Three of the 16 new Genome Canada bioinformatics grants announced today build on the IRIDA platform.

Genomic Epidemiology Application Ontology (GenEpiO)
Project leaders: William Hsiao, University of British Columbia; Andrew G. McArthur, McMaster
University; Fiona S.L. Brinkman, Simon Fraser University

Infectious disease outbreaks have significant impacts on human health, agri-food production,
animal health and the economy. Ineffective public health responses can result in outbreaks that
spread diseases like the Zika virus and food-borne illnesses, with enormous impacts on health
and high economic costs. DNA sequencing provides the complete “fingerprint” of a microbe,
enabling an unprecedented tracing of how infectious diseases spread. When outbreaks become
global, however (think SARS, or microbes resistant to antimicrobials) data needs to be shared
across public health organizations securely and efficiently. Unfortunately, data is often held in
institution-specific formats, making it difficult, time consuming and costly to do so.
Drs. William Hsiao (UBC), Andrew G. McArthur (McMaster University) and Fiona Brinkman
(Simon Fraser University) will improve data integration and sharing of infectious disease and
antimicrobial resistance information across public health agencies, with the Genomic
Epidemiology Application Ontology (GenOpiO). The platform will enable public health workers
to share outbreak-related information faster and to perform more powerful analyses, helping
to reduce the negative health and economic impact of disease outbreaks.


PathOGIST: Calibrated multi-criterion genomic analysis for public health
Project leaders: Leonid Chindelevitch, Simon Fraser University; William Hsiao, University of British
Columbia; Cedric Chauve, Simon Fraser University

Public health agencies protect the population from infectious diseases by monitoring diseasecausing
agents (pathogens) and intervening in disease outbreaks. Without these concerted
efforts, our population’s health would be endangered. Today, public health workers can
conduct pathogen whole-genome sequencing to investigate risks and determine the source of
outbreaks. But the challenge of translating that data into actionable interventions remains.
Drs. Leonid Chindelevitch, Cedric Chauve (Simon Fraser University) and William Hsiao (UBC) are
developing a statistically sound and robust computational framework called PathOGIST to
enable public health workers and others to quickly classify pathogens into epidemiologically
related groups based on sequencing data and generate interpreted genomic reports to inform
their actions. PathOGIST will revolutionize how disease outbreaks are managed, ensuring faster
responses that will significantly reduce the impact of these outbreaks on both health and the


Rapid prediction of antimicrobial resistance from metagenomic samples: data,
models, and methods
Project leaders: Robert Beiko, Dalhousie University; Andrew G. McArthur, McMaster University;
Fiona Brinkman, Simon Fraser University.

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, Andrew G. McArthur of McMaster University, and
Fiona Brinkman of Simon Fraser 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.