Phactboost Predictions

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. The results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, meta-predictors, and deep learning-based approaches as well as state-of-the-art tools, AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.绝大多数用于预测变异效应的算法均依赖于进化保守性分析。然而，此类方法大多仅通过多序列比对计算进化保守性，却忽略了替换事件的进化背景。本团队既往研究中曾提出PHACT，一款可利用系统发育树的错义突变致病性评分预测工具。在此基础上，本文提出PHACTboost——一种基于梯度提升树的分类器，可将PHACT评分与多序列比对、系统发育树及祖先序列重建的相关信息进行融合。通过在精心构建的变异数据集上开展的全面实验，结果表明PHACTboost的性能优于dbNSFP数据库中收录的40款主流致病性预测工具，涵盖传统工具、元预测器、基于深度学习的方法，以及前沿工具如AlphaMissense、EVE与CPT-1。相较于上述方法，PHACTboost的性能优势在那些不同致病性预测工具结果相悖的疑难变异样本中尤为显著。本数据集提供了覆盖20191种蛋白质的2.15亿个氨基酸变异位点的预测结果。PHACTboost可助力我们加深对遗传疾病的认知，并推动更精准的临床诊断。