VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning

by Jiwoong Kim, David E. Greenberg, Reed Pifer, Shuang Jiang, Guanghua Xiao, Samuel A. Shelburne, Andrew Koh, Yang Xie, Xiaowei Zhan Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combinations. Given the rapid, ongoing increase in ava ilability of high-density genomic data for a diverse array of bacteria, development of algorithms that could utilize genomic information to predict phenotype could both be useful clinically and assist with discovery of heretofore unrecognized AMR pathways. To facilitate understanding of the connecti ons between DNA variation and phenotypic AMR, we developed a new bioinformatics tool, variant mapping and prediction of antibiotic resistance (VAMPr), to (1) derive gene ortholog-based sequence features for protein variants; (2) interrogate these explainable gene-level variants for their known or no vel associations with AMR; and (3) build accurate models to predict AMR based on whole genome sequencing data. We curated the publicly available sequencing data for 3,393 bacterial isolates from 9 species that contained AMR phenotypes for 29 antibiotics. We detected 14,615 variant genotypes and buil t 93 association and prediction models. The association models confirmed known genetic antibiotic resistance mechanisms, such asblaKPC and carbapenem resistance con...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research