A context-based approach to identify the most likely mapping for RNA-seq experiments

BMC Bioinformatics

Proceedings

Thomas Bonfert¹  Caroline C Friedel¹  Gergely Csaba¹  Ralf Zimmer¹ 

[1] Institute for Informatics, Ludwig-Maximilians-University Munich, Amalienstr. 17, 80333, Munich, Germany;

关键词: Splice Site;    Priority Queue;    Splice Junction;    Read Coverage;    Seed Length;   

DOI  :  10.1186/1471-2105-13-S6-S9

来源: Springer

【 摘 要 】

BackgroundSequencing of mRNA (RNA-seq) by next generation sequencing technologies is widely used for analyzing the transcriptomic state of a cell. Here, one of the main challenges is the mapping of a sequenced read to its transcriptomic origin. As a simple alignment to the genome will fail to identify reads crossing splice junctions and a transcriptome alignment will miss novel splice sites, several approaches have been developed for this purpose. Most of these approaches have two drawbacks. First, each read is assigned to a location independent on whether the corresponding gene is expressed or not, i.e. information from other reads is not taken into account. Second, in case of multiple possible mappings, the mapping with the fewest mismatches is usually chosen which may lead to wrong assignments due to sequencing errors.ResultsTo address these problems, we developed ContextMap which efficiently uses information on the context of a read, i.e. reads mapping to the same expressed region. The context information is used to resolve possible ambiguities and, thus, a much larger degree of ambiguities can be allowed in the initial stage in order to detect all possible candidate positions. Although ContextMap can be used as a stand-alone version using either a genome or transcriptome as input, the version presented in this article is focused on refining initial mappings provided by other mapping algorithms. Evaluation results on simulated sequencing reads showed that the application of ContextMap to either TopHat or MapSplice mappings improved the mapping accuracy of both initial mappings considerably.ConclusionsIn this article, we show that the context of reads mapping to nearby locations provides valuable information for identifying the best unique mapping for a read. Using our method, mappings provided by other state-of-the-art methods can be refined and alignment accuracy can be further improved.Availabilityhttp://www.bio.ifi.lmu.de/ContextMap.

【 授权许可】

Unknown   
© Bonfert et al.; licensee BioMed Central Ltd. 2012. 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/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.