  RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of
transcriptomes in many areas of biology, including studies into gene
regulation, development and disease. Of particular interest is the discovery of
differentially expressed genes across different conditions (e.g., tissues,
perturbations), while optionally adjusting for other systematic factors that
affect the data collection process. There are a number of subtle yet critical
aspects of these analyses, such as read counting, appropriate treatment of
biological variability, quality control checks and appropriate setup of
statistical modeling. Several variations have been presented in the literature,
and there is a need for guidance on current best practices. This protocol
presents a "state-of-the-art" computational and statistical RNA-seq
differential expression analysis workflow largely based on the free open-source
R language and Bioconductor software and in particular, two widely-used tools
DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10
samples) can be <1 hour, with computation time <1 day using a standard desktop
PC.
