Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Division of Health Sciences Informatics, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Methods Mol Biol. 2020;2120:11-36. doi: 10.1007/978-1-0716-0327-7_2.
Somatic variant callers identify mutations found within cancer genome sequencing data through mapping sequencing reads to a universal reference genome and inferring likelihoods from statistical models. False positives, however, are common among various tools as mismatches with the universal reference can also occur due to germline variants. Previous applications of personalized reference construction are not amenable with cancer genome analysis. Here, we describe an individualized approach for somatic variant discovery through the step-by-step usage of Personalized Reference Editor for Somatic Mutation discovery in cancer genomics (PRESM), a personalized reference editor for somatic mutation discovery in cancer genomes.
体细胞变异调用者通过将测序reads 映射到通用参考基因组并从统计模型中推断可能性，来识别癌症基因组测序数据中发现的突变。然而，各种工具中普遍存在假阳性，因为与通用参考的不匹配也可能由于种系变异而发生。之前的个性化参考构建应用程序不适用于癌症基因组分析。在这里，我们通过逐步使用个性化参考编辑器用于癌症基因组中的体细胞突变发现（PRESM）来描述一种个体化的体细胞变异发现方法，这是一种用于癌症基因组中体细胞突变发现的个性化参考编辑器。
