Antimicrobial resistance(AMR)is an escalating global health challenge,with the rapid proliferation of antibiotic resistancegenes(ARGs)undermining the efficacy of existing treatments and threatening decades of medical ...Antimicrobial resistance(AMR)is an escalating global health challenge,with the rapid proliferation of antibiotic resistancegenes(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 revolutionizedARG 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 databasestructures,data curation methodologies,annotation depth,and coverage of resistance determinants.This reviewcomprehensively analyzes widely used ARG resources,focusing on databases and computational tools.We examinethe structural and functional characteristics of leading ARG databases,their strengths and limitations,and the diversityof 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 differentresearch contexts,including the detection of complex or low-abundance ARGs.This review bridges a criticalgap in the literature,which often focuses on either databases or algorithms in isolation.Moreover,our findings areexpected to support researchers in selecting appropriate resources for ARG detection and surveillance,enabling moreaccurate identification of resistance determinants and fostering the development of robust strategies to combat AMR.展开更多
文摘Antimicrobial resistance(AMR)is an escalating global health challenge,with the rapid proliferation of antibiotic resistancegenes(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 revolutionizedARG 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 databasestructures,data curation methodologies,annotation depth,and coverage of resistance determinants.This reviewcomprehensively analyzes widely used ARG resources,focusing on databases and computational tools.We examinethe structural and functional characteristics of leading ARG databases,their strengths and limitations,and the diversityof 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 differentresearch contexts,including the detection of complex or low-abundance ARGs.This review bridges a criticalgap in the literature,which often focuses on either databases or algorithms in isolation.Moreover,our findings areexpected to support researchers in selecting appropriate resources for ARG detection and surveillance,enabling moreaccurate identification of resistance determinants and fostering the development of robust strategies to combat AMR.
