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

Franck Rapaport1, Raya Khanin1, Yupu Liang1, Mono Pirun1, Azra Krek1, Paul Zumbo23, Christopher E Mason23, Nicholas D Socci1 and Doron Betel34*

Author Affiliations

1 Bioinformatics Core, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA

2 Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, 10021, USA

3 Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, 10021, USA

4 Division of Hematology/Oncology, Department of Medicine, Weill Cornell Medical College, New York, NY, 10021, USA

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Genome Biology 2013, 14:R95  doi:10.1186/gb-2013-14-9-r95

Published: 10 September 2013


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.