Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set
David Stein, Meltem Ece Kars, Yiming Wu, Çiğdem Sevim Bayrak, Peter D. Stenson, D.N. Cooper, Avner Schlessinger, Yuval Itan
Abstract
Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics. LoGoFunc outperforms other tools trained solely to predict pathogenicity for identifying pathogenic GOF and LOF variants and is available at https://itanlab.shinyapps.io/goflof/ .