To deal with the huge number of novel protein‐coding variants identified by genome and exome sequencing studies, many computational variant effect predictors (VEPs) have been developed. Such predictors are often trained and evaluated using different variant data sets, making a direct comparison between VEPs difficult. In this study, we use 31 previously published deep mutational scanning (DMS) experiments, which provide quantitative, independent phenotypic measurements for large numbers of single amino acid substitutions, in order to benchmark and compare 46 different VEPs. We also evaluate the ability of DMS measurements and VEPs to discriminate between pathogenic and benign missense variants. We find that DMS experiments tend to be superior to the top‐ranking predictors, demonstrating the tremendous potential of DMS for identifying novel human disease mutations. Among the VEPs, DeepSequence clearly stood out, showing both the strongest correlations with DMS data and having the best ability to predict pathogenic mutations, which is especially remarkable given that it is an unsupervised method. We further recommend SNAP2, DEOGEN2, SNPs&GO, SuSPect and REVEL based upon their performance in these analyses.

中文翻译：

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使用深度突变扫描来基准变异效应预测因子并识别疾病突变。

为了处理基因组和外显子组测序研究发现的大量新型蛋白质编码变异，人们开发了许多计算变异效应预测器（VEP）。此类预测器通常使用不同的变体数据集进行训练和评估，这使得 VEP 之间的直接比较变得困难。在这项研究中，我们使用了 31 个先前发表的深度突变扫描 (DMS) 实验，这些实验为大量单一氨基酸取代提供了定量、独立的表型测量，以便对 46 种不同的 VEP 进行基准测试和比较。我们还评估了 DMS 测量和 VEP 区分致病性和良性错义变异的能力。我们发现 DMS 实验往往优于顶级预测因子，这证明了 DMS 在识别新型人类疾病突变方面的巨大潜力。在 VEP 中，DeepSequence 明显脱颖而出，显示出与 DMS 数据最强的相关性，并且具有最好的预测致病突变的能力，考虑到它是一种无监督方法，这一点尤其引人注目。根据 SNAP2、DEOGEN2、SNPs&GO、SuSPect 和 REVEL 在这些分析中的表现，我们进一步推荐它们。