Improved variant discovery through localre-alignment of short-read next-generationsequencing data using SRMA

摘要

A primary component of next-generation sequencing analysis is to align short reads to a reference genome, witheach read aligned independently. However, reads that observe the same non-reference DNA sequence are highlycorrelated and can be used to better model the true variation in the target genome. A novel short-read micro realigner,SRMA, that leverages this correlation to better resolve a consensus of the underlying DNA sequence of thetargeted genome is described here. Whole-genome human re-sequencing is now feasibleusing next generation sequencing technology. Technologiessuch as those produced by Illumina, Life, andRoche 454 produce millions to billions of short DNAsequences that can be used to reconstruct the diploidsequence of a human genome. Ideally, such data alonecould be used to de novo assemble the genome in question[1-6]. However, the short read lengths (25 to 125bases), the size and repetitive nature of the human genome(3.2 × 109 bases), as well as the modest error rates(approximately 1% per base) make such de novoassembly of mammalian genomes intractable. Instead,short-read sequence alignment algorithms have beendeveloped to compare each short sequence to a referencegenome [7-12]. Observing multiple reads that differsimilarly from the reference sequence in their respectivealignments identifies variants. These alignment algorithmshave made it possible to accurately and efficientlycatalogue many types of variation between human individualsand those causative for specific diseases.