Identification of antibiotic resistance genes from whole genome and metagenome sequencing datasets

Abstract Antimicrobial resistance (AMR) is an escalating global health challenge, with the rapid proliferation of antibiotic resistance genes (ARGs) undermining the efficacy of existing treatments and threatening decades of medical progress. The advent of next-generation sequencing technologies, coupled with machine learning algorithms, has revolutionized ARG identification and prediction in high-throughput genomics and metagenomics. Despite these advancements, selecting the most appropriate ARG resources remains challenging owing to significant variability in database structures, data curation methodologies, annotation depth, and coverage of resistance determinants. This review comprehensively analyzes widely used ARG resources, focusing on databases and computational tools. We examine the structural and functional characteristics of leading ARG databases, their strengths and limitations, and the diversity of metadata they incorporate. Additionally, we explore cutting-edge computational tools, such as AMRFinderPlus, DeepARG, and HMD-ARG, evaluating their underlying algorithms, predictive capabilities, and suitability for different research contexts, including the detection of complex or low-abundance ARGs. This review bridges a critical gap in the literature, which often focuses on either databases or algorithms in isolation. Moreover, our findings are expected to support researchers in selecting appropriate resources for ARG detection and surveillance, enabling more accurate identification of resistance determinants and fostering the development of robust strategies to combat AMR.