Software for computing and annotating genomic ranges.
Michael F. Lawrence,Wolfgang Huber,Hervé Pagès,Patrick Aboyoun,Marc R. J. Carlson,Robert Gentleman,Martin Morgan,Vincent J. Carey +7 more
TLDR
This work describes Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions, including those for sequence analysis, differential expression analysis and visualization.Abstract:
We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable data structures for representing annotated ranges on the genome, with special support for transcript structures, read alignments and coverage vectors. Computational facilities include efficient algorithms for overlap and nearest neighbor detection, coverage calculation and other range operations. This infrastructure directly supports more than 80 other Bioconductor packages, including those for sequence analysis, differential expression analysis and visualization.read more
Citations
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Journal ArticleDOI
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Posted ContentDOI
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Journal ArticleDOI
HTSeq—a Python framework to work with high-throughput sequencing data
TL;DR: This work presents HTSeq, a Python library to facilitate the rapid development of custom scripts for high-throughput sequencing data analysis, and presents htseq-count, a tool developed with HTSequ that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes.
Journal ArticleDOI
Genome Regulation by Long Noncoding RNAs
John L. Rinn,Howard Y. Chang +1 more
TL;DR: Long noncoding RNAs (lncRNAs) as discussed by the authors form extensive networks of ribonucleoprotein (RNP) complexes with numerous chromatin regulators and then target these enzymatic activities to appropriate locations in the genome.
Journal ArticleDOI
Orchestrating high-throughput genomic analysis with Bioconductor
Wolfgang Huber,Vincent J. Carey,Robert Gentleman,Simon Anders,Marc R. J. Carlson,Benilton S. Carvalho,Héctor Corrada Bravo,Sean Davis,Laurent Gatto,Thomas Girke,Raphael Gottardo,Florian Hahne,Kasper D. Hansen,Rafael A. Irizarry,Michael S. Lawrence,Michael I. Love,James W. MacDonald,Valerie Obenchain,Andrzej K. Oleś,Hervé Pagès,Alejandro Reyes,Paul Shannon,Gordon K. Smyth,Dan Tenenbaum,Levi Waldron,Martin Morgan +25 more
TL;DR: An overview of Bioconductor, an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology, which comprises 934 interoperable packages contributed by a large, diverse community of scientists.
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