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.
A method based on the negative binomial distribution, with variance and mean linked by local regression, is proposed and an implementation, DESeq, as an R/Bioconductor package is presented.
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.
An error model that uses the negative binomial distribution, with variance and mean linked by local regression, to model the null distribution of the count data is proposed and provides good detection power.
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.
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.
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.
DEXSeq is presented, a statistical method to test for differential exon usage in RNA-seq data that uses generalized linear models and offers reliable control of false discoveries by taking biological variation into account.
It is shown that both SUTs and CUTs display distinct patterns of distribution at specific locations, changing the view of how a genome is transcribed and indicating that bidirectionality is an inherent feature of promoters.