Ultrafast SNP Analysis using the Burrows-Wheeler Transform of Short-read Data
This recent paper appears quite interesting (h/t: Ruibang). It starts with the BWT of short read library (e.g. BCR), and skips the alignment step altogether to go straight to SNP determination.
Motivation: Sequence-variation analysis is conventionally performed on mapping results that are highly redundant and occasionally contain undesirable heuristic biases. A straightforward approach to 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 supplementary data, FDC) using a desktop workstation in the case of human exome or transcriptome sequencing data and twenty minutes 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 fragment depth of coverage (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 was large enough.
Availability: A proof-of-concept binary code for a Linux platform is available on request to the corresponding author.
Contact: kouichi.kimura.hh@hitachi.com
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The authors are not new to using BWT and suffix arrays for analyzing genomic data. Here are a few of their previous papers -
2009 -
2009 -
Localized suffix array and its application to genome mapping problems for paired-end short reads
2011 -
Seed-set construction by equi-entropy partitioning for efficient and sensitive short-read mapping
