Comparison of software packages for detecting differential expression in RNA-seq studies

By Fatemeh Seyednasrollah, Asta Laiho, Laura L. Elo. Briefings Bioinform., 2013

404 citations recorded by the source corpus.

RNA-sequencing (RNA-seq) has rapidly become a popular tool to characterize transcriptomes. A fundamental research problem in many RNA-seq studies is the identification of reliable molecular markers that show differential expression between distinct sample groups. Together with the growing popularity of RNA-seq, a number of data analysis methods and pipelines have already been developed for this task. Currently, however, there is no clear consensus about the best practices yet, which makes the choice of an appropriate method a daunting task especially for a basic user without a strong statistical or computational background. To assist the choice, we perform here a systematic comparison of eight widely used software packages and pipelines for detecting differential expression between sample groups in a practical research setting and provide general guidelines for choosing a robust pipeline. In general, our results demonstrate how the data analysis tool utilized can markedly affect the outcome of the data analysis, highlighting the importance of this choice.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f9a710c3a5c31c70d4704c2df83f5912f6de91b3

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In Molecular Biology

Citations in the corpus

MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing (2014, background): “Several studies have been published comparing the differential expression methods and concluded that there are substantial differences in terms of sensitivity and specificity among the methods [33-35].”
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? (2015, methodology): “While there are differences between the normalizations used by these tools, it has been suggested that the details of which method is used to normalize the data does not significantly alter the downstream DGE results (Seyednasrollah et al. 2013).”