AllelePred: A Simple Allele Frequencies Ensemble Predictor for Different Single Nucleotide Variants

Turki Sobahy, Olaa Motwalli, Meshari Alazmi

Research output: Contribution to journalArticlepeer-review

Abstract

Background & Objective: Genomic medicine stands to be revolutionized by understanding single nucleotide variants (SNVs) and their expression in single-gene disorders (Mendelian diseases). Computational tools can play a vital role in the exploration of such variations and their pathogenicity. Consequently, we developed the ensemble prediction tool AllelePred to identify deleterious SNVs and disease causative genes. Results: The model utilizes different population genetics backgrounds and restricted criteria for features selection to help generate high accuracy results. In comparison to other tools, such as Eigen, PROVEAN, and fathmm-MKL our classifier achieves higher accuracy (98%), precision (96%), F1 score (93%), and coverage (100%) for different types of coding variants. The new method was also compared against a bioinformatics analytical workflow, which uses gnomAD overall AFs (less than 1%) and CADD (scaled C-score of at least 15). Furthermore, this research highlights the stature of genetic variant sharing and curation. We accumulated a list of highly probable deleterious variants and recommended further experimental validation before medical diagnostic usage. Conclusions: The ensemble prediction tool AllelePred enables increased accuracy in recognizing deleterious SNVs and the genetic determinants in real clinical data.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
DOIs
StatePublished - Mar 3 2022

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