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Rapid prediction of antimicrobial resistance from metagenomic samples: data, models, and methods

Status: 
Active
Competition: 
2015 Bioinformatics and Computational Biology B/CB
Sector: 
Health
Agriculture and Agri Food
Environment
Genome Centre(s):
Genome Atlantic, Ontario Genomics
Project Leader(s):
Robert Beiko (Dalhousie University), Andrew G. McArthur (McMaster University)
GE3LS: 
No
Fiscal Year Project Launched: 
2016-2017
Project Description: 

Antimicrobials (antibiotics), have been central to combating infectious disease for nearly a century. However, their effectiveness is slipping due to the increase in antimicrobial resistance (AMR). There is an increasingly urgent need to know more about AMR to better understand its consequences and monitor its presence in the environment, agri-foods industry, individual patients, and on a population level. Being able to analyze the genomes of resistant microorganisms is essential, but slow and costly to do one at a time. Metagenomics allows genetic profiling of microbes as a community, but datasets are huge and contain much irrelevant data. Currently, there is no software designed to specifically predict AMR profiles directly from metagenomic data, which would enable more rapid AMR profiling and aid prioritization of candidate genes for further research.

Drs. Robert Beiko of Dalhousie University and Andrew G. McArthur of McMaster University are leading a project to develop new software and database tools that will provide a near-instantaneous picture of AMR organisms in a sample, aiding AMR research and responding to AMR threats impacting both agri-food production and public health.