title: Comprehensive evaluation of differential gene  expression analysis methods for RNA-seq data.
abstract: 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.
authors.forename: Franck
authors.initials: F
authors.lastname: Rapaport
authors.forename: Raya
authors.initials: R
authors.lastname: Khanin
authors.forename: Yupu
authors.initials: Y
authors.lastname: Liang
authors.forename: Mono
authors.initials: M
authors.lastname: Pirun
authors.forename: Azra
authors.initials: A
authors.lastname: Krek
authors.forename: Paul
authors.initials: P
authors.lastname: Zumbo
authors.forename: Christopher E
authors.initials: CE
authors.lastname: Mason
authors.forename: Nicholas D
authors.initials: ND
authors.lastname: Socci
authors.forename: Doron
authors.initials: D
authors.lastname: Betel
mesh_terms: D001923:Brain Chemistry; D002460:Cell Line; D066264:Datasets as Topic; D015870:Gene Expression; D020869:Gene Expression Profiling; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data*; D006801:Humans; D009419:Nerve Tissue Proteins / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D012313:RNA / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods / Q000706:statistics & numerical data*; D059629:Signal-To-Noise Ratio; D012984:Software
chemical_list: D009419:Nerve Tissue Proteins; D012313:RNA
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