Accurate inference of isoforms from multiple sample RNA-Seq data

Abstract

Background

RNA-Seq based transcriptome assembly has become a fundamental technique for studying expressed mRNAs ( i.e., transcripts or isoforms) in a cell using high-throughput sequencing technologies, and is serving as a basis to analyze the structural and quantitative differences of expressed isoforms between samples. However, the current transcriptome assembly algorithms are not specifically designed to handle large amounts of errors that are inherent in real RNA-Seq datasets, especially those involving multiple samples, making downstream differential analysis applications difficult. On the other hand, multiple sample RNA-Seq datasets may provide more information than single sample datasets that can be utilized to improve the performance of transcriptome assembly and abundance estimation, but such information remains overlooked by the existing assembly tools.

Results

We formulate a computational framework of transcriptome assembly that is capable of handling noisy RNA-Seq reads and multiple sample RNA-Seq datasets efficiently. We show that finding an optimal solution under this framework is an NP-hard problem. Instead, we develop an efficient heuristic algorithm, called Iterative Shortest Path (ISP), based on linear programming (LP) and integer linear programming (ILP). Our preliminary experimental results on both simulated and real datasets and comparison with the existing assembly tools demonstrate that (i) the ISP algorithm is able to assemble transcriptomes with a greatly increased precision while keeping the same level of sensitivity, especially when many samples are involved, and (ii) its assembly results help improve downstream differential analysis. The source code of ISP is freely available at http://alumni.cs.ucr.edu/~liw/isp.html.

Contributors

Tao Jiang

Wei Li

Conference

Journal ID (nlm-ta): BMC Genomics

Journal ID (iso-abbrev): BMC Genomics

Title: BMC Genomics

Publisher: BioMed Central

ISSN (Electronic): 1471-2164

Publication date Collection: 2015

Publication date (Electronic): 21 January 2015

Volume: 16

Issue: Suppl 2

Page: S15

Affiliations

[1 ]Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, 92507, USA

[2 ]MOE Key Lab of Bioinformatics and Bioinformatics Division, TNLIST / Department of Automation, Tsinghua University, Beijing, 100084, China

[3 ]MOE Key Lab of Bioinformatics and Bioinformatics Division, TNLIST / Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

[4 ]Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA, 02215, USA

Article

Publisher ID: 1471-2164-16-S2-S15

DOI: 10.1186/1471-2164-16-S2-S15

PMC ID: 4331715

PubMed ID: 25708199

Copyright statement: Copyright © 2015 Tasnim et al.; licensee BioMed Central Ltd.

License:

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.

Conference name: The Thirteenth Asia Pacific Bioinformatics Conference (APBC 2015)

Conference location: HsinChu, Taiwan

Conference date: 21-23 January 2015

Product

Self URI: http://www.biomedcentral.com/qc/1471-2164/16/S2/S15

Categories

Subject: Proceedings