|Thimmapuram, J -|
Submitted to: BMC Research Notes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 10, 2012
Publication Date: September 14, 2013
Citation: Yendrek, C.R., Ainsworth, E.A., Thimmapuram, J. 2013. Method: The bench scientist's guide to RNA-Seq analysis. BMC Research Notes. 5:506. Interpretive Summary: Scientists now have the capcity to sequence all of the genes expressed in an organism at a given time. This capacity can be used to compare how plants respond to different important agronomic stresses, including rising atmospheric ozone concentrations. RNA-sequencing (RNA-Seq) is emerging as a gold-standard tool for comparing global gene expression, but the bioinformatic approaches for dealing with RNA-seq data can be overwhelming for the bench scientist. This paper describes a strategy for producing a conservative list of differentially expressed genes, and discusses sources of error in RNA-Seq analysis that could alter interpretation of RNA-Seq datasets.
Technical Abstract: RNA sequencing (RNA-Seq) is emerging as a highly accurate method to quantify transcript abundance. However, analyses of the large data sets obtained by sequencing the entire transcriptome of organisms have generally been performed by bioinformatic specialists. Here we outline a methods strategy designed for the bench scientist that results in a conservative list of differentially expressed genes. We also discuss potential sources of error in RNA-Seq analysis that could alter interpretation of global changes in gene expression.