Medical College of Wisconsin
CTSICores SearchResearch InformaticsREDCap

deGPS is a powerful tool for detecting differential expression in RNA-sequencing studies. BMC Genomics 2015 Jun 13;16:455

Date

06/14/2015

Pubmed ID

26070955

Pubmed Central ID

PMC4465298

DOI

10.1186/s12864-015-1676-0

Scopus ID

2-s2.0-84931273060   10 Citations

Abstract

BACKGROUND: The 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.

RESULTS: To 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.

CONCLUSIONS: Software implementing our deGPS was released within an R package with parallel computations ( https://github.com/LL-LAB-MCW/deGPS ). 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.

Author List

Chu C, Fang Z, Hua X, Yang Y, Chen E, Cowley AW Jr, Liang M, Liu P, Lu Y

Authors

Allen W. Cowley Jr PhD Professor in the Physiology department at Medical College of Wisconsin
Mingyu Liang PhD Center Director, Professor in the Physiology department at Medical College of Wisconsin
Pengyuan Liu PhD Adjunct Professor in the Physiology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Computational Biology
Databases, Genetic
Gene Expression Profiling
Gene Expression Regulation
Humans
Poisson Distribution
Sequence Analysis, RNA
Software
Transcriptome
jenkins-FCD Prod-484 8aa07fc50b7f6d102f3dda2f4c7056ff84294d1d