Kang Senbai, Borgsmüller Nico, Valecha Monica, Markowska Magda, Kuipers Jack, Beerenwinkel Niko, Posada David, Szczurek Ewa
Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.
Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland.
Genome Biol. 2025 Aug 25;26(1):255. doi: 10.1186/s13059-025-03738-9.
With rapid advancements in single-cell DNA sequencing (scDNA-seq), various computational methods have been developed to study evolution and call variants on single-cell level. However, modeling deletions remains challenging because they affect total coverage in ways that are difficult to distinguish from technical artifacts. We present DelSIEVE, a statistical method that infers cell phylogeny and single-nucleotide variants, accounting for deletions, from scDNA-seq data. DelSIEVE distinguishes deletions from mutations and artifacts, detecting more evolutionary events than previous methods. Simulations show high performance, and application to cancer samples reveals varying amounts of deletions and double mutants in different tumors.
随着单细胞DNA测序(scDNA-seq)的快速发展,已经开发出各种计算方法来研究进化并在单细胞水平上识别变异。然而,对缺失进行建模仍然具有挑战性,因为它们对总覆盖率的影响方式难以与技术假象区分开来。我们提出了DelSIEVE,这是一种统计方法,可从scDNA-seq数据中推断细胞系统发育和单核苷酸变异,并考虑缺失情况。DelSIEVE能够区分缺失与突变及假象,比以前的方法检测到更多的进化事件。模拟显示出高性能,将其应用于癌症样本揭示了不同肿瘤中不同数量的缺失和双突变体。