Read mapping on de Bruijn graphs

BMC Bioinformatics

Research Article

Pierre Peterlongo¹  Antoine Limasset¹  Eric Rivals²  Bastien Cazaux² 

[1] IRISA Inria Rennes Bretagne Atlantique, GenScale team, Campus de Beaulieu, 35042, Rennes, France;L.I.R.M.M., UMR 5506, Université de Montpellier et CNRS, 860 rue de St Priest, F-34392, Montpellier Cedex 5, France;Institut Biologie Computationnelle, Université de Montpellier, F-34392, Montpellier, France;

关键词: Read mapping;    De Bruijn graph;    NGS;    Sequence graph;    path;    Hamiltonian path;    Genomics;    Assembly;    NP-complete;   

DOI  :  10.1186/s12859-016-1103-9

 received in 2015-12-09, accepted in 2016-05-26,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundNext Generation Sequencing (NGS) has dramatically enhanced our ability to sequence genomes, but not to assemble them. In practice, many published genome sequences remain in the state of a large set of contigs. Each contig describes the sequence found along some path of the assembly graph, however, the set of contigs does not record all the sequence information contained in that graph. Although many subsequent analyses can be performed with the set of contigs, one may ask whether mapping reads on the contigs is as informative as mapping them on the paths of the assembly graph. Currently, one lacks practical tools to perform mapping on such graphs.ResultsHere, we propose a formal definition of mapping on a de Bruijn graph, analyse the problem complexity which turns out to be NP-complete, and provide a practical solution. We propose a pipeline called GGMAP (Greedy Graph MAPping). Its novelty is a procedure to map reads on branching paths of the graph, for which we designed a heuristic algorithm called BGREAT (de Bruijn Graph REAd mapping Tool). For the sake of efficiency, BGREAT rewrites a read sequence as a succession of unitigs sequences. GGMAP can map millions of reads per CPU hour on a de Bruijn graph built from a large set of human genomic reads. Surprisingly, results show that up to 22 % more reads can be mapped on the graph but not on the contig set.ConclusionsAlthough mapping reads on a de Bruijn graph is complex task, our proposal offers a practical solution combining efficiency with an improved mapping capacity compared to assembly-based mapping even for complex eukaryotic data.

【 授权许可】

CC BY   
© Limasset et al. 2016

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