biostudies-literatureMarini SNational Institute of Allergy and Infectious DiseasesNational Institute of Food and Agriculture, Agriculture and Food Research InitiativeNIAID NIH HHSUnited States Department of AgricultureNational Institutes of HealthNational Science Foundationbbac020https://www.ebi.ac.uk/biostudies/studies/S-EPMC8921637biostudies-literatureUnknown23(2)Antimicrobial resistance (AMR) is a growing threat to public health and farming at large. In clinical and veterinary practice, timely characterization of the antibiotic susceptibility profile of bacterial infections is a crucial step in optimizing treatment. High-throughput sequencing is a promising option for clinical point-of-care and ecological surveillance, opening the opportunity to develop genotyping-based AMR determination as a possibly faster alternative to phenotypic testing. In the present work, we compare the performance of state-of-the-art methods for detection of AMR using high-throughput sequencing data from clinical settings. We consider five computational approaches based on alignment (AMRPlusPlus), deep learning (DeepARG), k-mer genomic signatures (KARGA, ResFinder) or hidBriefings in bioinformaticsTowards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing.PMC89216372019-67017-29110R01AI1418102013998R01 AI141810Prosperi MMora RAMarini SBoucher CRobertson Noyes NfalseTowards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing.Antimicrobial resistance (AMR) is a growing threat to public health and farming at large. In clinical and veterinary practice, timely characterization of the antibiotic susceptibility profile of bacterial infections is a crucial step in optimizing treatment. High-throughput sequencing is a promising option for clinical point-of-care and ecological surveillance, opening the opportunity to develop genotyping-based AMR determination as a possibly faster alternative to phenotypic testing. In the present work, we compare the performance of state-of-the-art methods for detection of AMR using high-throughput sequencing data from clinical settings. We consider five computational approaches based on alignment (AMRPlusPlus), deep learning (DeepARG), k-mer genomic signatures (KARGA, ResFinder) or hid2022-01-01T00:00:00Z2022 Mar2026-05-28T19:01:14.664Z2025-02-19T00:14:06.515ZS-EPMC89216373521235410.1093/bib/bbac020