Computer Science, MIT, USA.
Brief Bioinform. 2010 Jan;11(1):3-14. doi: 10.1093/bib/bbp058. Epub 2010 Jan 6.
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.
高通量测序（HTS）技术的出现使得以更低的成本对人类基因组进行测序成为可能。这些基因组的可用性有望实现针对个体的新型医学诊断和治疗，从而开启个性化医疗的时代。HTS 机器目前生成的数据需要进行广泛的计算分析，以便识别测序个体中存在的基因组变体。在本文中，我们概述了 HTS 技术，并讨论了旨在分析 HTS 数据的众多算法和工具，包括读映射算法，以及从这些映射中识别单核苷酸多态性、插入/缺失和大规模结构变体和拷贝数变异的方法。
