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实证贝叶斯单核酸变体 - - 要求下一代测序数据

Ali Karimnezhad^1,2, Theodore J Perkins^3,4

¹Department of Mathematics and Statistics, University of Ottawa, Ottawa, K1N 9A7, Canada. a.karimnezhad@uottawa.ca.

Scientific reports

|January 17, 2024

概括

在癌症基因组学中准确识别单核酸变体 (SNV) 是一个挑战. 本研究引入了一种新的局部错误发现率 (LFDR) 方法,与SNV调用和优先级的现有方法相匹配或超越.

科学领域:

计算基因组学是一种计算基因组学.
生物信息学是一种生物信息学.
癌症研究癌症研究

背景情况:

精确识别单核酸变体 (SNVs) 对癌症基因组学至关重要.
现有的SNV调用算法在现实世界数据集上表现出显著的分歧.
需要强大可靠的方法来检测和优先考虑生殖线SNV.

研究的目的:

开发一种新的本地错误发现率 (LFDR) 估计器,用于生殖线SNV呼叫.
创建一个基于LFDR的算法来优先考虑其他变量调用工具所调用的SNV.
根据现有的最先进的方法,评估拟议的LFDR方法的性能.

主要方法:

使用经验贝叶斯方法来开发LFDR估计器.
该方法在没有事先信息的情况下学习模型参数,利用全区域基因组数据.
开发了基于LFDR的第二个算法,用于优先考虑其他算法的变量调用.

主要成果:

拟议的LFDR方法表现出与广泛使用的SNV呼叫程序在黄金标准蜂线数据上的性能相当或超过.
使用LFDR分数对变量调用进行优先排序,可使精度显著提高,灵敏度损失最小.
LFDR方法有效地解决了在不同最先进的SNV呼叫者之间观察到的差异.

结论:

开发的LFDR估计器为生殖线SNV提供了强大的和准确的方法,用于癌症基因组学.
基于LFDR的优先级算法为改进变量调用和提高精度提供了有价值的工具.
这种方法提高了SNV识别的可靠性,这对于推进癌症研究和诊断至关重要.

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