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Báo cáo y học: "Improved variant discovery through local re-alignment of short-read next-generation sequencing data using SRMA"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: Improved variant discovery through local re-alignment of short-read next-generation sequencing data using SRMA. | Homer and Nelson Genome Biology 2010 11 R99 http genomebiology.com 2010 11 10 R99 Genome Biology METHOD Open Access Improved variant discovery through local re-alignment of short-read next-generation sequencing data using SRMA Nils Homer1 2 3 Stanley F Nelson2 Abstract A primary component of next-generation sequencing analysis is to align short reads to a reference genome with each read aligned independently. However reads that observe the same non-reference DNA sequence are highly correlated 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 the targeted genome is described here. Background Whole-genome human re-sequencing is now feasible using next generation sequencing technology. Technologies such as those produced by Illumina Life and Roche 454 produce millions to billions of short DNA sequences that can be used to reconstruct the diploid sequence of a human genome. Ideally such data alone could be used to de novo assemble the genome in question 1-6 . However the short read lengths 25 to 125 bases the size and repetitive nature of the human genome 3.2 X 109 bases as well as the modest error rates approximately 1 per base make such de novo assembly of mammalian genomes intractable. Instead short-read sequence alignment algorithms have been developed to compare each short sequence to a reference genome 7-12 . Observing multiple reads that differ similarly from the reference sequence in their respective alignments identifies variants. These alignment algorithms have made it possible to accurately and efficiently catalogue many types of variation between human individuals and those causative for specific diseases. Because alignment algorithms map each read independently to the reference genome alignment artifacts could result such that SNPs insertions and deletions are improperly placed relative to their true