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Emma Griffiths ASM microbe gen_epio_poster

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

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Emma Griffiths ASM microbe gen_epio_poster

  1. 1. GenEpiO: The Genomic Epidemiology Application Ontology for 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. 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 • Whole genome sequencing (WGS) provides high resolution microbial pathogen typing for foodborne outbreak investigation Mapping Genomic 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 • Terms organized into hierarchies • Logical relationships being developed • Community contributions welcome. Contact: ontology-group-irida@googlegroups.com • Structured metadata is crucial for standardization, integration, querying and analysis i.e. to make sense of genomic data Genomic Epidemiology Ontology Will Help Integrate Genomics and Epidemiological Data Bioinformaticians Mapping GenomicFuture Directions: Formation of International Ontology Consortia • FoodOn (Food Ontology) Consortium: https://github.com/FoodOntology • GenEpiO (Genomic Epidemiology) Consortium http://github.com/Public-Health- Bioinformatics/IRIDA_ontology Acknowledgements Funded by Genome Canada, Genome BC, the Genomics R&D Initiative (GRDI), Cystic Fibrosis Canada and Compute Canada, with the support of AllerGen NCE Inc. www.fda.gov 4. Testing the IRIDA Ontology: Canada’s GRDI Pilot Project for Food and Water Safety • GenEpiO implemented in “Metadata Manager” NCBI BioSample- compliant genome upload form Line List visualizations based on GenEpiO fields: Timeline View 3. Implementing GenEpiO: IRIDA Visualizations Poster Number: 297 Presentation: Mon June 20 Simon Fraser University (778)782-5414 ega12@sfu.ca 2. Mapping Processes and Terms to Existing Ontologies Genomics Pathogen Taxonomy SOPS Diagnostic Tests Result Reports 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 Improved Public Health Investigation power! A Genomic Epidemiology Ontology has Advantages for Public Health. 1. Eliminates semantic ambiguity 2. Term-mapping allows customization 3. Faster data integration 4. Triggers actionable events in same way 5. Reproducibility (accreditation, validation) • No single existing ontology can adequately describe all the domains required for a genomic epidemiology Goal of Genomic Epidemiology Application Ontology (GenEpiO) To design and implement a genomic epidemiology application ontology to support the exchange and sharing of Public Health metadata and genomic sequence data. • HIPAA patient privacy fields flagged • Need for better: Food, Antimicrobial Resistance, Surveillance, Result Reporting vocabulary • Standardized, well-defined hierarchy terms • interconnected with logical relationships • “knowledge-generation engine” Ontologies Standardize Vocabulary and Enable Complex Querying. Resolves issues: • Synonyms • Taxonomy • Granularity • Specificity Join us! See draft version at https://github.com/GenEpiO/genepio www.irida.ca Example Food Hierarchies A) B)

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