BacARscan: an in silico resource to discern diversity in antibiotic resistance genes.

Antibiotic resistance has escalated as a significant problem of broad public health significance. Regular surveillance of antibiotic resistance genes (ARGs) in microbes and metagenomes from human, animal and environmental sources is vital to understanding ARGs' epidemiology and foreseeing the emergence of new antibiotic resistance determinants. Whole-genome sequencing (WGS)-based identification of the microbial ARGs using antibiotic resistance databases and prediction tools can significantly expedite the monitoring and characterization of ARGs in various niches. The major hindrance to the annotation of ARGs from WGS data is that most genome databases contain fragmented genes/genomes (due to incomplete assembly). Herein, we describe an Bacterial Antibiotic Resistance scan (BacARscan) (http://proteininformatics.org/mkumar/bacarscan/) that can detect, predict and characterize ARGs in -omics datasets, including short sequencing, reads, and fragmented contigs. Benchmarking on an independent non-redundant dataset revealed that the performance of BacARscan was better than other existing methods, with nearly 92% Precision and 95% F-measure on a combined dataset of ARG and non-ARG proteins. One of the most notable improvements of BacARscan over other ARG annotation methods is its ability to work on genomes and short-reads sequence libraries with equal efficiency and without any requirement for assembly of short reads. Thus, BacARscan can help monitor the prevalence and diversity of ARGs in microbial populations and metagenomic samples from animal, human, and environmental settings. The authors intend to constantly update the current version of BacARscan as and when new ARGs are discovered. Executable versions, source codes, sequences used for development and usage instructions are available at (http://www.proteininformatics.org/mkumar/bacarscan/downloads.html) and GitHub repository (https://github.com/mkubiophysics/BacARscan).