Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data Academic Article uri icon


MeSH Major

  • High-Throughput Nucleotide Sequencing
  • Nerve Tissue Proteins
  • RNA
  • Sequence Analysis, RNA
  • Software


  • 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 date

  • September 10, 2013



  • Academic Article



  • eng

PubMed Central ID

  • PMC4054597

Digital Object Identifier (DOI)

  • 10.1186/gb-2013-14-9-r95

PubMed ID

  • 24020486

Additional Document Info

start page

  • R95


  • 14


  • 9