ver: src/contrib/tidyprint_0.99.10.tar.gz vignettes: vignettes/tidyprint/inst/doc/Introduction.html vignetteTitles: tidyprint hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tidyprint/inst/doc/Introduction.R dependencyCount: 45 Package: tidysbml Version: 1.5.0 Depends: R (>= 4.4.0) Imports: xml2, methods Suggests: rmarkdown, knitr, BiocStyle, biomaRt, RCy3, testthat (>= 3.0.0) License: CC BY 4.0 MD5sum: bbbcd6cb33dd6e40d89e1d230e162a35 NeedsCompilation: no Title: Extract SBML's data into dataframes Description: Starting from one SBML file, it extracts information from each listOfCompartments, listOfSpecies and listOfReactions element by saving them into data frames. Each table provides one row for each entity (i.e. either compartment, species, reaction or speciesReference) and one set of columns for the attributes, one column for the content of the 'notes' subelement and one set of columns for the content of the 'annotation' subelement. biocViews: GraphAndNetwork, Network, Pathways, Software Author: Veronica Paparozzi [aut, cre] (ORCID: ), Christine Nardini [aut] (ORCID: ) Maintainer: Veronica Paparozzi URL: https://github.com/veronicapaparozzi/tidysbml VignetteBuilder: knitr BugReports: https://github.com/veronicapaparozzi/tidysbml/issues git_url: https://git.bioconductor.org/packages/tidysbml git_branch: devel git_last_commit: 64e3285 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tidysbml_1.5.0.tar.gz vignettes: vignettes/tidysbml/inst/doc/tidysbml-introduction.html vignetteTitles: Introduction to the tidysbml package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidysbml/inst/doc/tidysbml-introduction.R dependencyCount: 5 Package: tidySingleCellExperiment Version: 1.21.0 Depends: R (>= 4.4.0), SingleCellExperiment, ttservice (>= 0.4.0) Imports: dplyr, tidyr, SummarizedExperiment, tibble, ggplot2, magrittr, rlang, purrr, pkgconfig, lifecycle, methods, utils, S4Vectors, tidyselect, ellipsis, vctrs, pillar, stringr, cli, fansi, Matrix, stats Suggests: BiocStyle, testthat, knitr, markdown, rmarkdown, SingleCellSignalR, SingleR, scater, scran, tidyHeatmap, igraph, GGally, uwot, celldex, dittoSeq, plotly, rbibutils, prettydoc License: GPL-3 MD5sum: 476b5f9d062923f6a6eaf675aa1ffc9d NeedsCompilation: no Title: Brings SingleCellExperiment to the Tidyverse Description: 'tidySingleCellExperiment' is an adapter that abstracts the 'SingleCellExperiment' container in the form of a 'tibble'. This allows *tidy* data manipulation, nesting, and plotting. For example, a 'tidySingleCellExperiment' is directly compatible with functions from 'tidyverse' packages `dplyr` and `tidyr`, as well as plotting with `ggplot2` and `plotly`. In addition, the package provides various utility functions specific to single-cell omics data analysis (e.g., aggregation of cell-level data to pseudobulks). biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, SingleCell, GeneExpression, Normalization, Clustering, QualityControl, Sequencing Author: Stefano Mangiola [aut, cre] (ORCID: ) Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidySingleCellExperiment VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidySingleCellExperiment/issues git_url: https://git.bioconductor.org/packages/tidySingleCellExperiment git_branch: devel git_last_commit: 065fd60 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tidySingleCellExperiment_1.21.0.tar.gz vignettes: vignettes/tidySingleCellExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySingleCellExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySingleCellExperiment/inst/doc/introduction.R dependsOnMe: tidySpatialExperiment importsMe: tidyomics suggestsMe: CuratedAtlasQueryR, sccomp dependencyCount: 92 Package: tidySpatialExperiment Version: 1.7.2 Depends: R (>= 4.3.0), SpatialExperiment, tidySingleCellExperiment, ttservice Imports: SummarizedExperiment, SingleCellExperiment, BiocGenerics, S4Vectors, methods, utils, pkgconfig, tibble, dplyr, tidyr, ggplot2 (>= 4.0.0), plotly, rlang, purrr, stringr, vctrs, tidyselect, pillar, cli, fansi, lifecycle, magick, tidygate (>= 1.0.13), shiny Suggests: BiocStyle, testthat, knitr, markdown, scater, igraph, cowplot, DropletUtils, tidySummarizedExperiment License: GPL (>= 3) MD5sum: 5d84a7c9914629140328c6966c1b30fe NeedsCompilation: no Title: SpatialExperiment with tidy principles Description: tidySpatialExperiment provides a bridge between the SpatialExperiment package and the tidyverse ecosystem. It creates an invisible layer that allows you to interact with a SpatialExperiment object as if it were a tibble; enabling the use of functions from dplyr, tidyr, ggplot2 and plotly. But, underneath, your data remains a SpatialExperiment object. biocViews: Infrastructure, RNASeq, GeneExpression, Sequencing, Spatial, Transcriptomics, SingleCell Author: William Hutchison [aut, cre] (ORCID: ), Stefano Mangiola [aut] Maintainer: William Hutchison URL: https://github.com/william-hutchison/tidySpatialExperiment, https://william-hutchison.github.io/tidySpatialExperiment/ VignetteBuilder: knitr BugReports: https://github.com/william-hutchison/tidySpatialExperiment/issues git_url: https://git.bioconductor.org/packages/tidySpatialExperiment git_branch: devel git_last_commit: 002a6a5 git_last_commit_date: 2025-11-30 Date/Publication: 2026-04-20 source.ver: src/contrib/tidySpatialExperiment_1.7.2.tar.gz vignettes: vignettes/tidySpatialExperiment/inst/doc/overview.html vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySpatialExperiment/inst/doc/overview.R importsMe: tidyomics dependencyCount: 113 Package: tidySummarizedExperiment Version: 1.21.0 Depends: R (>= 4.3.0), SummarizedExperiment, ttservice (>= 0.5.0) Imports: dplyr, tibble (>= 3.0.4), magrittr, tidyr, ggplot2, rlang, purrr, lifecycle, methods, utils, S4Vectors, tidyselect, ellipsis, vctrs, pillar, stringr, cli, fansi, stats, pkgconfig, plyxp Suggests: BiocStyle, testthat, knitr, markdown, rmarkdown, plotly, rbibutils, prettydoc, airway License: GPL-3 MD5sum: 2ba714d363e97dec5faa50fea278fe84 NeedsCompilation: no Title: Brings SummarizedExperiment to the Tidyverse Description: The tidySummarizedExperiment package provides a set of tools for creating and manipulating tidy data representations of SummarizedExperiment objects. SummarizedExperiment is a widely used data structure in bioinformatics for storing high-throughput genomic data, such as gene expression or DNA sequencing data. The tidySummarizedExperiment package introduces a tidy framework for working with SummarizedExperiment objects. It allows users to convert their data into a tidy format, where each observation is a row and each variable is a column. This tidy representation simplifies data manipulation, integration with other tidyverse packages, and enables seamless integration with the broader ecosystem of tidy tools for data analysis. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidySummarizedExperiment VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidySummarizedExperiment/issues git_url: https://git.bioconductor.org/packages/tidySummarizedExperiment git_branch: devel git_last_commit: d35c29a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tidySummarizedExperiment_1.21.0.tar.gz vignettes: vignettes/tidySummarizedExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySummarizedExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySummarizedExperiment/inst/doc/introduction.R importsMe: tidyomics suggestsMe: tidybulk, tidyCoverage, tidySpatialExperiment dependencyCount: 92 Package: tigre Version: 1.65.0 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, grDevices, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle, BiocManager License: AGPL-3 MD5sum: ee17f175e45d55132647335799a19ac8 NeedsCompilation: yes Title: Transcription factor Inference through Gaussian process Reconstruction of Expression Description: The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF. biocViews: Microarray, TimeCourse, GeneExpression, Transcription, GeneRegulation, NetworkInference, Bayesian Author: Antti Honkela, Pei Gao, Jonatan Ropponen, Miika-Petteri Matikainen, Magnus Rattray, Neil D. Lawrence Maintainer: Antti Honkela URL: https://github.com/ahonkela/tigre BugReports: https://github.com/ahonkela/tigre/issues git_url: https://git.bioconductor.org/packages/tigre git_branch: devel git_last_commit: 4b818eb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tigre_1.65.0.tar.gz vignettes: vignettes/tigre/inst/doc/tigre.pdf vignetteTitles: tigre User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tigre/inst/doc/tigre.R dependencyCount: 50 Package: TileDBArray Version: 1.21.2 Depends: SparseArray (>= 1.5.20), DelayedArray (>= 0.31.7) Imports: methods, tiledb, S4Vectors Suggests: knitr, Matrix, rmarkdown, BiocStyle, BiocParallel, testthat License: MIT + file LICENSE MD5sum: 8d329a25bb9ae2312a9b681093773cee NeedsCompilation: no Title: Using TileDB as a DelayedArray Backend Description: Implements a DelayedArray backend for reading and writing dense or sparse arrays in the TileDB format. The resulting TileDBArrays are compatible with all Bioconductor pipelines that can accept DelayedArray instances. biocViews: DataRepresentation, Infrastructure, Software Author: Aaron Lun [aut, cre], Genentech, Inc. [cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/TileDBArray VignetteBuilder: knitr BugReports: https://github.com/LTLA/TileDBArray git_url: https://git.bioconductor.org/packages/TileDBArray git_branch: devel git_last_commit: 8b172c8 git_last_commit_date: 2026-04-10 Date/Publication: 2026-04-20 source.ver: src/contrib/TileDBArray_1.21.2.tar.gz vignettes: vignettes/TileDBArray/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TileDBArray/inst/doc/userguide.R importsMe: beachmat.tiledb dependencyCount: 33 Package: tilingArray Version: 1.89.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 MD5sum: c9f35ed67954d7e72d1c927dbdacd407 NeedsCompilation: yes Title: Transcript mapping with high-density oligonucleotide tiling arrays Description: The package provides functionality that can be useful for the analysis of high-density tiling microarray data (such as from Affymetrix genechips) for measuring transcript abundance and architecture. The main functionalities of the package are: 1. the class 'segmentation' for representing partitionings of a linear series of data; 2. the function 'segment' for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact; 3. the function 'confint' for calculating confidence intervals using the strucchange package; 4. the function 'plotAlongChrom' for generating pretty plots; 5. the function 'normalizeByReference' for probe-sequence dependent response adjustment from a (set of) reference hybridizations. biocViews: Microarray, OneChannel, Preprocessing, Visualization Author: Wolfgang Huber, Zhenyu Xu, Joern Toedling with contributions from Matt Ritchie Maintainer: Zhenyu Xu git_url: https://git.bioconductor.org/packages/tilingArray git_branch: devel git_last_commit: 1959ceb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tilingArray_1.89.0.tar.gz vignettes: vignettes/tilingArray/inst/doc/assessNorm.pdf, vignettes/tilingArray/inst/doc/costMatrix.pdf, vignettes/tilingArray/inst/doc/findsegments.pdf, vignettes/tilingArray/inst/doc/plotAlongChrom.pdf, vignettes/tilingArray/inst/doc/segmentation.pdf vignetteTitles: Normalisation with the normalizeByReference function in the tilingArray package, Supplement. Calculation of the cost matrix, Introduction to using the segment function to fit a piecewise constant curve, Introduction to the plotAlongChrom function, Segmentation demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tilingArray/inst/doc/findsegments.R, vignettes/tilingArray/inst/doc/plotAlongChrom.R dependsOnMe: davidTiling importsMe: ADaCGH2 dependencyCount: 74 Package: timecourse Version: 1.83.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 0ef71b9acdffc822b175a07fe7eefaba NeedsCompilation: no Title: Statistical Analysis for Developmental Microarray Time Course Data Description: Functions for data analysis and graphical displays for developmental microarray time course data. biocViews: Microarray, TimeCourse, DifferentialExpression Author: Yu Chuan Tai Maintainer: Yu Chuan Tai URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/timecourse git_branch: devel git_last_commit: 7b6e6b3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/timecourse_1.83.0.tar.gz vignettes: vignettes/timecourse/inst/doc/timecourse.pdf vignetteTitles: timecourse manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timecourse/inst/doc/timecourse.R dependencyCount: 12 Package: timeOmics Version: 1.23.0 Depends: mixOmics, R (>= 4.0) Imports: dplyr, tidyr, tibble, purrr, magrittr, ggplot2, stringr, ggrepel, lmtest, plyr, checkmate Suggests: BiocStyle, knitr, rmarkdown, testthat, snow, tidyverse, igraph, gplots License: GPL-3 MD5sum: 497bbf754dc52ea3e1838d23d8ef36a6 NeedsCompilation: no Title: Time-Course Multi-Omics data integration Description: timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step. biocViews: Clustering,FeatureExtraction,TimeCourse,DimensionReduction,Software, Sequencing, Microarray, Metabolomics, Metagenomics, Proteomics, Classification, Regression, ImmunoOncology, GenePrediction, MultipleComparison Author: Antoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le Cao [aut], Arnaud Droit [aut] Maintainer: Antoine Bodein VignetteBuilder: knitr BugReports: https://github.com/abodein/timeOmics/issues git_url: https://git.bioconductor.org/packages/timeOmics git_branch: devel git_last_commit: 5346f41 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/timeOmics_1.23.0.tar.gz vignettes: vignettes/timeOmics/inst/doc/vignette.html vignetteTitles: timeOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timeOmics/inst/doc/vignette.R dependencyCount: 90 Package: timescape Version: 1.35.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), stringr (>= 1.0.0), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 7d176d75a699ddabbc1d67cc2e032d8e NeedsCompilation: no Title: Patient Clonal Timescapes Description: TimeScape is an automated tool for navigating temporal clonal evolution data. The key attributes of this implementation involve the enumeration of clones, their evolutionary relationships and their shifting dynamics over time. TimeScape requires two inputs: (i) the clonal phylogeny and (ii) the clonal prevalences. Optionally, TimeScape accepts a data table of targeted mutations observed in each clone and their allele prevalences over time. The output is the TimeScape plot showing clonal prevalence vertically, time horizontally, and the plot height optionally encoding tumour volume during tumour-shrinking events. At each sampling time point (denoted by a faint white line), the height of each clone accurately reflects its proportionate prevalence. These prevalences form the anchors for bezier curves that visually represent the dynamic transitions between time points. biocViews: Visualization, BiomedicalInformatics Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/timescape git_branch: devel git_last_commit: a04609a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/timescape_1.35.0.tar.gz vignettes: vignettes/timescape/inst/doc/timescape_vignette.html vignetteTitles: TimeScape vignette hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timescape/inst/doc/timescape_vignette.R dependencyCount: 46 Package: TIN Version: 1.43.0 Depends: R (>= 2.12.0), data.table, impute, aroma.affymetrix Imports: WGCNA, squash, stringr Suggests: knitr, aroma.light, affxparser, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: ece62bbfd2befe120a850766f9e74358 NeedsCompilation: no Title: Transcriptome instability analysis Description: The TIN package implements a set of tools for transcriptome instability analysis based on exon expression profiles. Deviating exon usage is studied in the context of splicing factors to analyse to what degree transcriptome instability is correlated to splicing factor expression. In the transcriptome instability correlation analysis, the data is compared to both random permutations of alternative splicing scores and expression of random gene sets. biocViews: ExonArray, Microarray, GeneExpression, AlternativeSplicing, Genetics, DifferentialSplicing Author: Bjarne Johannessen, Anita Sveen and Rolf I. Skotheim Maintainer: Bjarne Johannessen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TIN git_branch: devel git_last_commit: bc093d8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TIN_1.43.0.tar.gz vignettes: vignettes/TIN/inst/doc/TIN.pdf vignetteTitles: Introduction to the TIN package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TIN/inst/doc/TIN.R dependencyCount: 99 Package: TissueEnrich Version: 1.31.0 Depends: R (>= 3.5), ggplot2 (>= 2.2.1), SummarizedExperiment (>= 1.6.5), GSEABase (>= 1.38.2) Imports: dplyr (>= 0.7.3), tidyr (>= 0.8.0), stats Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: dee249150829be5cdfd23e685831f89f NeedsCompilation: no Title: Tissue-specific gene enrichment analysis Description: The TissueEnrich package is used to calculate enrichment of tissue-specific genes in a set of input genes. For example, the user can input the most highly expressed genes from RNA-Seq data, or gene co-expression modules to determine which tissue-specific genes are enriched in those datasets. Tissue-specific genes were defined by processing RNA-Seq data from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx (Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using the algorithm from the HPA (Uhlén et al. 2015).The hypergeometric test is being used to determine if the tissue-specific genes are enriched among the input genes. Along with tissue-specific gene enrichment, the TissueEnrich package can also be used to define tissue-specific genes from expression datasets provided by the user, which can then be used to calculate tissue-specific gene enrichments. biocViews: GeneSetEnrichment, GeneExpression, Sequencing Author: Ashish Jain [aut, cre], Geetu Tuteja [aut] Maintainer: Ashish Jain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TissueEnrich git_branch: devel git_last_commit: 706448a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TissueEnrich_1.31.0.tar.gz vignettes: vignettes/TissueEnrich/inst/doc/TissueEnrich.html vignetteTitles: TissueEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TissueEnrich/inst/doc/TissueEnrich.R dependencyCount: 78 Package: tkWidgets Version: 1.89.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: b4904daf532165c166541765f80f5644 NeedsCompilation: no Title: R based tk widgets Description: Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. biocViews: Infrastructure Author: J. Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/tkWidgets git_branch: devel git_last_commit: 827eeaa git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tkWidgets_1.89.0.tar.gz vignettes: vignettes/tkWidgets/inst/doc/importWizard.pdf, vignettes/tkWidgets/inst/doc/tkWidgets.pdf vignetteTitles: tkWidgets importWizard, tkWidgets contents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tkWidgets/inst/doc/importWizard.R, vignettes/tkWidgets/inst/doc/tkWidgets.R importsMe: Mfuzz, OLINgui suggestsMe: affy, annotate, Biobase, genefilter, marray dependencyCount: 6 Package: TMixClust Version: 1.33.0 Depends: R (>= 3.4) Imports: gss, mvtnorm, stats, zoo, cluster, utils, BiocParallel, flexclust, grDevices, graphics, Biobase, SPEM Suggests: rmarkdown, knitr, BiocStyle, testthat License: GPL (>=2) MD5sum: 097415179f068f6562c0baecc5b2362a NeedsCompilation: no Title: Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines Description: Implementation of a clustering method for time series gene expression data based on mixed-effects models with Gaussian variables and non-parametric cubic splines estimation. The method can robustly account for the high levels of noise present in typical gene expression time series datasets. biocViews: Software, StatisticalMethod, Clustering, TimeCourse, GeneExpression Author: Monica Golumbeanu Maintainer: Monica Golumbeanu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TMixClust git_branch: devel git_last_commit: d55d2de git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TMixClust_1.33.0.tar.gz vignettes: vignettes/TMixClust/inst/doc/TMixClust.pdf vignetteTitles: Clustering time series gene expression data with TMixClust hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TMixClust/inst/doc/TMixClust.R dependencyCount: 42 Package: TMSig Version: 1.5.0 Depends: R (>= 4.4.0), limma Imports: circlize, ComplexHeatmap, data.table, grDevices, grid, GSEABase, Matrix, methods, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: 074dc8c55c16e090eed0e1703eb25bb0 NeedsCompilation: no Title: Tools for Molecular Signatures Description: The TMSig package contains tools to prepare, analyze, and visualize named lists of sets, with an emphasis on molecular signatures (such as gene or kinase sets). It includes fast, memory efficient functions to construct sparse incidence and similarity matrices and filter, cluster, invert, and decompose sets. Additionally, bubble heatmaps can be created to visualize the results of any differential or molecular signatures analysis. biocViews: Clustering, GeneSetEnrichment, GraphAndNetwork, Pathways, Visualization Author: Tyler Sagendorf [aut, cre] (ORCID: ), Di Wu [ctb], Gordon Smyth [ctb] Maintainer: Tyler Sagendorf URL: https://github.com/EMSL-Computing/TMSig VignetteBuilder: knitr BugReports: https://github.com/EMSL-Computing/TMSig/issues git_url: https://git.bioconductor.org/packages/TMSig git_branch: devel git_last_commit: cf64558 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TMSig_1.5.0.tar.gz vignettes: vignettes/TMSig/inst/doc/TMSig.html vignetteTitles: An Introduction to TMSig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TMSig/inst/doc/TMSig.R dependencyCount: 70 Package: TnT Version: 1.33.0 Depends: R (>= 3.4), GenomicRanges Imports: methods, stats, utils, grDevices, htmlwidgets, jsonlite, data.table, Biobase, GenomeInfoDb, IRanges, S4Vectors, knitr Suggests: GenomicFeatures, shiny, BiocManager, rmarkdown, testthat License: AGPL-3 MD5sum: d0b2e11b70eff4be5825ee6d8a9a89bc NeedsCompilation: no Title: Interactive Visualization for Genomic Features Description: A R interface to the TnT javascript library (https://github.com/ tntvis) to provide interactive and flexible visualization of track-based genomic data. biocViews: Infrastructure, Visualization Author: Jialin Ma [cre, aut], Miguel Pignatelli [aut], Toby Hocking [aut] Maintainer: Jialin Ma URL: https://github.com/Marlin-Na/TnT VignetteBuilder: knitr BugReports: https://github.com/Marlin-Na/TnT/issues git_url: https://git.bioconductor.org/packages/TnT git_branch: devel git_last_commit: a8c1dc4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TnT_1.33.0.tar.gz vignettes: vignettes/TnT/inst/doc/introduction.html vignetteTitles: Introduction to TnT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TnT/inst/doc/introduction.R dependencyCount: 48 Package: TOAST Version: 1.25.0 Depends: R (>= 3.6), EpiDISH, limma, nnls, quadprog Imports: stats, methods, SummarizedExperiment, corpcor, doParallel, parallel, ggplot2, tidyr, GGally Suggests: BiocStyle, knitr, rmarkdown, gplots, matrixStats, Matrix License: GPL-2 MD5sum: 55f7932b366c315b621761680a9fb18e NeedsCompilation: no Title: Tools for the analysis of heterogeneous tissues Description: This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge. biocViews: DNAMethylation, GeneExpression, DifferentialExpression, DifferentialMethylation, Microarray, GeneTarget, Epigenetics, MethylationArray Author: Ziyi Li and Weiwei Zhang and Luxiao Chen and Hao Wu Maintainer: Ziyi Li VignetteBuilder: knitr BugReports: https://github.com/ziyili20/TOAST/issues git_url: https://git.bioconductor.org/packages/TOAST git_branch: devel git_last_commit: dab62cf git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TOAST_1.25.0.tar.gz vignettes: vignettes/TOAST/inst/doc/TOAST.html vignetteTitles: The TOAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TOAST/inst/doc/TOAST.R importsMe: MICSQTL, RegionalST dependencyCount: 102 Package: tomoda Version: 1.21.0 Depends: R (>= 4.0.0) Imports: methods, stats, grDevices, reshape2, Rtsne, umap, RColorBrewer, ggplot2, ggrepel, SummarizedExperiment Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: f62bf76223aeacaa4536daecbaa7d31b NeedsCompilation: no Title: Tomo-seq data analysis Description: This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots. biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics, Spatial, Clustering, Visualization Author: Wendao Liu [aut, cre] (ORCID: ) Maintainer: Wendao Liu URL: https://github.com/liuwd15/tomoda VignetteBuilder: knitr BugReports: https://github.com/liuwd15/tomoda/issues git_url: https://git.bioconductor.org/packages/tomoda git_branch: devel git_last_commit: 2682c14 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tomoda_1.21.0.tar.gz vignettes: vignettes/tomoda/inst/doc/tomoda.html vignetteTitles: tomoda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tomoda/inst/doc/tomoda.R dependencyCount: 63 Package: tomoseqr Version: 1.15.0 Depends: R (>= 4.2) Imports: grDevices, graphics, animation, tibble, dplyr, stringr, purrr, methods, shiny, BiocFileCache, readr, tools, plotly, ggplot2 Suggests: rmarkdown, knitr, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: ad5771c2d5d75894fafd7973c568e4b4 NeedsCompilation: no Title: R Package for Analyzing Tomo-seq Data Description: `tomoseqr` is an R package for analyzing Tomo-seq data. Tomo-seq is a genome-wide RNA tomography method that combines combining high-throughput RNA sequencing with cryosectioning for spatially resolved transcriptomics. `tomoseqr` reconstructs 3D expression patterns from tomo-seq data and visualizes the reconstructed 3D expression patterns. biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics, Spatial, Visualization, Software Author: Ryosuke Matsuzawa [aut, cre] (ORCID: ) Maintainer: Ryosuke Matsuzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tomoseqr git_branch: devel git_last_commit: 5f3f9ac git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tomoseqr_1.15.0.tar.gz vignettes: vignettes/tomoseqr/inst/doc/tomoseqr.html vignetteTitles: tomoseqr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tomoseqr/inst/doc/tomoseqr.R dependencyCount: 95 Package: TOP Version: 1.11.0 Depends: R (>= 4.1.0) Imports: assertthat, caret, ClassifyR, directPA, doParallel, dplyr, ggnewscale, ggplot2, ggraph, ggrepel, ggthemes, glmnet, Hmisc, igraph, latex2exp, limma, magrittr, methods, plotly, pROC, purrr, reshape2, stats, stringr, survival, tibble, tidygraph, tidyr, statmod Suggests: knitr, rmarkdown, BiocStyle, Biobase, curatedOvarianData, ggbeeswarm, ggsci, survminer, tidyverse License: GPL-3 MD5sum: 03e7fb04cbd1aa62004f34020ca08d50 NeedsCompilation: no Title: TOP Constructs Transferable Model Across Gene Expression Platforms Description: TOP constructs a transferable model across gene expression platforms for prospective experiments. Such a transferable model can be trained to make predictions on independent validation data with an accuracy that is similar to a re-substituted model. The TOP procedure also has the flexibility to be adapted to suit the most common clinical response variables, including linear response, binomial and Cox PH models. biocViews: Software, Survival, GeneExpression Author: Harry Robertson [aut, cre] (ORCID: ), Nicholas Robertson [aut] Maintainer: Harry Robertson URL: https://github.com/Harry25R/TOP VignetteBuilder: knitr BugReports: https://github.com/Harry25R/TOP/issues git_url: https://git.bioconductor.org/packages/TOP git_branch: devel git_last_commit: 30abd4f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TOP_1.11.0.tar.gz vignettes: vignettes/TOP/inst/doc/BuildingATOPModel.html vignetteTitles: "Introduction to TOP" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TOP/inst/doc/BuildingATOPModel.R suggestsMe: ClassifyR dependencyCount: 223 Package: topconfects Version: 1.27.0 Depends: R (>= 3.6.0) Imports: methods, utils, stats, assertthat, ggplot2, scales, grid, grDevices Suggests: limma, edgeR, statmod, DESeq2, ashr, NBPSeq, dplyr, testthat, reshape2, tidyr, readr, org.At.tair.db, AnnotationDbi, knitr, rmarkdown, BiocStyle License: LGPL-2.1 | file LICENSE MD5sum: 66b5b3bcaff76dd6d563992165cdbe21 NeedsCompilation: no Title: Top Confident Effect Sizes Description: Rank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is an alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, RNASeq, mRNAMicroarray, Regression, MultipleComparison Author: Paul Harrison [aut, cre] (ORCID: ) Maintainer: Paul Harrison URL: https://github.com/pfh/topconfects VignetteBuilder: knitr BugReports: https://github.com/pfh/topconfects/issues git_url: https://git.bioconductor.org/packages/topconfects git_branch: devel git_last_commit: e1f11e5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/topconfects_1.27.0.tar.gz vignettes: vignettes/topconfects/inst/doc/an_overview.html, vignettes/topconfects/inst/doc/fold_change.html vignetteTitles: An overview of topconfects, Confident fold change hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/topconfects/inst/doc/an_overview.R, vignettes/topconfects/inst/doc/fold_change.R importsMe: GeoTcgaData, weitrix dependencyCount: 25 Package: topGO Version: 2.63.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.6), graph (>= 1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi (>= 1.7.19), SparseM (>= 0.73) Imports: lattice, matrixStats, DBI Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, multtest, Rgraphviz, globaltest, knitr, BiocStyle, rmarkdown License: LGPL MD5sum: 473d4307c629ecfea388394f33c6a06a NeedsCompilation: no Title: Enrichment Analysis for Gene Ontology Description: topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. biocViews: GeneExpression, Transcriptomics, GeneSetEnrichment, GO, Annotation, Pathways, SystemsBiology, Microarray, Sequencing, Visualization, Software Author: Adrian Alexa [aut], Jörg Rahnenführer [aut], Federico Marini [cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/topGO VignetteBuilder: knitr BugReports: https://github.com/federicomarini/topGO/issues git_url: https://git.bioconductor.org/packages/topGO git_branch: devel git_last_commit: 35ec429 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/topGO_2.63.0.tar.gz vignettes: vignettes/topGO/inst/doc/topGO_manual.html vignetteTitles: Gene set enrichment analysis with topGO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topGO/inst/doc/topGO_manual.R dependsOnMe: BgeeDB, compEpiTools, EGSEA, ideal, moanin, tRanslatome importsMe: APL, cellity, consICA, GRaNIE, mosdef, OmaDB, pcaExplorer, transcriptogramer, ViSEAGO suggestsMe: DeeDeeExperiment, fenr, FGNet, GeDi, geva, IntramiRExploreR, miRNAtap dependencyCount: 48 Package: toppgene Version: 0.99.2 Depends: R (>= 4.6.0) Imports: BiocFileCache, httr2, IRanges, jsonlite, methods, purrr, readr, S4Vectors, xml2, yaml Suggests: BiocStyle, DFplyr (>= 1.5.0), knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: 2de5151d63b1f1c0f741bbe943137529 NeedsCompilation: no Title: Gene List Enrichment Analysis using the ToppGene Suite Description: The ToppGene Suite is a one-stop portal for gene list enrichment analysis and candidate gene prioritization based on functional annotations and protein interactions network. Although the ToppCluster web application provides convenient graphical access to the ToppGene Suite, the OpenAPI 3.0 compliant interface of ToppGene is better suited for automation and reproducibility. This package includes Bioconductor class interfaces and biological examples. biocViews: Clustering, GeneExpression, GeneSetEnrichment, Genetics, MotifDiscovery, Network, NetworkEnrichment, Pathways, Pharmacogenetics, Proteomics, Software, ThirdPartyClient Author: Pariksheet Nanda [aut, cre] (ORCID: ), Jason Shoemaker [fnd] (ORCID: ) Maintainer: Pariksheet Nanda URL: https://github.com/ImmunoSystems-lab/toppgene VignetteBuilder: knitr BugReports: https://github.com/ImmunoSystems-lab/toppgene/issues git_url: https://git.bioconductor.org/packages/toppgene git_branch: devel git_last_commit: 3e5f69e git_last_commit_date: 2026-04-03 Date/Publication: 2026-04-20 source.ver: src/contrib/toppgene_0.99.2.tar.gz vignettes: vignettes/toppgene/inst/doc/toppgene.html vignetteTitles: toppgene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/toppgene/inst/doc/toppgene.R dependencyCount: 58 Package: ToxicoGx Version: 2.15.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, BiocGenerics, S4Vectors, Biobase, BiocParallel, ggplot2, tibble, dplyr, caTools, downloader, magrittr, methods, reshape2, tidyr, data.table, assertthat, scales, graphics, grDevices, parallel, stats, utils, limma, jsonlite Suggests: rmarkdown, testthat, BiocStyle, knitr, tinytex, devtools, PharmacoGx, xtable, markdown License: MIT + file LICENSE MD5sum: 54d5d4fa66d2badd03376c84c852b9e1 NeedsCompilation: no Title: Analysis of Large-Scale Toxico-Genomic Data Description: Contains a set of functions to perform large-scale analysis of toxicogenomic data, providing a standardized data structure to hold information relevant to annotation, visualization and statistical analysis of toxicogenomic data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software Author: Sisira Nair [aut], Esther Yoo [aut], Christopher Eeles [aut], Amy Tang [aut], Nehme El-Hachem [aut], Petr Smirnov [aut], Jermiah Joseph [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ToxicoGx git_branch: devel git_last_commit: 0781b14 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ToxicoGx_2.15.0.tar.gz vignettes: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.html vignetteTitles: ToxicoGx: An R Platform for Integrated Toxicogenomics Data Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.R dependencyCount: 132 Package: TPP2D Version: 1.27.0 Depends: R (>= 3.6.0), stats, utils, dplyr, methods Imports: ggplot2, tidyr, foreach, doParallel, openxlsx, stringr, RCurl, parallel, MASS, BiocParallel, limma Suggests: knitr, testthat, rmarkdown, BiocStyle License: GPL-3 MD5sum: d19aa36cca720382b9b7aa1643e1bbf6 NeedsCompilation: no Title: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP) Description: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP), Performs an FDR-controlled analysis of 2D-TPP experiments by functional analysis of dose-response curves across temperatures. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], Holger Franken [aut], Simon Anders [aut], Wolfgang Huber [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa URL: http://bioconductor.org/packages/TPP2D VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/TPP2D git_branch: devel git_last_commit: 4223558 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TPP2D_1.27.0.tar.gz vignettes: vignettes/TPP2D/inst/doc/TPP2D.html vignetteTitles: Introduction to TPP2D for 2D-TPP analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP2D/inst/doc/TPP2D.R dependencyCount: 56 Package: tpSVG Version: 1.7.0 Depends: mgcv, R (>= 4.4) Imports: stats, BiocParallel, MatrixGenerics, methods, SingleCellExperiment, SummarizedExperiment, SpatialExperiment Suggests: BiocStyle, knitr, nnSVG, rmarkdown, scran, scuttle, STexampleData, escheR, ggpubr, colorspace, BumpyMatrix, sessioninfo, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: f7fd0968f5e3b5a32654606b631f8890 NeedsCompilation: no Title: Thin plate models to detect spatially variable genes Description: The goal of `tpSVG` is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, `tpSVG` introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to currently available count-based model for spatially resolved data analysis, the `tpSVG` models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis. biocViews: Spatial, Transcriptomics, GeneExpression, Software, StatisticalMethod, DimensionReduction, Regression, Preprocessing Author: Boyi Guo [aut, cre] (ORCID: ), Lukas M. Weber [ctb] (ORCID: ), Stephanie C. Hicks [aut] (ORCID: ) Maintainer: Boyi Guo URL: https://github.com/boyiguo1/tpSVG VignetteBuilder: knitr BugReports: https://github.com/boyiguo1/tpSVG/issues git_url: https://git.bioconductor.org/packages/tpSVG git_branch: devel git_last_commit: 762d511 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tpSVG_1.7.0.tar.gz vignettes: vignettes/tpSVG/inst/doc/intro_to_tpSVG.html vignetteTitles: intro_to_tpSVG hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tpSVG/inst/doc/intro_to_tpSVG.R dependencyCount: 78 Package: tracktables Version: 1.45.0 Depends: R (>= 3.5.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: 6582cfd8feca39c686a2704eb38e55a2 NeedsCompilation: no Title: Build IGV tracks and HTML reports Description: Methods to create complex IGV genome browser sessions and dynamic IGV reports in HTML pages. biocViews: Sequencing, ReportWriting Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tracktables git_branch: devel git_last_commit: 6d7475c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tracktables_1.45.0.tar.gz vignettes: vignettes/tracktables/inst/doc/tracktables.pdf vignetteTitles: Creating IGV HTML reports with tracktables hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tracktables/inst/doc/tracktables.R dependencyCount: 45 Package: tradeSeq Version: 1.25.0 Depends: R (>= 3.6) Imports: mgcv, edgeR, SingleCellExperiment, SummarizedExperiment, slingshot, magrittr, RColorBrewer, BiocParallel, Biobase, pbapply, igraph, ggplot2, princurve, methods, S4Vectors, tibble, Matrix, TrajectoryUtils, viridis, matrixStats, MASS Suggests: knitr, rmarkdown, testthat, covr, clusterExperiment, DelayedMatrixStats License: MIT + file LICENSE MD5sum: c29d11592ed1b9edad7bf986a91d1e9c NeedsCompilation: no Title: trajectory-based differential expression analysis for sequencing data Description: tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM. biocViews: Clustering, Regression, TimeCourse, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Koen Van den Berge [aut], Hector Roux de Bezieux [aut, cre] (ORCID: ), Kelly Street [aut, ctb], Lieven Clement [aut, ctb], Sandrine Dudoit [ctb] Maintainer: Hector Roux de Bezieux URL: https://statomics.github.io/tradeSeq/index.html VignetteBuilder: knitr BugReports: https://github.com/statOmics/tradeSeq/issues git_url: https://git.bioconductor.org/packages/tradeSeq git_branch: devel git_last_commit: 4e78622 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tradeSeq_1.25.0.tar.gz vignettes: vignettes/tradeSeq/inst/doc/fitGAM.html, vignettes/tradeSeq/inst/doc/Monocle.html, vignettes/tradeSeq/inst/doc/multipleConditions.html, vignettes/tradeSeq/inst/doc/tradeSeq.html vignetteTitles: More details on working with fitGAM, Monocle + tradeSeq, Differential expression across conditions, The tradeSeq workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tradeSeq/inst/doc/fitGAM.R, vignettes/tradeSeq/inst/doc/Monocle.R, vignettes/tradeSeq/inst/doc/tradeSeq.R suggestsMe: blase dependencyCount: 73 Package: TrajectoryGeometry Version: 1.19.0 Depends: R (>= 4.1) Imports: pracma, rgl, ggplot2, stats, methods Suggests: dplyr, knitr, RColorBrewer, rmarkdown License: MIT + file LICENSE MD5sum: f8535b1a48f78b36aa5def29e1bb6e0a NeedsCompilation: no Title: This Package Discovers Directionality in Time and Pseudo-times Series of Gene Expression Patterns Description: Given a time series or pseudo-times series of gene expression data, we might wish to know: Do the changes in gene expression in these data exhibit directionality? Are there turning points in this directionality. Do different subsets of the data move in different directions? This package uses spherical geometry to probe these sorts of questions. In particular, if we are looking at (say) the first n dimensions of the PCA of gene expression, directionality can be detected as the clustering of points on the (n-1)-dimensional sphere. biocViews: BiologicalQuestion, StatisticalMethod, GeneExpression, SingleCell Author: Michael Shapiro [aut, cre] (ORCID: ) Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TrajectoryGeometry git_branch: devel git_last_commit: fede760 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TrajectoryGeometry_1.19.0.tar.gz vignettes: vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.html, vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.html vignetteTitles: SingleCellTrajectoryAnalysis, TrajectoryGeometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.R, vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.R dependencyCount: 49 Package: TrajectoryUtils Version: 1.19.0 Depends: SingleCellExperiment Imports: methods, stats, Matrix, igraph, S4Vectors, SummarizedExperiment Suggests: BiocNeighbors, DelayedArray, DelayedMatrixStats, BiocParallel, testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 3a85bfad1929e80c8a9313e53fed61a5 NeedsCompilation: no Title: Single-Cell Trajectory Analysis Utilities Description: Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results. biocViews: GeneExpression, SingleCell Author: Aaron Lun [aut, cre], Kelly Street [aut] Maintainer: Aaron Lun URL: https://bioconductor.org/packages/TrajectoryUtils VignetteBuilder: knitr BugReports: https://github.com/LTLA/TrajectoryUtils/issues git_url: https://git.bioconductor.org/packages/TrajectoryUtils git_branch: devel git_last_commit: 5806e25 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TrajectoryUtils_1.19.0.tar.gz vignettes: vignettes/TrajectoryUtils/inst/doc/overview.html vignetteTitles: Trajectory utilities hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TrajectoryUtils/inst/doc/overview.R dependsOnMe: slingshot, TSCAN importsMe: condiments, singleCellTK, tradeSeq dependencyCount: 35 Package: transcriptogramer Version: 1.33.0 Depends: R (>= 3.4), methods Imports: biomaRt, data.table, doSNOW, foreach, ggplot2, graphics, grDevices, igraph, limma, parallel, progress, RedeR, snow, stats, tidyr, topGO Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: b6380b1905f2cdcb09799e4a1766105f NeedsCompilation: no Title: Transcriptional analysis based on transcriptograms Description: R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expressions are altered. biocViews: Software, Network, Visualization, SystemsBiology, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Clustering, DifferentialExpression, Microarray, RNASeq, Transcription, ImmunoOncology Author: Diego Morais [aut, cre], Rodrigo Dalmolin [aut] Maintainer: Diego Morais URL: https://github.com/arthurvinx/transcriptogramer SystemRequirements: Java Runtime Environment (>= 6) VignetteBuilder: knitr BugReports: https://github.com/arthurvinx/transcriptogramer/issues git_url: https://git.bioconductor.org/packages/transcriptogramer git_branch: devel git_last_commit: c57474a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/transcriptogramer_1.33.0.tar.gz vignettes: vignettes/transcriptogramer/inst/doc/transcriptogramer.html vignetteTitles: The transcriptogramer user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptogramer/inst/doc/transcriptogramer.R dependencyCount: 91 Package: transcriptR Version: 1.39.4 Depends: R (>= 3.5.0), methods Imports: BiocGenerics, caret, chipseq, GenomicAlignments, GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2, graphics, grDevices, IRanges (>= 2.11.15), pROC, reshape2, Rsamtools, rtracklayer, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg19.knownGene, testthat, e1071 License: GPL-3 MD5sum: 8bced0fa8344031c726995da414fbff2 NeedsCompilation: no Title: An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification Description: The differences in the RNA types being sequenced have an impact on the resulting sequencing profiles. mRNA-seq data is enriched with reads derived from exons, while GRO-, nucRNA- and chrRNA-seq demonstrate a substantial broader coverage of both exonic and intronic regions. The presence of intronic reads in GRO-seq type of data makes it possible to use it to computationally identify and quantify all de novo continuous regions of transcription distributed across the genome. This type of data, however, is more challenging to interpret and less common practice compared to mRNA-seq. One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. The R package transcriptR combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample. biocViews: ImmunoOncology, Transcription, Software, Sequencing, RNASeq, Coverage Author: Armen R. Karapetyan Maintainer: Armen R. Karapetyan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transcriptR git_branch: devel git_last_commit: b0b2357 git_last_commit_date: 2025-12-22 Date/Publication: 2026-04-20 source.ver: src/contrib/transcriptR_1.39.4.tar.gz vignettes: vignettes/transcriptR/inst/doc/transcriptR.html vignetteTitles: transcriptR: an integrative tool for ChIP- and RNA-seq based primary transcripts detection and quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptR/inst/doc/transcriptR.R dependencyCount: 148 Package: transite Version: 1.29.0 Depends: R (>= 3.5) Imports: BiocGenerics (>= 0.26.0), Biostrings (>= 2.48.0), dplyr (>= 0.7.6), GenomicRanges (>= 1.32.6), ggplot2 (>= 3.0.0), grDevices, gridExtra (>= 2.3), methods, parallel, Rcpp (>= 1.0.4.8), scales (>= 1.0.0), stats, TFMPvalue (>= 0.0.8), stringr (>= 1.5.1), utils LinkingTo: Rcpp (>= 1.0.4.8) Suggests: knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: e829f88f4b3da8ec0e27cccbbdc7b28b NeedsCompilation: yes Title: RNA-binding protein motif analysis Description: transite is a computational method that allows comprehensive analysis of the regulatory role of RNA-binding proteins in various cellular processes by leveraging preexisting gene expression data and current knowledge of binding preferences of RNA-binding proteins. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, mRNAMicroarray, Genetics, GeneSetEnrichment Author: Konstantin Krismer [aut, cre, cph] (ORCID: ), Anna Gattinger [aut] (ORCID: ), Michael Yaffe [ths, cph] (ORCID: ), Ian Cannell [ths] (ORCID: ) Maintainer: Konstantin Krismer URL: https://transite.mit.edu SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transite git_branch: devel git_last_commit: 9302edb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/transite_1.29.0.tar.gz vignettes: vignettes/transite/inst/doc/spma.html vignetteTitles: Spectrum Motif Analysis (SPMA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/transite/inst/doc/spma.R dependencyCount: 47 Package: tRanslatome Version: 1.49.0 Depends: R (>= 2.15.0), methods, limma, anota, DESeq2, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 MD5sum: d038918b62f63bd5b345e4dbff13f1f7 NeedsCompilation: no Title: Comparison between multiple levels of gene expression Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression, DifferentialExpression, Microarray, HighThroughputSequencing, QualityControl, GO, MultipleComparisons, Bioinformatics Author: Toma Tebaldi, Erik Dassi, Galena Kostoska Maintainer: Toma Tebaldi , Erik Dassi git_url: https://git.bioconductor.org/packages/tRanslatome git_branch: devel git_last_commit: 6b31799 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tRanslatome_1.49.0.tar.gz vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf vignetteTitles: tRanslatome hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tRanslatome/inst/doc/tRanslatome_package.R dependencyCount: 110 Package: transmogR Version: 1.7.0 Depends: R (>= 4.1.0), Biostrings, GenomicRanges Imports: BSgenome, data.table, Seqinfo, GenomicFeatures, ggplot2 (>= 4.0.0), IRanges, jsonlite, matrixStats, methods, parallel, patchwork, scales, stats, S4Vectors, SummarizedExperiment, VariantAnnotation Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, edgeR, extraChIPs, InteractionSet, knitr, readr, rmarkdown, rtracklayer, SimpleUpset, testthat (>= 3.0.0) License: GPL-3 MD5sum: 5f2432a0b40e24a07a01e4e1e8a439b7 NeedsCompilation: yes Title: Modify a set of reference sequences using a set of variants Description: transmogR provides the tools needed to crate a new reference genome or reference transcriptome, using a set of variants. Variants can be any combination of SNPs, Insertions and Deletions. The intended use-case is to enable creation of variant-modified reference transcriptomes for incorporation into transcriptomic pseudo-alignment workflows, such as salmon. biocViews: Alignment, GenomicVariation, Sequencing, TranscriptomeVariant, VariantAnnotation Author: Stevie Pederson [aut, cre] (ORCID: ) Maintainer: Stevie Pederson URL: https://github.com/smped/transmogR VignetteBuilder: knitr BugReports: https://github.com/smped/transmogR/issues git_url: https://git.bioconductor.org/packages/transmogR git_branch: devel git_last_commit: ad3458d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/transmogR_1.7.0.tar.gz vignettes: vignettes/transmogR/inst/doc/creating_a_new_reference.html vignetteTitles: Creating a Variant-Modified Reference hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transmogR/inst/doc/creating_a_new_reference.R dependencyCount: 89 Package: transomics2cytoscape Version: 1.21.0 Imports: RCy3, KEGGREST, dplyr, purrr, tibble, pbapply Suggests: testthat, roxygen2, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 92124c9a1ad89051cc18bbc118d42f73 NeedsCompilation: no Title: A tool set for 3D Trans-Omic network visualization with Cytoscape Description: transomics2cytoscape generates a file for 3D transomics visualization by providing input that specifies the IDs of multiple KEGG pathway layers, their corresponding Z-axis heights, and an input that represents the edges between the pathway layers. The edges are used, for example, to describe the relationships between kinase on a pathway and enzyme on another pathway. This package automates creation of a transomics network as shown in the figure in Yugi.2014 (https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape automation (https://doi.org/10.1186/s13059-019-1758-4). biocViews: Network, Software, Pathways, DataImport, KEGG Author: Kozo Nishida [aut, cre] (ORCID: ), Katsuyuki Yugi [aut] (ORCID: ) Maintainer: Kozo Nishida SystemRequirements: Cytoscape >= 3.10.0 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transomics2cytoscape git_branch: devel git_last_commit: ec6e45e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/transomics2cytoscape_1.21.0.tar.gz vignettes: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.html vignetteTitles: transomics2cytoscape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.R dependencyCount: 66 Package: traseR Version: 1.41.0 Depends: R (>= 3.5.0), GenomicRanges, IRanges, BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL MD5sum: e4448db28a3781cdae67583b46809524 NeedsCompilation: no Title: GWAS trait-associated SNP enrichment analyses in genomic intervals Description: traseR performs GWAS trait-associated SNP enrichment analyses in genomic intervals using different hypothesis testing approaches, also provides various functionalities to explore and visualize the results. biocViews: Genetics,Sequencing, Coverage, Alignment, QualityControl, DataImport Author: Li Chen, Zhaohui S.Qin Maintainer: li chen git_url: https://git.bioconductor.org/packages/traseR git_branch: devel git_last_commit: f2dfbb1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/traseR_1.41.0.tar.gz vignettes: vignettes/traseR/inst/doc/traseR.pdf vignetteTitles: Perform GWAS trait-associated SNP enrichment analyses in genomic intervals hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/traseR/inst/doc/traseR.R dependencyCount: 59 Package: TreeAndLeaf Version: 1.23.2 Depends: R(>= 4.4) Imports: RedeR(>= 3.6.1), igraph, ape Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, stringr, ggtree, ggplot2, dplyr, dendextend, RColorBrewer License: Artistic-2.0 MD5sum: b863d2a0f2a9dac8d0a04ca519dfd88f NeedsCompilation: no Title: Displaying binary trees with focus on dendrogram leaves Description: TreeAndLeaf implements a hybrid layout strategy that enhances leaf-level visualization in dendrograms. By integrating force-directed graph and tree layout algorithms, it enables projection of multiple layers of information onto graph–tree diagrams. biocViews: GraphAndNetwork, Network, Visualization, DataRepresentation, Software, SystemsBiology Author: Milena Cardoso [aut], Luis Rizzardi [aut], Leonardo Kume [aut], Sheyla Trefflich [ctb], Clarice Groeneveld [ctb], Mauro Castro [aut, cre] (ORCID: ) Maintainer: Mauro Castro URL: https://doi.org/10.1093/bioinformatics/btab819 VignetteBuilder: knitr BugReports: https://github.com/sysbiolab/TreeAndLeaf/issues git_url: https://git.bioconductor.org/packages/TreeAndLeaf git_branch: devel git_last_commit: 63514dd git_last_commit_date: 2026-02-20 Date/Publication: 2026-04-20 source.ver: src/contrib/TreeAndLeaf_1.23.2.tar.gz vignettes: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.html vignetteTitles: TreeAndLeaf: an graph layout to dendrograms. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.R suggestsMe: RedeR dependencyCount: 29 Package: treeclimbR Version: 1.7.0 Depends: R (>= 4.4.0) Imports: TreeSummarizedExperiment (>= 1.99.0), edgeR, methods, SummarizedExperiment, S4Vectors, dirmult, dplyr, tibble, tidyr, ape, diffcyt, ggnewscale, ggplot2 (>= 3.4.0), viridis, ggtree, stats, utils, rlang Suggests: knitr, rmarkdown, scales, testthat (>= 3.0.0), BiocStyle, GenomeInfoDb License: Artistic-2.0 MD5sum: 6d75dfd13b525ce82462ebc9a63ae0ec NeedsCompilation: no Title: An algorithm to find optimal signal levels in a tree Description: The arrangement of hypotheses in a hierarchical structure appears in many research fields and often indicates different resolutions at which data can be viewed. This raises the question of which resolution level the signal should best be interpreted on. treeclimbR provides a flexible method to select optimal resolution levels (potentially different levels in different parts of the tree), rather than cutting the tree at an arbitrary level. treeclimbR uses a tuning parameter to generate candidate resolutions and from these selects the optimal one. biocViews: StatisticalMethod, CellBasedAssays Author: Ruizhu Huang [aut] (ORCID: ), Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/treeclimbR VignetteBuilder: knitr BugReports: https://github.com/csoneson/treeclimbR/issues git_url: https://git.bioconductor.org/packages/treeclimbR git_branch: devel git_last_commit: 3ba408c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/treeclimbR_1.7.0.tar.gz vignettes: vignettes/treeclimbR/inst/doc/treeclimbR.html vignetteTitles: treeclimbR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treeclimbR/inst/doc/treeclimbR.R dependencyCount: 194 Package: treeio Version: 1.35.0 Depends: R (>= 4.1.0) Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, stats, tibble, tidytree (>= 0.4.5), utils, yulab.utils (>= 0.1.6) Suggests: Biostrings, cli, ggplot2, ggtree, igraph, knitr, rmarkdown, phangorn, prettydoc, purrr, testthat, tidyr, vroom, xml2, yaml License: Artistic-2.0 MD5sum: e24b06bf6b5ba71639dbbe7f693a7fb8 NeedsCompilation: no Title: Base Classes and Functions for Phylogenetic Tree Input and Output Description: 'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats. biocViews: Software, Annotation, Clustering, DataImport, DataRepresentation, Alignment, MultipleSequenceAlignment, Phylogenetics Author: Guangchuang Yu [aut, cre] (ORCID: ), Tommy Tsan-Yuk Lam [ctb, ths], Shuangbin Xu [ctb] (ORCID: ), Bradley Jones [ctb], Casey Dunn [ctb], Tyler Bradley [ctb], Konstantinos Geles [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/contribution-tree-data/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/treeio/issues git_url: https://git.bioconductor.org/packages/treeio git_branch: devel git_last_commit: 3b17027 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/treeio_1.35.0.tar.gz vignettes: vignettes/treeio/inst/doc/treeio.html vignetteTitles: treeio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treeio/inst/doc/treeio.R importsMe: ggtree, lefser, MicrobiotaProcess, TreeSummarizedExperiment, geneplast.data, BioVizSeq, dowser, EvoPhylo, RevGadgets, RPesto, shinyTempSignal suggestsMe: ggtreeDendro, ggtreeExtra, rfaRm, FossilSim, idiogramFISH, MetaNet, nosoi, treestructure dependencyCount: 39 Package: treekoR Version: 1.19.0 Depends: R (>= 4.1) Imports: stats, utils, tidyr, dplyr, data.table, ggiraph, ggplot2, hopach, ape, ggtree, patchwork, SingleCellExperiment, diffcyt, edgeR, lme4, multcomp Suggests: knitr, rmarkdown, BiocStyle, CATALYST, testthat (>= 3.0.0) License: GPL-3 MD5sum: f80779328225881a015f31951b198527 NeedsCompilation: no Title: Cytometry Cluster Hierarchy and Cellular-to-phenotype Associations Description: treekoR is a novel framework that aims to utilise the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points. These associations are aimed to recapitulate the nested proportions prevalent in workflows inovlving manual gating, which are often overlooked in workflows using automatic clustering to identify cell populations. We developed treekoR to: Derive a hierarchical tree structure of cell clusters; quantify a cell types as a proportion relative to all cells in a sample (%total), and, as the proportion relative to a parent population (%parent); perform significance testing using the calculated proportions; and provide an interactive html visualisation to help highlight key results. biocViews: Clustering, DifferentialExpression, FlowCytometry, ImmunoOncology, MassSpectrometry, SingleCell, Software, StatisticalMethod, Visualization Author: Adam Chan [aut, cre], Ellis Patrick [ctb] Maintainer: Adam Chan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/treekoR git_branch: devel git_last_commit: 405f7ca git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/treekoR_1.19.0.tar.gz vignettes: vignettes/treekoR/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treekoR/inst/doc/vignette.R importsMe: Statial dependencyCount: 186 Package: TreeSummarizedExperiment Version: 2.19.0 Depends: R(>= 3.6.0), SingleCellExperiment, S4Vectors (>= 0.23.18), Biostrings Imports: methods, BiocGenerics, utils, ape, rlang, dplyr, SummarizedExperiment, BiocParallel, IRanges, treeio Suggests: ggtree, ggplot2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: 87b11c5dc6315fe1db552f2b5febcfb1 NeedsCompilation: no Title: TreeSummarizedExperiment: a S4 Class for Data with Tree Structures Description: TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data. biocViews: DataRepresentation, Infrastructure Author: Ruizhu Huang [aut, cre] (ORCID: ), Felix G.M. Ernst [ctb] (ORCID: ), Mark Robinson [ctb] (ORCID: ), Tuomas Borman [ctb] (ORCID: ) Maintainer: Ruizhu Huang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeSummarizedExperiment git_branch: devel git_last_commit: d592bc4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TreeSummarizedExperiment_2.19.0.tar.gz vignettes: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.html, vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.html vignetteTitles: 1. Introduction to TreeSE, 2. Combine TSEs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.R, vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.R dependsOnMe: ExperimentSubset, HoloFoodR, MGnifyR, mia, miaSim, miaViz, curatedMetagenomicData, MicrobiomeBenchmarkData, microbiomeDataSets importsMe: anansi, benchdamic, CrcBiomeScreen, DspikeIn, iSEEtree, maaslin3, miaDash, miaTime, treeclimbR, mikropml suggestsMe: ANCOMBC, dar, LimROTS, philr, PLSDAbatch, LegATo, file2meco, parafac4microbiome, radEmu dependencyCount: 67 Package: TREG Version: 1.15.0 Depends: R (>= 4.2), SummarizedExperiment Imports: Matrix, purrr, rafalib Suggests: BiocFileCache, BiocStyle, dplyr, ggplot2, knitr, pheatmap, sessioninfo, RefManageR, rmarkdown, testthat (>= 3.0.0), tibble, tidyr, SingleCellExperiment License: Artistic-2.0 MD5sum: 7cf004a27eafe109a86473ee30a3805c NeedsCompilation: no Title: Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data Description: RNA abundance and cell size parameters could improve RNA-seq deconvolution algorithms to more accurately estimate cell type proportions given the different cell type transcription activity levels. A Total RNA Expression Gene (TREG) can facilitate estimating total RNA content using single molecule fluorescent in situ hybridization (smFISH). We developed a data-driven approach using a measure of expression invariance to find candidate TREGs in postmortem human brain single nucleus RNA-seq. This R package implements the method for identifying candidate TREGs from snRNA-seq data. biocViews: Software, SingleCell, RNASeq, GeneExpression, Transcriptomics, Transcription, Sequencing Author: Louise Huuki-Myers [aut, cre] (ORCID: ), Leonardo Collado-Torres [ctb] (ORCID: ) Maintainer: Louise Huuki-Myers URL: https://github.com/LieberInstitute/TREG, http://research.libd.org/TREG/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/TREG git_url: https://git.bioconductor.org/packages/TREG git_branch: devel git_last_commit: 8161ab6 git_last_commit_date: 2026-03-31 Date/Publication: 2026-04-20 source.ver: src/contrib/TREG_1.15.0.tar.gz vignettes: vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.html vignetteTitles: How to find Total RNA Expression Genes (TREGs) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.R dependencyCount: 35 Package: Trendy Version: 1.33.0 Depends: R (>= 3.4) Imports: stats, utils, graphics, grDevices, segmented, gplots, parallel, magrittr, BiocParallel, DT, S4Vectors, SummarizedExperiment, methods, shiny, shinyFiles Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL-3 MD5sum: 9b265ac2770d0203f1cd378de33ac04e NeedsCompilation: no Title: Breakpoint analysis of time-course expression data Description: Trendy implements segmented (or breakpoint) regression models to estimate breakpoints which represent changes in expression for each feature/gene in high throughput data with ordered conditions. biocViews: TimeCourse, RNASeq, Regression, ImmunoOncology Author: Rhonda Bacher and Ning Leng Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/Trendy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Trendy git_branch: devel git_last_commit: 3ac921f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Trendy_1.33.0.tar.gz vignettes: vignettes/Trendy/inst/doc/Trendy_vignette.pdf vignetteTitles: Trendy Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Trendy/inst/doc/Trendy_vignette.R dependencyCount: 90 Package: TRESS Version: 1.17.0 Depends: R (>= 4.1.0), parallel, S4Vectors Imports: utils, rtracklayer, Matrix, matrixStats, stats, methods, graphics, GenomicRanges, GenomicFeatures, IRanges, Rsamtools, AnnotationDbi Suggests: knitr, rmarkdown,BiocStyle License: GPL-3 + file LICENSE MD5sum: 7b2e134fba1f9a86e3766b77abb74469 NeedsCompilation: no Title: Toolbox for mRNA epigenetics sequencing analysis Description: This package is devoted to analyzing MeRIP-seq data. Current functionalities include 1. detect transcriptome wide m6A methylation regions 2. detect transcriptome wide differential m6A methylation regions. biocViews: Epigenetics, RNASeq, PeakDetection, DifferentialMethylation Author: Zhenxing Guo [aut, cre], Hao Wu [ctb] Maintainer: Zhenxing Guo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TRESS git_branch: devel git_last_commit: 36f1f10 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TRESS_1.17.0.tar.gz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: magpie dependencyCount: 75 Package: tricycle Version: 1.19.0 Depends: R (>= 4.0), SingleCellExperiment Imports: methods, circular, ggplot2, ggnewscale, AnnotationDbi, scater, GenomicRanges, IRanges, S4Vectors, scattermore, dplyr, RColorBrewer, grDevices, stats, SummarizedExperiment, utils Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, CircStats, cowplot, htmltools, Seurat, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 2a971ca2dffbdbfa0fe3a28987f451a6 NeedsCompilation: no Title: tricycle: Transferable Representation and Inference of cell cycle Description: The package contains functions to infer and visualize cell cycle process using Single Cell RNASeq data. It exploits the idea of transfer learning, projecting new data to the previous learned biologically interpretable space. We provide a pre-learned cell cycle space, which could be used to infer cell cycle time of human and mouse single cell samples. In addition, we also offer functions to visualize cell cycle time on different embeddings and functions to build new reference. biocViews: SingleCell, Software, Transcriptomics, RNASeq, Transcription, BiologicalQuestion, DimensionReduction, ImmunoOncology Author: Shijie Zheng [aut, cre] Maintainer: Shijie Zheng URL: https://github.com/hansenlab/tricycle VignetteBuilder: knitr BugReports: https://github.com/hansenlab/tricycle/issues git_url: https://git.bioconductor.org/packages/tricycle git_branch: devel git_last_commit: 54701be git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tricycle_1.19.0.tar.gz vignettes: vignettes/tricycle/inst/doc/tricycle.html vignetteTitles: tricycle: Transferable Representation and Inference of Cell Cycle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tricycle/inst/doc/tricycle.R dependencyCount: 118 Package: TrIdent Version: 1.3.2 Depends: R (>= 4.2.0) Imports: graphics, utils, stats, dplyr, ggplot2, patchwork, stringr, tidyr, roll Suggests: BiocStyle, knitr, rmarkdown, kableExtra License: GPL-2 MD5sum: 02f4b397c099e209bc43d09ee1db99de NeedsCompilation: no Title: TrIdent - Transduction Identification Description: The `TrIdent` R package automates the analysis of transductomics data by detecting, classifying, and characterizing read coverage patterns associated with potential transduction events. Transductomics is a DNA sequencing-based method for the detection and characterization of transduction events in pure cultures and complex communities. Transductomics relies on mapping sequencing reads from a viral-like particle (VLP)-fraction of a sample to contigs assembled from the metagenome (whole-community) of the same sample. Reads from bacterial DNA carried by VLPs will map back to the bacterial contigs of origin creating read coverage patterns indicative of ongoing transduction. biocViews: Coverage, Metagenomics, PatternLogic, Classification, Sequencing Author: Jessie Maier [aut, cre] (ORCID: ), Jorden Rabasco [aut, ctb] (ORCID: ), Craig Gin [aut] (ORCID: ), Benjamin Callahan [aut] (ORCID: ), Manuel Kleiner [aut, ths] (ORCID: ) Maintainer: Jessie Maier URL: https://github.com/jlmaier12/TrIdent, https://jlmaier12.github.io/TrIdent/ VignetteBuilder: knitr BugReports: https://github.com/jlmaier12/TrIdent/issues git_url: https://git.bioconductor.org/packages/TrIdent git_branch: devel git_last_commit: eef7996 git_last_commit_date: 2026-01-05 Date/Publication: 2026-04-20 source.ver: src/contrib/TrIdent_1.3.2.tar.gz vignettes: vignettes/TrIdent/inst/doc/TrIdent-vignette.html vignetteTitles: TrIdent hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TrIdent/inst/doc/TrIdent-vignette.R dependencyCount: 41 Package: trio Version: 3.49.4 Depends: R (>= 3.0.1) Imports: grDevices, graphics, methods, stats, survival, utils, siggenes, LogicReg (>= 1.6.1), data.table Suggests: mcbiopi, splines, logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 MD5sum: 2cf336119699fdb93ddecbaf7c7d637c NeedsCompilation: no Title: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies Description: Testing SNPs and SNP interactions with a genotypic TDT. This package furthermore contains functions for computing pairwise values of LD measures and for identifying LD blocks, as well as functions for setting up matched case pseudo-control genotype data for case-parent trios in order to run trio logic regression, for imputing missing genotypes in trios, for simulating case-parent trios with disease risk dependent on SNP interaction, and for power and sample size calculation in trio data. biocViews: SNP, GeneticVariability, Microarray, Genetics Author: Holger Schwender, Qing Li, Philipp Berger, Christoph Neumann, Margaret Taub, Ingo Ruczinski Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/trio git_branch: devel git_last_commit: c6f8de7 git_last_commit_date: 2026-04-06 Date/Publication: 2026-04-20 source.ver: src/contrib/trio_3.49.4.tar.gz vignettes: vignettes/trio/inst/doc/trio.pdf vignetteTitles: Trio Logic Regression and genotypic TDT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trio/inst/doc/trio.R dependencyCount: 20 Package: triplex Version: 1.51.0 Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 2.5.27), XVector (>= 0.11.6), Biostrings (>= 2.39.10) Imports: methods, grid, GenomicRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer License: BSD_2_clause + file LICENSE MD5sum: 0524da025407dc738a9e150ee39191ff NeedsCompilation: yes Title: Search and visualize intramolecular triplex-forming sequences in DNA Description: This package provides functions for identification and visualization of potential intramolecular triplex patterns in DNA sequence. The main functionality is to detect the positions of subsequences capable of folding into an intramolecular triplex (H-DNA) in a much larger sequence. The potential H-DNA (triplexes) should be made of as many cannonical nucleotide triplets as possible. The package includes visualization showing the exact base-pairing in 1D, 2D or 3D. biocViews: SequenceMatching, GeneRegulation Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with contributions from Daniel Kopecek Maintainer: Jiri Hon URL: http://www.fi.muni.cz/~lexa/triplex/ git_url: https://git.bioconductor.org/packages/triplex git_branch: devel git_last_commit: 53b853d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/triplex_1.51.0.tar.gz vignettes: vignettes/triplex/inst/doc/triplex.pdf vignetteTitles: Triplex User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/triplex/inst/doc/triplex.R dependencyCount: 17 Package: tripr Version: 1.17.02 Depends: R (>= 4.1.0), shiny (>= 1.6.0), shinyBS Imports: shinyjs, shinyFiles, plyr, data.table, DT, stringr, stringdist, plot3D, gridExtra, RColorBrewer, plotly, dplyr, config (>= 0.3.1), golem (>= 0.3.1), methods, grDevices, graphics, stats, utils, vegan Suggests: BiocGenerics, shinycssloaders, tidyverse, BiocManager, Biostrings, xtable, rlist, motifStack, knitr, rmarkdown, testthat (>= 3.0.0), fs, BiocStyle, RefManageR, biocthis Enhances: parallel License: MIT + file LICENSE MD5sum: 2c5828aee0bfa4f660186c41219ac241 NeedsCompilation: no Title: T-cell Receptor/Immunoglobulin Profiler (TRIP) Description: TRIP is a software framework that provides analytics services on antigen receptor (B cell receptor immunoglobulin, BcR IG | T cell receptor, TR) gene sequence data. It is a web application written in R Shiny. It takes as input the output files of the IMGT/HighV-Quest tool. Users can select to analyze the data from each of the input samples separately, or the combined data files from all samples and visualize the results accordingly. biocViews: BatchEffect, MultipleComparison, GeneExpression, ImmunoOncology, TargetedResequencing Author: Maria Th. Kotouza [aut], Katerina Gemenetzi [aut], Chrysi Galigalidou [aut], Elisavet Vlachonikola [aut], Nikolaos Pechlivanis [cre], Andreas Agathangelidis [aut], Raphael Sandaltzopoulos [aut], Pericles A. Mitkas [aut], Kostas Stamatopoulos [aut], Anastasia Chatzidimitriou [aut], Fotis E. Psomopoulos [aut], Iason Ofeidis [aut], Aspasia Orfanou [aut] Maintainer: Nikolaos Pechlivanis URL: https://github.com/BiodataAnalysisGroup/tripr VignetteBuilder: knitr BugReports: https://github.com/BiodataAnalysisGroup/tripr/issues git_url: https://git.bioconductor.org/packages/tripr git_branch: devel git_last_commit: 4177ea9 git_last_commit_date: 2026-04-20 Date/Publication: 2026-04-20 source.ver: src/contrib/tripr_1.17.02.tar.gz vignettes: vignettes/tripr/inst/doc/tripr_guide.html vignetteTitles: tripr User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tripr/inst/doc/tripr_guide.R dependencyCount: 101 Package: tRNA Version: 1.29.0 Depends: R (>= 3.5), GenomicRanges, Structstrings Imports: stringr, S4Vectors, methods, BiocGenerics, IRanges, XVector, Biostrings, Modstrings, ggplot2, scales Suggests: knitr, rmarkdown, testthat, BiocStyle, tRNAscanImport License: GPL-3 + file LICENSE MD5sum: b7d814541ce57e6aa8d59dd6390b25c6 NeedsCompilation: no Title: Analyzing tRNA sequences and structures Description: The tRNA package allows tRNA sequences and structures to be accessed and used for subsetting. In addition, it provides visualization tools to compare feature parameters of multiple tRNA sets and correlate them to additional data. The tRNA package uses GRanges objects as inputs requiring only few additional column data sets. biocViews: Software, Visualization Author: Felix GM Ernst [aut, cre] (ORCID: ) Maintainer: Felix GM Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNA/issues git_url: https://git.bioconductor.org/packages/tRNA git_branch: devel git_last_commit: c4fadc0 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tRNA_1.29.0.tar.gz vignettes: vignettes/tRNA/inst/doc/tRNA.html vignetteTitles: tRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNA/inst/doc/tRNA.R dependsOnMe: tRNAdbImport, tRNAscanImport dependencyCount: 39 Package: tRNAdbImport Version: 1.29.0 Depends: R (>= 3.6), GenomicRanges, Modstrings, Structstrings, tRNA Imports: Biostrings, stringr, httr2, xml2, S4Vectors, methods, IRanges, utils Suggests: BiocGenerics, knitr, rmarkdown, testthat, httptest, BiocStyle, rtracklayer License: GPL-3 + file LICENSE MD5sum: e72db643724a63459248a28b3a11b244 NeedsCompilation: no Title: Importing from tRNAdb and mitotRNAdb as GRanges objects Description: tRNAdbImport imports the entries of the tRNAdb and mtRNAdb (http://trna.bioinf.uni-leipzig.de) as GRanges object. biocViews: Software, Visualization, DataImport Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAdbImport/issues git_url: https://git.bioconductor.org/packages/tRNAdbImport git_branch: devel git_last_commit: 4c8ebfd git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tRNAdbImport_1.29.0.tar.gz vignettes: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.html vignetteTitles: tRNAdbImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.R importsMe: EpiTxDb dependencyCount: 47 Package: tRNAscanImport Version: 1.31.0 Depends: R (>= 3.5), GenomicRanges, tRNA Imports: methods, stringr, BiocGenerics, Biostrings, Structstrings, S4Vectors, IRanges, XVector, Seqinfo, rtracklayer, BSgenome, Rsamtools Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2, BSgenome.Scerevisiae.UCSC.sacCer3 License: GPL-3 + file LICENSE MD5sum: c370d7f1d27ad850f8931b2db8fd0b74 NeedsCompilation: no Title: Importing a tRNAscan-SE result file as GRanges object Description: The package imports the result of tRNAscan-SE as a GRanges object. biocViews: Software, DataImport, WorkflowStep, Preprocessing, Visualization Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/tRNAscanImport VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAscanImport/issues git_url: https://git.bioconductor.org/packages/tRNAscanImport git_branch: devel git_last_commit: f9f96ff git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tRNAscanImport_1.31.0.tar.gz vignettes: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.html vignetteTitles: tRNAscanImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.R suggestsMe: Structstrings, tRNA dependencyCount: 79 Package: TRONCO Version: 2.43.1 Depends: R (>= 4.1.0), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, grDevices, graphics, stats, utils, methods Suggests: BiocGenerics, BiocStyle, testthat, knitr, rWikiPathways, magick License: GPL-3 MD5sum: 393262b2434aa4d680e214c20709c992 NeedsCompilation: no Title: TRONCO, an R package for TRanslational ONCOlogy Description: The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC). biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork, SomaticMutation, NetworkInference, Network, Clustering, DataImport, SingleCell, ImmunoOncology Author: Marco Antoniotti [ctb], Giulio Caravagna [aut], Luca De Sano [cre, aut] (ORCID: ), Alex Graudenzi [aut], Giancarlo Mauri [ctb], Bud Mishra [ctb], Daniele Ramazzotti [aut] (ORCID: ) Maintainer: Luca De Sano URL: https://sites.google.com/site/troncopackage/ VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/TRONCO git_url: https://git.bioconductor.org/packages/TRONCO git_branch: devel git_last_commit: 56ebe57 git_last_commit_date: 2026-04-01 Date/Publication: 2026-04-20 source.ver: src/contrib/TRONCO_2.43.1.tar.gz vignettes: vignettes/TRONCO/inst/doc/f1_introduction.html, vignettes/TRONCO/inst/doc/f2_loading_data.html, vignettes/TRONCO/inst/doc/f3_data_visualization.html, vignettes/TRONCO/inst/doc/f4_data_manipulation.html, vignettes/TRONCO/inst/doc/f5_model_inference.html, vignettes/TRONCO/inst/doc/f6_post_reconstruction.html, vignettes/TRONCO/inst/doc/f7_import_export.html vignetteTitles: f1_introduction.html, Loading data, Data visualization, Data manipulation, Model inference, Post reconstruction, Import/export from other tools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TRONCO/inst/doc/f1_introduction.R, vignettes/TRONCO/inst/doc/f2_loading_data.R, vignettes/TRONCO/inst/doc/f3_data_visualization.R, vignettes/TRONCO/inst/doc/f4_data_manipulation.R, vignettes/TRONCO/inst/doc/f5_model_inference.R, vignettes/TRONCO/inst/doc/f6_post_reconstruction.R, vignettes/TRONCO/inst/doc/f7_import_export.R dependencyCount: 47 Package: TSAR Version: 1.9.3 Depends: R (>= 4.3.0) Imports: dplyr (>= 1.0.7), ggplot2 (>= 3.3.5), ggpubr (>= 0.4.0), magrittr (>= 2.0.3), mgcv (>= 1.8.38), readxl (>= 1.4.0), stringr (>= 1.4.0), tidyr (>= 1.1.4), utils (>= 4.3.1), shiny (>= 1.7.4.1), plotly (>= 4.10.2), shinyjs (>= 2.1.0), jsonlite (>= 1.8.7), rhandsontable (>= 0.3.8), openxlsx (>= 4.2.5.2), shinyWidgets (>= 0.7.6), minpack.lm (>= 1.2.3) Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 MD5sum: 370f90615d794c09f474cf91485e3b95 NeedsCompilation: no Title: Thermal Shift Analysis in R Description: This package automates analysis workflow for Thermal Shift Analysis (TSA) data. Processing, analyzing, and visualizing data through both shiny applications and command lines. Package aims to simplify data analysis and offer front to end workflow, from raw data to multiple trial analysis. biocViews: Software, ShinyApps, Visualization, qPCR Author: Xinlin Gao [aut, cre] (ORCID: ), William M. McFadden [aut, fnd] (ORCID: ), Zhijiang Ye Ye [aut, fnd] (ORCID: ), Stefan G. Sarafianos [fnd, aut, ths] (ORCID: ) Maintainer: Xinlin Gao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSAR git_branch: devel git_last_commit: 15d3be9 git_last_commit_date: 2026-02-03 Date/Publication: 2026-04-20 source.ver: src/contrib/TSAR_1.9.3.tar.gz vignettes: vignettes/TSAR/inst/doc/FAQ_assistance.html, vignettes/TSAR/inst/doc/TSAR_Package_Structure.html, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Command.html, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Shiny.html vignetteTitles: Frequently Asked Questions, TSAR Package Structure, TSAR Workflow by Command, TSAR Workflow by Shiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSAR/inst/doc/FAQ_assistance.R, vignettes/TSAR/inst/doc/TSAR_Package_Structure.R, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Command.R, vignettes/TSAR/inst/doc/TSAR_Workflow_by_Shiny.R dependencyCount: 138 Package: TSCAN Version: 1.49.0 Depends: R (>= 4.4.0), SingleCellExperiment, TrajectoryUtils Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots, methods, stats, Matrix, SummarizedExperiment, SparseArray (>= 1.5.23), DelayedArray (>= 0.31.9), S4Vectors Suggests: knitr, testthat, scuttle, scran, metapod, BiocParallel, BiocNeighbors, batchelor License: GPL(>=2) MD5sum: 4a01b6a2c38e713e721a7a355cccbec0 NeedsCompilation: no Title: Tools for Single-Cell Analysis Description: Provides methods to perform trajectory analysis based on a minimum spanning tree constructed from cluster centroids. Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji [aut, cre], Hongkai Ji [aut], Aaron Lun [ctb] Maintainer: Zhicheng Ji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSCAN git_branch: devel git_last_commit: 19fa1c7 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TSCAN_1.49.0.tar.gz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R dependsOnMe: OSCA.multisample importsMe: FEAST, singleCellTK, DIscBIO suggestsMe: condiments dependencyCount: 82 Package: ttgsea Version: 1.19.0 Depends: keras Imports: tm, text2vec, tokenizers, textstem, stopwords, data.table, purrr, DiagrammeR, stats Suggests: fgsea, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: dca63e6ddda6bb5a56716065e156412e NeedsCompilation: no Title: Tokenizing Text of Gene Set Enrichment Analysis Description: Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ttgsea git_branch: devel git_last_commit: 46b2439 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ttgsea_1.19.0.tar.gz vignettes: vignettes/ttgsea/inst/doc/ttgsea.html vignetteTitles: ttgsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ttgsea/inst/doc/ttgsea.R importsMe: DeepPINCS, GenProSeq dependencyCount: 122 Package: TTMap Version: 1.33.0 Depends: rgl, colorRamps Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment, Biobase Suggests: BiocStyle, airway License: GPL-2 MD5sum: e375a02b50d2dd9d39514f4cc6c9fc49 NeedsCompilation: no Title: Two-Tier Mapper: a clustering tool based on topological data analysis Description: TTMap is a clustering method that groups together samples with the same deviation in comparison to a control group. It is specially useful when the data is small. It is parameter free. biocViews: Software, Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Rachel Jeitziner Maintainer: Rachel Jeitziner git_url: https://git.bioconductor.org/packages/TTMap git_branch: devel git_last_commit: a4bf3ca git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TTMap_1.33.0.tar.gz vignettes: vignettes/TTMap/inst/doc/TTMap.pdf vignetteTitles: Manual for the TTMap library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TTMap/inst/doc/TTMap.R dependencyCount: 54 Package: TurboNorm Version: 1.59.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata, hgu95av2cdf License: LGPL MD5sum: d344d7a24e632aa79b763e5d0362faf5 NeedsCompilation: yes Title: A fast scatterplot smoother suitable for microarray normalization Description: A fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, DNAMethylation, CpGIsland, MethylationArray, Normalization Author: Maarten van Iterson and Chantal van Leeuwen Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html git_url: https://git.bioconductor.org/packages/TurboNorm git_branch: devel git_last_commit: 037c11e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/TurboNorm_1.59.0.tar.gz vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf vignetteTitles: TurboNorm Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TurboNorm/inst/doc/turbonorm.R dependencyCount: 18 Package: TVTB Version: 1.37.0 Depends: R (>= 3.4), methods, utils, stats Imports: AnnotationFilter, BiocGenerics (>= 0.25.1), BiocParallel, Biostrings, ensembldb, Seqinfo, GenomicRanges, GGally, ggplot2, Gviz, limma, IRanges (>= 2.21.6), reshape2, Rsamtools, S4Vectors (>= 0.25.14), SummarizedExperiment, VariantAnnotation (>= 1.19.9) Suggests: EnsDb.Hsapiens.v75 (>= 0.99.7), shiny (>= 0.13.2.9005), DT (>= 0.1.67), rtracklayer, BiocStyle (>= 2.5.19), knitr (>= 1.12), rmarkdown, testthat, covr, pander License: Artistic-2.0 MD5sum: 65c1b262e1cbb6b7f925215f1cbeddcb NeedsCompilation: no Title: TVTB: The VCF Tool Box Description: The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE). biocViews: Software, Genetics, GeneticVariability, GenomicVariation, DataRepresentation, GUI, Genetics, DNASeq, WholeGenome, Visualization, MultipleComparison, DataImport, VariantAnnotation, Sequencing, Coverage, Alignment, SequenceMatching Author: Kevin Rue-Albrecht [aut, cre] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/TVTB VignetteBuilder: knitr BugReports: https://github.com/kevinrue/TVTB/issues git_url: https://git.bioconductor.org/packages/TVTB git_branch: devel git_last_commit: 5534769 git_last_commit_date: 2026-04-02 Date/Publication: 2026-04-20 source.ver: src/contrib/TVTB_1.37.0.tar.gz vignettes: vignettes/TVTB/inst/doc/Introduction.html, vignettes/TVTB/inst/doc/tSVE.html, vignettes/TVTB/inst/doc/VcfFilterRules.html vignetteTitles: Introduction to TVTB, The Shiny Variant Explorer, VCF filter rules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TVTB/inst/doc/Introduction.R, vignettes/TVTB/inst/doc/tSVE.R, vignettes/TVTB/inst/doc/VcfFilterRules.R dependencyCount: 159 Package: tweeDEseq Version: 1.57.0 Depends: R (>= 4.3.0) Imports: Rcpp (>= 1.0.10), MASS, limma, edgeR, parallel, cqn, grDevices, graphics, stats, utils LinkingTo: Rcpp Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) MD5sum: 4838e472a0ee9e42749ff5db3d718aa2 NeedsCompilation: yes Title: RNA-seq data analysis using the Poisson-Tweedie family of distributions Description: Differential expression analysis of RNA-seq using the Poisson-Tweedie (PT) family of distributions. PT distributions are described by a mean, a dispersion and a shape parameter and include Poisson and NB distributions, among others, as particular cases. An important feature of this family is that, while the Negative Binomial (NB) distribution only allows a quadratic mean-variance relationship, the PT distributions generalizes this relationship to any orde. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, Sequencing, RNASeq, DNASeq Author: Dolors Pelegri-Siso [aut, cre] (ORCID: ), Juan R. Gonzalez [aut] (ORCID: ), Mikel Esnaola [aut], Robert Castelo [aut] Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/tweeDEseq/ BugReports: https://github.com/isglobal-brge/tweeDEseq/issues git_url: https://git.bioconductor.org/packages/tweeDEseq git_branch: devel git_last_commit: d453ad1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/tweeDEseq_1.57.0.tar.gz vignettes: vignettes/tweeDEseq/inst/doc/tweeDEseq.pdf vignetteTitles: tweeDEseq: analysis of RNA-seq data using the Poisson-Tweedie family of distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tweeDEseq/inst/doc/tweeDEseq.R importsMe: ptmixed dependencyCount: 23 Package: twilight Version: 1.87.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, splines, stats Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2) License: GPL (>= 2) MD5sum: b11468cabb77a83aa28e848a938d5a6b NeedsCompilation: yes Title: Estimation of local false discovery rate Description: In a typical microarray setting with gene expression data observed under two conditions, the local false discovery rate describes the probability that a gene is not differentially expressed between the two conditions given its corrresponding observed score or p-value level. The resulting curve of p-values versus local false discovery rate offers an insight into the twilight zone between clear differential and clear non-differential gene expression. Package 'twilight' contains two main functions: Function twilight.pval performs a two-condition test on differences in means for a given input matrix or expression set and computes permutation based p-values. Function twilight performs a stochastic downhill search to estimate local false discovery rates and effect size distributions. The package further provides means to filter for permutations that describe the null distribution correctly. Using filtered permutations, the influence of hidden confounders could be diminished. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Stefanie Senger [cre, aut] (ORCID: ) Maintainer: Stefanie Senger URL: http://compdiag.molgen.mpg.de/software/twilight.shtml git_url: https://git.bioconductor.org/packages/twilight git_branch: devel git_last_commit: fc4af22 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/twilight_1.87.0.tar.gz vignettes: vignettes/twilight/inst/doc/tr_2004_01.pdf vignetteTitles: Estimation of Local False Discovery Rates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twilight/inst/doc/tr_2004_01.R dependsOnMe: OrderedList dependencyCount: 9 Package: twoddpcr Version: 1.35.1 Depends: R (>= 3.4) Imports: class, ggplot2, hexbin, methods, scales, shiny, stats, utils, RColorBrewer, S4Vectors Suggests: devtools, knitr, reshape2, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 6af45918ef75fcc3e6f9f0fc23e557b0 NeedsCompilation: no Title: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules Description: The twoddpcr package takes Droplet Digital PCR (ddPCR) droplet amplitude data from Bio-Rad's QuantaSoft and can classify the droplets. A summary of the positive/negative droplet counts can be generated, which can then be used to estimate the number of molecules using the Poisson distribution. This is the first open source package that facilitates the automatic classification of general two channel ddPCR data. Previous work includes 'definetherain' (Jones et al., 2014) and 'ddpcRquant' (Trypsteen et al., 2015) which both handle one channel ddPCR experiments only. The 'ddpcr' package available on CRAN (Attali et al., 2016) supports automatic gating of a specific class of two channel ddPCR experiments only. biocViews: ddPCR, Software, Classification Author: Anthony Chiu [aut, cre] Maintainer: Anthony Chiu URL: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/ VignetteBuilder: knitr BugReports: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/issues/ git_url: https://git.bioconductor.org/packages/twoddpcr git_branch: devel git_last_commit: 1db19f1 git_last_commit_date: 2026-04-15 Date/Publication: 2026-04-20 source.ver: src/contrib/twoddpcr_1.35.1.tar.gz vignettes: vignettes/twoddpcr/inst/doc/twoddpcr.html vignetteTitles: twoddpcr: A package for Droplet Digital PCR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twoddpcr/inst/doc/twoddpcr.R dependencyCount: 56 Package: tximport Version: 1.39.2 Imports: utils, stats, methods Suggests: knitr, rmarkdown, testthat, tximportData, TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), arrow, limma, edgeR (>= 4.9.2), DESeq2 (>= 1.11.6), rhdf5, jsonlite, matrixStats, Matrix, eds License: LGPL (>=2) MD5sum: 13d3d0bd676b3a07412cd698495c80d0 NeedsCompilation: no Title: Import and summarize transcript-level estimates for transcript- and gene-level analysis Description: Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts. biocViews: DataImport, Preprocessing, RNASeq, Transcriptomics, Transcription, GeneExpression, ImmunoOncology Author: Michael Love [cre,aut], Charlotte Soneson [aut], Mark Robinson [aut], Rob Patro [ctb], Andrew Parker Morgan [ctb], Ryan C. Thompson [ctb], Matt Shirley [ctb], Avi Srivastava [ctb] Maintainer: Michael Love URL: https://github.com/thelovelab/tximport VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximport git_branch: devel git_last_commit: 85b6972 git_last_commit_date: 2026-04-13 Date/Publication: 2026-04-20 source.ver: src/contrib/tximport_1.39.2.tar.gz vignettes: vignettes/tximport/inst/doc/tximport.html vignetteTitles: Importing transcript abundance datasets with tximport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximport/inst/doc/tximport.R importsMe: alevinQC, BgeeCall, CleanUpRNAseq, DifferentialRegulation, EventPointer, IsoformSwitchAnalyzeR, singleCellTK, TDbasedUFE, tximeta, cpam, EZbakR suggestsMe: BANDITS, DESeq2, variancePartition dependencyCount: 3 Package: UCell Version: 2.15.1 Depends: R(>= 4.3.0) Imports: methods, data.table(>= 1.13.6), Matrix, stats, BiocParallel, BiocNeighbors, SingleCellExperiment, SummarizedExperiment Suggests: scater, scRNAseq, reshape2, patchwork, ggplot2, BiocStyle, Seurat(>= 5.0.0), SeuratObject(>= 5.0.0), knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: ee4111e1afa5aaff7bf5094e547a9647 NeedsCompilation: no Title: Rank-based signature enrichment analysis for single-cell data Description: UCell is a package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with SingleCellExperiment and Seurat objects. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, GeneExpression, CellBasedAssays Author: Massimo Andreatta [aut, cre] (ORCID: ), Santiago Carmona [aut] (ORCID: ) Maintainer: Massimo Andreatta URL: https://github.com/carmonalab/UCell VignetteBuilder: knitr BugReports: https://github.com/carmonalab/UCell/issues git_url: https://git.bioconductor.org/packages/UCell git_branch: devel git_last_commit: 76c2c11 git_last_commit_date: 2026-02-11 Date/Publication: 2026-04-20 source.ver: src/contrib/UCell_2.15.1.tar.gz vignettes: vignettes/UCell/inst/doc/UCell_parameters.html, vignettes/UCell/inst/doc/UCell_sce.html, vignettes/UCell/inst/doc/UCell_Seurat.html, vignettes/UCell/inst/doc/UCell_vignette_basic.html vignetteTitles: 4. Some important parameters for UCell, 2. Using UCell with SingleCellExperiment, 3. Using UCell with Seurat, 1. Gene signature scoring with UCell hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/UCell/inst/doc/UCell_parameters.R, vignettes/UCell/inst/doc/UCell_sce.R, vignettes/UCell/inst/doc/UCell_Seurat.R, vignettes/UCell/inst/doc/UCell_vignette_basic.R importsMe: scGate suggestsMe: escape, GSABenchmark, scLANE, SCpubr dependencyCount: 41 Package: UCSC.utils Version: 1.7.1 Imports: methods, stats, httr, jsonlite, S4Vectors (>= 0.47.6) Suggests: DBI, RMariaDB, GenomeInfoDb, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: e64e96c4ec4feb2bdd41bc4eca78080d NeedsCompilation: no Title: Low-level utilities to retrieve data from the UCSC Genome Browser Description: A set of low-level utilities to retrieve data from the UCSC Genome Browser. Most functions in the package access the data via the UCSC REST API but some of them query the UCSC MySQL server directly. Note that the primary purpose of the package is to support higher-level functionalities implemented in downstream packages like GenomeInfoDb or txdbmaker. biocViews: Infrastructure, GenomeAssembly, Annotation, GenomeAnnotation, DataImport Author: Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/UCSC.utils VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/UCSC.utils/issues git_url: https://git.bioconductor.org/packages/UCSC.utils git_branch: devel git_last_commit: 54b281b git_last_commit_date: 2025-12-08 Date/Publication: 2026-04-20 source.ver: src/contrib/UCSC.utils_1.7.1.tar.gz vignettes: vignettes/UCSC.utils/inst/doc/UCSC.utils.html vignetteTitles: The UCSC.utils package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UCSC.utils/inst/doc/UCSC.utils.R importsMe: GenomeInfoDb, txdbmaker suggestsMe: GenomicRanges dependencyCount: 17 Package: UMI4Cats Version: 1.21.0 Depends: R (>= 4.1.0), SummarizedExperiment Imports: magick, cowplot, scales, GenomicRanges, ShortRead, zoo, ggplot2, reshape2, regioneR, IRanges, S4Vectors, dplyr, BSgenome, Biostrings, DESeq2, R.utils, Rsamtools, stringr, Rbowtie2, methods, GenomeInfoDb, GenomicAlignments, RColorBrewer, utils, grDevices, stats, annotate, rlang, GenomicFeatures, BiocFileCache, rappdirs, fda, BiocGenerics Suggests: knitr, rmarkdown, BiocStyle, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, tidyr, testthat License: Artistic-2.0 MD5sum: ab6ab953ba826e39c9554969dca61308 NeedsCompilation: no Title: UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data Description: UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers (UMIs) that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions. This package allows processing of UMI-4C data, starting from FastQ files provided by the sequencing facility. It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay. biocViews: QualityControl, Preprocessing, Alignment, Normalization, Visualization, Sequencing, Coverage Author: Mireia Ramos-Rodriguez [aut, cre] (ORCID: ), Marc Subirana-Granes [aut], Lorenzo Pasquali [aut] Maintainer: Mireia Ramos-Rodriguez URL: https://github.com/Pasquali-lab/UMI4Cats VignetteBuilder: knitr BugReports: https://github.com/Pasquali-lab/UMI4Cats/issues git_url: https://git.bioconductor.org/packages/UMI4Cats git_branch: devel git_last_commit: f049a0a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/UMI4Cats_1.21.0.tar.gz vignettes: vignettes/UMI4Cats/inst/doc/UMI4Cats.html vignetteTitles: Analyzing UMI-4C data with UMI4Cats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UMI4Cats/inst/doc/UMI4Cats.R dependencyCount: 148 Package: uncoverappLib Version: 1.21.0 Imports: markdown, shiny, shinyjs, shinyBS, shinyWidgets,shinycssloaders, DT, Gviz, Homo.sapiens, openxlsx, condformat, stringr, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, BiocFileCache,rappdirs, TxDb.Hsapiens.UCSC.hg19.knownGene, rlist, utils,S4Vectors, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v86, OrganismDbi, processx, Rsamtools, GenomicRanges Suggests: BiocStyle, knitr, testthat, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: 5329417cc7047aa28f45770db23c28e8 NeedsCompilation: no Title: Interactive graphical application for clinical assessment of sequence coverage at the base-pair level Description: a Shiny application containing a suite of graphical and statistical tools to support clinical assessment of low coverage regions.It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. uncoverAPP provides a statisticl summary of coverage given target file or genes name. biocViews: Software, Visualization, Annotation, Coverage Author: Emanuela Iovino [cre, aut], Tommaso Pippucci [aut] Maintainer: Emanuela Iovino URL: https://github.com/Manuelaio/uncoverappLib VignetteBuilder: knitr BugReports: https://github.com/Manuelaio/uncoverappLib/issues git_url: https://git.bioconductor.org/packages/uncoverappLib git_branch: devel git_last_commit: 99c7088 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/uncoverappLib_1.21.0.tar.gz vignettes: vignettes/uncoverappLib/inst/doc/uncoverappLib.html vignetteTitles: uncoverappLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/uncoverappLib/inst/doc/uncoverappLib.R dependencyCount: 184 Package: UNDO Version: 1.53.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: 1e83748520d0a42a2c18f1939de3bb77 NeedsCompilation: no Title: Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions Description: UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge. biocViews: Software Author: Niya Wang Maintainer: Niya Wang git_url: https://git.bioconductor.org/packages/UNDO git_branch: devel git_last_commit: a43bf55 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/UNDO_1.53.0.tar.gz vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf vignetteTitles: UNDO Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R dependencyCount: 11 Package: unifiedWMWqPCR Version: 1.47.0 Depends: methods Imports: BiocGenerics, limma, stats, graphics License: GPL (>=2) MD5sum: 83bd752f11f4584e06fc44bcbc789cab NeedsCompilation: no Title: Unified Wilcoxon-Mann Whitney Test for testing differential expression in qPCR data Description: This packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data. biocViews: DifferentialExpression, GeneExpression, MicrotitrePlateAssay, MultipleComparison, QualityControl, Software, Visualization, qPCR Author: Jan R. De Neve & Joris Meys Maintainer: Joris Meys git_url: https://git.bioconductor.org/packages/unifiedWMWqPCR git_branch: devel git_last_commit: 2c20907 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/unifiedWMWqPCR_1.47.0.tar.gz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: Using unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R dependencyCount: 9 Package: UniProt.ws Version: 2.51.1 Depends: R (>= 4.5.0) Imports: AnnotationDbi, BiocFileCache, BiocBaseUtils, BiocGenerics, httr2, jsonlite, methods, progress, rjsoncons, rlang, utils Suggests: BiocStyle, knitr, rmarkdown, tinytest License: Artistic-2.0 MD5sum: 12b894d996f38e4a566446df6c47a9c4 NeedsCompilation: no Title: R Interface to UniProt Web Services Description: The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. This package provides a collection of functions for retrieving, processing, and re-packaging UniProt web services. The package makes use of UniProt's modernized REST API and allows mapping of identifiers accross different databases. biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta Author: Marc Carlson [aut], Csaba Ortutay [ctb], Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/UniProt.ws VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/UniProt.ws/issues git_url: https://git.bioconductor.org/packages/UniProt.ws git_branch: devel git_last_commit: cbc13ee git_last_commit_date: 2026-02-04 Date/Publication: 2026-04-20 source.ver: src/contrib/UniProt.ws_2.51.1.tar.gz vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.html vignetteTitles: UniProt.ws: A package for retrieving data from the UniProt web service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UniProt.ws/inst/doc/UniProt.ws.R importsMe: dagLogo, drugTargetInteractions, ginmappeR, immunogenViewer suggestsMe: autonomics, cleaver, qPLEXanalyzer dependencyCount: 63 Package: Uniquorn Version: 2.31.0 Depends: R (>= 3.5) Imports: stringr, R.utils, WriteXLS, stats, doParallel, foreach, GenomicRanges, IRanges, VariantAnnotation, data.table Suggests: testthat, knitr, rmarkdown, BiocGenerics License: Artistic-2.0 MD5sum: 43334563dfaab53f2ecc140578cd99dc NeedsCompilation: no Title: Identification of cancer cell lines based on their weighted mutational/ variational fingerprint Description: 'Uniquorn' enables users to identify cancer cell lines. Cancer cell line misidentification and cross-contamination reprents a significant challenge for cancer researchers. The identification is vital and in the frame of this package based on the locations/ loci of somatic and germline mutations/ variations. The input format is vcf/ vcf.gz and the files have to contain a single cancer cell line sample (i.e. a single member/genotype/gt column in the vcf file). biocViews: ImmunoOncology, StatisticalMethod, WholeGenome, ExomeSeq Author: Raik Otto Maintainer: Raik Otto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Uniquorn git_branch: devel git_last_commit: baa8db3 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Uniquorn_2.31.0.tar.gz vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html vignetteTitles: Uniquorn vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 88 Package: universalmotif Version: 1.29.2 Depends: R (>= 4.1.0) Imports: methods, stats, utils, MASS, ggplot2, yaml, IRanges, Rcpp, Biostrings, BiocGenerics, S4Vectors, rlang, grid, MatrixGenerics LinkingTo: Rcpp, RcppThread Suggests: spelling, knitr, bookdown, TFBSTools, rmarkdown, MotifDb, testthat, BiocParallel, seqLogo, motifStack, dplyr, ape, ggtree, processx, ggseqlogo, cowplot, GenomicRanges, ggbio Enhances: PWMEnrich, rGADEM License: GPL-3 MD5sum: 3ba256276e8b8464a7a4a2859c7ec12c NeedsCompilation: yes Title: Import, Modify, and Export Motifs with R Description: Allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others. biocViews: MotifAnnotation, MotifDiscovery, DataImport, GeneRegulation Author: Benjamin Jean-Marie Tremblay [aut, cre] (ORCID: ), Spencer Nystrom [ctb] (ORCID: ) Maintainer: Benjamin Jean-Marie Tremblay URL: https://bioconductor.org/packages/universalmotif/ VignetteBuilder: knitr BugReports: https://github.com/bjmt/universalmotif/issues git_url: https://git.bioconductor.org/packages/universalmotif git_branch: devel git_last_commit: 47a70fe git_last_commit_date: 2026-04-10 Date/Publication: 2026-04-20 source.ver: src/contrib/universalmotif_1.29.2.tar.gz vignettes: vignettes/universalmotif/inst/doc/Introduction.pdf, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.pdf, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.pdf, vignettes/universalmotif/inst/doc/MotifManipulation.pdf, vignettes/universalmotif/inst/doc/SequenceSearches.pdf vignetteTitles: Introduction to "universalmotif", Introduction to sequence motifs, Motif comparisons and P-values, Motif import,, export,, and manipulation, Sequence manipulation and scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/universalmotif/inst/doc/Introduction.R, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.R, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.R, vignettes/universalmotif/inst/doc/MotifManipulation.R, vignettes/universalmotif/inst/doc/SequenceSearches.R importsMe: ChIPpeakAnno, circRNAprofiler, memes, MotifPeeker, SEMPLR suggestsMe: epiSeeker dependencyCount: 39 Package: updateObject Version: 1.15.0 Depends: R (>= 4.2.0), methods, BiocGenerics (>= 0.51.1), S4Vectors Imports: utils, digest Suggests: GenomicRanges, SummarizedExperiment, InteractionSet, SingleCellExperiment, MultiAssayExperiment, BiSeq, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 069ade1347d6cb9ce0398cb1a4fb5bea NeedsCompilation: no Title: Find/fix old serialized S4 instances Description: A set of tools built around updateObject() to work with old serialized S4 instances. The package is primarily useful to package maintainers who want to update the serialized S4 instances included in their package. This is still work-in-progress. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/updateObject SystemRequirements: git VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/updateObject/issues git_url: https://git.bioconductor.org/packages/updateObject git_branch: devel git_last_commit: 13c59ea git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/updateObject_1.15.0.tar.gz vignettes: vignettes/updateObject/inst/doc/updateObject.html vignetteTitles: A quick introduction to the updateObject package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/updateObject/inst/doc/updateObject.R dependencyCount: 9 Package: uSORT Version: 1.37.0 Depends: R (>= 3.3.0), tcltk Imports: igraph, Matrix, RANN, RSpectra, VGAM, gplots, parallel, plyr, methods, cluster, Biobase, fpc, BiocGenerics, monocle, grDevices, graphics, stats, utils Suggests: knitr, RUnit, testthat, ggplot2 License: Artistic-2.0 MD5sum: 2f60b919f60ad908ad3e11963e31e7bd NeedsCompilation: no Title: uSORT: A self-refining ordering pipeline for gene selection Description: This package is designed to uncover the intrinsic cell progression path from single-cell RNA-seq data. It incorporates data pre-processing, preliminary PCA gene selection, preliminary cell ordering, feature selection, refined cell ordering, and post-analysis interpretation and visualization. biocViews: ImmunoOncology, RNASeq, GUI, CellBiology, DNASeq Author: Mai Chan Lau, Hao Chen, Jinmiao Chen Maintainer: Hao Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/uSORT git_branch: devel git_last_commit: 8b952fe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/uSORT_1.37.0.tar.gz vignettes: vignettes/uSORT/inst/doc/uSORT_quick_start.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/uSORT/inst/doc/uSORT_quick_start.R dependencyCount: 92 Package: VAExprs Version: 1.17.0 Depends: keras, mclust Imports: SingleCellExperiment, SummarizedExperiment, tensorflow, scater, CatEncoders, DeepPINCS, purrr, DiagrammeR, stats Suggests: SC3, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: 34244d9bb07695481fb61f6c824b8007 NeedsCompilation: no Title: Generating Samples of Gene Expression Data with Variational Autoencoders Description: A fundamental problem in biomedical research is the low number of observations, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. By augmenting a few real observations with artificially generated samples, their analysis could lead to more robust and higher reproducible. One possible solution to the problem is the use of generative models, which are statistical models of data that attempt to capture the entire probability distribution from the observations. Using the variational autoencoder (VAE), a well-known deep generative model, this package is aimed to generate samples with gene expression data, especially for single-cell RNA-seq data. Furthermore, the VAE can use conditioning to produce specific cell types or subpopulations. The conditional VAE (CVAE) allows us to create targeted samples rather than completely random ones. biocViews: Software, GeneExpression, SingleCell Author: Dongmin Jung [cre, aut] (ORCID: ) Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VAExprs git_branch: devel git_last_commit: 360e803 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/VAExprs_1.17.0.tar.gz vignettes: vignettes/VAExprs/inst/doc/VAExprs.html vignetteTitles: VAExprs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VAExprs/inst/doc/VAExprs.R suggestsMe: GenProSeq dependencyCount: 202 Package: VanillaICE Version: 1.73.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.27.6), SummarizedExperiment (>= 1.5.3) Imports: MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges (>= 1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb (>= 1.11.4), crlmm, tools, stats, utils, BSgenome.Hsapiens.UCSC.hg18 Suggests: RUnit, human610quadv1bCrlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 MD5sum: 43cb7d4f99543aa33afdca28c238f4eb NeedsCompilation: yes Title: A Hidden Markov Model for high throughput genotyping arrays Description: Hidden Markov Models for characterizing chromosomal alteration in high throughput SNP arrays. biocViews: CopyNumberVariation Author: Robert Scharpf [aut, cre] Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/VanillaICE git_branch: devel git_last_commit: 22bb99f git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/VanillaICE_1.73.0.tar.gz vignettes: vignettes/VanillaICE/inst/doc/crlmmDownstream.pdf, vignettes/VanillaICE/inst/doc/VanillaICE.pdf vignetteTitles: crlmmDownstream, VanillaICE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VanillaICE/inst/doc/crlmmDownstream.R, vignettes/VanillaICE/inst/doc/VanillaICE.R dependsOnMe: MinimumDistance suggestsMe: oligoClasses dependencyCount: 95 Package: VarCon Version: 1.19.0 Depends: Biostrings, BSgenome, GenomicRanges, R (>= 4.1) Imports: methods, stats, IRanges, shiny, shinycssloaders, shinyFiles, ggplot2 Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: f2e17d34541a904147354ec5e81193af NeedsCompilation: no Title: VarCon: an R package for retrieving neighboring nucleotides of an SNV Description: VarCon is an R package which converts the positional information from the annotation of an single nucleotide variation (SNV) (either referring to the coding sequence or the reference genomic sequence). It retrieves the genomic reference sequence around the position of the single nucleotide variation. To asses, whether the SNV could potentially influence binding of splicing regulatory proteins VarCon calcualtes the HEXplorer score as an estimation. Besides, VarCon additionally reports splice site strengths of splice sites within the retrieved genomic sequence and any changes due to the SNV. biocViews: FunctionalGenomics, AlternativeSplicing Author: Johannes Ptok [aut, cre] Maintainer: Johannes Ptok VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VarCon git_branch: devel git_last_commit: 479604c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/VarCon_1.19.0.tar.gz vignettes: vignettes/VarCon/inst/doc/VarCon.html vignetteTitles: Analysing SNVs with VarCon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VarCon/inst/doc/VarCon.R dependencyCount: 101 Package: VariantAnnotation Version: 1.57.1 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), MatrixGenerics, Seqinfo, GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), Rsamtools (>= 2.25.1) Imports: utils, DBI, Biobase, S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), XVector (>= 0.29.2), Biostrings (>= 2.77.2), AnnotationDbi (>= 1.27.9), rtracklayer (>= 1.69.1), BSgenome (>= 1.77.1), GenomicFeatures (>= 1.61.4), curl LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib (>= 2.99.0) Suggests: GenomeInfoDb, RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle, knitr, magick, jsonlite, httr, rjsoncons License: Artistic-2.0 MD5sum: a623d17425448738492831cead7dc085 NeedsCompilation: yes Title: Annotation of Genetic Variants Description: Annotate variants, compute amino acid coding changes, predict coding outcomes. biocViews: DataImport, Sequencing, SNP, Annotation, Genetics, VariantAnnotation Author: Valerie Oberchain [aut], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer SystemRequirements: GNU make VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q git_url: https://git.bioconductor.org/packages/VariantAnnotation git_branch: devel git_last_commit: d9a5b88 git_last_commit_date: 2025-12-17 Date/Publication: 2026-04-20 source.ver: src/contrib/VariantAnnotation_1.57.1.tar.gz vignettes: vignettes/VariantAnnotation/inst/doc/ensemblVEP.html, vignettes/VariantAnnotation/inst/doc/filterVcf.html, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.html vignetteTitles: ensemblVEP: using the REST API with Bioconductor, 2. Using filterVcf to Select Variants from VCF Files, 1. Introduction to VariantAnnotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantAnnotation/inst/doc/ensemblVEP.R, vignettes/VariantAnnotation/inst/doc/filterVcf.R, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R dependsOnMe: alabaster.vcf, CNVrd2, deepSNV, demuxSNP, HelloRanges, myvariant, PureCN, R453Plus1Toolbox, RareVariantVis, seqCAT, SomaticSignatures, StructuralVariantAnnotation, svaNUMT, VariantFiltering, VariantTools, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, VariantToolsData, annotation, sequencing, variants, PlasmaMutationDetector importsMe: AllelicImbalance, APAlyzer, appreci8R, BadRegionFinder, BBCAnalyzer, biovizBase, biscuiteer, cardelino, CCAFE, CNVfilteR, CopyNumberPlots, crisprDesign, customProDB, DAMEfinder, decompTumor2Sig, DominoEffect, fcScan, fRagmentomics, G4SNVHunter, GA4GHclient, GenomicFiles, GenVisR, ggbio, gmapR, gwascat, gwasurvivr, icetea, igvR, karyoploteR, katdetectr, lineagespot, MungeSumstats, musicatk, MutationalPatterns, MutSeqR, parati, ProteoDisco, RAIDS, scoreInvHap, SEMPLR, signeR, SigsPack, SNPhood, svaRetro, tadar, tLOH, transmogR, TVTB, Uniquorn, UPDhmm, VCFArray, YAPSA, ZygosityPredictor, COSMIC.67, gpcp suggestsMe: alabaster.files, AnnotationHub, BiocParallel, cellbaseR, CrispRVariants, epialleleR, GenomicDataCommons, GenomicRanges, GenomicScores, GWASTools, igvShiny, ldblock, omicsPrint, podkat, Rsamtools, RVS, SeqArray, splatter, supersigs, systemPipeR, trackViewer, trio, vtpnet, AshkenazimSonChr21, GeuvadisTranscriptExpr, ldsep, MoBPS, polyRAD, SNPassoc, updog dependencyCount: 76 Package: VariantExperiment Version: 1.25.0 Depends: R (>= 3.6.0), S4Vectors (>= 0.21.24), SummarizedExperiment (>= 1.13.0), GenomicRanges, Imports: GDSArray (>= 1.11.1), DelayedDataFrame (>= 1.6.0), tools, utils, stats, methods, gdsfmt, SNPRelate, SeqArray, DelayedArray, Biostrings, IRanges Suggests: testthat, knitr, rmarkdown, markdown, BiocStyle License: GPL-3 MD5sum: 53c6c22869bbe89898add2d6d8567665 NeedsCompilation: no Title: A RangedSummarizedExperiment Container for VCF/GDS Data with GDS Backend Description: VariantExperiment is a Bioconductor package for saving data in VCF/GDS format into RangedSummarizedExperiment object. The high-throughput genetic/genomic data are saved in GDSArray objects. The annotation data for features/samples are saved in DelayedDataFrame format with mono-dimensional GDSArray in each column. The on-disk representation of both assay data and annotation data achieves on-disk reading and processing and saves memory space significantly. The interface of RangedSummarizedExperiment data format enables easy and common manipulations for high-throughput genetic/genomic data with common SummarizedExperiment metaphor in R and Bioconductor. biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, GenotypingArray Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/VariantExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/VariantExperiment/issues git_url: https://git.bioconductor.org/packages/VariantExperiment git_branch: devel git_last_commit: 230466e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/VariantExperiment_1.25.0.tar.gz vignettes: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.html vignetteTitles: VariantExperiment-class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.R dependencyCount: 35 Package: VariantTools Version: 1.53.2 Depends: R (>= 3.5.0), S4Vectors (>= 0.17.33), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.31.2), BiocGenerics, Biostrings, parallel, GenomicFeatures (>= 1.31.3), Matrix, rtracklayer (>= 1.39.7), BiocParallel, GenomeInfoDb, BSgenome, Biobase Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph, gmapR (>= 1.21.3), TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: Artistic-2.0 MD5sum: 729a21837be65cdde37a16ec134cb401 NeedsCompilation: no Title: Tools for Exploratory Analysis of Variant Calls Description: Explore, diagnose, and compare variant calls using filters. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/VariantTools git_branch: devel git_last_commit: faec495 git_last_commit_date: 2026-03-16 Date/Publication: 2026-04-20 source.ver: src/contrib/VariantTools_1.53.2.tar.gz vignettes: vignettes/VariantTools/inst/doc/tutorial.pdf, vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: tutorial.pdf, Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R suggestsMe: VariantToolsData dependencyCount: 79 Package: vbmp Version: 1.79.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) MD5sum: 8ed09bba7ff9cfc36d5b1138d418614d NeedsCompilation: no Title: Variational Bayesian Multinomial Probit Regression Description: Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior probability employing variational and sparse approximation to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. biocViews: Classification Author: Nicola Lama , Mark Girolami Maintainer: Nicola Lama URL: http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1 git_url: https://git.bioconductor.org/packages/vbmp git_branch: devel git_last_commit: 3fa9901 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/vbmp_1.79.0.tar.gz vignettes: vignettes/vbmp/inst/doc/vbmp.pdf vignetteTitles: vbmp Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vbmp/inst/doc/vbmp.R dependencyCount: 0 Package: VCFArray Version: 1.27.0 Depends: R (>= 3.6), methods, BiocGenerics, DelayedArray (>= 0.7.28) Imports: tools, GenomicRanges, VariantAnnotation (>= 1.29.3), GenomicFiles (>= 1.17.3), S4Vectors (>= 0.19.19), Rsamtools Suggests: SeqArray, BiocStyle, BiocManager, testthat, knitr, rmarkdown License: GPL-3 MD5sum: c6a8368711421aef0370b321488a1059 NeedsCompilation: no Title: Representing on-disk / remote VCF files as array-like objects Description: VCFArray extends the DelayedArray to represent VCF data entries as array-like objects with on-disk / remote VCF file as backend. Data entries from VCF files, including info fields, FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER) could be converted into VCFArray instances with different dimensions. biocViews: Infrastructure, DataRepresentation, Sequencing, VariantAnnotation Author: Qian Liu [aut, cre], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Liubuntu/VCFArray VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/VCFArray/issues git_url: https://git.bioconductor.org/packages/VCFArray git_branch: devel git_last_commit: c9fb606 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/VCFArray_1.27.0.tar.gz vignettes: vignettes/VCFArray/inst/doc/VCFArray.html vignetteTitles: VCFArray: DelayedArray objects with on-disk/remote VCF backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VCFArray/inst/doc/VCFArray.R dependencyCount: 81 Package: VDJdive Version: 1.13.0 Depends: R (>= 4.2) Imports: BiocParallel, cowplot, ggplot2, gridExtra, IRanges, Matrix, methods, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, utils LinkingTo: Rcpp Suggests: breakaway, covr, knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: e4e286a2d7d9f44aa419922c7bcaed85 NeedsCompilation: yes Title: Analysis Tools for 10X V(D)J Data Description: This package provides functions for handling and analyzing immune receptor repertoire data, such as produced by the CellRanger V(D)J pipeline. This includes reading the data into R, merging it with paired single-cell data, quantifying clonotype abundances, calculating diversity metrics, and producing common plots. It implements the E-M Algorithm for clonotype assignment, along with other methods, which makes use of ambiguous cells for improved quantification. biocViews: Software, ImmunoOncology, SingleCell, Annotation, RNASeq, TargetedResequencing Author: Kelly Street [aut, cre] (ORCID: ), Mercedeh Movassagh [aut] (ORCID: ), Jill Lundell [aut] (ORCID: ), Jared Brown [ctb], Linglin Huang [ctb], Mingzhi Ye [ctb] Maintainer: Kelly Street URL: https://github.com/kstreet13/VDJdive VignetteBuilder: knitr BugReports: https://github.com/kstreet13/VDJdive/issues git_url: https://git.bioconductor.org/packages/VDJdive git_branch: devel git_last_commit: 06997fb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/VDJdive_1.13.0.tar.gz vignettes: vignettes/VDJdive/inst/doc/workflow.html vignetteTitles: VDJdive Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VDJdive/inst/doc/workflow.R dependencyCount: 55 Package: VegaMC Version: 3.49.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods License: GPL-2 MD5sum: e54723a6395ec2a87065e5407c692bec NeedsCompilation: yes Title: VegaMC: A Package Implementing a Variational Piecewise Smooth Model for Identification of Driver Chromosomal Imbalances in Cancer Description: This package enables the detection of driver chromosomal imbalances including loss of heterozygosity (LOH) from array comparative genomic hybridization (aCGH) data. VegaMC performs a joint segmentation of a dataset and uses a statistical framework to distinguish between driver and passenger mutation. VegaMC has been implemented so that it can be immediately integrated with the output produced by PennCNV tool. In addition, VegaMC produces in output two web pages that allows a rapid navigation between both the detected regions and the altered genes. In the web page that summarizes the altered genes, the link to the respective Ensembl gene web page is reported. biocViews: aCGH, CopyNumberVariation Author: S. Morganella and M. Ceccarelli Maintainer: Sandro Morganella git_url: https://git.bioconductor.org/packages/VegaMC git_branch: devel git_last_commit: ad29267 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/VegaMC_3.49.0.tar.gz vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf vignetteTitles: VegaMC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VegaMC/inst/doc/VegaMC.R dependencyCount: 63 Package: veloviz Version: 1.17.0 Depends: R (>= 4.1) Imports: Rcpp, Matrix, igraph, mgcv, RSpectra, grDevices, graphics, stats LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 71e7c1ec736a12b35ae636100a0e901b NeedsCompilation: yes Title: VeloViz: RNA-velocity informed 2D embeddings for visualizing cell state trajectories Description: VeloViz uses each cell’s current observed and predicted future transcriptional states inferred from RNA velocity analysis to build a nearest neighbor graph between cells in the population. Edges are then pruned based on a cosine correlation threshold and/or a distance threshold and the resulting graph is visualized using a force-directed graph layout algorithm. VeloViz can help ensure that relationships between cell states are reflected in the 2D embedding, allowing for more reliable representation of underlying cellular trajectories. biocViews: Transcriptomics, Visualization, GeneExpression, Sequencing, RNASeq, DimensionReduction Author: Lyla Atta [aut, cre] (ORCID: ), Jean Fan [aut] (ORCID: ), Arpan Sahoo [aut] (ORCID: ) Maintainer: Lyla Atta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/veloviz git_branch: devel git_last_commit: 3dd61ba git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/veloviz_1.17.0.tar.gz vignettes: vignettes/veloviz/inst/doc/vignette.html vignetteTitles: Visualizing cell cycle trajectory in MERFISH data using VeloViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/veloviz/inst/doc/vignette.R dependencyCount: 23 Package: VennDetail Version: 1.27.0 Depends: R (>= 4.0.0) Imports: dplyr, DT, ggplot2, grid, gridExtra, magrittr, methods, patchwork, plotly, purrr, rlang, shiny, stats, tibble, tidyr, htmlwidgets, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0), markdown, RColorBrewer, rstudioapi License: GPL-2 MD5sum: 9608f08dc27ecc06b0479bcff5da7ce1 NeedsCompilation: no Title: Comprehensive Visualization and Analysis of Multi-Set Intersections Description: A comprehensive package for visualizing multi-set intersections and extracting detailed subset information. VennDetail generates high-resolution visualizations including traditional Venn diagrams, Venn-pie plots, and UpSet-style plots. It provides functions to extract and combine subset details with user datasets in various formats. The package is particularly useful for bioinformatics applications but can be used for any multi-set analysis. biocViews: DataRepresentation, GraphAndNetwork, Visualization, Software Author: Kai Guo [aut, cre], Brett McGregor [aut], James Porter [aut], Junguk Hur [aut] Maintainer: Kai Guo URL: https://github.com/guokai8/VennDetail VignetteBuilder: knitr BugReports: https://github.com/guokai8/VennDetail/issues git_url: https://git.bioconductor.org/packages/VennDetail git_branch: devel git_last_commit: 4ad6733 git_last_commit_date: 2026-03-05 Date/Publication: 2026-04-20 source.ver: src/contrib/VennDetail_1.27.0.tar.gz vignettes: vignettes/VennDetail/inst/doc/VennDetail.html vignetteTitles: VennDetail hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VennDetail/inst/doc/VennDetail.R dependencyCount: 78 Package: VERSO Version: 1.21.1 Depends: R (>= 4.1.0) Imports: utils, data.tree, ape, parallel, Rfast, stats Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: 4810e322d328f5b7cb579a2a911caa8d NeedsCompilation: no Title: Viral Evolution ReconStructiOn (VERSO) Description: Mutations that rapidly accumulate in viral genomes during a pandemic can be used to track the evolution of the virus and, accordingly, unravel the viral infection network. To this extent, sequencing samples of the virus can be employed to estimate models from genomic epidemiology and may serve, for instance, to estimate the proportion of undetected infected people by uncovering cryptic transmissions, as well as to predict likely trends in the number of infected, hospitalized, dead and recovered people. VERSO is an algorithmic framework that processes variants profiles from viral samples to produce phylogenetic models of viral evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a log-likelihood function. VERSO includes two separate and subsequent steps; in this package we provide an R implementation of VERSO STEP 1. biocViews: BiomedicalInformatics, Sequencing, SomaticMutation Author: Daniele Ramazzotti [aut] (ORCID: ), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] (ORCID: ) Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/VERSO VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/VERSO git_url: https://git.bioconductor.org/packages/VERSO git_branch: devel git_last_commit: abde525 git_last_commit_date: 2026-03-11 Date/Publication: 2026-04-20 source.ver: src/contrib/VERSO_1.21.1.tar.gz vignettes: vignettes/VERSO/inst/doc/v1_introduction.html, vignettes/VERSO/inst/doc/v2_running_VERSO.html vignetteTitles: Introduction, Running VERSO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VERSO/inst/doc/v1_introduction.R, vignettes/VERSO/inst/doc/v2_running_VERSO.R dependencyCount: 20 Package: vidger Version: 1.31.0 Depends: R (>= 3.5) Imports: Biobase, DESeq2, edgeR, GGally, ggplot2, ggrepel, knitr, RColorBrewer, rmarkdown, scales, stats, SummarizedExperiment, tidyr, utils Suggests: BiocStyle, testthat License: GPL-3 | file LICENSE MD5sum: 86d3e1e73423df94585b411a7bddb061 NeedsCompilation: no Title: Create rapid visualizations of RNAseq data in R Description: The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR. biocViews: ImmunoOncology, Visualization, RNASeq, DifferentialExpression, GeneExpression, ImmunoOncology Author: Brandon Monier [aut, cre], Adam McDermaid [aut], Jing Zhao [aut], Qin Ma [aut, fnd] Maintainer: Brandon Monier URL: https://github.com/btmonier/vidger, https://bioconductor.org/packages/release/bioc/html/vidger.html VignetteBuilder: knitr BugReports: https://github.com/btmonier/vidger/issues git_url: https://git.bioconductor.org/packages/vidger git_branch: devel git_last_commit: bdddd06 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/vidger_1.31.0.tar.gz vignettes: vignettes/vidger/inst/doc/vidger.html vignetteTitles: Visualizing RNA-seq data with ViDGER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vidger/inst/doc/vidger.R dependencyCount: 99 Package: viper Version: 1.45.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE MD5sum: d49a8d6649c360a4eea4fcb602b11015 NeedsCompilation: no Title: Virtual Inference of Protein-activity by Enriched Regulon analysis Description: Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/viper git_branch: devel git_last_commit: fbaf77d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/viper_1.45.0.tar.gz vignettes: vignettes/viper/inst/doc/viper.pdf vignetteTitles: Using VIPER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/viper/inst/doc/viper.R dependsOnMe: aracne.networks importsMe: diggit, RTN, diggitdata suggestsMe: decoupleR, easier, MethReg, MOMA, dorothea, vulcandata dependencyCount: 87 Package: ViSEAGO Version: 1.25.0 Depends: R (>= 3.6) Imports: data.table, AnnotationDbi, dendextend, dynamicTreeCut, GOSemSim, GO.db, heatmaply, topGO, AnnotationForge, DT, DiagrammeR, R.utils, RColorBrewer, UpSetR, biomaRt, fgsea, ggplot2, htmlwidgets, igraph, methods, plotly, scales, ComplexHeatmap, circlize Suggests: htmltools, org.Mm.eg.db, limma, Rgraphviz, BiocStyle, knitr, rmarkdown, corrplot, remotes, BiocManager, stats, utils, grDevices, processx License: GPL-3 bioconductor.org MD5sum: ce25e1d651d4f0547d4d2e1ff7900872 NeedsCompilation: no Title: ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity Description: The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. biocViews: Software, Annotation, GO, GeneSetEnrichment, MultipleComparison, Clustering, Visualization Author: Aurelien Brionne [aut, cre], Amelie Juanchich [aut], Christelle hennequet-antier [aut] Maintainer: Aurelien Brionne URL: https://www.bioconductor.org/packages/release/bioc/html/ViSEAGO.html, https://forgemia.inra.fr/UMR-BOA/ViSEAGO VignetteBuilder: knitr BugReports: https://forgemia.inra.fr/UMR-BOA/ViSEAGO/issues git_url: https://git.bioconductor.org/packages/ViSEAGO git_branch: devel git_last_commit: 646645b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ViSEAGO_1.25.0.tar.gz vignettes: vignettes/ViSEAGO/inst/doc/fgsea_alternative.html, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.html, vignettes/ViSEAGO/inst/doc/SS_choice.html, vignettes/ViSEAGO/inst/doc/ViSEAGO.html vignetteTitles: 3: fgsea_alternative, 2: mouse_bionconductor, 4: SS_choice, 1: ViSEAGO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ViSEAGO/inst/doc/fgsea_alternative.R, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.R, vignettes/ViSEAGO/inst/doc/SS_choice.R, vignettes/ViSEAGO/inst/doc/ViSEAGO.R dependencyCount: 173 Package: vissE Version: 1.19.0 Depends: R (>= 4.1) Imports: igraph, methods, plyr, ggplot2, scico, RColorBrewer, tm, ggwordcloud, GSEABase, reshape2, grDevices, ggforce, msigdb, ggrepel, textstem, tidygraph, stats, scales, ggraph Suggests: testthat, org.Hs.eg.db, org.Mm.eg.db, patchwork, singscore, knitr, rmarkdown, prettydoc, BiocStyle, pkgdown, covr License: GPL-3 MD5sum: ac4e0241907b2721626cec5e3d4849d5 NeedsCompilation: no Title: Visualising Set Enrichment Analysis Results Description: This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches. biocViews: Software, GeneExpression, GeneSetEnrichment, NetworkEnrichment, Network Author: Dharmesh D. Bhuva [aut, cre] (ORCID: ), Ahmed Mohamed [ctb] Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/vissE VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/vissE/issues git_url: https://git.bioconductor.org/packages/vissE git_branch: devel git_last_commit: 6362af1 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/vissE_1.19.0.tar.gz vignettes: vignettes/vissE/inst/doc/vissE.html vignetteTitles: vissE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vissE/inst/doc/vissE.R suggestsMe: msigdb dependencyCount: 138 Package: VISTA Version: 0.99.8 Depends: R (>= 4.3) Imports: AnnotationDbi, cli, clusterProfiler, colorspace, DESeq2, dplyr, edgeR, forcats, ggplot2, ggrepel, GGally, ggpubr, grid, matrixStats, methods, msigdbr, limma, purrr, rlang, S4Vectors, scales, stringr, SummarizedExperiment, tibble, tidyr, tidyselect, viridis Suggests: airway, BiocStyle, circlize, ComplexHeatmap, DT, EnhancedVolcano, ggpointdensity, ggridges, ggalluvial, ggcorrplot, ggrain, ggvenn, enrichplot, knitr, magrittr, patchwork, org.Hs.eg.db, org.Mm.eg.db, quarto, rmarkdown, yaml, writexl, testthat (>= 3.0.0), uwot, xCell2 License: GPL-3 MD5sum: 1f8aa7b6bdc746c939fffdd4ae1dc523 NeedsCompilation: no Title: Visualization and Integrated System for Transcriptomic Analysis Description: The VISTA (Visualization and Integrated System for Transcriptomic Analysis) platform streamlines differential expression workflows by wrapping DESeq2 and edgeR into a SummarizedExperiment-based container with consistent metadata. The package includes visualization utilities, MSigDB enrichment helpers, and optional deconvolution support to simplify interactive exploration of RNA-seq experiments. biocViews: RNASeq, DifferentialExpression, GeneExpression, Transcriptomics, Visualization Author: Chirag Parsania [aut, cre] Maintainer: Chirag Parsania URL: https://github.com/cparsania/VISTA, https://cparsania.github.io/VISTA/ VignetteBuilder: knitr BugReports: https://github.com/cparsania/VISTA/issues git_url: https://git.bioconductor.org/packages/VISTA git_branch: devel git_last_commit: 2772e28 git_last_commit_date: 2026-04-17 Date/Publication: 2026-04-20 source.ver: src/contrib/VISTA_0.99.8.tar.gz vignettes: vignettes/VISTA/inst/doc/VISTA-airway.html vignetteTitles: Complete RNA-seq Analysis Workflow with VISTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VISTA/inst/doc/VISTA-airway.R dependencyCount: 201 Package: VplotR Version: 1.21.0 Depends: R (>= 4.0), GenomicRanges, IRanges, ggplot2 Imports: cowplot, magrittr, Seqinfo, GenomeInfoDb, GenomicAlignments, RColorBrewer, zoo, Rsamtools, S4Vectors, parallel, reshape2, methods, graphics, stats Suggests: GenomicFeatures, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, testthat, covr, knitr, rmarkdown, pkgdown License: GPL (>= 3) MD5sum: c56dba4792d7f59fd6bdcff42901a838 NeedsCompilation: no Title: Set of tools to make V-plots and compute footprint profiles Description: The pattern of digestion and protection from DNA nucleases such as DNAse I, micrococcal nuclease, and Tn5 transposase can be used to infer the location of associated proteins. This package contains useful functions to analyze patterns of paired-end sequencing fragment density. VplotR facilitates the generation of V-plots and footprint profiles over single or aggregated genomic loci of interest. biocViews: NucleosomePositioning, Coverage, Sequencing, BiologicalQuestion, ATACSeq, Alignment Author: Jacques Serizay [aut, cre] (ORCID: ) Maintainer: Jacques Serizay URL: https://github.com/js2264/VplotR VignetteBuilder: knitr BugReports: https://github.com/js2264/VplotR/issues git_url: https://git.bioconductor.org/packages/VplotR git_branch: devel git_last_commit: c54c82d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/VplotR_1.21.0.tar.gz vignettes: vignettes/VplotR/inst/doc/VplotR.html vignetteTitles: VplotR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VplotR/inst/doc/VplotR.R dependencyCount: 75 Package: vsclust Version: 1.13.1 Depends: R (>= 4.2.0) Imports: matrixStats, limma, parallel, shiny, qvalue, grDevices, stats, MultiAssayExperiment, clusterProfiler, DOSE, httr, graphics LinkingTo: Rcpp Suggests: knitr, yaml, testthat (>= 3.0.0), rmarkdown, BiocStyle, httr, magick License: GPL-2 MD5sum: 3a5c233c37fc79897466462f978fc380 NeedsCompilation: yes Title: Feature-based variance-sensitive quantitative clustering Description: Feature-based variance-sensitive clustering of omics data. Optimizes cluster assignment by taking into account individual feature variance. Includes several modules for statistical testing, clustering and enrichment analysis. biocViews: Clustering, Annotation, PrincipalComponent, DifferentialExpression, Visualization, Proteomics, Metabolomics Author: Veit Schwammle [aut, cre] Maintainer: Veit Schwammle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/vsclust git_branch: devel git_last_commit: 44f2f72 git_last_commit_date: 2026-04-20 Date/Publication: 2026-04-20 source.ver: src/contrib/vsclust_1.13.1.tar.gz vignettes: vignettes/vsclust/inst/doc/Integrate_With_Bioconductor_Objects.html, vignettes/vsclust/inst/doc/Run_VSClust_Workflow.html vignetteTitles: VSClust on Bioconductor object, VSClust workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vsclust/inst/doc/Integrate_With_Bioconductor_Objects.R, vignettes/vsclust/inst/doc/Run_VSClust_Workflow.R dependencyCount: 149 Package: vsn Version: 3.79.6 Depends: R (>= 4.0.0), methods, Biobase Imports: affy, limma, lattice, ggplot2 Suggests: affydata, hgu95av2cdf, BiocStyle, knitr, rmarkdown, dplyr, testthat, hexbin License: Artistic-2.0 MD5sum: 9b49ec022d20f9d451c91b3c2a699502 NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities from single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing Author: Wolfgang Huber [aut, cre], Anja von Heydebreck [aut], Dennis Kostka [ctb], David Kreil [ctb], Hans-Ulrich Klein [ctb], Robert Gentleman [ctb], Deepayan Sarkar [ctb], Gordon Smyth [ctb], Federal Ministry of Research, Technology and Space of Germany, DHGP [fnd] Maintainer: Wolfgang Huber URL: https://github.com/Huber-group-EMBL/vsn VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/vsn/issues git_url: https://git.bioconductor.org/packages/vsn git_branch: devel git_last_commit: 3ee872b git_last_commit_date: 2026-03-27 Date/Publication: 2026-04-20 source.ver: src/contrib/vsn_3.79.6.tar.gz vignettes: vignettes/vsn/inst/doc/A-vsn.html, vignettes/vsn/inst/doc/C-likelihoodcomputations.html, vignettes/vsn/inst/doc/D-convergence.html vignetteTitles: Introduction to vsn (HTML version), Likelihood Calculations for vsn, Verifying and assessing the performance with simulated data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/A-vsn.R dependsOnMe: webbioc, rnaseqGene importsMe: arrayQualityMetrics, autonomics, bnem, Doscheda, MatrixQCvis, metaseqR2, MSnbase, NormalyzerDE, PRONE, pvca, SmartPhos, tilingArray, ExpressionNormalizationWorkflow, lfproQC suggestsMe: adSplit, beadarray, DAPAR, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, MsCoreUtils, PAA, QFeatures, qmtools, ribosomeProfilingQC, scp, twilight, estrogen, wrMisc dependencyCount: 33 Package: vtpnet Version: 0.51.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 3526debbeb3a43ce31d31fec47b1186c NeedsCompilation: no Title: variant-transcription factor-phenotype networks Description: variant-transcription factor-phenotype networks, inspired by Maurano et al., Science (2012), PMID 22955828 biocViews: Network Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/vtpnet git_branch: devel git_last_commit: 547ac9d git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/vtpnet_0.51.0.tar.gz vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf vignetteTitles: vtpnet: variant-transcription factor-network tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vtpnet/inst/doc/vtpnet.R dependencyCount: 115 Package: waddR Version: 1.25.0 Depends: R (>= 3.6.0) Imports: Rcpp (>= 1.0.1), arm (>= 1.10-1), eva, BiocFileCache (>= 2.6.0), BiocParallel, SingleCellExperiment, parallel, methods, stats LinkingTo: Rcpp, RcppArmadillo, Suggests: knitr, devtools, testthat, roxygen2, rprojroot, rmarkdown, scater License: MIT + file LICENSE MD5sum: 947dcab151c15fe89b0cbeaf6db25a5c NeedsCompilation: yes Title: Statistical tests for detecting differential distributions based on the 2-Wasserstein distance Description: The package offers statistical tests based on the 2-Wasserstein distance for detecting and characterizing differences between two distributions given in the form of samples. Functions for calculating the 2-Wasserstein distance and testing for differential distributions are provided, as well as a specifically tailored test for differential expression in single-cell RNA sequencing data. biocViews: Software, StatisticalMethod, SingleCell, DifferentialExpression Author: Roman Schefzik [aut], Julian Flesch [cre] Maintainer: Julian Flesch URL: https://github.com/goncalves-lab/waddR.git VignetteBuilder: knitr BugReports: https://github.com/goncalves-lab/waddR/issues git_url: https://git.bioconductor.org/packages/waddR git_branch: devel git_last_commit: e6c53eb git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/waddR_1.25.0.tar.gz vignettes: vignettes/waddR/inst/doc/waddR.html, vignettes/waddR/inst/doc/wasserstein_metric.html, vignettes/waddR/inst/doc/wasserstein_singlecell.html, vignettes/waddR/inst/doc/wasserstein_test.html vignetteTitles: waddR, wasserstein_metric, wasserstein_singlecell, wasserstein_test hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/waddR/inst/doc/waddR.R, vignettes/waddR/inst/doc/wasserstein_metric.R, vignettes/waddR/inst/doc/wasserstein_singlecell.R, vignettes/waddR/inst/doc/wasserstein_test.R dependencyCount: 98 Package: weaver Version: 1.77.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: 58cb6b6a172346e1420788c5b370ef6c NeedsCompilation: no Title: Tools and extensions for processing Sweave documents Description: This package provides enhancements on the Sweave() function in the base package. In particular a facility for caching code chunk results is included. biocViews: Infrastructure Author: Seth Falcon Maintainer: Seth Falcon git_url: https://git.bioconductor.org/packages/weaver git_branch: devel git_last_commit: c4894fe git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/weaver_1.77.0.tar.gz vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf vignetteTitles: Using weaver to process Sweave documents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/weaver/inst/doc/weaver_howTo.R dependencyCount: 4 Package: webbioc Version: 1.83.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocManager License: GPL (>= 2) MD5sum: 81bb02bcab6d1e67dd9fffb1c6aa0fc7 NeedsCompilation: no Title: Bioconductor Web Interface Description: An integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. (Currently only Affymetrix oligonucleotide analysis is supported.) biocViews: Infrastructure, Microarray, OneChannel, DifferentialExpression Author: Colin A. Smith Maintainer: Colin A. Smith URL: http://www.bioconductor.org/ SystemRequirements: Unix, Perl (>= 5.6.0), Netpbm git_url: https://git.bioconductor.org/packages/webbioc git_branch: devel git_last_commit: 8bbf95c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/webbioc_1.83.0.tar.gz vignettes: vignettes/webbioc/inst/doc/demoscript.pdf, vignettes/webbioc/inst/doc/webbioc.pdf vignetteTitles: webbioc Demo Script, webbioc Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 76 Package: weitrix Version: 1.23.0 Depends: R (>= 3.6), SummarizedExperiment Imports: methods, utils, stats, grDevices, assertthat, S4Vectors, DelayedArray, DelayedMatrixStats, BiocParallel, BiocGenerics, limma, topconfects, dplyr, purrr, ggplot2, rlang, scales, reshape2, splines, Ckmeans.1d.dp, glm2, RhpcBLASctl Suggests: knitr, rmarkdown, BiocStyle, tidyverse, airway, edgeR, EnsDb.Hsapiens.v86, org.Sc.sgd.db, AnnotationDbi, ComplexHeatmap, patchwork, testthat (>= 2.1.0) License: LGPL-2.1 | file LICENSE MD5sum: a87a6cda52df0bbee8a1a7d3f6df7bf4 NeedsCompilation: no Title: Tools for matrices with precision weights, test and explore weighted or sparse data Description: Data type and tools for working with matrices having precision weights and missing data. This package provides a common representation and tools that can be used with many types of high-throughput data. The meaning of the weights is compatible with usage in the base R function "lm" and the package "limma". Calibrate weights to account for known predictors of precision. Find rows with excess variability. Perform differential testing and find rows with the largest confident differences. Find PCA-like components of variation even with many missing values, rotated so that individual components may be meaningfully interpreted. DelayedArray matrices and BiocParallel are supported. biocViews: Software, DataRepresentation, DimensionReduction, GeneExpression, Transcriptomics, RNASeq, SingleCell, Regression Author: Paul Harrison [aut, cre] (ORCID: ) Maintainer: Paul Harrison VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/weitrix git_branch: devel git_last_commit: 0187a17 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/weitrix_1.23.0.tar.gz vignettes: vignettes/weitrix/inst/doc/V1_overview.html, vignettes/weitrix/inst/doc/V2_tail_length.html, vignettes/weitrix/inst/doc/V3_shift.html, vignettes/weitrix/inst/doc/V4_airway.html, vignettes/weitrix/inst/doc/V5_slam_seq.html vignetteTitles: 1. Concepts and practical details, 2. poly(A) tail length example, 3. Alternative polyadenylation, 4. RNA-Seq expression example, 5. Proportions data example with SLAM-Seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/weitrix/inst/doc/V2_tail_length.R, vignettes/weitrix/inst/doc/V3_shift.R, vignettes/weitrix/inst/doc/V4_airway.R, vignettes/weitrix/inst/doc/V5_slam_seq.R dependencyCount: 76 Package: widgetTools Version: 1.89.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: 617581be46db1a1aa071c428685e5aee NeedsCompilation: no Title: Creates an interactive tcltk widget Description: This packages contains tools to support the construction of tcltk widgets biocViews: Infrastructure Author: Jianhua Zhang Maintainer: Jianhua Zhang git_url: https://git.bioconductor.org/packages/widgetTools git_branch: devel git_last_commit: 0513252 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/widgetTools_1.89.0.tar.gz vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf vignetteTitles: widgetTools Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/widgetTools/inst/doc/widgetTools.R dependsOnMe: tkWidgets importsMe: OLINgui, SeqFeatR suggestsMe: affy dependencyCount: 3 Package: wiggleplotr Version: 1.35.2 Depends: R (>= 3.6) Imports: dplyr, ggplot2 (>= 2.2.0), GenomicRanges, rtracklayer, cowplot, assertthat, purrr, S4Vectors, IRanges, GenomeInfoDb Suggests: knitr, rmarkdown, biomaRt, GenomicFeatures, testthat, ensembldb, EnsDb.Hsapiens.v86, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi, AnnotationFilter, arrow License: Apache License 2.0 MD5sum: 77464048ecf26e4162a31848eb28287f NeedsCompilation: no Title: Make read coverage plots from BigWig files Description: Tools to visualise read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualisation of exonic read coverage. biocViews: ImmunoOncology, Coverage, RNASeq, ChIPSeq, Sequencing, Visualization, GeneExpression, Transcription, AlternativeSplicing Author: Kaur Alasoo [aut, cre] Maintainer: Kaur Alasoo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wiggleplotr git_branch: devel git_last_commit: d0fc81f git_last_commit_date: 2026-03-25 Date/Publication: 2026-04-20 source.ver: src/contrib/wiggleplotr_1.35.2.tar.gz vignettes: vignettes/wiggleplotr/inst/doc/wiggleplotr.html vignetteTitles: Introduction to wiggleplotr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wiggleplotr/inst/doc/wiggleplotr.R importsMe: chevreulPlot, chevreulShiny, factR dependencyCount: 84 Package: wpm Version: 1.21.0 Depends: R (>= 4.1.0) Imports: utils, methods, cli, Biobase, SummarizedExperiment, config, golem, shiny, DT, ggplot2, dplyr, rlang, stringr, shinydashboard, shinyWidgets, shinycustomloader, RColorBrewer, logging Suggests: MSnbase, testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 6b52d900a710f2f95ece0c2063710288 NeedsCompilation: no Title: Well Plate Maker Description: The Well-Plate Maker (WPM) is a shiny application deployed as an R package. Functions for a command-line/script use are also available. The WPM allows users to generate well plate maps to carry out their experiments while improving the handling of batch effects. In particular, it helps controlling the "plate effect" thanks to its ability to randomize samples over multiple well plates. The algorithm for placing the samples is inspired by the backtracking algorithm: the samples are placed at random while respecting specific spatial constraints. biocViews: GUI, Proteomics, MassSpectrometry, BatchEffect, ExperimentalDesign Author: Helene Borges [aut, cre], Thomas Burger [aut] Maintainer: Helene Borges URL: https://github.com/HelBor/wpm, https://bioconductor.org/packages/release/bioc/html/wpm.html VignetteBuilder: knitr BugReports: https://github.com/HelBor/wpm/issues git_url: https://git.bioconductor.org/packages/wpm git_branch: devel git_last_commit: 2fda63e git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/wpm_1.21.0.tar.gz vignettes: vignettes/wpm/inst/doc/wpm_vignette.html vignetteTitles: How to use Well Plate Maker hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wpm/inst/doc/wpm_vignette.R dependencyCount: 94 Package: Wrench Version: 1.29.0 Depends: R (>= 3.5.0) Imports: limma, matrixStats, locfit, stats, graphics Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR License: Artistic-2.0 MD5sum: c99466493a7476389de02a61b5f136e4 NeedsCompilation: no Title: Wrench normalization for sparse count data Description: Wrench is a package for normalization sparse genomic count data, like that arising from 16s metagenomic surveys. biocViews: Normalization, Sequencing, Software Author: Senthil Kumar Muthiah [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/Wrench VignetteBuilder: knitr BugReports: https://github.com/HCBravoLab/Wrench/issues git_url: https://git.bioconductor.org/packages/Wrench git_branch: devel git_last_commit: 9e3a020 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Wrench_1.29.0.tar.gz vignettes: vignettes/Wrench/inst/doc/vignette.html vignetteTitles: Wrench hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Wrench/inst/doc/vignette.R importsMe: metagenomeSeq dependencyCount: 11 Package: XAItest Version: 1.3.2 Depends: R (>= 3.5.0) Imports: limma, randomForest, kernelshap, caret, lime, DT, methods, SummarizedExperiment, ggplot2 Suggests: knitr, ggforce, shapr (>= 1.0.1), airway, xgboost, BiocGenerics, RUnit, S4Vectors License: MIT + file LICENSE MD5sum: e7305f7b2c6fa727da8fab1bf8e50837 NeedsCompilation: no Title: XAItest: Enhancing Feature Discovery with eXplainable AI Description: XAItest is an R Package that identifies features using eXplainable AI (XAI) methods such as SHAP or LIME. This package allows users to compare these methods with traditional statistical tests like t-tests, empirical Bayes, and Fisher's test. Additionally, it includes simThresh, a system that enables the comparison of feature importance with p-values by incorporating calibrated simulated data. biocViews: Software, StatisticalMethod, FeatureExtraction, Classification, Regression Author: Ghislain FIEVET [aut, cre] (ORCID: ), Sébastien HERGALANT [aut] (ORCID: ) Maintainer: Ghislain FIEVET URL: https://github.com/GhislainFievet/XAItest VignetteBuilder: knitr BugReports: https://github.com/GhislainFievet/XAItest/issues git_url: https://git.bioconductor.org/packages/XAItest git_branch: devel git_last_commit: 5a750fa git_last_commit_date: 2026-03-25 Date/Publication: 2026-04-20 source.ver: src/contrib/XAItest_1.3.2.tar.gz vignettes: vignettes/XAItest/inst/doc/XAItest.html vignetteTitles: 01_XAItest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/XAItest/inst/doc/XAItest.R dependencyCount: 133 Package: xCell2 Version: 1.3.0 Depends: R (>= 4.0.0) Imports: SummarizedExperiment, SingleCellExperiment, Rfast, singscore, AnnotationHub, ontologyIndex, tibble, dplyr, BiocParallel, Matrix, minpack.lm, pracma, methods, readr, magrittr, progress, quadprog Suggests: testthat, knitr, rmarkdown, ggplot2, randomForest, tidyr, EnhancedVolcano, BiocStyle License: GPL (>= 3) MD5sum: a210f8cc7fd919c5168602180b85e34c NeedsCompilation: no Title: A Tool for Generic Cell Type Enrichment Analysis Description: xCell2 provides methods for cell type enrichment analysis using cell type signatures. It includes three main functions - 1. xCell2Train for training custom references objects from bulk or single-cell RNA-seq datasets. 2. xCell2Analysis for conducting the cell type enrichment analysis using the custom reference. 3. xCell2GetLineage for identifying dependencies between different cell types using ontology. biocViews: GeneExpression, Transcriptomics, Microarray, RNASeq, SingleCell, DifferentialExpression, ImmunoOncology, GeneSetEnrichment Author: Almog Angel [aut, cre] (ORCID: ), Dvir Aran [aut] (ORCID: ) Maintainer: Almog Angel URL: https://github.com/AlmogAngel/xCell2 VignetteBuilder: knitr BugReports: https://github.com/AlmogAngel/xCell2/issues git_url: https://git.bioconductor.org/packages/xCell2 git_branch: devel git_last_commit: c6b6c48 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/xCell2_1.3.0.tar.gz vignettes: vignettes/xCell2/inst/doc/xCell2-vignette.html vignetteTitles: Introduction to xCell2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xCell2/inst/doc/xCell2-vignette.R suggestsMe: VISTA dependencyCount: 145 Package: xcore Version: 1.15.0 Depends: R (>= 4.2) Imports: DelayedArray (>= 0.18.0), edgeR (>= 3.34.1), foreach (>= 1.5.1), GenomicRanges (>= 1.44.0), glmnet (>= 4.1.2), IRanges (>= 2.26.0), iterators (>= 1.0.13), magrittr (>= 2.0.1), Matrix (>= 1.3.4), methods (>= 4.1.1), MultiAssayExperiment (>= 1.18.0), stats, S4Vectors (>= 0.30.0), utils Suggests: AnnotationHub (>= 3.0.2), BiocGenerics (>= 0.38.0), BiocParallel (>= 1.28), BiocStyle (>= 2.20.2), data.table (>= 1.14.0), devtools (>= 2.4.2), doParallel (>= 1.0.16), ExperimentHub (>= 2.2.0), knitr (>= 1.37), pheatmap (>= 1.0.12), proxy (>= 0.4.26), ridge (>= 3.0), rmarkdown (>= 2.11), rtracklayer (>= 1.52.0), testthat (>= 3.0.0), usethis (>= 2.0.1), xcoredata License: GPL-2 MD5sum: 519d3df5927ca08ff0ce49dc7b26e006 NeedsCompilation: no Title: xcore expression regulators inference Description: xcore is an R package for transcription factor activity modeling based on known molecular signatures and user's gene expression data. Accompanying xcoredata package provides a collection of molecular signatures, constructed from publicly available ChiP-seq experiments. xcore use ridge regression to model changes in expression as a linear combination of molecular signatures and find their unknown activities. Obtained, estimates can be further tested for significance to select molecular signatures with the highest predicted effect on the observed expression changes. biocViews: GeneExpression, GeneRegulation, Epigenetics, Regression, Sequencing Author: Maciej Migdał [aut, cre] (ORCID: ), Bogumił Kaczkowski [aut] (ORCID: ) Maintainer: Maciej Migdał VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/xcore git_branch: devel git_last_commit: a7dbfb4 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/xcore_1.15.0.tar.gz vignettes: vignettes/xcore/inst/doc/xcore_vignette.html vignetteTitles: xcore vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xcore/inst/doc/xcore_vignette.R suggestsMe: xcoredata dependencyCount: 59 Package: XDE Version: 2.57.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5) Imports: BiocGenerics, genefilter, graphics, grDevices, gtools, methods, stats, utils, mvtnorm, RColorBrewer, GeneMeta, siggenes Suggests: MASS, RUnit Enhances: coda License: LGPL-2 MD5sum: 59b1161724eebd99b234dd7a1a4208a5 NeedsCompilation: yes Title: XDE: a Bayesian hierarchical model for cross-study analysis of differential gene expression Description: Multi-level model for cross-study detection of differential gene expression. biocViews: Microarray, DifferentialExpression Author: R.B. Scharpf, G. Parmigiani, A.B. Nobel, and H. Tjelmeland Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/XDE git_branch: devel git_last_commit: b5fcf9b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/XDE_2.57.0.tar.gz vignettes: vignettes/XDE/inst/doc/XDE.pdf, vignettes/XDE/inst/doc/XdeParameterClass.pdf vignetteTitles: XDE Vignette, XdeParameterClass Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XDE/inst/doc/XDE.R, vignettes/XDE/inst/doc/XdeParameterClass.R dependencyCount: 61 Package: Xeva Version: 1.27.0 Depends: R (>= 3.6) Imports: methods, stats, utils, BBmisc, Biobase, grDevices, ggplot2, scales, ComplexHeatmap, parallel, doParallel, Rmisc, grid, nlme, PharmacoGx, downloader Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 67d56ccd557c93199a06397b3730c824 NeedsCompilation: no Title: Analysis of patient-derived xenograft (PDX) data Description: The Xeva package provides efficient and powerful functions for patient-drived xenograft (PDX) based pharmacogenomic data analysis. This package contains a set of functions to perform analysis of patient-derived xenograft data. This package was developed by the BHKLab, for further information please see our documentation. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Arvind Mer [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/Xeva/issues git_url: https://git.bioconductor.org/packages/Xeva git_branch: devel git_last_commit: ca1a253 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/Xeva_1.27.0.tar.gz vignettes: vignettes/Xeva/inst/doc/Xeva.pdf vignetteTitles: The Xeva User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Xeva/inst/doc/Xeva.R dependencyCount: 159 Package: XINA Version: 1.29.0 Depends: R (>= 3.5) Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools, grDevices, graphics, utils, STRINGdb Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 0c47141d91dc5522496bcc3e63ff9d50 NeedsCompilation: no Title: Multiplexes Isobaric Mass Tagged-based Kinetics Data for Network Analysis Description: The aim of XINA is to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions. XINA imports multiple datasets, tags dataset in silico, and combines the data for subsequent subgrouping into multiple clusters. The result is a single output depicting the variation across all conditions. XINA, not only extracts coabundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs. biocViews: SystemsBiology, Proteomics, RNASeq, Network Author: Lang Ho Lee and Sasha A. Singh Maintainer: Lang Ho Lee and Sasha A. Singh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XINA git_branch: devel git_last_commit: 6e730aa git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/XINA_1.29.0.tar.gz vignettes: vignettes/XINA/inst/doc/xina_user_code.html vignetteTitles: xina_user_code hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XINA/inst/doc/xina_user_code.R dependencyCount: 62 Package: xmapbridge Version: 1.69.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: ab9fa4118c3c47b5da535c1ec5cb61b7 NeedsCompilation: no Title: Export plotting files to the xmapBridge for visualisation in X:Map Description: xmapBridge can plot graphs in the X:Map genome browser. This package exports plotting files in a suitable format. biocViews: Annotation, ReportWriting, Visualization Author: Tim Yates and Crispin J Miller Maintainer: Chris Wirth URL: http://xmap.picr.man.ac.uk, http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/xmapbridge git_branch: devel git_last_commit: 2910a82 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/xmapbridge_1.69.0.tar.gz vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf vignetteTitles: xmapbridge primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xmapbridge/inst/doc/xmapbridge.R dependencyCount: 1 Package: XVector Version: 0.51.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.43.8) Imports: methods, utils, stats, tools, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 MD5sum: 35a97e73d9e18c1b0f375aca8e431db1 NeedsCompilation: yes Title: Foundation of external vector representation and manipulation in Bioconductor Description: Provides memory efficient S4 classes for storing sequences "externally" (e.g. behind an R external pointer, or on disk). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès and Patrick Aboyoun Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/XVector BugReports: https://github.com/Bioconductor/XVector/issues git_url: https://git.bioconductor.org/packages/XVector git_branch: devel git_last_commit: c527085 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/XVector_0.51.0.tar.gz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, triplex importsMe: Bioc.gff, BSgenome, ChIPsim, CNEr, compEpiTools, crisprScore, dada2, DECIPHER, gcrma, GenomAutomorphism, GenomicFeatures, Gviz, HiLDA, IONiseR, IsoformSwitchAnalyzeR, kebabs, MatrixRider, Modstrings, monaLisa, ProteoDisco, R453Plus1Toolbox, ribosomeProfilingQC, Rsamtools, rtracklayer, SparseArray, Structstrings, TFBSTools, tracktables, tRNA, tRNAscanImport, VariantAnnotation suggestsMe: CNVMetrics, fastRanges, IRanges, IWTomics, LOLA, musicatk, inDAGO linksToMe: Bioc.gff, Biostrings, CNEr, DECIPHER, kebabs, MatrixRider, posDemux, pwalign, Rsamtools, rtracklayer, ShortRead, SparseArray, triplex, VariantAnnotation, VariantFiltering dependencyCount: 10 Package: yamss Version: 1.37.0 Depends: R (>= 4.3.0), methods, BiocGenerics (>= 0.15.3), SummarizedExperiment Imports: IRanges, stats, S4Vectors, EBImage, Matrix, mzR, data.table, grDevices, limma Suggests: BiocStyle, knitr, rmarkdown, digest, mtbls2, testthat License: Artistic-2.0 MD5sum: 1a1da4e9364de2e2d4a680f3560f011e NeedsCompilation: no Title: Tools for high-throughput metabolomics Description: Tools to analyze and visualize high-throughput metabolomics data aquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis. At the core of these methods is a peak detection phase that pools information across all samples simultaneously. This is in contrast to other methods that detect peaks in a sample-by-sample basis. biocViews: MassSpectrometry, Metabolomics, PeakDetection, Software Author: Leslie Myint [cre, aut] (ORCID: ), Kasper Daniel Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/yamss VignetteBuilder: knitr BugReports: https://github.com/hansenlab/yamss/issues git_url: https://git.bioconductor.org/packages/yamss git_branch: devel git_last_commit: 9695dd5 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/yamss_1.37.0.tar.gz vignettes: vignettes/yamss/inst/doc/yamss.html vignetteTitles: yamss User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yamss/inst/doc/yamss.R dependencyCount: 70 Package: ZarrArray Version: 0.99.4 Depends: R (>= 3.4), methods, SparseArray, DelayedArray Imports: stats, tools, BiocGenerics, S4Vectors, IRanges, S4Arrays, Rarr (>= 1.11.33) Suggests: HDF5Array, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 3edefa8f587beba5c151eb1b2e3382fb NeedsCompilation: no Title: Bring Zarr datasets in R as DelayedArray objects Description: The ZarrArray package leverages the Rarr package to bring Zarr datasets in R as DelayedArray objects. The main class in the package is the ZarrArray class. A ZarrArray object is an array-like object that represents a Zarr dataset in R. ZarrArray objects are DelayedArray derivatives and therefore support all operations (delayed or block-processed) supported by DelayedArray objects. biocViews: Infrastructure, DataRepresentation, DataImport Author: Hervé Pagès [aut, cre] (ORCID: ), Mike Smith [aut] (ORCID: ), Hugo Gruson [aut] (ORCID: ), Artür Manukyan [aut] (ORCID: ), Levi Waldron [fnd] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/ZarrArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ZarrArray/issues git_url: https://git.bioconductor.org/packages/ZarrArray git_branch: devel git_last_commit: d3bb916 git_last_commit_date: 2026-04-16 Date/Publication: 2026-04-20 source.ver: src/contrib/ZarrArray_0.99.4.tar.gz vignettes: vignettes/ZarrArray/inst/doc/ZarrArray_overview.html vignetteTitles: ZarrArray overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ZarrArray/inst/doc/ZarrArray_overview.R suggestsMe: Rarr dependencyCount: 46 Package: zFPKM Version: 1.33.0 Depends: R (>= 3.4.0) Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment Suggests: knitr, limma, edgeR, GEOquery, stringr, printr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 030f342990b7bb36dea723fe4871128d NeedsCompilation: no Title: A suite of functions to facilitate zFPKM transformations Description: Perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113). Reference recommends using zFPKM > -3 to select expressed genes. Validated with encode open/closed chromosome data. Works well for gene level data using FPKM or TPM. Does not appear to calibrate well for transcript level data. biocViews: ImmunoOncology, RNASeq, FeatureExtraction, Software, GeneExpression Author: Ron Ammar [aut, cre], John Thompson [aut] Maintainer: Ron Ammar URL: https://github.com/ronammar/zFPKM/ VignetteBuilder: knitr BugReports: https://github.com/ronammar/zFPKM/issues git_url: https://git.bioconductor.org/packages/zFPKM git_branch: devel git_last_commit: a6e7c5c git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/zFPKM_1.33.0.tar.gz vignettes: vignettes/zFPKM/inst/doc/zFPKM.html vignetteTitles: Introduction to zFPKM Transformation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zFPKM/inst/doc/zFPKM.R suggestsMe: DGEobj.utils dependencyCount: 55 Package: zinbwave Version: 1.33.0 Depends: R (>= 3.4), methods, SummarizedExperiment, SingleCellExperiment Imports: BiocParallel, softImpute, stats, genefilter, edgeR, Matrix Suggests: knitr, rmarkdown, testthat, matrixStats, magrittr, scRNAseq, ggplot2, biomaRt, BiocStyle, Rtsne, DESeq2, sparseMatrixStats License: Artistic-2.0 MD5sum: 3a506f7afc2995381ff8e18c118dbe53 NeedsCompilation: no Title: Zero-Inflated Negative Binomial Model for RNA-Seq Data Description: Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data. biocViews: ImmunoOncology, DimensionReduction, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny Perraudeau [aut], Jean-Philippe Vert [aut], Clara Bagatin [aut] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/drisso/zinbwave/issues git_url: https://git.bioconductor.org/packages/zinbwave git_branch: devel git_last_commit: dee1bb8 git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/zinbwave_1.33.0.tar.gz vignettes: vignettes/zinbwave/inst/doc/intro.html vignetteTitles: zinbwave Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/zinbwave/inst/doc/intro.R importsMe: benchdamic, clusterExperiment, scBFA, singleCellTK suggestsMe: MAST, splatter dependencyCount: 74 Package: zitools Version: 1.5.0 Depends: R (>= 4.4.0), methods Imports: phyloseq, pscl, ggplot2, MatrixGenerics, SummarizedExperiment, stats, VGAM, matrixStats, tidyr, tibble, dplyr, DESeq2, reshape2, RColorBrewer, magrittr, BiocGenerics, graphics, utils Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), tidyverse, microbiome License: BSD_3_clause + file LICENSE MD5sum: e3db92af3b2719b1cb48ba0bf8ecb803 NeedsCompilation: no Title: Analysis of zero-inflated count data Description: zitools allows for zero inflated count data analysis by either using down-weighting of excess zeros or by replacing an appropriate proportion of excess zeros with NA. Through overloading frequently used statistical functions (such as mean, median, standard deviation), plotting functions (such as boxplots or heatmap) or differential abundance tests, it allows a wide range of downstream analyses for zero-inflated data in a less biased manner. This becomes applicable in the context of microbiome analyses, where the data is often overdispersed and zero-inflated, therefore making data analysis extremly challenging. biocViews: Software, StatisticalMethod, Microbiome Author: Carlotta Meyring [aut, cre] (ORCID: ) Maintainer: Carlotta Meyring URL: https://github.com/kreutz-lab/zitools VignetteBuilder: knitr BugReports: https://github.com/kreutz-lab/zitools/issues git_url: https://git.bioconductor.org/packages/zitools git_branch: devel git_last_commit: 822919b git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/zitools_1.5.0.tar.gz vignettes: vignettes/zitools/inst/doc/zitools_tutorial.pdf vignetteTitles: An Introduction to zitools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zitools/inst/doc/zitools_tutorial.R dependencyCount: 94 Package: ZygosityPredictor Version: 1.11.0 Depends: R (>= 4.3.0) Imports: GenomicAlignments, GenomicRanges, Rsamtools, IRanges, VariantAnnotation, DelayedArray, dplyr, stringr, purrr, tibble, methods, knitr, igraph, readr, stats, magrittr, rlang Suggests: rmarkdown, testthat, BiocStyle License: GPL-2 MD5sum: 8f2c265bdc9d65e80a1ccd61fc7bf012 NeedsCompilation: no Title: Package for prediction of zygosity for variants/genes in NGS data Description: The ZygosityPredictor allows to predict how many copies of a gene are affected by small variants. In addition to the basic calculations of the affected copy number of a variant, the Zygosity-Predictor can integrate the influence of several variants on a gene and ultimately make a statement if and how many wild-type copies of the gene are left. This information proves to be of particular use in the context of translational medicine. For example, in cancer genomes, the Zygosity-Predictor can address whether unmutated copies of tumor-suppressor genes are present. Beyond this, it is possible to make this statement for all genes of an organism. The Zygosity-Predictor was primarily developed to handle SNVs and INDELs (later addressed as small-variants) of somatic and germline origin. In order not to overlook severe effects outside of the small-variant context, it has been extended with the assessment of large scale deletions, which cause losses of whole genes or parts of them. biocViews: BiomedicalInformatics, FunctionalPrediction, SomaticMutation, GenePrediction Author: Marco Rheinnecker [aut, cre] (ORCID: ), Marc Ruebsam [aut], Daniel Huebschmann [aut], Martina Froehlich [aut], Barbara Hutter [aut] Maintainer: Marco Rheinnecker VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ZygosityPredictor git_branch: devel git_last_commit: 21ba67a git_last_commit_date: 2025-10-29 Date/Publication: 2026-04-20 source.ver: src/contrib/ZygosityPredictor_1.11.0.tar.gz vignettes: vignettes/ZygosityPredictor/inst/doc/Usage.html vignetteTitles: Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ZygosityPredictor/inst/doc/Usage.R dependencyCount: 99 Package: GOstats Version: 2.77.0 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.43.2), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), AnnotationForge, Rgraphviz Suggests: hgu95av2.db (>= 1.13.0), ALL, multtest, genefilter, RColorBrewer, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 Title: Tools for manipulating GO and microarrays Description: A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations. biocViews: Annotation, GO, MultipleComparison, GeneExpression, Microarray, Pathways, GeneSetEnrichment, GraphAndNetwork Author: Robert Gentleman [aut], Seth Falcon [ctb], Robert Castelo [ctb], Sonali Kumari [ctb] (Converted vignettes from Sweave to R Markdown / HTML.), Dennis Ndubi [ctb] (Converted GOstatsHyperG vignette from Sweave to R Markdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Package: safe Version: 3.51.0 Depends: R (>= 2.4.0), AnnotationDbi, Biobase, methods, SparseM Suggests: GO.db, PFAM.db, reactome.db, hgu133a.db, breastCancerUPP, survival, foreach, doRNG, Rgraphviz, GOstats License: GPL (>= 2) Title: Significance Analysis of Function and Expression Description: SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions. biocViews: DifferentialExpression, Pathways, GeneSetEnrichment, StatisticalMethod, Software Author: William T. Barry Maintainer: Ludwig Geistlinger Package: xcms Version: 4.9.2 Depends: R (>= 4.1.0), BiocParallel (>= 1.8.0) Imports: MSnbase (>= 2.33.3), mzR (>= 2.25.3), methods, Biobase, BiocGenerics, ProtGenerics (>= 1.37.1), lattice, MassSpecWavelet (>= 1.66.0), S4Vectors, IRanges, SummarizedExperiment, MsCoreUtils (>= 1.19.2), MsFeatures, MsExperiment (>= 1.5.4), Spectra (>= 1.16.1), progress, RColorBrewer, MetaboCoreUtils (>= 1.11.2), data.table Suggests: BiocStyle, caTools, knitr (>= 1.1.0), faahKO, msdata (>= 0.25.1), ncdf4, testthat (>= 3.1.9), pander, rmarkdown, MALDIquant, pheatmap, RANN, multtest, MsBackendMgf, signal, mgcv, rhdf5 Enhances: Rgraphviz, rgl License: GPL (>= 2) + file LICENSE Title: LC-MS and GC-MS Data Analysis Description: Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Colin A. Smith [aut], Ralf Tautenhahn [aut], Steffen Neumann [aut, cre] (ORCID: ), Paul Benton [aut], Christopher Conley [aut], Johannes Rainer [aut] (ORCID: ), Michael Witting [ctb], William Kumler [aut] (ORCID: ), Philippine Louail [aut] (ORCID: ), Pablo Vangeenderhuysen [ctb] (ORCID: ), Carl Brunius [ctb] (ORCID: ) Maintainer: Steffen Neumann URL: https://github.com/sneumann/xcms VignetteBuilder: knitr BugReports: https://github.com/sneumann/xcms/issues/new Package: Category Version: 2.77.0 Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, DBI Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGGREST, karyoploteR, geneplotter, limma, lattice, RUnit, org.Sc.sgd.db, GOstats, GO.db License: Artistic-2.0 Title: Category Analysis Description: A collection of tools for performing category (gene set enrichment) analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: Robert Gentleman [aut], Seth Falcon [ctb], Deepayan Sarkar [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer Package: affycoretools Version: 1.83.0 Depends: Biobase, methods Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi, ggplot2, gplots, oligoClasses, ReportingTools, hwriter, lattice, S4Vectors, edgeR, RSQLite, BiocGenerics, DBI, Glimma Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl, rmarkdown License: Artistic-2.0 Title: Functions useful for those doing repetitive analyses with Affymetrix GeneChips Description: Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see. biocViews: ReportWriting, Microarray, OneChannel, GeneExpression Author: James W. MacDonald Maintainer: James W. MacDonald VignetteBuilder: knitr Package: lumi Version: 2.63.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures, GenomicRanges, annotate, lattice, mgcv (>= 1.4-0), nleqslv, KernSmooth, preprocessCore, RSQLite, DBI, AnnotationDbi, MASS, graphics, stats, stats4, methods Suggests: beadarray, limma, vsn, lumiBarnes, lumiHumanAll.db, lumiHumanIDMapping, genefilter, RColorBrewer License: LGPL (>= 2) NeedsCompilation: no Title: BeadArray Specific Methods for Illumina Methylation and Expression Microarrays Description: The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. biocViews: Microarray, OneChannel, Preprocessing, DNAMethylation, QualityControl, TwoChannel Author: Pan Du, Richard Bourgon, Gang Feng, Simon Lin Maintainer: Lei Huang Package: arrayMvout Version: 1.69.0 Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy Imports: mdqc, affyContam, lumi Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata, hgu133atagcdf License: Artistic-2.0 Title: multivariate outlier detection for expression array QA Description: This package supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate biocViews: Infrastructure, Microarray, QualityControl Author: Z. Gao, A. Asare, R. Wang, V. Carey Maintainer: V. Carey Package: flagme Version: 1.67.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography-massspectrometry metabolomics data. biocViews: DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli Package: CAMERA Version: 1.67.0 Depends: R (>= 2.1.0), methods, Biobase, xcms (>= 1.13.5) Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils, Hmisc, igraph Suggests: faahKO, RUnit, BiocGenerics, multtest Enhances: Rmpi, snow License: GPL (>= 2) Title: Collection of annotation related methods for mass spectrometry data Description: Annotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, isotope validation, EIC correlation based tagging of unknown adducts and fragments biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Carsten Kuhl, Ralf Tautenhahn, Hendrik Treutler, Steffen Neumann {ckuhl|htreutle|sneumann}@ipb-halle.de, rtautenh@scripps.edu Maintainer: Steffen Neumann URL: http://msbi.ipb-halle.de/msbi/CAMERA/ BugReports: https://github.com/sneumann/CAMERA/issues/new Package: methylumi Version: 2.57.0 Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2, matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, Biobase, graphics, lattice, annotate, genefilter, AnnotationDbi, minfi, stats4, illuminaio, GenomicFeatures Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, TCGAMethylation450k, IlluminaHumanMethylation450kanno.ilmn12.hg19, FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr License: GPL-2 Title: Handle Illumina methylation data Description: This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl, CpGIsland Author: Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr., Moiz Bootwalla Maintainer: Sean Davis VignetteBuilder: knitr BugReports: https://github.com/seandavi/methylumi/issues/new Package: ChIPpeakAnno Version: 3.45.3 Depends: R (>= 3.5), methods, IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), S4Vectors (>= 0.17.25) Imports: AnnotationDbi, BiocGenerics (>= 0.1.0), Biostrings (>= 2.47.6), pwalign, DBI, dplyr, GenomeInfoDb, GenomicAlignments, GenomicFeatures, RBGL, Rsamtools, SummarizedExperiment, VennDiagram, biomaRt, ggplot2, grDevices, graph, graphics, grid, InteractionSet, KEGGREST, matrixStats, multtest, regioneR, rtracklayer, stats, utils, universalmotif, stringr, tibble, tidyr, data.table, scales, ensembldb Suggests: AnnotationHub, BSgenome, limma, reactome.db, BiocManager, BiocStyle, BSgenome.Ecoli.NCBI.20080805, BSgenome.Hsapiens.UCSC.hg19, org.Ce.eg.db, org.Hs.eg.db, BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7, BSgenome.Hsapiens.UCSC.hg38, DelayedArray, idr, seqinr, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, GO.db, gplots, UpSetR, knitr, rmarkdown, reshape2, testthat, trackViewer, motifStack, OrganismDbi, BiocFileCache License: GPL (>= 2) Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments, or any experiments that result in large number of genomic interval data Description: The package encompasses a range of functions for identifying the closest gene, exon, miRNA, or custom features—such as highly conserved elements and user-supplied transcription factor binding sites. Additionally, users can retrieve sequences around the peaks and obtain enriched Gene Ontology (GO) or Pathway terms. In version 2.0.5 and beyond, new functionalities have been introduced. These include features for identifying peaks associated with bi-directional promoters along with summary statistics (peaksNearBDP), summarizing motif occurrences in peaks (summarizePatternInPeaks), and associating additional identifiers with annotated peaks or enrichedGO (addGeneIDs). The package integrates with various other packages such as biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest, and stat to enhance its analytical capabilities. biocViews: Annotation, ChIPSeq, ChIPchip Author: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Kai Hu, Haibo Liu, Junhui Li, Hervé Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe, Michael Green Maintainer: Jianhong Ou , Lihua Julie Zhu , Kai Hu , Junhui Li VignetteBuilder: knitr Package: AgiMicroRna Version: 2.61.0 Depends: R (>= 2.10),methods,Biobase,limma,affy (>= 1.22),preprocessCore,affycoretools Imports: Biobase Suggests: geneplotter,marray,gplots,gtools,gdata,codelink License: GPL-3 Title: Processing and Differential Expression Analysis of Agilent microRNA chips Description: Processing and Analysis of Agilent microRNA data biocViews: Microarray, AgilentChip, OneChannel, Preprocessing, DifferentialExpression Author: Pedro Lopez-Romero Maintainer: Pedro Lopez-Romero Package: attract Version: 1.63.0 Depends: R (>= 3.4.0), AnnotationDbi Imports: Biobase, limma, cluster, GOstats, graphics, stats, reactome.db, KEGGREST, org.Hs.eg.db, utils, methods Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) NeedsCompilation: no Title: Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape Description: This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression. biocViews: ImmunoOncology, KEGG, Reactome, GeneExpression, Pathways, GeneSetEnrichment, Microarray, RNASeq Author: Jessica Mar Maintainer: Samuel Zimmerman Package: phenoTest Version: 1.59.0 Depends: R (>= 3.6.0), Biobase, methods, annotate, Heatplus, BMA, ggplot2, Hmisc Imports: survival, limma, gplots, Category, AnnotationDbi, hopach, biomaRt, GSEABase, genefilter, xtable, annotate, mgcv, hgu133a.db, ellipse Suggests: GSEABase, GO.db Enhances: parallel, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, org.Dm.eg.db License: GPL (>=2) Title: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation. Description: Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Evarist Planet Maintainer: Evarist Planet Package: mosaics Version: 2.49.0 Depends: R (>= 3.0.0), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, Seqinfo, S4Vectors LinkingTo: Rcpp Suggests: mosaicsExample Enhances: parallel License: GPL (>= 2) Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Description: This package provides functions for fitting MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data of transcription factor binding and histone modification. biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics Author: Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles Maintainer: Dongjun Chung URL: http://groups.google.com/group/mosaics_user_group SystemRequirements: Perl Package: MSnbase Version: 2.37.3 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR (>= 2.29.3), S4Vectors, ProtGenerics (>= 1.29.1) Imports: MsCoreUtils, PSMatch (>= 1.15.3), PTMods (>= 0.99.5), BiocParallel, IRanges (>= 2.13.28), plyr, vsn, grid, stats4, affy, impute, pcaMethods, MALDIquant (>= 1.16), mzID (>= 1.5.2), digest, lattice, ggplot2, scales, MASS, Rcpp LinkingTo: Rcpp Suggests: testthat, gridExtra, microbenchmark, zoo, knitr (>= 1.1.0), Rdisop, pRoloc, pRolocdata (>= 1.43.3), magick, MsDataHub, msdata, roxygen2, rgl, rpx, AnnotationHub, BiocStyle (>= 2.5.19), rmarkdown, imputeLCMD, norm, gplots, XML, shiny, magrittr, SummarizedExperiment, Spectra License: Artistic-2.0 Title: Base Functions and Classes for Mass Spectrometry and Proteomics Description: MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto, Johannes Rainer and Sebastian Gibb with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie Cotton, Arne Smits, Martina Fisher, Ludger Goeminne, Adriaan Sticker, Lieven Clement and Pascal Maas. Maintainer: Laurent Gatto URL: https://lgatto.github.io/MSnbase VignetteBuilder: knitr BugReports: https://github.com/lgatto/MSnbase/issues Package: GSVA Version: 2.5.39 Depends: R (>= 4.0.0) Imports: methods, stats, utils, graphics, BiocGenerics, MatrixGenerics, S4Vectors, S4Arrays, HDF5Array, SparseArray, DelayedArray, IRanges, Biobase, SummarizedExperiment, GSEABase, Matrix (>= 1.5-0), DelayedMatrixStats, BiocParallel, SingleCellExperiment, BiocSingular, SpatialExperiment, sparseMatrixStats, cli, memuse LinkingTo: cli Suggests: RUnit, BiocStyle, knitr, rmarkdown, limma, RColorBrewer, org.Hs.eg.db, genefilter, edgeR, GSVAdata, sva, TENxPBMCData, TENxVisiumData, scrapper, bluster, igraph, shiny, shinydashboard, ggplot2, data.table, plotly, future, promises, shinybusy, shinyjs License: Artistic-2.0 Title: Gene Set Variation Analysis for Microarray and RNA-Seq Data Description: Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. biocViews: FunctionalGenomics, Microarray, RNASeq, Pathways, GeneSetEnrichment Author: Robert Castelo [aut, cre] (ORCID: ), Justin Guinney [aut], Alexey Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb], Axel Klenk [ctb], Chan Zuckerberg Initiative (CZI) [fnd], Spanish Ministry of Science, Innovation and Universities (MCIU) [fnd] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GSVA VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GSVA/issues Package: REDseq Version: 1.57.0 Depends: R (>= 3.5.0), BiocGenerics, BSgenome.Celegans.UCSC.ce2, multtest, Biostrings, BSgenome, ChIPpeakAnno Imports: AnnotationDbi, graphics, IRanges (>= 1.13.5), stats, utils License: GPL (>=2) Title: Analysis of high-throughput sequencing data processed by restriction enzyme digestion Description: The package includes functions to build restriction enzyme cut site (RECS) map, distribute mapped sequences on the map with five different approaches, find enriched/depleted RECSs for a sample, and identify differentially enriched/depleted RECSs between samples. biocViews: Sequencing, SequenceMatching, Preprocessing Author: Lihua Julie Zhu, Junhui Li and Thomas Fazzio Maintainer: Lihua Julie Zhu Package: rqubic Version: 1.57.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 Title: Qualitative biclustering algorithm for expression data analysis in R Description: This package implements the QUBIC algorithm introduced by Li et al. for the qualitative biclustering with gene expression data. biocViews: Clustering Author: Jitao David Zhang [aut, cre, ctb] (ORCID: ) Maintainer: Jitao David Zhang Package: DiffBind Version: 3.21.0 Depends: R (>= 4.0), GenomicRanges, SummarizedExperiment Imports: RColorBrewer, amap, gplots, grDevices, limma, GenomicAlignments, locfit, stats, utils, IRanges, lattice, systemPipeR, tools, Rcpp, dplyr, ggplot2, BiocParallel, parallel, S4Vectors, Rsamtools (>= 2.13.1), DESeq2, methods, graphics, ggrepel, apeglm, ashr, GreyListChIP LinkingTo: Rhtslib (>= 1.99.1), Rcpp Suggests: BiocStyle, testthat, xtable, rgl, XLConnect, edgeR, csaw, BSgenome, GenomeInfoDb, profileplyr, rtracklayer, grid License: Artistic-2.0 Title: Differential Binding Analysis of ChIP-Seq Peak Data Description: Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions. biocViews: Sequencing, ChIPSeq,ATACSeq, DNaseSeq, MethylSeq, RIPSeq, DifferentialPeakCalling, DifferentialMethylation, GeneRegulation, HistoneModification, PeakDetection, BiomedicalInformatics, CellBiology, MultipleComparison, Normalization, ReportWriting, Epigenetics, FunctionalGenomics Author: Rory Stark [aut, cre], Gord Brown [aut] Maintainer: Rory Stark URL: https://www.cruk.cam.ac.uk/core-facilities/bioinformatics-core/software/DiffBind SystemRequirements: GNU make Package: minfi Version: 1.57.0 Depends: methods, BiocGenerics (>= 0.15.3), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), Biostrings (>= 2.77.2), bumphunter (>= 1.1.9) Imports: S4Vectors, Seqinfo, Biobase (>= 2.33.2), IRanges, beanplot, RColorBrewer, lattice, nor1mix, siggenes, limma, preprocessCore, illuminaio (>= 0.23.2), DelayedMatrixStats (>= 1.3.4), mclust, genefilter, nlme, reshape, MASS, quadprog, data.table, GEOquery, stats, grDevices, graphics, utils, DelayedArray (>= 0.15.16), HDF5Array, BiocParallel Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1), minfiData (>= 0.18.0), minfiDataEPIC, FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest, BiocStyle, knitr, rmarkdown, tools License: Artistic-2.0 Title: Analyze Illumina Infinium DNA methylation arrays Description: Tools to analyze & visualize Illumina Infinium methylation arrays. biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, MultiChannel, TwoChannel, DataImport, Normalization, Preprocessing, QualityControl Author: Kasper Daniel Hansen [cre, aut], Martin Aryee [aut], Rafael A. Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb], E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim Triche [ctb], Shan V. Andrews [ctb], Peter F. Hickey [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/hansenlab/minfi VignetteBuilder: knitr BugReports: https://github.com/hansenlab/minfi/issues Package: ggbio Version: 1.59.0 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales, reshape2, gtable, Hmisc, biovizBase (>= 1.29.2), Biobase, S4Vectors (>= 0.13.13), IRanges (>= 2.11.16), Seqinfo, GenomeInfoDb (>= 1.45.5), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), Biostrings (>= 2.77.2), Rsamtools (>= 2.25.1), GenomicAlignments (>= 1.45.1), BSgenome (>= 1.77.1), VariantAnnotation (>= 1.55.1), rtracklayer (>= 1.69.1), GenomicFeatures (>= 1.61.4), OrganismDbi, ensembldb (>= 2.33.1), AnnotationDbi, AnnotationFilter, rlang Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat, EnsDb.Hsapiens.v75, tinytex License: Artistic-2.0 Title: Visualization tools for genomic data Description: The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries. biocViews: Infrastructure, Visualization Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence URL: https://lawremi.github.io/ggbio/ VignetteBuilder: knitr BugReports: https://github.com/lawremi/ggbio/issues Package: BAGS Version: 2.51.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Title: A Bayesian Approach for Geneset Selection Description: R package providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest. biocViews: Bayesian Author: Alejandro Quiroz-Zarate Maintainer: Alejandro Quiroz-Zarate Package: ffpe Version: 1.55.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) Title: Quality assessment and control for FFPE microarray expression data Description: Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots). biocViews: Microarray, GeneExpression, QualityControl Author: Levi Waldron Maintainer: Levi Waldron Package: iBBiG Version: 1.55.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 Title: Iterative Binary Biclustering of Genesets Description: iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes biocViews: Clustering, Annotation, GeneSetEnrichment Author: Daniel Gusenleitner, Aedin Culhane Maintainer: Aedin Culhane URL: http://bcb.dfci.harvard.edu/~aedin/publications/ Package: categoryCompare Version: 1.55.0 Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8), Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1), GOstats, annotate, colorspace, graph, RCy3 (>= 1.99.29), methods, grDevices, utils Suggests: knitr, GO.db, KEGGREST, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter, rmarkdown License: GPL-2 Title: Meta-analysis of high-throughput experiments using feature annotations Description: Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested). biocViews: Annotation, GO, MultipleComparison, Pathways, GeneExpression Author: Robert M. Flight Maintainer: Robert M. Flight URL: https://github.com/rmflight/categoryCompare SystemRequirements: Cytoscape (>= 3.6.1) (if used for visualization of results, heavily suggested) VignetteBuilder: knitr BugReports: https://github.com/rmflight/categoryCompare/issues Package: DSS Version: 2.59.1 Depends: R (>= 3.5.0), methods, Biobase, BiocParallel, bsseq, parallel Imports: utils, graphics, stats, splines Suggests: BiocStyle, knitr, rmarkdown, edgeR License: GPL Title: Dispersion shrinkage for sequencing data Description: DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions. biocViews: Sequencing, RNASeq, DNAMethylation,GeneExpression, DifferentialExpression,DifferentialMethylation Author: Hao Wu, Hao Feng Maintainer: Hao Wu , Hao Feng VignetteBuilder: knitr Package: bsseq Version: 1.47.1 Depends: R (>= 4.0), methods, BiocGenerics, GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5) Imports: IRanges (>= 2.23.9), Seqinfo, scales, stats, parallel, tools, graphics, Biobase, locfit, gtools, data.table (>= 1.11.8), S4Vectors (>= 0.27.12), R.utils (>= 2.0.0), DelayedMatrixStats (>= 1.5.2), permute, limma, DelayedArray (>= 0.15.16), Rcpp, BiocParallel, BSgenome, Biostrings, utils, HDF5Array (>= 1.19.11), rhdf5, beachmat (>= 2.23.2) LinkingTo: Rcpp, beachmat, assorthead (>= 1.1.4) Suggests: testthat, bsseqData, BiocStyle, rmarkdown, knitr, Matrix, doParallel, rtracklayer, BSgenome.Hsapiens.UCSC.hg38, batchtools License: Artistic-2.0 NeedsCompilation: yes Title: Analyze, manage and store whole-genome methylation data Description: A collection of tools for analyzing and visualizing whole-genome methylation data from sequencing. This includes whole-genome bisulfite sequencing and Oxford nanopore data. biocViews: DNAMethylation Author: Kasper Daniel Hansen [aut, cre] (ORCID: ), Peter Hickey [aut] (ORCID: ), Hervé Pagès [ctb], Aaron Lun [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues Package: RMassBank Version: 3.21.0 Depends: Rcpp Imports: assertthat, Biobase, ChemmineR, data.table, digest, dplyr, enviPat, glue, httr, httr2, logger, methods, MSnbase, mzR, purrr, R.utils, rcdk, readJDX, readr, rjson, S4Vectors, tibble, tidyselect, webchem, XML, yaml Suggests: BiocStyle, CAMERA, gplots, knitr, magick, rmarkdown, RMassBankData (>= 1.33.1), RUnit, xcms (>= 1.37.1) License: Artistic-2.0 Title: Workflow to process tandem MS files and build MassBank records Description: Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records. biocViews: ImmunoOncology, Bioinformatics, MassSpectrometry, Metabolomics, Software Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, Paul Stahlhofen, Tobias Schulze with contributions of Hendrik Treutler Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel VignetteBuilder: knitr Package: ReportingTools Version: 2.51.0 Depends: methods, knitr, utils Imports: Biobase,hwriter,Category,GOstats,limma(>= 3.17.5),lattice,AnnotationDbi,edgeR, annotate,PFAM.db, GSEABase, BiocGenerics(>= 0.1.6), grid, XML, R.utils, DESeq2(>= 1.3.41), ggplot2, ggbio, IRanges Suggests: RUnit, ALL, hgu95av2.db, org.Mm.eg.db, shiny, pasilla, org.Sc.sgd.db, rmarkdown, markdown License: Artistic-2.0 Title: Tools for making reports in various formats Description: The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools. biocViews: ImmunoOncology, Software, Visualization, Microarray, RNASeq, GO, DataRepresentation, GeneSetEnrichment Author: Jason A. Hackney, Melanie Huntley, Jessica L. Larson, Christina Chaivorapol, Gabriel Becker, and Josh Kaminker Maintainer: Jason A. Hackney , Gabriel Becker , Jessica L. Larson VignetteBuilder: utils, rmarkdown Package: pRoloc Version: 1.51.0 Depends: R (>= 3.5), MSnbase (>= 1.19.20), MLInterfaces (>= 1.67.10), methods, Rcpp (>= 0.10.3), BiocParallel Imports: stats4, Biobase, mclust (>= 4.3), caret, e1071, sampling, class, kernlab, lattice, nnet, randomForest, proxy, FNN, hexbin, BiocGenerics, stats, dendextend, RColorBrewer, scales, MASS, knitr, mvtnorm, LaplacesDemon, coda, mixtools, gtools, plyr, ggplot2, biomaRt, utils, grDevices, graphics, colorspace LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, rmarkdown, pRolocdata (>= 1.43.2), roxygen2, xtable, rgl, BiocStyle (>= 2.5.19), hpar (>= 1.41.0), dplyr, akima, fields, vegan, GO.db, AnnotationDbi, Rtsne (>= 0.13), nipals, reshape, magick, umap License: GPL-2 Title: A unifying bioinformatics framework for spatial proteomics Description: The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Classification, Clustering, QualityControl Author: Laurent Gatto [aut], Lisa Breckels [aut, cre], Thomas Burger [ctb], Samuel Wieczorek [ctb], Charlotte Hutchings [ctb], Oliver Crook [aut] Maintainer: Lisa Breckels URL: https://github.com/lgatto/pRoloc VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRoloc/issues Package: wateRmelon Version: 2.17.0 Depends: R (>= 3.5.0), Biobase, limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19, illuminaio Imports: Biobase Suggests: RPMM, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BiocStyle, knitr, rmarkdown, IlluminaHumanMethylationEPICmanifest, irlba, FlowSorted.Blood.EPIC, FlowSorted.Blood.450k, preprocessCore Enhances: minfi License: GPL-3 NeedsCompilation: no Title: Illumina DNA methylation array normalization and metrics Description: 15 flavours of betas and three performance metrics, with methods for objects produced by methylumi and minfi packages. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Leo C Schalkwyk [cre, aut], Tyler J Gorrie-Stone [aut], Ruth Pidsley [aut], Chloe CY Wong [aut], Nizar Touleimat [ctb], Matthieu Defrance [ctb], Andrew Teschendorff [ctb], Jovana Maksimovic [ctb], Louis Y El Khoury [ctb], Yucheng Wang [ctb], Alexandria Andrayas [ctb] Maintainer: Leo C Schalkwyk VignetteBuilder: knitr Package: QuasR Version: 1.51.0 Depends: R (>= 4.4), parallel, GenomicRanges, Rbowtie Imports: methods, grDevices, graphics, utils, stats, tools, BiocGenerics, S4Vectors, IRanges, Biobase, Biostrings, BSgenome, Rsamtools (>= 2.13.1), GenomicFeatures, txdbmaker, ShortRead, BiocParallel, Seqinfo, rtracklayer, GenomicFiles, AnnotationDbi LinkingTo: Rhtslib (>= 1.99.1) Suggests: Gviz, BiocStyle, GenomeInfoDbData, GenomicAlignments, Rhisat2, knitr, rmarkdown, covr, testthat License: GPL-2 Archs: x64 Title: Quantify and Annotate Short Reads in R Description: This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. Read alignments are either generated through Rbowtie (data from DNA/ChIP/ATAC/Bis-seq experiments) or Rhisat2 (data from RNA-seq experiments that require spliced alignments), or can be provided in the form of bam files. biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq, MethylSeq, Coverage, Alignment, QualityControl, ImmunoOncology Author: Anita Lerch [aut], Adam Alexander Thil SMITH [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Dimos Gaidatzis [aut], Michael Stadler [aut, cre] (ORCID: ) Maintainer: Michael Stadler URL: https://bioconductor.org/packages/QuasR SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/QuasR/issues Package: casper Version: 2.45.2 Depends: R (>= 3.6.0), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics (>= 0.31.6), coda, EBarrays, gaga, gtools, Seqinfo, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors (>= 0.9.25), sqldf, survival, txdbmaker, VGAM Enhances: parallel License: GPL (>=2) Title: Characterization of Alternative Splicing Based on Paired-End Reads Description: Infer alternative splicing from paired-end RNA-seq data. The model is based on counting paths across exons, rather than pairwise exon connections, and estimates the fragment size and start distributions non-parametrically, which improves estimation precision. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Transcription, RNASeq, Sequencing Author: David Rossell [aut, cre], Camille Stephan-Otto [aut], Manuel Kroiss [aut], Miranda Stobbe [aut], Victor Pena [aut] Maintainer: David Rossell Package: EBSeq Version: 2.9.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) Imports: Rcpp (>= 0.12.11), RcppEigen (>= 0.3.2.9.0), BH (<= 1.87.0-1) LinkingTo: Rcpp,RcppEigen,BH License: Artistic-2.0 NeedsCompilation: no Title: An R package for gene and isoform differential expression analysis of RNA-seq data Description: Differential Expression analysis at both gene and isoform level using RNA-seq data biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing Author: Xiuyu Ma [cre, aut], Ning Leng [aut], Christina Kendziorski [ctb], Michael A. Newton [ctb] Maintainer: Xiuyu Ma SystemRequirements: c++14 git_url: https://github.com/wiscstatman/EBSeq git_branch: RELEASE_3_9 git_last_commit: f0aec48 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-02 Package: customProDB Version: 1.51.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi, biomaRt(>= 2.17.1) Imports: S4Vectors (>= 0.9.25), DBI, GenomeInfoDb, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.32.0), stringr, RCurl, plyr, VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite, txdbmaker, AhoCorasickTrie, methods Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search Description: Database search is the most widely used approach for peptide and protein identification in mass spectrometry-based proteomics studies. Our previous study showed that sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in the samples and thus improve protein identification. More importantly, single nucleotide variations, short insertion and deletions and novel junctions identified from RNA-Seq data make protein database more complete and sample-specific. Here, we report an R package customProDB that enables the easy generation of customized databases from RNA-Seq data for proteomics search. This work bridges genomics and proteomics studies and facilitates cross-omics data integration. biocViews: ImmunoOncology, Sequencing, MassSpectrometry, Proteomics, SNP, RNASeq, Software, Transcription, AlternativeSplicing, FunctionalGenomics Author: Xiaojing Wang Maintainer: Xiaojing Wang Bo Wen Package: intansv Version: 1.51.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: MIT + file LICENSE Title: Integrative analysis of structural variations Description: This package provides efficient tools to read and integrate structural variations predicted by popular softwares. Annotation and visulation of structural variations are also implemented in the package. biocViews: Genetics, Annotation, Sequencing, Software Author: Wen Yao Maintainer: Wen Yao Package: msmsEDA Version: 1.49.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 Title: Exploratory Data Analysis of LC-MS/MS data by spectral counts Description: Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori Package: msmsTests Version: 1.49.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue Suggests: xtable License: GPL-2 Title: LC-MS/MS Differential Expression Tests Description: Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package.The three models admit blocking factors to control for nuissance variables.To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori i Font Package: ChAMP Version: 2.41.0 Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0),DMRcate, Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest, DT, RPMM Imports: prettydoc,Hmisc,globaltest,sva,illuminaio,rmarkdown,IlluminaHumanMethylation450kmanifest,IlluminaHumanMethylationEPICanno.ilm10b4.hg19, limma, DNAcopy, preprocessCore,impute, marray, wateRmelon, plyr,goseq,missMethyl,ggplot2, GenomicRanges,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly (>= 4.5.6),RColorBrewer,dendextend, matrixStats,combinat Suggests: knitr,rmarkdown License: GPL-3 NeedsCompilation: no Title: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC Description: The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber, DNAMethylation Author: Yuan Tian [cre,aut], Tiffany Morris [ctb], Lee Stirling [ctb], Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur Chakravarthy [ctb] Maintainer: Yuan Tian VignetteBuilder: knitr Package: trackViewer Version: 1.47.1 Depends: R (>= 3.5.0), grDevices, methods, GenomicRanges, grid Imports: Seqinfo, GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz, Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools, IRanges, AnnotationDbi, grImport, htmlwidgets, InteractionSet, utils, rhdf5, strawr, txdbmaker Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, org.Hs.eg.db, BiocStyle, knitr, VariantAnnotation, httr, htmltools, rmarkdown, motifStack License: GPL (>= 2) Title: A R/Bioconductor package with web interface for drawing elegant interactive tracks or lollipop plot to facilitate integrated analysis of multi-omics data Description: Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data. biocViews: Visualization Author: Jianhong Ou [aut, cre] (ORCID: ), Julie Lihua Zhu [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr Package: SomaticSignatures Version: 2.47.0 Depends: R (>= 3.1.0), VariantAnnotation, GenomicRanges, NMF Imports: S4Vectors, IRanges, Seqinfo, Biostrings, ggplot2, ggbio, reshape2, NMF, pcaMethods, Biobase, methods, proxy Suggests: testthat, knitr, parallel, GenomeInfoDb, BSgenome.Hsapiens.1000genomes.hs37d5, SomaticCancerAlterations, ggdendro, fastICA, sva License: MIT + file LICENSE Title: Somatic Signatures Description: The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms. biocViews: Sequencing, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod Author: Julian Gehring Maintainer: Julian Gehring URL: https://github.com/juliangehring/SomaticSignatures VignetteBuilder: knitr BugReports: https://support.bioconductor.org Package: COMPASS Version: 1.49.0 Depends: R (>= 3.0.3) Imports: methods, Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils, pdist, magrittr, reshape2, dplyr, tidyr, rlang, BiocStyle, rmarkdown, foreach, coda LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.33.1), flowCore, ncdfFlow, shiny, testthat, devtools, flowWorkspaceData, ggplot2, progress License: Artistic-2.0 Title: Combinatorial Polyfunctionality Analysis of Single Cells Description: COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination. biocViews: ImmunoOncology, FlowCytometry Maintainer: Greg Finak VignetteBuilder: knitr BugReports: https://github.com/RGLab/COMPASS/issues Package: DMRcate Version: 3.7.0 Depends: R (>= 4.3.0) Imports: AnnotationHub, ExperimentHub, bsseq, Seqinfo, limma, edgeR, minfi, missMethyl, GenomicRanges, plyr, Gviz, IRanges, stats, utils, S4Vectors, methods, graphics, SummarizedExperiment, biomaRt, grDevices Suggests: knitr, RUnit, BiocGenerics, GenomeInfoDb, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICv2anno.20a1.hg38, FlowSorted.Blood.EPIC, tissueTreg, DMRcatedata, EPICv2manifest License: file LICENSE NeedsCompilation: no Title: Methylation array and sequencing spatial analysis methods Description: De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions. biocViews: DifferentialMethylation, GeneExpression, Microarray, MethylationArray, Genetics, DifferentialExpression, GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel, MultipleComparison, QualityControl, TimeCourse, Sequencing, WholeGenome, Epigenetics, Coverage, Preprocessing, DataImport Maintainer: Tim Peters VignetteBuilder: knitr Package: CAFE Version: 1.47.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Title: Chromosmal Aberrations Finder in Expression data Description: Detection and visualizations of gross chromosomal aberrations using Affymetrix expression microarrays as input biocViews: GeneExpression, Microarray, OneChannel, GeneSetEnrichment Author: Sander Bollen Maintainer: Sander Bollen Package: BiocCheck Version: 1.47.25 Depends: R (>= 4.4.0) Imports: BiocBaseUtils, BiocFileCache, BiocManager, biocViews, callr, cli, codetools, commonmark, graph, httr2, knitr, methods, rvest, stringdist, tools, utils, xml2 Suggests: BiocStyle, devtools, gert, jsonlite, rmarkdown, tinytest, usethis License: Artistic-2.0 Title: Bioconductor-specific package checks Description: BiocCheck guides maintainers through Bioconductor best practicies. It runs Bioconductor-specific package checks by searching through package code, examples, and vignettes. Maintainers are required to address all errors, warnings, and most notes produced. biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut], Lori Shepherd [aut], Daniel von Twisk [ctb], Kevin Rue [ctb], Marcel Ramos [aut, cre] (ORCID: ), Leonardo Collado-Torres [ctb], Federico Marini [ctb] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/BiocCheck VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocCheck/issues Package: metaMS Version: 1.47.0 Depends: R (>= 4.0), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics, graphics, stats, utils Suggests: metaMSdata, RUnit License: GPL (>= 2) Title: MS-based metabolomics annotation pipeline Description: MS-based metabolomics data processing and compound annotation pipeline. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Ron Wehrens [aut] (author of GC-MS part, Initial Maintainer), Pietro Franceschi [aut] (author of LC-MS part), Nir Shahaf [ctb], Matthias Scholz [ctb], Georg Weingart [ctb] (development of GC-MS approach), Elisabete Carvalho [ctb] (testing and feedback of GC-MS pipeline), Yann Guitton [ctb, cre] (ORCID: ), Julien Saint-Vanne [ctb] Maintainer: Yann Guitton URL: https://github.com/yguitton/metaMS Package: VariantFiltering Version: 1.47.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.25.1), VariantAnnotation (>= 1.13.29) Imports: utils, stats, Biobase, S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Seqinfo (>= 0.99.2), GenomeInfoDb (>= 1.45.7), Biostrings (>= 2.77.2), GenomicRanges (>= 1.61.1), SummarizedExperiment (>= 1.39.1), GenomicFeatures (>= 1.61.4), Rsamtools (>= 2.25.1), BSgenome (>= 1.77.1), GenomicScores (>= 2.21.4), Gviz (>= 1.53.1), shiny, shinythemes, shinyjs, DT, shinyTree LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit, BiocStyle, org.Hs.eg.db, BSgenome.Hsapiens.1000genomes.hs37d5, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, MafDb.1Kgenomes.phase1.hs37d5, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 Title: Filtering of coding and non-coding genetic variants Description: Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc. biocViews: Genetics, Homo_sapiens, Annotation, SNP, Sequencing, HighThroughputSequencing Author: Robert Castelo [aut, cre], Dei Martinez Elurbe [ctb], Pau Puigdevall [ctb], Joan Fernandez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering BugReports: https://github.com/rcastelo/VariantFiltering/issues Package: meshr Version: 2.17.0 Depends: R (>= 4.1.0) Imports: markdown, rmarkdown, BiocStyle, knitr, methods, stats, utils, fdrtool, MeSHDbi, Category, S4Vectors, BiocGenerics, RSQLite License: Artistic-2.0 Title: Tools for conducting enrichment analysis of MeSH Description: A set of annotation maps describing the entire MeSH assembled using data from MeSH. biocViews: AnnotationData, FunctionalAnnotation, Bioinformatics, Statistics, Annotation, MultipleComparisons, MeSHDb Author: Koki Tsuyuzaki, Itoshi Nikaido, Gota Morota Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr BugReports: https://github.com/rikenbit/meshr/issues Package: MethylAid Version: 1.45.0 Depends: R (>= 3.4) Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase, grDevices, graphics, hexbin, matrixStats, minfi (>= 1.22.0), methods, RColorBrewer, shiny, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, MethylAidData, minfiData, minfiDataEPIC, RUnit License: GPL (>= 2) Title: Visual and interactive quality control of large Illumina DNA Methylation array data sets Description: A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored. biocViews: DNAMethylation, MethylationArray, Microarray, TwoChannel, QualityControl, BatchEffect, Visualization, GUI Author: Maarten van Iterson [aut, cre], Elmar Tobi[ctb], Roderick Slieker[ctb], Wouter den Hollander[ctb], Rene Luijk[ctb] and Bas Heijmans[ctb] Maintainer: L.J.Sinke VignetteBuilder: knitr Package: cosmiq Version: 1.45.0 Depends: R (>= 3.6), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Title: cosmiq - COmbining Single Masses Into Quantities Description: cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: David Fischer [aut, cre], Christian Panse [aut] (ORCID: ), Endre Laczko [ctb] Maintainer: David Fischer URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html Package: wavClusteR Version: 2.45.1 Depends: R (>= 3.2), GenomicRanges (>= 1.31.8), Rsamtools Imports: methods, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Biostrings (>= 2.47.6), foreach, GenomicFeatures (>= 1.31.3), ggplot2, Hmisc, mclust, rtracklayer (>= 1.39.7), seqinr, stringr, txdbmaker Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 Title: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data Description: The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq). biocViews: ImmunoOncology, Sequencing, Technology, RIPSeq, RNASeq, Bayesian Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio VignetteBuilder: knitr Package: pRolocGUI Version: 2.21.0 Depends: methods, R (>= 3.1.0), pRoloc (>= 1.27.6), Biobase, MSnbase (>= 2.1.11) Imports: shiny (>= 0.9.1), scales, dplyr, DT (>= 0.1.40), graphics, utils, ggplot2, shinydashboardPlus (>= 2.0.0), colourpicker, shinyhelper, shinyWidgets, shinyjs, colorspace, stats, grDevices, grid, BiocGenerics, shinydashboard Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown, testthat (>= 3.0.0) License: GPL-2 Title: Interactive visualisation of spatial proteomics data Description: The package pRolocGUI comprises functions to interactively visualise spatial proteomics data on the basis of pRoloc, pRolocdata and shiny. biocViews: Proteomics, Visualization, GUI Author: Lisa Breckels [aut, cre] (ORCID: ), Thomas Naake [aut], Laurent Gatto [aut] (ORCID: ) Maintainer: Lisa Breckels URL: https://github.com/lgatto/pRolocGUI VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRolocGUI/issues Package: shinyMethyl Version: 1.47.0 Imports: Biobase, BiocGenerics, graphics, grDevices, htmltools, MatrixGenerics, methods, minfi, RColorBrewer, shiny, stats, utils Suggests: shinyMethylData, minfiData, BiocStyle, knitr, testthat License: Artistic-2.0 Title: Interactive visualization for Illumina methylation arrays Description: Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl, MethylationArray Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/shinyMethyl VignetteBuilder: knitr BugReports: https://github.com/Jfortin1/shinyMethyl Package: missMethyl Version: 1.45.0 Depends: R (>= 3.6.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICv2anno.20a1.hg38 Imports: AnnotationDbi, BiasedUrn, Biobase, BiocGenerics, GenomeInfoDb, GenomicRanges, GO.db, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylationEPICv2manifest, IRanges, limma, methods, methylumi, minfi, org.Hs.eg.db, ruv, S4Vectors, statmod, stringr, SummarizedExperiment Suggests: BiocStyle, edgeR, knitr, minfiData, rmarkdown, tweeDEseqCountData, DMRcate, ExperimentHub License: GPL-2 Title: Analysing Illumina HumanMethylation BeadChip Data Description: Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes. biocViews: Normalization, DNAMethylation, MethylationArray, GenomicVariation, GeneticVariability, DifferentialMethylation, GeneSetEnrichment Author: Belinda Phipson and Jovana Maksimovic Maintainer: Belinda Phipson , Jovana Maksimovic , Andrew Lonsdale , Calandra Grima VignetteBuilder: knitr Package: quantro Version: 1.45.0 Depends: R (>= 4.0) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: rmarkdown, knitr, RUnit, BiocGenerics, BiocStyle License: GPL-3 Title: A test for when to use quantile normalization Description: A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing Author: Stephanie Hicks [aut, cre] (ORCID: ), Rafael Irizarry [aut] (ORCID: ) Maintainer: Stephanie Hicks VignetteBuilder: knitr Package: EnrichmentBrowser Version: 2.41.0 Depends: SummarizedExperiment, graph Imports: AnnotationDbi, BiocFileCache, BiocManager, GSEABase, GO.db, KEGGREST, KEGGgraph, Rgraphviz, S4Vectors, SPIA, edgeR, graphite, hwriter, limma, methods, pathview, safe Suggests: ALL, BiocStyle, ComplexHeatmap, DESeq2, ReportingTools, airway, biocGraph, hgu95av2.db, geneplotter, knitr, msigdbr, rmarkdown, statmod License: Artistic-2.0 Title: Seamless navigation through combined results of set-based and network-based enrichment analysis Description: The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Mirko Signorelli [ctb], Rohit Satyam [ctb], Marcel Ramos [ctb], Levi Waldron [ctb], Ralf Zimmer [aut] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/lgeistlinger/EnrichmentBrowser/issues Package: MSnID Version: 1.45.2 Depends: R (>= 2.10), Rcpp Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach, doParallel, parallel, methods, iterators, data.table, Biobase, ProtGenerics, reshape2, dplyr, mzR, BiocStyle, msmsTests, ggplot2, RUnit, BiocGenerics, Biostrings, purrr, rlang, stringr, tibble, AnnotationHub, AnnotationDbi, xtable License: Artistic-2.0 Title: Utilities for Exploration and Assessment of Confidence of LC-MSn Proteomics Identifications Description: Extracts MS/MS ID data from mzIdentML (leveraging mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. Also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc. biocViews: Proteomics, MassSpectrometry, ImmunoOncology Author: Vlad Petyuk with contributions from Laurent Gatto Maintainer: Vlad Petyuk Package: derfinderPlot Version: 1.45.0 Depends: R(>= 3.2) Imports: derfinder (>= 1.1.0), Seqinfo, GenomeInfoDb (>= 1.45.9), GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>= 1.13.13), ggplot2, graphics, grDevices, IRanges (>= 1.99.28), limma, methods, plyr, RColorBrewer, reshape2, S4Vectors (>= 0.9.38), scales, utils Suggests: biovizBase (>= 1.27.2), bumphunter (>= 1.7.6), derfinderData (>= 0.99.0), sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), org.Hs.eg.db, RefManageR, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 Title: Plotting functions for derfinder Description: This package provides plotting functions for results from the derfinder package. This helps separate the graphical dependencies required for making these plots from the core functionality of derfinder. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (ORCID: ), Andrew E. Jaffe [aut] (ORCID: ), Jeffrey T. Leek [aut, ths] (ORCID: ) Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderPlot VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderPlot Package: proBAMr Version: 1.45.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, txdbmaker, rtracklayer Suggests: GenomeInfoDbData, RUnit, BiocGenerics License: Artistic-2.0 NeedsCompilation: no Title: Generating SAM file for PSMs in shotgun proteomics data Description: Mapping PSMs back to genome. The package builds SAM file from shotgun proteomics data The package also provides function to prepare annotation from GTF file. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Software, Visualization Author: Xiaojing Wang Maintainer: Xiaojing Wang Package: MAIT Version: 1.45.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Suggests: faahKO Enhances: rgl License: GPL-2 Title: Statistical Analysis of Metabolomic Data Description: The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Software Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina Andres-LaCueva, Alexandre Perera Maintainer: Pol Sola-Santos Package: mygene Version: 1.47.0 Depends: R (>= 3.2.1), GenomicFeatures, txdbmaker Imports: methods, utils, stats, httr (>= 0.3), jsonlite (>= 0.9.7), Hmisc, sqldf, plyr, S4Vectors Suggests: BiocStyle License: Artistic-2.0 Title: Access MyGene.Info_ services Description: MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. biocViews: Annotation Author: Adam Mark, Ryan Thompson, Cyrus Afrasiabi, Chunlei Wu Maintainer: Adam Mark, Cyrus Afrasiabi, Chunlei Wu Package: seq2pathway Version: 1.43.0 Depends: R (>= 3.6.2) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 Title: a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data Description: Seq2pathway is a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data, consisting of "seq2gene" and "gene2path" components. The seq2gene links sequence-level measurements of genomic regions (including SNPs or point mutation coordinates) to gene-level scores, and the gene2pathway summarizes gene scores to pathway-scores for each sample. The seq2gene has the feasibility to assign both coding and non-exon regions to a broader range of neighboring genes than only the nearest one, thus facilitating the study of functional non-coding regions. The gene2pathway takes into account the quantity of significance for gene members within a pathway compared those outside a pathway. The output of seq2pathway is a general structure of quantitative pathway-level scores, thus allowing one to functional interpret such datasets as RNA-seq, ChIP-seq, GWAS, and derived from other next generational sequencing experiments. biocViews: Software Author: Xinan Yang ; Bin Wang Maintainer: Arjun Kinstlick Package: sincell Version: 1.43.0 Depends: R (>= 3.0.2), igraph Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2, reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster, statmod LinkingTo: Rcpp Suggests: BiocStyle, knitr, biomaRt, stringr, monocle License: GPL (>= 2) Title: R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data Description: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology Author: Miguel Julia , Amalio Telenti , Antonio Rausell Maintainer: Miguel Julia , Antonio Rausell URL: http://bioconductor.org/ VignetteBuilder: knitr Package: skewr Version: 1.43.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 Title: Visualize Intensities Produced by Illumina's Human Methylation 450k BeadChip Description: The skewr package is a tool for visualizing the output of the Illumina Human Methylation 450k BeadChip to aid in quality control. It creates a panel of nine plots. Six of the plots represent the density of either the methylated intensity or the unmethylated intensity given by one of three subsets of the 485,577 total probes. These subsets include Type I-red, Type I-green, and Type II.The remaining three distributions give the density of the Beta-values for these same three subsets. Each of the nine plots optionally displays the distributions of the "rs" SNP probes and the probes associated with imprinted genes as series of 'tick' marks located above the x-axis. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl Author: Ryan Putney [cre, aut], Steven Eschrich [aut], Anders Berglund [aut] Maintainer: Ryan Putney VignetteBuilder: knitr Package: canceR Version: 1.45.0 Depends: R (>= 4.3), tcltk, cBioPortalData Imports: GSEABase, tkrplot, geNetClassifier, RUnit, Formula, rpart, survival, Biobase, phenoTest, circlize, plyr, tidyr, dplyr, graphics, stats, utils, grDevices, R.oo, R.methodsS3 Suggests: testthat (>= 3.1), knitr, rmarkdown, BiocStyle License: GPL-2 Title: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC Description: The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC). biocViews: GUI, GeneExpression, Clustering, GO, GeneSetEnrichment, KEGG, MultipleComparison Author: Karim Mezhoud. Nuclear Safety & Security Department. Nuclear Science Center of Tunisia. Maintainer: Karim Mezhoud SystemRequirements: Tktable, BWidget VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/canceR/issues Package: TPP Version: 3.39.2 Depends: R (>= 3.4), Biobase, dplyr, magrittr, tidyr Imports: biobroom, data.table, doParallel, foreach, futile.logger, ggplot2, grDevices, gridExtra, grid, knitr, limma, MASS, mefa, nls2, openxlsx (>= 2.4.0), parallel, plyr, purrr, RColorBrewer, RCurl, reshape2, rlang, rmarkdown, splines, stats, stringr, tibble, utils, VennDiagram, VGAM Suggests: BiocStyle, testthat License: Artistic-2.0 Title: Analyze thermal proteome profiling (TPP) experiments Description: Analyze thermal proteome profiling (TPP) experiments with varying temperatures (TR) or compound concentrations (CCR). biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Dorothee Childs, Nils Kurzawa, Holger Franken, Carola Doce, Mikhail Savitski and Wolfgang Huber Maintainer: Dorothee Childs VignetteBuilder: knitr Package: conumee Version: 1.45.0 Depends: R (>= 3.0), minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges, Seqinfo Suggests: BiocStyle, knitr, rmarkdown, minfiData, RCurl License: GPL (>= 2) Title: Enhanced copy-number variation analysis using Illumina DNA methylation arrays Description: This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays. biocViews: CopyNumberVariation, DNAMethylation, MethylationArray, Microarray, Normalization, Preprocessing, QualityControl, Software Author: Volker Hovestadt, Marc Zapatka Maintainer: Volker Hovestadt VignetteBuilder: knitr Package: ENmix Version: 1.47.3 Depends: parallel,doParallel,foreach,SummarizedExperiment,stats,R (>= 3.5.0) Imports: grDevices,graphics,matrixStats,methods,utils,irlba, geneplotter,impute,minfi,RPMM,illuminaio,dynamicTreeCut,IRanges,gtools, Biobase,ExperimentHub,AnnotationHub,genefilter,gplots,quadprog,S4Vectors Suggests: minfiData, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 NeedsCompilation: no Title: Quality control and analysis tools for Illumina DNA methylation BeadChip Description: Tools for quanlity control, analysis and visulization of Illumina DNA methylation array data. biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel, Microarray, OneChannel, MethylationArray, BatchEffect, Normalization, DataImport, Regression, PrincipalComponent,Epigenetics, MultiChannel, DifferentialMethylation, ImmunoOncology Author: Zongli Xu [cre, aut], Liang Niu [aut], Jack Taylor [ctb] Maintainer: Zongli Xu URL: https://github.com/Bioconductor/ENmix VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ENmix/issues git_url: https://git.bioconductor.org/packages/ENmix Package: R3CPET Version: 1.43.0 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: methods, parallel, ggplot2, pheatmap, clValid, igraph, data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors, IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), ggbio LinkingTo: Rcpp Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene, biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny, ChIPpeakAnno License: GPL (>=2) NeedsCompilation: yes Title: 3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process Description: The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results. biocViews: NetworkInference, GenePrediction, Bayesian, GraphAndNetwork, Network, GeneExpression, HiC Author: Djekidel MN, Yang Chen et al. Maintainer: Mohamed Nadhir Djekidel VignetteBuilder: knitr BugReports: https://github.com/sirusb/R3CPET/issues Package: RnBeads Version: 2.29.1 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, grid, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr, reshape2 Imports: IRanges Suggests: Category, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RnBeads.hg19, RnBeads.mm9, RnBeads.hg38, XML, annotate, biomaRt, foreach, doParallel, ggbio, isva, mclust, mgcv, minfi, nlme, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, quadprog, rtracklayer, qvalue, sva, wateRmelon, wordcloud, qvalue, argparse, glmnet, IlluminaHumanMethylation450kanno.ilmn12.hg19, scales, missMethyl, impute, shiny, shinyjs, plotrix, hexbin, RUnit, MethylSeekR, sesame, dplyr License: GPL-3 Title: RnBeads Description: RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale. biocViews: DNAMethylation, MethylationArray, MethylSeq, Epigenetics, QualityControl, Preprocessing, BatchEffect, DifferentialMethylation, Sequencing, CpGIsland, ImmunoOncology, TwoChannel, DataImport Author: Yassen Assenov [aut], Christoph Bock [aut], Pavlo Lutsik [aut], Michael Scherer [aut], Fabian Mueller [aut, cre] Maintainer: Fabian Mueller Package: RTCGAToolbox Version: 2.41.0 Depends: R (>= 4.3.0) Imports: BiocGenerics, data.table, DelayedArray, GenomicRanges, Seqinfo, httr, methods, RaggedExperiment, RCurl, RJSONIO, rvest, S4Vectors, stats, stringr, SummarizedExperiment, TCGAutils, utils Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown License: GPL-2 Title: A new tool for exporting TCGA Firehose data Description: Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis. biocViews: DifferentialExpression, GeneExpression, Sequencing Author: Mehmet Samur [aut], Marcel Ramos [aut, cre] (ORCID: ), Ludwig Geistlinger [ctb] Maintainer: Marcel Ramos URL: http://mksamur.github.io/RTCGAToolbox/ VignetteBuilder: knitr BugReports: https://github.com/mksamur/RTCGAToolbox/issues Package: ELMER Version: 2.35.1 Depends: R (>= 3.4.0), ELMER.data (>= 2.9.3) Imports: GenomicRanges, ggplot2, reshape, grid, grDevices, graphics, methods, parallel, stats, utils, IRanges, Seqinfo, S4Vectors, GenomicFeatures, TCGAbiolinks (>= 2.23.7), plyr, Matrix, dplyr, Gviz, ComplexHeatmap, circlize, MultiAssayExperiment, SummarizedExperiment, biomaRt, doParallel, downloader, ggrepel, lattice, magrittr, readr, scales, rvest, xml2, plotly, gridExtra, rmarkdown, stringr, tibble, tidyr, progress, purrr, reshape2, ggpubr, rtracklayer (>= 1.61.2), DelayedArray Suggests: BiocStyle, AnnotationHub, ExperimentHub, knitr, testthat, data.table, DT, GenomicInteractions, webshot, R.utils, covr, sesameData License: GPL-3 Title: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes Description: ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue. biocViews: DNAMethylation, GeneExpression, MotifAnnotation, Software, GeneRegulation, Transcription, Network Author: Tiago Chedraoui Silva [aut, cre], Lijing Yao [aut], Simon Coetzee [aut], Nicole Gull [ctb], Hui Shen [ctb], Peter Laird [ctb], Peggy Farnham [aut], Dechen Li [ctb], Benjamin Berman [aut] Maintainer: Tiago Chedraoui Silva VignetteBuilder: knitr Package: RareVariantVis Version: 2.39.0 Depends: BiocGenerics, VariantAnnotation, googleVis, GenomicFeatures Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, gtools, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, SummarizedExperiment, GenomicScores Suggests: knitr License: Artistic-2.0 NeedsCompilation: no Title: A suite for analysis of rare genomic variants in whole genome sequencing data Description: Second version of RareVariantVis package aims to provide comprehensive information about rare variants for your genome data. It annotates, filters and presents genomic variants (especially rare ones) in a global, per chromosome way. For discovered rare variants CRISPR guide RNAs are designed, so the user can plan further functional studies. Large structural variants, including copy number variants are also supported. Package accepts variants directly from variant caller - for example GATK or Speedseq. Output of package are lists of variants together with adequate visualization. Visualization of variants is performed in two ways - standard that outputs png figures and interactive that uses JavaScript d3 package. Interactive visualization allows to analyze trio/family data, for example in search for causative variants in rare Mendelian diseases, in point-and-click interface. The package includes homozygous region caller and allows to analyse whole human genomes in less than 30 minutes on a desktop computer. RareVariantVis disclosed novel causes of several rare monogenic disorders, including one with non-coding causative variant - keratolythic winter erythema. biocViews: GenomicVariation, Sequencing, WholeGenome Author: Adam Gudys and Tomasz Stokowy Maintainer: Tomasz Stokowy VignetteBuilder: knitr Package: ropls Version: 1.43.0 Depends: R (>= 3.5.0) Imports: Biobase, ggplot2, graphics, grDevices, methods, plotly, stats, MultiAssayExperiment, MultiDataSet, SummarizedExperiment, utils Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, phenomis, rmarkdown, testthat License: CeCILL NeedsCompilation: no Title: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data Description: Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). biocViews: Regression, Classification, PrincipalComponent, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry, ImmunoOncology Author: Etienne A. Thevenot [aut, cre] (ORCID: ) Maintainer: Etienne A. Thevenot URL: https://doi.org/10.1021/acs.jproteome.5b00354 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ropls git_branch: master git_last_commit: 4d77cd4 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 Package: miRLAB Version: 1.41.0 Imports: methods, stats, utils, RCurl, httr, stringr, Hmisc, energy, entropy, gplots, glmnet, impute, limma, pcalg,TCGAbiolinks,dplyr,SummarizedExperiment, ctc, InvariantCausalPrediction, Category, GOstats, org.Hs.eg.db Suggests: knitr,BiocGenerics, AnnotationDbi,RUnit,rmarkdown License: GPL (>=2) NeedsCompilation: no Title: Dry lab for exploring miRNA-mRNA relationships Description: Provide tools exploring miRNA-mRNA relationships, including popular miRNA target prediction methods, ensemble methods that integrate individual methods, functions to get data from online resources, functions to validate the results, and functions to conduct enrichment analyses. biocViews: miRNA, GeneExpression, NetworkInference, Network Author: Thuc Duy Le, Junpeng Zhang, Mo Chen, Vu Viet Hoang Pham Maintainer: Thuc Duy Le URL: https://github.com/pvvhoang/miRLAB VignetteBuilder: knitr Package: variancePartition Version: 1.41.5 Depends: R (>= 4.3.0), ggplot2, limma (>= 3.62.2), BiocParallel Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, Matrix (>= 1.4.0), iterators, gplots, corpcor, reformulas, matrixStats, RhpcBLASctl, reformulas, reshape2, gtools, remaCor (>= 0.0.15), fANCOVA, aod, scales, Rdpack, rlang, lme4 (>= 2.0-1), grDevices, graphics, Biobase, methods, utils, stats Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, RUnit, cowplot, Rfast, zenith, statmod, BiocGenerics, r2glmm, readr License: GPL-2 Title: Quantify and interpret drivers of variation in multilevel gene expression experiments Description: Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures. biocViews: RNASeq, GeneExpression, GeneSetEnrichment, DifferentialExpression, BatchEffect, QualityControl, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Preprocessing, Microarray, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] (ORCID: ) Maintainer: Gabriel E. Hoffman URL: http://bioconductor.org/packages/variancePartition, https://DiseaseNeuroGenomics.github.io/variancePartition VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeuroGenomics/variancePartition/issues Package: Oscope Version: 1.41.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 NeedsCompilation: no Title: Oscope - A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq Description: Oscope is a statistical pipeline developed to identifying and recovering the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group. biocViews: ImmunoOncology, StatisticalMethod,RNASeq, Sequencing, GeneExpression Author: Ning Leng Maintainer: Ning Leng Package: ChIPComp Version: 1.41.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,rtracklayer,Seqinfo,S4Vectors Imports: Rsamtools,limma,BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9,BiocGenerics Suggests: BiocStyle,RUnit License: GPL NeedsCompilation: yes Title: Quantitative comparison of multiple ChIP-seq datasets Description: ChIPComp detects differentially bound sharp binding sites across multiple conditions considering matching control. biocViews: ChIPSeq, Sequencing, Transcription, Genetics,Coverage, MultipleComparison, DataImport Author: Hao Wu, Li Chen, Zhaohui S.Qin, Chi Wang Maintainer: Li Chen Package: AnnotationHubData Version: 1.41.1 Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub (>= 2.15.15) Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics, jsonlite, BiocManager, biocViews, BiocCheck, graph, AnnotationDbi, Biobase, Biostrings, DBI, Seqinfo, GenomeInfoDb (>= 1.45.5), OrganismDbi, RSQLite, AnnotationForge, futile.logger (>= 1.3.0), XML, RCurl Suggests: RUnit, knitr, BiocStyle, grasp2db, GenomeInfoDbData, rmarkdown, HubPub License: Artistic-2.0 Title: Transform public data resources into Bioconductor Data Structures Description: These recipes convert a wide variety and a growing number of public bioinformatic data sets into easily-used standard Bioconductor data structures. biocViews: DataImport Author: Martin Morgan [ctb], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Paul Shannon [ctb], Lori Shepherd [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Package: motifbreakR Version: 2.25.0 Depends: R (>= 4.4.0), grid, MotifDb Imports: methods, grDevices, stringr, parallel, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Biostrings, BSgenome, rtracklayer, VariantAnnotation, BiocParallel, motifStack, Gviz, matrixStats, TFMPvalue, SummarizedExperiment, pwalign, DT, bsicons, BiocFileCache, biomaRt, bslib, shiny, vroom Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP155.GRCh37, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer7, BiocStyle, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Hsapiens.UCSC.hg38 License: GPL-2 NeedsCompilation: no Title: A Package For Predicting The Disruptiveness Of Single Nucleotide Polymorphisms On Transcription Factor Binding Sites Description: We introduce motifbreakR, which allows the biologist to judge in the first place whether the sequence surrounding the polymorphism is a good match, and in the second place how much information is gained or lost in one allele of the polymorphism relative to another. MotifbreakR is both flexible and extensible over previous offerings; giving a choice of algorithms for interrogation of genomes with motifs from public sources that users can choose from; these are 1) a weighted-sum probability matrix, 2) log-probabilities, and 3) weighted by relative entropy. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within Bioconductor (currently there are 32 species, a total of 109 versions). biocViews: ChIPSeq, Visualization, MotifAnnotation, Transcription Author: Simon Gert Coetzee [aut, cre] (ORCID: ), Dennis J. Hazelett [aut] Maintainer: Simon Gert Coetzee VignetteBuilder: knitr BugReports: https://github.com/Simon-Coetzee/motifbreakR/issues Package: DAPAR Version: 1.43.0 Depends: R (>= 4.3.0) Imports: Biobase, MSnbase, DAPARdata (>= 1.30.0), utils, highcharter, foreach Suggests: testthat, BiocStyle, AnnotationDbi, clusterProfiler, graph, diptest, cluster, vioplot, visNetwork, vsn, igraph, FactoMineR, factoextra, dendextend, parallel, doParallel, Mfuzz, apcluster, forcats, readxl, openxlsx, multcomp, purrr, tibble, knitr, norm, scales, tidyverse, cp4p, imp4p (>= 1.1),lme4, dplyr, limma, preprocessCore, stringr, tidyr, impute, gplots, grDevices, reshape2, graphics, stats, methods, ggplot2, RColorBrewer, Matrix, org.Sc.sgd.db License: Artistic-2.0 NeedsCompilation: no Title: Tools for the Differential Analysis of Proteins Abundance with R Description: The package DAPAR is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required (see `Prostar` package). biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, GO, DataImport Author: c(person(given = "Samuel", family = "Wieczorek", email = "samuel.wieczorek@cea.fr", role = c("aut","cre")), person(given = "Florence", family ="Combes", email = "florence.combes@cea.fr", role = "aut"), person(given = "Thomas", family ="Burger", email = "thomas.burger@cea.fr", role = "aut"), person(given = "Vasile-Cosmin", family ="Lazar", email = "vcosminlazar@gmail.com", role = "ctb"), person(given = "Enora", family ="Fremy", email = "enora.fremy@cea.fr", role = "ctb"), person(given = "Helene", family ="Borges", email = "helene.borges@cea.fr", role = "ctb")) Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/edyp-lab/DAPAR/issues Package: iCheck Version: 1.41.0 Depends: R (>= 3.2.0), Biobase, lumi, gplots Imports: stats, graphics, preprocessCore, grDevices, randomForest, affy, limma, parallel, GeneSelectMMD, rgl, MASS, lmtest, scatterplot3d, utils License: GPL (>= 2) NeedsCompilation: no Title: QC Pipeline and Data Analysis Tools for High-Dimensional Illumina mRNA Expression Data Description: QC pipeline and data analysis tools for high-dimensional Illumina mRNA expression data. biocViews: GeneExpression, DifferentialExpression, Microarray, Preprocessing, DNAMethylation, OneChannel, TwoChannel, QualityControl Author: Weiliang Qiu [aut, cre], Brandon Guo [aut, ctb], Christopher Anderson [aut, ctb], Barbara Klanderman [aut, ctb], Vincent Carey [aut, ctb], Benjamin Raby [aut, ctb] Maintainer: Weiliang Qiu Package: MEAL Version: 1.41.0 Depends: R (>= 3.6.0), Biobase, MultiDataSet Imports: GenomicRanges, limma, vegan, BiocGenerics, minfi, IRanges, S4Vectors, methods, parallel, ggplot2 (>= 2.0.0), permute, Gviz, missMethyl, isva, SummarizedExperiment, SmartSVA, graphics, stats, utils, matrixStats Suggests: testthat, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData, BiocStyle, rmarkdown, brgedata License: Artistic-2.0 Title: Perform methylation analysis Description: Package to integrate methylation and expression data. It can also perform methylation or expression analysis alone. Several plotting functionalities are included as well as a new region analysis based on redundancy analysis. Effect of SNPs on a region can also be estimated. biocViews: DNAMethylation, Microarray, Software, WholeGenome Author: Carlos Ruiz-Arenas [aut, cre], Juan R. Gonzalez [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr Package: Prostar Version: 1.43.0 Depends: R (>= 4.4.0) Imports: DAPAR (>= 1.35.1), DAPARdata (>= 1.30.0), rhandsontable, data.table, shiny, shinyBS, shinyAce, highcharter, htmlwidgets, webshot, shinythemes, later, shinycssloaders, future, promises, shinyjqui, tibble, ggplot2, gplots, shinyjs, vioplot, Biobase, DT, R.utils, RColorBrewer, XML, colourpicker, gtools, markdown, rclipboard, sass, shinyTree, shinyWidgets Suggests: BiocStyle, BiocManager, testthat, knitr License: Artistic-2.0 NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for the DAPAR package. The package Prostar (Proteomics statistical analysis with R) is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required. biocViews: Proteomics, MassSpectrometry, Normalization, Preprocessing, Software, GUI Author: Thomas Burger [aut], Florence Combes [aut], Samuel Wieczorek [cre, aut] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/edyp-lab/Prostar/issues Package: GUIDEseq Version: 1.41.0 Depends: R (>= 3.5.0), GenomicRanges, BiocGenerics Imports: Biostrings, pwalign, CRISPRseek, ChIPpeakAnno, data.table, matrixStats, BSgenome, parallel, IRanges (>= 2.5.5), S4Vectors (>= 0.9.6), stringr, multtest, GenomicAlignments (>= 1.7.3), GenomeInfoDb, Rsamtools, hash, limma,dplyr, GenomicFeatures, rio, tidyr, tools, methods, purrr, ggplot2, openxlsx, patchwork, rlang Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat (>= 3.0.0) License: GPL (>= 2) NeedsCompilation: no Title: GUIDE-seq and PEtag-seq analysis pipeline Description: The package implements GUIDE-seq and PEtag-seq analysis workflow including functions for filtering UMI and reads with low coverage, obtaining unique insertion sites (proxy of cleavage sites), estimating the locations of the insertion sites, aka, peaks, merging estimated insertion sites from plus and minus strand, and performing off target search of the extended regions around insertion sites with mismatches and indels. biocViews: ImmunoOncology, GeneRegulation, Sequencing, WorkflowStep, CRISPR Author: Lihua Julie Zhu, Michael Lawrence, Ankit Gupta, Hervé Pagès , Alper Kucukural, Manuel Garber, Scot A. Wolfe Maintainer: Lihua Julie Zhu VignetteBuilder: knitr Package: SNPhood Version: 1.41.0 Depends: R (>= 3.1), GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb (>= 1.34.8), BiocParallel, VariantAnnotation, BiocGenerics, IRanges, methods, SummarizedExperiment, RColorBrewer, Biostrings, grDevices, gridExtra, stats, grid, utils, reshape2, scales, S4Vectors Suggests: BiocStyle, knitr, pryr, rmarkdown, SNPhoodData, corrplot License: LGPL (>= 3) Title: SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data Description: To date, thousands of single nucleotide polymorphisms (SNPs) have been found to be associated with complex traits and diseases. However, the vast majority of these disease-associated SNPs lie in the non-coding part of the genome, and are likely to affect regulatory elements, such as enhancers and promoters, rather than function of a protein. Thus, to understand the molecular mechanisms underlying genetic traits and diseases, it becomes increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin environment or transcription factor (TF) binding. Towards this aim, we developed SNPhood, a user-friendly *Bioconductor* R package to investigate and visualize the local neighborhood of a set of SNPs of interest for NGS data such as chromatin marks or transcription factor binding sites from ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. Finally, SNPhood can be employed for determining, investigating, and visualizing allele-specific binding patterns around the SNPs of interest. biocViews: Software Author: Christian Arnold [aut, cre], Pooja Bhat [aut], Judith Zaugg [aut] Maintainer: Christian Arnold URL: https://bioconductor.org/packages/SNPhood VignetteBuilder: knitr BugReports: mailto: Package: ExperimentHubData Version: 1.37.0 Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData (>= 1.21.3) Imports: methods, ExperimentHub, BiocManager, DBI, httr, curl Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle, rmarkdown, HubPub License: Artistic-2.0 Title: Add resources to ExperimentHub Description: Functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Package: HDF5Array Version: 1.39.1 Depends: R (>= 3.4), methods, SparseArray (>= 1.7.5), DelayedArray (>= 0.33.5), h5mread (>= 1.3.3) Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.51.2), S4Vectors, IRanges, S4Arrays (>= 1.1.1), rhdf5 Suggests: BiocParallel, GenomicRanges, SummarizedExperiment (>= 1.15.1), h5vcData, ExperimentHub, TENxBrainData, zellkonverter, GenomicFeatures, SingleCellExperiment, DelayedMatrixStats, genefilter, RSpectra, RUnit, knitr, rmarkdown, BiocStyle License: Artistic-2.0 Title: HDF5 datasets as array-like objects in R Description: The HDF5Array package is an HDF5 backend for DelayedArray objects. It implements the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects. biocViews: Infrastructure, DataRepresentation, DataImport, Sequencing, RNASeq, Coverage, Annotation, GenomeAnnotation, SingleCell, ImmunoOncology Author: Hervé Pagès [aut, cre] (ORCID: ) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/HDF5Array VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HDF5Array/issues Package: biosigner Version: 1.39.0 Imports: Biobase, methods, e1071, grDevices, graphics, MultiAssayExperiment, MultiDataSet, randomForest, ropls, stats, SummarizedExperiment, utils Suggests: BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, omicade4, rmarkdown, testthat License: CeCILL NeedsCompilation: no Title: Signature discovery from omics data Description: Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics. biocViews: Classification, FeatureExtraction, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry Author: Philippe Rinaudo [aut], Etienne A. Thevenot [aut, cre] (ORCID: ) Maintainer: Etienne A. Thevenot URL: http://dx.doi.org/10.3389/fmolb.2016.00026 VignetteBuilder: knitr Package: recoup Version: 1.39.2 Depends: R (>= 4.0.0), GenomicRanges, GenomicAlignments, ggplot2, ComplexHeatmap Imports: BiocGenerics, biomaRt, Biostrings, circlize, Seqinfo, GenomicFeatures, graphics, grDevices, httr, IRanges, methods, parallel, RSQLite, Rsamtools, rtracklayer, S4Vectors, stats, stringr, txdbmaker, utils Suggests: GenomeInfoDb, grid, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocManager, BSgenome, RMySQL License: GPL (>= 3) Title: An R package for the creation of complex genomic profile plots Description: recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively. biocViews: ImmunoOncology, Software, GeneExpression, Preprocessing, QualityControl, RNASeq, ChIPSeq, Sequencing, Coverage, ATACSeq, ChipOnChip, Alignment, DataImport Maintainer: ERROR URL: https://github.com/pmoulos/recoup VignetteBuilder: knitr Package: Glimma Version: 2.21.0 Depends: R (>= 4.0.0) Imports: htmlwidgets, edgeR, DESeq2, limma, SummarizedExperiment, stats, jsonlite, methods, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle, IRanges, GenomicRanges, pryr, AnnotationHub, scRNAseq, scater, scran, scRNAseq, License: GPL-3 Title: Interactive visualizations for gene expression analysis Description: This package produces interactive visualizations for RNA-seq data analysis, utilizing output from limma, edgeR, or DESeq2. It produces interactive htmlwidgets versions of popular RNA-seq analysis plots to enhance the exploration of analysis results by overlaying interactive features. The plots can be viewed in a web browser or embedded in notebook documents. biocViews: DifferentialExpression, GeneExpression, Microarray, ReportWriting, RNASeq, Sequencing, Visualization Author: Shian Su [aut, cre], Hasaru Kariyawasam [aut], Oliver Voogd [aut], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee [ctb], Isaac Virshup [ctb] Maintainer: Shian Su URL: https://github.com/hasaru-k/GlimmaV2 VignetteBuilder: knitr BugReports: https://github.com/hasaru-k/GlimmaV2/issues Package: BatchQC Version: 2.7.3 Depends: R (>= 4.5.0) Imports: data.table, DESeq2, dplyr, EBSeq, edgeR, FNN, ggdendro, ggnewscale, ggplot2, ggpubr, Harman, limma, matrixStats, methods, MASS, pheatmap, RColorBrewer, reader, reshape2, scran, shiny, shinyjs, shinythemes, stats, SummarizedExperiment, sva, S4Vectors, tibble, tidyr, tidyverse, umap, utils Suggests: BiocManager, BiocStyle, bladderbatch, curatedTBData, devtools, knitr, lintr, MultiAssayExperiment, plotly, rmarkdown, spelling, testthat (>= 3.0.0) License: MIT + file LICENSE Title: Batch Effects Quality Control Software Description: Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA. biocViews: BatchEffect, GeneExpression, GraphAndNetwork, Microarray, Normalization, PrincipalComponent, Sequencing, Software, Visualization, QualityControl, RNASeq, Preprocessing, DifferentialExpression, ImmunoOncology Author: Jessica Anderson [aut] (ORCID: ), W. Evan Johnson [aut, fnd] (ORCID: ), Yaoan Leng [ctb, cre] (ORCID: ), Solaiappan Manimaran [aut], Heather Selby [ctb], Claire Ruberman [ctb], Kwame Okrah [ctb], Hector Corrada Bravo [ctb], Michael Silverstein [ctb], Regan Conrad [ctb], Zhaorong Li [ctb], Evan Holmes [ctb], Solomon Joseph [ctb], Howard Fan [ctb], Sean Lu [ctb] Maintainer: Yaoan Leng URL: https://github.com/wejlab/BatchQC VignetteBuilder: knitr BugReports: https://github.com/wejlab/BatchQC/issues Package: pcaExplorer Version: 3.5.1 Imports: DESeq2, SummarizedExperiment, mosdef (>= 1.1.0), GenomicRanges, IRanges, S4Vectors, genefilter, ggplot2 (>= 2.0.0), heatmaply, plotly, scales, NMF, plyr, topGO, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, ggrepel, DT, shinyAce, threejs, biomaRt, pheatmap, knitr, rmarkdown, base64enc, tidyr, grDevices, methods Suggests: testthat, BiocStyle, markdown, airway, org.Hs.eg.db, htmltools License: MIT + file LICENSE NeedsCompilation: no Title: Interactive Visualization of RNA-seq Data Using a Principal Components Approach Description: This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis. biocViews: ImmunoOncology, Visualization, RNASeq, DimensionReduction, PrincipalComponent, QualityControl, GUI, ReportWriting, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/pcaExplorer, https://federicomarini.github.io/pcaExplorer/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/pcaExplorer/issues Package: oppar Version: 1.39.0 Depends: R (>= 3.3) Imports: Biobase, methods, GSEABase, GSVA Suggests: knitr, rmarkdown, limma, org.Hs.eg.db, GO.db, snow, parallel License: GPL-2 Title: Outlier profile and pathway analysis in R Description: The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis. biocViews: Pathways, GeneSetEnrichment, SystemsBiology, GeneExpression, Software Author: Chenwei Wang [aut], Alperen Taciroglu [aut], Stefan R Maetschke [aut], Colleen C Nelson [aut], Mark Ragan [aut], Melissa Davis [aut], Soroor Hediyeh zadeh [cre], Momeneh Foroutan [ctr] Maintainer: Soroor Hediyeh zadeh VignetteBuilder: knitr Package: BgeeDB Version: 2.37.0 Depends: R (>= 3.6.0), topGO, tidyr Imports: R.utils, data.table, curl, RCurl, digest, methods, stats, utils, dplyr, RSQLite, graph, Biobase, zellkonverter, anndata, HDF5Array, bread Suggests: knitr, BiocStyle, testthat, rmarkdown, markdown License: GPL-3 + file LICENSE NeedsCompilation: no Title: Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology Description: A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns. biocViews: Software, DataImport, Sequencing, GeneExpression, Microarray, GO, GeneSetEnrichment Author: Andrea Komljenovic [aut, cre], Julien Roux [aut, cre] Maintainer: Julien Wollbrett , Julien Roux , Andrea Komljenovic , Frederic Bastian URL: https://github.com/BgeeDB/BgeeDB_R VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeDB_R/issues Package: EGSEA Version: 1.39.0 Depends: R (>= 4.3.0), Biobase, gage (>= 2.14.4), AnnotationDbi, topGO (>= 2.16.0), pathview (>= 1.4.2) Imports: PADOG (>= 1.6.0), GSVA (>= 1.12.0), globaltest (>= 5.18.0), limma (>= 3.20.9), edgeR (>= 3.6.8), HTMLUtils (>= 0.1.5), hwriter (>= 1.2.2), gplots (>= 2.14.2), ggplot2 (>= 1.0.0), safe (>= 3.4.0), stringi (>= 0.5.0), parallel, stats, metap, grDevices, graphics, utils, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, RColorBrewer, methods, EGSEAdata (>= 1.3.1), htmlwidgets, plotly, DT Suggests: BiocStyle, knitr, testthat License: GPL-3 NeedsCompilation: no Title: Ensemble of Gene Set Enrichment Analyses Description: This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. EGSEA algorithm utilizes the analysis results of twelve prominent GSE algorithms in the literature to calculate collective significance scores for each gene set. biocViews: ImmunoOncology, DifferentialExpression, GO, GeneExpression, GeneSetEnrichment, Genetics, Microarray, MultipleComparison, OneChannel, Pathways, RNASeq, Sequencing, Software, SystemsBiology, TwoChannel,Metabolomics, Proteomics, KEGG, GraphAndNetwork, GeneSignaling, GeneTarget, NetworkEnrichment, Network, Classification Author: Monther Alhamdoosh [aut, cre], Luyi Tian [aut], Milica Ng [aut], Matthew Ritchie [ctb] Maintainer: Monther Alhamdoosh VignetteBuilder: knitr Package: bioCancer Version: 1.39.0 Depends: R (>= 3.6.0), radiant.data (>= 0.9.1), cBioPortalData, XML(>= 3.98) Imports: R.oo, R.methodsS3, DT (>= 0.3), dplyr (>= 0.7.2), tidyr, shiny (>= 1.0.5), AlgDesign (>= 1.1.7.3), import (>= 1.1.0), methods, AnnotationDbi, shinythemes, Biobase, geNetClassifier, org.Hs.eg.db, org.Bt.eg.db, DOSE, clusterProfiler, reactome.db, ReactomePA, DiagrammeR(<= 1.01), visNetwork, htmlwidgets, plyr, tibble, GO.db Suggests: BiocStyle, prettydoc, rmarkdown, knitr, testthat (>= 0.10.0) License: AGPL-3 | file LICENSE Title: Interactive Multi-Omics Cancers Data Visualization and Analysis Description: This package is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data. biocViews: GUI, DataRepresentation, Network, MultipleComparison, Pathways, Reactome, Visualization,GeneExpression,GeneTarget Author: Karim Mezhoud [aut, cre] Maintainer: Karim Mezhoud URL: https://kmezhoud.github.io/bioCancer/ VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/bioCancer/issues Package: Pigengene Version: 1.37.0 Depends: R (>= 4.0.3), graph, BiocStyle (>= 2.28.0) Imports: bnlearn (>= 4.7), C50 (>= 0.1.2), MASS, matrixStats, partykit, Rgraphviz, WGCNA, GO.db, impute, preprocessCore, grDevices, graphics, stats, utils, parallel, pheatmap (>= 1.0.8), dplyr, gdata, clusterProfiler, ReactomePA, ggplot2, openxlsx, DBI, DOSE Suggests: org.Hs.eg.db (>= 3.7.0), org.Mm.eg.db (>= 3.7.0), biomaRt (>= 2.30.0), knitr, AnnotationDbi, energy License: GPL (>=2) NeedsCompilation: no Title: Infers biological signatures from gene expression data Description: Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes. biocViews: GeneExpression, RNASeq, NetworkInference, Network, GraphAndNetwork, BiomedicalInformatics, SystemsBiology, Transcriptomics, Classification, Clustering, DecisionTree, DimensionReduction, PrincipalComponent, Microarray, Normalization, ImmunoOncology Author: Habil Zare, Amir Foroushani, Rupesh Agrahari, Meghan Short, Isha Mehta, Neda Emami, and Sogand Sajedi Maintainer: Habil Zare VignetteBuilder: knitr Package: MGFR Version: 1.37.0 Depends: R (>= 3.5) Imports: biomaRt, annotate License: GPL-3 NeedsCompilation: no Title: Marker Gene Finder in RNA-seq data Description: The package is designed to detect marker genes from RNA-seq data. biocViews: ImmunoOncology, Genetics, GeneExpression, RNASeq Author: Khadija El Amrani Maintainer: Khadija El Amrani Package: clusterExperiment Version: 2.31.3 Depends: R (>= 3.6.0), SingleCellExperiment, SummarizedExperiment (>= 1.15.4), BiocGenerics Imports: methods, NMF, RColorBrewer, ape (>= 5.0), cluster, stats, limma, locfdr, matrixStats, graphics, parallel, BiocSingular, kernlab, stringr, S4Vectors, grDevices, DelayedArray (>= 0.7.48), HDF5Array (>= 1.7.10), Matrix, Rcpp, edgeR, scales, zinbwave, phylobase, pracma, mbkmeans LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, MAST, Rtsne, scran, igraph, rmarkdown License: Artistic-2.0 Title: Compare Clusterings for Single-Cell Sequencing Description: Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets. biocViews: Clustering, RNASeq, Sequencing, Software, SingleCell Author: Elizabeth Purdom [aut, cre, cph], Davide Risso [aut] Maintainer: Elizabeth Purdom VignetteBuilder: knitr BugReports: https://github.com/epurdom/clusterExperiment/issues Package: LOBSTAHS Version: 1.37.0 Depends: R (>= 3.4), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE NeedsCompilation: no Title: Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences Description: LOBSTAHS is a multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico data for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Lipidomics, DataImport Author: James Collins [aut, cre], Helen Fredricks [aut], Bethanie Edwards [aut], Henry Holm [aut], Benjamin Van Mooy [aut], Daniel Lowenstein [aut] Maintainer: Henry Holm , Daniel Lowenstein , James Collins URL: http://bioconductor.org/packages/LOBSTAHS VignetteBuilder: knitr BugReports: https://github.com/vanmooylipidomics/LOBSTAHS/issues/new Package: RCAS Version: 1.37.0 Depends: R (>= 3.3.0), plotly (>= 4.5.2), DT (>= 0.2), data.table, Imports: GenomicRanges, IRanges, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDb (>= 1.12.0), Biostrings, rtracklayer, GenomicFeatures, txdbmaker, rmarkdown (>= 0.9.5), genomation (>= 1.5.5), knitr (>= 1.12.3), BiocGenerics, S4Vectors, plotrix, pbapply, RSQLite, proxy, pheatmap, ggplot2, cowplot, seqLogo, utils, ranger, gprofiler2 Suggests: testthat, covr, BiocManager License: Artistic-2.0 Title: RNA Centric Annotation System Description: RCAS is an R/Bioconductor package designed as a generic reporting tool for the functional analysis of transcriptome-wide regions of interest detected by high-throughput experiments. Such transcriptomic regions could be, for instance, signal peaks detected by CLIP-Seq analysis for protein-RNA interaction sites, RNA modification sites (alias the epitranscriptome), CAGE-tag locations, or any other collection of query regions at the level of the transcriptome. RCAS produces in-depth annotation summaries and coverage profiles based on the distribution of the query regions with respect to transcript features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions). Moreover, RCAS can carry out functional enrichment analyses and discriminative motif discovery. biocViews: Software, GeneTarget, MotifAnnotation, MotifDiscovery, GO, Transcriptomics, GenomeAnnotation, GeneSetEnrichment, Coverage Author: Bora Uyar [aut, cre], Dilmurat Yusuf [aut], Ricardo Wurmus [aut], Altuna Akalin [aut] Maintainer: Bora Uyar SystemRequirements: pandoc (>= 1.12.3) VignetteBuilder: knitr Package: ASpli Version: 2.21.0 Depends: methods, grDevices, stats, utils, parallel, edgeR, limma, AnnotationDbi Imports: GenomicRanges, GenomicFeatures, BiocGenerics, IRanges, GenomicAlignments, Gviz, S4Vectors, Rsamtools, BiocStyle, igraph, htmltools, data.table, UpSetR, tidyr, DT, MASS, grid, graphics, pbmcapply, txdbmaker License: GPL Title: Analysis of Alternative Splicing Using RNA-Seq Description: Integrative pipeline for the analysis of alternative splicing using RNAseq. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, TimeCourse, RNASeq, GenomeAnnotation, Sequencing, Alignment Author: Estefania Mancini, Andres Rabinovich, Javier Iserte, Marcelo Yanovsky and Ariel Chernomoretz Maintainer: Ariel Chernomoretz Package: IPO Version: 1.37.0 Depends: xcms (>= 1.50.0), rsm, CAMERA, grDevices, graphics, stats, utils Imports: BiocParallel Suggests: RUnit, BiocGenerics, msdata, mtbls2, faahKO, knitr Enhances: parallel License: GPL (>= 2) + file LICENSE NeedsCompilation: no Title: Automated Optimization of XCMS Data Processing parameters Description: The outcome of XCMS data processing strongly depends on the parameter settings. IPO (`Isotopologue Parameter Optimization`) is a parameter optimization tool that is applicable for different kinds of samples and liquid chromatography coupled to high resolution mass spectrometry devices, fast and free of labeling steps. IPO uses natural, stable 13C isotopes to calculate a peak picking score. Retention time correction is optimized by minimizing the relative retention time differences within features and grouping parameters are optimized by maximizing the number of features showing exactly one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiment. The resulting scores are evaluated using response surface models. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Gunnar Libiseller , Christoph Magnes , Thomas Lieb Maintainer: Thomas Lieb URL: https://github.com/rietho/IPO VignetteBuilder: knitr BugReports: https://github.com/rietho/IPO/issues/new Package: YAPSA Version: 1.37.1 Depends: R (>= 4.0.0), GenomicRanges, ggplot2, grid Imports: limSolve, SomaticSignatures, VariantAnnotation, Seqinfo, reshape2, gridExtra, corrplot, dendextend, GetoptLong, circlize, gtrellis, doParallel, parallel, PMCMRplus, ggbeeswarm, ComplexHeatmap, KEGGREST, grDevices, Biostrings, BSgenome.Hsapiens.UCSC.hg19, magrittr, pracma, dplyr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-3 Title: Yet Another Package for Signature Analysis Description: This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata. biocViews: Sequencing, DNASeq, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod, BiologicalQuestion Author: Daniel Huebschmann [aut, cre], Lea Jopp-Saile [aut], Carolin Andresen [aut], Zuguang Gu [aut], Matthias Schlesner [aut] Maintainer: Daniel Huebschmann VignetteBuilder: knitr Package: MetaboSignal Version: 1.41.0 Depends: R(>= 3.3) Imports: KEGGgraph, hpar, igraph, RCurl, KEGGREST, EnsDb.Hsapiens.v75, stats, graphics, utils, org.Hs.eg.db, biomaRt, AnnotationDbi, MWASTools, mygene Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: GPL-3 NeedsCompilation: no Title: MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways Description: MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes. biocViews: GraphAndNetwork, GeneSignaling, GeneTarget, Network, Pathways, KEGG, Reactome, Software Author: Andrea Rodriguez-Martinez, Rafael Ayala, Joram M. Posma, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr Package: bigmelon Version: 1.37.0 Depends: R (>= 3.3), wateRmelon (>= 1.25.0), gdsfmt (>= 1.0.4), methods, minfi (>= 1.21.0), Biobase, methylumi Imports: stats, utils, GEOquery, graphics, BiocGenerics, illuminaio Suggests: BiocGenerics, RUnit, BiocStyle, minfiData, parallel, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, bumphunter License: GPL-3 NeedsCompilation: no Title: Illumina methylation array analysis for large experiments Description: Methods for working with Illumina arrays using gdsfmt. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl, MethylationArray, DataImport, CpGIsland Author: Tyler J. Gorrie-Stone [aut], Ayden Saffari [aut], Karim Malki [aut], Leonard C. Schalkwyk [cre, aut] Maintainer: Leonard C. Schalkwyk Package: GOpro Version: 1.37.0 Depends: R (>= 3.4) Imports: AnnotationDbi, dendextend, doParallel, foreach, parallel, org.Hs.eg.db, GO.db, Rcpp, stats, graphics, MultiAssayExperiment, IRanges, S4Vectors LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, RTCGA.PANCAN12, BiocStyle, testthat License: GPL-3 Title: Find the most characteristic gene ontology terms for groups of human genes Description: Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng). biocViews: Annotation, Clustering, GO, GeneExpression, GeneSetEnrichment, MultipleComparison Author: Lidia Chrabaszcz Maintainer: Lidia Chrabaszcz URL: https://github.com/mi2-warsaw/GOpro VignetteBuilder: knitr BugReports: https://github.com/mi2-warsaw/GOpro/issues Package: DEsubs Version: 1.37.0 Depends: R (>= 3.3), locfit Imports: graph, igraph, RBGL, circlize, limma, edgeR, EBSeq, NBPSeq, stats, grDevices, graphics, pheatmap, utils, ggplot2, Matrix, jsonlite, tools, DESeq2, methods Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL-3 NeedsCompilation: no Title: DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments Description: DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG, GeneExpression, NetworkEnrichment, Network, RNASeq, DifferentialExpression, Normalization, ImmunoOncology Author: Aristidis G. Vrahatis and Panos Balomenos Maintainer: Aristidis G. Vrahatis , Panos Balomenos VignetteBuilder: knitr Package: yarn Version: 1.37.0 Depends: Biobase Imports: biomaRt, downloader, edgeR, gplots, graphics, limma, matrixStats, preprocessCore, readr, RColorBrewer, stats, quantro Suggests: knitr, rmarkdown, testthat (>= 0.8) License: Artistic-2.0 Title: YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization Description: Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments. biocViews: Software, QualityControl, GeneExpression, Sequencing, Preprocessing, Normalization, Annotation, Visualization, Clustering Author: Joseph N Paulson [aut, cre], Cho-Yi Chen [aut], Camila Lopes-Ramos [aut], Marieke Kuijjer [aut], John Platig [aut], Abhijeet Sonawane [aut], Maud Fagny [aut], Kimberly Glass [aut], John Quackenbush [aut] Maintainer: Joseph N Paulson VignetteBuilder: knitr Package: scDD Version: 1.35.0 Depends: R (>= 3.4) Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran Suggests: BiocStyle, knitr, gridExtra License: GPL-2 NeedsCompilation: yes Title: Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions Description: This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions. biocViews: ImmunoOncology, Bayesian, Clustering, RNASeq, SingleCell, MultipleComparison, Visualization, DifferentialExpression Author: Keegan Korthauer [cre, aut] (ORCID: ) Maintainer: Keegan Korthauer URL: https://github.com/kdkorthauer/scDD VignetteBuilder: knitr BugReports: https://github.com/kdkorthauer/scDD/issues Package: funtooNorm Version: 1.35.0 Depends: R(>= 3.4) Imports: pls, matrixStats, minfi, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomeInfoDb, grDevices, graphics, stats Suggests: prettydoc, minfiData, knitr, rmarkdown License: GPL-3 Title: Normalization Procedure for Infinium HumanMethylation450 BeadChip Kit Description: Provides a function to normalize Illumina Infinium Human Methylation 450 BeadChip (Illumina 450K), correcting for tissue and/or cell type. biocViews: DNAMethylation, Preprocessing, Normalization Author: Celia Greenwood ,Stepan Grinek , Maxime Turgeon , Kathleen Klein Maintainer: Kathleen Klein VignetteBuilder: knitr Package: splatter Version: 1.35.2 Depends: R (>= 4.0), SingleCellExperiment Imports: BiocGenerics, BiocParallel, checkmate (>= 2.0.0), crayon, edgeR, fitdistrplus, grDevices, lifecycle, locfit, matrixStats, methods, rlang, S4Vectors, scrapper, scuttle, stats, SummarizedExperiment, utils, withr Suggests: BASiCS (>= 1.7.10), BiocManager, BiocSingular, BiocStyle, Biostrings, covr, cowplot, GenomeInfoDb, GenomicRanges, ggplot2 (>= 3.4.0), igraph, IRanges, knitr, limSolve, lme4, magick, mfa, phenopath, preprocessCore, progress, pscl, rmarkdown, scales, scater (>= 1.15.16), scDD, scran, SparseDC, spelling, testthat, VariantAnnotation, zinbwave License: GPL-3 + file LICENSE Title: Simple Simulation of Single-cell RNA Sequencing Data Description: Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets. biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, Software, ImmunoOncology Author: Luke Zappia [aut, cre] (ORCID: , GitHub: lazappi), Belinda Phipson [aut] (ORCID: , GitHub: bphipson), Christina Azodi [ctb] (ORCID: , GitHub: azodichr), Alicia Oshlack [aut] (ORCID: ) Maintainer: Luke Zappia URL: https://bioconductor.org/packages/splatter/, https://github.com/Oshlack/splatter, http://oshlacklab.com/splatter/ VignetteBuilder: knitr BugReports: https://github.com/Oshlack/splatter/issues Package: karyoploteR Version: 1.37.0 Depends: R (>= 3.4), regioneR, GenomicRanges, methods Imports: regioneR, GenomicRanges, IRanges, Rsamtools, stats, graphics, memoise, rtracklayer, Seqinfo, GenomeInfoDb, S4Vectors, biovizBase, digest, bezier, GenomicFeatures, bamsignals, AnnotationDbi, grDevices, VariantAnnotation Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, magrittr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Hs.eg.db, org.Mm.eg.db, pasillaBamSubset License: Artistic-2.0 NeedsCompilation: no Title: Plot customizable linear genomes displaying arbitrary data Description: karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones. biocViews: Visualization, CopyNumberVariation, Sequencing, Coverage, DNASeq, ChIPSeq, MethylSeq, DataImport, OneChannel Author: Bernat Gel [aut, cre] (ORCID: ) Maintainer: Bernat Gel URL: https://github.com/bernatgel/karyoploteR VignetteBuilder: knitr BugReports: https://github.com/bernatgel/karyoploteR/issues Package: REMP Version: 1.35.0 Depends: R (>= 3.6), SummarizedExperiment(>= 1.1.6), minfi (>= 1.22.0) Imports: readr, rtracklayer, graphics, stats, utils, methods, settings, BiocGenerics, S4Vectors, Biostrings, GenomicRanges, IRanges, Seqinfo, BiocParallel, doParallel, parallel, foreach, caret, kernlab, ranger, BSgenome, AnnotationHub, org.Hs.eg.db, impute, iterators Suggests: IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, minfiDataEPIC License: GPL-3 Title: Repetitive Element Methylation Prediction Description: Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE. biocViews: DNAMethylation, Microarray, MethylationArray, Sequencing, GenomeWideAssociation, Epigenetics, Preprocessing, MultiChannel, TwoChannel, DifferentialMethylation, QualityControl, DataImport Author: Yinan Zheng [aut, cre], Lei Liu [aut], Wei Zhang [aut], Warren Kibbe [aut], Lifang Hou [aut, cph] Maintainer: Yinan Zheng URL: https://github.com/YinanZheng/REMP BugReports: https://github.com/YinanZheng/REMP/issues Package: EventPointer Version: 3.19.1 Depends: R (>= 3.4), SGSeq, Matrix, SummarizedExperiment Imports: txdbmaker, stringr, GenomeInfoDb, igraph, MASS, nnls, limma, matrixStats, RBGL, prodlim, graph, methods, utils, stats, doParallel, foreach, affxparser, GenomicRanges, GenomicAlignments, Rsamtools, S4Vectors, IRanges, qvalue, cobs, rhdf5, BSgenome, Biostrings, glmnet, abind, aroma.light, iterators, lpSolve, poibin, speedglm, tximport, fgsea Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, dplyr, kableExtra License: Artistic-2.0 Title: An effective identification of alternative splicing events using junction arrays and RNA-Seq data Description: EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). In the latter, EventPointer can work with annotated splicing events or can build a splicing graph from the RNA-Seq reads and then identify new and specific alternative splicing events. The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation. biocViews: AlternativeSplicing, DifferentialSplicing, mRNAMicroarray, RNASeq, Transcription, Sequencing, TimeCourse, ImmunoOncology Author: Juan Pablo Romero [aut], Juan A. Ferrer-Bonsoms [aut, cre], Pablo Sacristan [aut], Ander Muniategui [aut], Fernando Carazo [aut], Ander Aramburu [aut], Angel Rubio [aut] Maintainer: Juan A. Ferrer-Bonsoms VignetteBuilder: knitr BugReports: https://github.com/jpromeror/EventPointer/issues Package: rqt Version: 1.37.0 Depends: R (>= 3.4), SummarizedExperiment Imports: stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls Suggests: BiocStyle, knitr, rmarkdown License: GPL Title: rqt: utilities for gene-level meta-analysis Description: Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, . biocViews: GenomeWideAssociation, Regression, Survival, PrincipalComponent, StatisticalMethod, Sequencing Author: Ilya Zhbannikov [aut, cre], Konstantin Arbeev [aut], Anatoliy Yashin [aut] Maintainer: Ilya Zhbannikov URL: https://github.com/izhbannikov/rqt VignetteBuilder: knitr BugReports: https://github.com/izhbannikov/rqt/issues Package: IntEREst Version: 1.35.2 Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, SummarizedExperiment, edgeR, S4Vectors, GenomicFiles Imports: seqLogo, Biostrings, GenomicFeatures (>= 1.39.4), txdbmaker, IRanges, seqinr, graphics, grDevices, stats, utils, grid, methods, DBI, RMariaDB, GenomicAlignments, BiocParallel, BiocGenerics, DEXSeq, DESeq2 Suggests: clinfun, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-2 NeedsCompilation: no Title: Intron-Exon Retention Estimator Description: This package performs Intron-Exon Retention analysis on RNA-seq data (.bam files). biocViews: Software, AlternativeSplicing, Coverage, DifferentialSplicing, Sequencing, RNASeq, Alignment, Normalization, DifferentialExpression, ImmunoOncology Author: Ali Oghabian , Dario Greco , Mikko Frilander Maintainer: Ali Oghabian VignetteBuilder: knitr Package: DaMiRseq Version: 2.23.0 Depends: R (>= 3.4), SummarizedExperiment, ggplot2 Imports: DESeq2, limma, EDASeq, RColorBrewer, sva, Hmisc, pheatmap, FactoMineR, corrplot, randomForest, e1071, caret, MASS, lubridate, plsVarSel, kknn, FSelector, methods, stats, utils, graphics, grDevices, reshape2, ineq, arm, pls, RSNNS, edgeR, plyr Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Title: Data Mining for RNA-seq data: normalization, feature selection and classification Description: The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot. biocViews: Sequencing, RNASeq, Classification, ImmunoOncology Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr Package: NADfinder Version: 1.35.0 Depends: R (>= 3.4), BiocGenerics, IRanges, GenomicRanges, S4Vectors, SummarizedExperiment Imports: graphics, methods, baseline, signal, GenomicAlignments, GenomeInfoDb, rtracklayer, limma, trackViewer, stats, utils, Rsamtools, metap, EmpiricalBrownsMethod,ATACseqQC, corrplot, csaw Suggests: RUnit, BiocStyle, knitr, BSgenome.Mmusculus.UCSC.mm10, testthat, BiocManager, rmarkdown License: GPL (>= 2) Title: Call wide peaks for sequencing data Description: Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation. biocViews: Sequencing, DNASeq, GeneRegulation, PeakDetection Author: Jianhong Ou, Haibo Liu, Jun Yu, Hervé Pagès, Paul Kaufman, Lihua Julie Zhu Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr Package: GenomicScores Version: 2.23.0 Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges, methods, BiocGenerics (>= 0.13.8) Imports: stats, utils, XML, httr, Biobase, BiocManager, BiocFileCache, IRanges (>= 2.3.23), Biostrings, Seqinfo, GenomeInfoDb (>= 1.45.5), AnnotationHub, rhdf5, DelayedArray, HDF5Array Suggests: RUnit, BiocStyle, knitr, rmarkdown, VariantAnnotation, gwascat, RColorBrewer, shiny, shinyjs, shinycustomloader, data.table, DT, magrittr, shinydashboard, BSgenome.Hsapiens.UCSC.hg38, phastCons100way.UCSC.hg38, MafDb.1Kgenomes.phase1.hs37d5, MafH5.gnomAD.v4.0.GRCh38, SNPlocs.Hsapiens.dbSNP144.GRCh37, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 Title: Infrastructure to work with genomewide position-specific scores Description: Provide infrastructure to store and access genomewide position-specific scores within R and Bioconductor. biocViews: Infrastructure, Genetics, Annotation, Sequencing, Coverage, AnnotationHubSoftware Author: Robert Castelo [aut, cre], Pau Puigdevall [ctb], Pablo Rodríguez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GenomicScores VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GenomicScores/issues Package: ideal Version: 2.5.0 Depends: topGO Imports: DESeq2, SummarizedExperiment, mosdef (>= 1.1.0), GenomicRanges, IRanges, S4Vectors, ggplot2 (>= 2.0.0), heatmaply, plotly, pheatmap, IHW, gplots, UpSetR, goseq, stringr, dplyr, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, DT, rentrez, rintrojs, rlang, ggrepel, knitr, rmarkdown, shinyAce, BiocParallel, grDevices, graphics, base64enc, methods, utils, stats Suggests: testthat, BiocStyle, markdown, airway, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, htmltools, edgeR License: MIT + file LICENSE Title: Interactive Differential Expression AnaLysis Description: This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. Support for reproducibility of the whole analysis is provided by means of a template report which gets automatically compiled and can be stored/shared. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, RNASeq, Sequencing, Visualization, QualityControl, GUI, GeneSetEnrichment, ReportWriting, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/ideal, https://federicomarini.github.io/ideal/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/ideal/issues Package: ATACseqQC Version: 1.35.0 Depends: R (>= 3.4), BiocGenerics, S4Vectors Imports: BSgenome, Biostrings, ChIPpeakAnno, IRanges, GenomicRanges, GenomicAlignments, GenomeInfoDb, GenomicScores, graphics, grid, limma, Rsamtools (>= 1.31.2), randomForest, rtracklayer, stats, motifStack, preseqR, utils, KernSmooth, edgeR, BiocParallel Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, MotifDb, trackViewer, testthat, rmarkdown License: GPL (>= 2) Title: ATAC-seq Quality Control Description: ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints. biocViews: Sequencing, DNASeq, ATACSeq, GeneRegulation, QualityControl, Coverage, NucleosomePositioning, ImmunoOncology Author: Jianhong Ou, Haibo Liu, Feng Yan, Jun Yu, Michelle Kelliher, Lucio Castilla, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr Package: RITAN Version: 1.35.0 Depends: R (>= 4.0), Imports: graphics, methods, stats, utils, grid, gridExtra, reshape2, gplots, ggplot2, plotrix, RColorBrewer, STRINGdb, MCL, dynamicTreeCut, gsubfn, hash, png, sqldf, igraph, BgeeDB, knitr, RITANdata, GenomicFeatures, ensembldb, AnnotationFilter, EnsDb.Hsapiens.v86 Suggests: rmarkdown, BgeeDB License: file LICENSE NeedsCompilation: no Title: Rapid Integration of Term Annotation and Network resources Description: Tools for comprehensive gene set enrichment and extraction of multi-resource high confidence subnetworks. RITAN facilitates bioinformatic tasks for enabling network biology research. biocViews: QualityControl, Network, NetworkEnrichment, NetworkInference, GeneSetEnrichment, FunctionalGenomics, GraphAndNetwork Author: Michael Zimmermann [aut, cre] Maintainer: Michael Zimmermann VignetteBuilder: knitr Package: msgbsR Version: 1.35.0 Depends: R (>= 3.4), GenomicRanges, methods Imports: BSgenome, easyRNASeq, edgeR, GenomicAlignments, GenomicFeatures, Seqinfo, ggbio, ggplot2, IRanges, parallel, plyr, Rsamtools, R.utils, stats, SummarizedExperiment, S4Vectors, utils Suggests: roxygen2, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-2 Title: msgbsR: methylation sensitive genotyping by sequencing (MS-GBS) R functions Description: Pipeline for the anaysis of a MS-GBS experiment. biocViews: ImmunoOncology, DifferentialMethylation, DataImport, Epigenetics, MethylSeq Author: Benjamin Mayne Maintainer: Benjamin Mayne Package: IsoformSwitchAnalyzeR Version: 2.11.1 Depends: R (>= 4.2), limma, DEXSeq, satuRn (>= 1.7.0), sva, ggplot2 (>= 3.3.5), pfamAnalyzeR Imports: methods, BSgenome, plyr, reshape2, gridExtra, Biostrings (>= 2.50.0), IRanges, GenomicRanges, RColorBrewer, rtracklayer, VennDiagram, DBI, grDevices, graphics, stats, utils, Seqinfo, grid, tximport (>= 1.7.1), tximeta (>= 1.7.12), edgeR, futile.logger, stringr, dplyr, magrittr, readr, tibble, XVector, BiocGenerics, RCurl, Biobase, SummarizedExperiment, tidyr, S4Vectors, BiocParallel, pwalign Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, rmarkdown License: GPL (>= 2) Title: Identify, Annotate and Visualize Isoform Switches with Functional Consequences from both short- and long-read RNA-seq data Description: Analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNA-seq (short/long) by tools such as Kallisto, Salmon, StringTie, Tallon, IsoQuant etc. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Visualization, StatisticalMethod, TranscriptomeVariant, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, Transcriptomics, RNASeq, Annotation, FunctionalPrediction, GenePrediction, DataImport, MultipleComparison, BatchEffect, ImmunoOncology Author: Kristoffer Vitting-Seerup [cre, aut] (ORCID: ), Chunxu Han [ctb], Jeroen Gilis [ctb] (ORCID: ), Elena Iriondo Delgado [ctb] Maintainer: Kristoffer Vitting-Seerup URL: http://bioconductor.org/packages/IsoformSwitchAnalyzeR/ VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/IsoformSwitchAnalyzeR/issues Package: consensusOV Version: 1.33.0 Title: ERROR Maintainer: ERROR Package: rexposome Version: 1.33.1 Depends: R (>= 3.5), Biobase Imports: methods, utils, stats, lsr, FactoMineR, stringr, circlize, corrplot, ggplot2, ggridges, reshape2, pryr, S4Vectors, imputeLCMD, scatterplot3d, glmnet, gridExtra, grid, Hmisc, gplots, gtools, scales, lme4, grDevices, graphics, ggrepel, mice Suggests: mclust, flexmix, testthat, BiocStyle, knitr, formatR, rmarkdown License: MIT + file LICENSE Title: Exposome exploration and outcome data analysis Description: Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes. biocViews: Software, BiologicalQuestion, Infrastructure, DataImport, DataRepresentation, BiomedicalInformatics, ExperimentalDesign, MultipleComparison, Classification, Clustering Author: Carles Hernandez-Ferrer [aut, cre], Juan R. Gonzalez [aut], Xavier Escribà-Montagut [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr Package: BiocSklearn Version: 1.33.0 Depends: R (>= 4.0), reticulate, methods, SummarizedExperiment Imports: basilisk Suggests: testthat, HDF5Array, BiocStyle, rmarkdown, knitr License: Artistic-2.0 Title: interface to python sklearn via Rstudio reticulate Description: This package provides interfaces to selected sklearn elements, and demonstrates fault tolerant use of python modules requiring extensive iteration. biocViews: StatisticalMethod, DimensionReduction, Infrastructure Author: Vince Carey [cre, aut] Maintainer: Vince Carey SystemRequirements: python (>= 2.7), sklearn, numpy, pandas, h5py VignetteBuilder: knitr Package: miRspongeR Version: 2.15.2 Depends: R (>= 4.4.0) Imports: corpcor, SPONGE, parallel, igraph, MCL, clusterProfiler, ReactomePA, DOSE, survival, grDevices, graphics, stats, utils, Rcpp, RColorBrewer, grid, org.Hs.eg.db, foreach, doParallel Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Title: Identification and analysis of miRNA sponge regulation Description: This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network. biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment, Survival, Microarray, Software, SingleCell, Spatial, RNASeq Author: Junpeng Zhang [aut, cre] Maintainer: Junpeng Zhang URL: VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRspongeR/issues Package: SPONGE Version: 1.33.0 Depends: R (>= 3.6) Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table, MASS, expm, gRbase, glmnet, igraph, iterators, tidyverse, caret, dplyr, biomaRt, randomForest, ggridges, cvms, ComplexHeatmap, ggplot2, MetBrewer, rlang, tnet, ggpubr, stringr, tidyr Suggests: testthat, knitr, rmarkdown, visNetwork, ggrepel, gridExtra, digest, doParallel, bigmemory, GSVA License: GPL (>=3) Title: Sparse Partial Correlations On Gene Expression Description: This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape. biocViews: GeneExpression, Transcription, GeneRegulation, NetworkInference, Transcriptomics, SystemsBiology, Regression, RandomForest, MachineLearning Author: Markus List [aut, cre] (ORCID: ), Markus Hoffmann [aut] (ORCID: ), Lena Strasser [aut] (ORCID: ) Maintainer: Markus List VignetteBuilder: knitr Package: TFHAZ Version: 1.33.0 Depends: R(>= 3.4) Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils, IRanges, methods, ORFik Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 Title: Transcription Factor High Accumulation Zones Description: It finds trascription factor (TF) high accumulation DNA zones, i.e., regions along the genome where there is a high presence of different transcription factors. Starting from a dataset containing the genomic positions of TF binding regions, for each base of the selected chromosome the accumulation of TFs is computed. Three different types of accumulation (TF, region and base accumulation) are available, together with the possibility of considering, in the single base accumulation computing, the TFs present not only in that single base, but also in its neighborhood, within a window of a given width. Two different methods for the search of TF high accumulation DNA zones, called "binding regions" and "overlaps", are available. In addition, some functions are provided in order to analyze, visualize and compare results obtained with different input parameters. biocViews: Software, BiologicalQuestion, Transcription, ChIPSeq, Coverage Author: Alberto Marchesi, Silvia Cascianelli, Marco Masseroli Maintainer: Gaia Ceddia VignetteBuilder: knitr Package: multiMiR Version: 1.33.0 Depends: R (>= 3.4) Imports: stats, XML, RCurl, purrr (>= 0.2.2), tibble (>= 2.0), methods, BiocGenerics, AnnotationDbi, dplyr, Suggests: BiocStyle, edgeR, knitr, rmarkdown, testthat (>= 1.0.2) License: MIT + file LICENSE NeedsCompilation: no Title: Integration of multiple microRNA-target databases with their disease and drug associations Description: A collection of microRNAs/targets from external resources, including validated microRNA-target databases (miRecords, miRTarBase and TarBase), predicted microRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan) and microRNA-disease/drug databases (miR2Disease, Pharmaco-miR VerSe and PhenomiR). biocViews: miRNAData, Homo_sapiens_Data, Mus_musculus_Data, Rattus_norvegicus_Data, OrganismData Author: Yuanbin Ru [aut], Matt Mulvahill [aut], Spencer Mahaffey [cre, aut], Katerina Kechris [aut, cph, ths] Maintainer: Spencer Mahaffey URL: https://github.com/KechrisLab/multiMiR VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/multiMiR/issues Package: MSstatsQC Version: 2.29.3 Imports: dplyr,plyr, plotly, ggplot2, ggExtra, stats, grid, MSnbase, qcmetrics, h2o, FrF2, car, reshape2, jsonlite Suggests: knitr, rmarkdown, testthat, RforProteomics License: Artistic License 2.0 Title: Longitudinal system suitability monitoring and quality control for proteomic experiments Description: MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry, Normalization Author: Eralp Dogu [aut, cre] (ORCID: ), Sara Taheri [aut] (ORCID: ), Olga Vitek [aut] (ORCID: ) Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc Package: cbaf Version: 1.33.0 Depends: R (>= 4.1) Imports: BiocFileCache, RColorBrewer, cBioPortalData, genefilter, gplots, grDevices, stats, utils, openxlsx, zip Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 NeedsCompilation: no Title: Automated functions for comparing various omic data from cbioportal.org Description: This package contains functions that allow analysing and comparing omic data across various cancers/cancer subgroups easily. So far, it is compatible with RNA-seq, microRNA-seq, microarray and methylation datasets that are stored on cbioportal.org. biocViews: Software, AssayDomain, DNAMethylation, GeneExpression, Transcription, Microarray,ResearchField, BiomedicalInformatics, ComparativeGenomics, Epigenetics, Genetics, Transcriptomics Author: Arman Shahrisa [aut, cre, cph], Maryam Tahmasebi Birgani [aut] Maintainer: Arman Shahrisa VignetteBuilder: knitr Package: vulcan Version: 1.33.0 Depends: R (>= 4.0), ChIPpeakAnno,TxDb.Hsapiens.UCSC.hg19.knownGene, zoo, GenomicRanges, S4Vectors, viper, DiffBind, locfit Imports: wordcloud, csaw, gplots, stats, utils, caTools, graphics, DESeq2, Biobase Suggests: vulcandata License: LGPL-3 NeedsCompilation: no Title: VirtUaL ChIP-Seq data Analysis using Networks Description: Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a package that interrogates gene regulatory networks to infer cofactors significantly enriched in a differential binding signature coming from ChIP-Seq data. In order to do so, our package combines strategies from different BioConductor packages: DESeq for data normalization, ChIPpeakAnno and DiffBind for annotation and definition of ChIP-Seq genomic peaks, csaw to define optimal peak width and viper for applying a regulatory network over a differential binding signature. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, ChIPSeq Author: Federico M. Giorgi, Andrew N. Holding, Florian Markowetz Maintainer: Federico M. Giorgi Package: MIRA Version: 1.33.0 Depends: R (>= 3.5) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, ggplot2, Biobase, stats, bsseq, methods Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 Title: Methylation-Based Inference of Regulatory Activity Description: DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets. biocViews: ImmunoOncology, DNAMethylation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing, Epigenetics, Coverage Author: Nathan Sheffield [aut], Christoph Bock [ctb], John Lawson [aut, cre] Maintainer: John Lawson URL: http://databio.org/mira VignetteBuilder: knitr BugReports: https://github.com/databio/MIRA Package: omicRexposome Version: 1.33.0 Depends: R (>= 3.4), Biobase Imports: stats, utils, grDevices, graphics, methods, rexposome, limma, sva, ggplot2, ggrepel, PMA, omicade4, gridExtra, MultiDataSet, SmartSVA, isva, parallel, SummarizedExperiment, stringr Suggests: BiocStyle, knitr, rmarkdown, snpStats, brgedata License: MIT + file LICENSE Title: Exposome and omic data associatin and integration analysis Description: omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA). biocViews: ImmunoOncology, WorkflowStep, MultipleComparison, Visualization, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneRegulation, Epigenetics, Proteomics, Transcriptomics, StatisticalMethod, Regression Author: Carles Hernandez-Ferrer [aut, cre], Juan R. González [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr Package: chromVAR Version: 1.33.0 Depends: R (>= 3.4) Imports: IRanges, Seqinfo, GenomicRanges, ggplot2, nabor, BiocParallel, BiocGenerics, Biostrings, TFBSTools, Rsamtools, S4Vectors, methods, Rcpp, grid, plotly, shiny, miniUI, stats, utils, graphics, DT, Rtsne, Matrix, SummarizedExperiment, RColorBrewer, BSgenome LinkingTo: Rcpp, RcppArmadillo Suggests: JASPAR2016, BSgenome.Hsapiens.UCSC.hg19, readr, testthat, knitr, rmarkdown, pheatmap, motifmatchr License: MIT + file LICENSE Title: Chromatin Variation Across Regions Description: Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. biocViews: SingleCell, Sequencing, GeneRegulation, ImmunoOncology Author: Alicia Schep [aut, cre], Jason Buenrostro [ctb], Caleb Lareau [ctb], William Greenleaf [ths], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr Package: bnbc Version: 1.33.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, GenomicRanges Imports: Rcpp (>= 0.12.12), IRanges, rhdf5, data.table, Seqinfo, S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage, utils, HiCBricks LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, RUnit, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 Title: Bandwise normalization and batch correction of Hi-C data Description: Tools to normalize (several) Hi-C data from replicates. biocViews: HiC, Preprocessing, Normalization, Software Author: Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Kipper Fletez-Brant URL: https://github.com/hansenlab/bnbc VignetteBuilder: knitr BugReports: https://github.com/hansenlab/bnbc/issues Package: scPipe Version: 2.11.0 Depends: R (>= 4.2.0), SingleCellExperiment Imports: AnnotationDbi, basilisk, BiocGenerics, biomaRt, Biostrings, data.table, dplyr, DropletUtils, flexmix, GenomicRanges, GenomicAlignments, GGally, ggplot2, glue (>= 1.3.0), grDevices, graphics, hash, IRanges, magrittr, MASS, Matrix (>= 1.5.0), mclust, methods, MultiAssayExperiment, org.Hs.eg.db, org.Mm.eg.db, purrr, Rcpp (>= 0.11.3), reshape, reticulate, Rhtslib, rlang, robustbase, Rsamtools, Rsubread, rtracklayer, SummarizedExperiment, S4Vectors, scales, stats, stringr, tibble, tidyr, tools, utils, vctrs (>= 0.5.2) LinkingTo: Rcpp, Rhtslib (>= 1.13.1), testthat Suggests: BiocStyle, DT, GenomicFeatures, grid, igraph, kableExtra, knitr, locStra, plotly, rmarkdown, RColorBrewer, readr, reshape2, RANN, shiny, scater (>= 1.11.0), testthat, xml2, umap License: GPL (>= 2) NeedsCompilation: yes Title: Pipeline for single cell multi-omic data pre-processing Description: A preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols. biocViews: ImmunoOncology, Software, Sequencing, RNASeq, GeneExpression, SingleCell, Visualization, SequenceMatching, Preprocessing, QualityControl, GenomeAnnotation, DataImport Author: Luyi Tian [aut], Shian Su [aut, cre], Shalin Naik [ctb], Shani Amarasinghe [aut], Oliver Voogd [aut], Phil Yang [aut], Matthew Ritchie [ctb] Maintainer: Shian Su URL: https://github.com/LuyiTian/scPipe SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/LuyiTian/scPipe Package: progeny Version: 1.33.0 Depends: R (>= 3.6.0) Imports: Biobase, stats, dplyr, tidyr, ggplot2, ggrepel, gridExtra, decoupleR, reshape2 Suggests: airway, biomaRt, BiocFileCache, broom, Seurat, SingleCellExperiment, DESeq2, BiocStyle, knitr, readr, readxl, pheatmap, tibble, rmarkdown, testthat (>= 2.1.0) License: Apache License (== 2.0) | file LICENSE Title: Pathway RespOnsive GENes for activity inference from gene expression Description: PROGENy is resource that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway responsive genes for human and mouse. These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics. biocViews: SystemsBiology, GeneExpression, FunctionalPrediction, GeneRegulation Author: Michael Schubert [aut], Alberto Valdeolivas [ctb] (ORCID: ), Christian H. Holland [ctb] (ORCID: ), Igor Bulanov [ctb], Aurélien Dugourd [cre, ctb] Maintainer: Aurélien Dugourd URL: https://github.com/saezlab/progeny VignetteBuilder: knitr BugReports: https://github.com/saezlab/progeny/issues Package: topdownr Version: 1.33.1 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.20.0), ProtGenerics (>= 1.10.0), Biostrings (>= 2.42.1), S4Vectors (>= 0.12.2) Imports: grDevices, stats, tools, utils, Biobase, Matrix (>= 1.4-2), MSnbase (>= 2.33.5), PSMatch (>= 1.11.4), ggplot2 (>= 2.2.1), mzR (>= 2.27.5) Suggests: topdownrdata (>= 0.2), knitr, rmarkdown, ranger, testthat, BiocStyle, xml2 License: GPL (>= 3) Title: Investigation of Fragmentation Conditions in Top-Down Proteomics Description: The topdownr package allows automatic and systemic investigation of fragment conditions. It creates Thermo Orbitrap Fusion Lumos method files to test hundreds of fragmentation conditions. Additionally it provides functions to analyse and process the generated MS data and determine the best conditions to maximise overall fragment coverage. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, Coverage Author: Sebastian Gibb [aut, cre] (ORCID: ), Pavel Shliaha [aut] (ORCID: ), Ole Nørregaard Jensen [aut] (ORCID: ) Maintainer: Sebastian Gibb URL: https://codeberg.org/sgibb/topdownr/ VignetteBuilder: knitr BugReports: https://codeberg.org/sgibb/topdownr/issues/ Package: ontoProc Version: 2.5.2 Depends: R (>= 4.1), ontologyIndex Imports: Biobase, S4Vectors, methods, stats, utils, BiocFileCache, shiny, graph, Rgraphviz, ontologyPlot, dplyr, magrittr, DT, igraph, AnnotationHub, SummarizedExperiment, reticulate, R.utils, httr, basilisk, jsonlite, RBGL, ellmer Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle, SingleCellExperiment, celldex, rmarkdown, AnnotationDbi, magick, License: Artistic-2.0 Title: processing of ontologies of anatomy, cell lines, and so on Description: Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies. biocViews: Infrastructure, GO Author: Vincent Carey [ctb, cre] (ORCID: ), Sara Stankiewicz [ctb], Victor Tarca [ctb] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/ontoProc VignetteBuilder: knitr BugReports: https://github.com/vjcitn/ontoProc/issues Package: tenXplore Version: 1.33.0 Depends: R (>= 4.0), shiny Imports: methods, ontoProc (>= 0.99.7), SummarizedExperiment, AnnotationDbi, matrixStats, org.Mm.eg.db, stats, utils, BiocFileCache Suggests: org.Hs.eg.db, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 Title: ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics Description: Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics. biocViews: ImmunoOncology, DimensionReduction, PrincipalComponent, Transcriptomics, SingleCell Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr Package: runibic Version: 1.33.0 Depends: R (>= 3.4.0), biclust, SummarizedExperiment Imports: Rcpp (>= 0.12.12), testthat, methods LinkingTo: Rcpp Suggests: knitr, rmarkdown, GEOquery, affy, airway, QUBIC License: MIT + file LICENSE NeedsCompilation: yes Title: runibic: row-based biclustering algorithm for analysis of gene expression data in R Description: This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data. biocViews: Microarray, Clustering, GeneExpression, Sequencing, Coverage Author: Patryk Orzechowski, Artur Pańszczyk Maintainer: Patryk Orzechowski URL: http://github.com/athril/runibic SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: http://github.com/athril/runibic/issues Package: gep2pep Version: 1.31.0 Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods, Biobase, XML, rhdf5, digest, iterators Suggests: WriteXLS, testthat, knitr, rmarkdown License: GPL-3 Title: Creation and Analysis of Pathway Expression Profiles (PEPs) Description: Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "drug set enrichment analysis" and "gene2drug" drug discovery analysis respectively. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DimensionReduction, Pathways, GO Author: Francesco Napolitano Maintainer: Francesco Napolitano VignetteBuilder: knitr Package: GOfuncR Version: 1.31.0 Depends: R (>= 3.4), vioplot (>= 0.2), Imports: Rcpp (>= 0.11.5), mapplots (>= 1.5), gtools (>= 3.5.0), GenomicRanges (>= 1.28.4), IRanges, AnnotationDbi, utils, grDevices, graphics, stats, LinkingTo: Rcpp Suggests: Homo.sapiens, BiocStyle, knitr, markdown, rmarkdown, testthat License: GPL (>= 2) NeedsCompilation: yes Title: Gene ontology enrichment using FUNC Description: GOfuncR performs a gene ontology enrichment analysis based on the ontology enrichment software FUNC. GO-annotations are obtained from OrganismDb or OrgDb packages ('Homo.sapiens' by default); the GO-graph is included in the package and updated regularly (01-May-2021). GOfuncR provides the standard candidate vs. background enrichment analysis using the hypergeometric test, as well as three additional tests: (i) the Wilcoxon rank-sum test that is used when genes are ranked, (ii) a binomial test that is used when genes are associated with two counts and (iii) a Chi-square or Fisher's exact test that is used in cases when genes are associated with four counts. To correct for multiple testing and interdependency of the tests, family-wise error rates are computed based on random permutations of the gene-associated variables. GOfuncR also provides tools for exploring the ontology graph and the annotations, and options to take gene-length or spatial clustering of genes into account. It is also possible to provide custom gene coordinates, annotations and ontologies. biocViews: GeneSetEnrichment, GO Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr Package: DropletUtils Version: 1.31.1 Depends: SingleCellExperiment Imports: utils, stats, methods, Matrix, Rcpp, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, BiocParallel, SparseArray (>= 1.5.18), DelayedArray (>= 0.31.9), DelayedMatrixStats, HDF5Array, rhdf5, edgeR, R.utils, dqrng, beachmat, scuttle (>= 1.21.4) LinkingTo: Rcpp, beachmat, assorthead, Rhdf5lib, BH, dqrng, scuttle Suggests: testthat, knitr, BiocStyle, rmarkdown, jsonlite, DropletTestFiles License: GPL-3 NeedsCompilation: yes Title: Utilities for Handling Single-Cell Droplet Data Description: Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix. biocViews: ImmunoOncology, SingleCell, Sequencing, RNASeq, GeneExpression, Transcriptomics, DataImport, Coverage Author: Aaron Lun [aut], Jonathan Griffiths [ctb, cre], Davis McCarthy [ctb], Dongze He [ctb], Rob Patro [ctb] Maintainer: Jonathan Griffiths SystemRequirements: C++17, GNU make VignetteBuilder: knitr Package: mCSEA Version: 1.31.3 Depends: R (>= 3.5), mCSEAdata, Homo.sapiens Imports: biomaRt, fgsea, GenomicFeatures, GenomicRanges, ggplot2, graphics, grDevices, Gviz, IRanges, limma, methods, parallel, S4Vectors, stats, SummarizedExperiment, utils Suggests: Biobase, BiocGenerics, BiocStyle, FlowSorted.Blood.450k, knitr, leukemiasEset, minfi, minfiData, rmarkdown, RUnit License: GPL-2 Title: Methylated CpGs Set Enrichment Analysis Description: Identification of diferentially methylated regions (DMRs) in predefined regions (promoters, CpG islands...) from the human genome using Illumina's 450K or EPIC microarray data. Provides methods to rank CpG probes based on linear models and includes plotting functions. biocViews: ImmunoOncology, DifferentialMethylation, DNAMethylation, Epigenetics, Genetics, GenomeAnnotation, MethylationArray, Microarray, MultipleComparison, TwoChannel Author: Jordi Martorell-Marugán and Pedro Carmona-Sáez Maintainer: Jordi Martorell-Marugán VignetteBuilder: knitr Package: pogos Version: 1.31.0 Depends: R (>= 3.5.0), rjson (>= 0.2.15), httr (>= 1.3.1) Imports: methods, S4Vectors, utils, shiny, ontoProc, ggplot2, graphics Suggests: knitr, DT, ontologyPlot, testthat, rmarkdown, BiocStyle License: Artistic-2.0 Title: PharmacOGenomics Ontology Support Description: Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies. biocViews: Pharmacogenomics, PooledScreens, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr Package: kissDE Version: 1.31.1 Imports: aods3, Biobase, DESeq2, DSS, ggplot2, gplots, graphics, grDevices, matrixStats, stats, utils, foreach, doParallel, parallel, shiny, shinycssloaders, ade4, factoextra, DT, rlang Suggests: BiocStyle, quarto, testthat License: GPL (>= 2) Title: Retrieves Condition-Specific Variants in RNA-Seq Data Description: Retrieves condition-specific variants in RNA-seq data (SNVs, alternative-splicings, indels). It has been developed as a post-treatment of 'KisSplice' but can also be used with user's own data. biocViews: AlternativeSplicing, DifferentialSplicing, ExperimentalDesign, GenomicVariation, RNASeq, Transcriptomics Author: Clara Benoit-Pilven [aut], Camille Marchet [aut], Janice Kielbassa [aut], Lilia Brinza [aut], Audric Cologne [aut], Aurelie Siberchicot [aut, cre] (ORCID: ), Vincent Lacroix [aut], Frank Picard [ctb], Laurent Jacob [ctb], Vincent Miele [ctb] Maintainer: Aurelie Siberchicot URL: https://github.com/lbbe-software/kissDE VignetteBuilder: quarto BugReports: https://github.com/lbbe-software/kissDE/issues Package: scmeth Version: 1.31.0 Depends: R (>= 3.5.0) Imports: BiocGenerics, bsseq, AnnotationHub, Seqinfo, GenomicRanges, reshape2, stats, utils, BSgenome, DelayedArray (>= 0.5.15), annotatr, SummarizedExperiment (>= 1.5.6), GenomeInfoDb, Biostrings, DT, HDF5Array (>= 1.7.5) Suggests: knitr, rmarkdown, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, Biobase, ggplot2, ggthemes License: GPL-2 Title: Functions to conduct quality control analysis in methylation data Description: Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report. biocViews: DNAMethylation, QualityControl, Preprocessing, SingleCell, ImmunoOncology Author: Divy Kangeyan Maintainer: Divy Kangeyan VignetteBuilder: knitr BugReports: https://github.com/aryeelab/scmeth/issues Package: dmrseq Version: 1.31.0 Depends: R (>= 3.5), bsseq Imports: GenomicRanges, nlme, ggplot2, S4Vectors, RColorBrewer, bumphunter, DelayedMatrixStats (>= 1.1.13), matrixStats, BiocParallel, outliers, methods, locfit, IRanges, grDevices, graphics, stats, utils, annotatr, AnnotationHub, rtracklayer, Seqinfo, splines Suggests: knitr, rmarkdown, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: MIT + file LICENSE Title: Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing Description: This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, MultipleComparison, Software, Sequencing, DifferentialMethylation, WholeGenome, Regression, FunctionalGenomics Author: Keegan Korthauer [cre, aut] (ORCID: ), Rafael Irizarry [aut] (ORCID: ), Yuval Benjamini [aut], Sutirtha Chakraborty [aut] Maintainer: Keegan Korthauer VignetteBuilder: knitr Package: GARS Version: 1.31.0 Depends: R (>= 3.5), ggplot2, cluster Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Title: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets Description: Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets. biocViews: Classification, FeatureExtraction, Clustering Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr Package: DEScan2 Version: 1.31.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocParallel, BiocGenerics, ChIPpeakAnno, data.table, DelayedArray, Seqinfo, GenomeInfoDb, GenomicAlignments, glue, IRanges, plyr, Rcpp (>= 0.12.13), rtracklayer, S4Vectors (>= 0.23.19), SummarizedExperiment, tools, utils LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, edgeR, limma, EDASeq, RUVSeq, RColorBrewer, statmod License: Artistic-2.0 Title: Differential Enrichment Scan 2 Description: Integrated peak and differential caller, specifically designed for broad epigenomic signals. biocViews: ImmunoOncology, PeakDetection, Epigenetics, Software, Sequencing, Coverage Author: Dario Righelli [aut, cre], John Koberstein [aut], Bruce Gomes [aut], Nancy Zhang [aut], Claudia Angelini [aut], Lucia Peixoto [aut], Davide Risso [aut] Maintainer: Dario Righelli VignetteBuilder: knitr Package: GSEABenchmarkeR Version: 1.31.0 Depends: R (>= 4.5.0), Biobase, SummarizedExperiment Imports: AnnotationDbi, AnnotationHub, BiocFileCache, BiocParallel, edgeR, EnrichmentBrowser, ExperimentHub, grDevices, graphics, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, methods, S4Vectors, stats, utils Suggests: BiocStyle, GSE62944, knitr, rappdirs, rmarkdown License: Artistic-2.0 Title: Reproducible GSEA Benchmarking Description: The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Lucas Schiffer [ctb], Marcel Ramos [ctb], Ralf Zimmer [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/GSEABenchmarkeR VignetteBuilder: knitr BugReports: https://github.com/waldronlab/GSEABenchmarkeR/issues Package: ASICS Version: 2.27.0 Depends: R (>= 3.5) Imports: BiocParallel, ggplot2, glmnet, grDevices, gridExtra, methods, mvtnorm, PepsNMR, plyr, quadprog, ropls, stats, SummarizedExperiment, utils, Matrix, zoo Suggests: knitr, rmarkdown, BiocStyle, testthat, ASICSdata License: GPL (>= 2) Title: Automatic Statistical Identification in Complex Spectra Description: With a set of pure metabolite reference spectra, ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty. The method and its statistical properties are described in Tardivel et al. (2017) . biocViews: Software, DataImport, Cheminformatics, Metabolomics Author: Gaëlle Lefort [aut, cre], Rémi Servien [aut], Patrick Tardivel [aut], Nathalie Vialaneix [aut] Maintainer: Gaëlle Lefort VignetteBuilder: knitr Package: TCGAutils Version: 1.31.5 Depends: R (>= 4.5.0) Imports: AnnotationDbi, BiocGenerics, BiocBaseUtils, GenomeInfoDb, GenomicFeatures, GenomicRanges, GenomicDataCommons, glue, IRanges, methods, MultiAssayExperiment, RaggedExperiment, rvest, S4Vectors, Seqinfo, stats, stringr, SummarizedExperiment, utils, xml2 Suggests: AnnotationHub, Bioc.gff, BiocFileCache, BiocStyle, curatedTCGAData, ComplexHeatmap, devtools, dplyr, httr, IlluminaHumanMethylation450kanno.ilmn12.hg19, impute, knitr, magrittr, miRNAmeConverter, org.Hs.eg.db, RColorBrewer, readr, rmarkdown, RTCGAToolbox, rtracklayer, R.utils, testthat, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 Title: TCGA utility functions for data management Description: A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. Exported functions include those that import data from flat files into Bioconductor objects, convert row annotations, and identifier translation via the GDC API. biocViews: Software, WorkflowStep, Preprocessing, DataImport Author: Marcel Ramos [aut, cre] (ORCID: ), Lucas Schiffer [aut], Sean Davis [ctb], Levi Waldron [aut], NCI [fnd] (GrantNo.: U24CA289073) Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TCGAutils/issues Package: MSstatsQCgui Version: 1.31.0 Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, grid Suggests: knitr License: Artistic License 2.0 Title: A graphical user interface for MSstatsQC package Description: MSstatsQCgui is a Shiny app which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry, GUI Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc Package: netSmooth Version: 1.31.0 Depends: R (>= 3.5), scater (>= 1.15.11), clusterExperiment (>= 2.1.6) Imports: entropy, SummarizedExperiment, SingleCellExperiment, Matrix, cluster, data.table, stats, methods, DelayedArray, HDF5Array (>= 1.15.13) Suggests: knitr, testthat, Rtsne, biomaRt, igraph, STRINGdb, NMI, pheatmap, ggplot2, BiocStyle, rmarkdown, BiocParallel, uwot License: GPL-3 Title: Network smoothing for scRNAseq Description: netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data. biocViews: Network, GraphAndNetwork, SingleCell, RNASeq, GeneExpression, Sequencing, Transcriptomics, Normalization, Preprocessing, Clustering, DimensionReduction Author: Jonathan Ronen [aut, cre], Altuna Akalin [aut] Maintainer: Jonathan Ronen URL: https://github.com/BIMSBbioinfo/netSmooth VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/netSmooth/issues Package: ORFik Version: 1.31.3 Depends: R (>= 4.1.0), IRanges (>= 2.17.1), GenomicRanges (>= 1.35.1), GenomicAlignments (>= 1.19.0) Imports: AnnotationDbi (>= 1.45.0), Biostrings (>= 2.51.1), biomaRt, biomartr (>= 1.0.7), BiocFileCache, BiocGenerics (>= 0.29.1), BiocParallel (>= 1.19.0), BSgenome, cowplot (>= 1.0.0), data.table (>= 1.11.8), DESeq2 (>= 1.24.0), fst (>= 0.9.2), GenomeInfoDb (>= 1.15.5), GenomicFeatures (>= 1.31.10), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), httr (>= 1.3.0), jsonlite, methods (>= 3.6.0), qs2, R.utils, Rcpp (>= 1.0.0), Rsamtools (>= 1.35.0), rtracklayer (>= 1.43.0), stats, SummarizedExperiment (>= 1.14.0), S4Vectors (>= 0.21.3), tools, txdbmaker, utils, XML, xml2 (>= 1.2.0), withr LinkingTo: Rcpp Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDbData License: MIT + file LICENSE Title: Open Reading Frames in Genomics Description: R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more. biocViews: ImmunoOncology, Software, Sequencing, RiboSeq, RNASeq, FunctionalGenomics, Coverage, Alignment, DataImport Author: Haakon Tjeldnes [aut, cre, dtc] (ORCID: ), Kornel Labun [aut, cph], Michal Swirski [ctb], Katarzyna Chyzynska [ctb, dtc], Yamila Torres Cleuren [ctb, ths], Eivind Valen [ths, fnd] Maintainer: Haakon Tjeldnes URL: https://github.com/Roleren/ORFik VignetteBuilder: knitr BugReports: https://github.com/Roleren/ORFik/issues Package: Ularcirc Version: 1.29.0 Depends: R (>= 3.4.0) Imports: AnnotationHub, AnnotationDbi, BiocGenerics, Biostrings, BSgenome, data.table (>= 1.9.4), DT, GenomicFeatures, GenomeInfoDb, GenomeInfoDbData, GenomicAlignments, GenomicRanges, ggplot2, ggrepel, gsubfn, moments, Organism.dplyr, plotgardener,R.utils, S4Vectors, shiny, shinydashboard, shinyFiles, shinyjs, yaml Suggests: BSgenome.Hsapiens.UCSC.hg38, BiocStyle, httpuv, knitr, org.Hs.eg.db, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE Title: Shiny app for canonical and back splicing analysis (i.e. circular and mRNA analysis) Description: Ularcirc reads in STAR aligned splice junction files and provides visualisation and analysis tools for splicing analysis. Users can assess backsplice junctions and forward canonical junctions. biocViews: DataRepresentation,Visualization, Genetics, Sequencing, Annotation, Coverage, AlternativeSplicing, DifferentialSplicing Author: David Humphreys [aut, cre] Maintainer: David Humphreys VignetteBuilder: knitr Package: qPLEXanalyzer Version: 1.29.6 Depends: R (>= 4.0), Biobase, MSnbase Imports: assertthat, BiocGenerics, Biostrings, dplyr (>= 1.0.0), ggdendro, ggplot2, graphics, grDevices, IRanges, limma, magrittr, MSnbase, preprocessCore, purrr, RColorBrewer, readr, rlang, scales, stats, stringr, tibble, tidyr, tidyselect, utils Suggests: patchwork, knitr, MSnbase, qPLEXdata, rmarkdown, statmod, testthat, UniProt.ws, vdiffr License: GPL-2 Title: Tools for quantitative proteomics data analysis Description: Tools for TMT based quantitative proteomics data analysis. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Normalization, Preprocessing, QualityControl, DataImport Author: Matthew Eldridge [aut], Kamal Kishore [aut], Ashley Sawle [aut, cre] Maintainer: Ashley Sawle VignetteBuilder: knitr BugReports: https://github.com/crukci-bioinformatics/qPLEXanalyzer/issues Package: methylGSA Version: 1.29.0 Depends: R (>= 3.5) Imports: RobustRankAggreg, ggplot2, stringr, stats, clusterProfiler, missMethyl, org.Hs.eg.db, reactome.db, BiocParallel, GO.db, AnnotationDbi, shiny, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Suggests: knitr, rmarkdown, testthat, enrichplot License: GPL-2 Title: Gene Set Analysis Using the Outcome of Differential Methylation Description: The main functions for methylGSA are methylglm and methylRRA. methylGSA implements logistic regression adjusting number of probes as a covariate. methylRRA adjusts multiple p-values of each gene by Robust Rank Aggregation. For more detailed help information, please see the vignette. biocViews: DNAMethylation,DifferentialMethylation,GeneSetEnrichment,Regression, GeneRegulation,Pathways Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/methylGSA VignetteBuilder: knitr BugReports: https://github.com/reese3928/methylGSA/issues Package: appreci8R Version: 1.29.0 Imports: shiny, shinyjs, DT, VariantAnnotation, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, Homo.sapiens, SNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, Biostrings, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.hs37d5, MafDb.gnomADex.r2.1.hs37d5, COSMIC.67, rentrez, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137, seqinr, openxlsx, Rsamtools, stringr, stats, GenomicRanges, S4Vectors, GenomicFeatures, IRanges, GenomicScores, SummarizedExperiment Suggests: GO.db, org.Hs.eg.db, utils License: LGPL-3 Title: appreci8R: an R/Bioconductor package for filtering SNVs and short indels with high sensitivity and high PPV Description: The appreci8R is an R version of our appreci8-algorithm - A Pipeline for PREcise variant Calling Integrating 8 tools. Variant calling results of our standard appreci8-tools (GATK, Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and VarDict), as well as up to 5 additional tools is combined, evaluated and filtered. biocViews: VariantDetection, GeneticVariability, SNP, VariantAnnotation, Sequencing, Author: Sarah Sandmann Maintainer: Sarah Sandmann Package: OUTRIDER Version: 1.29.1 Depends: R (>= 3.6), BiocParallel, GenomicFeatures, SummarizedExperiment, methods Imports: BBmisc, BiocGenerics, data.table, DESeq2 (>= 1.16.1), generics, GenomicRanges, ggplot2, ggrepel, graphics, grDevices, heatmaply, IRanges, matrixStats, pcaMethods, pheatmap, plotly, plyr, pracma, PRROC, RColorBrewer, reshape2, RMTstat, S4Vectors, scales, splines, stats, txdbmaker, utils LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, RMariaDB, AnnotationDbi, beeswarm, covr, GenomeInfoDb, ggbio, biovizBase License: file LICENSE NeedsCompilation: yes Title: OUTRIDER - OUTlier in RNA-Seq fInDER Description: Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results. biocViews: ImmunoOncology, RNASeq, Transcriptomics, Alignment, Sequencing, GeneExpression, Genetics Author: Felix Brechtmann [aut] (ORCID: ), Christian Mertes [aut, cre] (ORCID: ), Agne Matuseviciute [aut], Michaela Fee Müller [ctb], Andrea Raithel [ctb], Vicente Yepez [aut] (ORCID: ), Julien Gagneur [aut] (ORCID: ) Maintainer: Christian Mertes URL: https://github.com/gagneurlab/OUTRIDER VignetteBuilder: knitr BugReports: https://github.com/gagneurlab/OUTRIDER/issues Package: scruff Version: 1.29.0 Depends: R (>= 4.0) Imports: data.table, GenomicAlignments, GenomicFeatures, txdbmaker, GenomicRanges, Rsamtools, ShortRead, parallel, plyr, BiocGenerics, BiocParallel, S4Vectors, AnnotationDbi, Biostrings, methods, ggplot2, ggthemes, scales, GenomeInfoDb, stringdist, ggbio, rtracklayer, SingleCellExperiment, SummarizedExperiment, Rsubread, parallelly, patchwork Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE Title: Single Cell RNA-Seq UMI Filtering Facilitator (scruff) Description: A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics. biocViews: Software, Technology, Sequencing, Alignment, RNASeq, SingleCell, WorkflowStep, Preprocessing, QualityControl, Visualization, ImmunoOncology Author: Zhe Wang [aut, cre], Junming Hu [aut], Joshua Campbell [aut] Maintainer: Zhe Wang VignetteBuilder: knitr BugReports: https://github.com/campbio/scruff/issues Package: tximeta Version: 1.29.13 Depends: R (>= 4.1.0) Imports: SummarizedExperiment (>= 1.39.1), tximport, jsonlite, S4Vectors, IRanges, GenomicRanges (>= 1.61.1), AnnotationDbi, DBI, GenomicFeatures, txdbmaker, ensembldb, BiocFileCache, AnnotationHub, Biostrings, tibble, Seqinfo, tools, utils, methods, Matrix Suggests: knitr, rmarkdown, testthat, tximportData (>= 1.37.5), org.Dm.eg.db, DESeq2, edgeR (>= 4.9.2), limma, devtools, macrophage License: GPL-2 Title: Transcript Quantification Import with Automatic Metadata Description: Transcript quantification import from Salmon and other quantifiers with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility. biocViews: Annotation, GenomeAnnotation, DataImport, Preprocessing, RNASeq, LongRead, SingleCell, Transcriptomics, Transcription, GeneExpression, FunctionalGenomics, ReproducibleResearch, ReportWriting, ImmunoOncology Author: Michael Love [aut, cre], Charlotte Soneson [aut, ctb], Peter Hickey [aut, ctb], Rob Patro [aut, ctb], NIH NHGRI [fnd], CZI [fnd] Maintainer: Michael Love URL: https://github.com/thelovelab/tximeta VignetteBuilder: knitr, rmarkdown Package: primirTSS Version: 1.29.0 Depends: R (>= 3.5.0) Imports: GenomicRanges (>= 1.32.2), S4Vectors (>= 0.18.2), rtracklayer (>= 1.40.3), dplyr (>= 0.7.6), stringr (>= 1.3.1), tidyr (>= 0.8.1), Biostrings (>= 2.48.0), purrr (>= 0.2.5), BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.1), phastCons100way.UCSC.hg38 (>= 3.7.1), GenomicScores (>= 1.4.1), shiny (>= 1.0.5), Gviz (>= 1.24.0), BiocGenerics (>= 0.26.0), IRanges (>= 2.14.10), TFBSTools (>= 1.18.0), JASPAR2018 (>= 1.1.1), tibble (>= 1.4.2), R.utils (>= 2.6.0), stats, utils Suggests: knitr, rmarkdown License: GPL-2 Title: Prediction of pri-miRNA Transcription Start Site Description: A fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs. biocViews: ImmunoOncology, Sequencing, RNASeq, Genetics, Preprocessing, Transcription, GeneRegulation Author: Pumin Li [aut, cre], Qi Xu [aut], Jie Li [aut], Jin Wang [aut] Maintainer: Pumin Li URL: https://github.com/ipumin/primirTSS VignetteBuilder: knitr BugReports: http://github.com/ipumin/primirTSS/issues Package: NormalyzerDE Version: 1.29.0 Depends: R (>= 4.1.0) Imports: vsn, preprocessCore, limma, MASS, ape, car, ggplot2, methods, utils, stats, SummarizedExperiment, matrixStats, ggforce Suggests: knitr, testthat, rmarkdown, roxygen2, hexbin, BiocStyle License: Artistic-2.0 Title: Evaluation of normalization methods and calculation of differential expression analysis statistics Description: NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis. biocViews: Normalization, MultipleComparison, Visualization, Bayesian, Proteomics, Metabolomics, DifferentialExpression Maintainer: Jakob Willforss URL: https://github.com/ComputationalProteomics/NormalyzerDE VignetteBuilder: knitr Package: glmSparseNet Version: 1.29.0 Depends: R (>= 4.3.0) Imports: biomaRt, checkmate, dplyr, forcats, futile.logger, ggplot2, glue, httr, lifecycle, methods, parallel, readr, rlang, glmnet, Matrix, MultiAssayExperiment, SummarizedExperiment, survminer, TCGAutils, utils Suggests: BiocStyle, curatedTCGAData, knitr, magrittr, reshape2, pROC, rmarkdown, survival, testthat, VennDiagram, withr License: GPL-3 NeedsCompilation: no Title: Network Centrality Metrics for Elastic-Net Regularized Models Description: glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian". biocViews: Software, StatisticalMethod, DimensionReduction, Regression, Classification, Survival, Network, GraphAndNetwork Author: André Veríssimo [aut, cre] (ORCID: ), Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb] Maintainer: André Veríssimo URL: https://www.github.com/sysbiomed/glmSparseNet VignetteBuilder: knitr BugReports: https://www.github.com/sysbiomed/glmSparseNet/issues Package: HiCBricks Version: 1.29.0 Depends: R (>= 3.6), utils, curl, rhdf5, R6, grid Imports: ggplot2, viridis, RColorBrewer, scales, reshape2, stringr, data.table, Seqinfo, GenomicRanges, stats, IRanges, grDevices, S4Vectors, digest, tibble, jsonlite, BiocParallel, R.utils, readr, methods Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE Title: Framework for Storing and Accessing Hi-C Data Through HDF Files Description: HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization. biocViews: DataImport, Infrastructure, Software, Technology, Sequencing, HiC Author: Koustav Pal [aut, cre], Carmen Livi [ctb], Ilario Tagliaferri [ctb] Maintainer: Koustav Pal VignetteBuilder: knitr Package: cTRAP Version: 1.29.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationHub, binr, cowplot, data.table, dplyr, DT, fastmatch, fgsea, ggplot2, ggrepel, graphics, highcharter, htmltools, httr, limma, methods, parallel, pbapply, purrr, qs, R.utils, readxl, reshape2, rhdf5, rlang, scales, shiny (>= 1.7.0), shinycssloaders, stats, tibble, tools, utils Suggests: testthat, knitr, covr, rmarkdown, spelling, biomaRt, remotes License: MIT + file LICENSE Title: Identification of candidate causal perturbations from differential gene expression data Description: Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations. biocViews: DifferentialExpression, GeneExpression, RNASeq, Transcriptomics, Pathways, ImmunoOncology, GeneSetEnrichment Author: Bernardo P. de Almeida [aut], Nuno Saraiva-Agostinho [aut, cre], Nuno L. Barbosa-Morais [aut, led] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/cTRAP, https://github.com/nuno-agostinho/cTRAP VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/cTRAP/issues Package: COCOA Version: 2.25.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocGenerics, S4Vectors, IRanges, data.table, ggplot2, Biobase, stats, methods, ComplexHeatmap, MIRA, tidyr, grid, grDevices, simpleCache, fitdistrplus Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 Title: Coordinate Covariation Analysis Description: COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data. biocViews: Epigenetics, DNAMethylation, ATACSeq, DNaseSeq, MethylSeq, MethylationArray, PrincipalComponent, GenomicVariation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, Sequencing, ImmunoOncology Author: John Lawson [aut, cre], Nathan Sheffield [aut] (http://www.databio.org), Jason Smith [ctb] Maintainer: John Lawson URL: http://code.databio.org/COCOA/ VignetteBuilder: knitr BugReports: https://github.com/databio/COCOA Package: consensusDE Version: 1.29.0 Depends: R (>= 3.5), BiocGenerics Imports: airway, AnnotationDbi, BiocParallel, Biobase, Biostrings, data.table, dendextend, DESeq2 (>= 1.20.0), EDASeq, ensembldb, edgeR, EnsDb.Hsapiens.v86, GenomicAlignments, GenomicFeatures, limma, org.Hs.eg.db, pcaMethods, RColorBrewer, Rsamtools, RUVSeq, S4Vectors, stats, SummarizedExperiment, TxDb.Dmelanogaster.UCSC.dm3.ensGene, utils Suggests: knitr, rmarkdown License: GPL-3 Title: RNA-seq analysis using multiple algorithms Description: This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports "Voom", "EdgeR" and "DESeq". It uses RUV-seq (optional) to remove unwanted sources of variation. biocViews: Transcriptomics, MultipleComparison, Clustering, Sequencing, Software Author: Ashley J. Waardenberg [aut, cre], Martha M. Cooper [ctb] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr Package: artMS Version: 1.29.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, bit64, circlize, cluster, corrplot, data.table, dplyr, getopt, ggdendro, ggplot2, gplots, ggrepel, graphics, grDevices, grid, limma, MSstats, openxlsx, org.Hs.eg.db, pheatmap, plotly, plyr, RColorBrewer, scales, seqinr, stats, stringr, tidyr, UpSetR, utils, VennDiagram, yaml Suggests: BiocStyle, ComplexHeatmap, factoextra, FactoMineR, gProfileR, knitr, PerformanceAnalytics, org.Mm.eg.db, rmarkdown, testthat License: GPL (>= 3) + file LICENSE NeedsCompilation: no Title: Analytical R tools for Mass Spectrometry Description: artMS provides a set of tools for the analysis of proteomics label-free datasets. It takes as input the MaxQuant search result output (evidence.txt file) and performs quality control, relative quantification using MSstats, downstream analysis and integration. artMS also provides a set of functions to re-format and make it compatible with other analytical tools, including, SAINTq, SAINTexpress, Phosfate, and PHOTON. Check [http://artms.org](http://artms.org) for details. biocViews: Proteomics, DifferentialExpression, BiomedicalInformatics, SystemsBiology, MassSpectrometry, Annotation, QualityControl, GeneSetEnrichment, Clustering, Normalization, ImmunoOncology, MultipleComparison Author: David Jimenez-Morales [aut, cre] (ORCID: ), Alexandre Rosa Campos [aut, ctb] (ORCID: ), John Von Dollen [aut], Nevan Krogan [aut] (ORCID: ), Danielle Swaney [aut, ctb] (ORCID: ) Maintainer: David Jimenez-Morales URL: http://artms.org VignetteBuilder: knitr BugReports: https://github.com/biodavidjm/artMS/issues Package: LoomExperiment Version: 1.29.0 Depends: R (>= 3.5.0), S4Vectors, SingleCellExperiment, SummarizedExperiment, methods, rhdf5, BiocIO Imports: DelayedArray, GenomicRanges, HDF5Array, Matrix, stats, stringr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, reticulate License: Artistic-2.0 Title: LoomExperiment container Description: The LoomExperiment package provide a means to easily convert the Bioconductor "Experiment" classes to loom files and vice versa. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Martin Morgan, Daniel Van Twisk Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Package: scTensor Version: 2.21.0 Depends: R (>= 4.1.0) Imports: methods, RSQLite, igraph, S4Vectors, plotly, reactome.db, AnnotationDbi, SummarizedExperiment, SingleCellExperiment, nnTensor (>= 1.1.5), ccTensor (>= 1.0.2), rTensor (>= 1.4.8), abind, plotrix, heatmaply, tagcloud, rmarkdown, BiocStyle, knitr, AnnotationHub, MeSHDbi (>= 1.29.2), grDevices, graphics, stats, utils, outliers, Category, meshr (>= 1.99.1), GOstats, ReactomePA, DOSE, crayon, checkmate, BiocManager, visNetwork, schex, ggplot2 Suggests: testthat, LRBaseDbi, Seurat, scTGIF, Homo.sapiens License: Artistic-2.0 Title: Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition Description: The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor. biocViews: DimensionReduction, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr Package: CluMSID Version: 1.27.0 Depends: R (>= 3.6) Imports: mzR, S4Vectors, dbscan, RColorBrewer, ape, network, GGally, ggplot2, plotly, methods, utils, stats, sna, grDevices, graphics, Biobase, gplots, MSnbase Suggests: knitr, rmarkdown, testthat, dplyr, readr, stringr, magrittr, CluMSIDdata, metaMS, metaMSdata, xcms License: MIT + file LICENSE Title: Clustering of MS2 Spectra for Metabolite Identification Description: CluMSID is a tool that aids the identification of features in untargeted LC-MS/MS analysis by the use of MS2 spectra similarity and unsupervised statistical methods. It offers functions for a complete and customisable workflow from raw data to visualisations and is interfaceable with the xmcs family of preprocessing packages. biocViews: Metabolomics, Preprocessing, Clustering Author: Tobias Depke [aut, cre], Raimo Franke [ctb], Mark Broenstrup [ths] Maintainer: Tobias Depke URL: https://github.com/tdepke/CluMSID VignetteBuilder: knitr BugReports: https://github.com/tdepke/CluMSID/issues Package: Rhisat2 Version: 1.27.0 Depends: R (>= 4.4.0) Imports: txdbmaker, SGSeq, GenomicRanges, methods, utils Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 Title: R Wrapper for HISAT2 Aligner Description: An R interface to the HISAT2 spliced short-read aligner by Kim et al. (2015). The package contains wrapper functions to create a genome index and to perform the read alignment to the generated index. biocViews: Alignment, Sequencing, SplicedAlignment Author: Charlotte Soneson [aut, cre] (ORCID: ) Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/Rhisat2 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rhisat2/issues Package: adductomicsR Version: 1.27.0 Depends: R (>= 3.6), adductData, ExperimentHub, AnnotationHub Imports: parallel (>= 3.3.2), data.table (>= 1.10.4), OrgMassSpecR (>= 0.4.6), foreach (>= 1.4.3), mzR (>= 2.14.0), ade4 (>= 1.7.6), rvest (>= 0.3.2), pastecs (>= 1.3.18), reshape2 (>= 1.4.2), pracma (>= 2.0.4), DT (>= 0.2), fpc (>= 2.1.10), doSNOW (>= 1.0.14), fastcluster (>= 1.1.22), RcppEigen (>= 0.3.3.3.0), bootstrap (>= 2017.2), smoother (>= 1.1), dplyr (>= 0.7.5), zoo (>= 1.8), stats (>= 3.5.0), utils (>= 3.5.0), graphics (>= 3.5.0), grDevices (>= 3.5.0), methods (>= 3.5.0), datasets (>= 3.5.0) Suggests: knitr (>= 1.15.1), rmarkdown (>= 1.5), Rdisop (>= 1.34.0), testthat License: Artistic-2.0 Title: Processing of adductomic mass spectral datasets Description: Processes MS2 data to identify potentially adducted peptides from spectra that has been corrected for mass drift and retention time drift and quantifies MS1 level mass spectral peaks. biocViews: MassSpectrometry,Metabolomics,Software,ThirdPartyClient,DataImport, GUI Author: Josie Hayes Maintainer: Josie Hayes VignetteBuilder: knitr BugReports: https://github.com/JosieLHayes/adductomicsR/issues Package: Rcwl Version: 1.27.0 Depends: R (>= 3.6), yaml, methods, S4Vectors Imports: utils, stats, BiocParallel, batchtools, DiagrammeR, shiny, R.utils, codetools, basilisk Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 | file LICENSE Title: An R interface to the Common Workflow Language Description: The Common Workflow Language (CWL) is an open standard for development of data analysis workflows that is portable and scalable across different tools and working environments. Rcwl provides a simple way to wrap command line tools and build CWL data analysis pipelines programmatically within R. It increases the ease of usage, development, and maintenance of CWL pipelines. biocViews: Software, WorkflowStep, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut] Maintainer: Qiang Hu VignetteBuilder: knitr Package: PrInCE Version: 1.27.0 Depends: R (>= 3.6.0) Imports: purrr (>= 0.2.4), dplyr (>= 0.7.4), tidyr (>= 0.8.99), forecast (>= 8.2), progress (>= 1.1.2), Hmisc (>= 4.0), naivebayes (>= 0.9.1), robustbase (>= 0.92-7), ranger (>= 0.8.0), LiblineaR (>= 2.10-8), speedglm (>= 0.3-2), tester (>= 0.1.7), magrittr (>= 1.5), Biobase (>= 2.40.0), MSnbase (>= 2.8.3), stats, utils, methods, Rdpack (>= 0.7) Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 + file LICENSE Title: Predicting Interactomes from Co-Elution Description: PrInCE (Predicting Interactomes from Co-Elution) uses a naive Bayes classifier trained on dataset-derived features to recover protein-protein interactions from co-elution chromatogram profiles. This package contains the R implementation of PrInCE. biocViews: Proteomics, SystemsBiology, NetworkInference Author: Michael Skinnider [aut, trl, cre], R. Greg Stacey [ctb], Nichollas Scott [ctb], Anders Kristensen [ctb], Leonard Foster [aut, led] Maintainer: Michael Skinnider VignetteBuilder: knitr BugReports: https://github.com/fosterlab/PrInCE/issues Package: RcwlPipelines Version: 1.27.0 Depends: R (>= 3.6), Rcwl, BiocFileCache Imports: rappdirs, methods, utils, git2r, httr, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 Title: Bioinformatics pipelines based on Rcwl Description: A collection of Bioinformatics tools and pipelines based on R and the Common Workflow Language. biocViews: Software, WorkflowStep, Alignment, Preprocessing, QualityControl, DNASeq, RNASeq, DataImport, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut], Shuang Gao [aut] Maintainer: Qiang Hu SystemRequirements: nodejs VignetteBuilder: knitr Package: profileplyr Version: 1.27.1 Depends: R (>= 3.6), BiocGenerics, SummarizedExperiment Imports: GenomicRanges, stats, soGGi, methods, utils, S4Vectors, R.utils, dplyr, magrittr, tidyr, IRanges, rjson, ChIPseeker,GenomicFeatures,TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene,org.Hs.eg.db,org.Mm.eg.db,rGREAT, pheatmap, ggplot2, EnrichedHeatmap, ComplexHeatmap, grid, circlize, BiocParallel, rtracklayer, Seqinfo, grDevices, rlang, tiff, Rsamtools Suggests: GenomeInfoDb, BiocStyle, testthat, knitr, rmarkdown, png, Cairo License: GPL (>= 3) Title: Visualization and annotation of read signal over genomic ranges with profileplyr Description: Quick and straightforward visualization of read signal over genomic intervals is key for generating hypotheses from sequencing data sets (e.g. ChIP-seq, ATAC-seq, bisulfite/methyl-seq). Many tools both inside and outside of R and Bioconductor are available to explore these types of data, and they typically start with a bigWig or BAM file and end with some representation of the signal (e.g. heatmap). profileplyr leverages many Bioconductor tools to allow for both flexibility and additional functionality in workflows that end with visualization of the read signal. biocViews: ChIPSeq, DataImport, Sequencing, ChipOnChip, Coverage Author: Tom Carroll and Doug Barrows Maintainer: Tom Carroll , Doug Barrows VignetteBuilder: knitr Package: CopyNumberPlots Version: 1.27.0 Depends: R (>= 3.6), karyoploteR Imports: regioneR, IRanges, Rsamtools, SummarizedExperiment, VariantAnnotation, methods, stats, GenomeInfoDb, GenomicRanges, cn.mops, rhdf5, utils Suggests: BiocStyle, knitr, rmarkdown, panelcn.mops, BSgenome.Hsapiens.UCSC.hg19.masked, DNAcopy, testthat License: Artistic-2.0 Title: Create Copy-Number Plots using karyoploteR functionality Description: CopyNumberPlots have a set of functions extending karyoploteRs functionality to create beautiful, customizable and flexible plots of copy-number related data. biocViews: Visualization, CopyNumberVariation, Coverage, OneChannel, DataImport, Sequencing, DNASeq Author: Bernat Gel and Miriam Magallon Maintainer: Bernat Gel URL: https://github.com/bernatgel/CopyNumberPlots VignetteBuilder: knitr BugReports: https://github.com/bernatgel/CopyNumberPlots/issues Package: lipidr Version: 2.25.0 Depends: R (>= 3.6.0), SummarizedExperiment Imports: methods, stats, utils, data.table, S4Vectors, rlang, dplyr, tidyr, forcats, ggplot2, limma, fgsea, ropls, imputeLCMD, magrittr Suggests: knitr, rmarkdown, BiocStyle, ggrepel, plotly, spelling, testthat License: MIT + file LICENSE Title: Data Mining and Analysis of Lipidomics Datasets Description: lipidr an easy-to-use R package implementing a complete workflow for downstream analysis of targeted and untargeted lipidomics data. lipidomics results can be imported into lipidr as a numerical matrix or a Skyline export, allowing integration into current analysis frameworks. Data mining of lipidomics datasets is enabled through integration with Metabolomics Workbench API. lipidr allows data inspection, normalization, univariate and multivariate analysis, displaying informative visualizations. lipidr also implements a novel Lipid Set Enrichment Analysis (LSEA), harnessing molecular information such as lipid class, total chain length and unsaturation. biocViews: Lipidomics, MassSpectrometry, Normalization, QualityControl, Visualization Author: Ahmed Mohamed [cre] (ORCID: ), Ahmed Mohamed [aut], Jeffrey Molendijk [aut] Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/lipidr VignetteBuilder: knitr BugReports: https://github.com/ahmohamed/lipidr/issues/ Package: RNAmodR Version: 1.25.0 Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomicRanges, Modstrings Imports: methods, stats, grDevices, matrixStats, BiocGenerics, Biostrings (>= 2.57.2), BiocParallel, txdbmaker, GenomicFeatures, GenomicAlignments, Seqinfo, rtracklayer, Rsamtools, BSgenome, RColorBrewer, colorRamps, ggplot2, Gviz (>= 1.31.0), reshape2, graphics, ROCR Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data License: Artistic-2.0 Title: Detection of post-transcriptional modifications in high throughput sequencing data Description: RNAmodR provides classes and workflows for loading/aggregation data from high througput sequencing aimed at detecting post-transcriptional modifications through analysis of specific patterns. In addition, utilities are provided to validate and visualize the results. The RNAmodR package provides a core functionality from which specific analysis strategies can be easily implemented as a seperate package. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR/issues Package: RNAmodR.RiboMethSeq Version: 1.25.0 Depends: R (>= 4.0), RNAmodR (>= 1.5.3) Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, Seqinfo, RNAmodR.Data License: Artistic-2.0 Title: Detection of 2'-O methylations by RiboMethSeq Description: RNAmodR.RiboMethSeq implements the detection of 2'-O methylations on RNA from experimental data generated with the RiboMethSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.RiboMethSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.RiboMethSeq/issues Package: RNAmodR.AlkAnilineSeq Version: 1.25.0 Depends: R (>= 4.0), RNAmodR (>= 1.5.3) Imports: methods, S4Vectors, IRanges, BiocGenerics, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, Biostrings, Seqinfo, RNAmodR.Data License: Artistic-2.0 Title: Detection of m7G, m3C and D modification by AlkAnilineSeq Description: RNAmodR.AlkAnilineSeq implements the detection of m7G, m3C and D modifications on RNA from experimental data generated with the AlkAnilineSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq/issues Package: RNAmodR.ML Version: 1.25.0 Depends: R (>= 3.6), RNAmodR Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, stats, ranger Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data, RNAmodR.AlkAnilineSeq, GenomicFeatures, Rsamtools, rtracklayer, keras License: Artistic-2.0 Title: Detecting patterns of post-transcriptional modifications using machine learning Description: RNAmodR.ML extend the functionality of the RNAmodR package and classical detection strategies towards detection through machine learning models. RNAmodR.ML provides classes, functions and an example workflow to establish a detection stratedy, which can be packaged. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (ORCID: ), Denis L.J. Lafontaine [ctb] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.ML VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.ML/issues Package: KnowSeq Version: 1.25.0 Depends: R (>= 4.0), cqn (>= 1.28.1) Imports: stringr, methods, ggplot2 (>= 3.3.0), jsonlite, kernlab, rlist, rmarkdown, reshape2, e1071, randomForest, caret, XML, praznik, R.utils, httr, sva (>= 3.30.1), edgeR (>= 3.24.3), limma (>= 3.38.3), grDevices, graphics, stats, utils, Hmisc (>= 4.4.0), gridExtra Suggests: knitr License: GPL (>=2) Title: KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline Description: KnowSeq proposes a novel methodology that comprises the most relevant steps in the Transcriptomic gene expression analysis. KnowSeq expects to serve as an integrative tool that allows to process and extract relevant biomarkers, as well as to assess them through a Machine Learning approaches. Finally, the last objective of KnowSeq is the biological knowledge extraction from the biomarkers (Gene Ontology enrichment, Pathway listing and Visualization and Evidences related to the addressed disease). Although the package allows analyzing all the data manually, the main strenght of KnowSeq is the possibilty of carrying out an automatic and intelligent HTML report that collect all the involved steps in one document. It is important to highligh that the pipeline is totally modular and flexible, hence it can be started from whichever of the different steps. KnowSeq expects to serve as a novel tool to help to the experts in the field to acquire robust knowledge and conclusions for the data and diseases to study. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataImport, Classification, FeatureExtraction, Sequencing, RNASeq, BatchEffect, Normalization, Preprocessing, QualityControl, Genetics, Transcriptomics, Microarray, Alignment, Pathways, SystemsBiology, GO, ImmunoOncology Author: Daniel Castillo-Secilla [aut, cre], Juan Manuel Galvez [ctb], Francisco Carrillo-Perez [ctb], Marta Verona-Almeida [ctb], Daniel Redondo-Sanchez [ctb], Francisco Manuel Ortuno [ctb], Luis Javier Herrera [ctb], Ignacio Rojas [ctb] Maintainer: Daniel Castillo-Secilla VignetteBuilder: knitr git_url: https://github.com/CasedUgr/KnowSeq Package: ncGTW Version: 1.25.0 Depends: methods, BiocParallel, xcms Imports: Rcpp, grDevices, graphics, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-2 Title: Alignment of LC-MS Profiles by Neighbor-wise Compound-specific Graphical Time Warping with Misalignment Detection Description: The purpose of ncGTW is to help XCMS for LC-MS data alignment. Currently, ncGTW can detect the misaligned feature groups by XCMS, and the user can choose to realign these feature groups by ncGTW or not. biocViews: Software, MassSpectrometry, Metabolomics, Alignment Author: Chiung-Ting Wu Maintainer: Chiung-Ting Wu VignetteBuilder: knitr BugReports: https://github.com/ChiungTingWu/ncGTW/issues Package: GenomicOZone Version: 1.25.0 Depends: R (>= 4.0.0), Ckmeans.1d.dp (>= 4.3.0), GenomicRanges, biomaRt, ggplot2 Imports: grDevices, stats, utils, plyr, gridExtra, lsr, parallel, ggbio, S4Vectors, IRanges, Seqinfo, Rdpack Suggests: readxl, GEOquery, knitr, rmarkdown License: LGPL (>=3) NeedsCompilation: no Title: Delineate outstanding genomic zones of differential gene activity Description: The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. It enables characterization of effects on multiple genes within adaptive genomic neighborhoods, which could arise from genome reorganization, structural variation, or epigenome alteration. It guarantees cluster optimality, linear runtime to sample size, and reproducibility. One can apply it on genome-wide activity measurements such as copy number, transcriptomic, proteomic, and methylation data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, FunctionalPrediction, GeneRegulation, BiomedicalInformatics, CellBiology, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Clustering, Regression, RNASeq, Annotation, Visualization, Sequencing, Coverage, DifferentialMethylation, GenomicVariation, StructuralVariation, CopyNumberVariation Author: Hua Zhong, Mingzhou Song Maintainer: Hua Zhong, Mingzhou Song VignetteBuilder: knitr Package: BUSpaRse Version: 1.25.0 Depends: R (>= 3.6) Imports: AnnotationDbi, AnnotationFilter, AnnotationHub, biomaRt, BiocGenerics, Biostrings, BSgenome, dplyr, ensembldb, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, IRanges, lifecycle, magrittr, Matrix, methods, plyranges, Rcpp, S4Vectors, stats, stringr, tibble, tidyr, utils, zeallot LinkingTo: Rcpp, RcppArmadillo, RcppProgress, BH Suggests: knitr, rmarkdown, testthat, BiocStyle, txdbmaker, TENxBUSData, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: BSD_2_clause + file LICENSE Title: kallisto | bustools R utilities Description: The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Here biotypes can be filtered and scaffolds and haplotypes can be removed, and the filtered transcriptome can be extracted and written to disk. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices. biocViews: SingleCell, RNASeq, WorkflowStep Author: Lambda Moses [aut, cre] (ORCID: ), Lior Pachter [aut, ths] (ORCID: ) Maintainer: Lambda Moses URL: https://github.com/BUStools/BUSpaRse SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/BUStools/BUSpaRse/issues Package: muscat Version: 1.25.4 Depends: R (>= 4.6) Imports: BiocParallel, blme, ComplexHeatmap, dplyr, edgeR, ggplot2, glmmTMB, grDevices, grid, limma, lmerTest, lme4, Matrix, MatrixGenerics, matrixStats, methods, progress, rlang, S4Vectors, scales, scater, scuttle, stats, SingleCellExperiment, SummarizedExperiment, variancePartition Suggests: BiocStyle, countsimQC, DESeq2, AnnotationHub, ExperimentHub, iCOBRA, IHW, knitr, patchwork, phylogram, RColorBrewer, reshape2, rmarkdown, sctransform, statmod, stageR, testthat, tidyr, UpSetR License: GPL-3 Title: Multi-sample multi-group scRNA-seq data analysis tools Description: `muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data. biocViews: ImmunoOncology, DifferentialExpression, Sequencing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre] (ORCID: ), Pierre-Luc Germain [aut], Charlotte Soneson [aut], Anthony Sonrel [aut], Jeroen Gilis [aut], Davide Risso [aut], Lieven Clement [aut], Mark D. Robinson [aut, fnd] Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/muscat VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/muscat/issues Package: SharedObject Version: 1.25.0 Depends: R (>= 3.6.0) Imports: Rcpp, methods, stats, BiocGenerics LinkingTo: BH, Rcpp Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle License: GPL-3 Title: Sharing R objects across multiple R processes without memory duplication Description: This package is developed for facilitating parallel computing in R. It is capable to create an R object in the shared memory space and share the data across multiple R processes. It avoids the overhead of memory dulplication and data transfer, which make sharing big data object across many clusters possible. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/SharedObject/issues Package: ReactomeGSA Version: 1.25.1 Imports: Biobase, BiocSingular, dplyr, ggplot2, gplots, httr, igraph, jsonlite, methods, progress, RColorBrewer, SummarizedExperiment, tidyr Suggests: devtools, knitr, ReactomeGSA.data, rmarkdown, scater, scran, scRNAseq, scuttle, Seurat (>= 3.0), SingleCellExperiment, testthat License: MIT + file LICENSE Title: Client for the Reactome Analysis Service for comparative multi-omics gene set analysis Description: The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited. biocViews: GeneSetEnrichment, Proteomics, Transcriptomics, SystemsBiology, GeneExpression, Reactome Author: Johannes Griss [aut, cre] () Maintainer: Johannes Griss URL: https://github.com/reactome/ReactomeGSA VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGSA/issues Package: Spaniel Version: 1.25.0 Depends: R (>= 4.0) Imports: Seurat, SingleCellExperiment, SummarizedExperiment, dplyr, methods, ggplot2, scater (>= 1.13), scran, igraph, shiny, jpeg, magrittr, utils, S4Vectors, DropletUtils, jsonlite, png Suggests: knitr, rmarkdown, testthat, devtools License: MIT + file LICENSE Title: Spatial Transcriptomics Analysis Description: Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. Spaniel can import data from either the original Spatial Transcriptomics system or 10X Visium technology. The package contains functions to create a SingleCellExperiment Seurat object and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage, Clustering Author: Rachel Queen [aut, cre] Maintainer: Rachel Queen VignetteBuilder: knitr Package: peakPantheR Version: 1.25.0 Depends: R (>= 4.5) Imports: foreach (>= 1.4.4), doParallel (>= 1.0.11), ggplot2 (>= 3.5.0), gridExtra (>= 2.3), MSnbase (>= 2.4.0), mzR (>= 2.12.0), stringr (>= 1.2.0), methods (>= 3.4.0), XML (>= 3.98.1.10), minpack.lm (>= 1.2.1), scales(>= 0.5.0), shiny (>= 1.0.5), bslib, shinycssloaders (>= 1.0.0), DT (>= 0.15), pracma (>= 2.2.3), utils, lubridate, svglite (>= 2.1.1) Suggests: testthat, devtools, faahKO, msdata, knitr, rmarkdown, pander, BiocStyle License: GPL-3 Title: Peak Picking and Annotation of High Resolution Experiments Description: An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained. biocViews: MassSpectrometry, Metabolomics, PeakDetection Author: Arnaud Wolfer [aut, cre] (ORCID: ), Goncalo Correia [aut] (ORCID: ), Jake Pearce [ctb], Caroline Sands [ctb] Maintainer: Arnaud Wolfer URL: https://github.com/phenomecentre/peakPantheR VignetteBuilder: knitr BugReports: https://github.com/phenomecentre/peakPantheR/issues/new Package: GmicR Version: 1.25.0 Imports: AnnotationDbi, ape, bnlearn, Category, DT, doParallel, foreach, gRbase, GSEABase, gRain, GOstats, org.Hs.eg.db, org.Mm.eg.db, shiny, WGCNA, data.table, grDevices, graphics, reshape2, stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 + file LICENSE Title: Combines WGCNA and xCell readouts with bayesian network learrning to generate a Gene-Module Immune-Cell network (GMIC) Description: This package uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell. It is a hypothesis generating tool. biocViews: Software, SystemsBiology, GraphAndNetwork, Network, NetworkInference, GUI, ImmunoOncology, GeneExpression, QualityControl, Bayesian, Clustering Author: Richard Virgen-Slane Maintainer: Richard Virgen-Slane VignetteBuilder: knitr Package: methylCC Version: 1.25.2 Depends: R (>= 3.6), FlowSorted.Blood.450k Imports: Biobase, GenomicRanges, IRanges, S4Vectors, dplyr, magrittr, minfi, bsseq, quadprog, stats, utils, bumphunter, genefilter, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19 Suggests: rmarkdown, knitr, testthat (>= 2.1.0), BiocGenerics, BiocStyle, tidyr, ggplot2 License: GPL-3 Title: Estimate the cell composition of whole blood in DNA methylation samples Description: A tool to estimate the cell composition of DNA methylation whole blood sample measured on any platform technology (microarray and sequencing). biocViews: Microarray, Sequencing, DNAMethylation, MethylationArray, MethylSeq, WholeGenome Author: Stephanie C. Hicks [aut, cre] (ORCID: ), Rafael Irizarry [aut] (ORCID: ) Maintainer: Stephanie C. Hicks URL: https://github.com/stephaniehicks/methylCC/ VignetteBuilder: knitr BugReports: https://github.com/stephaniehicks/methylCC/ Package: CNVfilteR Version: 1.25.0 Depends: R (>= 4.3) Imports: IRanges, GenomicRanges, SummarizedExperiment, pracma, regioneR, assertthat, karyoploteR, CopyNumberPlots, graphics, utils, VariantAnnotation, Rsamtools, GenomeInfoDb, Biostrings, methods Suggests: knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, rmarkdown License: Artistic-2.0 Title: Identifies false positives of CNV calling tools by using SNV calls Description: CNVfilteR identifies those CNVs that can be discarded by using the single nucleotide variant (SNV) calls that are usually obtained in common NGS pipelines. biocViews: CopyNumberVariation, Sequencing, DNASeq, Visualization, DataImport Author: Jose Marcos Moreno-Cabrera [aut, cre] (ORCID: ), Bernat Gel [aut] Maintainer: Jose Marcos Moreno-Cabrera URL: https://github.com/jpuntomarcos/CNVfilteR VignetteBuilder: knitr BugReports: https://github.com/jpuntomarcos/CNVfilteR/issues Package: OmnipathR Version: 3.19.12 Depends: R(>= 4.0) Imports: checkmate, crayon, curl, digest, dplyr(>= 1.1.0), fs, httr2, igraph, jsonlite, later, logger, lubridate, magrittr, progress, purrr, rappdirs, readr(>= 2.0.0), readxl, rlang, rmarkdown, RSQLite, R.utils, rvest, sessioninfo, stats, stringi, stringr, tibble, tidyr, tidyselect, tools, utils, vctrs, withr, XML, xml2, yaml, zip Suggests: BiocStyle, bookdown, ggplot2, ggraph, gprofiler2, knitr, mlrMBO, parallelMap, ParamHelpers, R.matlab, sigmajs, smoof, testthat License: MIT + file LICENSE Title: OmniPath web service client and more Description: A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github). biocViews: GraphAndNetwork, Network, Pathways, Software, ThirdPartyClient, DataImport, DataRepresentation, GeneSignaling, GeneRegulation, SystemsBiology, Transcriptomics, SingleCell, Annotation, KEGG Author: Alberto Valdeolivas [aut] (ORCID: ), Denes Turei [cre, aut] (ORCID: ), Attila Gabor [aut] (ORCID: ), Diego Mananes [aut] (ORCID: ), Aurelien Dugourd [aut] (ORCID: ) Maintainer: Denes Turei URL: https://r.omnipathdb.org/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/OmnipathR/issues Package: biscuiteer Version: 1.25.1 Depends: R (>= 4.1.0), biscuiteerData, bsseq Imports: readr, qualV, Matrix, impute, HDF5Array, S4Vectors, Rsamtools, data.table, Biobase, GenomicRanges, IRanges, BiocGenerics, VariantAnnotation, DelayedMatrixStats, SummarizedExperiment, GenomeInfoDb, Mus.musculus, Homo.sapiens, matrixStats, rtracklayer, QDNAseq, dmrseq, methods, utils, R.utils, gtools, BiocParallel Suggests: covr, knitr, rmarkdown, markdown, rlang, scmeth, pkgdown, roxygen2, testthat, QDNAseq.hg19, QDNAseq.mm10, BiocStyle License: GPL-3 Title: Convenience Functions for Biscuit Description: A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc. biocViews: DataImport, MethylSeq, DNAMethylation Author: Tim Triche [aut], Wanding Zhou [aut], Benjamin Johnson [aut], Jacob Morrison [aut, cre], Lyong Heo [aut], James Eapen [aut] Maintainer: Jacob Morrison URL: https://github.com/trichelab/biscuiteer VignetteBuilder: knitr BugReports: https://github.com/trichelab/biscuiteer/issues Package: methrix Version: 1.25.0 Depends: R (>= 3.6), data.table (>= 1.12.4), SummarizedExperiment Imports: rtracklayer, DelayedArray, HDF5Array, BSgenome, DelayedMatrixStats, parallel, methods, ggplot2, S4Vectors, matrixStats, graphics, stats, utils, GenomicRanges, IRanges Suggests: knitr, rmarkdown, DSS, bsseq, plotly, BSgenome.Mmusculus.UCSC.mm9, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, BSgenome.Hsapiens.UCSC.hg19, GenomicScores, Biostrings, RColorBrewer, GenomeInfoDb, testthat (>= 2.1.0) License: MIT + file LICENSE Title: Fast and efficient summarization of generic bedGraph files from Bisufite sequencing Description: Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions. biocViews: DNAMethylation, Sequencing, Coverage Author: Anand Mayakonda [aut, cre] (ORCID: ), Reka Toth [aut] (ORCID: ), Rajbir Batra [ctb], Clarissa Feuerstein-Akgöz [ctb], Joschka Hey [ctb], Maximilian Schönung [ctb], Pavlo Lutsik [ctb] Maintainer: Anand Mayakonda URL: https://github.com/CompEpigen/methrix VignetteBuilder: knitr BugReports: https://github.com/CompEpigen/methrix/issues Package: signatureSearch Version: 1.25.1 Depends: R(>= 4.5.0), Rcpp, SummarizedExperiment, org.Hs.eg.db Imports: AnnotationDbi, ggplot2, data.table, ExperimentHub, HDF5Array, magrittr, RSQLite, dplyr, fgsea, scales, methods, qvalue, stats, utils, reshape2, visNetwork, BiocParallel, fastmatch, reactome.db, Matrix, readr, rhdf5, GSEABase, DelayedArray, GO.db, BiocGenerics, tibble, DOSE, AnnotationHub, stringr LinkingTo: Rcpp Suggests: knitr, testthat, rmarkdown, BiocStyle, signatureSearchData, DT License: Artistic-2.0 Title: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis Description: This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. biocViews: Software, GeneExpression, GO, NetworkEnrichment, Sequencing, Coverage, DifferentialExpression Author: Yuzhu Duan [aut], Brendan Gongol [cre, aut], Thomas Girke [aut] Maintainer: Brendan Gongol URL: https://github.com/yduan004/signatureSearch/ SystemRequirements: C++20 VignetteBuilder: knitr BugReports: https://github.com/yduan004/signatureSearch/issues Package: BiocSet Version: 1.25.0 Depends: R (>= 3.6), dplyr Imports: methods, tibble, utils, rlang, plyr, S4Vectors, BiocIO, AnnotationDbi, KEGGREST, ontologyIndex, tidyr Suggests: GSEABase, airway, org.Hs.eg.db, DESeq2, limma, BiocFileCache, GO.db, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 Title: Representing Different Biological Sets Description: BiocSet displays different biological sets in a triple tibble format. These three tibbles are `element`, `set`, and `elementset`. The user has the abilty to activate one of these three tibbles to perform common functions from the dplyr package. Mapping functionality and accessing web references for elements/sets are also available in BiocSet. biocViews: GeneExpression, GO, KEGG, Software Author: Kayla Morrell [aut, cre], Martin Morgan [aut], Kevin Rue-Albrecht [ctb], Lluís Revilla Sancho [ctb] Maintainer: Kayla Morrell VignetteBuilder: knitr Package: BgeeCall Version: 1.27.1 Depends: R (>= 3.6) Imports: AnnotationDbi, curl, ggplot2, scales, GenomicFeatures, tximport, Biostrings, readr, sjmisc, RCurl, RSQLite, tools, stringr, rtracklayer, jsonlite, methods, dplyr, data.table, sjmisc, grDevices, graphics, stats, utils, rslurm, rhdf5, txdbmaker, IRanges, spatstat.univar Suggests: knitr, testthat, rmarkdown, AnnotationHub, GenomeInfoDb, httr License: GPL-3 + file LICENSE NeedsCompilation: no Title: Automatic RNA-Seq present/absent gene expression calls generation Description: BgeeCall allows to generate present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee (https://bgee.org). biocViews: Software, GeneExpression, RNASeq Author: Julien Wollbrett [aut, cre], Alessandro Brandulas Cammarata [aut], Sara Fonseca Costa [aut], Julien Roux [aut], Marc Robinson Rechavi [ctb], Frederic Bastian [aut] Maintainer: Julien Wollbrett URL: https://github.com/BgeeDB/BgeeCall SystemRequirements: kallisto VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeCall/issues Package: GeneTonic Version: 3.5.0 Depends: R (>= 4.0.0) Imports: AnnotationDbi, backbone, bs4Dash (>= 2.0.0), circlize, colorspace, colourpicker, ComplexHeatmap, ComplexUpset, dendextend, DESeq2, dplyr, DT, dynamicTreeCut, expm, ggforce, ggplot2 (>= 3.5.0), ggrepel, ggridges, GO.db, graphics, grDevices, grid, igraph, matrixStats, methods, mosdef (>= 1.1.0), plotly, RColorBrewer, rintrojs, rlang, rmarkdown, S4Vectors, scales, shiny, shinyAce, shinycssloaders, shinyWidgets, stats, SummarizedExperiment, tidyr, tippy, tools, utils, viridis, visNetwork Suggests: knitr, BiocStyle, htmltools, clusterProfiler, macrophage, org.Hs.eg.db, magrittr, testthat (>= 2.1.0) License: MIT + file LICENSE Title: Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis Description: This package provides functionality to combine the existing pieces of the transcriptome data and results, making it easier to generate insightful observations and hypothesis. Its usage is made easy with a Shiny application, combining the benefits of interactivity and reproducibility e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. Using the GeneTonicList as a standardized container for all the required components, it is possible to simplify the generation of multiple visualizations and summaries. biocViews: GUI, GeneExpression, Software, Transcription, Transcriptomics, Visualization, DifferentialExpression, Pathways, ReportWriting, GeneSetEnrichment, Annotation, GO, ShinyApps Author: Federico Marini [aut, cre] (ORCID: ), Annekathrin Nedwed [aut] (ORCID: ), Edoardo Filippi [ctb] (ORCID: ) Maintainer: Federico Marini URL: https://github.com/federicomarini/GeneTonic VignetteBuilder: knitr BugReports: https://github.com/federicomarini/GeneTonic/issues Package: MEAT Version: 1.23.0 Depends: R (>= 4.0) Imports: impute (>= 1.58), dynamicTreeCut (>= 1.63), glmnet (>= 2.0), grDevices, graphics, stats, utils, stringr, tibble, RPMM (>= 1.25), minfi (>= 1.30), dplyr, SummarizedExperiment, wateRmelon Suggests: knitr, markdown, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: MIT + file LICENSE NeedsCompilation: no Title: Muscle Epigenetic Age Test Description: This package estimates epigenetic age in skeletal muscle, using DNA methylation data generated with the Illumina Infinium technology (HM27, HM450 and HMEPIC). biocViews: Epigenetics, DNAMethylation, Microarray, Normalization, BiomedicalInformatics, MethylationArray, Preprocessing Author: Sarah Voisin [aut, cre] (), Steve Horvath [ctb] () Maintainer: Sarah Voisin URL: https://github.com/sarah-voisin/MEAT VignetteBuilder: knitr BugReports: https://github.com/sarah-voisin/MEAT/issues Package: basilisk Version: 1.23.0 Depends: reticulate Imports: utils, methods, parallel, dir.expiry Suggests: knitr, rmarkdown, BiocStyle, testthat, callr License: GPL-3 Title: Freezing Python Dependencies Inside Bioconductor Packages Description: Installs a self-contained conda instance that is managed by the R/Bioconductor installation machinery. This aims to provide a consistent Python environment that can be used reliably by Bioconductor packages. Functions are also provided to enable smooth interoperability of multiple Python environments in a single R session. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph], Vince Carey [ctb] Maintainer: Aaron Lun VignetteBuilder: knitr BugReports: https://github.com/LTLA/basilisk/issues Package: VaSP Version: 1.23.0 Depends: R (>= 4.0), ballgown Imports: IRanges, GenomicRanges, S4Vectors, parallel, matrixStats, GenomicAlignments, GenomeInfoDb, Rsamtools, cluster, stats, graphics, methods Suggests: knitr, rmarkdown License: GPL (>= 2.0) Title: Quantification and Visualization of Variations of Splicing in Population Description: Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures in a population. (Warning: The visualizing function is removed due to the dependent package Sushi deprecated. If you want to use it, please change back to an older version.) biocViews: RNASeq, AlternativeSplicing, DifferentialSplicing, StatisticalMethod, Visualization, Preprocessing, Clustering, DifferentialExpression, KEGG, ImmunoOncology Author: Huihui Yu [aut, cre] (ORCID: ), Qian Du [aut] (ORCID: ), Chi Zhang [aut] (ORCID: ) Maintainer: Huihui Yu URL: https://github.com/yuhuihui2011/VaSP VignetteBuilder: knitr BugReports: https://github.com/yuhuihui2011/VaSP/issues Package: ribosomeProfilingQC Version: 1.23.0 Depends: R (>= 4.0), GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, BSgenome, EDASeq, GenomicAlignments, GenomicFeatures, Seqinfo, GenomeInfoDb, IRanges, methods, motifStack, rtracklayer, Rsamtools, RUVSeq, Rsubread, S4Vectors, XVector, ggplot2, ggfittext, scales, ggrepel, utils, cluster, stats, graphics, grid, txdbmaker, ggExtra Suggests: RUnit, BiocStyle, knitr, BSgenome.Drerio.UCSC.danRer10, GenomeInfoDbData, edgeR, DESeq2, limma, ashr, testthat, rmarkdown, vsn, Biobase License: GPL (>=3) + file LICENSE Title: Ribosome Profiling Quality Control Description: Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis. biocViews: RiboSeq, Sequencing, GeneRegulation, QualityControl, Visualization, Coverage Author: Jianhong Ou [aut, cre] (ORCID: ), Mariah Hoye [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr Package: EpiTxDb Version: 1.23.0 Depends: R (>= 4.0), AnnotationDbi, Modstrings Imports: methods, utils, httr, xml2, curl, rex, GenomicFeatures, txdbmaker, GenomicRanges, Seqinfo, BiocGenerics, BiocFileCache, S4Vectors, IRanges, RSQLite, DBI, Biostrings, tRNAdbImport Suggests: BiocStyle, knitr, rmarkdown, testthat, httptest, AnnotationHub, ensembldb, ggplot2, EpiTxDb.Hs.hg38, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Scerevisiae.UCSC.sacCer3, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 Title: Storing and accessing epitranscriptomic information using the AnnotationDbi interface Description: EpiTxDb facilitates the storage of epitranscriptomic information. More specifically, it can keep track of modification identity, position, the enzyme for introducing it on the RNA, a specifier which determines the position on the RNA to be modified and the literature references each modification is associated with. biocViews: Software, Epitranscriptomics Author: Felix G.M. Ernst [aut, cre] (ORCID: ) Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/EpiTxDb VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/EpiTxDb/issues Package: spicyR Version: 1.23.0 Depends: R (>= 4.1) Imports: BiocParallel, ClassifyR, S4Vectors, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, cli, concaveman, coxme, data.table, dplyr, ggforce, ggh4x, ggnewscale, ggplot2, ggthemes, grDevices, lifecycle, lmerTest, magrittr, methods, pheatmap, rlang, scales, scam, simpleSeg, spatstat.explore, spatstat.geom, stats, survival, tibble, tidyr Suggests: SpatialDatasets, BiocStyle, knitr, rmarkdown, pkgdown, imcRtools, testthat (>= 3.0.0) License: GPL (>=2) Title: Spatial analysis of in situ cytometry data Description: The spicyR package provides a framework for performing inference on changes in spatial relationships between pairs of cell types for cell-resolution spatial omics technologies. spicyR consists of three primary steps: (i) summarizing the degree of spatial localization between pairs of cell types for each image; (ii) modelling the variability in localization summary statistics as a function of cell counts and (iii) testing for changes in spatial localizations associated with a response variable. biocViews: SingleCell, CellBasedAssays, Spatial Author: Nicolas Canete [aut], Ellis Patrick [aut, cre], Nicholas Robertson [ctb], Alex Qin [ctb], Farhan Ameen [ctb], Shreya Rao [ctb] Maintainer: Ellis Patrick URL: https://ellispatrick.github.io/spicyR/ https://github.com/SydneyBioX/spicyR, https://sydneybiox.github.io/spicyR/ VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/spicyR/issues Package: cytomapper Version: 1.23.0 Depends: R (>= 4.0), EBImage, SingleCellExperiment, methods Imports: SpatialExperiment, S4Vectors, BiocParallel, HDF5Array, DelayedArray, RColorBrewer, viridis, utils, SummarizedExperiment, tools, graphics, raster, grDevices, stats, ggplot2, ggbeeswarm, svgPanZoom, svglite, shiny, shinydashboard, matrixStats, rhdf5, nnls Suggests: BiocStyle, knitr, rmarkdown, markdown, cowplot, testthat, shinytest License: GPL (>= 2) Title: Visualization of highly multiplexed imaging data in R Description: Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells. biocViews: ImmunoOncology, Software, SingleCell, OneChannel, TwoChannel, MultipleComparison, Normalization, DataImport Author: Nils Eling [aut] (ORCID: ), Nicolas Damond [aut] (ORCID: ), Tobias Hoch [ctb], Lasse Meyer [cre, ctb] (ORCID: ) Maintainer: Lasse Meyer URL: https://github.com/BodenmillerGroup/cytomapper VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/cytomapper/issues Package: glmGamPoi Version: 1.23.0 Imports: Rcpp, beachmat, DelayedMatrixStats, matrixStats, MatrixGenerics, SparseArray (>= 1.5.21), S4Vectors, DelayedArray, HDF5Array, Matrix, SummarizedExperiment, SingleCellExperiment, BiocGenerics, methods, stats, utils, splines, rlang, vctrs LinkingTo: Rcpp, RcppArmadillo, beachmat, assorthead Suggests: testthat (>= 2.1.0), zoo, DESeq2, edgeR, limma, MASS, statmod, ggplot2, bench, BiocParallel, knitr, rmarkdown, BiocStyle, TENxPBMCData, muscData, scran, dplyr License: GPL-3 Title: Fit a Gamma-Poisson Generalized Linear Model Description: Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments. biocViews: Regression, RNASeq, Software, SingleCell Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ), Nathan Lubock [ctb] (ORCID: ), Michael Love [ctb] Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/glmGamPoi SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/const-ae/glmGamPoi/issues Package: rfaRm Version: 1.23.0 Imports: httr, stringi, rsvg, magick, data.table, Biostrings, utils, rvest, xml2, IRanges, S4Vectors, jsonlite Suggests: R4RNA, treeio, knitr, BiocStyle, rmarkdown, BiocGenerics, RUnit License: GPL-3 NeedsCompilation: no Title: An R interface to the Rfam database Description: rfaRm provides a client interface to the Rfam database of RNA families. Data that can be retrieved include RNA families, secondary structure images, covariance models, sequences within each family, alignments leading to the identification of a family and secondary structures in the dot-bracket format. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, MultipleSequenceAlignment Author: Lara Selles Vidal, Rafael Ayala, Guy-Bart Stan, Rodrigo Ledesma-Amaro Maintainer: Lara Selles Vidal , Rafael Ayala VignetteBuilder: knitr Package: randRotation Version: 1.23.0 Imports: methods, graphics, utils, stats, Rdpack (>= 0.7) Suggests: knitr, BiocParallel, lme4, nlme, rmarkdown, BiocStyle, testthat (>= 2.1.0), limma, sva License: GPL-3 Title: Random Rotation Methods for High Dimensional Data with Batch Structure Description: A collection of methods for performing random rotations on high-dimensional, normally distributed data (e.g. microarray or RNA-seq data) with batch structure. The random rotation approach allows exact testing of dependent test statistics with linear models following arbitrary batch effect correction methods. biocViews: Software, Sequencing, BatchEffect, BiomedicalInformatics, RNASeq, Preprocessing, Microarray, DifferentialExpression, GeneExpression, Genetics, MicroRNAArray, Normalization, StatisticalMethod Author: Peter Hettegger [aut, cre] (ORCID: ) Maintainer: Peter Hettegger URL: https://github.com/phettegger/randRotation VignetteBuilder: knitr BugReports: https://github.com/phettegger/randRotation/issues Package: scClassify Version: 1.23.0 Depends: R (>= 4.0) Imports: S4Vectors, limma, ggraph, igraph, methods, cluster, minpack.lm, mixtools, BiocParallel, proxy, proxyC, Matrix, ggplot2, hopach, diptest, mgcv, stats, graphics, statmod, Cepo Suggests: knitr, rmarkdown, BiocStyle, pkgdown License: GPL-3 Title: scClassify: single-cell Hierarchical Classification Description: scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references. biocViews: SingleCell, GeneExpression, Classification Author: Yingxin Lin Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scClassify/issues Package: methylSig Version: 1.23.0 Depends: R (>= 3.6) Imports: bsseq, DelayedArray, DelayedMatrixStats, DSS, IRanges, Seqinfo, GenomicRanges, methods, parallel, stats, S4Vectors Suggests: BiocStyle, bsseqData, knitr, rmarkdown, testthat (>= 2.1.0), covr License: GPL-3 Title: MethylSig: Differential Methylation Testing for WGBS and RRBS Data Description: MethylSig is a package for testing for differentially methylated cytosines (DMCs) or regions (DMRs) in whole-genome bisulfite sequencing (WGBS) or reduced representation bisulfite sequencing (RRBS) experiments. MethylSig uses a beta binomial model to test for significant differences between groups of samples. Several options exist for either site-specific or sliding window tests, and variance estimation. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, Regression, MethylSeq Author: Yongseok Park [aut], Raymond G. Cavalcante [aut, cre] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://github.com/sartorlab/methylSig/issues Package: FRASER Version: 2.7.1 Depends: BiocParallel, Rsamtools, SummarizedExperiment Imports: AnnotationDbi, BBmisc, Biobase, BiocGenerics, biomaRt, BSgenome, cowplot, data.table, DelayedArray (>= 0.5.11), DelayedMatrixStats, extraDistr, generics, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges, grDevices, ggplot2, ggrepel, HDF5Array, matrixStats, methods, OUTRIDER, pcaMethods, pheatmap, plotly, PRROC, RColorBrewer, rhdf5, Rsubread, R.utils, S4Vectors, stats, tibble, tools, txdbmaker, utils, VGAM, RMTstat, pracma LinkingTo: RcppArmadillo, Rcpp Suggests: magick, BiocStyle, knitr, rmarkdown, testthat, covr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, rtracklayer, SGSeq, ggbio, biovizBase, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.UCSC.hg19 License: file LICENSE Title: Find RAre Splicing Events in RNA-Seq Data Description: Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects. biocViews: RNASeq, AlternativeSplicing, Sequencing, Software, Genetics, Coverage Author: Christian Mertes [aut, cre] (ORCID: ), Ines Scheller [aut] (ORCID: ), Karoline Lutz [ctb], Ata Jadid Ahari [ctb] (ORCID: ), Vicente Yepez [aut] (ORCID: ), Julien Gagneur [aut] (ORCID: ) Maintainer: Christian Mertes URL: https://github.com/gagneurlab/FRASER VignetteBuilder: knitr BugReports: https://github.com/gagneurlab/FRASER/issues Package: TBSignatureProfiler Version: 1.23.0 Depends: R (>= 4.4.0) Imports: ASSIGN (>= 1.23.1), BiocParallel, ComplexHeatmap, DESeq2, DT, edgeR, gdata, ggplot2, glmnet, GSVA (>= 1.51.3), HGNChelper, magrittr, methods, pROC, RColorBrewer, reshape2, ROCit, S4Vectors, singscore, stats, SummarizedExperiment, tibble Suggests: BiocStyle, caret, circlize, class, covr, dplyr, e1071, impute, knitr, lintr, MASS, plyr, randomForest, rmarkdown, shiny, spelling, sva, testthat License: MIT + file LICENSE Title: Profile RNA-Seq Data Using TB Pathway Signatures Description: Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates known signatures and provides computational tools to enlist their usage on other datasets. The TBSignatureProfiler makes it easy to profile RNA-Seq data using these signatures and includes common signature profiling tools including ASSIGN, GSVA, and ssGSEA. Original models for some gene signatures are also available. A shiny app provides some functionality alongside for detailed command line accessibility. biocViews: GeneExpression, DifferentialExpression Author: Kiloni Quiles [cre] (ORCID: ), Aubrey R. Odom [aut, dtm] (ORCID: ), David Jenkins [aut, org] (ORCID: ), Xutao Wang [aut], Yue Zhao [ctb] (ORCID: ), Christian Love [ctb], W. Evan Johnson [aut] Maintainer: Kiloni Quiles URL: https://github.com/wejlab/TBSignatureProfiler, https://wejlab.github.io/TBSignatureProfiler-docs/ VignetteBuilder: knitr BugReports: https://github.com/wejlab/TBSignatureProfiler/issues Package: metaseqR2 Version: 1.23.2 Depends: R (>= 4.0.0), DESeq2, limma, locfit, splines Imports: ABSSeq, Biobase, BiocGenerics, BiocParallel, biomaRt, Biostrings, corrplot, DSS, DT, EDASeq, edgeR, genefilter, Seqinfo, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, gplots, graphics, grDevices, harmonicmeanp, heatmaply, htmltools, httr, IRanges, jsonlite, lattice, log4r, magrittr, MASS, Matrix, methods, NBPSeq, pander, parallel, qvalue, rmarkdown, rmdformats, Rsamtools, RSQLite, rtracklayer, S4Vectors, stats, stringr, SummarizedExperiment, survcomp, txdbmaker, utils, VennDiagram, vsn, yaml, zoo Suggests: BiocStyle, BiocManager, BSgenome, knitr, RMySQL, RUnit Enhances: TCC License: GPL (>= 3) NeedsCompilation: yes Title: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms Description: Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way. biocViews: Software, GeneExpression, DifferentialExpression, WorkflowStep, Preprocessing, QualityControl, Normalization, ReportWriting, RNASeq, Transcription, Sequencing, Transcriptomics, Bayesian, Clustering, CellBiology, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, ImmunoOncology, AlternativeSplicing, DifferentialSplicing, MultipleComparison, TimeCourse, DataImport, ATACSeq, Epigenetics, Regression, ProprietaryPlatforms, GeneSetEnrichment, BatchEffect, ChIPSeq Author: Panagiotis Moulos [aut, cre] Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr BugReports: https://github.com/pmoulos/metaseqR2/issues Package: AnVIL Version: 1.23.10 Depends: R (>= 4.6.0), dplyr, AnVILBase Imports: stats, utils, methods, futile.logger, GCPtools, jsonlite, httr, digest, rapiclient, yaml, tibble, shiny, DT, miniUI, htmltools, BiocBaseUtils Suggests: knitr, rmarkdown, testthat, withr, readr, BiocStyle, devtools, AnVILAz, AnVILGCP, lifecycle License: Artistic-2.0 Title: Bioconductor on the AnVIL compute environment Description: The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVIL package provides programatic access to the Dockstore, Leonardo, Rawls, TDR, and Terra RESTful programming interfaces. For platform-specific user-level functionality, see either the AnVILGCP or AnVILAz package. biocViews: Infrastructure Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut] (ORCID: ), Kayla Interdonato [aut], Yubo Cheng [aut], Nitesh Turaga [aut], BJ Stubbs [ctb], Vincent Carey [ctb], Sehyun Oh [ctb], Sweta Gopaulakrishnan [ctb], Valerie Obenchain [ctb] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/AnVIL VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnVIL/issues Package: cBioPortalData Version: 2.23.5 Depends: R (>= 4.5.0), AnVIL (>= 1.19.5), MultiAssayExperiment Imports: BiocBaseUtils, BiocFileCache (>= 1.5.3), digest, dplyr, Seqinfo, GenomicRanges, httr, IRanges, methods, readr, RaggedExperiment, RTCGAToolbox (>= 2.19.7), S4Vectors, SummarizedExperiment, stats, tibble, tidyr, TCGAutils (>= 1.9.4), utils Suggests: BiocStyle, jsonlite, knitr, survival, survminer, rmarkdown, testthat License: AGPL-3 Title: Exposes and Makes Available Data from the cBioPortal Web Resources Description: The cBioPortalData R package accesses study datasets from the cBio Cancer Genomics Portal. It accesses the data either from the pre-packaged zip / tar files or from the API interface that was recently implemented by the cBioPortal Data Team. The package can provide data in either tabular format or with MultiAssayExperiment object that uses familiar Bioconductor data representations. biocViews: Software, Infrastructure, ThirdPartyClient Author: Levi Waldron [aut], Marcel Ramos [aut, cre] (ORCID: ), Karim Mezhoud [ctb] Maintainer: Marcel Ramos URL: https://github.com/waldronlab/cBioPortalData VignetteBuilder: knitr BugReports: https://github.com/waldronlab/cBioPortalData/issues Package: DegNorm Version: 1.21.0 Depends: R (>= 4.0.0), methods Imports: Rcpp (>= 1.0.2),GenomicFeatures, txdbmaker, parallel, foreach, S4Vectors, doParallel, Rsamtools (>= 1.31.2), GenomicAlignments, heatmaply, data.table, stats, ggplot2, GenomicRanges, IRanges, plyr, plotly, utils,viridis LinkingTo: Rcpp, RcppArmadillo,S4Vectors,IRanges Suggests: knitr,rmarkdown,formatR License: LGPL (>= 3) NeedsCompilation: yes Title: DegNorm: degradation normalization for RNA-seq data Description: This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy. It provides estimates for each gene within each sample. biocViews: RNASeq, Normalization, GeneExpression, Alignment,Coverage, DifferentialExpression, BatchEffect,Software,Sequencing, ImmunoOncology, QualityControl, DataImport Author: Ji-Ping Wang [aut, cre] (ORCID: ) Maintainer: Ji-Ping Wang VignetteBuilder: knitr BugReports: https://github.com/jipingw/DegNorm/issues Package: multicrispr Version: 1.21.0 Depends: R (>= 4.0) Imports: BiocGenerics, Biostrings, BSgenome, CRISPRseek, data.table, Seqinfo, GenomicFeatures, GenomicRanges, ggplot2, grid, karyoploteR, magrittr, methods, parallel, plyranges, Rbowtie, reticulate, rtracklayer, stats, stringi, tidyr, tidyselect, utils Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Scerevisiae.UCSC.sacCer1, ensembldb, IRanges, GenomeInfoDb, knitr, magick, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-2 Title: Multi-locus multi-purpose Crispr/Cas design Description: This package is for designing Crispr/Cas9 and Prime Editing experiments. It contains functions to (1) define and transform genomic targets, (2) find spacers (4) count offtarget (mis)matches, and (5) compute Doench2016/2014 targeting efficiency. Care has been taken for multicrispr to scale well towards large target sets, enabling the design of large Crispr/Cas9 libraries. biocViews: CRISPR, Software Author: Aditya Bhagwat [aut, cre], Richie ´Cotton [aut], Rene Wiegandt [ctb], Mette Bentsen [ctb], Jens Preussner [ctb], Michael Lawrence [ctb], Hervé Pagès [ctb], Johannes Graumann [sad], Mario Looso [sad, rth] Maintainer: Aditya Bhagwat URL: https://github.com/bhagwataditya/multicrispr VignetteBuilder: knitr BugReports: https://github.com/bhagwataditya/multicrispr/issues Package: RegEnrich Version: 1.21.0 Depends: R (>= 4.0.0), S4Vectors, dplyr, tibble, BiocSet, SummarizedExperiment Imports: randomForest, fgsea, DOSE, BiocParallel, DESeq2, limma, WGCNA, ggplot2 (>= 2.2.0), methods, reshape2, magrittr, BiocStyle Suggests: GEOquery, rmarkdown, knitr, BiocManager, testthat License: GPL (>= 2) Title: Gene regulator enrichment analysis Description: This package is a pipeline to identify the key gene regulators in a biological process, for example in cell differentiation and in cell development after stimulation. There are four major steps in this pipeline: (1) differential expression analysis; (2) regulator-target network inference; (3) enrichment analysis; and (4) regulators scoring and ranking. biocViews: GeneExpression, Transcriptomics, RNASeq, TwoChannel, Transcription, GeneTarget, NetworkEnrichment, DifferentialExpression, Network, NetworkInference, GeneSetEnrichment, FunctionalPrediction Author: Weiyang Tao [cre, aut], Aridaman Pandit [aut] Maintainer: Weiyang Tao VignetteBuilder: knitr Package: Rtpca Version: 1.21.0 Depends: R (>= 4.0.0), stats, dplyr, tidyr Imports: Biobase, methods, ggplot2, pROC, fdrtool, splines, utils, tibble Suggests: knitr, BiocStyle, TPP, testthat, rmarkdown License: GPL-3 Title: Thermal proximity co-aggregation with R Description: R package for performing thermal proximity co-aggregation analysis with thermal proteome profiling datasets to analyse protein complex assembly and (differential) protein-protein interactions across conditions. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], André Mateus [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ Package: scCB2 Version: 1.21.0 Depends: R (>= 3.6.0) Imports: SingleCellExperiment, SummarizedExperiment, Matrix, methods, utils, stats, edgeR, rhdf5, parallel, DropletUtils, doParallel, iterators, foreach, Seurat Suggests: testthat (>= 2.1.0), KernSmooth, beachmat, knitr, BiocStyle, rmarkdown License: GPL-3 NeedsCompilation: yes Title: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data Description: scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled. biocViews: DataImport, RNASeq, SingleCell, Sequencing, GeneExpression, Transcriptomics, Preprocessing, Clustering Author: Zijian Ni [aut, cre], Shuyang Chen [ctb], Christina Kendziorski [ctb] Maintainer: Zijian Ni URL: https://github.com/zijianni/scCB2 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/zijianni/scCB2/issues Package: MultiBaC Version: 1.21.0 Imports: Matrix, ggplot2, MultiAssayExperiment, ropls, graphics, methods, plotrix, grDevices, pcaMethods Suggests: knitr, rmarkdown, BiocStyle, devtools License: GPL-3 NeedsCompilation: no Title: Multiomic Batch effect Correction Description: MultiBaC is a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. MultiBaC is the first Batch effect correction algorithm that dealing with batch effect correction in multiomics datasets. MultiBaC is able to remove batch effects across different omics generated within separate batches provided that at least one common omic data type is included in all the batches considered. biocViews: Software, StatisticalMethod, PrincipalComponent, DataRepresentation, GeneExpression, Transcription, BatchEffect Author: person("Manuel", "Ugidos", email = "manuelugidos@gmail.com"), person("Sonia", "Tarazona", email = "sotacam@gmail.com"), person("María José", "Nueda", email = "mjnueda@ua.es") Maintainer: The package maintainer VignetteBuilder: knitr Package: snifter Version: 1.21.0 Depends: R (>= 4.0.0) Imports: basilisk, reticulate, irlba, stats, assertthat Suggests: knitr, rmarkdown, BiocStyle, ggplot2, testthat (>= 3.0.0) License: GPL-3 Title: R wrapper for the python openTSNE library Description: Provides an R wrapper for the implementation of FI-tSNE from the python package openTNSE. See Poličar et al. (2019) and the algorithm described by Linderman et al. (2018) . biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Aaron Lun [aut] Maintainer: Alan O'Callaghan URL: https://bioconductor.org/packages/snifter VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/snifter/issues Package: zellkonverter Version: 1.21.2 Imports: basilisk, cli, DelayedArray, Matrix, methods, reticulate, S4Vectors, SingleCellExperiment (>= 1.11.6), SparseArray, SummarizedExperiment, utils Suggests: anndata, BiocFileCache, BiocStyle, covr, HDF5Array, knitr, pkgload, rhdf5 (>= 2.45.1), rmarkdown, scRNAseq, SpatialExperiment, spelling, testthat, withr License: MIT + file LICENSE Title: Conversion Between scRNA-seq Objects Description: Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk. biocViews: SingleCell, DataImport, DataRepresentation Author: Luke Zappia [aut, cre] (ORCID: , github: lazappi), Aaron Lun [aut] (ORCID: , github: LTLA), Jack Kamm [ctb] (ORCID: , github: jackkamm), Robrecht Cannoodt [ctb] (ORCID: , github: rcannood), Gabriel Hoffman [ctb] (ORCID: , github: GabrielHoffman), Marek Cmero [ctb] (ORCID: , github: mcmero) Maintainer: Luke Zappia URL: https://github.com/theislab/zellkonverter VignetteBuilder: knitr BugReports: https://github.com/theislab/zellkonverter/issues Package: ISAnalytics Version: 1.21.0 Depends: R (>= 4.5) Imports: utils, dplyr, readr, tidyr, purrr, rlang, tibble, stringr, fs, lubridate, lifecycle, ggplot2, ggrepel, stats, readxl, tools, grDevices, forcats, glue, shiny, shinyWidgets, datamods, bslib, vegan, data.table, DT Suggests: testthat, covr, knitr, BiocStyle, sessioninfo, rmarkdown, roxygen2, withr, extraDistr, ggalluvial, scales, gridExtra, R.utils, RefManageR, flexdashboard, circlize, plotly, gtools, eulerr, openxlsx, jsonlite, pheatmap, BiocParallel, progressr, future, doFuture, foreach, psych, Rcapture License: CC BY 4.0 Title: Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies Description: In gene therapy, stem cells are modified using viral vectors to deliver the therapeutic transgene and replace functional properties since the genetic modification is stable and inherited in all cell progeny. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites (IS), essential for monitoring the evolution of genetically modified cells in vivo. A comprehensive toolkit for the analysis of IS is required to foster clonal trackign studies and supporting the assessment of safety and long term efficacy in vivo. This package is aimed at (1) supporting automation of IS workflow, (2) performing base and advance analysis for IS tracking (clonal abundance, clonal expansions and statistics for insertional mutagenesis, etc.), (3) providing basic biology insights of transduced stem cells in vivo. biocViews: BiomedicalInformatics, Sequencing, SingleCell, CellBiology, FunctionalGenomics, DataImport Author: Francesco Gazzo [cre] (ORCID: ), Giulia Pais [aut] (ORCID: ), Andrea Calabria [aut], Giulio Spinozzi [aut] Maintainer: Francesco Gazzo URL: https://calabrialab.github.io/ISAnalytics, https://github.com//calabrialab/isanalytics, https://calabrialab.github.io/ISAnalytics/ VignetteBuilder: knitr BugReports: https://github.com/calabrialab/ISAnalytics/issues Package: recountmethylation Version: 1.21.0 Depends: R (>= 4.1) Imports: minfi, HDF5Array, rhdf5, S4Vectors, utils, methods, RCurl, R.utils, BiocFileCache, basilisk, reticulate, DelayedMatrixStats Suggests: minfiData, minfiDataEPIC, knitr, testthat, ggplot2, gridExtra, rmarkdown, BiocStyle, GenomicRanges, limma, ExperimentHub, AnnotationHub License: Artistic-2.0 Title: Access and analyze public DNA methylation array data compilations Description: Resources for cross-study analyses of public DNAm array data from NCBI GEO repo, produced using Illumina's Infinium HumanMethylation450K (HM450K) and MethylationEPIC (EPIC) platforms. Provided functions enable download, summary, and filtering of large compilation files. Vignettes detail background about file formats, example analyses, and more. Note the disclaimer on package load and consult the main manuscripts for further info. biocViews: DNAMethylation, Epigenetics, Microarray, MethylationArray, ExperimentHub Author: Sean K Maden [cre, aut] (ORCID: ), Brian Walsh [aut] (ORCID: ), Kyle Ellrott [aut] (ORCID: ), Kasper D Hansen [aut] (ORCID: ), Reid F Thompson [aut] (ORCID: ), Abhinav Nellore [aut] (ORCID: ) Maintainer: Sean K Maden URL: https://github.com/metamaden/recountmethylation VignetteBuilder: knitr BugReports: https://github.com/metamaden/recountmethylation/issues Package: ANCOMBC Version: 2.13.1 Depends: R (>= 4.5.0) Imports: stats, CVXR (>= 1.8.1), DescTools, Hmisc, MASS, Matrix, Rdpack, doParallel, doRNG, energy, foreach, gtools, lme4, lmerTest, multcomp, nloptr, parallel, utils Suggests: mia (>= 1.18.0), DT, S4Vectors, SingleCellExperiment, SummarizedExperiment, TreeSummarizedExperiment, dplyr, knitr, magrittr, microbiome, phyloseq, rmarkdown, testthat, tidyr, tidyverse License: Artistic-2.0 Title: Microbiome differential abudance and correlation analyses with bias correction Description: ANCOMBC is a package containing differential abundance (DA) and correlation analyses for microbiome data. Specifically, the package includes Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2), Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), and Analysis of Composition of Microbiomes (ANCOM) for DA analysis, and Sparse Estimation of Correlations among Microbiomes (SECOM) for correlation analysis. Microbiome data are typically subject to two sources of biases: unequal sampling fractions (sample-specific biases) and differential sequencing efficiencies (taxon-specific biases). Methodologies included in the ANCOMBC package are designed to correct these biases and construct statistically consistent estimators. biocViews: DifferentialExpression, Microbiome, Normalization, Sequencing, Software Author: Huang Lin [cre, aut] (ORCID: ) Maintainer: Huang Lin URL: https://github.com/FrederickHuangLin/ANCOMBC VignetteBuilder: knitr BugReports: https://github.com/FrederickHuangLin/ANCOMBC/issues Package: ncRNAtools Version: 1.21.0 Imports: httr, xml2, utils, methods, grDevices, ggplot2, IRanges, GenomicRanges, S4Vectors Suggests: knitr, BiocStyle, rmarkdown, RUnit, BiocGenerics License: GPL-3 NeedsCompilation: no Title: An R toolkit for non-coding RNA Description: ncRNAtools provides a set of basic tools for handling and analyzing non-coding RNAs. These include tools to access the RNAcentral database and to predict and visualize the secondary structure of non-coding RNAs. The package also provides tools to read, write and interconvert the file formats most commonly used for representing such secondary structures. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, StructuralPrediction Author: Lara Selles Vidal [cre, aut] (ORCID: ), Rafael Ayala [aut] (ORCID: ), Guy-Bart Stan [aut] (ORCID: ), Rodrigo Ledesma-Amaro [aut] (ORCID: ) Maintainer: Lara Selles Vidal VignetteBuilder: knitr BugReports: https://github.com/LaraSellesVidal/ncRNAtools/issues Package: proActiv Version: 1.21.0 Depends: R (>= 4.0.0) Imports: AnnotationDbi, BiocParallel, data.table, dplyr, DESeq2, IRanges, GenomicRanges, GenomicFeatures, GenomicAlignments, GenomeInfoDb, ggplot2, gplots, graphics, methods, rlang, scales, S4Vectors, SummarizedExperiment, stats, tibble, txdbmaker Suggests: GenomeInfoDbData, testthat, rmarkdown, knitr, Rtsne, gridExtra License: MIT + file LICENSE Title: Estimate Promoter Activity from RNA-Seq data Description: Most human genes have multiple promoters that control the expression of different isoforms. The use of these alternative promoters enables the regulation of isoform expression pre-transcriptionally. Alternative promoters have been found to be important in a wide number of cell types and diseases. proActiv is an R package that enables the analysis of promoters from RNA-seq data. proActiv uses aligned reads as input, and generates counts and normalized promoter activity estimates for each annotated promoter. In particular, proActiv accepts junction files from TopHat2 or STAR or BAM files as inputs. These estimates can then be used to identify which promoter is active, which promoter is inactive, and which promoters change their activity across conditions. proActiv also allows visualization of promoter activity across conditions. biocViews: RNASeq, GeneExpression, Transcription, AlternativeSplicing, GeneRegulation, DifferentialSplicing, FunctionalGenomics, Epigenetics, Transcriptomics, Preprocessing Author: Deniz Demircioglu [aut] (ORCID: ), Jonathan Göke [aut], Joseph Lee [cre] Maintainer: Joseph Lee URL: https://github.com/GoekeLab/proActiv VignetteBuilder: knitr Package: velociraptor Version: 1.21.3 Depends: SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, reticulate, S4Vectors, DelayedArray, basilisk, zellkonverter, scuttle, SingleCellExperiment, BiocParallel, BiocSingular Suggests: BiocStyle, testthat, knitr, rmarkdown, pkgdown, scran, scater, scRNAseq, Rtsne, graphics, grDevices, ggplot2, cowplot, GGally, patchwork, metR License: MIT + file LICENSE Title: Toolkit for Single-Cell Velocity Description: This package provides Bioconductor-friendly wrappers for RNA velocity calculations in single-cell RNA-seq data. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment (R) and AnnData (Python). The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class. biocViews: SingleCell, GeneExpression, Sequencing, Coverage Author: Kevin Rue-Albrecht [aut, cre] (ORCID: ), Aaron Lun [aut] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Michael Stadler [aut] (ORCID: ) Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/velociraptor VignetteBuilder: knitr BugReports: https://github.com/kevinrue/velociraptor/issues Package: spatialHeatmap Version: 2.17.3 Depends: R (>= 4.5.0) Imports: data.table, dplyr, edgeR, genefilter, ggplot2, grImport, grid, gridExtra, igraph, methods, Matrix, rsvg, shiny, grDevices, graphics, ggplotify, parallel, reshape2, stats, SummarizedExperiment, SingleCellExperiment, shinydashboard, S4Vectors, spsComps (>= 0.3.3.0), tibble, utils, xml2 Suggests: AnnotationDbi, av, BiocParallel, BiocFileCache, BiocGenerics, BiocStyle, BiocSingular, Biobase, cachem, DESeq2, dendextend, DT, dynamicTreeCut, flashClust, gplots, ggdendro, HDF5Array, htmltools, htmlwidgets, kableExtra, knitr, limma, magick, memoise, ExpressionAtlas, GEOquery, org.Hs.eg.db, org.Mm.eg.db, org.At.tair.db, org.Dr.eg.db, org.Dm.eg.db, pROC, plotly, rmarkdown, rols, rappdirs, RUnit, Rtsne, rhdf5, scater, scuttle, scran, shinyWidgets, shinyjs, shinyBS, sortable, Seurat, sparkline, spsUtil, uwot, UpSetR, visNetwork, WGCNA, xlsx, yaml License: Artistic-2.0 Title: spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions Description: The spatialHeatmap package offers the primary functionality for visualizing cell-, tissue- and organ-specific assay data in spatial anatomical images. Additionally, it provides extended functionalities for large-scale data mining routines and co-visualizing bulk and single-cell data. A description of the project is available here: https://spatialheatmap.org. biocViews: Spatial, Visualization, Microarray, Sequencing, GeneExpression, DataRepresentation, Network, Clustering, GraphAndNetwork, CellBasedAssays, ATACSeq, DNASeq, TissueMicroarray, SingleCell, CellBiology, GeneTarget Author: Jianhai Zhang [aut, trl, cre], Le Zhang [aut], Jordan Hayes [aut], Brendan Gongol [aut], Alexander Borowsky [aut], Julia Bailey-Serres [aut], Thomas Girke [aut] Maintainer: Jianhai Zhang URL: https://spatialheatmap.org, https://github.com/jianhaizhang/spatialHeatmap VignetteBuilder: knitr BugReports: https://github.com/jianhaizhang/spatialHeatmap/issues Package: RnaSeqSampleSize Version: 2.21.0 Depends: R (>= 4.0.0), ggplot2, RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,methods,grDevices, graphics, stats, Rcpp (>= 0.11.2),recount,ggpubr,SummarizedExperiment,tidyr,dplyr,tidyselect,utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Title: RnaSeqSampleSize Description: RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data. It controls FDR for multiple testing and utilizes the average read count and dispersion distributions from real data to estimate a more reliable sample size. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization. biocViews: ImmunoOncology, ExperimentalDesign, Sequencing, RNASeq, GeneExpression, DifferentialExpression Author: Shilin Zhao Developer [aut, cre], Chung-I Li [aut], Yan Guo [aut], Quanhu Sheng [aut], Yu Shyr [aut] Maintainer: Shilin Zhao Developer VignetteBuilder: knitr Package: AnVILPublish Version: 1.21.1 Imports: AnVIL, AnVILGCP, BiocBaseUtils, BiocManager, httr, jsonlite, rmarkdown, yaml, readr, whisker, tools, utils, stats Suggests: knitr, BiocStyle, GCPtools, testthat (>= 3.0.0) License: Artistic-2.0 Title: Publish Packages and Other Resources to AnVIL Workspaces Description: Use this package to create or update AnVIL workspaces from resources such as R / Bioconductor packages. The metadata about the package (e.g., select information from the package DESCRIPTION file and from vignette YAML headings) are used to populate the 'DASHBOARD'. Vignettes are translated to python notebooks ready for evaluation in AnVIL. biocViews: Infrastructure, Software Author: Marcel Ramos [aut, cre] (ORCID: ), Martin Morgan [aut] (ORCID: ), Kayla Interdonato [aut], Vincent Carey [ctb] (ORCID: ) Maintainer: Marcel Ramos VignetteBuilder: knitr Package: MOFA2 Version: 1.21.3 Depends: R (>= 4.0) Imports: rhdf5, dplyr, tidyr, reshape2, pheatmap, ggplot2, methods, RColorBrewer, cowplot, ggrepel, reticulate, HDF5Array, grDevices, stats, magrittr, forcats, utils, corrplot, DelayedArray, Rtsne, uwot, basilisk, stringi Suggests: knitr, testthat, Seurat, SeuratObject, ggpubr, foreach, psych, MultiAssayExperiment, SummarizedExperiment, SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown, data.table, tidyverse, BiocStyle, Matrix, markdown License: file LICENSE NeedsCompilation: yes Title: Multi-Omics Factor Analysis v2 Description: The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, visualisation, imputation etc are available. biocViews: DimensionReduction, Bayesian, Visualization Author: Ricard Argelaguet [aut, cre] (ORCID: ), Damien Arnol [aut] (ORCID: ), Danila Bredikhin [aut] (ORCID: ), Britta Velten [aut] (ORCID: ) Maintainer: ERROR URL: https://biofam.github.io/MOFA2/index.html SystemRequirements: Python (>=3), numpy, pandas, h5py, scipy, argparse, sklearn, mofapy2 VignetteBuilder: knitr BugReports: https://github.com/bioFAM/MOFA2 Package: NanoMethViz Version: 3.7.3 Depends: R (>= 4.0.0), methods, ggplot2 (>= 3.4.0) Imports: cpp11 (>= 0.2.5), readr, cli, S4Vectors, SummarizedExperiment, BiocSingular, bsseq, forcats, assertthat, AnnotationDbi, Rcpp, dplyr, dbscan, e1071, fs, GenomicRanges, Biostrings, ggrastr, glue, graphics, IRanges, limma (>= 3.44.0), patchwork, purrr, rlang, R.utils, Rsamtools, scales (>= 1.2.0), stats, stringr, tibble, tidyr, utils, withr LinkingTo: Rcpp Suggests: BiocStyle, Mus.musculus (>= 1.3.1), Homo.sapiens (>= 1.3.1), org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, knitr, rmarkdown, rtracklayer, testthat (>= 3.0.0), covr License: Apache License (>= 2.0) Title: Visualise methylation data from Oxford Nanopore sequencing Description: NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features. biocViews: Software, LongRead, Visualization, DifferentialMethylation, DNAMethylation, Epigenetics, DataImport Author: Shian Su [cre, aut] Maintainer: Shian Su URL: https://github.com/shians/NanoMethViz, https://shians.github.io/NanoMethViz/ SystemRequirements: C++20 VignetteBuilder: knitr BugReports: https://github.com/Shians/NanoMethViz/issues Package: densvis Version: 1.21.1 Imports: Rcpp, basilisk, assertthat, reticulate, Rtsne, irlba LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, ggplot2, uwot, testthat License: MIT + file LICENSE Title: Density-Preserving Data Visualization via Non-Linear Dimensionality Reduction Description: Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) . The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space. biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Ashwinn Narayan [aut], Hyunghoon Cho [aut] Maintainer: Alan O'Callaghan URL: https://bioconductor.org/packages/densvis VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/densvis/issues Package: NewWave Version: 1.21.0 Depends: R (>= 4.0), SummarizedExperiment Imports: methods, SingleCellExperiment, parallel, irlba, Matrix, DelayedArray, BiocSingular, SharedObject, stats Suggests: testthat, rmarkdown, splatter, mclust, Rtsne, ggplot2, Rcpp, BiocStyle, knitr License: GPL-3 Title: Negative binomial model for scRNA-seq Description: A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise. biocViews: Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Sequencing, Coverage, Regression Author: Federico Agostinis [aut, cre], Chiara Romualdi [aut], Gabriele Sales [aut], Davide Risso [aut] Maintainer: Federico Agostinis VignetteBuilder: knitr BugReports: https://github.com/fedeago/NewWave/issues Package: moanin Version: 1.19.0 Depends: R (>= 4.0), SummarizedExperiment, topGO, stats Imports: S4Vectors, MASS (>= 1.0.0), limma, viridis, edgeR, graphics, methods, grDevices, reshape2, NMI, zoo, ClusterR, splines, matrixStats Suggests: testthat (>= 1.0.0), timecoursedata, knitr, rmarkdown, markdown, covr, BiocStyle License: BSD 3-clause License + file LICENSE Title: An R Package for Time Course RNASeq Data Analysis Description: Simple and efficient workflow for time-course gene expression data, built on publictly available open-source projects hosted on CRAN and bioconductor. moanin provides helper functions for all the steps required for analysing time-course data using functional data analysis: (1) functional modeling of the timecourse data; (2) differential expression analysis; (3) clustering; (4) downstream analysis. biocViews: TimeCourse, GeneExpression, RNASeq, Microarray, DifferentialExpression, Clustering Author: Elizabeth Purdom [aut] (ORCID: ), Nelle Varoquaux [aut, cre] (ORCID: ) Maintainer: Nelle Varoquaux VignetteBuilder: knitr Package: shinyepico Version: 1.19.0 Depends: R (>= 4.3.0) Imports: DT (>= 0.15.0), data.table (>= 1.13.0), doParallel (>= 1.0.0), dplyr (>= 1.0.9), foreach (>= 1.5.0), GenomicRanges (>= 1.38.0), ggplot2 (>= 3.3.0), gplots (>= 3.0.0), heatmaply (>= 1.1.0), limma (>= 3.42.0), minfi (>= 1.32.0), plotly (>= 4.9.2), reshape2 (>= 1.4.0), rlang (>= 1.0.2), rmarkdown (>= 2.3.0), rtracklayer (>= 1.46.0), shiny (>= 1.5.0), shinyWidgets (>= 0.5.0), shinycssloaders (>= 0.3.0), shinyjs (>= 1.1.0), shinythemes (>= 1.1.0), statmod (>= 1.4.0), tidyr (>= 1.2.0), zip (>= 2.1.0) Suggests: knitr (>= 1.30.0), mCSEA (>= 1.10.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, testthat, minfiData, BiocStyle License: AGPL-3 + file LICENSE Title: ShinyÉPICo Description: ShinyÉPICo is a graphical pipeline to analyze Illumina DNA methylation arrays (450k or EPIC). It allows to calculate differentially methylated positions and differentially methylated regions in a user-friendly interface. Moreover, it includes several options to export the results and obtain files to perform downstream analysis. biocViews: DifferentialMethylation,DNAMethylation,Microarray,Preprocessing,QualityControl Author: Octavio Morante-Palacios [cre, aut] Maintainer: Octavio Morante-Palacios URL: https://github.com/omorante/shiny_epico VignetteBuilder: knitr BugReports: https://github.com/omorante/shiny_epico/issues Package: sampleClassifier Version: 1.35.0 Depends: R (>= 4.0), MGFM, MGFR, annotate Imports: e1071, ggplot2, stats, utils Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db License: Artistic-2.0 NeedsCompilation: no Title: Sample Classifier Description: The package is designed to classify microarray RNA-seq gene expression profiles. biocViews: ImmunoOncology, Classification, Microarray, RNASeq, GeneExpression Author: Khadija El Amrani [aut, cre] Maintainer: Khadija El Amrani Package: RiboDiPA Version: 1.19.0 Depends: R (>= 4.1), Rsamtools, GenomicFeatures, GenomicAlignments Imports: Rcpp (>= 1.0.2), graphics, stats, data.table, elitism, methods, S4Vectors, IRanges, GenomicRanges, matrixStats, reldist, doParallel, foreach, parallel, qvalue, DESeq2, ggplot2, BiocFileCache, BiocGenerics, txdbmaker LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL (>= 3) NeedsCompilation: yes Title: Differential pattern analysis for Ribo-seq data Description: This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization. biocViews: RiboSeq, GeneExpression, GeneRegulation, DifferentialExpression, Sequencing, Coverage, Alignment, RNASeq, ImmunoOncology, QualityControl, DataImport, Software, Normalization Author: Keren Li [aut], Matt Hope [aut], Xiaozhong Wang [aut], Ji-Ping Wang [aut, cre] Maintainer: Ji-Ping Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboDiPA git_branch: master git_last_commit: 7a819c5 git_last_commit_date: 2021-05-19 Date/Publication: 2021-05-19 Package: SOMNiBUS Version: 1.19.0 Depends: R (>= 4.1.0) Imports: Matrix, mgcv, stats, VGAM, IRanges, GenomeInfoDb, GenomicRanges, rtracklayer, S4Vectors, BiocManager, annotatr, yaml, utils, bsseq, reshape2, data.table, ggplot2, tidyr, Suggests: BiocStyle, covr, devtools, dplyr, knitr, magick, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, License: MIT + file LICENSE Title: Smooth modeling of bisulfite sequencing Description: This package aims to analyse count-based methylation data on predefined genomic regions, such as those obtained by targeted sequencing, and thus to identify differentially methylated regions (DMRs) that are associated with phenotypes or traits. The method is built a rich flexible model that allows for the effects, on the methylation levels, of multiple covariates to vary smoothly along genomic regions. At the same time, this method also allows for sequencing errors and can adjust for variability in cell type mixture. biocViews: DNAMethylation, Regression, Epigenetics, DifferentialMethylation, Sequencing, FunctionalPrediction Author: Kaiqiong Zhao [aut], Kathleen Klein [cre], Audrey Lemaçon [ctb, ctr], Simon Laurin-Lemay [ctb, ctr], My Intelligent Machines Inc. [ctr], Celia Greenwood [ths, aut] Maintainer: Kathleen Klein URL: https://github.com/kaiqiong/SOMNiBUS VignetteBuilder: knitr BugReports: https://github.com/kaiqiong/SOMNiBUS/issues Package: MACSr Version: 1.19.0 Depends: R (>= 4.1.0) Imports: utils, reticulate, S4Vectors, methods, basilisk, ExperimentHub, AnnotationHub Suggests: testthat, knitr, rmarkdown, BiocStyle, MACSdata License: BSD_3_clause + file LICENSE Title: MACS: Model-based Analysis for ChIP-Seq Description: The Model-based Analysis of ChIP-Seq (MACS) is a widely used toolkit for identifying transcript factor binding sites. This package is an R wrapper of the lastest MACS3. biocViews: Software, ChIPSeq, ATACSeq, ImmunoOncology Author: Philippa Doherty [aut], Qiang Hu [aut, cre] Maintainer: Qiang Hu VignetteBuilder: knitr Package: MAGAR Version: 1.19.0 Depends: R (>= 4.1), HDF5Array, RnBeads, snpStats, crlmm Imports: doParallel, igraph, bigstatsr, rjson, plyr, data.table, UpSetR, reshape2, jsonlite, methods, ff, argparse, impute, RnBeads.hg19, RnBeads.hg38, utils, stats Suggests: gridExtra, VennDiagram, qqman, LOLA, RUnit, rmutil, rmarkdown, JASPAR2018, TFBSTools, seqLogo, knitr, devtools, BiocGenerics, BiocManager License: GPL-3 Title: MAGAR: R-package to compute methylation Quantitative Trait Loci (methQTL) from DNA methylation and genotyping data Description: "Methylation-Aware Genotype Association in R" (MAGAR) computes methQTL from DNA methylation and genotyping data from matched samples. MAGAR uses a linear modeling stragety to call CpGs/SNPs that are methQTLs. MAGAR accounts for the local correlation structure of CpGs. biocViews: Regression, Epigenetics, DNAMethylation, SNP, GeneticVariability, MethylationArray, Microarray, CpGIsland, MethylSeq, Sequencing, mRNAMicroarray, Preprocessing, CopyNumberVariation, TwoChannel, ImmunoOncology, DifferentialMethylation, BatchEffect, QualityControl, DataImport, Network, Clustering, GraphAndNetwork Author: Michael Scherer [cre, aut] (ORCID: ) Maintainer: Michael Scherer URL: https://github.com/MPIIComputationalEpigenetics/MAGAR VignetteBuilder: knitr BugReports: https://github.com/MPIIComputationalEpigenetics/MAGAR/issues Package: autonomics Version: 1.19.0 Depends: R (>= 4.0) Imports: abind, arrow, BiocFileCache, BiocGenerics, bit64, cluster, codingMatrices, colorspace, data.table, dplyr, edgeR, ggforce, ggplot2, ggrepel, graphics, grDevices, grid, gridExtra, limma, lme4, magrittr, matrixStats, methods, MultiAssayExperiment, parallel, RColorBrewer, rlang, R.utils, readxl, S4Vectors, scales, stats, stringi, SummarizedExperiment, survival, tidyr, tidyselect, tools, utils, vsn Suggests: affy, AnnotationDbi, AnnotationHub, apcluster, Biobase, BiocManager, BiocStyle, Biostrings, coin, diagram, DBI, e1071, ensembldb, GenomicDataCommons, GenomicRanges, GEOquery, ggstance, ggridges, ggtext, hgu95av2.db, ICSNP, jsonlite, knitr, lmerTest, MASS, mclust, mixOmics, mixtools, mpm, nlme, OlinkAnalyze, org.Hs.eg.db, org.Mm.eg.db, patchwork, pcaMethods, pheatmap, progeny, propagate, RCurl, RSQLite, remotes, rmarkdown, ropls, Rsubread, readODS, rtracklayer, statmod, testthat, UniProt.ws, writexl, XML License: GPL-3 Title: Unified Statistical Modeling of Omics Data Description: This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). Across survival methods (coxph, survdiff, coin). It provides a fast enrichment analysis implementation. biocViews: Software, DataImport, Preprocessing, DimensionReduction, PrincipalComponent, Regression, DifferentialExpression, GeneSetEnrichment, Transcriptomics, Transcription, GeneExpression, RNASeq, Microarray, Proteomics, Metabolomics, MassSpectrometry, Author: Aditya Bhagwat [aut, cre], Richard Cotton [aut], Vanessa Beutgen [ctb], Witold Szymanski [ctb], Shahina Hayat [ctb], Laure Cougnaud [ctb], Hinrich Goehlmann [sad], Karsten Suhre [sad], Johannes Graumann [aut, sad] Maintainer: Aditya Bhagwat VignetteBuilder: knitr BugReports: https://gitlab.uni-marburg.de/fb20/ag-graumann/software/autonomics/issues Package: lisaClust Version: 1.19.0 Title: ERROR Maintainer: ERROR Package: sitadela Version: 1.19.2 Depends: R (>= 4.1.0) Imports: Biobase, BiocGenerics, biomaRt, Biostrings, Seqinfo, GenomicFeatures, GenomicRanges, IRanges, methods, parallel, Rsamtools, RSQLite, rtracklayer, S4Vectors, tools, txdbmaker, utils Suggests: GenomeInfoDb, BiocStyle, BSgenome, knitr, rmarkdown, RMySQL, RUnit License: Artistic-2.0 Title: An R package for the easy provision of simple but complete tab-delimited genomic annotation from a variety of sources and organisms Description: Provides an interface to build a unified database of genomic annotations and their coordinates (gene, transcript and exon levels). It is aimed to be used when simple tab-delimited annotations (or simple GRanges objects) are required instead of the more complex annotation Bioconductor packages. Also useful when combinatorial annotation elements are reuired, such as RefSeq coordinates with Ensembl biotypes. Finally, it can download, construct and handle annotations with versioned genes and transcripts (where available, e.g. RefSeq and latest Ensembl). This is particularly useful in precision medicine applications where the latter must be reported. biocViews: Software, WorkflowStep, RNASeq, Transcription, Sequencing, Transcriptomics, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, AlternativeSplicing, DataImport, ChIPSeq Author: Panagiotis Moulos [aut, cre] Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/sitadela VignetteBuilder: knitr BugReports: https://github.com/pmoulos/sitadela/issues Package: fedup Version: 1.19.0 Depends: R (>= 4.1) Imports: openxlsx, tibble, dplyr, data.table, ggplot2, ggthemes, forcats, RColorBrewer, RCy3, utils, stats Suggests: biomaRt, tidyr, testthat, knitr, rmarkdown, devtools, covr License: MIT + file LICENSE Title: Fisher's Test for Enrichment and Depletion of User-Defined Pathways Description: An R package that tests for enrichment and depletion of user-defined pathways using a Fisher's exact test. The method is designed for versatile pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run pathway analysis on custom annotations. This package is also integrated with Cytoscape to provide network-based pathway visualization that enhances the interpretability of the results. biocViews: GeneSetEnrichment, Pathways, NetworkEnrichment, Network Author: Catherine Ross [aut, cre] Maintainer: Catherine Ross URL: https://github.com/rosscm/fedup VignetteBuilder: knitr BugReports: https://github.com/rosscm/fedup/issues Package: ptairMS Version: 1.19.0 Imports: Biobase, bit64, chron, data.table, doParallel, DT, enviPat, foreach, ggplot2, graphics, grDevices, ggpubr, gridExtra, Hmisc, methods, minpack.lm, MSnbase, parallel, plotly, rhdf5, rlang, Rcpp, shiny, shinyscreenshot, signal, scales, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), ptairData, ropls License: GPL-3 NeedsCompilation: yes Title: Pre-processing PTR-TOF-MS Data Description: This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a singl VignetteBuilder: knitr BugReports: https://github.com/camilleroquencourt/ptairMS/issues Package: wppi Version: 1.19.0 Depends: R(>= 4.1) Imports: dplyr, igraph, logger, methods, magrittr, Matrix, OmnipathR(>= 2.99.8), progress, purrr, rlang, RCurl, stats, tibble, tidyr Suggests: knitr, testthat, rmarkdown License: MIT + file LICENSE NeedsCompilation: no Title: Weighting protein-protein interactions Description: Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm. biocViews: GraphAndNetwork, Network, Pathways, Software, GeneSignaling, GeneTarget, SystemsBiology, Transcriptomics, Annotation Author: Ana Galhoz [cre, aut] (ORCID: ), Denes Turei [aut] (ORCID: ), Michael P. Menden [aut] (ORCID: ), Albert Krewinkel [ctb, cph] (pagebreak Lua filter) Maintainer: Ana Galhoz URL: https://github.com/AnaGalhoz37/wppi VignetteBuilder: knitr BugReports: https://github.com/AnaGalhoz37/wppi/issues Package: CNViz Version: 1.19.0 Depends: R (>= 4.0), shiny (>= 1.5.0) Imports: dplyr, stats, utils, grDevices, plotly, karyoploteR, CopyNumberPlots, GenomicRanges, magrittr, DT, scales, graphics Suggests: rmarkdown, knitr License: Artistic-2.0 Title: Copy Number Visualization Description: CNViz takes probe, gene, and segment-level log2 copy number ratios and launches a Shiny app to visualize your sample's copy number profile. You can also integrate loss of heterozygosity (LOH) and single nucleotide variant (SNV) data. biocViews: Visualization, CopyNumberVariation, Sequencing, DNASeq Author: Rebecca Greenblatt [aut, cre] Maintainer: Rebecca Greenblatt VignetteBuilder: knitr Package: cbpManager Version: 1.19.0 Depends: shiny, shinydashboard Imports: utils, DT, htmltools, vroom, plyr, dplyr, magrittr, jsonlite, rapportools, basilisk, reticulate, shinyBS, shinycssloaders, rintrojs, rlang, markdown Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE Title: Generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics Description: This R package provides an R Shiny application that enables the user to generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics. Create cancer studies and edit its metadata. Upload mutation data of a patient that will be concatenated to the data_mutation_extended.txt file of the study. Create and edit clinical patient data, sample data, and timeline data. Create custom timeline tracks for patients. biocViews: ImmunoOncology, DataImport, DataRepresentation, GUI, ThirdPartyClient, Preprocessing, Visualization Author: Arsenij Ustjanzew [aut, cre, cph] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Arsenij Ustjanzew URL: https://arsenij-ust.github.io/cbpManager/index.html VignetteBuilder: knitr BugReports: https://github.com/arsenij-ust/cbpManager/issues Package: cosmosR Version: 1.19.1 Depends: R (>= 4.1) Imports: CARNIVAL, dorothea, dplyr, GSEABase, igraph, progress, purrr, rlang, stringr, utils, visNetwork, decoupleR Suggests: testthat, knitr, rmarkdown, htmltools, markdown, ggplot2, stringi, reshape2 License: GPL-3 Title: COSMOS (Causal Oriented Search of Multi-Omic Space) Description: COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets. biocViews: CellBiology, Pathways, Network, Proteomics, Metabolomics, Transcriptomics, GeneSignaling Author: Aurélien Dugourd [aut, cre] (ORCID: ), Attila Gabor [aut] (ORCID: ), Katharina Zirngibl [aut] (ORCID: ) Maintainer: Aurélien Dugourd URL: https://github.com/saezlab/COSMOSR VignetteBuilder: knitr BugReports: https://github.com/saezlab/COSMOSR/issues Package: decoupleR Version: 2.17.0 Depends: R (>= 4.0) Imports: BiocParallel, broom, dplyr, magrittr, Matrix, parallelly, purrr, rlang, stats, stringr, tibble, tidyr, tidyselect, withr Suggests: glmnet (>= 4.1-7), GSVA, viper, fgsea (>= 1.15.4), AUCell, SummarizedExperiment, rpart, ranger, BiocStyle, covr, knitr, pkgdown, RefManageR, rmarkdown, roxygen2, sessioninfo, pheatmap, testthat, OmnipathR, Seurat, ggplot2, ggrepel, patchwork, magick License: GPL-3 + file LICENSE Title: decoupleR: Ensemble of computational methods to infer biological activities from omics data Description: Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase. biocViews: DifferentialExpression, FunctionalGenomics, GeneExpression, GeneRegulation, Network, Software, StatisticalMethod, Transcription, Author: Pau Badia-i-Mompel [aut, cre] (ORCID: ), Jesús Vélez-Santiago [aut] (ORCID: ), Jana Braunger [aut] (ORCID: ), Celina Geiss [aut] (ORCID: ), Daniel Dimitrov [aut] (ORCID: ), Sophia Müller-Dott [aut] (ORCID: ), Petr Taus [aut] (ORCID: ), Aurélien Dugourd [aut] (ORCID: ), Christian H. Holland [aut] (ORCID: ), Ricardo O. Ramirez Flores [aut] (ORCID: ), Julio Saez-Rodriguez [aut] (ORCID: ) Maintainer: Pau Badia-i-Mompel URL: https://saezlab.github.io/decoupleR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/decoupleR/issues Package: epigraHMM Version: 1.19.1 Imports: Rcpp, magrittr, data.table, SummarizedExperiment, methods, Seqinfo, GenomicRanges, rtracklayer, IRanges, Rsamtools, bamsignals, csaw, S4Vectors, limma, stats, Rhdf5lib, rhdf5, Matrix, MASS, scales, ggpubr, ggplot2, GreyListChIP, pheatmap, grDevices LinkingTo: Rcpp, RcppArmadillo, Rhdf5lib Suggests: GenomeInfoDb, testthat, knitr, rmarkdown, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, gcapc, genomationData License: MIT + file LICENSE Title: Epigenomic R-based analysis with hidden Markov models Description: epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions. biocViews: ChIPSeq, ATACSeq, DNaseSeq, HiddenMarkovModel, Epigenetics Author: Pedro Baldoni [aut, cre] Maintainer: Pedro Baldoni SystemRequirements: GNU make VignetteBuilder: knitr Package: SingleMoleculeFootprinting Version: 2.5.7 Depends: R (>= 4.4.0) Imports: BiocGenerics, Biostrings, BSgenome, cluster, dplyr, Seqinfo, GenomicRanges, ggpointdensity, ggplot2, ggrepel, grDevices, IRanges, magrittr, Matrix, methods, miscTools, parallel, parallelDist, patchwork, plyranges, qs, QuasR, RColorBrewer, rlang, S4Vectors, stats, stringr, tibble, tidyr, utils, viridis Suggests: BSgenome.Mmusculus.UCSC.mm10, devtools, ExperimentHub, knitr, qs, rmarkdown, readr, rrapply, SingleMoleculeFootprintingData, testthat (>= 3.0.0), tidyverse License: GPL-3 Title: Analysis tools for Single Molecule Footprinting (SMF) data Description: SingleMoleculeFootprinting provides functions to analyze Single Molecule Footprinting (SMF) data. Following the workflow exemplified in its vignette, the user will be able to perform basic data analysis of SMF data with minimal coding effort. Starting from an aligned bam file, we show how to perform quality controls over sequencing libraries, extract methylation information at the single molecule level accounting for the two possible kind of SMF experiments (single enzyme or double enzyme), classify single molecules based on their patterns of molecular occupancy, plot SMF information at a given genomic location. biocViews: DNAMethylation, Coverage, NucleosomePositioning, DataRepresentation, Epigenetics, MethylSeq, QualityControl, Sequencing Author: Guido Barzaghi [aut, cre] (ORCID: ), Arnaud Krebs [aut] (ORCID: ), Mike Smith [ctb] (ORCID: ) Maintainer: Guido Barzaghi URL: https://www.bioconductor.org/packages/release/bioc/html/SingleMoleculeFootprinting.html VignetteBuilder: knitr BugReports: https://github.com/Krebslabrep/SingleMoleculeFootprinting/issues Package: DelayedTensor Version: 1.17.0 Depends: R (>= 4.1.0) Imports: methods, utils, S4Arrays, SparseArray, DelayedArray (>= 0.31.8), HDF5Array, BiocSingular, rTensor, DelayedRandomArray (>= 1.13.1), irlba, Matrix, einsum, Suggests: markdown, rmarkdown, BiocStyle, knitr, testthat, magrittr, dplyr, reticulate License: Artistic-2.0 Title: R package for sparse and out-of-core arithmetic and decomposition of Tensor Description: DelayedTensor operates Tensor arithmetic directly on DelayedArray object. DelayedTensor provides some generic function related to Tensor arithmetic/decompotision and dispatches it on the DelayedArray class. DelayedTensor also suppors Tensor contraction by einsum function, which is inspired by numpy einsum. biocViews: Software, Infrastructure, DataRepresentation, DimensionReduction Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr BugReports: https://github.com/rikenbit/DelayedTensor/issues Package: segmenter Version: 1.17.0 Depends: R (>= 4.1) Imports: ChIPseeker, GenomicRanges, SummarizedExperiment, IRanges, S4Vectors, bamsignals, ComplexHeatmap, graphics, stats, utils, methods, chromhmmData Suggests: testthat, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg18.knownGene, Gviz License: GPL-3 Title: Perform Chromatin Segmentation Analysis in R by Calling ChromHMM Description: Chromatin segmentation analysis transforms ChIP-seq data into signals over the genome. The latter represents the observed states in a multivariate Markov model to predict the chromatin's underlying states. ChromHMM, written in Java, integrates histone modification datasets to learn the chromatin states de-novo. The goal of this package is to call chromHMM from within R, capture the output files in an S4 object and interface to other relevant Bioconductor analysis tools. In addition, segmenter provides functions to test, select and visualize the output of the segmentation. biocViews: Software, HistoneModification Author: Mahmoud Ahmed [aut, cre] (ORCID: ) Maintainer: Mahmoud Ahmed VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/segmenter/issues Package: methylclock Version: 1.17.0 Depends: R (>= 4.1.0), methylclockData, devtools, quadprog Imports: Rcpp (>= 1.0.6), ExperimentHub, dplyr, impute, PerformanceAnalytics, Biobase, ggpmisc, tidyverse, ggplot2, ggpubr, minfi, tibble, RPMM, stats, graphics, tidyr, gridExtra, preprocessCore, dynamicTreeCut, planet LinkingTo: Rcpp Suggests: BiocStyle, knitr, GEOquery, rmarkdown License: MIT + file LICENSE Title: Methylclock - DNA methylation-based clocks Description: This package allows to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. Chronological DNAm age (in years) : Horvath's clock, Hannum's clock, BNN, Horvath's skin+blood clock, PedBE clock and Wu's clock. Gestational DNAm age : Knight's clock, Bohlin's clock, Mayne's clock and Lee's clocks. Biological DNAm clocks : Levine's clock and Telomere Length's clock. biocViews: DNAMethylation, BiologicalQuestion, Preprocessing, StatisticalMethod, Normalization Author: Dolors Pelegri-Siso [aut, cre] (ORCID: ), Juan R. Gonzalez [aut] (ORCID: ) Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/methylclock VignetteBuilder: knitr BugReports: https://github.com/isglobal-brge/methylclock/issues Package: FLAMES Version: 2.5.5 Depends: R (>= 4.2.0) Imports: abind, basilisk, bambu, BiocParallel, Biostrings, BiocGenerics, crew, circlize, ComplexHeatmap, cowplot, cli, dplyr, GenomicRanges, GenomicFeatures, GenomicAlignments, Seqinfo, ggplot2, grid, gridExtra, igraph, jsonlite, magrittr, magick, Matrix, MatrixGenerics, readr, reticulate, Rsamtools, rtracklayer, RColorBrewer, R.utils, S4Arrays, ShortRead, SingleCellExperiment, SummarizedExperiment, SpatialExperiment, scater, scatterpie, scrapper (>= 1.5.17), S4Vectors, scuttle, stats, scran, stringr, tidyr, utils, withr, methods, tibble, tidyselect, IRanges LinkingTo: Rcpp, Rhtslib, testthat Suggests: BiocStyle, GEOquery, ggrastr, knitr, rmarkdown, uwot, testthat (>= 3.0.0), xml2 License: GPL (>= 3) Title: FLAMES: Full Length Analysis of Mutations and Splicing in long read RNA-seq data Description: Semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution. biocViews: RNASeq, SingleCell, Transcriptomics, DataImport, DifferentialSplicing, AlternativeSplicing, GeneExpression, LongRead Author: Changqing Wang [aut, cre], Luyi Tian [aut], Oliver Voogd [aut], Jakob Schuster [aut], Shian Su [aut], Yair D.J. Prawer [aut], Yupei You [aut], Matthew Ritchie [ctb] Maintainer: Changqing Wang URL: https://mritchielab.github.io/FLAMES SystemRequirements: GNU make, C++17 VignetteBuilder: knitr BugReports: https://github.com/mritchielab/FLAMES/issues Package: cageminer Version: 1.17.0 Depends: R (>= 4.1) Imports: ggplot2, rlang, ggbio, ggtext, GenomeInfoDb, GenomicRanges, IRanges, reshape2, methods, BioNERO Suggests: testthat (>= 3.0.0), SummarizedExperiment, knitr, BiocStyle, rmarkdown, covr, sessioninfo License: GPL-3 Title: Candidate Gene Miner Description: This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments. biocViews: Software, SNP, FunctionalPrediction, GenomeWideAssociation, GeneExpression, NetworkEnrichment, VariantAnnotation, FunctionalGenomics, Network Author: Fabrício Almeida-Silva [aut, cre] (ORCID: ), Thiago Venancio [aut] (ORCID: ) Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/cageminer VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/cageminer Package: scanMiRApp Version: 1.17.1 Depends: R (>= 4.0), scanMiR Imports: AnnotationDbi, AnnotationFilter, AnnotationHub, BiocParallel, Biostrings, data.table, digest, DT, ensembldb, fst, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, htmlwidgets, IRanges, Matrix, methods, plotly, rintrojs, rtracklayer, S4Vectors, scanMiRData, shiny, shinycssloaders, shinydashboard, shinyjqui, stats, utils, txdbmaker, waiter Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), shinytest, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-3 Title: scanMiR shiny application Description: A shiny interface to the scanMiR package. The application enables the scanning of transcripts and custom sequences for miRNA binding sites, the visualization of KdModels and binding results, as well as browsing predicted repression data. In addition contains the IndexedFst class for fast indexed reading of large GenomicRanges or data.frames, and some utilities for facilitating scans and identifying enriched miRNA-target pairs. biocViews: miRNA, SequenceMatching, GUI, ShinyApps Author: Pierre-Luc Germain [cre, aut] (ORCID: ), Michael Soutschek [aut], Fridolin Gross [ctb] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr Package: spatialDE Version: 1.17.0 Depends: R (>= 4.3) Imports: reticulate, basilisk (>= 1.9.10), checkmate, stats, SpatialExperiment, methods, SummarizedExperiment, Matrix, ggplot2, ggrepel, scales, gridExtra Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE Title: R wrapper for SpatialDE Description: SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk. biocViews: Software, Transcriptomics Author: Davide Corso [aut] (ORCID: ), Milan Malfait [aut] (ORCID: ), Lambda Moses [aut] (ORCID: ), Gabriele Sales [cre] Maintainer: Gabriele Sales URL: https://github.com/sales-lab/spatialDE, https://bioconductor.org/packages/spatialDE/ VignetteBuilder: knitr BugReports: https://github.com/sales-lab/spatialDE/issues Package: transformGamPoi Version: 1.17.0 Imports: glmGamPoi, DelayedArray, Matrix, MatrixGenerics, SummarizedExperiment, HDF5Array, methods, utils, Rcpp LinkingTo: Rcpp Suggests: testthat, TENxPBMCData, scran, knitr, rmarkdown, BiocStyle License: GPL-3 Title: Variance Stabilizing Transformation for Gamma-Poisson Models Description: Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals). biocViews: SingleCell, Normalization, Preprocessing, Regression Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/transformGamPoi VignetteBuilder: knitr BugReports: https://github.com/const-ae/transformGamPoi/issues Package: imcRtools Version: 1.17.1 Depends: R (>= 4.1), SpatialExperiment Imports: S4Vectors, stats, utils, SummarizedExperiment, methods, pheatmap, scuttle, stringr, readr, EBImage, cytomapper, abind, BiocParallel, viridis, dplyr, magrittr, DT, igraph, SingleCellExperiment, vroom, BiocNeighbors, RTriangle, ggraph, tidygraph, ggplot2, data.table, sf, concaveman, tidyselect, distances, MatrixGenerics, rlang, grDevices Suggests: CATALYST, grid, tidyr, BiocStyle, knitr, rmarkdown, markdown, testthat License: GPL-3 Title: Methods for imaging mass cytometry data analysis Description: This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected. biocViews: ImmunoOncology, SingleCell, Spatial, DataImport, Clustering Author: Nils Eling [aut], Tobias Hoch [ctb], Vito Zanotelli [ctb], Jana Fischer [ctb], Daniel Schulz [ctb, cre] (ORCID: ), Lasse Meyer [ctb], Lutz Marlene [ctb], Schiller Chiara [ctb], Ibañez Victor [ctb] Maintainer: Daniel Schulz URL: https://github.com/BodenmillerGroup/imcRtools VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/imcRtools/issues Package: sparrow Version: 1.17.0 Depends: R (>= 4.0) Imports: babelgene (>= 21.4), BiocGenerics, BiocParallel, BiocSet, checkmate, circlize, ComplexHeatmap (>= 2.0), data.table (>= 1.10.4), DelayedMatrixStats, edgeR (>= 3.18.1), ggplot2 (>= 2.2.0), graphics, grDevices, GSEABase, irlba, limma, Matrix, methods, plotly (>= 4.9.0), stats, utils, viridis Suggests: AnnotationDbi, BiasedUrn, Biobase (>= 2.24.0), BiocStyle, DESeq2, dplyr, dtplyr, fgsea, GSVA, GO.db, goseq, hexbin, KernSmooth, knitr, magrittr, matrixStats, msigdbr (>= 10.0), orthogene, PANTHER.db (>= 1.0.3), R.utils, reactome.db, rmarkdown, SummarizedExperiment, statmod, stringr, testthat, webshot License: MIT + file LICENSE Title: Take command of set enrichment analyses through a unified interface Description: Provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package. biocViews: GeneSetEnrichment, Pathways Author: Steve Lianoglou [aut, cre] (ORCID: ), Arkadiusz Gladki [ctb], Aratus Informatics, LLC [fnd] (2023+), Denali Therapeutics [fnd] (2018-2022), Genentech [fnd] (2014 - 2017) Maintainer: Steve Lianoglou URL: https://github.com/lianos/sparrow VignetteBuilder: knitr BugReports: https://github.com/lianos/sparrow/issues Package: MicrobiomeProfiler Version: 1.17.1 Depends: R (>= 4.2.0) Imports: enrichit, config, DT, enrichplot, golem, gson, methods, magrittr, shiny (>= 1.6.0), shinyWidgets, shinycustomloader, htmltools, ggplot2, graphics, stats, utils, yulab.utils Suggests: clusterProfiler (>= 4.5.2), rmarkdown, knitr, testthat (>= 3.0.0), prettydoc License: GPL-2 Title: An R/shiny package for microbiome functional enrichment analysis Description: This is an R/shiny package to perform functional enrichment analysis for microbiome data. This package was based on clusterProfiler. Moreover, MicrobiomeProfiler support KEGG enrichment analysis, COG enrichment analysis, Microbe-Disease association enrichment analysis, Metabo-Pathway analysis. biocViews: Microbiome, Software, Visualization,KEGG Author: Guangchuang Yu [cre, aut] (ORCID: ), Meijun Chen [aut] (ORCID: ) Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/MicrobiomeProfiler/, https://yulab-smu.top/contribution-knowledge-mining/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiomeProfiler/issues Package: CyTOFpower Version: 1.17.3 Depends: R (>= 4.1) Imports: CytoGLMM, diffcyt, DT, dplyr, ggplot2, magrittr, methods, rlang, stats, shiny, shinyFeedback, shinyjs, shinyMatrix, SummarizedExperiment, tibble, tidyr Suggests: testthat (>= 3.0.0), BiocStyle, knitr License: LGPL-3 Title: Power analysis for CyTOF experiments Description: This package is a tool to predict the power of CyTOF experiments in the context of differential state analyses. The package provides a shiny app with two options to predict the power of an experiment: i. generation of in-sicilico CyTOF data, using users input ii. browsing in a grid of parameters for which the power was already precomputed. biocViews: FlowCytometry, SingleCell, CellBiology, StatisticalMethod, Software Author: Anne-Maud Ferreira [cre, aut] (ORCID: ), Catherine Blish [aut], Susan Holmes [aut] Maintainer: Anne-Maud Ferreira VignetteBuilder: knitr Package: benchdamic Version: 1.17.0 Depends: R (>= 4.3.0) Imports: stats, stats4, utils, methods, phyloseq, TreeSummarizedExperiment, BiocParallel, zinbwave, edgeR, DESeq2, limma, ALDEx2, corncob, SummarizedExperiment, MAST, Seurat, ANCOMBC, microbiome, mixOmics, lme4, NOISeq, dearseq, MicrobiomeStat, Maaslin2, maaslin3, GUniFrac, metagenomeSeq, MGLM, ggplot2, RColorBrewer, plyr, reshape2, ggdendro, ggridges, graphics, cowplot, grDevices, tidytext Suggests: knitr, rmarkdown, kableExtra, BiocStyle, magick, SPsimSeq, testthat License: Artistic-2.0 Title: Benchmark of differential abundance methods on microbiome data Description: Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization. biocViews: Metagenomics, Microbiome, DifferentialExpression, MultipleComparison, Normalization, Preprocessing, Software Author: Matteo Calgaro [aut, cre] (ORCID: ), Chiara Romualdi [aut] (ORCID: ), Davide Risso [aut] (ORCID: ), Nicola Vitulo [aut] (ORCID: ) Maintainer: Matteo Calgaro VignetteBuilder: knitr BugReports: https://github.com/mcalgaro93/benchdamic/issues Package: deconvR Version: 1.17.0 Depends: R (>= 4.1), data.table (>= 1.14.0) Imports: S4Vectors (>= 0.30.0), methylKit (>= 1.18.0), IRanges (>= 2.26.0), GenomicRanges (>= 1.44.0), BiocGenerics (>= 0.38.0), stats, methods, foreach (>= 1.5.1), magrittr (>= 2.0.1), matrixStats (>= 0.61.0), e1071 (>= 1.7.9), quadprog (>= 1.5.8), nnls (>= 1.4), rsq (>= 2.2), MASS, utils, dplyr (>= 1.0.7), tidyr (>= 1.1.3), assertthat, minfi Suggests: testthat (>= 3.0.0), roxygen2 (>= 7.1.2), doParallel (>= 1.0.16), parallel, knitr (>= 1.34), BiocStyle (>= 2.20.2), reshape2 (>= 1.4.4), ggplot2 (>= 3.3.5), rmarkdown, devtools (>= 2.4.2), sessioninfo (>= 1.1.1), covr, granulator, RefManageR License: Artistic-2.0 Title: Simulation and Deconvolution of Omic Profiles Description: This package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. Users are given the option to create or extend a reference atlas and,also simulate the desired size of the bulk signature profile of the reference cell types.The package includes the cell-type-specific methylation atlas and, Illumina Epic B5 probe ids that can be used in deconvolution. Additionally,we included BSmeth2Probe, to make mapping WGBS data to their probe IDs easier. biocViews: DNAMethylation, Regression, GeneExpression, RNASeq, SingleCell, StatisticalMethod, Transcriptomics Author: Irem B. Gündüz [aut, cre] (ORCID: ), Veronika Ebenal [aut] (ORCID: ), Altuna Akalin [aut] (ORCID: ) Maintainer: Irem B. Gündüz URL: https://github.com/BIMSBbioinfo/deconvR VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/deconvR Package: Cepo Version: 1.17.0 Depends: GSEABase, R (>= 4.1) Imports: DelayedMatrixStats, DelayedArray, HDF5Array, S4Vectors, methods, SingleCellExperiment, SummarizedExperiment, ggplot2, rlang, grDevices, patchwork, reshape2, BiocParallel, stats, dplyr, purrr Suggests: knitr, rmarkdown, BiocStyle, testthat, covr, UpSetR, scater, scMerge, fgsea, escape, pheatmap License: MIT + file LICENSE Title: Cepo for the identification of differentially stable genes Description: Defining the identity of a cell is fundamental to understand the heterogeneity of cells to various environmental signals and perturbations. We present Cepo, a new method to explore cell identities from single-cell RNA-sequencing data using differential stability as a new metric to define cell identity genes. Cepo computes cell-type specific gene statistics pertaining to differential stable gene expression. biocViews: Classification, GeneExpression, SingleCell, Software, Sequencing, DifferentialExpression Author: Hani Jieun Kim [aut, cre] (ORCID: ), Kevin Wang [aut] (ORCID: ) Maintainer: Hani Jieun Kim VignetteBuilder: knitr Package: mosbi Version: 1.17.0 Depends: R (>= 4.1) Imports: Rcpp, BH, xml2, methods, igraph, fabia, RcppParallel, biclust, isa2, QUBIC, akmbiclust, RColorBrewer LinkingTo: Rcpp, BH, RcppParallel Suggests: knitr, rmarkdown, BiocGenerics, runibic, BiocStyle, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE Title: Molecular Signature identification using Biclustering Description: This package is a implementation of biclustering ensemble method MoSBi (Molecular signature Identification from Biclustering). MoSBi provides standardized interfaces for biclustering results and can combine their results with a multi-algorithm ensemble approach to compute robust ensemble biclusters on molecular omics data. This is done by computing similarity networks of biclusters and filtering for overlaps using a custom error model. After that, the louvain modularity it used to extract bicluster communities from the similarity network, which can then be converted to ensemble biclusters. Additionally, MoSBi includes several network visualization methods to give an intuitive and scalable overview of the results. MoSBi comes with several biclustering algorithms, but can be easily extended to new biclustering algorithms. biocViews: Software, StatisticalMethod, Clustering, Network Author: Tim Daniel Rose [cre, aut], Josch Konstantin Pauling [aut], Nikolai Koehler [aut] Maintainer: Tim Daniel Rose SystemRequirements: C++17, GNU make VignetteBuilder: knitr Package: txcutr Version: 1.17.0 Depends: R (>= 4.5.0) Imports: AnnotationDbi, GenomicFeatures, txdbmaker, IRanges, GenomicRanges, BiocGenerics, Biostrings, S4Vectors, rtracklayer, BiocParallel, stats, methods, utils Suggests: RefManageR, BiocStyle, knitr, sessioninfo, rmarkdown, testthat (>= 3.0.0), TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, BSgenome.Scerevisiae.UCSC.sacCer3, GenomeInfoDbData License: GPL-3 Title: Transcriptome CUTteR Description: Various mRNA sequencing library preparation methods generate sequencing reads specifically from the transcript ends. Analyses that focus on quantification of isoform usage from such data can be aided by using truncated versions of transcriptome annotations, both at the alignment or pseudo-alignment stage, as well as in downstream analysis. This package implements some convenience methods for readily generating such truncated annotations and their corresponding sequences. biocViews: Alignment, Annotation, RNASeq, Sequencing, Transcriptomics Author: Mervin Fansler [aut, cre] (ORCID: ) Maintainer: Mervin Fansler URL: https://github.com/mfansler/txcutr VignetteBuilder: knitr BugReports: https://github.com/mfansler/txcutr/issues Package: RiboCrypt Version: 1.17.0 Depends: R (>= 3.6.0), ORFik (>= 1.13.12) Imports: bslib, BiocGenerics, BiocParallel, Biostrings, ComplexHeatmap, cowplot, crosstalk, data.table, dplyr, DT, fst, Seqinfo, GenomicFeatures, GenomicRanges, ggplot2, grid, htmlwidgets, httr, IRanges, jsonlite, knitr, markdown, NGLVieweR, plotly, rlang, rclipboard, RCurl, rtracklayer, shiny, shinycssloaders, shinyhelper, shinyjs, shinyjqui, shinyWidgets, stringr, writexl Suggests: testthat, rmarkdown, BiocStyle, BSgenome, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE Title: Interactive visualization in genomics Description: R Package for interactive visualization and browsing NGS data. It contains a browser for both transcript and genomic coordinate view. In addition a QC and general metaplots are included, among others differential translation plots and gene expression plots. The package is still under development. biocViews: Software, Sequencing, RiboSeq, RNASeq, Author: Michal Swirski [aut, cre, cph], Haakon Tjeldnes [aut, ctb], Kornel Labun [ctb] Maintainer: Michal Swirski URL: https://github.com/m-swirski/RiboCrypt VignetteBuilder: knitr BugReports: https://github.com/m-swirski/RiboCrypt/issues Package: easier Version: 1.17.0 Depends: R (>= 4.2.0) Imports: progeny, easierData, dorothea (>= 1.6.0), decoupleR, quantiseqr, ROCR, grDevices, stats, graphics, ggplot2, ggpubr, DESeq2, utils, dplyr, tidyr, tibble, matrixStats, rlang, BiocParallel, reshape2, rstatix, ggrepel, magrittr, coin Suggests: knitr, rmarkdown, BiocStyle, testthat, SummarizedExperiment, viper License: MIT + file LICENSE Title: Estimate Systems Immune Response from RNA-seq data Description: This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in TCGA data to identify how these descriptors can simultaneously predict several state-of-the-art hallmarks of anti-cancer immune response. In this way we derive cancer-specific models and identify cancer-specific systems biomarkers of immune response. These biomarkers have been experimentally validated in the literature and the performance of EaSIeR predictions has been validated using independent datasets form four different cancer types with patients treated with anti-PD1 or anti-PDL1 therapy. biocViews: GeneExpression, Software, Transcription, SystemsBiology, Pathways, GeneSetEnrichment, ImmunoOncology, Epigenetics, Classification, BiomedicalInformatics, Regression, ExperimentHubSoftware Author: Oscar Lapuente-Santana [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ), Arsenij Ustjanzew [aut] (ORCID: ), Francesca Finotello [aut] (ORCID: ), Federica Eduati [aut] (ORCID: ) Maintainer: Oscar Lapuente-Santana VignetteBuilder: knitr Package: BOBaFIT Version: 1.15.0 Depends: R (>= 2.10) Imports: dplyr, NbClust, ggplot2, ggbio, grDevices, stats, tidyr, GenomicRanges, ggforce, stringr, plyranges, methods, utils, magrittr Suggests: rmarkdown, markdown, BiocStyle, knitr, testthat (>= 3.0.0), utils, testthat License: GPL (>= 3) Title: Refitting diploid region profiles using a clustering procedure Description: This package provides a method to refit and correct the diploid region in copy number profiles. It uses a clustering algorithm to identify pathology-specific normal (diploid) chromosomes and then use their copy number signal to refit the whole profile. The package is composed by three functions: DRrefit (the main function), ComputeNormalChromosome and PlotCluster. biocViews: CopyNumberVariation, Clustering, Visualization, Normalization, Software Author: Andrea Poletti [aut], Gaia Mazzocchetti [aut, cre], Vincenza Solli [aut] Maintainer: Gaia Mazzocchetti URL: https://github.com/andrea-poletti-unibo/BOBaFIT VignetteBuilder: knitr BugReports: https://github.com/andrea-poletti-unibo/BOBaFIT/issues Package: beer Version: 1.15.1 Depends: R (>= 4.2.0), PhIPData (>= 1.1.1), rjags Imports: cli, edgeR, BiocParallel, methods, progressr, stats, SummarizedExperiment, utils Suggests: testthat (>= 3.0.0), BiocStyle, covr, codetools, knitr, rmarkdown, dplyr, ggplot2, spelling License: MIT + file LICENSE Title: Bayesian Enrichment Estimation in R Description: BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses. biocViews: Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Athena Chen [aut, cre] (ORCID: ), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen URL: https://github.com/athchen/beer/ SystemRequirements: JAGS (4.3.0) VignetteBuilder: knitr BugReports: https://github.com/athchen/beer/issues Package: ASURAT Version: 1.15.0 Depends: R (>= 4.0.0) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, Rcpp (>= 1.0.7), cluster, utils, plot3D, ComplexHeatmap, circlize, grid, grDevices, graphics LinkingTo: Rcpp Suggests: ggplot2, TENxPBMCData, dplyr, Rtsne, Seurat, AnnotationDbi, BiocGenerics, stringr, org.Hs.eg.db, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 + file LICENSE Title: Functional annotation-driven unsupervised clustering for single-cell data Description: ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs). biocViews: GeneExpression, SingleCell, Sequencing, Clustering, GeneSignaling Author: Keita Iida [aut, cre] (ORCID: ), Johannes Nicolaus Wibisana [ctb] Maintainer: Keita Iida VignetteBuilder: knitr Package: bandle Version: 1.15.0 Depends: R (>= 4.1), S4Vectors, Biobase, MSnbase, pRoloc Imports: Rcpp (>= 1.0.4.6), pRolocdata, lbfgs, ggplot2, dplyr, plyr, knitr, methods, BiocParallel, robustbase, BiocStyle, ggalluvial, ggrepel, tidyr, circlize, graphics, stats, utils, grDevices, rlang, RColorBrewer, gtools, gridExtra, coda (>= 0.19-4) LinkingTo: Rcpp, RcppArmadillo, BH Suggests: testthat, interp, fields, pheatmap, viridis, rmarkdown, spelling License: Artistic-2.0 Title: An R package for the Bayesian analysis of differential subcellular localisation experiments Description: The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental methods supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data. biocViews: Bayesian, Classification, Clustering, ImmunoOncology, QualityControl,DataImport, Proteomics, MassSpectrometry Author: Oliver M. Crook [aut, cre] (ORCID: ), Lisa Breckels [aut] (ORCID: ) Maintainer: Oliver M. Crook URL: http://github.com/ococrook/bandle VignetteBuilder: knitr BugReports: https://github.com/ococrook/bandle/issues Package: epimutacions Version: 1.15.0 Depends: R (>= 4.3.0), epimutacionsData Imports: minfi, bumphunter, isotree, robustbase, ggplot2, GenomicRanges, GenomicFeatures, IRanges, SummarizedExperiment, stats, matrixStats, BiocGenerics, S4Vectors, utils, biomaRt, BiocParallel, GenomeInfoDb, Homo.sapiens, purrr, tibble, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, rtracklayer, AnnotationDbi, AnnotationHub, ExperimentHub, reshape2, grid, ensembldb, gridExtra, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, ggrepel Suggests: testthat, knitr, rmarkdown, BiocStyle, a4Base, kableExtra, methods, grDevices License: MIT + file LICENSE Title: Robust outlier identification for DNA methylation data Description: The package includes some statistical outlier detection methods for epimutations detection in DNA methylation data. The methods included in the package are MANOVA, Multivariate linear models, isolation forest, robust mahalanobis distance, quantile and beta. The methods compare a case sample with a suspected disease against a reference panel (composed of healthy individuals) to identify epimutations in the given case sample. It also contains functions to annotate and visualize the identified epimutations. biocViews: DNAMethylation, BiologicalQuestion, Preprocessing, StatisticalMethod, Normalization Author: Dolors Pelegri-Siso [aut, cre] (ORCID: ), Juan R. Gonzalez [aut] (ORCID: ), Carlos Ruiz-Arenas [aut] (ORCID: ), Carles Hernandez-Ferrer [aut] (ORCID: ), Leire Abarrategui [aut] (ORCID: ) Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/epimutacions VignetteBuilder: knitr BugReports: https://github.com/isglobal-brge/epimutacions/issues Package: terraTCGAdata Version: 1.15.2 Depends: AnVILGCP, MultiAssayExperiment Imports: AnVIL, BiocFileCache, dplyr, GenomicRanges, methods, RaggedExperiment, readr, S4Vectors, stats, tidyr, TCGAutils, utils Suggests: AnVILBase, GCPtools, knitr, rmarkdown, BiocStyle, withr, testthat (>= 3.0.0) License: Artistic-2.0 Title: OpenAccess TCGA Data on Terra as MultiAssayExperiment Description: Leverage the existing open access TCGA data on Terra with well-established Bioconductor infrastructure. Make use of the Terra data model without learning its complexities. With a few functions, you can copy / download and generate a MultiAssayExperiment from the TCGA example workspaces provided by Terra. biocViews: Software, Infrastructure, DataImport Author: Marcel Ramos [aut, cre] (ORCID: ) Maintainer: Marcel Ramos URL: https://github.com/waldronlab/terraTCGAdata VignetteBuilder: knitr BugReports: https://github.com/waldronlab/terraTCGAdata/issues Package: borealis Version: 1.15.0 Depends: R (>= 4.2.0), Biobase Imports: doParallel, snow, purrr, plyr, foreach, gamlss, gamlss.dist, bsseq, methods, DSS, R.utils, utils, stats, ggplot2, cowplot, dplyr, rlang, GenomicRanges Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, annotatr, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL-3 Title: Bisulfite-seq OutlieR mEthylation At singLe-sIte reSolution Description: Borealis is an R library performing outlier analysis for count-based bisulfite sequencing data. It detectes outlier methylated CpG sites from bisulfite sequencing (BS-seq). The core of Borealis is modeling Beta-Binomial distributions. This can be useful for rare disease diagnoses. biocViews: Sequencing, Coverage, DNAMethylation, DifferentialMethylation Author: Garrett Jenkinson [aut, cre] (ORCID: ) Maintainer: Garrett Jenkinson VignetteBuilder: knitr Package: scDDboost Version: 1.13.0 Depends: R (>= 4.2), ggplot2 Imports: Rcpp (>= 0.12.11), RcppEigen (>= 0.3.2.9.0),EBSeq, BiocParallel, mclust, SingleCellExperiment, cluster, Oscope, SummarizedExperiment, stats, methods LinkingTo: Rcpp, RcppEigen, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) Title: A compositional model to assess expression changes from single-cell rna-seq data Description: scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions. biocViews: SingleCell, Software, Clustering, Sequencing, GeneExpression, DifferentialExpression, Bayesian Author: Xiuyu Ma [cre, aut], Michael A. Newton [ctb] Maintainer: Xiuyu Ma URL: https://github.com/wiscstatman/scDDboost SystemRequirements: c++14 VignetteBuilder: knitr BugReports: https://github.com/wiscstatman/scDDboost/issues Package: SpliceWiz Version: 1.13.0 Depends: NxtIRFdata Imports: ompBAM, methods, stats, utils, tools, parallel, scales, magrittr, Rcpp (>= 1.0.5), data.table, fst, ggplot2, AnnotationHub, RSQLite, BiocFileCache, BiocGenerics, BiocParallel, Biostrings, BSgenome, DelayedArray, DelayedMatrixStats, genefilter, GenomeInfoDb, GenomicRanges, HDF5Array, h5mread, htmltools, IRanges, patchwork, pheatmap, progress, plotly, R.utils, rhdf5, rtracklayer, SummarizedExperiment, S4Vectors, shiny, shinyFiles, shinyWidgets, shinydashboard, stringi, rhandsontable, DT, grDevices, heatmaply, matrixStats, RColorBrewer, rvest, httr LinkingTo: ompBAM, Rcpp, RcppProgress Suggests: knitr, rmarkdown, crayon, splines, testthat (>= 3.0.0), DESeq2, limma, DoubleExpSeq, edgeR, DBI, GO.db, AnnotationDbi, fgsea, Rsubread License: MIT + file LICENSE Title: interactive analysis and visualization of alternative splicing in R Description: The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across conditions. SpliceWiz features a shiny-based GUI facilitating interactive data exploration of results including gene ontology enrichment. It is performance optimized with multi-threaded processing of BAM files and a new COV file format for fast recall of sequencing coverage. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization. biocViews: Software, Transcriptomics, RNASeq, AlternativeSplicing, Coverage, DifferentialSplicing, DifferentialExpression, GUI, Sequencing Author: Alex Chit Hei Wong [aut, cre, cph], Ulf Schmitz [ctb], William Ritchie [cph] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/SpliceWiz SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ Package: EpiMix Version: 1.13.0 Depends: R (>= 4.2.0), EpiMix.data (>= 1.2.2) Imports: AnnotationHub, AnnotationDbi, Biobase, biomaRt, data.table, doParallel, doSNOW, downloader, dplyr, ELMER.data, ExperimentHub, foreach, Seqinfo, GenomicFeatures, GenomicRanges, ggplot2, graphics, grDevices, impute, IRanges, limma, methods, parallel, plyr, progress, R.matlab, RColorBrewer, RCurl, rlang, RPMM, S4Vectors, stats, SummarizedExperiment, tibble, tidyr, utils Suggests: BiocStyle, clusterProfiler, DT, GEOquery, karyoploteR, knitr, org.Hs.eg.db, regioneR, Seurat, survival, survminer, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, multiMiR, miRBaseConverter License: GPL-3 Title: EpiMix: an integrative tool for the population-level analysis of DNA methylation Description: EpiMix is a comprehensive tool for the integrative analysis of high-throughput DNA methylation data and gene expression data. EpiMix enables automated data downloading (from TCGA or GEO), preprocessing, methylation modeling, interactive visualization and functional annotation.To identify hypo- or hypermethylated CpG sites across physiological or pathological conditions, EpiMix uses a beta mixture modeling to identify the methylation states of each CpG probe and compares the methylation of the experimental group to the control group.The output from EpiMix is the functional DNA methylation that is predictive of gene expression. EpiMix incorporates specialized algorithms to identify functional DNA methylation at various genetic elements, including proximal cis-regulatory elements of protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs. biocViews: Software, Epigenetics, Preprocessing, DNAMethylation, GeneExpression, DifferentialMethylation Author: Yuanning Zheng [aut, cre], Markus Sujansky [aut], John Jun [aut], Olivier Gevaert [aut] Maintainer: Yuanning Zheng VignetteBuilder: knitr BugReports: https://github.com/gevaertlab/EpiMix/issues Package: crisprDesign Version: 1.13.10 Depends: R (>= 4.2.0), crisprBase (>= 1.1.3) Imports: AnnotationDbi, BiocGenerics, Biostrings (>= 2.77.2), BSgenome (>= 1.77.1), crisprBowtie (>= 0.99.8), crisprScore (>= 1.15.2), GenomeInfoDb (>= 1.45.7), GenomicFeatures (>= 1.61.4), GenomicRanges (>= 1.61.1), IRanges, Matrix, MatrixGenerics, methods, reticulate, rtracklayer (>= 1.69.1), S4Vectors, Seqinfo, stats, txdbmaker (>= 1.5.6), utils, VariantAnnotation (>= 1.55.1) Suggests: biomaRt, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BiocStyle, crisprBwa (>= 0.99.7), knitr, rmarkdown, Rbowtie, Rbwa, RCurl, testthat License: MIT + file LICENSE Title: Comprehensive design of CRISPR gRNAs for nucleases and base editors Description: Provides a comprehensive suite of functions to design and annotate CRISPR guide RNA (gRNAs) sequences. This includes on- and off-target search, on-target efficiency scoring, off-target scoring, full gene and TSS contextual annotations, and SNP annotation (human only). It currently support five types of CRISPR modalities (modes of perturbations): CRISPR knockout, CRISPR activation, CRISPR inhibition, CRISPR base editing, and CRISPR knockdown. All types of CRISPR nucleases are supported, including DNA- and RNA-target nucleases such as Cas9, Cas12a, and Cas13d. All types of base editors are also supported. gRNA design can be performed on reference genomes, transcriptomes, and custom DNA and RNA sequences. Both unpaired and paired gRNA designs are enabled. biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprDesign VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprDesign/issues Package: zenith Version: 1.13.0 Depends: R (>= 4.2.0), limma, methods Imports: variancePartition (>= 1.26.0), EnrichmentBrowser (>= 2.22.0), GSEABase (>= 1.54.0), msigdbr, Rfast, ggplot2, tidyr, dplyr, reshape2, progress, utils, Rdpack, stats Suggests: BiocStyle, BiocGenerics, knitr, pander, rmarkdown, tweeDEseqCountData, edgeR, kableExtra, RUnit License: Artistic-2.0 Title: Gene set analysis following differential expression using linear (mixed) modeling with dream Description: Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream(). biocViews: RNASeq, GeneExpression, GeneSetEnrichment, DifferentialExpression, BatchEffect, QualityControl, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Preprocessing, Microarray, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] (ORCID: ) Maintainer: Gabriel Hoffman URL: https://DiseaseNeuroGenomics.github.io/zenith VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeuroGenomics/zenith/issues Package: SimBu Version: 1.13.0 Imports: basilisk, BiocParallel, data.table, dplyr, ggplot2, tools, Matrix (>= 1.3.3), methods, phyloseq, proxyC, RColorBrewer, RCurl, reticulate, sparseMatrixStats, SummarizedExperiment, tidyr Suggests: curl, knitr, matrixStats, rmarkdown, Seurat (>= 5.0.0), SeuratObject (>= 5.0.0), testthat (>= 3.0.0) License: GPL-3 + file LICENSE Title: Simulate Bulk RNA-seq Datasets from Single-Cell Datasets Description: SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations. biocViews: Software, RNASeq, SingleCell Author: Alexander Dietrich [aut, cre] Maintainer: Alexander Dietrich URL: https://github.com/omnideconv/SimBu VignetteBuilder: knitr BugReports: https://github.com/omnideconv/SimBu/issues Package: crisprVerse Version: 1.13.1 Depends: R (>= 4.2.0) Imports: BiocManager, cli, crisprBase, crisprBowtie, crisprScore, crisprScoreData, crisprDesign, crisprViz, rlang, tools, utils Suggests: BiocStyle, knitr, testthat License: MIT + file LICENSE Title: Easily install and load the crisprVerse ecosystem for CRISPR gRNA design Description: The crisprVerse is a modular ecosystem of R packages developed for the design and manipulation of CRISPR guide RNAs (gRNAs). All packages share a common language and design principles. This package is designed to make it easy to install and load the crisprVerse packages in a single step. To learn more about the crisprVerse, visit . biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprVerse VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprVerse/issues Package: BASiCStan Version: 1.13.0 Depends: R (>= 4.2), BASiCS, rstan (>= 2.18.1) Imports: methods, glmGamPoi, scran, scuttle, stats, utils, SingleCellExperiment, SummarizedExperiment, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstantools (>= 2.1.1) LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: testthat (>= 3.0.0), knitr, rmarkdown License: GPL-3 Title: Stan implementation of BASiCS Description: Provides an interface to infer the parameters of BASiCS using the variational inference (ADVI), Markov chain Monte Carlo (NUTS), and maximum a posteriori (BFGS) inference engines in the Stan programming language. BASiCS is a Bayesian hierarchical model that uses an adaptive Metropolis within Gibbs sampling scheme. Alternative inference methods provided by Stan may be preferable in some situations, for example for particularly large data or posterior distributions with difficult geometries. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology Author: Alan O'Callaghan [aut, cre], Catalina Vallejos [aut] Maintainer: Alan O'Callaghan URL: https://github.com/Alanocallaghan/BASiCStan SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/BASiCStan/issues Package: EasyCellType Version: 1.13.0 Depends: R (>= 4.2.0) Imports: clusterProfiler, dplyr, forcats, ggplot2, magrittr, rlang, stats, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, vctrs (>= 0.6.4), BiocStyle Suggests: knitr, rmarkdown, testthat (>= 3.0.0), Seurat, BiocManager, devtools, BiocStyle License: Artistic-2.0 Title: Annotate cell types for scRNA-seq data Description: We developed EasyCellType which can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA) and a modified versio of Fisher's exact test. The function presents annotation recommendations in graphical outcomes: bar plots for each cluster showing candidate cell types, as well as a dot plot summarizing the top 5 significant annotations for each cluster. biocViews: SingleCell, Software, GeneExpression, GeneSetEnrichment Author: Ruoxing Li [aut, cre, ctb], Ziyi Li [ctb] Maintainer: Ruoxing Li VignetteBuilder: knitr Package: phenomis Version: 1.13.0 Depends: SummarizedExperiment Imports: Biobase, biodb, biodbChebi, data.table, futile.logger, ggplot2, ggrepel, graphics, grDevices, grid, htmlwidgets, igraph, limma, methods, MultiAssayExperiment, MultiDataSet, PMCMRplus, plotly, ranger, RColorBrewer, ropls, stats, tibble, tidyr, utils, VennDiagram Suggests: BiocGenerics, BiocStyle, biosigner, CLL, knitr, omicade4, rmarkdown, testthat License: CeCILL Title: Postprocessing and univariate analysis of omics data Description: The 'phenomis' package provides methods to perform post-processing (i.e. quality control and normalization) as well as univariate statistical analysis of single and multi-omics data sets. These methods include quality control metrics, signal drift and batch effect correction, intensity transformation, univariate hypothesis testing, but also clustering (as well as annotation of metabolomics data). The data are handled in the standard Bioconductor formats (i.e. SummarizedExperiment and MultiAssayExperiment for single and multi-omics datasets, respectively; the alternative ExpressionSet and MultiDataSet formats are also supported for convenience). As a result, all methods can be readily chained as workflows. The pipeline can be further enriched by multivariate analysis and feature selection, by using the 'ropls' and 'biosigner' packages, which support the same formats. Data can be conveniently imported from and exported to text files. Although the methods were initially targeted to metabolomics data, most of the methods can be applied to other types of omics data (e.g., transcriptomics, proteomics). biocViews: BatchEffect, Clustering, Coverage, KEGG, MassSpectrometry, Metabolomics, Normalization, Proteomics, QualityControl, Sequencing, StatisticalMethod, Transcriptomics Author: Etienne A. Thevenot [aut, cre] (ORCID: ), Natacha Lenuzza [ctb], Marie Tremblay-Franco [ctb], Alyssa Imbert [ctb], Pierrick Roger [ctb], Eric Venot [ctb], Sylvain Dechaumet [ctb] Maintainer: Etienne A. Thevenot URL: https://doi.org/10.1038/s41597-021-01095-3 VignetteBuilder: knitr Package: scifer Version: 1.13.1 Imports: dplyr, rmarkdown, data.table, Biostrings, stats, plyr, knitr, ggplot2, gridExtra, DECIPHER, stringr, sangerseqR, kableExtra, tibble, scales, rlang, flowCore, methods, basilisk, basilisk.utils, reticulate, here, pwalign, utils Suggests: BiocBaseUtils, fs, BiocStyle, testthat (>= 3.0.0) Enhances: parallel License: MIT + file LICENSE Title: Scifer: Single-Cell Immunoglobulin Filtering of Sanger Sequences Description: Have you ever index sorted cells in a 96 or 384-well plate and then sequenced using Sanger sequencing? If so, you probably had some struggles to either check the electropherogram of each cell sequenced manually, or when you tried to identify which cell was sorted where after sequencing the plate. Scifer was developed to solve this issue by performing basic quality control of Sanger sequences and merging flow cytometry data from probed single-cell sorted B cells with sequencing data. scifer can export summary tables, 'fasta' files, electropherograms for visual inspection, and generate reports. biocViews: Preprocessing, QualityControl, SangerSeq, Sequencing, Software, FlowCytometry, SingleCell Author: Rodrigo Arcoverde Cerveira [aut, cre, cph] (ORCID: ), Marcel Martin [ctb], Matthew James Hinchcliff [ctb], Sebastian Ols [aut, dtc] (ORCID: ), Karin Loré [dtc, ths, fnd] (ORCID: ) Maintainer: Rodrigo Arcoverde Cerveira URL: https://github.com/rodrigarc/scifer VignetteBuilder: knitr BugReports: https://github.com/rodrigarc/scifer/issues Package: crisprViz Version: 1.13.0 Depends: R (>= 4.2.0), crisprBase (>= 0.99.15), crisprDesign (>= 0.99.77) Imports: BiocGenerics, Biostrings, BSgenome, Seqinfo, GenomicFeatures, GenomicRanges, grDevices, Gviz, IRanges, methods, S4Vectors, txdbmaker Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, rtracklayer, testthat, utils License: MIT + file LICENSE Title: Visualization Functions for CRISPR gRNAs Description: Provides functionalities to visualize and contextualize CRISPR guide RNAs (gRNAs) on genomic tracks across nucleases and applications. Works in conjunction with the crisprBase and crisprDesign Bioconductor packages. Plots are produced using the Gviz framework. biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprViz VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprViz/issues Package: CircSeqAlignTk Version: 1.13.0 Depends: R (>= 4.2) Imports: stats, tools, utils, R.utils, methods, S4Vectors, rlang, magrittr, dplyr, tidyr, ggplot2, BiocGenerics, Biostrings, IRanges, ShortRead, Rsamtools, Rbowtie2, Rhisat2, shiny, shinyFiles, shinyjs, plotly, parallel, htmltools Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE Title: End-to-End Analysis of Small RNA-Seq Data from Viroids Description: CircSeqAlignTk is a toolkit for the analysis of RNA-Seq data derived from circular genome sequences, with a primary focus on viroids, circular RNAs typically consisting of a few hundred nucleotides. The toolkit supports an end-to-end analysis pipeline, from alignment to visualization. biocViews: Sequencing, SmallRNA, Alignment, Software Author: Jianqiang Sun [cre, aut] (ORCID: ), Xi Fu [ctb], Wei Cao [ctb] Maintainer: Jianqiang Sun URL: https://github.com/bitdessin/CircSeqAlignTk VignetteBuilder: knitr BugReports: https://github.com/bitdessin/CircSeqAlignTk/issues Package: SpatialFeatureExperiment Version: 1.13.2 Depends: R (>= 4.3.0) Imports: Biobase, BiocGenerics (>= 0.51.2), BiocNeighbors, BiocParallel, data.table, DropletUtils, EBImage, grDevices, lifecycle, Matrix, methods, rjson, rlang, S4Vectors, sf, sfheaders, SingleCellExperiment, SpatialExperiment, spatialreg, spdep (>= 1.1-7), SummarizedExperiment, stats, terra, utils, zeallot Suggests: arrow, BiocStyle, dplyr, gmp, knitr, OSTA.data, RBioFormats, rhdf5, rmarkdown, scater, sfarrow, SFEData (>= 1.5.3), Seurat, SeuratObject, sparseMatrixStats, testthat (>= 3.0.0), tidyr, VisiumIO, Voyager (>= 1.7.2), withr, xml2 License: Artistic-2.0 Title: Integrating SpatialExperiment with Simple Features in sf Description: A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used. biocViews: DataRepresentation, Transcriptomics, Spatial Author: Lambda Moses [aut, cre] (ORCID: ), Alik Huseynov [aut] (ORCID: ), Lior Pachter [aut, ths] (ORCID: ) Maintainer: Lambda Moses URL: https://github.com/pachterlab/SpatialFeatureExperiment VignetteBuilder: knitr BugReports: https://github.com/pachterlab/SpatialFeatureExperiment/issues Package: Voyager Version: 1.13.1 Depends: R (>= 4.2.0), SpatialFeatureExperiment (>= 1.7.3) Imports: BiocParallel, bluster, DelayedArray, ggnewscale, ggplot2 (>= 3.4.0), grDevices, grid, lifecycle, Matrix, MatrixGenerics, memuse, methods, patchwork, rlang, RSpectra, S4Vectors, scales, scico, sf, SingleCellExperiment, SpatialExperiment, spdep, stats, SummarizedExperiment, terra, utils, zeallot Suggests: arrow, automap, BiocSingular, BiocStyle, biscale, cowplot, data.table, DelayedMatrixStats, EBImage, ExperimentHub, ggh4x, gstat, hexbin, knitr, matrixStats, pheatmap, RBioFormats, rhdf5, rmarkdown, scater, scattermore, scran, sfarrow, SFEData, testthat (>= 3.0.0), vdiffr, xml2 License: Artistic-2.0 Title: From geospatial to spatial omics Description: SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results. biocViews: GeneExpression, Spatial, Transcriptomics, Visualization Author: Lambda Moses [aut, cre] (ORCID: ), Alik Huseynov [aut] (ORCID: ), Kayla Jackson [aut] (ORCID: ), Laura Luebbert [aut] (ORCID: ), Lior Pachter [aut, rev] (ORCID: ) Maintainer: Lambda Moses URL: https://github.com/pachterlab/voyager VignetteBuilder: knitr BugReports: https://github.com/pachterlab/voyager/issues Package: TENxIO Version: 1.13.4 Depends: R (>= 4.5.0), SingleCellExperiment, SummarizedExperiment Imports: BiocBaseUtils, BiocGenerics, BiocIO, Seqinfo, GenomicRanges, HDF5Array, Matrix, MatrixGenerics, methods, readr, rhdf5, R.utils, S4Vectors, utils Suggests: BiocStyle, DropletTestFiles, ExperimentHub, knitr, RaggedExperiment (>= 1.33.3), rmarkdown, Rsamtools, tinytest License: Artistic-2.0 Title: Import methods for 10X Genomics files Description: Provides a structured S4 approach to importing data files from the 10X pipelines. It mainly supports Single Cell Multiome ATAC + Gene Expression data among other data types. The main Bioconductor data representations used are SingleCellExperiment and RaggedExperiment. biocViews: Software, Infrastructure, DataImport, SingleCell Author: Marcel Ramos [aut, cre] (ORCID: ), NCI [fnd] (GrantNo.: U24CA289073) Maintainer: Marcel Ramos URL: https://github.com/waldronlab/TENxIO VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TENxIO/issues Package: signifinder Version: 1.13.0 Depends: R (>= 4.4.0) Imports: AnnotationDbi, BiocGenerics, ComplexHeatmap, consensusOV, cowplot, DGEobj.utils, dplyr, ensembldb, ggplot2, ggridges, GSVA, IRanges, magrittr, matrixStats, maxstat, methods, openair, org.Hs.eg.db, patchwork, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, SpatialExperiment, stats, scales, SummarizedExperiment, survival, survminer, viridis Suggests: BiocStyle, edgeR, grid, kableExtra, knitr, limma, testthat (>= 3.0.0) License: AGPL-3 Title: Collection and implementation of public transcriptional cancer signatures Description: signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures coming from public literature, based on gene expression values, and return single-sample (-cell/-spot) scores. Currently, signifinder collects more than 70 distinct signatures, relating to multiple tumors and multiple cancer processes. biocViews: GeneExpression, GeneTarget, ImmunoOncology, BiomedicalInformatics, RNASeq, Microarray, ReportWriting, Visualization, SingleCell, Spatial, GeneSignaling Author: Stefania Pirrotta [cre, aut] (ORCID: ), Enrica Calura [aut] (ORCID: ) Maintainer: Stefania Pirrotta URL: https://github.com/CaluraLab/signifinder VignetteBuilder: knitr BugReports: https://github.com/CaluraLab/signifinder/issues Package: stJoincount Version: 1.13.0 Depends: R (>= 4.2.0) Imports: graphics, stats, dplyr, magrittr, sp, raster, spdep, ggplot2, pheatmap, grDevices, Seurat, SpatialExperiment, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE Title: stJoincount - Join count statistic for quantifying spatial correlation between clusters Description: stJoincount facilitates the application of join count analysis to spatial transcriptomic data generated from the 10x Genomics Visium platform. This tool first converts a labeled spatial tissue map into a raster object, in which each spatial feature is represented by a pixel coded by label assignment. This process includes automatic calculation of optimal raster resolution and extent for the sample. A neighbors list is then created from the rasterized sample, in which adjacent and diagonal neighbors for each pixel are identified. After adding binary spatial weights to the neighbors list, a multi-categorical join count analysis is performed to tabulate "joins" between all possible combinations of label pairs. The function returns the observed join counts, the expected count under conditions of spatial randomness, and the variance calculated under non-free sampling. The z-score is then calculated as the difference between observed and expected counts, divided by the square root of the variance. biocViews: Transcriptomics, Clustering, Spatial, BiocViews, Software Author: Jiarong Song [cre, aut] (ORCID: ), Rania Bassiouni [aut], David Craig [aut] Maintainer: Jiarong Song URL: https://github.com/Nina-Song/stJoincount VignetteBuilder: knitr Package: simpleSeg Version: 1.13.0 Imports: BiocParallel, EBImage, terra, stats, spatstat.geom, S4Vectors, grDevices, SummarizedExperiment, methods, cytomapper Suggests: BiocStyle, testthat (>= 3.0.0), knitr, ggplot2 License: GPL-3 Title: A package to perform simple cell segmentation Description: Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained from multiple images. biocViews: Classification, Survival, SingleCell, Normalization, Spatial Author: Nicolas Canete [aut], Alexander Nicholls [aut], Ellis Patrick [aut, cre] Maintainer: Ellis Patrick URL: https://sydneybiox.github.io/simpleSeg/ https://github.com/SydneyBioX/simpleSeg VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/simpleSeg/issues Package: octad Version: 1.13.6 Depends: R (>= 4.2.0), magrittr, dplyr, ggplot2, edgeR, RUVSeq, DESeq2, limma, rhdf5, foreach, Rfast, octad.db, stats, httr, qpdf, ExperimentHub, AnnotationHub, Biobase, S4Vectors Imports: EDASeq, GSVA, data.table, htmlwidgets, plotly, reshape2, grDevices, utils Suggests: knitr, rmarkdown License: Artistic-2.0 Title: Open Cancer TherApeutic Discovery (OCTAD) Description: OCTAD provides a platform for virtually screening compounds targeting precise cancer patient groups. The essential idea is to identify drugs that reverse the gene expression signature of disease by tamping down over-expressed genes and stimulating weakly expressed ones. The package offers deep-learning based reference tissue selection, disease gene expression signature creation, pathway enrichment analysis, drug reversal potency scoring, cancer cell line selection, drug enrichment analysis and in silico hit validation. It currently covers ~20,000 patient tissue samples covering 50 cancer types, and expression profiles for ~12,000 distinct compounds. biocViews: Classification, GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, GeneSetEnrichment Author: E. Chekalin [aut, cre], S. Paithankar [aut], B. Zeng [aut], B. Glicksberg [ctb], P. Newbury [ctb], J. Xing [ctb], K. Liu [ctb], A. Wen [ctb], D. Joseph [ctb], B. Chen [aut] Maintainer: E. Chekalin VignetteBuilder: knitr Package: MSstatsShiny Version: 1.13.3 Depends: R (>= 4.2) Imports: shiny (>= 1.5.0), shinyBS, shinyjs, shinybusy, dplyr, ggplot2, plotly, data.table, Hmisc, shinyFiles, MSstats,MSstatsBig, MSstatsTMT, MSstatsPTM, MSstatsConvert, gplots, marray, DT, readxl, ggrepel, uuid, utils, stats, htmltools, methods, tidyr, grDevices, graphics, mockery, MSstatsBioNet, shinydashboard, arrow, tools, MSstatsResponse, stringr Suggests: rmarkdown, tinytest, sessioninfo, knitr, testthat (>= 3.0.0), shinytest2, License: Artistic-2.0 Title: MSstats GUI for Statistical Anaylsis of Proteomics Experiments Description: MSstatsShiny is an R-Shiny graphical user interface (GUI) integrated with the R packages MSstats, MSstatsTMT, and MSstatsPTM. It provides a point and click end-to-end analysis pipeline applicable to a wide variety of experimental designs. These include data-dependedent acquisitions (DDA) which are label-free or tandem mass tag (TMT)-based, as well as DIA, SRM, and PRM acquisitions and those targeting post-translational modifications (PTMs). The application automatically saves users selections and builds an R script that recreates their analysis, supporting reproducible data analysis. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, ShinyApps, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl, GUI Author: Devon Kohler [aut], Anthony Wu [aut, cre], Deril Raju [aut], Maanasa Kaza [aut], Cristina Pasi [aut], Ting Huang [aut], Mateusz Staniak [aut], Dhaval Mohandas [aut], Eduard Sabido [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Anthony Wu VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsShiny/issues Package: ReUseData Version: 1.11.0 Imports: Rcwl, RcwlPipelines, BiocFileCache, S4Vectors, stats, tools, utils, methods, jsonlite, yaml, basilisk Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: GPL-3 Title: Reusable and reproducible Data Management Description: ReUseData is an _R/Bioconductor_ software tool to provide a systematic and versatile approach for standardized and reproducible data management. ReUseData facilitates transformation of shell or other ad hoc scripts for data preprocessing into workflow-based data recipes. Evaluation of data recipes generate curated data files in their generic formats (e.g., VCF, bed). Both recipes and data are cached using database infrastructure for easy data management and reuse. Prebuilt data recipes are available through ReUseData portal ("https://rcwl.org/dataRecipes/") with full annotation and user instructions. Pregenerated data are available through ReUseData cloud bucket that is directly downloadable through "getCloudData()". biocViews: Software, Infrastructure, DataImport, Preprocessing, ImmunoOncology Author: Qian Liu [aut, cre] (ORCID: ) Maintainer: Qian Liu URL: https://github.com/rworkflow/ReUseData VignetteBuilder: knitr BugReports: https://github.com/rworkflow/ReUseData/issues Package: scFeatures Version: 1.11.7 Depends: R (>= 4.2.0) Imports: DelayedArray, DelayedMatrixStats, EnsDb.Hsapiens.v79, EnsDb.Mmusculus.v79, GSVA, ape, glue, dplyr, ensembldb, gtools, msigdbr, proxyC, reshape2, spatstat.explore, spatstat.geom, tidyr, AUCell, BiocParallel, rmarkdown, methods, stats, cli, MatrixGenerics, Seurat, DT Suggests: knitr, S4Vectors, survival, survminer, BiocStyle, ClassifyR, org.Hs.eg.db, clusterProfiler, pheatmap, limma, ggplot2, plotly, igraph, data.table, enrichplot, DOSE, rmarkdown License: GPL-3 Title: scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction Description: scFeatures constructs multi-view representations of single-cell and spatial data. scFeatures is a tool that generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types. These features can then be used for a variety of analyses using other software in Biocondutor. biocViews: CellBasedAssays, SingleCell, Spatial, Software, Transcriptomics Author: Yue Cao [aut, cre], Yingxin Lin [aut], Ellis Patrick [aut], Pengyi Yang [aut], Jean Yee Hwa Yang [aut] Maintainer: Yue Cao URL: https://sydneybiox.github.io/scFeatures/ https://github.com/SydneyBioX/scFeatures/ VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scFeatures/issues Package: AnVILWorkflow Version: 1.11.0 Depends: R (>= 4.4.0), AnVILGCP, AnVILBase, httr Imports: AnVIL, dplyr, jsonlite, rlang, tibble, tidyr, utils, methods, plyr, stringr Suggests: knitr, BiocStyle License: Artistic-2.0 Title: Run workflows implemented in Terra/AnVIL workspace Description: The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The main cloud-based genomics platform deported by the AnVIL project is Terra. The AnVILWorkflow package allows remote access to Terra implemented workflows, enabling end-user to utilize Terra/ AnVIL provided resources - such as data, workflows, and flexible/scalble computing resources - through the conventional R functions. biocViews: Infrastructure, Software Author: Sehyun Oh [aut, cre] (ORCID: ), Marcel Ramos [ctb] (ORCID: ), Kai Gravel-Pucillo [aut] Maintainer: Sehyun Oh URL: https://github.com/shbrief/AnVILWorkflow VignetteBuilder: knitr BugReports: https://github.com/shbrief/AnVILWorkflow/issues Package: alabaster.matrix Version: 1.11.0 Depends: alabaster.base Imports: methods, BiocGenerics, S4Vectors, DelayedArray (>= 0.33.3), S4Arrays, SparseArray (>= 1.5.22), rhdf5 (>= 2.47.1), HDF5Array, Matrix, Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, chihaya, BiocSingular, ResidualMatrix License: MIT + file LICENSE Title: Load and Save Artifacts from File Description: Save matrices, arrays and similar objects into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr Package: alabaster.se Version: 1.11.0 Depends: SummarizedExperiment, alabaster.base Imports: methods, alabaster.ranges, alabaster.matrix, BiocGenerics, S4Vectors, IRanges, GenomicRanges, jsonlite Suggests: rmarkdown, knitr, testthat, BiocStyle License: MIT + file LICENSE Title: Load and Save SummarizedExperiments from File Description: Save SummarizedExperiments into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr Package: alabaster.sce Version: 1.11.0 Depends: SingleCellExperiment, alabaster.base Imports: methods, alabaster.se, jsonlite Suggests: knitr, testthat, BiocStyle, rmarkdown License: MIT + file LICENSE Title: Load and Save SingleCellExperiment from File Description: Save SingleCellExperiment into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr Package: alabaster.spatial Version: 1.11.1 Depends: SpatialExperiment, alabaster.base Imports: methods, utils, grDevices, S4Vectors, alabaster.sce, rhdf5 Suggests: testthat, knitr, rmarkdown, BiocStyle, DropletUtils, magick, png, digest License: MIT + file LICENSE Title: Save and Load Spatial 'Omics Data to/from File Description: Save SpatialExperiment objects and their images into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr Package: alabaster.mae Version: 1.11.0 Depends: MultiAssayExperiment, alabaster.base Imports: methods, alabaster.se, S4Vectors, jsonlite, rhdf5 Suggests: testthat, knitr, SummarizedExperiment, BiocParallel, BiocStyle, rmarkdown License: MIT + file LICENSE Title: Load and Save MultiAssayExperiments Description: Save MultiAssayExperiments into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr Package: alabaster.vcf Version: 1.11.0 Depends: alabaster.base, VariantAnnotation Imports: methods, S4Vectors, alabaster.se, alabaster.string, Rsamtools Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE Title: Save and Load Variant Data to/from File Description: Save variant calling SummarizedExperiment to file and load them back as VCF objects. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties. biocViews: DataImport, DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr Package: alabaster Version: 1.11.0 Depends: alabaster.base Imports: alabaster.matrix, alabaster.ranges, alabaster.se, alabaster.sce, alabaster.spatial, alabaster.string, alabaster.vcf, alabaster.bumpy, alabaster.mae Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Title: Umbrella for the Alabaster Framework Description: Umbrella for the alabaster suite, providing a single-line import for all alabaster.* packages. Installing this package ensures that all known alabaster.* packages are also installed, avoiding problems with missing packages when a staging method or loading function is dynamically requested. Obviously, this comes at the cost of needing to install more packages, so advanced users and application developers may prefer to install the required alabaster.* packages individually. biocViews: DataRepresentation, DataImport Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr Package: chihaya Version: 1.11.0 Depends: DelayedArray Imports: methods, Matrix, rhdf5, Rcpp, HDF5Array LinkingTo: Rcpp, Rhdf5lib Suggests: BiocGenerics, S4Vectors, BiocSingular, ResidualMatrix, BiocStyle, testthat, rmarkdown, knitr License: GPL-3 Title: Save Delayed Operations to a HDF5 File Description: Saves the delayed operations of a DelayedArray to a HDF5 file. This enables efficient recovery of the DelayedArray's contents in other languages and analysis frameworks. biocViews: DataImport, DataRepresentation Author: Aaron Lun [cre, aut] Maintainer: Aaron Lun URL: https://github.com/ArtifactDB/chihaya-R SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/ArtifactDB/chihaya-R/issues Package: HiCool Version: 1.11.3 Depends: R (>= 4.2), HiCExperiment Imports: BiocIO, S4Vectors, GenomicRanges, IRanges, InteractionSet, vroom, basilisk.utils, basilisk, reticulate, rmarkdown, rmdformats, plotly, dplyr, stringr, sessioninfo, utils Suggests: HiContacts, HiContactsData, AnnotationHub, BiocFileCache, BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE Title: HiCool Description: HiCool provides an R interface to process and normalize Hi-C paired-end fastq reads into .(m)cool files. .(m)cool is a compact, indexed HDF5 file format specifically tailored for efficiently storing HiC-based data. On top of processing fastq reads, HiCool provides a convenient reporting function to generate shareable reports summarizing Hi-C experiments and including quality controls. biocViews: HiC, DNA3DStructure, DataImport Author: Jacques Serizay [aut, cre] Maintainer: Jacques Serizay URL: https://github.com/js2264/HiCool VignetteBuilder: knitr BugReports: https://github.com/js2264/HiCool/issues Package: AHMassBank Version: 1.11.0 Depends: R (>= 4.2) Imports: AnnotationHubData (>= 1.5.24) Suggests: BiocStyle, knitr, AnnotationHub (>= 2.7.13), rmarkdown, methods, CompoundDb (>= 1.1.4) License: Artistic-2.0 Title: MassBank Annotation Resources for AnnotationHub Description: Supplies AnnotationHub with MassBank metabolite/compound annotations bundled in CompDb SQLite databases. CompDb SQLite databases contain general compound annotation as well as fragment spectra representing fragmentation patterns of compounds' ions. MassBank data is retrieved from https://massbank.eu/MassBank and processed using helper functions from the CompoundDb Bioconductor package into redistributable SQLite databases. biocViews: MassSpectrometry, AnnotationHubSoftware Author: Johannes Rainer [cre] (ORCID: ) Maintainer: Johannes Rainer URL: https://github.com/jorainer/AHMassBank VignetteBuilder: knitr BugReports: https://github.com/jorainer/AHMassBank/issues Package: TDbasedUFEadv Version: 1.11.0 Imports: TDbasedUFE, Biobase, GenomicRanges, utils, rTensor, methods, graphics, RTCGA, stats, enrichplot, DOSE, STRINGdb, enrichR, hash, shiny Suggests: knitr, rmarkdown, testthat (>= 3.0.0), RTCGA.rnaseq, RTCGA.clinical, BiocStyle, MOFAdata License: GPL-3 Title: Advanced package of tensor decomposition based unsupervised feature extraction Description: This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality. biocViews: GeneExpression, FeatureExtraction, MethylationArray, SingleCell, Software Author: Y-h. Taguchi [aut, cre] (ORCID: ) Maintainer: Y-h. Taguchi URL: https://github.com/tagtag/TDbasedUFEadv VignetteBuilder: knitr BugReports: https://github.com/tagtag/TDbasedUFEadv/issues Package: cytoviewer Version: 1.11.0 Imports: shiny, shinydashboard, utils, colourpicker, shinycssloaders, svgPanZoom, viridis, archive, grDevices, RColorBrewer, svglite, EBImage, methods, cytomapper, SingleCellExperiment, S4Vectors, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat License: GPL-3 Title: An interactive multi-channel image viewer for R Description: This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image downloads. biocViews: ImmunoOncology, Software, SingleCell, OneChannel, TwoChannel, MultiChannel, Spatial, DataImport Author: Lasse Meyer [aut, cre] (ORCID: ), Nils Eling [aut] (ORCID: ) Maintainer: Lasse Meyer URL: https://github.com/BodenmillerGroup/cytoviewer VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/cytoviewer/issues Package: cfTools Version: 1.11.0 Imports: Rcpp, utils, GenomicRanges, basilisk, R.utils, stats, cfToolsData, grDevices, graphics LinkingTo: Rcpp, BH Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: file LICENSE Title: Informatics Tools for Cell-Free DNA Study Description: The cfTools R package provides methods for cell-free DNA (cfDNA) methylation data analysis to facilitate cfDNA-based studies. Given the methylation sequencing data of a cfDNA sample, for each cancer marker or tissue marker, we deconvolve the tumor-derived or tissue-specific reads from all reads falling in the marker region. Our read-based deconvolution algorithm exploits the pervasiveness of DNA methylation for signal enhancement, therefore can sensitively identify a trace amount of tumor-specific or tissue-specific cfDNA in plasma. cfTools provides functions for (1) cancer detection: sensitively detect tumor-derived cfDNA and estimate the tumor-derived cfDNA fraction (tumor burden); (2) tissue deconvolution: infer the tissue type composition and the cfDNA fraction of multiple tissue types for a plasma cfDNA sample. These functions can serve as foundations for more advanced cfDNA-based studies, including cancer diagnosis and disease monitoring. biocViews: Software, BiomedicalInformatics, Epigenetics, Sequencing, MethylSeq, DNAMethylation, DifferentialMethylation Author: Ran Hu [aut, cre] (ORCID: ), Mary Louisa Stackpole [aut] (ORCID: ), Shuo Li [aut] (ORCID: ), Xianghong Jasmine Zhou [aut] (ORCID: ), Wenyuan Li [aut] (ORCID: ) Maintainer: Ran Hu URL: https://github.com/jasminezhoulab/cfTools VignetteBuilder: knitr BugReports: https://github.com/jasminezhoulab/cfTools/issues Package: BiocHail Version: 1.11.0 Depends: R (>= 4.3.0), graphics, stats, utils Imports: reticulate, basilisk, BiocFileCache, methods, dplyr, BiocGenerics Suggests: knitr, testthat, BiocStyle, ggplot2, DT License: Artistic-2.0 Title: basilisk and hail Description: Use hail via basilisk when appropriate, or via reticulate. This package can be used in terra.bio to interact with UK Biobank resources processed by hail.is. biocViews: Infrastructure Author: Vincent Carey [aut, cre] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/BiocHail VignetteBuilder: knitr BugReports: https://github.com/vjcitn/BiocHail/issues Package: SVMDO Version: 1.11.0 Depends: R(>= 4.4), shiny (>= 1.7.4) Imports: shinyFiles (>= 0.9.3), shinytitle (>= 0.1.0), golem (>= 0.3.5), nortest (>= 1.0-4), e1071 (>= 1.7-12), BSDA (>= 1.2.1), data.table (>= 1.14.6), sjmisc (>= 2.8.9), klaR (>= 1.7-1), caTools (>= 1.18.2), caret (>= 6.0-93), survival (>= 3.4-0), DT (>= 0.33.0), DOSE (>= 3.24.2), AnnotationDbi (>= 1.60.0), org.Hs.eg.db (>= 3.16.0), dplyr (>= 1.0.10), SummarizedExperiment (>= 1.28.0), grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.1.6) License: GPL-3 NeedsCompilation: no Title: Identification of Tumor-Discriminating mRNA Signatures via Support Vector Machines Supported by Disease Ontology Description: It is an easy-to-use GUI using disease information for detecting tumor/normal sample discriminating gene sets from differentially expressed genes. Our approach is based on an iterative algorithm filtering genes with disease ontology enrichment analysis and wilk and wilks lambda criterion connected to SVM classification model construction. Along with gene set extraction, SVMDO also provides individual prognostic marker detection. The algorithm is designed for FPKM and RPKM normalized RNA-Seq transcriptome datasets. biocViews: GeneSetEnrichment, DifferentialExpression, GUI, Classification, RNASeq, Transcriptomics, Survival Author: Mustafa Erhan Ozer [aut, cre] (ORCID: ), Pemra Ozbek Sarica [aut], Kazim Yalcin Arga [aut] Maintainer: Mustafa Erhan Ozer VignetteBuilder: knitr BugReports: https://github.com/robogeno/SVMDO/issues Package: mariner Version: 1.11.3 Depends: R (>= 4.2.0) Imports: methods, S4Vectors, BiocGenerics, BiocManager, GenomicRanges, InteractionSet, data.table, stats, rlang, glue, assertthat, dplyr, magrittr, dbscan, purrr, progress, GenomeInfoDb, strawr (>= 0.0.91), DelayedArray, HDF5Array, abind, BiocParallel, IRanges, SummarizedExperiment, rhdf5, plotgardener, RColorBrewer, colourvalues, utils, grDevices, graphics, grid Suggests: knitr, testthat (>= 3.0.0), rmarkdown, ExperimentHub, marinerData, TxDb.Hsapiens.UCSC.hg38.knownGene, fields License: MIT + file LICENSE Title: Mariner: Explore the Hi-Cs Description: Tools for manipulating paired ranges and working with Hi-C data in R. Functionality includes manipulating/merging paired regions, generating paired ranges, extracting/aggregating interactions from `.hic` files, and visualizing the results. Designed for compatibility with plotgardener for visualization. biocViews: FunctionalGenomics, Visualization, HiC Author: Eric Davis [aut, cre] (ORCID: ), Sarah Parker [aut] (ORCID: ) Maintainer: Eric Davis URL: https://ericscottdavis.com/mariner/, https://github.com/EricSDavis/mariner VignetteBuilder: knitr Package: gDRimport Version: 1.9.10 Depends: R (>= 4.2) Imports: assertthat, BumpyMatrix, checkmate, CoreGx, PharmacoGx, data.table, futile.logger, gDRutils (>= 1.7.1), magrittr, methods, MultiAssayExperiment, readxl, rio, S4Vectors, stats, stringi, SummarizedExperiment, tibble, tools, utils, XML, yaml, openxlsx, qs2 Suggests: BiocStyle, gDRtestData (>= 1.7.1), gDRstyle (>= 1.7.1), knitr, purrr, testthat License: Artistic-2.0 Title: Package for handling the import of dose-response data Description: The package is a part of the gDR suite. It helps to prepare raw drug response data for downstream processing. It mainly contains helper functions for importing/loading/validating dose-response data provided in different file formats. biocViews: Software, Infrastructure, DataImport Author: Arkadiusz Gladki [aut, cre] (ORCID: ), Bartosz Czech [aut] (ORCID: ), Marc Hafner [aut] (ORCID: ), Sergiu Mocanu [aut], Dariusz Scigocki [aut], Allison Vuong [aut], Luca Gerosa [aut] (ORCID: ), Janina Smola [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRimport, https://gdrplatform.github.io/gDRimport/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRimport/issues Package: gDRutils Version: 1.9.8 Depends: R (>= 4.2) Imports: BiocParallel, BumpyMatrix, checkmate, data.table, digest, drc, jsonlite, jsonvalidate, methods, MultiAssayExperiment, S4Vectors, stats, stringr, SummarizedExperiment, qs2, utils Suggests: BiocManager, BiocStyle, futile.logger, gDRstyle (>= 1.7.1), gDRtestData (>= 1.7.1), IRanges, knitr, lintr, mockery, purrr, rcmdcheck, rmarkdown, scales, testthat, tools, withr, yaml License: Artistic-2.0 Title: A package with helper functions for processing drug response data Description: This package contains utility functions used throughout the gDR platform to fit data, manipulate data, and convert and validate data structures. This package also has the necessary default constants for gDR platform. Many of the functions are utilized by the gDRcore package. biocViews: Software, Infrastructure Author: Bartosz Czech [aut] (ORCID: ), Arkadiusz Gladki [cre, aut] (ORCID: ), Aleksander Chlebowski [aut], Marc Hafner [aut] (ORCID: ), Pawel Piatkowski [aut], Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut], Allison Vuong [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRutils, https://gdrplatform.github.io/gDRutils/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRutils/issues Package: iNETgrate Version: 1.9.0 Depends: R (>= 4.3.0), BiocStyle (>= 2.18.1) Imports: SummarizedExperiment, GenomicRanges (>= 1.24.1), stats, WGCNA, grDevices, graphics, survival, igraph, Pigengene (>= 1.19.26), Homo.sapiens, glmnet, caret, gplots, minfi, matrixStats, Rfast, tidyr, tidyselect, utils Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, IlluminaHumanMethylation450kanno.ilmn12.hg19, AnnotationDbi, sesameData, TCGAbiolinks (>= 2.29.4) License: GPL-3 NeedsCompilation: no Title: Integrates DNA methylation data with gene expression in a single gene network Description: The iNETgrate package provides functions to build a correlation network in which nodes are genes. DNA methylation and gene expression data are integrated to define the connections between genes. This network is used to identify modules (clusters) of genes. The biological information in each of the resulting modules is represented by an eigengene. These biological signatures can be used as features e.g., for classification of patients into risk categories. The resulting biological signatures are very robust and give a holistic view of the underlying molecular changes. biocViews: GeneExpression, RNASeq, DNAMethylation, NetworkInference, Network, GraphAndNetwork, BiomedicalInformatics, SystemsBiology, Transcriptomics, Classification, Clustering, DimensionReduction, PrincipalComponent, mRNAMicroarray, Normalization, GenePrediction, KEGG, Survival Author: Isha Mehta [aut] (), Ghazal Ebrahimi [aut], Hanie Samimi [aut], Habil Zare [aut, cre] () Maintainer: Habil Zare VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocManager/issues Package: gDRstyle Version: 1.9.1 Depends: R (>= 4.2) Imports: BiocCheck, BiocManager, BiocStyle, checkmate, desc, git2r, lintr (>= 3.0.0), rcmdcheck, remotes, yaml, rjson, pkgbuild, withr Suggests: knitr, pkgdown, testthat (>= 3.0.0) License: Artistic-2.0 Title: A package with style requirements for the gDR suite Description: Package fills a helper package role for whole gDR suite. It helps to support good development practices by keeping style requirements and style tests for other packages. It also contains build helpers to make all package requirements met. biocViews: Software, Infrastructure Author: Allison Vuong [aut], Dariusz Scigocki [aut], Marcin Kamianowski [aut], Aleksander Chlebowski [ctb], Janina Smola [aut], Arkadiusz Gladki [cre, aut] (ORCID: ), Bartosz Czech [aut] (ORCID: ) Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRstyle, https://gdrplatform.github.io/gDRstyle/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRstyle/issues Package: gDR Version: 1.9.1 Depends: R (>= 4.2), gDRcore (>= 1.7.1), gDRimport (>= 1.7.1), gDRutils (>= 1.7.1) Suggests: BiocStyle, BumpyMatrix, futile.logger, gDRstyle (>= 1.7.1), gDRtestData (>= 1.7.1), kableExtra, knitr, markdown, purrr, rmarkdown, SummarizedExperiment, testthat, yaml License: Artistic-2.0 Title: Umbrella package for R packages in the gDR suite Description: Package is a part of the gDR suite. It reexports functions from other packages in the gDR suite that contain critical processing functions and utilities. The vignette walks through the full processing pipeline for drug response analyses that the gDR suite offers. biocViews: Software, DataImport, ShinyApps Author: Allison Vuong [aut], Bartosz Czech [aut] (ORCID: ), Arkadiusz Gladki [cre, aut] (ORCID: ), Marc Hafner [aut] (ORCID: ), Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDR, https://gdrplatform.github.io/gDR/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDR/issues Package: gDRcore Version: 1.9.7 Depends: R (>= 4.2) Imports: BumpyMatrix, BiocParallel, checkmate, futile.logger, gDRutils (>= 1.7.1), MultiAssayExperiment, purrr, stringr, S4Vectors, SummarizedExperiment, data.table Suggests: BiocStyle, gDRstyle (>= 1.7.1), gDRimport (>= 1.7.1), gDRtestData (>= 1.7.1), IRanges, knitr, pkgbuild, qs2, testthat, yaml License: Artistic-2.0 NeedsCompilation: yes Title: Processing functions and interface to process and analyze drug dose-response data Description: This package contains core functions to process and analyze drug response data. The package provides tools for normalizing, averaging, and calculation of gDR metrics data. All core functions are wrapped into the pipeline function allowing analyzing the data in a straightforward way. biocViews: Software, ShinyApps Author: Bartosz Czech [aut] (ORCID: ), Arkadiusz Gladki [cre, aut] (ORCID: ), Marc Hafner [aut] (ORCID: ), Pawel Piatkowski [aut], Natalia Potocka [aut], Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut], Marcin Kamianowski [aut], Allison Vuong [aut] Maintainer: Arkadiusz Gladki URL: https://github.com/gdrplatform/gDRcore, https://gdrplatform.github.io/gDRcore/ VignetteBuilder: knitr BugReports: https://github.com/gdrplatform/gDRcore/issues Package: orthos Version: 1.9.0 Depends: R (>= 4.3), SummarizedExperiment Imports: AnnotationHub, basilisk, BiocParallel, colorspace, cowplot, DelayedArray, dplyr, ExperimentHub, ggplot2 (>= 3.4.0), ggpubr, ggrepel, ggsci, grDevices, grid, HDF5Array, keras (>= 2.16.0), methods, orthosData, parallel, plyr, reticulate, rlang, S4Vectors, stats, tensorflow, tidyr Suggests: BiocManager, BiocStyle, htmltools, knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE Title: `orthos` is an R package for variance decomposition using conditional variational auto-encoders Description: `orthos` decomposes RNA-seq contrasts, for example obtained from a gene knock-out or compound treatment experiment, into unspecific and experiment-specific components. Original and decomposed contrasts can be efficiently queried against a large database of contrasts (derived from ARCHS4, https://maayanlab.cloud/archs4/) to identify similar experiments. `orthos` furthermore provides plotting functions to visualize the results of such a search for similar contrasts. biocViews: RNASeq, DifferentialExpression, GeneExpression Author: Panagiotis Papasaikas [aut, cre] (ORCID: ), Charlotte Soneson [aut] (ORCID: ), Michael Stadler [aut] (ORCID: ), Friedrich Miescher Institute for Biomedical Research [cph] Maintainer: Panagiotis Papasaikas VignetteBuilder: knitr Package: beachmat.hdf5 Version: 1.9.1 Imports: methods, beachmat, HDF5Array, DelayedArray, Rcpp LinkingTo: Rcpp, assorthead, beachmat, Rhdf5lib Suggests: testthat, BiocStyle, knitr, rmarkdown, rhdf5, Matrix License: GPL-3 NeedsCompilation: yes Title: beachmat bindings for HDF5-backed matrices Description: Extends beachmat to support initialization of tatami matrices from HDF5-backed arrays. This allows C++ code in downstream packages to directly call the HDF5 C/C++ library to access array data, without the need for block processing via DelayedArray. Some utilities are also provided for direct creation of an in-memory tatami matrix from a HDF5 file. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++17, GNU make VignetteBuilder: knitr Package: GenomicPlot Version: 1.9.3 Depends: R (>= 4.4.0), GenomicRanges (>= 1.46.1) Imports: methods, Rsamtools, parallel, tidyr, rtracklayer (>= 1.54.0), plyranges (>= 1.14.0), cowplot (>= 1.1.1), VennDiagram, ggplotify, Seqinfo, IRanges, ComplexHeatmap, RCAS (>= 1.20.0), scales (>= 1.2.0), GenomicAlignments (>= 1.30.0), edgeR, circlize, viridis, ggsignif (>= 0.6.3), ggsci (>= 2.9), ggpubr, grDevices, graphics, stats, utils, GenomicFeatures, genomation (>= 1.36.0), txdbmaker, ggplot2 (>= 3.3.5), BiocGenerics, dplyr, grid, GenomeInfoDb Suggests: knitr, rmarkdown, R.utils, Biobase, BiocStyle, testthat, AnnotationDbi License: GPL-2 Title: Plot profiles of next generation sequencing data in genomic features Description: Visualization of next generation sequencing (NGS) data is essential for interpreting high-throughput genomics experiment results. 'GenomicPlot' facilitates plotting of NGS data in various formats (bam, bed, wig and bigwig); both coverage and enrichment over input can be computed and displayed with respect to genomic features (such as UTR, CDS, enhancer), and user defined genomic loci or regions. Statistical tests on signal intensity within user defined regions of interest can be performed and represented as boxplots or bar graphs. Parallel processing is used to speed up computation on multicore platforms. In addition to genomic plots which is suitable for displaying of coverage of genomic DNA (such as ChIPseq data), metagenomic (without introns) plots can also be made for RNAseq or CLIPseq data as well. biocViews: AlternativeSplicing, ChIPSeq, Coverage, GeneExpression, RNASeq, Sequencing, Software, Transcription, Visualization, Annotation Author: Shuye Pu Maintainer: Shuye Pu URL: https://github.com/shuye2009/GenomicPlot VignetteBuilder: knitr BugReports: https://github.com/shuye2009/GenomicPlot/issues Package: dreamlet Version: 1.9.2 Depends: R (>= 4.3.0), variancePartition (>= 1.36.1), SingleCellExperiment, ggplot2 Imports: edgeR, SummarizedExperiment, DelayedMatrixStats, sparseMatrixStats, MatrixGenerics, Matrix, methods, purrr, GSEABase, data.table, zenith (>= 1.1.2), mashr (>= 0.2.52), ashr, dplyr, reformulas, BiocParallel, ggbeeswarm, S4Vectors, IRanges, irlba, limma, metafor, remaCor, broom, tidyr, rlang, BiocGenerics, S4Arrays, SparseArray, DelayedArray, gtools, reshape2, ggrepel, scattermore, Rcpp, MASS, Rdpack, utils, stats LinkingTo: Rcpp, beachmat Suggests: BiocStyle, knitr, pander, rmarkdown, muscat, ExperimentHub, RUnit, muscData, scater, scuttle License: Artistic-2.0 Title: Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs Description: Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample. biocViews: RNASeq, GeneExpression, DifferentialExpression, BatchEffect, QualityControl, Regression, GeneSetEnrichment, GeneRegulation, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, SingleCell, Preprocessing, Sequencing, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] (ORCID: ) Maintainer: Gabriel Hoffman URL: https://DiseaseNeurogenomics.github.io/dreamlet SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeurogenomics/dreamlet/issues Package: Moonlight2R Version: 1.9.2 Depends: R (>= 4.5), doParallel, foreach Imports: parmigene, randomForest, gplots, circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase, grDevices, graphics, GEOquery, stats, purrr, RISmed, grid, utils, ComplexHeatmap, GenomicRanges, dplyr, fuzzyjoin, rtracklayer, magrittr, qpdf, readr, seqminer, stringr, tibble, tidyHeatmap, tidyr, AnnotationHub, easyPubMed, org.Hs.eg.db, EpiMix, BiocGenerics, ggplot2, ExperimentHub, rlang, withr, data.table, fgsea Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), devtools, roxygen2, png License: GPL-3 Title: Identify oncogenes and tumor suppressor genes from omics data Description: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. An additional mechanistic layer evaluates if there are mutations affecting the protein stability of the transcription factors (TFs) of the TSGs and OCGs, as that may have an effect on the expression of the genes. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Mona Nourbakhsh [aut], Astrid Saksager [aut], Nikola Tom [aut], Katrine Meldgård [aut], Anna Melidi [aut], Xi Steven Chen [aut], Antonio Colaprico [aut], Catharina Olsen [aut], Alessia Campo [aut], Matteo Tiberti [cre, aut], Elena Papaleo [aut] Maintainer: Matteo Tiberti URL: https://github.com/ELELAB/Moonlight2R SystemRequirements: CScapeSomatic VignetteBuilder: knitr BugReports: https://github.com/ELELAB/Moonlight2R/issues Package: lemur Version: 1.9.0 Depends: R (>= 4.1) Imports: stats, utils, irlba, methods, SingleCellExperiment, SummarizedExperiment, rlang (>= 1.1.0), vctrs (>= 0.6.0), glmGamPoi (>= 1.12.0), BiocGenerics, S4Vectors, Matrix, DelayedMatrixStats, HDF5Array, MatrixGenerics, matrixStats, Rcpp, harmony (>= 1.2.0), limma, BiocNeighbors LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), tidyverse, uwot, dplyr, edgeR, knitr, quarto, BiocStyle License: MIT + file LICENSE Title: Latent Embedding Multivariate Regression Description: Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed. biocViews: Transcriptomics, DifferentialExpression, SingleCell, DimensionReduction, Regression Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID: ) Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/lemur VignetteBuilder: quarto BugReports: https://github.com/const-ae/lemur/issues Package: GNOSIS Version: 1.9.0 Depends: R (>= 4.3.0), shiny, shinydashboard, shinydashboardPlus, dashboardthemes, shinyWidgets, shinymeta, tidyverse, operator.tools, maftools Imports: DT, fontawesome, shinycssloaders, cBioPortalData, shinyjs, reshape2, RColorBrewer, survival, survminer, stats, compareGroups, rpart, partykit, DescTools, car, rstatix, fabricatr, shinylogs, magrittr Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE Title: Genomics explorer using statistical and survival analysis in R Description: GNOSIS incorporates a range of R packages enabling users to efficiently explore and visualise clinical and genomic data obtained from cBioPortal. GNOSIS uses an intuitive GUI and multiple tab panels supporting a range of functionalities. These include data upload and initial exploration, data recoding and subsetting, multiple visualisations, survival analysis, statistical analysis and mutation analysis, in addition to facilitating reproducible research. biocViews: Software, ShinyApps, Survival, GUI Author: Lydia King [aut, cre] (ORCID: ), Marcel Ramos [ctb] Maintainer: Lydia King URL: https://github.com/Lydia-King/GNOSIS/ VignetteBuilder: knitr Video: https://doi.org/10.5281/zenodo.5788544 BugReports: https://github.com/Lydia-King/GNOSIS/issues Package: pgxRpi Version: 1.7.0 Depends: R (>= 4.2) Imports: utils, methods, grDevices, graphics, circlize, httr, dplyr, attempt, lubridate, survival, survminer, ggplot2, GenomicRanges, SummarizedExperiment, S4Vectors, yaml, parallel, future, future.apply Suggests: BiocStyle, rmarkdown, knitr, testthat License: Artistic-2.0 Title: R wrapper for Progenetix Description: The package is an R wrapper for Progenetix REST API built upon the Beacon v2 protocol. Its purpose is to provide a seamless way for retrieving genomic data from Progenetix database—an open resource dedicated to curated oncogenomic profiles. Empowered by this package, users can effortlessly access and visualize data from Progenetix. biocViews: CopyNumberVariation, GenomicVariation, DataImport, Software Author: Hangjia Zhao [aut, cre] (ORCID: ), Michael Baudis [aut] (ORCID: ) Maintainer: Hangjia Zhao URL: https://github.com/progenetix/pgxRpi VignetteBuilder: knitr BugReports: https://github.com/progenetix/pgxRpi/issues Package: AlphaMissenseR Version: 1.7.1 Depends: R (>= 4.3.0), dplyr Imports: rjsoncons (>= 1.0.1), DBI, duckdb (>= 1.3.1), rlang, curl, BiocFileCache, spdl, memoise, BiocBaseUtils, utils, stats, tools, methods, whisker, ggplot2 Suggests: BiocManager, BiocGenerics, S4Vectors, Seqinfo, GenomeInfoDb, GenomicRanges, AnnotationHub, ExperimentHub, ensembldb, httr, tidyr, r3dmol, bio3d, shiny, shiny.gosling, ggdist, colorspace, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 Title: Accessing AlphaMissense Data Resources in R Description: The AlphaMissense publication outlines how a variant of AlphaFold / DeepMind was used to predict missense variant pathogenicity. Supporting data on Zenodo include, for instance, 71M variants across hg19 and hg38 genome builds. The 'AlphaMissenseR' package allows ready access to the data, downloading individual files to DuckDB databases for exploration and integration into *R* and *Bioconductor* workflows. biocViews: SNP, Annotation, FunctionalGenomics, StructuralPrediction, Transcriptomics, VariantAnnotation, GenePrediction, ImmunoOncology Author: Martin Morgan [aut, cre] (ORCID: ), Tram Nguyen [aut] (ORCID: ), Tyrone Lee [ctb], Nitesh Turaga [ctb], Chan Zuckerberg Initiative DAF CZF2019-002443 [fnd], NIH NCI ITCR U24CA180996 [fnd], NIH NCI IOTN U24CA232979 [fnd], NIH NCI ARTNet U24CA274159 [fnd] Maintainer: Martin Morgan URL: https://mtmorgan.github.io/AlphaMissenseR/ VignetteBuilder: knitr BugReports: https://github.com/mtmorgan/AlphaMissenseR/issues Package: saseR Version: 1.7.0 Depends: R (>= 4.3.0) Imports: ASpli, BiocGenerics, BiocParallel, data.table, DESeq2, dplyr, edgeR, GenomicAlignments, GenomicFeatures, GenomicRanges, igraph, IRanges, limma, methods, MASS, MatrixGenerics, S4Vectors, stats, SummarizedExperiment, parallel, PRROC Suggests: rrcov, knitr, txdbmaker License: Artistic-2.0 Title: Scalable Aberrant Splicing and Expression Retrieval Description: saseR is a highly performant and fast framework for aberrant expression and splicing analyses. The main functions are: \itemize{ \item \code{\link{BamtoAspliCounts}} - Process BAM files to ASpli counts \item \code{\link{convertASpli}} - Get gene, bin or junction counts from ASpli SummarizedExperiment \item \code{\link{calculateOffsets}} - Create an offsets assays for aberrant expression or splicing analysis \item \code{\link{saseRfindEncodingDim}} - Estimate the optimal number of latent factors to include when estimating the mean expression \item \code{\link{saseRfit}} - Parameter estimation of the negative binomial distribution and compute p-values for aberrant expression and splicing } For information upon how to use these functions, check out our vignette at \url{https://github.com/statOmics/saseR/blob/main/vignettes/Vignette.Rmd} and the saseR paper: Segers, A. et al. (2023). Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. bioRxiv. \url{https://doi.org/10.1101/2023.06.29.547014}. biocViews: DifferentialExpression, DifferentialSplicing, Regression, GeneExpression, AlternativeSplicing, RNASeq, Sequencing, Software Author: Alexandre Segers [aut, cre], Jeroen Gilis [ctb], Mattias Van Heetvelde [ctb], Elfride De Baere [ctb], Lieven Clement [ctb] Maintainer: Alexandre Segers URL: https://github.com/statOmics/saseR, https://doi.org/10.1101/2023.06.29.547014 VignetteBuilder: knitr BugReports: https://github.com/statOmics/saseR/issues Package: methodical Version: 1.7.0 Depends: GenomicRanges, ggplot2, R (>= 4.0), SummarizedExperiment Imports: AnnotationHub, annotatr, BiocCheck, BiocManager, BiocParallel, BiocStyle, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, bsseq, cowplot, data.table, DelayedArray, devtools, dplyr, ExperimentHub, foreach, GenomeInfoDb, HDF5Array, IRanges, knitr, MatrixGenerics, R.utils, rcmdcheck, RcppRoll, remotes, rhdf5, rtracklayer, S4Vectors, scales, tibble, tidyr, tools, TumourMethData, usethis Suggests: DESeq2, methrix, rmarkdown License: GPL (>= 3) Title: Discovering genomic regions where methylation is strongly associated with transcriptional activity Description: DNA methylation is generally considered to be associated with transcriptional silencing. However, comprehensive, genome-wide investigation of this relationship requires the evaluation of potentially millions of correlation values between the methylation of individual genomic loci and expression of associated transcripts in a relatively large numbers of samples. Methodical makes this process quick and easy while keeping a low memory footprint. It also provides a novel method for identifying regions where a number of methylation sites are consistently strongly associated with transcriptional expression. In addition, Methodical enables housing DNA methylation data from diverse sources (e.g. WGBS, RRBS and methylation arrays) with a common framework, lifting over DNA methylation data between different genome builds and creating base-resolution plots of the association between DNA methylation and transcriptional activity at transcriptional start sites. biocViews: DNAMethylation, MethylationArray, Transcription, GenomeWideAssociation, Software Author: Richard Heery [aut, cre] (ORCID: ) Maintainer: Richard Heery URL: https://github.com/richardheery/methodical VignetteBuilder: knitr BugReports: https://github.com/richardheery/methodical/issues Package: VisiumIO Version: 1.7.7 Depends: R (>= 4.5.0), TENxIO Imports: BiocBaseUtils, BiocGenerics, BiocIO (>= 1.15.1), jsonlite, methods, S4Vectors, SingleCellExperiment, SpatialExperiment, SummarizedExperiment Suggests: arrow, BiocStyle, data.table, knitr, readr, rmarkdown, sf, tinytest License: Artistic-2.0 Title: Import Visium data from the 10X Space Ranger pipeline Description: The package allows users to readily import spatial data obtained from either the 10X website or from the Space Ranger pipeline. Supported formats include tar.gz, h5, and mtx files. Multiple files can be imported at once with *List type of functions. The package represents data mainly as SpatialExperiment objects. biocViews: Software, Infrastructure, DataImport, SingleCell, Spatial Author: Marcel Ramos [aut, cre] (ORCID: ), Estella YiXing Dong [aut, ctb], Dario Righelli [aut, ctb], Helena Crowell [aut, ctb], NCI [fnd] (GrantNo.: U24CA289073) Maintainer: Marcel Ramos URL: https://github.com/waldronlab/VisiumIO VignetteBuilder: knitr BugReports: https://github.com/waldronlab/VisiumIO/issues Package: dar Version: 1.7.0 Depends: R (>= 4.5.0) Imports: cli, ComplexHeatmap, crayon, dplyr, generics, ggplot2, glue, gplots, heatmaply, magrittr, methods, mia, phyloseq, purrr, readr, rlang (>= 0.4.11), scales, stringr, tibble, tidyr, UpSetR, waldo Suggests: ALDEx2, ANCOMBC, apeglm, ashr, Biobase, corncob, covr, DESeq2, devtools, furrr, future, knitr, lefser, limma, Maaslin2, metagenomeSeq, microbiome, rmarkdown, roxygen2, roxyglobals, roxytest, rstatix, SummarizedExperiment, TreeSummarizedExperiment, testthat (>= 3.0.0), GenomeInfoDb License: MIT + file LICENSE Title: Differential Abundance Analysis by Consensus Description: Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way. biocViews: Software, Sequencing, Microbiome, Metagenomics, MultipleComparison, Normalization Author: Francesc Catala-Moll [aut, cre] (ORCID: ) Maintainer: Francesc Catala-Moll URL: https://github.com/MicrobialGenomics-IrsicaixaOrg/dar, https://microbialgenomics-irsicaixaorg.github.io/dar/ VignetteBuilder: knitr BugReports: https://github.com/MicrobialGenomics-IrsicaixaOrg/dar/issues Package: crisprShiny Version: 1.7.2 Depends: R (>= 4.4.0), shiny Imports: BiocGenerics, Biostrings, BSgenome, crisprBase, crisprDesign, crisprScore, crisprViz, DT, Seqinfo, htmlwidgets, methods, pwalign, S4Vectors, shinyBS, shinyjs, utils, waiter Suggests: BiocStyle, knitr, rmarkdown, shinyFeedback, testthat (>= 3.0.0), BSgenome.Hsapiens.UCSC.hg38 License: MIT + file LICENSE Title: Exploring curated CRISPR gRNAs via Shiny Description: Provides means to interactively visualize guide RNAs (gRNAs) in GuideSet objects via Shiny application. This GUI can be self-contained or as a module within a larger Shiny app. The content of the app reflects the annotations present in the passed GuideSet object, and includes intuitive tools to examine, filter, and export gRNAs, thereby making gRNA design more user-friendly. biocViews: CRISPR, FunctionalGenomics, GeneTarget, GUI Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprShiny VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprShiny/issues Package: sketchR Version: 1.7.1 Imports: basilisk, Biobase, DelayedArray, dplyr, ggplot2, methods, reticulate, rlang, scales, stats Suggests: rmarkdown, knitr, testthat (>= 3.0.0), TENxPBMCData, scuttle, scran, scater, SingleR, celldex, cowplot, SummarizedExperiment, beachmat.hdf5, BiocStyle, BiocManager, SingleCellExperiment, snifter, uwot, bluster, class License: MIT + file LICENSE Title: An R interface for python subsampling/sketching algorithms Description: Provides an R interface for various subsampling algorithms implemented in python packages. Currently, interfaces to the geosketch and scSampler python packages are implemented. In addition it also provides diagnostic plots to evaluate the subsampling. biocViews: SingleCell Author: Charlotte Soneson [aut, cre] (ORCID: ), Michael Stadler [aut] (ORCID: ), Friedrich Miescher Institute for Biomedical Research [cph] Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/sketchR VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/sketchR/issues Package: scMitoMut Version: 1.7.0 Depends: R (>= 4.3.0) Imports: data.table, Rcpp, magrittr, plyr, stringr, utils, stats, methods, ggplot2, pheatmap, RColorBrewer, rhdf5, readr, parallel, grDevices LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, VGAM, R.utils License: Artistic-2.0 NeedsCompilation: yes Title: Single-cell Mitochondrial Mutation Analysis Tool Description: This package is designed for calling lineage-informative mitochondrial mutations using single-cell sequencing data, such as scRNASeq and scATACSeq (preferably the latter due to RNA editing issues). It includes functions for mutation calling and visualization. Mutation calling is done using beta-binomial distribution. biocViews: Preprocessing, Sequencing, SingleCell Author: Wenjie Sun [cre, aut] (ORCID: ), Leila Perie [ctb] Maintainer: Wenjie Sun URL: http://github.com/wenjie1991/scMitoMut VignetteBuilder: knitr BugReports: https://github.com/wenjie1991/scMitoMut/issues Package: txdbmaker Version: 1.7.3 Depends: BiocGenerics, S4Vectors (>= 0.47.6), Seqinfo, GenomicRanges (>= 1.61.1), GenomicFeatures (>= 1.61.4) Imports: methods, utils, stats, tools, httr, rjson, DBI, RSQLite (>= 2.0), IRanges, UCSC.utils (>= 1.7.1), GenomeInfoDb, AnnotationDbi, Biobase, BiocIO, rtracklayer, biomaRt (>= 2.59.1) Suggests: RMariaDB, ensembldb, GenomeInfoDbData, RUnit, BiocStyle, knitr License: Artistic-2.0 Title: Tools for making TxDb objects from genomic annotations Description: A set of tools for making TxDb objects from genomic annotations from various sources (e.g. UCSC, Ensembl, and GFF files). These tools allow the user to download the genomic locations of transcripts, exons, and CDS, for a given assembly, and to import them in a TxDb object. TxDb objects are implemented in the GenomicFeatures package, together with flexible methods for extracting the desired features in convenient formats. biocViews: Infrastructure, DataImport, Annotation, GenomeAnnotation, GenomeAssembly, Genetics, Sequencing Author: H. Pagès [aut, cre], M. Carlson [aut], P. Aboyoun [aut], S. Falcon [aut], M. Morgan [aut], R. Castelo [ctb], M. Lawrence [ctb], I-Hsuan Lin [ctb], J. MacDonald [ctb], M. Ramos [ctb], S. Saini [ctb], L. Shepherd [ctb] Maintainer: H. Pagès URL: https://bioconductor.org/packages/txdbmaker VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/txdbmaker/issues Package: UPDhmm Version: 1.7.1 Depends: R (>= 4.1.0) Imports: HMM, utils, VariantAnnotation, GenomicRanges, S4Vectors, IRanges, SummarizedExperiment, stats, BiocParallel, GenomeInfoDb Suggests: knitr, testthat (>= 2.1.0), BiocStyle, rmarkdown, markdown, karyoploteR, regioneR, dplyr, BiocManager License: MIT + file LICENSE Title: Detecting Uniparental Disomy through NGS trio data Description: Uniparental disomy (UPD) is a genetic condition where an individual inherits both copies of a chromosome or part of it from one parent, rather than one copy from each parent. This package contains a HMM for detecting UPDs through HTS (High Throughput Sequencing) data from trio assays. By analyzing the genotypes in the trio, the model infers a hidden state (normal, father isodisomy, mother isodisomy, father heterodisomy and mother heterodisomy). biocViews: Software, HiddenMarkovModel, Genetics Author: Marta Sevilla [aut, cre] (ORCID: ), Carlos Ruiz-Arenas [aut] (ORCID: ) Maintainer: Marta Sevilla URL: https://github.com/martasevilla/UPDhmm VignetteBuilder: knitr BugReports: https://github.com/martasevilla/UPDhmm/issues Package: Damsel Version: 1.7.0 Depends: R (>= 4.4.0) Imports: AnnotationDbi, Biostrings, ComplexHeatmap, dplyr, edgeR, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggbio, ggplot2, goseq, magrittr, patchwork, plyranges, reshape2, rlang, Rsamtools, Rsubread, stats, stringr, tidyr, utils Suggests: BiocStyle, biomaRt, biovizBase, BSgenome.Dmelanogaster.UCSC.dm6, knitr, limma, org.Dm.eg.db, rmarkdown, testthat (>= 3.0.0), TxDb.Dmelanogaster.UCSC.dm6.ensGene License: MIT + file LICENSE Title: Damsel: an end to end analysis of DamID Description: Damsel provides an end to end analysis of DamID data. Damsel takes bam files from Dam-only control and fusion samples and counts the reads matching to each GATC region. edgeR is utilised to identify regions of enrichment in the fusion relative to the control. Enriched regions are combined into peaks, and are associated with nearby genes. Damsel allows for IGV style plots to be built as the results build, inspired by ggcoverage, and using the functionality and layering ability of ggplot2. Damsel also conducts gene ontology testing with bias correction through goseq, and future versions of Damsel will also incorporate motif enrichment analysis. Overall, Damsel is the first package allowing for an end to end analysis with visual capabilities. The goal of Damsel was to bring all the analysis into one place, and allow for exploratory analysis within R. biocViews: DifferentialMethylation, PeakDetection, GenePrediction, GeneSetEnrichment Author: Caitlin Page [aut, cre] (ORCID: ) Maintainer: Caitlin Page URL: https://github.com/Oshlack/Damsel VignetteBuilder: knitr BugReports: https://github.com/Oshlack/Damsel Package: GeDi Version: 1.7.1 Depends: R (>= 4.4.0) Imports: Matrix, shiny, shinyWidgets, bs4Dash, rintrojs, utils, DT, dplyr, shinyBS, STRINGdb, igraph, visNetwork, shinycssloaders, fontawesome, grDevices, parallel, stats, ggplot2, plotly, expm, RColorBrewer, scales, readxl, ggdendro, ComplexHeatmap, BiocNeighbors, tm, wordcloud2, tools, BiocParallel, BiocFileCache, cluster, methods, circlize, proxyC, simona Suggests: knitr, rmarkdown, testthat (>= 3.0.0), DESeq2, mosdef, GeneTonic, htmltools, AnnotationDbi, macrophage, topGO, biomaRt, ReactomePA, clusterProfiler, BiocStyle, org.Hs.eg.db License: MIT + file LICENSE Title: Defining and visualizing the distances between different genesets Description: The package provides different distances measurements to calculate the difference between genesets. Based on these scores the genesets are clustered and visualized as graph. This is all presented in an interactive Shiny application for easy usage. biocViews: GUI, GeneSetEnrichment, Software, Transcription, RNASeq, Visualization, Clustering, Pathways, ReportWriting, GO, KEGG, Reactome, ShinyApps Author: Annekathrin Nedwed [aut, cre] (ORCID: ), Federico Marini [aut] (ORCID: ) Maintainer: Annekathrin Nedwed URL: https://github.com/AnnekathrinSilvia/GeDi VignetteBuilder: knitr BugReports: https://github.com/AnnekathrinSilvia/GeDi/issues Package: singleCellTK Version: 2.21.1 Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment, DelayedArray, Biobase Imports: ape, anndata, AnnotationHub, batchelor, BiocParallel, celldex, colourpicker, colorspace, cowplot, cluster, ComplexHeatmap, data.table, DelayedMatrixStats, DESeq2, dplyr, DT, ExperimentHub, ensembldb, fields, ggplot2, ggplotify, ggrepel, ggtree, gridExtra, grid, GSVA (>= 1.50.0), GSVAdata, igraph, KernSmooth, limma, MAST, Matrix (>= 1.6-1), matrixStats, methods, msigdbr, multtest, plotly, plyr, ROCR, Rtsne, S4Vectors, scater, scMerge (>= 1.2.0), scran, Seurat (>= 3.1.3), shiny, shinyjs, SingleR, stringr, SoupX, sva, reshape2, shinyalert, circlize, enrichR (>= 3.2), celda, shinycssloaders, DropletUtils, scds (>= 1.2.0), reticulate (>= 1.14), tools, tximport, tidyr, eds, withr, GSEABase, R.utils, zinbwave, scRNAseq (>= 2.0.2), TENxPBMCData, yaml, rmarkdown, magrittr, scDblFinder, metap, VAM (>= 0.5.3), tibble, rlang, TSCAN, TrajectoryUtils, scuttle, utils, stats, zellkonverter, lifecycle Suggests: testthat, Rsubread, BiocStyle, knitr, lintr, spelling, org.Mm.eg.db, kableExtra, shinythemes, shinyBS, shinyjqui, shinyWidgets, shinyFiles, BiocGenerics, RColorBrewer, fastmap (>= 1.1.0), harmony, SeuratObject, optparse License: MIT + file LICENSE Title: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data Description: The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk. biocViews: SingleCell, GeneExpression, DifferentialExpression, Alignment, Clustering, ImmunoOncology, BatchEffect, Normalization, QualityControl, DataImport, GUI Author: Yichen Wang [aut] (ORCID: ), Irzam Sarfraz [aut] (ORCID: ), Rui Hong [aut], Yusuke Koga [aut], Salam Alabdullatif [aut], Nida Pervaiz [aut], David Jenkins [aut] (ORCID: ), Vidya Akavoor [aut], Xinyun Cao [aut], Shruthi Bandyadka [aut], Anastasia Leshchyk [aut], Tyler Faits [aut], Mohammed Muzamil Khan [aut], Zhe Wang [aut], W. Evan Johnson [aut] (ORCID: ), Ming Liu [aut], Joshua David Campbell [aut, cre] (ORCID: ) Maintainer: Joshua David Campbell URL: https://www.camplab.net/sctk/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/singleCellTK/issues Package: gINTomics Version: 1.7.0 Depends: R (>= 4.4.0) Imports: BiocParallel, biomaRt, OmnipathR, edgeR, ggplot2, ggridges, gtools, MultiAssayExperiment, plyr, stringi, stringr, SummarizedExperiment, methods, stats, reshape2, randomForest, limma, org.Hs.eg.db, org.Mm.eg.db, BiocGenerics, GenomicFeatures, ReactomePA, clusterProfiler, dplyr, AnnotationDbi, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, shiny, GenomicRanges, ggtree, shinydashboard, plotly, DT, MASS, InteractiveComplexHeatmap, ComplexHeatmap, visNetwork, shiny.gosling, ggvenn, RColorBrewer, utils, grDevices, callr, circlize, MethylMix, shinyjs Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 Title: Multi-Omics data integration Description: gINTomics is an R package for Multi-Omics data integration and visualization. gINTomics is designed to detect the association between the expression of a target and of its regulators, taking into account also their genomics modifications such as Copy Number Variations (CNV) and methylation. What is more, gINTomics allows integration results visualization via a Shiny-based interactive app. biocViews: GeneExpression, RNASeq, Microarray, Visualization, CopyNumberVariation, GeneTarget Author: Angelo Velle [cre, aut] (ORCID: ), Francesco Patane' [aut] (ORCID: ), Chiara Romualdi [aut] (ORCID: ) Maintainer: Angelo Velle URL: https://github.com/angelovelle96/gINTomics VignetteBuilder: knitr BugReports: https://github.com/angelovelle96/gINTomics/issues Package: Pirat Version: 1.5.8 Depends: R (>= 4.5.0) Imports: basilisk, reticulate, progress, ggplot2, MASS, invgamma, grDevices, stats, graphics, SummarizedExperiment, S4Vectors Suggests: knitr, BiocStyle License: GPL-2 Title: Precursor or Peptide Imputation under Random Truncation Description: Pirat enables the imputation of missing values (either MNARs or MCARs) in bottom-up LC-MS/MS proteomics data using a penalized maximum likelihood strategy. It does not require any parameter tuning, it models the instrument censorship from the data available. It accounts for sibling peptides correlations and it can leverage complementary transcriptomics measurements. biocViews: Proteomics, MassSpectrometry, Preprocessing, Software Author: Lucas Etourneau [cre, aut] (ORCID: ), Laura Fancello [aut], Manon Gaudin [aut], Samuel Wieczorek [aut] (ORCID: ), Nelle Varoquaux [aut], Thomas Burger [aut] Maintainer: Lucas Etourneau URL: https://github.com/edyp-lab/Pirat VignetteBuilder: knitr BugReports: https://github.com/edyp-lab/Pirat/issues Package: broadSeq Version: 1.5.2 Depends: dplyr, ggpubr, SummarizedExperiment Imports: BiocStyle, DELocal, EBSeq (>= 1.38.0), DESeq2 (>= 1.38.2), NOISeq, forcats (>= 1.0.0), genefilter, ggplot2, ggplotify, plyr, clusterProfiler (>= 4.8.2), pheatmap, sechm (>= 1.6.0), stringr, purrr (>= 0.3.5), edgeR (>= 3.40.1) Suggests: knitr, limma (>= 3.54.0), rmarkdown, stats (>= 4.2.2), samr License: MIT + file LICENSE Title: broadSeq : for streamlined exploration of RNA-seq data Description: This package helps user to do easily RNA-seq data analysis with multiple methods (usually which needs many different input formats). Here the user will provid the expression data as a SummarizedExperiment object and will get results from different methods. It will help user to quickly evaluate different methods. biocViews: GeneExpression, DifferentialExpression, RNASeq, Transcriptomics, Sequencing, Coverage, GeneSetEnrichment, GO Author: Rishi Das Roy [aut, cre] (ORCID: ) Maintainer: Rishi Das Roy URL: https://github.com/dasroy/broadSeq VignetteBuilder: knitr BugReports: https://github.com/dasroy/broadSeq/issues Package: squallms Version: 1.5.0 Depends: R (>= 3.5.0) Imports: xcms, MSnbase, MsExperiment, RaMS, dplyr, tidyr, tibble, ggplot2, shiny, plotly, data.table, caret, stats, graphics, utils, keys Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE Title: Speedy quality assurance via lasso labeling for LC-MS data Description: squallms is a Bioconductor R package that implements a "semi-labeled" approach to untargeted mass spectrometry data. It pulls in raw data from mass-spec files to calculate several metrics that are then used to label MS features in bulk as high or low quality. These metrics of peak quality are then passed to a simple logistic model that produces a fully-labeled dataset suitable for downstream analysis. biocViews: MassSpectrometry, Metabolomics, Proteomics, Lipidomics, ShinyApps, Classification, Clustering, FeatureExtraction, PrincipalComponent, Regression, Preprocessing, QualityControl, Visualization Author: William Kumler [aut, cre, cph] (ORCID: ) Maintainer: William Kumler URL: https://github.com/wkumler/squallms VignetteBuilder: knitr BugReports: https://github.com/wkumler/squallms/issues Package: cliqueMS Version: 1.25.0 Depends: R (>= 4.3.0) Imports: Rcpp (>= 0.12.15), xcms(>= 3.0.0), MSnbase, igraph, coop, slam, matrixStats, methods LinkingTo: Rcpp, BH, RcppArmadillo Suggests: BiocParallel, knitr, rmarkdown, testthat, CAMERA License: GPL (>= 2) Title: Annotation of Isotopes, Adducts and Fragmentation Adducts for in-Source LC/MS Metabolomics Data Description: Annotates data from liquid chromatography coupled to mass spectrometry (LC/MS) metabolomics experiments. Based on a network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O. Yanes, R.Guimerà and M. Sales-Pardo, Bioinformatics, 35(20), 2019), 'CliqueMS' builds a weighted similarity network where nodes are features and edges are weighted according to the similarity of this features. Then it searches for the most plausible division of the similarity network into cliques (fully connected components). Finally it annotates metabolites within each clique, obtaining for each annotated metabolite the neutral mass and their features, corresponding to isotopes, ionization adducts and fragmentation adducts of that metabolite. biocViews: Metabolomics, MassSpectrometry, Network, NetworkInference Author: Oriol Senan Campos [aut, cre], Antoni Aguilar-Mogas [aut], Jordi Capellades [aut], Miriam Navarro [aut], Oscar Yanes [aut], Roger Guimera [aut], Marta Sales-Pardo [aut] Maintainer: Oriol Senan Campos URL: http://cliquems.seeslab.net SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/osenan/cliqueMS/issues Package: tidytof Version: 1.5.1 Depends: R (>= 4.3) Imports: doParallel, dplyr, flowCore, foreach, ggplot2, ggraph, glmnet, methods, parallel, purrr, readr, recipes, rlang, stringr, survival, tidygraph, tidyr, tidyselect, yardstick, Rcpp, tibble, stats, utils, RcppHNSW LinkingTo: Rcpp Suggests: ConsensusClusterPlus, Biobase, broom, covr, diffcyt, emdist, FlowSOM, forcats, ggrepel, HDCytoData, knitr, markdown, philentropy, rmarkdown, Rtsne, statmod, SummarizedExperiment, testthat (>= 3.0.0), lmerTest, lme4, ggridges, spelling, scattermore, preprocessCore, SingleCellExperiment, Seurat, SeuratObject, embed, rsample, BiocGenerics License: MIT + file LICENSE Title: Analyze High-dimensional Cytometry Data Using Tidy Data Principles Description: This package implements an interactive, scientific analysis pipeline for high-dimensional cytometry data built using tidy data principles. It is specifically designed to play well with both the tidyverse and Bioconductor software ecosystems, with functionality for reading/writing data files, data cleaning, preprocessing, clustering, visualization, modeling, and other quality-of-life functions. tidytof implements a "grammar" of high-dimensional cytometry data analysis. biocViews: SingleCell, FlowCytometry Author: Timothy Keyes [cre] (ORCID: ), Kara Davis [rth, own], Garry Nolan [rth, own] Maintainer: Timothy Keyes URL: https://keyes-timothy.github.io/tidytof, https://keyes-timothy.github.io/tidytof/ VignetteBuilder: knitr BugReports: https://github.com/keyes-timothy/tidytof/issues PackageStatus: Deprecated Package: xenLite Version: 1.5.0 Depends: R (>= 4.1) Imports: SpatialExperiment, BiocFileCache, Matrix, S4Vectors, SummarizedExperiment, methods, utils, EBImage, shiny, HDF5Array, arrow, ggplot2, SingleCellExperiment, TENxIO, dplyr, graphics, stats Suggests: knitr, testthat, BiocStyle, yesno, terra, SpatialFeatureExperiment, SFEData, tiff License: Artistic-2.0 Title: Simple classes and methods for managing Xenium datasets Description: Define a relatively light class for managing Xenium data using Bioconductor. Address use of parquet for coordinates, SpatialExperiment for assay and sample data. Address serialization and use of cloud storage. biocViews: Infrastructure Author: Vincent Carey [aut, cre] (ORCID: ) Maintainer: Vincent Carey URL: https://github.com/vjcitn/xenLite VignetteBuilder: knitr BugReports: https://github.com/vjcitn/xenLite/issues Package: koinar Version: 1.5.1 Depends: R (>= 4.3) Imports: httr, jsonlite, methods, utils Suggests: BiocManager, BiocStyle (>= 2.26), httptest, knitr, lattice, testthat, OrgMassSpecR, mzR, msdata, OrgMassSpecR, protViz, S4Vectors, Spectra, testthat, mzR License: Apache License 2.0 NeedsCompilation: no Title: KoinaR - Remote machine learning inference using Koina Description: A client to simplify fetching predictions from the Koina web service. Koina is a model repository enabling the remote execution of models. Predictions are generated as a response to HTTP/S requests, the standard protocol used for nearly all web traffic. biocViews: MassSpectrometry, Proteomics, Infrastructure, Software Author: Ludwig Lautenbacher [aut, cre] (ORCID: ), Christian Panse [aut] (ORCID: ) Maintainer: Ludwig Lautenbacher URL: https://github.com/wilhelm-lab/koina VignetteBuilder: knitr BugReports: https://github.com/wilhelm-lab/koina/issues Package: omXplore Version: 1.5.0 Depends: R (>= 4.5.0), methods Imports: DT, shiny, MSnbase, PSMatch, SummarizedExperiment, MultiAssayExperiment, shinyBS, shinyjs, shinyjqui, RColorBrewer, gplots, highcharter, visNetwork, tibble, grDevices, stats, utils, htmlwidgets, vioplot, graphics, FactoMineR, dendextend, dplyr, factoextra, tidyr, nipals Suggests: knitr, rmarkdown, BiocStyle, testthat, Matrix, graph License: Artistic-2.0 NeedsCompilation: no Title: Vizualization tools for 'omics' datasets with R Description: This package contains a collection of functions (written as shiny modules) for the visualisation and the statistical analysis of omics data. These plots can be displayed individually or embedded in a global Shiny module. Additionaly, it is possible to integrate third party modules to the main interface of the package omXplore. biocViews: Software, ShinyApps, MassSpectrometry, DataRepresentation, GUI, QualityControl Author: Samuel Wieczorek [aut, cre] (ORCID: ), Thomas Burger [aut], Enora Fremy [ctb], Cyril Ariztegui [ctb] Maintainer: Samuel Wieczorek URL: https://github.com/edyp-lab/omXplore, https://edyp-lab.github.io/omXplore/ VignetteBuilder: knitr BugReports: https://github.com/edyp-lab/omXplore/issues Package: geomeTriD Version: 1.5.1 Depends: R (>= 4.4.0) Imports: aricode, BiocGenerics, Biostrings, clue, cluster, dbscan, future.apply, Seqinfo, GenomicRanges, graphics, grDevices, grid, htmlwidgets, igraph, InteractionSet, IRanges, MASS, Matrix, methods, plotrix, progressr, RANN, rgl, rjson, S4Vectors, scales, stats, trackViewer Suggests: RUnit, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, manipulateWidget, shiny, BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE Title: A R/Bioconductor package for interactive 3D plot of epigenetic data or single cell data Description: The geomeTriD (Three-Dimensional Geometry) Package provides interactive 3D visualization of chromatin structures using the WebGL-based 'three.js' (https://threejs.org/) or the rgl rendering library. It is designed to identify and explore spatial chromatin patterns within genomic regions. The package generates dynamic 3D plots and HTML widgets that integrate seamlessly with Shiny applications, enabling researchers to visualize chromatin organization, detect spatial features, and compare structural dynamics across different conditions and data types. biocViews: Visualization Author: Jianhong Ou [aut, cre] (ORCID: ), Kenneth Poss [aut, fnd] Maintainer: Jianhong Ou URL: https://github.com/jianhong/geomeTriD VignetteBuilder: knitr BugReports: https://github.com/jianhong/geomeTriD/issues Package: PRONE Version: 1.5.1 Depends: R (>= 4.4.0), SummarizedExperiment Imports: dplyr, magrittr, data.table, RColorBrewer, ggplot2, S4Vectors, ComplexHeatmap, stringr, NormalyzerDE, tibble, limma, MASS, edgeR, matrixStats, preprocessCore, stats, gtools, methods, ROTS, ComplexUpset, tidyr, purrr, circlize, gprofiler2, plotROC, MSnbase, UpSetR, dendsort, vsn, Biobase, reshape2, POMA, ggtext, scales, DEqMS, vegan Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, DT License: GPL (>= 3) Title: The PROteomics Normalization Evaluator Description: High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed. biocViews: Proteomics, Preprocessing, Normalization, DifferentialExpression, Visualization Author: Lis Arend [aut, cre] (ORCID: ) Maintainer: Lis Arend URL: https://github.com/daisybio/PRONE VignetteBuilder: knitr BugReports: https://github.com/daisybio/PRONE/issues Package: OmicsMLRepoR Version: 1.5.5 Depends: R (>= 4.4.0) Imports: dplyr, stringr, rols, tidyr, methods, stats, tibble, data.tree, jsonlite, plyr, BiocFileCache, readr, DiagrammeR, rlang, lubridate Suggests: arrow, knitr, BiocStyle, curatedMetagenomicData, testthat (>= 3.0.0), cBioPortalData License: Artistic-2.0 Title: Search harmonized metadata created under the OmicsMLRepo project Description: This package provides functions to browse the harmonized metadata for large omics databases. This package also supports data navigation if the metadata incorporates ontology. biocViews: Software, Infrastructure, DataRepresentation Author: Sehyun Oh [aut, cre] (ORCID: ), Kaelyn Long [aut] Maintainer: Sehyun Oh URL: https://github.com/shbrief/OmicsMLRepoR VignetteBuilder: knitr BugReports: https://github.com/shbrief/OmicsMLRepoR/issues Package: visiumStitched Version: 1.3.0 Depends: R (>= 4.4), SpatialExperiment Imports: BiocBaseUtils, BiocGenerics, clue, dplyr, DropletUtils, grDevices, imager, Matrix, methods, pkgcond, readr, rjson, S4Vectors, SingleCellExperiment, spatialLIBD (>= 1.17.8), stringr, SummarizedExperiment, tibble, tidyr, xml2 Suggests: BiocFileCache, BiocStyle, ggplot2, knitr, RefManageR, rmarkdown, sessioninfo, Seurat, testthat (>= 3.0.0) License: Artistic-2.0 Title: Enable downstream analysis of Visium capture areas stitched together with Fiji Description: This package provides helper functions for working with multiple Visium capture areas that overlap each other. This package was developed along with the companion example use case data available from https://github.com/LieberInstitute/visiumStitched_brain. visiumStitched prepares SpaceRanger (10x Genomics) output files so you can stitch the images from groups of capture areas together with Fiji. Then visiumStitched builds a SpatialExperiment object with the stitched data and makes an artificial hexagonal grid enabling the seamless use of spatial clustering methods that rely on such grid to identify neighboring spots, such as PRECAST and BayesSpace. The SpatialExperiment objects created by visiumStitched are compatible with spatialLIBD, which can be used to build interactive websites for stitched SpatialExperiment objects. visiumStitched also enables casting SpatialExperiment objects as Seurat objects. biocViews: Software, Spatial, Transcriptomics, Transcription, GeneExpression, Visualization, DataImport Author: Nicholas J. Eagles [aut, cre] (ORCID: ), Leonardo Collado-Torres [ctb] (ORCID: ) Maintainer: Nicholas J. Eagles URL: https://github.com/LieberInstitute/visiumStitched VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/visiumStitched Package: LimROTS Version: 1.3.25 Depends: R (>= 4.5.0), SummarizedExperiment Imports: limma, stringr, qvalue, utils, stats, BiocParallel, S4Vectors, dplyr, survival, cmprsk, variancePartition Suggests: BiocStyle, ggplot2, testthat (>= 3.0.0), knitr, rmarkdown, caret, ROTS, mia, miaTime, TreeSummarizedExperiment License: GPL (>= 2) Title: LimROTS: A Hybrid Method Integrating Empirical Bayes and Reproducibility-Optimized Statistics for Robust Differential Expression Analysis Description: Differential expression analysis is commonly used to study diverse biological datasets. The reproducibility-optimized test statistic (ROTS) (Elo et al., 2008, ) uses a modified t-statistic to prioritise features that differ between two or more groups. However, the ROTS Bioconductor implementation (Suomi et al., 2017, ) did not accommodate technical or biological covariates. LimROTS (Anwar et al., 2025, ) addressed this limitation by combining a reproducibility-optimized test statistic with the limma empirical Bayes approach (Ritchie et al., 2015, ). This enables the analysis of more complex experimental designs and the incorporation of covariates. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics, ImmunoOncology, Metabolomics, mRNAMicroarray Author: Ali Mostafa Anwar [aut, cre] (ORCID: ), Leo Lahti [aut, ths] (ORCID: ), Akewak Jeba [aut, ctb] (ORCID: ), Eleanor Coffey [aut, ths] (ORCID: ), Rasmus Hindström [ctb] (ORCID: ) Maintainer: Ali Mostafa Anwar URL: https://github.com/AliYoussef96/LimROTS, https://aliyoussef96.github.io/LimROTS/ VignetteBuilder: knitr BugReports: https://github.com/AliYoussef96/LimROTS/issues Package: scQTLtools Version: 1.3.5 Depends: R (>= 4.4.1.0) Imports: ggplot2(>= 3.5.1), Matrix (>= 1.7-0), stats (>= 4.4.1), progress(>= 1.2.3), stringr(>= 1.5.1), dplyr(>= 1.1.4), SeuratObject(>= 5.0.2), methods(>= 4.4.1), magrittr(>= 2.0.3), patchwork(>= 1.2.0), DESeq2 (>= 1.45.3), VGAM (>= 1.1-11), limma (>= 3.61.9), biomaRt(>= 2.61.3), gamlss (>= 5.4-22), SingleCellExperiment(>= 1.27.2), SummarizedExperiment(>= 1.32.0), yulab.utils (>= 0.2.3) Suggests: BiocStyle, knitr, rmarkdown, org.Hs.eg.db, org.Mm.eg.db, org.Ce.eg.db, org.At.tair.db, testthat (>= 3.2.1.1) License: MIT + file LICENSE Title: scQTLtools: an R/Bioconductor package for comprehensive identification and visualization of single-cell eQTLs Description: scQTLtools is a comprehensive R/Bioconductor package that facilitates end-to-end single-cell eQTL analysis, from preprocessing to visualization biocViews: Software, GeneExpression, GeneticVariability, SNP, DifferentialExpression, GenomicVariation, VariantDetection, Genetics, FunctionalGenomics, SystemsBiology, Regression, SingleCell, Normalization, Visualization, Preprocessing Author: Xiaofeng Wu [aut, cre, cph] (ORCID: ), Xin Huang [aut, cph] (ORCID: ), Jingtong Kang [com] (ORCID: ), Siwen Xu [aut, cph] (ORCID: ) Maintainer: Xiaofeng Wu <1427972815@qq.com> URL: https://github.com/XFWuCN/scQTLtools VignetteBuilder: knitr BugReports: https://github.com/XFWuCN/scQTLtools/issues Package: SpatialExperimentIO Version: 1.3.0 Imports: DropletUtils, SpatialExperiment, SingleCellExperiment, methods, data.table, arrow, purrr, S4Vectors Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: Artistic-2.0 Title: Read in Xenium, CosMx, MERSCOPE or STARmapPLUS data as SpatialExperiment object Description: Read in imaging-based spatial transcriptomics technology data. Current available modules are for Xenium by 10X Genomics, CosMx by Nanostring, MERSCOPE by Vizgen, or STARmapPLUS from Broad Institute. You can choose to read the data in as a SpatialExperiment or a SingleCellExperiment object. biocViews: DataRepresentation, DataImport, Infrastructure, Transcriptomics, SingleCell, Spatial, GeneExpression Author: Yixing E. Dong [aut, cre] (ORCID: ) Maintainer: Yixing E. Dong URL: https://github.com/estellad/SpatialExperimentIO VignetteBuilder: knitr BugReports: https://github.com/estellad/SpatialExperimentIO/issues Package: ELViS Version: 1.3.0 Depends: R (>= 4.5.0) Imports: reticulate, BiocGenerics, circlize, ComplexHeatmap, data.table, dplyr, GenomicFeatures, GenomicRanges, ggplot2, glue, graphics, grDevices, igraph, IRanges, magrittr, memoise, methods, parallel, patchwork, scales, segclust2d, stats, stringr, txdbmaker, utils, uuid, zoo Suggests: Rsamtools, BiocManager, knitr, testthat (>= 3.0.0) License: MIT + file LICENSE Title: An R Package for Estimating Copy Number Levels of Viral Genome Segments Using Base-Resolution Read Depth Profile Description: Base-resolution copy number analysis of viral genome. Utilizes base-resolution read depth data over viral genome to find copy number segments with two-dimensional segmentation approach. Provides publish-ready figures, including histograms of read depths, coverage line plots over viral genome annotated with copy number change events and viral genes, and heatmaps showing multiple types of data with integrative clustering of samples. biocViews: CopyNumberVariation, Coverage, GenomicVariation, BiomedicalInformatics, Sequencing, Normalization, Visualization, Clustering Author: Hyo Young Choi [aut, cph] (ORCID: ), Jin-Young Lee [aut, cre, cph] (ORCID: ), Xiaobei Zhao [ctb] (ORCID: ), Jeremiah R. Holt [ctb] (ORCID: ), Katherine A. Hoadley [aut] (ORCID: ), D. Neil Hayes [aut, fnd, cph] (ORCID: ) Maintainer: Jin-Young Lee URL: https://github.com/hyochoi/ELViS VignetteBuilder: knitr BugReports: https://github.com/hyochoi/ELViS/issues Package: XeniumIO Version: 1.3.3 Depends: TENxIO, R (>= 4.5.0) Imports: BiocBaseUtils, BiocGenerics, BiocIO, jsonlite, methods, S4Vectors, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, VisiumIO (>= 1.7.5) Suggests: arrow, BiocFileCache, BiocStyle, knitr, rmarkdown, tinytest License: Artistic-2.0 Title: Import and represent Xenium data from the 10X Xenium Analyzer Description: The package allows users to readily import spatial data obtained from the 10X Xenium Analyzer pipeline. Supported formats include 'parquet', 'h5', and 'mtx' files. The package mainly represents data as SpatialExperiment objects. biocViews: Software, Infrastructure, DataImport, SingleCell, Spatial Author: Marcel Ramos [aut, cre] (ORCID: ), Dario Righelli [ctb], Estella Dong [ctb], NCI [fnd] (GrantNo.: U24CA289073) Maintainer: Marcel Ramos URL: https://github.com/waldronlab/XeniumIO VignetteBuilder: knitr BugReports: https://github.com/waldronlab/XeniumIO/issues Package: bedbaser Version: 1.3.6 Depends: R (>= 4.5.0) Imports: AnVIL (>= 1.16.0), BiocFileCache, curl, dplyr, GenomeInfoDb, GenomicRanges, httr, methods, purrr, rtracklayer, rlang, R.utils, stats, stringr, tibble, tidyr, tools, utils Suggests: BiocStyle, knitr, liftOver, testthat (>= 3.0.0), withr License: Artistic License 2.0 Title: A BEDbase client Description: A client for BEDbase. bedbaser provides access to the API at api.bedbase.org. It also includes convenience functions to import BED files into GRanges objects and BEDsets into GRangesLists. biocViews: Software, DataImport, ThirdPartyClient Author: Andres Wokaty [aut, cre] (ORCID: ), Levi Waldron [aut] (ORCID: ) Maintainer: Andres Wokaty URL: https://github.com/waldronlab/bedbaser VignetteBuilder: knitr BugReports: https://github.com/waldronlab/bedbaser/issues Package: OSTA.data Version: 1.3.1 Depends: R (>= 4.5) Imports: osfr, utils, BiocFileCache Suggests: BiocStyle, DropletUtils, knitr, VisiumIO, SpatialExperimentIO License: Artistic-2.0 Title: OSTA book data Description: 'OSTA.data' is a companion package for the "Orchestrating Spatial Transcriptomics Analysis" (OSTA) with Bioconductor online book. Throughout OSTA, we rely on a set of publicly available datasets that cover different sequencing- and imaging-based platforms, such as Visium, Visium HD, Xenium (10x Genomics) and CosMx (NanoString). In addition, we rely on scRNA-seq (Chromium) data for tasks, e.g., spot deconvolution and label transfer (i.e., supervised clustering). These data been deposited in an Open Storage Framework (OSF) repository, and can be queried and downloaded using functions from the 'osfr' package. For convenience, we have implemented 'OSTA.data' to query and retrieve data from our OSF node, and cache retrieved Zip archives using 'BiocFileCache'. biocViews: DataImport, DataRepresentation, ExperimentHubSoftware, Infrastructure, ImmunoOncology, GeneExpression, Transcriptomics, SingleCell, Spatial Author: Yixing E. Dong [aut, cre] (ORCID: ), Helena L. Crowell [aut] (ORCID: ), Vince Carey [aut] (ORCID: ) Maintainer: Yixing E. Dong URL: https://github.com/estellad/OSTA.data VignetteBuilder: knitr BugReports: https://github.com/estellad/OSTA.data Package: DNAcycP2 Version: 1.3.0 Depends: R (>= 4.4.0) Imports: basilisk, reticulate Suggests: knitr, rmarkdown, BiocGenerics, RUnit, tinytest, BiocStyle, Biostrings License: Artistic-2.0 Title: DNA Cyclizability Prediction Description: This package performs prediction of intrinsic cyclizability of of every 50-bp subsequence in a DNA sequence. The input could be a file either in FASTA or text format. The output will be the C-score, the estimated intrinsic cyclizability score for each 50 bp sequences in each entry of the sequence set. biocViews: NeuralNetwork, StructuralPrediction Author: Ji-Ping Wang [aut, cre] (ORCID: ) Maintainer: Ji-Ping Wang URL: https://github.com/jipingw/DNAcycP2 VignetteBuilder: knitr BugReports: https://github.com/jipingw/DNAcycP2 Package: alabaster.sfe Version: 1.3.0 Depends: SpatialFeatureExperiment (>= 1.9.3), alabaster.base Imports: alabaster.sce, alabaster.spatial (>= 1.5.2), EBImage, jsonlite, methods, RBioFormats, S4Vectors, sfarrow, SingleCellExperiment, spatialreg, spdep, SummarizedExperiment, terra, xml2 Suggests: BiocStyle, fs, knitr, rmarkdown, scater, sf, SFEData, testthat (>= 3.0.0), Voyager (>= 1.9.1) License: MIT + file LICENSE Title: Language agnostic on disk serialization of SpatialFeatureExperiment Description: Builds upon the existing ArtifactDB project, expending alabaster.spatial for language agnostic on disk serialization of SpatialFeatureExperiment. biocViews: DataRepresentation, Spatial Author: Lambda Moses [aut, cre] (ORCID: ) Maintainer: Lambda Moses URL: https://pachterlab.github.io/alabaster.sfe/ VignetteBuilder: knitr BugReports: https://github.com/pachterlab/alabaster.sfe/issues Package: crumblr Version: 1.3.0 Depends: R (>= 4.4.0), ggplot2, methods Imports: Rdpack, viridis, tidytree, variancePartition (>= 1.36.3), SingleCellExperiment, ggtree, dplyr, stats, MASS, Rfast Suggests: BiocStyle, RUnit, knitr, rmarkdown, dreamlet, muscat, ExperimentHub, scater, HMP, reshape2, glue, tidyverse, BiocGenerics, compositions License: Artistic-2.0 NeedsCompilation: no Title: Count ratio uncertainty modeling base linear regression Description: Crumblr enables analysis of count ratio data using precision weighted linear (mixed) models. It uses an asymptotic normal approximation of the variance following the centered log ration transform (CLR) that is widely used in compositional data analysis. Crumblr provides a fast, flexible alternative to GLMs and GLMM's while retaining high power and controlling the false positive rate. biocViews: RNASeq, GeneExpression, DifferentialExpression, BatchEffect, QualityControl, SingleCell, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Clustering, DimensionReduction, Preprocessing, Software Author: Gabriel Hoffman [aut, cre] (ORCID: ) Maintainer: Gabriel Hoffman URL: https://DiseaseNeurogenomics.github.io/crumblr VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeurogenomics/crumblr/issues Package: RFLOMICS Version: 1.3.0 Depends: R (>= 4.4.0), SummarizedExperiment, MultiAssayExperiment, shinyBS, dplyr, ggplot2, htmltools, knitr, coseq Imports: vroom, org.At.tair.db, AnnotationDbi, clusterProfiler, ComplexHeatmap, data.table, DT, edgeR, FactoMineR, ggpubr, ggnetwork, ggrepel, grDevices, grid, httr, limma, magrittr, methods, mixOmics, MOFA2, plotly, purrr, RColorBrewer, reshape2, reticulate, rmarkdown, S4Vectors, shiny, shinydashboard, shinyWidgets, stats, stringr, tidyr, tibble, tidyselect, UpSetR, Suggests: testthat, shinytest2, BiocStyle, org.Hs.eg.db License: Artistic-2.0 Title: Interactive web application for Omics-data analysis Description: R-package with shiny interface, provides a framework for the analysis of transcriptomics, proteomics and/or metabolomics data. The interface offers a guided experience for the user, from the definition of the experimental design to the integration of several omics table together. A report can be generated with all settings and analysis results. biocViews: ShinyApps, DifferentialExpression, Metabolomics, Proteomics, Transcriptomics Author: Nadia Bessoltane [aut, cre] (ORCID: ), Delphine Charif [aut] (ORCID: ), Audrey Hulot [aut] (ORCID: ), Christine Paysant-Leroux [aut] (ORCID: ), Gwendal Cueff [aut] Maintainer: Nadia Bessoltane URL: https://github.com/RFLOMICS/RFLOMICS SystemRequirements: Python (>=3), numpy, pandas, h5py, scipy, argparse, sklearn, mofapy2 (>=0.7.1) VignetteBuilder: knitr BugReports: https://github.com/RFLOMICS/RFLOMICS/issues Package: RbowtieCuda Version: 1.3.2 Depends: R (>= 4.5.0) Imports: methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: BSD_3_clause + file LICENSE Archs: x64 NeedsCompilation: yes Title: An R Wrapper for nvBowtie and nvBWT, a rewritten version of Bowtie2 for cuda Description: This package provides an R wrapper for the popular Bowtie2 sequencing read aligner, optimized to run on NVIDIA graphics cards. It includes wrapper functions that enable both genome indexing and alignment to the generated indexes, ensuring high performance and ease of use within the R environment. biocViews: Sequencing, Alignment, Preprocessing, Coverage Author: c(Jacopo Pantaleoni [aut], Nuno Subtil [aut], Samuel Simon Sanchez [aut], Franck RICHARD [aut, cre], role = c("aut", "cre")) Maintainer: Franck RICHARD URL: https://github.com/FranckRICHARD01/RbowtieCuda, https://belacqua-labo.ovh/bioinformatic/RbowtieCuda SystemRequirements: C++20, GNU make, cmake, CUDA Toolkit(>=10), MSVC, libthrust-dev, libcub-dev, gcc, g++ VignetteBuilder: knitr BugReports: https://github.com/FranckRICHARD01/RbowtieCuda/issues Package: vmrseq Version: 1.3.0 Depends: R (>= 4.5.0) Imports: bumphunter, dplyr, BiocParallel, DelayedArray, GenomicRanges, ggplot2, methods, tidyr, locfit, gamlss.dist, recommenderlab, HDF5Array, data.table, SummarizedExperiment, IRanges, S4Vectors, devtools Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE Title: Probabilistic Modeling of Single-cell Methylation Heterogeneity Description: High-throughput single-cell measurements of DNA methylation allows studying inter-cellular epigenetic heterogeneity, but this task faces the challenges of sparsity and noise. We present vmrseq, a statistical method that overcomes these challenges and identifies variably methylated regions accurately and robustly. biocViews: Software, ImmunoOncology, DNAMethylation, Epigenetics, SingleCell, Sequencing, WholeGenome Author: Ning Shen [aut, cre] Maintainer: Ning Shen URL: https://github.com/nshen7/vmrseq VignetteBuilder: knitr BugReports: https://github.com/nshen7/vmrseq/issues Package: pathMED Version: 1.3.0 Depends: R (>= 4.5.0) Imports: BiocParallel, caret, caretEnsemble, decoupleR, ggplot2, GSVA, factoextra, FactoMineR, magrittr, matrixStats, methods, metrica, pbapply, reshape2, singscore, stats, stringi, dplyr, Suggests: ada, AUCell, Biobase, BiocGenerics, BiocStyle, fgsea (>= 1.15.4), gam, GSEABase, import, kernlab, klaR, knitr, mboost, MLeval, randomForest, ranger, rmarkdown, RUnit, SummarizedExperiment, utils, xgboost License: GPL-2 Title: Scoring Personalized Molecular Portraits Description: PathMED is a collection of tools to facilitate precision medicine studies with omics data (e.g. transcriptomics). Among its funcionalities, genesets scores for individual samples may be calculated with several methods. These scores may be used to train machine learning models and to predict clinical features on new data. For this, several machine learning methods are evaluated in order to select the best method based on internal validation and to tune the hyperparameters. Performance metrics and a ready-to-use model to predict the outcomes for new patients are returned. biocViews: Pathways, Classification, FeatureExtraction, Transcriptomics Author: Jordi Martorell-Marugán [cre, aut] (ORCID: ), Daniel Toro-Domínguez [aut] (ORCID: ), Raúl López-Domínguez [aut] (ORCID: ), Iván Ellson [aut] (ORCID: ) Maintainer: Jordi Martorell-Marugán URL: https://github.com/jordimartorell/pathMED VignetteBuilder: knitr BugReports: https://github.com/jordimartorell/pathMED/issues Package: crupR Version: 1.3.0 Depends: R (>= 4.4.0) Imports: bamsignals, Rsamtools, GenomicRanges, preprocessCore, randomForest, rtracklayer, Seqinfo, S4Vectors, ggplot2, matrixStats, dplyr, IRanges, GenomicAlignments, GenomicFeatures, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, reshape2, magrittr, stats, utils, grDevices, SummarizedExperiment, BiocParallel, fs, methods Suggests: GenomeInfoDb, testthat, BiocStyle, knitr, rmarkdown License: GPL-3 Title: An R package to predict condition-specific enhancers from ChIP-seq data Description: An R package that offers a workflow to predict condition-specific enhancers from ChIP-seq data. The prediction of regulatory units is done in four main steps: Step 1 - the normalization of the ChIP-seq counts. Step 2 - the prediction of active enhancers binwise on the whole genome. Step 3 - the condition-specific clustering of the putative active enhancers. Step 4 - the detection of possible target genes of the condition-specific clusters using RNA-seq counts. biocViews: DifferentialPeakCalling, GeneTarget, FunctionalPrediction, HistoneModification, PeakDetection Author: Persia Akbari Omgba [cre], Verena Laupert [aut], Martin Vingron [aut] Maintainer: Persia Akbari Omgba URL: https://github.com/akbariomgba/crupR VignetteBuilder: knitr BugReports: https://github.com/akbariomgba/crupR/issues Package: scafari Version: 1.1.0 Depends: R (>= 4.5.0) Imports: magrittr, shiny, shinycssloaders, DT, dplyr, waiter, ggplot2, tibble, stringr, reshape2, shinyjs, shinyBS, shinycustomloader, factoextra, markdown, plotly, ggbio, GenomicRanges, rhdf5, ComplexHeatmap, biomaRt, org.Hs.eg.db, SummarizedExperiment, SingleCellExperiment, S4Vectors, parallel, httr, jsonlite, scales, tidyr, txdbmaker, circlize, R.utils, dbscan, igraph, RANN Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: LGPL-3 Title: Analysis of scDNA-seq data Description: Scafari is a Shiny application designed for the analysis of single-cell DNA sequencing (scDNA-seq) data provided in .h5 file format. The analysis process is structured into the four key steps "Sequencing", "Panel", "Variants", and "Explore Variants". It supports various analyses and visualizations. biocViews: Software, ShinyApps, SingleCell, Sequencing Author: Sophie Wind [aut, cre] (ORCID: ) Maintainer: Sophie Wind URL: https://github.com/sophiewind/scafari VignetteBuilder: knitr BugReports: https://github.com/sophiewind/scafari/issues Package: SpaceTrooper Version: 1.1.7 Depends: R (>= 4.4.0), SpatialExperiment Imports: DropletUtils, S4Vectors, SummarizedExperiment, arrow, data.table, dplyr, e1071, ggplot2, ggpubr, robustbase, scater, scuttle, sf, sfheaders, cowplot, glmnet, rhdf5, methods, rlang, SpatialExperimentIO Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), withr, viridis License: MIT + file LICENSE Title: SpaceTrooper performs Quality Control analysis of Image-Based spatial Description: SpaceTrooper performs Quality Control analysis using data driven GLM models of Image-Based spatial data, providing exploration plots, QC metrics computation, outlier detection. It implements a GLM strategy for the detection of low quality cells in imaging-based spatial data (Transcriptomics and Proteomics). It additionally implements several plots for the visualization of imaging based polygons through the ggplot2 package. biocViews: Software, Transcriptomics, GeneExpression, QualityControl, Spatial, SingleCell, DataImport, ImmunoOncology Author: Dario Righelli [aut, cre] (ORCID: ), Benedetta Banzi [aut], Matteo Marchionni [aut], Oriana Romano [ctb], Mattia Forcato [ctb], Silvio Bicciato [aut], Davide Risso [ctb] Maintainer: Dario Righelli URL: https://github.com/drighelli/SpaceTrooper VignetteBuilder: knitr BugReports: https://github.com/drighelli/SpaceTrooper/issues Package: linkSet Version: 1.1.0 Depends: GenomicRanges, S4Vectors, R (>= 4.5.0) Imports: methods, IRanges, GenomeInfoDb, BiocGenerics, Organism.dplyr, InteractionSet, ggplot2, patchwork, scales, foreach, iterators, stats, rlang, MASS, data.table, DBI, doParallel, AnnotationDbi Suggests: knitr, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Mm.eg.db, org.Hs.eg.db, GenomicFeatures, GenomicInteractions, gamlss, gamlss.tr, BiocStyle, rtracklayer License: MIT + file LICENSE Title: Base Classes for Storing Genomic Link Data Description: Provides a comprehensive framework for representing, analyzing, and visualizing genomic interactions, particularly focusing on gene-enhancer relationships. The package extends the GenomicRanges infrastructure to handle paired genomic regions with specialized methods for chromatin interaction data from Hi-C, Promoter Capture Hi-C (PCHi-C), and single-cell ATAC-seq experiments. Key features include conversion from common interaction formats, annotation of promoters and enhancers, distance-based analyses, interaction strength metrics, statistical modeling using CHiCANE methodology, and tailored visualization tools. The package aims to standardize the representation of genomic interaction data while providing domain-specific functions not available in general genomic interaction packages. biocViews: Software, HiC, DataRepresentation, Sequencing, SingleCell, Coverage Author: Gilbert Han [aut, cre] (ORCID: ) Maintainer: Gilbert Han URL: https://github.com/GilbertHan1011/linkSet, https://gilberthan1011.github.io/linkSet VignetteBuilder: knitr BugReports: https://github.com/GilbertHan1011/linkSet/issues/new Package: stPipe Version: 1.1.2 Depends: R (>= 4.5.0) Imports: basilisk, data.table, DropletUtils, dplyr, ggplot2, methods, pbmcapply, reticulate, rmarkdown, Rcpp, Rhtslib, Rsubread, Rtsne, Seurat, SeuratObject, scPipe, shiny, SummarizedExperiment, SingleCellExperiment, SpatialExperiment, stats, umap, yaml LinkingTo: Rcpp, Rhdf5lib, testthat, Rhtslib Suggests: knitr, plotly, BiocStyle, testthat (>= 3.0.0) License: GPL-3 Title: Upstream pre-processing for Sequencing-Based Spatial Transcriptomics Description: This package serves as an upstream pipeline for pre-processing sequencing-based spatial transcriptomics data. Functions includes FASTQ trimming, BAM file reformatting, index building, spatial barcode detection, demultiplexing, gene count matrix generation with UMI deduplication, QC, and revelant visualization. Config is an essential input for most of the functions which aims to improve reproducibility. biocViews: ImmunoOncology, Software, Sequencing, RNASeq, GeneExpression, SingleCell, Visualization, SequenceMatching, Preprocessing, QualityControl, GenomeAnnotation, DataImport, Spatial, Transcriptomics, Clustering Author: Yang Xu [aut, cre] (ORCID: ), Callum Sargeant [aut], Shian Su [aut], Luyi Tian [aut], Yunshun Chen [ctb], Matthew Ritchie [ctb, fnd] Maintainer: Yang Xu URL: https://github.com/mritchielab/stPipe SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/mritchielab/stPipe/issues/new Package: Ibex Version: 1.1.1 Depends: R (>= 4.5.0) Imports: basilisk, immApex (>= 1.3.2), methods, Matrix, reticulate (>= 1.43.0), SeuratObject, scRepertoire, SingleCellExperiment, stats, SummarizedExperiment, tensorflow, tools Suggests: basilisk.utils, BiocStyle, bluster, dplyr, ggplot2, kableExtra, knitr, lifecycle, markdown, mumosa, patchwork, Peptides, rmarkdown, scater, spelling, testthat (>= 3.0.0), utils, viridis License: MIT + file LICENSE Title: Methods for BCR single-cell embedding Description: Implementation of the Ibex algorithm for single-cell embedding based on BCR sequences. The package includes a standalone function to encode BCR sequence information by amino acid properties or sequence order using tensorflow-based autoencoder. In addition, the package interacts with SingleCellExperiment or Seurat data objects. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing Author: Nick Borcherding [aut, cre, cph], Qile Yang [ctb] (ORCID: ) Maintainer: Nick Borcherding URL: https://github.com/BorchLab/Ibex/ SystemRequirements: Python (via basilisk) VignetteBuilder: knitr BugReports: https://github.com/BorchLab/Ibex/issues Package: DOtools Version: 1.1.9 Depends: R (>= 4.5.0) Imports: Seurat (>= 5.2.0), SeuratObject (>= 5.1.0), ggplot2 (>= 3.5.0), ggpubr (>= 0.6.0), ggtext (>= 0.1.2), ggalluvial (>= 0.12.5), ggrastr (>= 1.0.2), tidyverse (>= 2.0.0), reshape2 (>= 1.4.4), dplyr (>= 1.1.4), tidyr (>= 1.3.1), rstatix (>= 0.7.2), cowplot (>= 1.1.3), reticulate (>= 1.41.0.1), zellkonverter (>= 1.16.0), progress (>= 1.2.3), ggiraphExtra (>= 0.3.0), grid (>= 4.4.3), SCpubr (>= 2.0.2), DropletUtils (>= 1.26.0), scCustomize (>= 3.0.1), openxlsx (>= 4.2.8), tibble (>= 3.2.1), scDblFinder (>= 1.20.0), ggcorrplot (>= 0.1.4.1), DESeq2 (>= 1.48.1), enrichR (>= 3.4), cli (>= 3.6.5), curl(>= 6.3.0), magrittr (>= 2.0.3), Matrix (>= 1.7.3), purrr(>= 1.0.4), rlang(>= 1.1.6), scales (>= 1.4.0), SingleCellExperiment (>= 1.30.1), S4Vectors (>= 0.46.0), basilisk (>= 1.20.0), FNN (>= 1.1.4.1), ks, methods, stats, utils Suggests: SummarizedExperiment, knitr, kableExtra, pkgdown, RefManageR, BiocStyle, roxygen2, httr, magick, rmarkdown, assertthat, plyr, rsvg, scran, scater, igraph, sessioninfo, testthat (>= 3.0.0), leidenbase (>= 0.1.36), mockery License: MIT + file LICENSE Title: Convenient functions to streamline your single cell data analysis workflow Description: This package provides functions for creating various visualizations, convenient wrappers, and quality-of-life utilities for single cell experiment objects. It offers a streamlined approach to visualize results and integrates different tools for easy use. biocViews: SingleCell, RNASeq, Visualization, Clustering, Annotation, WorkflowStep, QualityControl, GeneExpression Author: Mariano Ruz Jurado [aut, cre] (ORCID: ), David Rodriguez Morales [aut] (ORCID: ), David John [aut] (ORCID: ), DFG SFB 1366, Project B04 [fnd], DFG SFB 1531, Project 456687919 [fnd] Maintainer: Mariano Ruz Jurado URL: https://marianoruzjurado.github.io/DOtools/ VignetteBuilder: knitr BugReports: https://github.com/MarianoRuzJurado/DOtools/issues Package: SmartPhos Version: 1.1.0 Depends: R (>= 4.4.0) Imports: MultiAssayExperiment, SummarizedExperiment, data.table, shiny, shinythemes, shinyjs, shinyBS, shinyWidgets, parallel, DT, tools, stats, ggplot2, plotly, ggbeeswarm, pheatmap, grid, XML, MsCoreUtils, imputeLCMD, missForest, limma, proDA, decoupleR, piano, BiocParallel, doParallel, doRNG, e1071, magrittr, matrixStats, rlang, stringr, tibble, dplyr, tidyr, Biobase, vsn, factoextra, cowplot Suggests: knitr, BiocStyle, PhosR, testthat License: GPL-3 Title: A phosphoproteomics data analysis package with an interactive ShinyApp Description: To facilitate and streamline phosphoproteomics data analysis, we developed SmartPhos, an R package for the pre-processing, quality control, and exploratory analysis of phosphoproteomics data generated by MaxQuant and Spectronaut. The package can be used either through the R command line or through an interactive ShinyApp called SmartPhos Explorer. The package contains methods such as normalization and normalization correction, transformation, imputation, batch effect correction, PCA, heatmap, differential expression, time-series clustering, gene set enrichment analysis, and kinase activity inference. biocViews: Visualization, ShinyApps, GUI, QualityControl, Proteomics, DifferentialExpression, Normalization, Preprocessing, GeneSetEnrichment, Clustering, GeneExpression, MassSpectrometry, BatchEffect Author: Shubham Agrawal [aut, cre] (ORCID: ), Junyan Lu [aut] (ORCID: ) Maintainer: Shubham Agrawal URL: https://lu-group-ukhd.github.io/SmartPhos/ VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SmartPhos/issues Package: StatescopeR Version: 0.99.36 Depends: R (>= 4.6.0) Imports: S4Vectors, SummarizedExperiment, reticulate, methods, SingleCellExperiment, matrixStats, scran, basilisk, Matrix, ComplexHeatmap, ggplot2, cowplot, utils Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo, scRNAseq, scuttle, testthat License: MIT + file LICENSE Title: StatescopeR framework for discovery of cell states from cell type-specific gene expression profiles inferred from bulk mRNA profiles Description: StatescopeR is an R wrapper around Statescope, a computational framework designed to discover cell states from cell type-specific gene expression profiles inferred from bulk RNA profiles. biocViews: GeneExpression, RNASeq, SingleCell, Bayesian, Transcriptomics, Software Author: Mischa Steketee [aut, cre] (ORCID: ), Bauke Ylstra [ths] (ORCID: ), Yongsoo Kim [ths] (ORCID: ), KWF Kankerbestrijding [fnd] Maintainer: Mischa Steketee URL: https://github.com/tgac-vumc/StatescopeR VignetteBuilder: knitr BugReports: https://github.com/tgac-vumc/StatescopeR/issues Package: Aerith Version: 0.99.11 Depends: R (>= 4.4.0) Imports: mzR, MSnbase, methods, Rcpp, ggplot2, ggrepel, ggnewscale, dplyr, tidyr, stringr, data.table, scales LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, License: GPL-3 Title: visualization and annotation of isotopic enrichment patterns of peptides and metabolites with stable isotope labeling from proteomics and metabolomics Description: Visualisation of peptide isotopic peaks and SIP peptide spectra match (PSM). Filtration of high quality PSM. Accurate isotopic abundance calculation of peptide and metabolites. Visualisation of SIP proteomics results. biocViews: Proteomics, Metabolomics, MassSpectrometry, Software, Visualization, QualityControl, Annotation Author: Yi Xiong [aut, cre] (ORCID: ) Maintainer: Yi Xiong URL: https://github.com/xyz1396/Aerith SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://github.com/xyz1396/Aerith/issues Package: DOTSeq Version: 0.99.8 Imports: ashr, boot, data.table, emmeans, glmmTMB, Matrix, methods, Rcpp, stats, utils, graphics, grDevices, pbapply, AnnotationDbi, BiocGenerics, BiocParallel, Biostrings, BSgenome, txdbmaker, DESeq2, GenomicAlignments, GenomicFeatures, GenomeInfoDb, GenomeInfoDbData, GenomicRanges, IRanges, rtracklayer, Rsamtools, S4Vectors, SummarizedExperiment LinkingTo: Rcpp Suggests: BSgenome.Hsapiens.UCSC.hg38, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, org.Hs.eg.db, curl, pasillaBamSubset, BiocStyle, biomaRt, DHARMa, eulerr, ggplot2, ggsignif, knitr, rmarkdown, testthat, withr, magick License: MIT + file LICENSE Title: Genome-wide Detection of Differential ORF Usage Description: Differential open reading frame (ORF) translation analysis framework for ribosome profiling (Ribo-seq) with matched RNA-seq. Implements (i) Differential ORF Usage (DOU), a beta-binomial generalized linear model that models the expected proportion of Ribo-seq versus RNA-seq reads mapping to each ORF within a gene, and (ii) ORF-level Differential Translation Efficiency (DTE), a negative binomial GLM that capture changes in translation efficiency of individual ORFs across experimental conditions. Supports ORF-level read summarization for bulk and single-cell Ribo-seq. biocViews: RiboSeq, SingleCell, GeneRegulation, GeneExpression, DifferentialExpression, Genetics, Sequencing, Software, RNASeq, Bayesian, Regression, MultipleComparison Author: Chun Shen Lim [aut, cre] (ORCID: ), Gabrielle Chieng [aut, ctb] (ORCID: ), Marsden [fnd] Maintainer: Chun Shen Lim URL: https://github.com/compgenom/DOTSeq VignetteBuilder: knitr BugReports: https://github.com/compgenom/DOTSeq/issues Package: epiSeeker Version: 0.99.15 Depends: R (>= 4.6.0) Imports: AnnotationDbi, aplot, bsseq, BiocGenerics, Biostrings, boot, dplyr, enrichplot, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, graphics, grDevices, magrittr, methods, plotrix, parallel, RColorBrewer, rlang, RSQLite, rtracklayer, S4Vectors, scales, stats, SummarizedExperiment, tibble, tidyselect, tidyr, utils, yulab.utils (>= 0.2.0), grid Suggests: ape, BSgenome, BSgenome.Hsapiens.UCSC.hg38, clusterProfiler, data.table, GEOmetadb, GEOquery, gggenes, ggimage, ggiraph, ggplotify, ggtree, gginnards, gridBase, gtools, ggupset, ggVennDiagram, JASPAR2024, knitr, org.Hs.eg.db, prettydoc, ReactomePA, rmarkdown, testthat, TFBSTools, TxDb.Hsapiens.UCSC.hg38.knownGene, universalmotif License: Artistic-2.0 Title: epiSeeker: an R package for Annotation, Comparison and Visualization of multi-omics epigenetic data Description: This package implements functions to analyze multi-omics epigenetic data. Data of fragment type and base type are supported by epiSeeker. It provides functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statistical methods to estimate the significance of overlap among peak data sets, and motif analysis. It incorporates the GEO database for users to compare their own dataset with those deposited in the database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, overlap of peaks or genes, and the single-base resolution epigenetic data by considering the strand, motif, and additional information. biocViews: Annotation, ChIPSeq, Software, Visualization, MultipleComparison, Coverage, MotifAnnotation, GeneRegulation Author: Guangchuang Yu [aut, cre, fnd] (ORCID: ), Ming Li [ctb], Qianwen Wang [ctb], Yun Yan [ctb], Hervé Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb], Zhougeng Xu [ctb], Chun-Hui Gao [ctb] Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/epiSeeker VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/epiSeeker/issues Package: plaid Version: 0.99.19 Depends: R (>= 4.3.3) Imports: Matrix, MatrixGenerics, matrixStats, methods, parallel, Rfast, qlcMatrix, GSVA, fgsea, SummarizedExperiment, BiocSet, stats, utils Suggests: BiocStyle, knitr, rmarkdown, sparseMatrixStats, testthat (>= 3.0.0) License: GPL-3 Title: PLAID ultrafast gene set enrichment scoring Description: PLAID (Pathway Level Average Intensity Detection) is an ultra-fast method to compute single-sample enrichment scores for gene expression or proteomics data. For each sample, plaid computes the gene set score as the average intensity of the genes/proteins in the gene set. The output is a gene set score matrix suitable for further analyses. biocViews: GeneSetEnrichment, GeneExpression, Proteomics Author: Ivo Kwee [aut] (ORCID: ), Antonino Zito [cre] (ORCID: ) Maintainer: Antonino Zito URL: https://github.com/bigomics/plaid, https://bigomics.github.io/plaid/ VignetteBuilder: knitr BugReports: https://github.com/bigomics/plaid/issues Package: singIST Version: 0.99.85 Depends: R (>= 4.6.0) Imports: msigdb, GSEABase, checkmate, stats, asmbPLS, BiocParallel, stringr, FactoMineR, Seurat, SeuratObject, biomaRt, data.table, purrr, SingleCellExperiment, SummarizedExperiment, scran, scuttle, missMDA, S4Vectors Suggests: testthat (>= 3.0.0), BiocStyle, knitr, qpdf, utils, RcppAlgos, glmGamPoi, methods, sp License: MIT + file LICENSE Title: comparative single-cell transcriptomics between disease models and a human condition Description: Provides with toolkits to implement a full singIST analysis with pseudobulked Seurat objects of disease models and human data. biocViews: SingleCell, Classification, Transcriptomics Author: Aitor Moruno-Cuenca [aut, cre] (ORCID: ), Dr. Sergio Picart-Armada [rev] (ORCID: ), Dr. Alexandre Perera-Lluna [ths] (ORCID: ), Dr. Francesc Fernández-Albert [ths] (ORCID: ) Maintainer: Aitor Moruno-Cuenca URL: https://github.com/DataScienceRD-Almirall/singIST VignetteBuilder: knitr BugReports: https://github.com/DataScienceRD-Almirall/singIST/issues Package: annoLinker Version: 0.99.7 Depends: R (>= 4.5.0) Imports: AnnotationDbi, BiocGenerics, future.apply, GenomicRanges, GenomeInfoDb, igraph, IRanges, InteractionSet, methods, progressr, S4Vectors, Seqinfo, trackViewer, visNetwork Suggests: BiocStyle, knitr, rtracklayer, rmarkdown, testthat, TxDb.Drerio.UCSC.danRer10.refGene, org.Dr.eg.db, future License: GPL-3 Title: Annotating genomic regions through chromatin interaction links Description: Fast annotation of genomic peaks using DNA interaction data by constructing interaction networks with igraph, where peaks overlapping any node in a connected subgraph are annotated with all genes in that subgraph. The annotation evidence could be visualized as either a network graph or a genomic track integrated with gene annotation information. biocViews: Network, Annotation, Visualization Author: Jianhong Ou [aut, cre] (ORCID: ), Kenneth Poss [aut, fnd] Maintainer: Jianhong Ou URL: https://github.com/jianhong/annoLinker VignetteBuilder: knitr BugReports: https://github.com/jianhong/annoLinker/issues Package: carnation Version: 0.99.10 Depends: R (>= 4.6.0) Imports: BiocParallel, colorspace, ComplexUpset, dendextend, DESeq2, dplyr, DT, enrichplot, GeneTonic, ggplot2, ggrepel, heatmaply, htmltools, igraph, methods, MatrixGenerics, plotly, reticulate, RColorBrewer, rintrojs, scales, shiny, shinyBS, shinycssloaders, shinymanager, shinythemes, shinyWidgets, sortable, SummarizedExperiment, tools, utils, viridisLite, visNetwork, yaml Suggests: airway, BiocStyle, DEGreport, GenomicFeatures, goseq, knitr, org.Hs.eg.db, rmarkdown, testthat License: MIT + file LICENSE Title: Interactive Exploration & Management of RNA-Seq Analyses Description: Highly interactive & modular shiny app to explore three facets of RNA-Seq analysis: differential expression (DE), functional enrichment and pattern analysis. Several visualizations are implemented to provide a wide-ranging view of data sets. For DE analysis, we provide PCA plot, MA plot, Upset plot & heatmaps, in addition to a highly customizable gene plot. Seven different visualizations are available for functional enrichment analysis, and we also support gene pattern analysis. Genes of interest can be tracked across all modules using the gene scratchpad. In addition, carnation provides an integrated platform to manage multiple projects and user access that can be run on a central server to share with collaborators. biocViews: GUI, GeneExpression, Software, ShinyApps, GO, Transcription, Transcriptomics, Visualization, DifferentialExpression, Pathways, GeneSetEnrichment Author: Apratim Mitra [aut, cre] (ORCID: ), Matthew Tyler Menold [ctb] (ORCID: ), Ryan Dale [fnd] (ORCID: ) Maintainer: Apratim Mitra URL: https://nichd-bspc.github.io/carnation/ VignetteBuilder: knitr BugReports: https://github.com/NICHD-BSPC/carnation/issues Package: lcmsPlot Version: 0.99.21 Depends: R (>= 4.4.0) Imports: methods, rlang, dplyr, tibble, tidyr, BiocParallel, MSnbase, xcms, MsExperiment, mzR, Spectra, MsBackendMsp, S4Vectors, ggplot2, scales, patchwork, DBI, RSQLite Suggests: knitr, rmarkdown, BiocStyle, openxlsx, faahKO, rawrr, testthat (>= 3.0.0) License: GPL-3 Title: Comprehensive Liquid Chromatography-Mass Spectrometry (LC-MS) data visualisation package Description: lcmsPlot is an R package designed for visualising Liquid Chromatography-Mass Spectrometry (LC-MS) data with publication-ready high-quality plots. The package enables users to generate and customise chromatograms, mass traces, spectra, and more with fine-tuned aesthetics and annotation options. biocViews: Metabolomics, MassSpectrometry Author: Ossama Edbali [aut, cre] (ORCID: ), Ralf Johannes Maria Weber [aut] (ORCID: ) Maintainer: Ossama Edbali URL: https://github.com/computational-metabolomics/lcmsPlot VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/lcmsPlot/issues Package: OAtools Version: 0.99.15 Depends: R (>= 4.6) Imports: basilisk (>= 1.20.0), Biobase (>= 2.70.0), dplyr (>= 1.1.4), DT (>= 0.34.0), ggplot2 (>= 3.5.2), janitor (>= 2.2.1), methods (>= 4.5.2), plotly (>= 4.11.0), purrr (>= 1.2.0), ReadqPCR (>= 1.56.0), readxl (>= 1.4.5), reticulate (>= 1.43.0), rlang (>= 1.1.6), rmarkdown (>= 2.29), S4Vectors (>= 0.48.0), shiny (>= 1.13.0), SummarizedExperiment (>= 1.40.0), tibble (>= 3.3.0), tidyr (>= 1.3.1), writexl (>= 1.5.4) Suggests: testthat (>= 3.0.0), knitr, kableExtra (>= 1.4.0), NormqPCR (>= 1.56.0), BiocManager (>= 1.30.27) License: GPL (>= 3) Title: Analysis of OpenArray PCR Data Description: Provides a suite of R functions to analyze gene expression experiments on the OpenArray real-time PCR platform. OAtools fits logistic regressions to fluorescence curves to distinguish between real amplification and false positives. OAtools supports data import, analysis, and visualization through plots and a dynamic HTML report. biocViews: qPCR, GeneExpression, DataImport, Regression Author: Aidan Shea [aut, cre] (ORCID: ) Maintainer: Aidan Shea URL: https://github.com/uwvirology-ngs/OAtools VignetteBuilder: knitr BugReports: https://github.com/uwvirology-ngs/OAtools/issues Package: RBedMethyl Version: 0.99.1 Imports: methods, HDF5Array, rhdf5, DelayedArray, DelayedMatrixStats, SummarizedExperiment, bsseq, GenomicRanges, S4Vectors, IRanges, data.table Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 2) Title: Disk-backed Representation of ONT bedMethyl Files Description: Bioconductor-native infrastructure for handling large nanoporetech modkit bedMethyl pileup files from ONT data using HDF5Array and DelayedArray. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, Infrastructure, DataImport, Software Author: Vasileios Lemonidis [aut, cre, cph] (ORCID: ), Center for Oncological Research, University of Antwerp [cph, fnd], Stichting Tegen Kanker [fnd] Maintainer: Vasileios Lemonidis URL: https://github.com/CMG-UA/RBedMethyl VignetteBuilder: knitr BugReports: https://github.com/CMG-UA/RBedMethyl/issues Package: netZooR Version: 1.15.0 Depends: R (>= 4.2.0), igraph, reticulate, pandaR, Biobase, Imports: cmdstanr, AnnotationDbi, assertthat, biomaRt, cmdstanr, corpcor, data.table, doParallel, downloader, dplyr, edgeR, foreach, GeneNet, ggdendro, ggplot2, GO.db, GOstats, gplots, graphics, grid, limma, loo, MASS, Matrix, matrixcalc, matrixStats, matrixTests, methods, nnet, org.Hs.eg.db, parallel, penalized, preprocessCore, quantro, rARPACK, RColorBrewer, RCy3, readr, reshape, reshape2, stats, STRINGdb, tidyr, utils, vegan, viridisLite, Suggests: dorothea, knitr, pkgdown, rmarkdown, testthat (>= 2.1.0), License: GPL-3 Title: A menagerie of methods for the inference and analysis of gene regulatory networks Description: netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information. biocViews: GeneExpression, GeneRegulation, GraphAndNetwork, Microarray, Network, NetworkInference, Transcription Author: Tara Eicher [aut] (ORCID: ), Marouen Ben Guebila [aut, cre] (ORCID: ), Tian Wang [aut] (ORCID: ), John Platig [aut], Marieke Kuijjer [aut] (ORCID: ), Megha Padi [aut] (ORCID: ), Rebekka Burkholz [aut], Des Weighill [aut] (ORCID: ), Chen Chen [aut] (ORCID: ), Kate Shutta [aut] (ORCID: ) Maintainer: Marouen Ben Guebila URL: https://github.com/netZoo/netZooR, https://netzoo.github.io/ VignetteBuilder: knitr BugReports: https://github.com/netZoo/netZooR/issues PackageStatus: Deprecated Package: fourSynergy Version: 0.99.12 Depends: R (>= 4.5.0), GenomicRanges Imports: magrittr, dplyr, ggplot2, tibble, org.Hs.eg.db, org.Mm.eg.db, reshape2, tidyr, methods, jsonlite, karyoploteR, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, bamsignals, DESeq2, GenomeInfoDb, graphics, stringr, yaml Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: LGPL-3 Title: Ensemble algorithm for 4C-seq data Description: fourSynergy is an ensemble algorithm leveraging synergies among the existing 4C-seq algorithms r3C-seq, peakC, r.4cker and fourSig. It uses a weighted voting approach to perform improved interaction calling. fourSynergy supports also differential interaction calling. biocViews: Sequencing, Software, DifferentialPeakCalling Author: Sophie Wind [aut, cre] (ORCID: ), Carolin Walter [aut, fnd] (ORCID: ) Maintainer: Sophie Wind URL: https://github.com/sophiewind/fourSynergy VignetteBuilder: knitr BugReports: https://github.com/sophiewind/fourSynergy/issues Package: MetaProViz Version: 3.99.50 Depends: R (>= 4.4) Imports: broom, ComplexUpset (>= 1.3.3), cosmosR, DBI, dplyr, EnhancedVolcano, factoextra, ggbeeswarm, ggfortify, ggplot2 (>= 3.3.5), ggpubr, ggrepel, grid, gridExtra, gtools, hash, igraph, ggraph, inflection, limma, logger, magrittr, methods, OmnipathR (>= 3.19.12), patchwork, pheatmap, Polychrome, purrr, qcc, qvalue, rappdirs, readr, rlang, rstatix, S4Vectors, stringr, SummarizedExperiment, tibble, tidyr, tidyselect, writexl Suggests: BiocStyle, ggupset, ggVennDiagram, kableExtra, knitr, pkgdown, svglite, testthat (>= 3.1.4) License: BSD_3_clause + file LICENSE Title: METabolomics pre-PRocessing, functiOnal analysis and VIZualisation Description: MetaProViz can analyse standard metabolomics and exometabolomics data (CoRe). It performs pre-processing including feature filtering, missing value imputation, normalisation and outlier detection. It performs functional analysis including differential metabolite analysis (DMA), clustering based on regulatory rules (MCA) and contains different visualisation methods to extract biological interpretable graphs and saves them in a publication ready format. biocViews: Clustering, Metabolomics, Pathways, QualityControl, Software, SystemsBiology, Visualization Author: Christina Schmidt [aut, cre, fnd] (ORCID: ), Denes Turei [aut] (ORCID: ), Dimitrios Prymidis [aut] (ORCID: ), Macabe Daley [aut] (ORCID: ), Jannik Franken [aut], Julio Saez-Rodriguez [aut] (ORCID: ), Christian Frezza [aut] (ORCID: ) Maintainer: Christina Schmidt URL: https://saezlab.github.io/MetaProViz VignetteBuilder: knitr BugReports: https://github.com/saezlab/MetaProViz/issues Package: MetaboAnnotatoR Version: 0.99.21 Depends: R (>= 4.5.0), xcms (>= 3.0.0), MSnbase (>= 2.16.1), ProtGenerics Imports: ggplot2 (>= 3.3.3), gridExtra (>= 2.3) Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle, MsDataHub License: GPL-3 Title: Automated Annotation of All-Ion Fragmentation LC-MS Metabolomic Features Description: Performs feature annotations on LC-MS All-ion fragmentation datasets using fragment ion libraries. biocViews: MassSpectrometry, Metabolomics Author: Goncalo Graca [aut, cre] (ORCID: ), Yuheng (Rene) Cai [aut], Timothy Ebbels [aut] Maintainer: Goncalo Graca URL: https://github.com/gggraca/MetaboAnnotatoR VignetteBuilder: knitr BugReports: https://github.com/gggraca/MetaboAnnotatoR/issues Package: GSABenchmark Version: 0.99.7 Imports: abdiv, CSOA, decoupleR, dplyr, escape, fabR, ggplot2, ggrepel, GSVA, hammers, henna, jaccard, lsa, Matrix, MLmetrics, methods, mltools, pagoda2, paletteer, reshape2, rlang, scLang, singscore, SiPSiC, stringr, stats, VAM, withr Suggests: AUCell, BiocStyle, knitr, qs2, ranger, rmarkdown, rpart, scater, scRNAseq, scuttle, Seurat, testthat (>= 3.0.0), UCell License: MIT + file LICENSE Title: Tools for benchmarking single-cell gene set analysis methods Description: GSABenchmark is a package designed for benchmarking scRNA-seq gene set analysis (scGSA) methods. It provides both traditional and novel benchmark metrics, as well as visualization tools. Currently, GSABenchmark supports 17 scGSA methods. biocViews: Software, SingleCell, GeneSetEnrichment, GeneExpression, Visualization Author: Andrei-Florian Stoica [aut, cre] (ORCID: ) Maintainer: Andrei-Florian Stoica URL: https://github.com/andrei-stoica26/GSABenchmark VignetteBuilder: knitr BugReports: https://github.com/andrei-stoica26/GSABenchmark/issues Package: LRDE Version: 0.99.7 Title: ERROR Maintainer: ERROR Package: imageFeatureTCGA Version: 0.99.69 Depends: R (>= 4.5.0) Imports: BiocBaseUtils, BiocFileCache, BiocIO, BumpyMatrix, dplyr, httr2, IRanges, methods, readr, rjsoncons, S4Vectors, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, TCGAutils, TENxIO, tibble, utils Suggests: AnnotationDbi, anndataR, BiocStyle, BiocParallel, cowplot, curatedTCGAData, curl, ggplot2, imageTCGAutils, knitr, png, RColorBrewer, rhdf5, rmarkdown, SpatialFeatureExperiment, tinytest, yaml License: Artistic-2.0 Title: Import features from hovernet, provgigapath into a MultiAssayExperiment Description: The package imports data from HoverNet, and ProvGigaPath pipelines. Pipeline output data are hosted in a self-owned online repository. Package functionality conveniently incorporates pipeline data into existing MultiAssayExperiment instances from curatedTCGAData. biocViews: Software, Infrastructure, DataImport, DataRepresentation Author: Marcel Ramos [aut] (ORCID: , affiliation: CUNY Graduate School of Public Health and Health Policy, New York, NY USA), Ilaria Billato [aut, cre] (ORCID: , affiliation: Department of Biology, University of Padova), Eslam Abousamra [aut] (affiliation: CUNY Graduate School of Public Health and Health Policy, New York, NY USA), Sehyun Oh [aut] (ORCID: , affiliation: CUNY Graduate School of Public Health and Health Policy, New York, NY USA) Maintainer: Ilaria Billato URL: https://github.com/waldronlab/imageFeatureTCGA VignetteBuilder: knitr BugReports: https://github.com/waldronlab/imageFeatureTCGA/issues Package: HistoImagePlot Version: 0.99.10 Depends: R (>= 4.5.0) Imports: BiocBaseUtils, cowplot, ggplot2, grDevices, imageFeatureTCGA, methods, RColorBrewer, S4Vectors, SpatialExperiment, SummarizedExperiment Suggests: anndataR, BiocStyle, dplyr, knitr, magick, rmarkdown, tinytest License: Artistic-2.0 Title: Plotting functionality for Histopathology pipeline datasets Description: Create side-by-side visualizations of tissue thumbnail image and HoverNet cell segmentation with colored cell type labels. Functionality automatically retrieves the thumbnail image associated with a HoverNet JSON file and overlays the segmentation data. This package is intended for researchers working with histopathological images, facilitating exploratory analysis, and integrates with the imageFeatureTCGA Bioconductor package. biocViews: Software, Visualization, Spatial Author: Ilaria Billato [aut, cre] (ORCID: , affiliation: Department of Biology, University of Padova) Maintainer: Ilaria Billato URL: https://github.com/waldronlab/HistoImagePlot VignetteBuilder: knitr BugReports: https://github.com/waldronlab/HistoImagePlot/issues