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This article is part of the supplement: Selected articles from the International Conference on Intelligent Biology and Medicine (ICIBM 2013): Genomics

Open Access Open Badges Research

Evaluation of read count based RNAseq analysis methods

Yan Guo*, Chung-I Li, Fei Ye and Yu Shyr*

Author Affiliations

Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA

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BMC Genomics 2013, 14(Suppl 8):S2  doi:10.1186/1471-2164-14-S8-S2

Published: 9 December 2013



RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. To date, there has not been a consensus on the best approach for conducting robust RNAseq analysis.


In this study, we designed a thorough experiment to evaluate six read count-based RNAseq analysis methods (DESeq, DEGseq, edgeR, NBPSeq, TSPM and baySeq) using both real and simulated data. We found the six methods produce similar fold changes and reasonable overlapping of differentially expressed genes based on p-values. However, all six methods suffer from over-sensitivity.


Based on the evaluation of runtime using real data and area under the receiver operating characteristic curve (AUC-ROC) using simulated data, we found that edgeR achieves a better balance between speed and accuracy than the other methods.

RNAseq; expression; DESeq; DEGseq; edger; NBPSeq; TSPM; baySeq