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RNA sequencing (RNA-seq) has been rapidly adopted for the profiling oftranscriptomes in many areas of biology, including studies into generegulation, development and disease. Of particular interest is the discovery ofdifferentially expressed genes across different conditions (e.g., tissues,perturbations), while optionally adjusting for other systematic factors thataffect the data collection process. There are a number of subtle yet criticalaspects of these analyses, such as read counting, appropriate treatment ofbiological variability, quality control checks and appropriate setup ofstatistical modeling. Several variations have been presented in the literature,and there is a need for guidance on current best practices. This protocolpresents a "state-of-the-art" computational and statistical RNA-seqdifferential expression analysis workflow largely based on the free open-sourceR language and Bioconductor software and in particular, two widely-used toolsDESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10samples) can be <1 hour, with computation time <1 day using a standard desktopPC.

Count-based differential expression analysis of RNA sequencing data using R and Bioconductor