Scientific Reports
| Autor: | Rick Stevens, John Santerre, Thomas Brettin, Chunhong Mao, Sébastien Boisvert, Maulik Shukla, Ross Overbeek, Rebecca Will, Fangfang Xia, Robert Olson, Alice R. Wattam, James J. Davis, Ronald W. Kenyon |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Clinical Decision-Making Computational biology Bacterial genome size Drug resistance Microbial Sensitivity Tests mycobacterium-tuberculosis resource Bioinformatics Article Bacterial genetics acinetobacter-baumannii Mycobacterium tuberculosis Machine Learning 03 medical and health sciences Antibiotic resistance Databases Genetic medicine emergence Humans gene machine database Data Curation Multidisciplinary biology staphylococcus-aureus Computational Biology Drug Resistance Microbial Molecular Sequence Annotation Bacterial Infections biology.organism_classification mutations Prognosis United States Acinetobacter baumannii Anti-Bacterial Agents 030104 developmental biology antibiotic-resistance National Institutes of Health (U.S.) Microbial genetics Rifampicin Genome Bacterial medicine.drug |
| Zdroj: | Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | The 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. United States National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Service [HHSN272201400027C] This 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. |
| Databáze: | OpenAIRE |
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