High-throughput sequencing technology has rapidly become a popular tool to calculate RNA expression level. There are many computational methods that have been designed to identify genes that are differentially expressed by using RNA-Seq data. The set of differentially-expressed genes detected varies greatly. Some of them are identified in all or most of software packages, whereas, some genes are detected by just one method. In this study, we compare four widely-used software packages for identifying differential expression between gene samples: DESeq2, EdgeR, Cuffdiff2, and DEGSeq. This paper shows that Cuffdiff2 has a slight advantage over other software packages but takes the longest runtime.
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Compare and Contrast of Differential Gene Expression Software Packages of RNA-Seq
Semantic Scholar · Computer Science · 2018
Abstract
High-throughput sequencing technology has rapidly become a popular tool to calculate RNA expression level. There are many computational methods that have been designed to identify genes that are differentially expressed by using RNA-Seq data. The set of differentially-expressed genes detected varies greatly. Some of them are identified in all or most of software packages, whereas, some genes are detected by just one method. In this study, we compare four widely-used software packages for identifying differential expression between gene samples: DESeq2, EdgeR, Cuffdiff2, and DEGSeq. This paper shows that Cuffdiff2 has a slight advantage over other software packages but takes the longest runtime.