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Title: Antimicrobial resistance prediction in PATRIC and RAST

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 (, 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%. Lastly, 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.
 [1] ;  [2] ;  [1] ;  [3] ;  [3] ;  [1] ;  [4] ;  [5] ;  [1] ;  [3] ;  [3] ;  [1] ;  [1]
  1. Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Lemont, IL (United States)
  2. Gydle Inc., Chanoine Morel Quebec, QC (Canada)
  3. Biocomplexity Institute of Virginia Tech., Blacksburg, VA (United States)
  4. Argonne National Lab. (ANL), Lemont, IL (United States); The Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States)
  5. Univ. of Chicago, Chicago, IL (United States)
Publication Date:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 6; Journal ID: ISSN 2045-2322
Nature Publishing Group
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
Country of Publication:
United States
59 BASIC BIOLOGICAL SCIENCES adaptive boosting; genome annotation; machine learning; random forest; support vector machines; computational biology and bioinformatics; genetic databases; machine learning