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评估驱动基因识别工具对目标基因组测序数据的有效性

Felipe Rojas-Rodriguez¹, Marjanka K Schmidt^1,2,3, Sander Canisius^1,4

¹Division of Molecular Pathology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, 1066 CX Amsterdam, The Netherlands.

Bioinformatics advances

|May 29, 2024

概括

在针对性测序数据上测试了七种癌症驱动基因识别工具. 四个工具 (OncodriveFML,OncodriveCLUSTL,20/20+,dNdSCv,ActiveDriver) 建议用于目标测序,而两个则不适合.

科学领域:

基因组学就是基因组学.
癌症基因组学癌症基因组学
生物信息学是一种生物信息学.

背景情况:

全外因子测序 (WES) 是癌症驱动基因识别的标准.
针对性测序为大型癌症研究提供了具有成本效益的深度.
基于WES的工具对目标测序数据的适用性尚不清楚.

研究的目的:

评估现有的驱动基因识别工具对目标测序数据的性能.
确定流行工具对分析向癌症测序面板的适用性.
提供针对性测序研究中最佳工具选择的建议.

主要方法:

评估了七种流行的驱动基因识别工具.
利用了来自14种癌症类型 (TCGA) 的全外因组测序数据.
创建了模仿MSK-IMPACT和B-CAST面板的目标数据集.

主要成果:

在针对性测序数据上,工具的性能差异很大.
背景突变率建模中的差异影响了工具的有效性.
在OncodriveFML,OncodriveCLUSTL,20/20+,dNdSCv和ActiveDriver中显示出可靠的性能.
发现MutSigCV和DriverML不适合针对性测序.

结论:

为WES开发的驱动基因识别工具可能在针对性测序数据上表现不佳.
建议在目标测序中使用特定工具来准确发现驱动基因.
了解背景突变率建模对于工具选择至关重要.

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