ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences

BMC Genomics

Software

Philip C. Rosenstiel¹  Wentao Yang²  Hinrich Schulenburg² 

[1] Centre for Molecular Biology, Institute for Clinical Molecular Biology, CAU Kiel, Am Botanischen Garten 11, 24118, Kiel, Germany;Evolutionary Ecology and Genetics, Zoological Institute, CAU Kiel, Am Botanischen Garten 9, 24118, Kiel, Germany;

关键词: RNA-Seq;    Transcriptome analysis;    Differential gene expression;    ABSSeq;    Negative binomial distribution;   

DOI  :  10.1186/s12864-016-2848-2

 received in 2015-12-16, accepted in 2016-06-20,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundThe recent advances in next generation sequencing technology have made the sequencing of RNA (i.e., RNA-Seq) an extemely popular approach for gene expression analysis. Identification of significant differential expression represents a crucial initial step in these analyses, on which most subsequent inferences of biological functions are built. Yet, for identification of these subsequently analysed genes, most studies use an additional minimal threshold of differential expression that is not captured by the applied statistical procedures.ResultsHere we introduce a new analysis approach, ABSSeq, which uses a negative binomal distribution to model absolute expression differences between conditions, taking into account variations across genes and samples as well as magnitude of differences. In comparison to alternative methods, ABSSeq shows higher performance on controling type I error rate and at least a similar ability to correctly identify differentially expressed genes.ConclusionsABSSeq specifically considers the overall magnitude of expression differences, which enhances the power in detecting truly differentially expressed genes by reducing false positives at both very low and high expression level. In addition, ABSSeq offers to calculate shrinkage of fold change to facilitate gene ranking and effective outlier detection.

【 授权许可】

CC BY   
© The Author(s). 2016

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