BatMis: A fast algorithm for k-mismatch mapping

Abstract

Motivation: Second-generation sequencing (SGS) generates millions of reads that need to be aligned to a reference genome allowing errors. Although current aligners can efficiently map reads allowing a small number of mismatches, they are not well suited for handling a large number of mismatches. The efficiency of aligners can be improved using various heuristics, but the sensitivity and accuracy of the alignments are sacrificed. In this article, we introduce Basic Alignment tool for Mismatches (BatMis)-an efficient method to align short reads to a reference allowing k mismatches. BatMis is a Burrows-Wheeler transformation based aligner that uses a seed and extend approach, and it is an exact method. Results: Benchmark tests show that BatMis performs better than competing aligners in solving the k-mismatch problem. Furthermore, it can compete favorably even when compared with the heuristic modes of the other aligners. BatMis is a useful alternative for applications where fast k-mismatch mappings, unique mappings or multiple mappings of SGS data are required. © The Author 2012. Published by Oxford University Press. All rights reserved.