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GenEpiO: The Genomic Epidemiology Application Ontology for the Standardization and Integration of Microbial Genomic, Clinical and Epidemiological Data

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  1. 1. GenEpiO: The Genomic Epidemiology Application Ontology for the Standardization and Integration of Microbial Genomic, Clinical and Epidemiological Data Emma Griffiths1, Damion Dooley2, Mélanie Courtot3, Josh Adam4, Franklin Bristow4, João A Carriço5, Bhavjinder K. Dhillon1, Alex Keddy6, Matthew Laird3, Thomas Matthews4, Aaron Petkau4, Julie Shay1, Geoff Winsor1, the IRIDA Ontology Advisory Group7, Robert Beiko6, Lynn M Schriml8, Eduardo Taboada9, Gary Van Domselaar4, Morag Graham4, Fiona Brinkman1 and William Hsiao2. www.irida.ca 1Simon Fraser University, Greater Vancouver, BC, Canada; 2 BC Public Health Microbiology and Reference Laboratory, Vancouver, BC, Canada; 3 European Bioinformatics Institute, Hinxton, Cambridge, UK; 4National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada; 5Faculty of Medicine, University of Lisbon, Lisbon, Portugal; 6Dalhousie University, Halifax, NS, Canada; 7BC Centre for Disease Control, Vancouver, BC, Canada; 8University of Maryland School of Medicine, Baltimore, MD, USA; 9National Microbiology Laboratory, Public Health Agency of Canada, Lethbridge, AB, Canada Background • No single existing ontology can adequately describe all the domains required for a genomic epidemiology • Information sharing between jurisdictions is complex • Not all jurisdictions collect/store/report the same information • Fears of compromising investigations, IP concerns make Provinces “metadata sharing risk averse” Public Health Data Sharing and Information Flow in Canada Mapping Genomic Goal of IRIDA Ontology To design and implement a genomic epidemiology application ontology suite to support the exchange and sharing of Public Health metadata and genomic sequence data. Methods 1. Interview users to model data flow 2. Resource reviews 3. Test application with real public health data Results and Deliverables 1. OWL File Encoding Required Metadata Elements • GenEpiO combines different Epi, Lab, Genomics and Clinical data fields • Community contributions welcome. Contact: ontology-group-irida@googlegroups.com Acknowledgements Funded by Genome Canada, Genome BC, the Genomics R&D Initiative (GRDI), Cystic Fibrosis Canada and Compute Canada 3. Testing the IRIDA Ontology: Canada’s GRDI Pilot Project for Food and Water Safety • Disease, age, sex, status of case, episode dates, episode identifier and geographic indicator shared between provinces and federal agencies • For some diseases, there has been further agreement to provide an additional set of variables ("minimum data set”). Provincial Epidemiology National Microbiology Lab Provincial Lab Doctor Private or Hospital Lab LHA Patient Local Public Health Provincial Public Health National Public Health Federal Epidemiology Genomics Pathogen Taxonomy SOPS Diagnostic Test Result Report Laboratory- test centric Clinical- patient centric Epidemiology -case centric Host Taxonomy Symptoms Demographics Treatment Vaccines Drugs Geography Public Health Intervention Exposure Contact Food Travel Environment Temporal Info • Structured metadata is crucial for standardization, integration, querying and analysis i.e. to make sense of genomic data Future Directions: Formation of Ontology Consortia • FoodOn (Food Ontology) Consortium: https://github.com/FoodOntology • GenEpiO (Genomic Epidemiology) Consortium: http://github.com/Public-Health- Bioinformatics/IRIDA_ontology • Community contributions welcome. Contact: ontology-group-irida@googlegroups.com • GenEpiO implemented in “Metadata Manager” NCBI BioSample-compliant genome upload app, Timeline Line List visualizations • Need for standardized Food, Antimicrobial Resistance, Surveillance, Result Reporting vocabulary Line List visualizations based on GenEpiO fields: Timeline View Genomic Epidemiology Ontology Will Help Integrate Genomics and Epidemiological Data Acknowledgements Funded by Genome Canada, Genome BC, the Genomics R&D Initiative (GRDI), Cystic Fibrosis Canada and Compute Canada 2. Mapping Processes and Terms to Existing Ontologies Bioinformaticians

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