Koboldt Daniel C, Larson David E, Wilson Richard K
The Genome Institute at Washington University in St. Louis, Missouri 63108, USA.
The Genome Institute at Washington University in St. Louis, Missouri, USA, 63108.
Curr Protoc Bioinformatics. 2013 Dec;44:15.4.1-17. doi: 10.1002/0471250953.bi1504s44.
The identification of small sequence variants remains a challenging but critical step in the analysis of next-generation sequencing data. Our variant calling tool, VarScan 2, employs heuristic and statistic thresholds based on user-defined criteria to call variants using SAMtools mpileup data as input. Here, we provide guidelines for generating that input, and describe protocols for using VarScan 2 to (1) identify germline variants in individual samples; (2) call somatic mutations, copy number alterations, and LOH events in tumor-normal pairs; and (3) identify germline variants, de novo mutations, and Mendelian inheritance errors in family trios. Further, we describe a strategy for variant filtering that removes likely false positives associated with common sequencing- and alignment-related artifacts.
在分析下一代测序数据时,识别小序列变异仍然是一个具有挑战性但至关重要的步骤。我们的变异检测工具VarScan 2,基于用户定义的标准采用启发式和统计阈值,以SAMtools mpileup数据作为输入来检测变异。在此,我们提供生成该输入的指南,并描述使用VarScan 2的方案,以(1)识别个体样本中的种系变异;(2)检测肿瘤-正常样本对中的体细胞突变、拷贝数改变和杂合性缺失事件;以及(3)识别家系三联体中的种系变异、新生突变和孟德尔遗传错误。此外,我们描述了一种变异过滤策略,该策略可去除与常见测序和比对相关伪影相关的可能假阳性。