IRcall and IRclassifier: two methods for flexible detection of intron retention events from RNA-Seq data

BMC Genomics

Proceedings

Shufan Ji¹  Yadong Wang²  Yang Bai² 

[1] School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, 43 Xueyuan Road, 100083, HaiDian District, Beijing, China;School of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, 150001, Nan Gang District, Harbin, China;

关键词: Splice Site;    Random Forest;    Information Gain;    Read Count;    Intron Retention;   

DOI  :  10.1186/1471-2164-16-S2-S9

来源: Springer

【 摘 要 】

BackgroundThe emergence of next-generation RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate detection of intron retention (IR) events from RNA-Seq data has remained an unresolved challenge in next-generation sequencing (NGS) studies.ResultsWe propose two new methods: IRcall and IRclassifier to detect IR events from RNA-Seq data. Our methods combine together gene expression information, read coverage within an intron, and read counts (within introns, within flanking exons, supporting splice junctions, and overlapping with 5' splice site/ 3' splice site), employing ranking strategy and classifiers to detect IR events. We applied our approaches to one published RNA-Seq data on contrasting skip mutant and wild-type in Arabidopsis thaliana. Compared with three state-of-the-art methods, IRcall and IRclassifier could effectively filter out false positives, and predict more accurate IR events.AvailabilityThe data and codes of IRcall and IRclassifier are available at http://mlg.hit.edu.cn/ybai/IR/IRcallAndIRclass.html

【 授权许可】

Unknown   
© Bai et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.