BacARscan: A Comprehensive and Interactive Web-Resource to Discern Antibiotic Resistance Gene Diversity in –Omics Datasets

Abstract Regular surveillance of antibiotic resistance genes (ARGs) is important to understand the emergence and epidemiology of antibiotic resistance (AR) in clinical and environmental niches. With diminishing costs, NGS technologies are anticipated to replace classical microbiological and molecular methods for determination of AR. One major hindrance underlying identification and annotation of ARGs from WGS data is that a major part of genome databases contain fragmented genes/genomes (due to incomplete assembly). Herein, we propose a web resource of Bacterial ARGs, named as BacARscan (Bacterial Antibiotic Resistance scan), to detect, predict and characterize ARGs in metagenomic, genomic and proteomic data. The current version of BacARscan comprises 254 ARG models, each annotated with a resistant profile against different classes of antibiotics, resistance mechanism etc. Benchmarking on a combined dataset of AR and non-AR proteins found 92% precision & 95% F-measure. BacARscan can also discriminate between the protein families that are homologous but not all families are involved in the AR. BacARscan identified more ARGs in (a) gut microbiome and (b) datasets comprising short read genomic and proteomic sequences of ESKAPE pathogens. Analysis of clinical metagenomic data indicated its potential to complement and/or supplement WGS based identification of ARGs in clinical samples. BacARscan standalone software and web-server are freely available at http://proteininformatics.org/mkumar/bacarscan and github repository (https://github.com/University-of-Delhi-south-campus/BacARscan).