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使用全基因组测序数据系统评估新的突变调用工具

Anushi Shah^1,2, Steven Monger^1,2, Michael Troup¹

¹Victor Chang Cardiac Research Institute, 405 Liverpool St, Darlinghurst, Sydney, 2010 NSW, Australia.

Briefings in bioinformatics

|November 8, 2025

概括

准确检测新的突变 (DNM) 对于诊断发育障碍至关重要. 这项研究系统地比较了使用真实和模拟全基因组测序数据的五种DNM调用工具,揭示了低一致率,并为工具选择提供了建议.

科学领域:

遗传学是一个遗传学.
生物信息学是一种生物信息学.
基因组医学是基因组医学.

背景情况:

新生突变 (DNM) 是后代的新型遗传变异,经常与严重的发育障碍有关.
准确识别DNM至关重要,特别是随着下一代测序 (NGS) 的日益使用.
有许多生物信息学工具用于DNM检测,但缺乏系统的比较.

研究的目的:

系统地评估和比较五个新突变 (DNM) 调用工具的性能.
通过真实和模拟的全基因组测序 (WGS) 三组数据来评估这些工具的一致性和准确性.
提供基于证据的建议,以选择合适的DNM呼叫者进行WGS三重分析.

主要方法:

利用了来自1000个基因组项目 (1000G) 的真实全基因组测序 (WGS) 三组数据.
采用了内部模拟的三组数据集,增加了100个已知的新突变 (DNM).
评估了五个DNM调用工具:DeNovoGear,TrioDeNovo,PhaseByTransmission,VarScan 2和DeNovoCNN. 这些工具包括:

主要成果:

观察到的低一致率:真实数据为8.4%,模拟数据为3.9%,大多数DNM只用一个工具识别.
在真实的1000G数据集上,DeNovoGear展示了最高的F1得分.
在模拟的WGS数据集上,DeNovoCNN获得了最高的F1分数.

结论:

目前的新突变 (DNM) 调用工具对全基因组测序 (WGS) 三组数据的一致性有限.
工具的性能在真实和模拟数据集之间有所不同,这突显了需要仔细选择的需要.
这种比较分析为研究人员和临床医生在选择和应用DNM呼叫者方面提供了宝贵的指导.

关键词:

在新突变 (DNMs) 的新突变.有错误的阳性结果.变量调用工具是变量调用工具.整个基因组的测序.

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