Abstract：
: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 crucial 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, on two widely used tools, DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 h, with computation time <1 d using a standard desktop PC.
journal_name
Nat Protocjournal_title
Nature protocolsauthors
Anders S,McCarthy DJ,Chen Y,Okoniewski M,Smyth GK,Huber W,Robinson MDdoi
10.1038/nprot.2013.099subject
Has Abstractpub_date
2013-09-01 00:00:00pages
1765-86issue
9eissn
1754-2189issn
1750-2799pii
nprot.2013.099journal_volume
8pub_type
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