BADGE: a novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data

BMC Bioinformatics
Jinghua GuJianhua Xuan

Abstract

Recent advances in RNA sequencing (RNA-Seq) technology have offered unprecedented scope and resolution for transcriptome analysis. However, precise quantification of mRNA abundance and identification of differentially expressed genes are complicated due to biological and technical variations in RNA-Seq data. We systematically study the variation in count data and dissect the sources of variation into between-sample variation and within-sample variation. A novel Bayesian framework is developed for joint estimate of gene level mRNA abundance and differential state, which models the intrinsic variability in RNA-Seq to improve the estimation. Specifically, a Poisson-Lognormal model is incorporated into the Bayesian framework to model within-sample variation; a Gamma-Gamma model is then used to model between-sample variation, which accounts for over-dispersion of read counts among multiple samples. Simulation studies, where sequencing counts are synthesized based on parameters learned from real datasets, have demonstrated the advantage of the proposed method in both quantification of mRNA abundance and identification of differentially expressed genes. Moreover, performance comparison on data from the Sequencing Quality Control (SEQC...Continue Reading

Citations

Nov 6, 2015·PeerJ·Tsung Fei Khang, Ching Yee Lau
Jan 23, 2018·Journal of the Royal Statistical Society. Series C, Applied Statistics·Panagiotis Papastamoulis, Magnus Rattray

Related Concepts

Bayesian Prediction
In Silico
Poly(A) Tail
Computer Programs and Programming
Sequence Determinations, RNA
MRNA Differential Display
Malignant Neoplasm of Breast
Genes
RNA
RNA, Messenger

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