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

Antimicrobial Resistance Prediction in PATRIC and RAST.

Author information

1
University of Chicago, Computation Institute, 5735 South Ellis Avenue, Chicago, IL 60637, USA.
2
Argonne National Laboratory, 9700 Cass Ave, Lemont, IL 60439, USA.
3
Gydle Inc. 101-1332 Chanoine Morel Quebec, QC, G1S, 4B4, Canada.
4
Biocomplexity Institute of Virginia Tech, 1015 Life Science Cir, Blacksburg, VA 24061, USA.
5
The Fellowship for Interpretation of Genomes, 15w155 81st St, Burr Ridge, IL 60527, USA.
6
University of Chicago, Department of Computer Science, Ryerson Physical Laboratory, 1100 E 58th St, Chicago, IL 60637, USA.

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.

PMID:
27297683
PMCID:
PMC4906388
DOI:
10.1038/srep27930
[Indexed for MEDLINE]
Free PMC Article

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