Antimicrobial Resistance Prediction in PATRIC and RAST

Sci Rep. 2016 Jun 14;6:27930. doi: 10.1038/srep27930.

Abstract

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Anti-Bacterial Agents / therapeutic use*
  • Bacterial Infections / drug therapy*
  • Clinical Decision-Making
  • Computational Biology
  • Data Curation
  • Databases, Genetic*
  • Drug Resistance, Microbial / genetics*
  • Genome, Bacterial / genetics*
  • Humans
  • Machine Learning
  • Microbial Sensitivity Tests
  • Molecular Sequence Annotation
  • National Institutes of Health (U.S.)
  • Prognosis
  • United States

Substances

  • Anti-Bacterial Agents