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

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.