Genome variation discovery with high-throughput sequencing data

被引：47

作者：

Dalca, Adrian V. ^{[2
]}

Brudno, Michael ^{[1
]}

机构：

[1] Univ Toronto, Dept Comp Sci, Toronto, ON, Canada

[2] MIT, Cambridge, MA 02139 USA

来源：

BRIEFINGS IN BIOINFORMATICS | 2010年 / 11卷 / 01期

关键词：

high-throughput sequencing; genome variation; personal genomics; HUMAN RESEQUENCING DATA; STRUCTURAL VARIATION; SHORT-READ; SNP DETECTION; COPY-NUMBER; LOCAL ALIGNMENT; DNA-SEQUENCE; PROGRAM; IDENTIFICATION; ALGORITHMS;

D O I：

10.1093/bib/bbp058

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

The advent of high-throughput sequencing (HTS) technologies is enabling sequencing of human genomes at a significantly lower cost. The availability of these genomes is hoped to enable novel medical diagnostics and treatment, specific to the individual, thus launching the era of personalized medicine. The data currently generated by HTS machines require extensive computational analysis in order to identify genomic variants present in the sequenced individual. In this paper, we overview HTS technologies and discuss several of the plethora of algorithms and tools designed to analyze HTS data, including algorithms for read mapping, as well as methods for identification of single-nucleotide polymorphisms, insertions/deletions and large-scale structural variants and copy-number variants from these mappings.

引用

页码：3 / 14

页数：12

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