SpliceJumper: a classification-based approach for calling splicing junctions from RNA-seq data

BMC Bioinformatics

Research

Yufeng Wu¹  Chong Chu¹  Xin Li¹ 

[1] Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, Unit 2155, 06269, Storrs, CT, U.S.A;

关键词: splicing junction calling;    RNA-seq;    sequence reads alignment;    support vector machine;   

DOI  :  10.1186/1471-2105-16-S17-S10

来源: Springer

【 摘 要 】

BackgroundNext-generation RNA sequencing technologies have been widely applied in transcriptome profiling. This facilitates further studies of gene structure and expression on the genome wide scale. It is an important step to align reads to the reference genome and call out splicing junctions for the following analysis, such as the analysis of alternative splicing and isoform construction. However, because of the existence of introns, when RNA-seq reads are aligned to the reference genome, reads can not be fully mapped at splicing sites. Thus, it is challenging to align reads and call out splicing junctions accurately.ResultsIn this paper, we present a classification based approach for calling splicing junctions from RNA-seq data, which is implemented in the program SpliceJumper. SpliceJumper uses a machine learning approach which combines multiple features extracted from RNA-seq data. We compare SpliceJumper with two existing RNA-seq analysis approaches, TopHat2 and MapSplice2, on both simulated and real data. Our results show that SpliceJumper outperforms TopHat2 and MapSplice2 in accuracy. The program SpliceJumper can be downloaded at https://github.com/Reedwarbler/SpliceJumper.

【 授权许可】

Unknown   
© Chu et al. 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.