Interpreting protein variant effects with computational predictors and deep mutational scanning.
Interpreting protein variant effects with computational predictors and deep mutational scanning.
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DOI：
10.1242/dmm.049510
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发表时间：
2022-06-01
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
近年来，遗传变异效应的计算预测方法发展迅速。这些项目为临床和研究实验室提供了快速和可扩展的方法来评估新变体的可能影响。然而，很难知道我们可以在多大程度上信任他们的结果。为了对它们的性能进行基准测试，预测因子通常针对已知致病性和良性变异的大型数据集进行测试。这些基准测试数据可能与用于训练一些监督预测器的数据重叠，这导致数据重用或循环，从而导致这些预测器的性能估计过高。此外，新的预测因子通常被其作者发现上级所有以前的预测因子，这表明在其基准测试中存在一定程度的计算偏差。被称为深度突变扫描的大规模功能测定为这个问题提供了一种可能的解决方案，提供了变异效应测量的独立数据集。在这篇综述中，我们讨论了预测方法学的一些关键进展，当前的基准测试策略以及如何使用来自深度突变扫描的数据来克服数据循环问题。我们还讨论了这种功能检测直接预测突变的临床影响的能力，以及这可能如何影响未来对变异效应预测因子的需求。摘要：变异效应预测因子（VEP）是旨在预测遗传或蛋白质变异致病的可能性的算法。在此，我们回顾基准程序和深度突变扫描作为验证VEP的方法。
Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. To benchmark their performance, predictors are often tested against large datasets of known pathogenic and benign variants. These benchmarking data may overlap with the data used to train some supervised predictors, which leads to data re-use or circularity, resulting in inflated performance estimates for those predictors. Furthermore, new predictors are usually found by their authors to be superior to all previous predictors, which suggests some degree of computational bias in their benchmarking. Large-scale functional assays known as deep mutational scans provide one possible solution to this problem, providing independent datasets of variant effect measurements. In this Review, we discuss some of the key advances in predictor methodology, current benchmarking strategies and how data derived from deep mutational scans can be used to overcome the issue of data circularity. We also discuss the ability of such functional assays to directly predict clinical impacts of mutations and how this might affect the future need for variant effect predictors. Summary: Variant effect predictors (VEPs) are algorithms that aim to predict the likelihood that a genetic or protein variant will be pathogenic. Herein, we review benchmarking procedures and deep mutational scanning as methods for validating VEPs.
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影响因子：
4.3
作者：
Bendl J;Stourac J;Salanda O;Pavelka A;Wieben ED;Zendulka J;Brezovsky J;Damborsky J
通讯作者：
Damborsky J
影响因子：
64.8
作者：
Findlay GM;Daza RM;Martin B;Zhang MD;Leith AP;Gasperini M;Janizek JD;Huang X;Starita LM;Shendure J
通讯作者：
Shendure J
影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
影响因子：
48
作者：
Fowler, Douglas M.;Fields, Stanley
通讯作者：
Fields, Stanley
影响因子：
14.9
作者：
Bromberg Y;Rost B
通讯作者：
Rost B