cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA.

cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA.
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DOI:
10.1038/s41596-023-00807-w
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发表时间:
2023-05
期刊:
影响因子:
14.8
通讯作者:
Li, Wenyuan
Li, Wenyuan
中科院分区:
生物学1区
文献类型:
--
作者:
Li, Shuo;Hu, Ran;Small, Colin;Kang, Ting-Yu;Liu, Chun-Chi;Zhou, Xianghong Jasmine;Li, Wenyuan

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血液中的游离 DNA (cfDNA) 被视为肿瘤活检的替代品,具有许多临床应用,包括诊断癌症、指导癌症治疗和监测治疗反应。所有这些应用都依赖于一项不可或缺但尚未开发的任务:检测 cfDNA 的体细胞突变。由于 cfDNA 中的肿瘤分数较低,这项任务具有挑战性。最近,我们开发了cfSNV计算方法,这是第一个综合考虑cfDNA特性的用于灵敏检测cfDNA突变的方法。 cfSNV 远远优于主要用于识别实体瘤组织突变的传统方法。即使采用中等覆盖度(例如≥200×)测序,cfSNV 也可以准确检测 cfDNA 中的突变,这使得 cfDNA 的全外显子组测序 (WES) 成为各种临床应用的可行选择。在这里,我们提出了一个用户友好的 cfSNV 包,它具有快速计算和方便的用户选项。我们还为其构建了一个 Docker 镜像,旨在使计算背景有限的研究人员和临床医生能够轻松地在高性能计算平台和本地计算机上进行分析。从标准预处理的 WES 数据集(约 250× 和约 7000 万碱基对目标大小)进行突变调用可以在具有 8 个虚拟 CPU 和 32 GB 随机存取存储器的服务器上在 3 小时内完成。
Cell-free DNA (cfDNA) in blood, viewed as a surrogate for tumor biopsy, has many clinical applications, including diagnosing cancer, guiding cancer treatment and monitoring treatment response. All these applications depend on an indispensable, yet underdeveloped task: detecting somatic mutations from cfDNA. The task is challenging because of the low tumor fraction in cfDNA. Recently, we developed the computational method cfSNV, the first method that comprehensively considers the properties of cfDNA for the sensitive detection of mutations from cfDNA. cfSNV vastly outperformed the conventional methods that were developed primarily for calling mutations from solid tumor tissues. cfSNV can accurately detect mutations in cfDNA even with medium-coverage (e.g., ≥200×) sequencing, which makes whole-exome sequencing (WES) of cfDNA a viable option for various clinical utilities. Here, we present a user-friendly cfSNV package that exhibits fast computation and convenient user options. We also built a Docker image of it, which is designed to enable researchers and clinicians with a limited computational background to easily carry out analyses on both high-performance computing platforms and local computers. Mutation calling from a standard preprocessed WES dataset (~250× and ~70 million base pair target size) can be carried out in 3 h on a server with eight virtual CPUs and 32 GB of random access memory.
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