| Literature DB >> 29624415 |
Clarissa M Koch1, Stephen F Chiu1,2, Mahzad Akbarpour2, Ankit Bharat1,2, Karen M Ridge1,3, Elizabeth T Bartom4, Deborah R Winter5.
Abstract
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.Keywords: RNA sequencing; bioinformatics; data analysis; transcriptomics
Mesh:
Year: 2018 PMID: 29624415 PMCID: PMC6096346 DOI: 10.1165/rcmb.2017-0430TR
Source DB: PubMed Journal: Am J Respir Cell Mol Biol ISSN: 1044-1549 Impact factor: 6.914