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

ABSTRACT: A large number of computational methods have been recently developed for analyzing differential gene expression (DE) in RNA-seq data. We report on a comprehensive evaluation of the commonly used DE methods using the SEQC benchmark data set and data from ENCODE project. We evaluated a number of key features including: normalization, accuracy of DE detection and DE analysis when one condition has no detectable expression. We found significant differences among the methods. Furthermore, computational methods designed for DE detection from expression array data perform comparably to methods customized for RNA-seq. Most importantly, our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

ORGANISM(S): Homo sapiens

PROVIDER: GSE49712 | GEO | 2013/08/20

SECONDARY ACCESSION(S): PRJNA214799

REPOSITORIES: GEO

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

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