{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Marini S"],"funding":["National Institute of Allergy and Infectious Diseases","National Institute of Food and Agriculture, Agriculture and Food Research Initiative","NIAID NIH HHS","United States Department of Agriculture","National Institutes of Health","National Science Foundation"],"pagination":["bbac020"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8921637"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(2)"],"pubmed_abstract":["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 hid"],"journal":["Briefings in bioinformatics"],"pubmed_title":["Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing."],"pmcid":["PMC8921637"],"funding_grant_id":["2019-67017-29110","R01AI141810","2013998","R01 AI141810"],"pubmed_authors":["Prosperi M","Mora RA","Marini S","Boucher C","Robertson Noyes N"],"additional_accession":[]},"is_claimable":false,"name":"Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing.","description":"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 hid","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2026-05-28T19:01:14.664Z","creation":"2025-02-19T00:14:06.515Z"},"accession":"S-EPMC8921637","cross_references":{"pubmed":["35212354"],"doi":["10.1093/bib/bbac020"]}}