Translational Genome Informatics Laboratory, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Nat Methods. 2023 Dec;20(12):2058-2067. doi: 10.1038/s41592-023-02043-2. Epub 2023 Oct 12.
Rapid advances in sequencing and analysis technologies have enabled the accurate detection of diverse forms of genomic variants represented as heterozygous, homozygous and mosaic mutations. However, the best practices for mosaic variant calling remain disorganized owing to the technical and conceptual difficulties faced in evaluation. Here we present our benchmark of 11 feasible mosaic variant detection approaches based on a systematically designed whole-exome-level reference standard that mimics mosaic samples, supported by 354,258 control positive mosaic single-nucleotide variants and insertion-deletion mutations and 33,111,725 control negatives. We identified not only the best practice for mosaic variant detection but also the condition-dependent strengths and weaknesses of the current methods. Furthermore, feature-level evaluation and their combinatorial usage across multiple algorithms direct the way for immediate to prolonged improvements in mosaic variant detection. Our results will guide researchers in selecting suitable calling algorithms and suggest future strategies for developers.
测序和分析技术的快速发展使得能够准确检测代表杂合子、纯合子和镶嵌突变的各种形式的基因组变体。然而,由于在评估过程中面临的技术和概念上的困难,镶嵌变体调用的最佳实践仍然没有组织。在这里,我们提出了我们的基准,基于一个系统设计的全外显子水平的参考标准,模拟镶嵌样本,支持 354258 个控制阳性镶嵌单核苷酸变体和插入缺失突变和 33111725 个控制阴性。我们不仅确定了镶嵌变体检测的最佳实践,还确定了当前方法的条件依赖性的优缺点。此外,特征级别的评估及其在多个算法中的组合使用为镶嵌变体检测的即时和长期改进指明了方向。我们的结果将指导研究人员选择合适的调用算法,并为开发人员提供未来的策略。