Ultrafast SNP analysis using the Burrows-Wheeler transform of short-read data

Abstract

Motivation: Sequence-variation analysis is conventionally performed on mapping results that are highly redundant and occasionally contain undesirable heuristic biases. A straightforward approach to single-nucleotide polymorphism (SNP) analysis, using the Burrows-Wheeler transform (BWT) of short-read data, is proposed. Results: The BWT makes it possible to simultaneously process collections of read fragments of the same sequences; accordingly, SNPs were found from the BWT much faster than from the mapping results. It took only a few minutes to find SNPs from the BWT (with a supplementary data, fragment depth of coverage [FDC]) using a desktop workstation in the case of human exome or transcriptome sequencing data and 20 min using a dual-CPU server in the case of human genome sequencing data. The SNPs found with the proposed method almost agreed with those found by a time-consuming state-of-the-art tool, except for the cases in which the use of fragments of reads led to sensitivity loss or sequencing depth was not sufficient. These exceptions were predictable in advance on the basis of minimum length for uniqueness (MLU) and FDC defined on the reference genome. Moreover, BWT and FDC were computed in less time than it took to get the mapping results, provided that the data were large enough.