用于结构变异表征的大规模平行测序方法。
Massively parallel sequencing approaches for characterization of structural variation.
作者信息
Koboldt Daniel C, Larson David E, Chen Ken, Ding Li, Wilson Richard K
机构信息
The Genome Institute at Washington University School of Medicine, St. Louis, MO, USA.
出版信息
Methods Mol Biol. 2012;838:369-84. doi: 10.1007/978-1-61779-507-7_18.
Abstract
The emergence of next-generation sequencing (NGS) technologies offers an incredible opportunity to comprehensively study DNA sequence variation in human genomes. Commercially available platforms from Roche (454), Illumina (Genome Analyzer and Hiseq 2000), and Applied Biosystems (SOLiD) have the capability to completely sequence individual genomes to high levels of coverage. NGS data is particularly advantageous for the study of structural variation (SV) because it offers the sensitivity to detect variants of various sizes and types, as well as the precision to characterize their breakpoints at base pair resolution. In this chapter, we present methods and software algorithms that have been developed to detect SVs and copy number changes using massively parallel sequencing data. We describe visualization and de novo assembly strategies for characterizing SV breakpoints and removing false positives.
摘要
新一代测序（NGS）技术的出现为全面研究人类基因组中的DNA序列变异提供了绝佳机会。罗氏公司（454）、Illumina公司（基因组分析仪和Hiseq 2000）以及应用生物系统公司（SOLiD）的商用平台有能力对个体基因组进行高覆盖度的完全测序。NGS数据在结构变异（SV）研究中特别有利，因为它既能灵敏地检测各种大小和类型的变异，又能精确地在碱基对分辨率下表征其断点。在本章中，我们介绍了利用大规模平行测序数据检测SV和拷贝数变化所开发的方法和软件算法。我们描述了用于表征SV断点和去除假阳性的可视化及从头组装策略。
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