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The bench scientist's guide to statistical analysis of RNA-Seq data

Craig R Yendrek1*, Elizabeth A Ainsworth12 and Jyothi Thimmapuram34

Author Affiliations

1 USDA ARS Global Change and Photosynthesis Research Unit, 1201 W. Gregory Drive, Urbana, IL 61801, USA

2 Department of Plant Biology, University of Illinois, Urbana-Champaign, Urbana, IL, 61801, USA

3 Roy J. Carver Biotechnology Center, University of Illinois, Urbana-Champaign, Urbana, IL, 61801, USA

4 Current Address: Bioinformatics Core, Discovery Park, Purdue University, West Lafayette, IN, 47907, USA

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BMC Research Notes 2012, 5:506  doi:10.1186/1756-0500-5-506

Published: 14 September 2012



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 bioinformatics specialists. Here we provide a step-by-step guide and outline a strategy using currently available statistical tools 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.


When comparing statistical tools, the negative binomial distribution-based methods, edgeR and DESeq, respectively identified 11,995 and 11,317 differentially expressed genes from an RNA-seq dataset generated from soybean leaf tissue grown in elevated O3. However, the number of genes in common between these two methods was only 10,535, resulting in 2,242 genes determined to be differentially expressed by only one method. Upon analysis of the non-significant genes, several limitations of these analytic tools were revealed, including evidence for overly stringent parameters for determining statistical significance of differentially expressed genes as well as increased type II error for high abundance transcripts.


Because of the high variability between methods for determining differential expression of RNA-Seq data, we suggest using several bioinformatics tools, as outlined here, to ensure that a conservative list of differentially expressed genes is obtained. We also conclude that despite these analytical limitations, RNA-Seq provides highly accurate transcript abundance quantification that is comparable to qRT-PCR.

RNA-Seq; Differential Expression; Statistical analysis