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使用深度突变扫描对变异效应预测者的更新基准测试

Benjamin J Livesey¹, Joseph A Marsh¹

¹MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.

Molecular systems biology

|June 13, 2023

概括

深度突变扫描 (DMS) 为评估变异效应预测器 (VEP) 提供了一种不那么偏见的方法. 蛋白质语言模型和监督VEP显示有希望,DMS数据与临床变异识别相关.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
蛋白质科学蛋白质科学

背景情况:

评估变异效应预测器 (VEP) 性能受到临床数据基准测试偏差的挑战.
之前的工作建立了深度突变扫描 (DMS) 作为VEP评估的独立数据源.

研究的目的:

用独立的深度突变扫描 (DMS) 数据对26种人类蛋白质进行55种变异效应预测器 (VEP) 的基准测试.
评估VEP和DMS数据集在病原和良性误解变异之间区分的性能.
评估数据循环性和偏差对VEP绩效的影响.

主要方法:

使用26个人类蛋白质的DMS数据对55个VEP进行基准分析,最大限度地减少数据循环性.
使用无监督的方法 (EVE,DeepSequence,ESM-1v) 和监督的方法 (VARITY).
评估VEP和DMS在分类已知的致病性与假定良性误解变异中的表现.

主要成果:

没有监督的VEP,特别是蛋白质语言模型ESM-1v,以及像VARITY这样的监督VEP显示出强的表现.
在变种分类中,DMS数据集的性能差异很大.
观察到VEP与DMS数据的一致性及其识别临床相关变异的能力之间存在强烈的相关性.

结论:

使用DMS数据的独立基准测试对于公正的VEP评估至关重要.
蛋白质语言模型和监督VEP正在推进,解决了数据循环性问题.
DMS数据显示了用于验证VEP性能和识别临床显著变异的实用性.

关键词:

一个基准的基准.循环性是一种循环性.在 DMS 中使用 DMS.马维 (MAVE) 是一个很棒的平台.这是VEPEP.

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