XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylation

BMC Bioinformatics

Research

Teresa L. Johnson-Pais¹  Ping Wu¹  Ronald Rodriguez¹  Wasim H. Chowdhury¹  Zhao Lai²  Jingqi Zhou³  Yuanhang Liu⁴  Yidong Chen⁵ 

[1] Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA;Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA;Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA;Cornell university, Ithaca, NY, USA;Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA;Department of Cellular and Structure Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA;Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA;Department of Epidemiology & Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA;

关键词: Differential expression analysis;    XBSeq;    XBSeq2;    Alternative polyadenylation;    RNA-seq;   

DOI  :  10.1186/s12859-017-1803-9

来源: Springer

【 摘 要 】

BackgroundRNA sequencing (RNA-seq) is a high throughput technology that profiles gene expression in a genome-wide manner. RNA-seq has been mainly used for testing differential expression (DE) of transcripts between two conditions and has recently been used for testing differential alternative polyadenylation (APA). In the past, many algorithms have been developed for detecting differentially expressed genes (DEGs) from RNA-seq experiments, including the one we developed, XBSeq, which paid special attention to the context-specific background noise that is ignored in conventional gene expression quantification and DE analysis of RNA-seq data.ResultsWe present several major updates in XBSeq2, including alternative statistical testing and parameter estimation method for detecting DEGs, capacity to directly process alignment files and methods for testing differential APA usage. We evaluated the performance of XBSeq2 against several other methods by using simulated datasets in terms of area under the receiver operating characteristic (ROC) curve (AUC), number of false discoveries and statistical power. We also benchmarked different methods concerning execution time and computational memory consumed. Finally, we demonstrated the functionality of XBSeq2 by using a set of in-house generated clear cell renal carcinoma (ccRCC) samples.ConclusionsWe present several major updates to XBSeq. By using simulated datasets, we demonstrated that, overall, XBSeq2 performs equally well as XBSeq in terms of several statistical metrics and both perform better than DESeq2 and edgeR. In addition, XBSeq2 is faster in speed and consumes much less computational memory compared to XBSeq, allowing users to evaluate differential expression and APA events in parallel. XBSeq2 is available from Bioconductor: http://bioconductor.org/packages/XBSeq/

【 授权许可】

CC BY   
© The Author(s). 2017

【 图 表 】

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