Massively parallel sequencing approaches for characterization of structural variation.
Massively parallel sequencing approaches for characterization of structural variation.
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DOI：
10.1007/978-1-61779-507-7_18
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发表时间：
2012
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影响因子：
--
通讯作者：
Wilson, Richard K
中科院分区：
文献类型：
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作者：
Koboldt, Daniel C;Larson, David E;Chen, Ken;Ding, Li;Wilson, Richard K
下一代测序技术的出现为全面研究人类基因组中的DNA序列变异提供了一个令人难以置信的机会。罗氏(454)、Illumina(Genome Analyzer和HiSeq 2000)和应用生物系统公司(Solid)的商用平台能够对单个基因组进行完全测序，覆盖范围很广。NGS数据对于结构变异(SV)的研究特别有利，因为它提供了检测各种大小和类型的变异的灵敏度，以及在碱基对分辨率下表征它们的断裂点的精度。在本章中，我们介绍了利用大规模并行测序数据检测SVS和拷贝数变化的方法和软件算法。我们描述了用于表征SV断点和去除假阳性的可视化和从头组装策略。
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.