In comparative high-throughput sequencing assays, a fundamental task is the 
analysis of count data, such as read counts per gene in RNA-seq, for evidence of 
systematic changes across experimental conditions. Small replicate numbers, 
discreteness, large dynamic range and the presence of outliers require a 
suitable statistical approach. We present DESeq2, a method for differential 
analysis of count data, using shrinkage estimation for dispersions and fold 
changes to improve stability and interpretability of estimates. This enables a 
more quantitative analysis focused on the strength rather than the mere presence 
of differential expression. The DESeq2 package is available at 
http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html webcite.