Antimicrobial Resistance Prediction in PATRIC and RAST

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dc.contributor.authorDavis, James J.en
dc.contributor.authorBoisvert, Sebastienen
dc.contributor.authorBrettin, Thomasen
dc.contributor.authorKenyon, Ronald W.en
dc.contributor.authorMao, Chunhongen
dc.contributor.authorOlson, Robert D.en
dc.contributor.authorOverbeek, Rossen
dc.contributor.authorSanterre, Johnen
dc.contributor.authorShukla, Mauliken
dc.contributor.authorWattam, Alice R.en
dc.contributor.authorWill, Rebeccaen
dc.contributor.authorXia, Fangfangen
dc.contributor.authorStevens, Rick L.en
dc.date.accessioned2019-01-23T14:27:29Zen
dc.date.available2019-01-23T14:27:29Zen
dc.date.issued2016-06-14en
dc.description.abstractThe emergence and spread of antimicrobial resistance (AMR) mechanisms in bacterial pathogens, coupled with the dwindling number of effective antibiotics, has created a global health crisis. Being able to identify the genetic mechanisms of AMR and predict the resistance phenotypes of bacterial pathogens prior to culturing could inform clinical decision-making and improve reaction time. At PATRIC (http://patricbrc.org/), we have been collecting bacterial genomes with AMR metadata for several years. In order to advance phenotype prediction and the identification of genomic regions relating to AMR, we have updated the PATRIC FTP server to enable access to genomes that are binned by their AMR phenotypes, as well as metadata including minimum inhibitory concentrations. Using this infrastructure, we custom built AdaBoost (adaptive boosting) machine learning classifiers for identifying carbapenem resistance in Acinetobacter baumannii, methicillin resistance in Staphylococcus aureus, and beta-lactam and co-trimoxazole resistance in Streptococcus pneumoniae with accuracies ranging from 88-99%. We also did this for isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin resistance in Mycobacterium tuberculosis, achieving accuracies ranging from 71-88%. This set of classifiers has been used to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.en
dc.description.notesThis work was supported by the United States National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Service [Contract No. HHSN272201400027C]. We thank Emily Dietrich for her careful editing.en
dc.description.sponsorshipUnited States National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Service [HHSN272201400027C]en
dc.format.extent12en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/srep27930en
dc.identifier.issn2045-2322en
dc.identifier.other27930en
dc.identifier.pmid27297683en
dc.identifier.urihttp://hdl.handle.net/10919/86847en
dc.identifier.volume6en
dc.language.isoen_USen
dc.publisherSpringer Natureen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectmycobacterium-tuberculosisen
dc.subjectantibiotic-resistanceen
dc.subjectstaphylococcus-aureusen
dc.subjectacinetobacter-baumanniien
dc.subjectmutationsen
dc.subjectgeneen
dc.subjectemergenceen
dc.subjectresourceen
dc.subjectdatabaseen
dc.subjectmachineen
dc.titleAntimicrobial Resistance Prediction in PATRIC and RASTen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
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