Fast and accurate read mapping with approximate seeds and multiple backtracking

Siragusa, E.; Weese, D.; Reinert, K.

doi:10.1093/nar/gkt005

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Fast and accurate read mapping with approximate seeds and multiple backtracking

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Siragusa, E.
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;
Department of Mathematics and Computer Science, Freie Universität Berlin;

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Citation

Siragusa, E., Weese, D., & Reinert, K. (2013). Fast and accurate read mapping with approximate seeds and multiple backtracking. Nucleic Acids Research (London), 41(7), e78-e78. doi:10.1093/nar/gkt005.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0014-7BB1-F

Abstract

We present Masai, a read mapper representing the state-of-the-art in terms of speed and accuracy. Our tool is an order of magnitude faster than RazerS 3 and mrFAST, 2-4 times faster and more accurate than Bowtie 2 and BWA. The novelties of our read mapper are filtration with approximate seeds and a method for multiple backtracking. Approximate seeds, compared with exact seeds, increase filtration specificity while preserving sensitivity. Multiple backtracking amortizes the cost of searching a large set of seeds by taking advantage of the repetitiveness of next-generation sequencing data. Combined together, these two methods significantly speed up approximate search on genomic data sets. Masai is implemented in C++ using the SeqAn library. The source code is distributed under the BSD license and binaries for Linux, Mac OS X and Windows can be freely downloaded from http://www.seqan.de/projects/masai.