Targeting bioinformatics tools to study the dissemination and spread of antibiotic resistant genes in the environment and clinical settings.

Abstract

Antibiotic resistance has expanded as a result of the careless use of antibiotics in the medical field, the food industry, agriculture, and other industries. By means of genetic recombination between commensal and pathogenic bacteria, the microbes obtain antibiotic resistance genes (ARGs). In bacteria, horizontal gene transfer (HGT) is the main mechanism for acquiring ARGs. With the development of high-throughput sequencing, ARG sequence analysis is now feasible and widely available. Preventing the spread of AMR in the environment requires the implementation of ARGs mapping. The metagenomic technique, in particular, has helped in identifying antibiotic resistance within microbial communities. Due to the exponential growth of experimental and clinical data, significant investments in computer capacity, and advancements in algorithmic techniques, the application of machine learning (ML) algorithms to the problem of AMR has attracted increasing attention over the past five years. The review article sheds a light on the application of bioinformatics for the antibiotic resistance monitoring. The most advanced tool currently being employed to catalog the resistome of various habitats are metagenomics and metatranscriptomics. The future lies in the hands of artificial intelligence (AI) and machine learning (ML) methods, to predict and optimize the interaction of antibiotic-resistant compounds with target proteins.