Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
Genome Biol. 2022 May 6;23(1):110. doi: 10.1186/s13059-022-02666-2.
Variant benchmarking is often performed by comparing a test callset to a gold standard set of variants. In repetitive regions of the genome, it may be difficult to establish what is the truth for a call, for example, when different alignment scoring metrics provide equally supported but different variant calls on the same data. Here, we provide an alternative approach, TT-Mars, that takes advantage of the recent production of high-quality haplotype-resolved genome assemblies by providing false discovery rates for variant calls based on how well their call reflects the content of the assembly, rather than comparing calls themselves.
变异基准测试通常通过将测试调用集与变异的黄金标准集进行比较来完成。在基因组的重复区域，确定调用的真实性可能很困难，例如，当不同的比对评分指标在相同的数据上提供同样支持但不同的变异调用时。在这里，我们提供了一种替代方法 TT-Mars，它利用了最近产生的高质量单倍型解析基因组组装，通过基于调用反映组装内容的程度而不是比较调用本身来为变异调用提供错误发现率。
