Genome variation discovery with high-throughput sequencing data
Genome variation discovery with high-throughput sequencing data
复制标题
DOI：
10.1093/bib/bbp058
复制
发表时间：
2010-01-01
影响因子：
9.5
通讯作者：
Brudno, Michael
中科院分区：
文献类型：
--
作者：
Dalca, Adrian V.;Brudno, Michael
高通量测序（HTS）技术的出现使得人类基因组的测序成本大大降低。这些基因组的可用性有望使新的医学诊断和治疗，具体到个人，从而启动个性化医疗时代。目前由HTS机器生成的数据需要大量的计算分析，以识别测序个体中存在的基因组变异。在本文中，我们概述了HTS技术，并讨论了几个过多的算法和工具，旨在分析HTS数据，包括算法读取映射，以及识别单核苷酸多态性，插入/缺失和大规模的结构变异和拷贝数变异的方法，从这些映射。
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.