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评估低频变量调用工具的性能,用于从短读深度测序数据中检测变量

Xudong Xiang¹, Bowen Lu¹, Dongyang Song¹

¹Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.

Scientific reports

|November 22, 2023

概括

准确检测低频变异对于生物医学研究至关重要. 基于UMI的工具,如DeepSNVMiner和UMI-VarCal,在敏感和精确的变体检测方面,比原始读取方法表现出更高的性能.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
分子生物学分子生物学

背景情况:

准确检测低频变异对于生物医学研究和临床应用至关重要.
下一代测序 (NGS) 由于高错误率而带来挑战,使真正的低级别变体的识别变得复杂.
现有的变体调用工具缺乏用于低频变体检测的系统性性能比较.

研究的目的:

系统地比较基于原始读数和基于UMI的变异调用器的性能,以检测低频单核酸变异 (SNV).
评估这些工具在深度测序数据中以低至0.025%的基因频率识别SNV的能力.

主要方法:

评估了四个基于原始读取的 (SiNVICT,outLyzer,Pisces,LoFreq) 和四个基于UMI的 (DeepSNVMiner,MAGERI,smCounter2,UMI-VarCal) 变体调用器.
利用了54个模拟数据集,其测序深度和变异性等位基因频率 (VAF) 不同,再加上两个参考数据和Horizon Tru-Q样本数据.
根据检测极限,灵敏度和精度评估变异调用性能.

主要成果:

基于UMI的调用器 (smCounter2除外) 在检测极限方面通常比基于原始读取的调用器表现更好.
序列深度对基于原始读取的呼叫者产生了重大影响,但对基于UMI的呼叫者影响很小.
DeepSNVMiner和UMI-VarCal表现出高灵敏度 (88%和84%) 和精度 (两者均为100%) 的最佳整体性能.

结论:

基于UMI的变体调用器,特别是DeepSNVMiner和UMI-VarCal,由于其优越的灵敏度和精度,建议用于可靠的低频变体检测.
这些发现为开发用于基因研究和临床实践中低频变异检测的先进算法提供了关键的见解.

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