crisprScoreData can be installed from the Bioconductor devel
branch using the following commands in a fresh R session:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version="devel")
BiocManager::install("crisprScoreData")We first load the crisprScoreData package:
library(crisprScoreData)## Loading required package: ExperimentHub## Loading required package: BiocGenerics## Loading required package: generics##
## Attaching package: 'generics'## The following objects are masked from 'package:base':
##
## as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
## setequal, union##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
## mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
## unsplit, which.max, which.min## Loading required package: AnnotationHub## Loading required package: BiocFileCache## Loading required package: dbplyr## Registered S3 method overwritten by 'bit64':
## method from
## print.bitstring toolsThis package contains several pre-trained models for different on-target activity prediction algorithms to be used in the package crisprScore.
We can access the file paths of the different pre-trained models directly with named functions:
# For DeepHF model:
DeepWt.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## EH6123
## "/home/biocbuild/.cache/R/ExperimentHub/77b38aa3cf8_6166"DeepWt_T7.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## EH6124
## "/home/biocbuild/.cache/R/ExperimentHub/77b3839a53ad3_6167"DeepWt_U6.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## EH6125
## "/home/biocbuild/.cache/R/ExperimentHub/77b388602745_6168"esp_rnn_model.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## EH6126
## "/home/biocbuild/.cache/R/ExperimentHub/77b381a850a77_6169"hf_rnn_model.hdf5()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## EH6127
## "/home/biocbuild/.cache/R/ExperimentHub/77b381ac3c433_6170"# For Lindel model:
Model_weights.pkl()## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## EH6128
## "/home/biocbuild/.cache/R/ExperimentHub/77b385c2c6031_6171"Or we can access them using the ExperimentHub interface:
eh <- ExperimentHub()
query(eh, "crisprScoreData")## ExperimentHub with 9 records
## # snapshotDate(): 2026-04-13
## # $dataprovider: Fudan University, UCSF, University of Washington, New York ...
## # $species: NA
## # $rdataclass: character
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH6123"]]'
##
## title
## EH6123 | DeepWt.hdf5
## EH6124 | DeepWt_T7.hdf5
## EH6125 | DeepWt_U6.hdf5
## EH6126 | esp_rnn_model.hdf5
## EH6127 | hf_rnn_model.hdf5
## EH6128 | Model_weights.pkl
## EH7304 | CRISPRa_model.pkl
## EH7305 | CRISPRi_model.pkl
## EH7356 | RFcombined.rdseh[["EH6127"]]## see ?crisprScoreData and browseVignettes('crisprScoreData') for documentation## loading from cache## EH6127
## "/home/biocbuild/.cache/R/ExperimentHub/77b381ac3c433_6170"For details on the source of these files, and on their construction
see ?crisprScoreData and the scripts:
inst/scripts/make-metadata.Rinst/scripts/make-data.RmdsessionInfo()## R version 4.6.0 RC (2026-04-17 r89917)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] crisprScoreData_1.15.0 ExperimentHub_3.1.0 AnnotationHub_4.1.0
## [4] BiocFileCache_3.1.0 dbplyr_2.5.2 BiocGenerics_0.57.1
## [7] generics_0.1.4 BiocStyle_2.39.0
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.4 sass_0.4.10 BiocVersion_3.23.1
## [4] RSQLite_2.4.6 digest_0.6.39 magrittr_2.0.5
## [7] evaluate_1.0.5 bookdown_0.46 fastmap_1.2.0
## [10] blob_1.3.0 jsonlite_2.0.0 AnnotationDbi_1.73.1
## [13] DBI_1.3.0 BiocManager_1.30.27 httr_1.4.8
## [16] purrr_1.2.2 Biostrings_2.79.5 httr2_1.2.2
## [19] jquerylib_0.1.4 cli_3.6.6 crayon_1.5.3
## [22] rlang_1.2.0 XVector_0.51.0 Biobase_2.71.0
## [25] bit64_4.6.0-1 withr_3.0.2 cachem_1.1.0
## [28] yaml_2.3.12 otel_0.2.0 tools_4.6.0
## [31] memoise_2.0.1 dplyr_1.2.1 filelock_1.0.3
## [34] curl_7.0.0 vctrs_0.7.3 R6_2.6.1
## [37] png_0.1-9 stats4_4.6.0 lifecycle_1.0.5
## [40] Seqinfo_1.1.0 KEGGREST_1.51.1 S4Vectors_0.49.2
## [43] IRanges_2.45.0 bit_4.6.0 pkgconfig_2.0.3
## [46] pillar_1.11.1 bslib_0.10.0 glue_1.8.1
## [49] xfun_0.57 tibble_3.3.1 tidyselect_1.2.1
## [52] knitr_1.51 htmltools_0.5.9 rmarkdown_2.31
## [55] compiler_4.6.0