Journal
AMERICAN JOURNAL OF RESPIRATORY CELL AND MOLECULAR BIOLOGY
Volume 59, Issue 2, Pages 145-157Publisher
AMER THORACIC SOC
DOI: 10.1165/rcmb.2017-0430TR
Keywords
RNA sequencing; transcriptomics; bioinformatics; data analysis
Funding
- National Institutes of Health (NIH)/National Institute of Diabetes and Digestive and Kidney Diseases grant [T32DK077662]
- NIH/National Heart, Lung, and Blood Institute (NHLBI) [HL128194, HL071643, HL125940]
- Thoracic Surgery Foundation
- American Society of Transplant Surgeons Foundation
- U.S. Department of Defense [W81XWH-15-1-0214]
- Northwestern Memorial Foundation Dixon Award
- Arthritis National Research Foundation
- American Lung Association
Ask authors/readers for more resources
Since the first publications coining the term RNA-seq (RNA sequencing) appeared in 2008, the number of publications containing RNA-seq data has grown exponentially, hitting an all-time high of 2,808 publications in 2016 (PubMed). With this wealth of RNA-seq data being generated, it is a challenge to extract maximal meaning from these datasets, and without the appropriate skills and background, there is risk of misinterpretation of these data. However, a general understanding of the principles underlying each step of RNA-seq data analysis allows investigators without a background in programming and bioinformatics to critically analyze their own datasets as well as published data. Our goals in the present review are to break down the steps of a typical RNA-seq analysis and to highlight the pitfalls and checkpoints along the way that are vital for bench scientists and biomedical researchers performing experiments that use RNA-seq.
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