Krishnamachari Kiran, Carrié Hanaé, Skanderup Anders Jacobsen
Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
Methods Mol Biol. 2025;2932:291-301. doi: 10.1007/978-1-0716-4566-6_16.
Somatic variant detection is an important step in the analysis of cancer genomes for basic research as well as precision oncology. Here, we review existing computational methods for identifying somatic mutations from tissue as well as liquid biopsy samples. We then describe steps to run VarNet (Krishnamachari et al., Nat Commun 13:4248, 2022), a variant caller using deep learning, to accurately identify single nucleotide variants (SNVs) and short insertion-deletion (indels) mutations from next-generation sequencing (NGS) of tumor tissue samples.
体细胞变异检测是癌症基因组分析中基础研究以及精准肿瘤学的重要步骤。在此，我们综述了从组织以及液体活检样本中识别体细胞突变的现有计算方法。然后，我们描述了运行VarNet（Krishnamachari等人，《自然通讯》13:4248，2022）的步骤，VarNet是一种使用深度学习的变异检测工具，用于从肿瘤组织样本的下一代测序（NGS）中准确识别单核苷酸变异（SNV）和短插入缺失（indel）突变。
