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

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)