Institute of Computational Biology, Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany.
Department of Computer Science, TUM School of Computation, Information and Technology, Technical University Munich, 85748 Garching, Germany.
Bioinformatics. 2023 Jan 1;39(1). doi: 10.1093/bioinformatics/btac828.
Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole-genome sequencing (WGS) samples.
We present the convolutional neural network-based approach called DeepSom for detecting somatic single nucleotide polymorphism and short insertion and deletion variants in tumor WGS samples without a matched normal. We validate DeepSom by reporting its performance on five different cancer datasets. We also demonstrate that on WGS samples DeepSom outperforms previously proposed methods for tumor-only somatic variant calling.
DeepSom is available as a GitHub repository at https://github.com/heiniglab/DeepSom.
Supplementary data are available at Bioinformatics online.
体细胞突变通常通过分析肿瘤样本的 DNA 序列并结合匹配的正常样本进行调用。然而，并非总是可以获得匹配的正常样本，例如在回顾性分析或诊断环境中。对于这种情况，需要设计仅针对肿瘤的体细胞变异调用工具。以前提出的方法在全基因组测序 (WGS) 样本上表现不佳。
我们提出了一种基于卷积神经网络的方法，称为 DeepSom，用于在没有匹配正常样本的情况下检测肿瘤 WGS 样本中的体细胞单核苷酸多态性和短插入缺失变异。我们通过在五个不同的癌症数据集上报告其性能来验证 DeepSom。我们还证明，在 WGS 样本上，DeepSom 优于以前提出的仅针对肿瘤的体细胞变异调用方法。
DeepSom 可在 GitHub 存储库 https://github.com/heiniglab/DeepSom 上获得。
补充数据可在 Bioinformatics 在线获得。
