Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

Genome Biol. 2013;14(9):R95. doi: 10.1186/gb-2013-14-9-r95.


A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain Chemistry
  • Cell Line
  • Datasets as Topic
  • Gene Expression
  • Gene Expression Profiling
  • High-Throughput Nucleotide Sequencing / statistics & numerical data*
  • Humans
  • Nerve Tissue Proteins / genetics*
  • Oligonucleotide Array Sequence Analysis
  • RNA / genetics*
  • Sequence Analysis, RNA / methods
  • Sequence Analysis, RNA / statistics & numerical data*
  • Signal-To-Noise Ratio
  • Software*


  • Nerve Tissue Proteins
  • RNA