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@article{Trapnell:2009p156,
author = {C Trapnell and L Pachter and S. L Salzberg}, 
journal = {Bioinformatics},
title = {TopHat: discovering splice junctions with RNA-Seq},
abstract = {TeX output 2009.04.21:1940},
number = {9},
pages = {1105--1111},
volume = {25},
year = {2009},
month = {May},
date-added = {2010-02-15 10:09:23 +0100},
date-modified = {2010-02-15 10:09:24 +0100},
doi = {10.1093/bioinformatics/btp120},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2009/Bioinformatics/Trapnell_Bioinformatics_2009.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p156},
rating = {0}
}

@article{Mortazavi:2008p740,
author = {Ali Mortazavi and Brian A Williams and Kenneth Mccue and Lorian Schaeffer and Barbara Wold}, 
journal = {Nature Methods},
title = {Mapping and quantifying mammalian transcriptomes by RNA-Seq},
abstract = {We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41-52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3' untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 x 10(5) distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.},
affiliation = {Division of Biology, MC 156-29, California Institute of Technology, Pasadena, California 91125, USA.},
number = {7},
pages = {621--8},
volume = {5},
year = {2008},
month = {Jul},
language = {eng},
keywords = {Promoter Regions: Genetic, Brain, Oligonucleotide Array Sequence Analysis, Mice: Inbred C57BL, Muscle: Skeletal, Sequence Analysis: RNA, Algorithms, Databases: Nucleic Acid, Liver, Chromosome Mapping, RNA, Exons, Sensitivity and Specificity, RNA: Messenger, 3' Untranslated Regions, Mice, Animals, Gene Expression Profiling, Alternative Splicing, RNA Splicing, Software}, 
date-added = {2010-06-07 10:04:28 +0200},
date-modified = {2010-06-07 10:04:41 +0200},
doi = {10.1038/nmeth.1226},
pii = {nmeth.1226},
pmid = {18516045},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2008/Nature%20Methods/Mortazavi_Nature%20Methods_2008.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p740},
rating = {0}
}

@article{Anders:2010p741,
author = {Simon Anders and Wolfgang Huber}, 
title = {Differential expression analysis for sequence count data},
affiliation = {European Molecular Biology Laboratory (EMBL), Heidelberg, Germany},
date-added = {2010-06-07 10:07:48 +0200},
date-modified = {2010-06-07 10:10:00 +0200},
URL = {http://precedings.nature.com/documents/4282/version/1},
local-url = {file://localhost/Users/delhomme/Documents/Papers/Unknown/Unknown/Anders__.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p741},
rating = {0}
}

@article{Hansen:2010p495,
author = {Kasper D Hansen and Steven E Brenner and Sandrine Dudoit}, 
journal = {Nucleic Acids Research},
title = {Biases in Illumina transcriptome sequencing caused by random hexamer priming},
abstract = {Generation of cDNA using random hexamer priming induces biases in the nucleotide composition at the beginning of transcriptome sequencing reads from the Illumina Genome Analyzer. The bias is independent of organism and laboratory and impacts the uniformity of the reads along the transcriptome. We provide a read count reweighting scheme, based on the nucleotide frequencies of the reads, that mitigates the impact of the bias.},
affiliation = {Division of Biostatistics, School of Public Health, UC Berkeley, 101 Haviland Hall, Berkeley, CA 94720-7358, Department of Plant and Microbial Biology, UC Berkeley, 461 Koshland Hall, Berkeley, CA 94720-3102 and Department of Statistics, UC Berkeley, 367 Evans Hall, Berkeley, CA 94720-3860, USA.},
pages = {},
year = {2010},
month = {Apr},
language = {ENG},
date-added = {2010-04-29 20:19:30 +0200},
date-modified = {2010-04-29 20:19:42 +0200},
doi = {10.1093/nar/gkq224},
pii = {gkq224},
pmid = {20395217},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2010/Nucleic%20Acids%20Research/Hansen_Nucleic%20Acids%20Research_2010.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p495},
rating = {0}
}

@article{Morgan:2009p739,
author = {Martin Morgan and Simon Anders and Michael Lawrence and Patrick Aboyoun and Herv{\'e} Pag{\`e}s and Robert Gentleman}, 
journal = {Bioinformatics},
title = {ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data},
abstract = {ShortRead is a package for input, quality assessment, manipulation and output of high-throughput sequencing data. ShortRead is provided in the R and Bioconductor environments, allowing ready access to additional facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources. AVAILABILITY AND IMPLEMENTATION: This package is implemented in R and available at the Bioconductor web site; the package contains a 'vignette' outlining typical work flows.},
affiliation = {Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. mtmorgan@fhcrc.org},
number = {19},
pages = {2607--8},
volume = {25},
year = {2009},
month = {Oct},
language = {eng},
keywords = {Sequence Analysis: DNA, Gene Expression Profiling, Computational Biology, Software, Databases: Genetic}, 
date-added = {2010-06-07 10:01:41 +0200},
date-modified = {2010-06-07 10:01:51 +0200},
doi = {10.1093/bioinformatics/btp450},
pii = {btp450},
pmid = {19654119},
URL = {http://bioinformatics.oxfordjournals.org/cgi/content/full/25/19/2607?view=long&pmid=19654119},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2009/Bioinformatics/Morgan_Bioinformatics_2009.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p739},
rating = {0}
}

@article{Zerbino:2008p749,
author = {Daniel R Zerbino and Ewan Birney}, 
journal = {Genome Research},
title = {Velvet: algorithms for de novo short read assembly using de Bruijn graphs},
abstract = {We have developed a new set of algorithms, collectively called "Velvet," to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25-50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to real Solexa data sets without read pairs, Velvet generated contigs of approximately 8 kb in a prokaryote and 2 kb in a mammalian BAC, in close agreement with our simulated results without read-pair information. Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies.},
affiliation = {EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom.},
number = {5},
pages = {821--9},
volume = {18},
year = {2008},
month = {May},
language = {eng},
keywords = {Genome: Human, Genomics, Mammals, Streptococcus, Humans, Computer Simulation, Genome: Bacterial, Sequence Analysis: DNA, Chromosomes: Artificial: Bacterial, Computational Biology, Algorithms, Animals}, 
date-added = {2010-06-07 10:11:46 +0200},
date-modified = {2010-06-07 10:12:22 +0200},
doi = {10.1101/gr.074492.107},
pii = {gr.074492.107},
pmid = {18349386},
URL = {http://genome.cshlp.org/content/18/5/821.long},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2008/Genome%20Research/Zerbino_Genome%20Research_2008.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p749},
rating = {0}
}

@article{Zerbino:2009p743,
author = {Daniel R Zerbino and Gayle K McEwen and Elliott H Margulies and Ewan Birney}, 
journal = {PLoS ONE},
title = {Pebble and rock band: heuristic resolution of repeats and scaffolding in the velvet short-read de novo assembler},
abstract = {BACKGROUND: Despite the short length of their reads, micro-read sequencing technologies have shown their usefulness for de novo sequencing. However, especially in eukaryotic genomes, complex repeat patterns are an obstacle to large assemblies. PRINCIPAL FINDINGS: We present a novel heuristic algorithm, Pebble, which uses paired-end read information to resolve repeats and scaffold contigs to produce large-scale assemblies. In simulations, we can achieve weighted median scaffold lengths (N50) of above 1 Mbp in Bacteria and above 100 kbp in more complex organisms. Using real datasets we obtained a 96 kbp N50 in Pseudomonas syringae and a unique 147 kbp scaffold of a ferret BAC clone. We also present an efficient algorithm called Rock Band for the resolution of repeats in the case of mixed length assemblies, where different sequencing platforms are combined to obtain a cost-effective assembly. CONCLUSIONS: These algorithms extend the utility of short read only assemblies into large complex genomes. They have been implemented and made available within the open-source Velvet short-read de novo assembler.},
affiliation = {European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. zerbino@ebi.ac.uk},
number = {12},
pages = {e8407},
volume = {4},
year = {2009},
month = {Jan},
language = {eng},
keywords = {Sequence Analysis: DNA, Pseudomonas syringae, Computer Simulation, Chromosomes: Artificial: Bacterial, Animals, Ferrets, Algorithms, Repetitive Sequences: Nucleic Acid}, 
date-added = {2010-06-07 10:11:49 +0200},
date-modified = {2010-06-07 10:12:15 +0200},
doi = {10.1371/journal.pone.0008407},
pmid = {20027311},
URL = {http://www.plosone.org/article/info%253Adoi%252F10.1371%252Fjournal.pone.0008407},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2009/PLoS%20ONE/Zerbino_PLoS%20ONE_2009.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p743},
rating = {0}
}

@article{Kim:2010p572,
author = {Tae-Kyung Kim and Martin Hemberg and Jesse M Gray and Allen M Costa and Daniel M Bear and Jing Wu and David A Harmin and Mike Laptewicz and Kellie Barbara-Haley and Scott Kuersten and Eirene Markenscoff-Papadimitriou and Dietmar Kuhl and Haruhiko Bito and Paul F Worley and Gabriel Kreiman and Michael E Greenberg}, 
journal = {Nature},
title = {Widespread transcription at neuronal activity-regulated enhancers},
abstract = {We used genome-wide sequencing methods to study stimulus-dependent enhancer function in mouse cortical neurons. We identified approximately 12,000 neuronal activity-regulated enhancers that are bound by the general transcriptional co-activator CBP in an activity-dependent manner. A function of CBP at enhancers may be to recruit RNA polymerase II (RNAPII), as we also observed activity-regulated RNAPII binding to thousands of enhancers. Notably, RNAPII at enhancers transcribes bi-directionally a novel class of enhancer RNAs (eRNAs) within enhancer domains defined by the presence of histone H3 monomethylated at lysine 4. The level of eRNA expression at neuronal enhancers positively correlates with the level of messenger RNA synthesis at nearby genes, suggesting that eRNA synthesis occurs specifically at enhancers that are actively engaged in promoting mRNA synthesis. These findings reveal that a widespread mechanism of enhancer activation involves RNAPII binding and eRNA synthesis.},
affiliation = {Department of Neurobiology, Harvard Medical School, 220 Longwood Avenue, Boston, Massachusetts 02115, USA.},
number = {7295},
pages = {182--7},
volume = {465},
year = {2010},
month = {May},
language = {eng},
date-added = {2010-05-28 13:54:18 +0200},
date-modified = {2010-05-28 13:55:12 +0200},
doi = {10.1038/nature09033},
pii = {nature09033},
pmid = {20393465},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2010/Nature/Kim_Nature_2010.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p572},
rating = {0}
}

@article{Ameur:2010p280,
author = {Adam Ameur and Anna Wetterbom and Lars Feuk and Ulf Gyllensten}, 
journal = {Genome Biology 2010 11:202},
title = {Global and unbiased detection of splice junctions from RNA-seq data},
abstract = {ABSTRACT: We have developed a new strategy for de novo prediction of splice junctions in short read RNA-seq data, suitable for detection of novel splicing events and chimeric transcripts. When tested on mouse RNA-seq data, over 31,000 splice events were predicted, of which 88% bridged between two regions separated by at most 100 kb, and 74% connected two exons of the same RefSeq gene. Our method also reports genomic rearrangements such as insertions and deletions.},
number = {3},
pages = {R34},
volume = {11},
year = {2010},
month = {Mar},
language = {ENG},
date-added = {2010-03-25 09:12:16 +0100},
date-modified = {2010-04-13 09:24:57 +0200},
doi = {10.1186/gb-2010-11-3-r34},
pii = {gb-2010-11-3-r34},
pmid = {20236510},
URL = {http://genomebiology.com/2010/11/3/R34},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2010/Genome%20Biology%202010%2011202/Ameur_Genome%20Biology%202010%2011202_2010.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p280},
read = {Yes},
rating = {0}
}

@article{Li:2010p732,
author = {Jun Li and Hui Jiang and Wing Hung Wong}, 
journal = {Genome Biology 2010 11:202},
title = {Modeling non-uniformity in short-read rates in RNA-Seq data},
abstract = {ABSTRACT: After mapping, RNA-Seq data can be summarized by a sequence of read counts commonly modeled as Poisson variables with constant rates along each transcript, which actually fit data poorly. We suggest using variable rates for different positions, and propose two models to predict these rates based on local sequences. These models explain more than 50% of the variations and can lead to improved estimates of gene and isoform expressions for both Illumina and Applied Biosystems (ABI) data.},
number = {5},
pages = {R50},
volume = {11},
year = {2010},
month = {May},
language = {ENG},
date-added = {2010-06-02 14:19:35 +0200},
date-modified = {2010-06-02 14:21:04 +0200},
doi = {10.1186/gb-2010-11-5-r50},
pii = {gb-2010-11-5-r50},
pmid = {20459815},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2010/Genome%20Biology%202010%2011202/Li_Genome%20Biology%202010%2011202_2010.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p732},
rating = {0}
}

@Manual{ref:R,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2009},
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org},
}

@article{Robinson:2010p775,
author = {Mark D Robinson and Davis J McCarthy and Gordon K Smyth}, 
journal = {Bioinformatics},
title = {edgeR: a Bioconductor package for differential expression analysis of digital gene expression data},
abstract = {SUMMARY: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. AVAILABILITY: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).},
affiliation = {Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia. mrobinson@wehi.edu.au},
number = {1},
pages = {139--40},
volume = {26},
year = {2010},
month = {Jan},
language = {eng},
keywords = {Programming Languages, Gene Expression Profiling, Software, Algorithms, Oligonucleotide Array Sequence Analysis, Signal Processing: Computer-Assisted}, 
date-added = {2010-06-07 10:27:27 +0200},
date-modified = {2010-06-07 10:27:56 +0200},
doi = {10.1093/bioinformatics/btp616},
pii = {btp616},
pmid = {19910308},
URL = {http://bioinformatics.oxfordjournals.org/cgi/content/full/26/1/139?view=long&pmid=19910308},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2010/Bioinformatics/Robinson_Bioinformatics_2010.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p775},
rating = {0}
}

@article{Lefrancois:2009p778,
author = {Philippe Lefran{\c c}ois and Ghia M Euskirchen and Raymond K Auerbach and Joel Rozowsky and Theodore Gibson and Christopher M Yellman and Mark Gerstein and Michael Snyder}, 
journal = {BMC Genomics},
title = {Efficient yeast ChIP-Seq using multiplex short-read DNA sequencing},
abstract = {BACKGROUND: Short-read high-throughput DNA sequencing technologies provide new tools to answer biological questions. However, high cost and low throughput limit their widespread use, particularly in organisms with smaller genomes such as S. cerevisiae. Although ChIP-Seq in mammalian cell lines is replacing array-based ChIP-chip as the standard for transcription factor binding studies, ChIP-Seq in yeast is still underutilized compared to ChIP-chip. We developed a multiplex barcoding system that allows simultaneous sequencing and analysis of multiple samples using Illumina's platform. We applied this method to analyze the chromosomal distributions of three yeast DNA binding proteins (Ste12, Cse4 and RNA PolII) and a reference sample (input DNA) in a single experiment and demonstrate its utility for rapid and accurate results at reduced costs. RESULTS: We developed a barcoding ChIP-Seq method for the concurrent analysis of transcription factor binding sites in yeast. Our multiplex strategy generated high quality data that was indistinguishable from data obtained with non-barcoded libraries. None of the barcoded adapters induced differences relative to a non-barcoded adapter when applied to the same DNA sample. We used this method to map the binding sites for Cse4, Ste12 and Pol II throughout the yeast genome and we found 148 binding targets for Cse4, 823 targets for Ste12 and 2508 targets for PolII. Cse4 was strongly bound to all yeast centromeres as expected and the remaining non-centromeric targets correspond to highly expressed genes in rich media. The presence of Cse4 non-centromeric binding sites was not reported previously. CONCLUSION: We designed a multiplex short-read DNA sequencing method to perform efficient ChIP-Seq in yeast and other small genome model organisms. This method produces accurate results with higher throughput and reduced cost. Given constant improvements in high-throughput sequencing technologies, increasing multiplexing will be possible to further decrease costs per sample and to accelerate the completion of large consortium projects such as modENCODE.},
affiliation = {Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520, USA. philippe.lefrancois@yale.edu},
pages = {37},
volume = {10},
year = {2009},
month = {Jan},
language = {eng},
keywords = {Centromere, Genomic Library, Genomics, Saccharomyces cerevisiae, Chromatin Immunoprecipitation, DNA: Fungal, Transcription Factors, Genome: Fungal, Binding Sites, Chromosome Mapping, Sequence Analysis: DNA, Oligonucleotide Array Sequence Analysis}, 
date-added = {2010-06-07 10:33:12 +0200},
date-modified = {2010-06-07 10:33:49 +0200},
doi = {10.1186/1471-2164-10-37},
pii = {1471-2164-10-37},
pmid = {19159457},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2009/BMC%20Genomics/Lefran%C3%A7ois_BMC%20Genomics_2009.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p778},
rating = {0}
}

@article{Smith:2010p777,
author = {Andrew M Smith and Lawrence E Heisler and Robert P St Onge and Eveline Farias-Hesson and Iain M Wallace and John Bodeau and Adam N Harris and Kathleen M Perry and Guri Giaever and Nader Pourmand and Corey Nislow}, 
journal = {Nucleic Acids Research},
title = {Highly-multiplexed barcode sequencing: an efficient method for parallel analysis of pooled samples},
abstract = {Next-generation sequencing has proven an extremely effective technology for molecular counting applications where the number of sequence reads provides a digital readout for RNA-seq, ChIP-seq, Tn-seq and other applications. The extremely large number of sequence reads that can be obtained per run permits the analysis of increasingly complex samples. For lower complexity samples, however, a point of diminishing returns is reached when the number of counts per sequence results in oversampling with no increase in data quality. A solution to making next-generation sequencing as efficient and affordable as possible involves assaying multiple samples in a single run. Here, we report the successful 96-plexing of complex pools of DNA barcoded yeast mutants and show that such 'Bar-seq' assessment of these samples is comparable with data provided by barcode microarrays, the current benchmark for this application. The cost reduction and increased throughput permitted by highly multiplexed sequencing will greatly expand the scope of chemogenomics assays and, equally importantly, the approach is suitable for other sequence counting applications that could benefit from massive parallelization.},
affiliation = {Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Toronto, Ontario M5S 1A8, Banting and Best Department of Medical Research, University of Toronto, 112 College Street, Toronto, Ontario M5G 1L6, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Department of Pharmaceutical Sciences, University of Toronto, 144 College Street, Toronto, Ontario M5S 3M2, Canada, Department of Biochemistry, Stanford University, Stanford, CA 94305, Stanford Genome Technology Center, Stanford University, Palo Alto, CA 94304, Biomolecular Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, Life Technologies Corporation, 850 Lincoln Centre Drive, Foster City, CA 94404 and Life Technologies Corporation, 5791 Van Allen Way, Carlsbad, CA 92009, USA.},
pages = {},
year = {2010},
month = {May},
language = {ENG},
date-added = {2010-06-07 10:29:21 +0200},
date-modified = {2010-06-07 10:29:38 +0200},
doi = {10.1093/nar/gkq368},
pii = {gkq368},
pmid = {20460461},
URL = {http://nar.oxfordjournals.org/cgi/content/full/gkq368v1?view=long&pmid=20460461},
local-url = {file://localhost/Users/delhomme/Documents/Papers/2010/Nucleic%20Acids%20Research/Smith_Nucleic%20Acids%20Research_2010.pdf},
uri = {papers://D894A898-86A1-436D-98B2-BBADFC487BE6/Paper/p777},
rating = {0}
}

