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BMC Genomics

, 16:455

First Online: 13 June 2015Received: 17 November 2014Accepted: 01 June 2015DOI: 10.1186-s12864-015-1676-0

Cite this article as: Chu, C., Fang, Z., Hua, X. et al. BMC Genomics 2015 16: 455. doi:10.1186-s12864-015-1676-0


BackgroundThe advent of the NGS technologies has permitted profiling of whole-genome transcriptomes i.e., RNA-Seq at unprecedented speed and very low cost. RNA-Seq provides a far more precise measurement of transcript levels and their isoforms compared to other methods such as microarrays. A fundamental goal of RNA-Seq is to better identify expression changes between different biological or disease conditions. However, existing methods for detecting differential expression from RNA-Seq count data have not been comprehensively evaluated in large-scale RNA-Seq datasets. Many of them suffer from inflation of type I error and failure in controlling false discovery rate especially in the presence of abnormal high sequence read counts in RNA-Seq experiments.

ResultsTo address these challenges, we propose a powerful and robust tool, termed deGPS, for detecting differential expression in RNA-Seq data. This framework contains new normalization methods based on generalized Poisson distribution modeling sequence count data, followed by permutation-based differential expression tests. We systematically evaluated our new tool in simulated datasets from several large-scale TCGA RNA-Seq projects, unbiased benchmark data from compcodeR package, and real RNA-Seq data from the development transcriptome of Drosophila. deGPS can precisely control type I error and false discovery rate for the detection of differential expression and is robust in the presence of abnormal high sequence read counts in RNA-Seq experiments.

ConclusionsSoftware implementing our deGPS was released within an R package with parallel computations deGPS is a powerful and robust tool for data normalization and detecting different expression in RNA-Seq experiments. Beyond RNA-Seq, deGPS has the potential to significantly enhance future data analysis efforts from many other high-throughput platforms such as ChIP-Seq, MBD-Seq and RIP-Seq.

KeywordsNext-generation sequencing Differential expression Generalized Poisson RNA-Seq AbbreviationsAUCArea under curve

ChIP-SeqChromatin immunoprecipitation sequencing

DEDifferential expression

FDRFalse discovery rate

FPRFalse positive rate

GPGeneralized Poisson

lncRNALong noncoding RNAs

LowessLocally weighted least squares

LRTLikelihood ratio test

MBD-SeqMethyl-CpG binding domain protein-enriched genome sequencing


mRNAMessenger RNA

NBNegative binomial

NGSNext-generation sequencing

RIP-seqRNA-immunoprecipitation sequencing

RNA-SeqRNA sequencing

ROCOperating characteristic curve

siRNASmall interfering RNAs

TCTAThe cancer Genome Atlas

TMMTrimmed mean method

TPRTrue positive rate

Electronic supplementary materialThe online version of this article doi:10.1186-s12864-015-1676-0 contains supplementary material, which is available to authorized users.

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Autor: Chen Chu - Zhaoben Fang - Xing Hua - Yaning Yang - Enguo Chen - Allen W. CowleyJr. - Mingyu Liang - Pengyuan Liu - Yan L


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