一种基于回归的方法来从多样本的瘤大批量DNA测序中进行遗传学重建
Henri Schmidt1, Benjamin J Raphael1
1Department of Computer Science, Princeton University, New Jersey, United States of America.
PLoS computational biology
|December 4, 2024
概括
我们开发了fastBE,这是一个可扩展的计算方法,用于从大量DNA测序数据中重建癌症系谱. fastBE准确地推断瘤演变,在准确性,效率和运行时间方面超过现有方法.
科学领域:
- 计算生物学 计算生物学
- 癌症基因组学 癌症基因组学
- 遗传学上的推理.
背景情况:
- 从批量DNA测序中重建癌症系谱是由于瘤异质性的计算挑战.
- 现有的遗传学重建方法难以扩展到具有众多子群的大型数据集.
- 准确的瘤基因重建需要同时对癌症克隆进行解,并推断祖先关系.
研究的目的:
- 开发一种可扩展和准确的计算方法,用于从多样本批量DNA测序数据的基因结构重建.
- 提高瘤基因推断的准确性,样本效率和运行时间,特别是对于大型和复杂的数据集.
主要方法:
- 开发了fastBE,这是一种新的方法,将族系遗传重建分为结构化回归和树空间优化.
- 为回归子问题提取了一种高效的算法,其性能优于线性编程 (LP) 方法.
- 实现了用于遗传学推断的fastBE,使得从单个突变直接重建和新的遗传学约束突变聚类算法成为可能.
主要成果:
- fastBE在数百个样本和数千个子群体的模拟数据上展示了卓越的重建精度,样本效率和运行时间.
- 在真实白血病患者数据上,fastBE推断出具有较少进化约束违规性和更好的突变频率一致性的族系.
- 与最先进的方法相比,基于fastBE的突变聚类实现了较低的扭曲,并在结直肠癌模型中显示了较少的进化约束违规.
结论:
- fastBE提供了一种可扩展和准确的解决方案,用于从批量DNA测序数据中重建瘤系谱.
- 该方法可以直接重建突变原始物和改进突变聚类,推进癌症亚克隆进化研究.
- fastBE在真实患者数据上的表现验证了其在理解癌症演变和异质性方面的有用性.
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