gen epio immem_griffiths
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IRIDA’s Genomic Epidemiology Application Ontology (GenEpiO): Genomic, Clinical and Epidemiological Data
Standardization and Integration
Emma GriffithsBrinkman Lab
Simon Fraser University, Greater Vancouver, Canada
On behalf of the IRIDA Ontology WG (Will Hsiao & Damion Dooley (BC Public Health Lab), Fiona Brinkman (SFU)
IMMEM XI, Estoril, PortugalMarch 11, 2016
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Contextual Information is Crucial for Interpreting Genomics Data.
Microbial genomics is a high resolution tool for identification.
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Contextual Information Needs to be Shared…..So Keep the Next User in Mind.
International Partners Intervention Partners
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The
of Contextual InformationIsn’t
STANDARDIZED
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When Words Can Mean Different Things.
Semantic Ambiguity.
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“Ontologies are for the digital age what dictionaries were in the age of print.”
Logic
VocabularyHierarchy
Knowledge Extraction
Ontology
Ontology, A Way of Structuring Information.
• Standardized, well-defined hierarchy terms • interconnected with logical relationships• “knowledge-generation engine”
=
Ontologies Standardize Vocabulary and Enable Complex Querying.
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Simple Food Ontology Hierarchy
Animal Feed Poultry Water
Pellets Nuggets Deli Meats Bottled Well
Produce
Spinach Sprouts Whole Mice
Transmission through_ ingestion or contact
Treated by_filtration
Taxonomy_Spniacea oleracea
Preparation_Ready-to-Eat
Animal (Consumer)_Snake
Synonym_Cold Cuts
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Case Studies: Ontology Can Help Resolve Issues of Taxonomy, Granularity and Specificity.
Leafy Greens
Spinach Lettuce
EndiveIcebergSpinacia oleracea Amaranthus hybridus
Taxonomy_species found in N. America
Taxonomy_species found in S. Africa Equivalent Subtypes
of Lettuce
a) Taxonomy & Granularity
Poultry
Chicken Nuggets
b) Specificity
Breast
Processing_Ready-to-Eat
Composition_breading, spices, chicken breast
Location of Purchase_Retail (Grocery Store vs Butcher)
Preparation_marinated
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Ontology Acts Like A Rosetta Stone.
• Need a common language
• Humans AND computers need to read it
• Mapping allows interoperability AND customization
*ontologies can be translated into different human languages as wellRosetta Stone – Egypt, 196 BC• stone tablet translating same text
into different ancient languages
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Ontology Offers Faster, More Accurate Data Integration.
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The Mission: Developing an Ontology Resource for Genomic Epidemiology in Canada
To Develop a Useful Gen Epi Ontology, Engaging the End Users is Your TOP Priority.
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Medical & Environmental Microbiologists
Bioinformaticians
Surveillance Analysts & Lab Personnel
EpidemiologistsSoftware and Work Flows
Investigation ToolsInstrumentation
+ =
Interview users Examine resources
GenEpiO(Genomic Epidemiology Application Ontology)
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GenEpiO Combines Different Epi, Lab, Genomics and Clinical Data Fields.
Lab AnalyticsGenomics, PFGE
Serotyping, Phage typingMLST, AMR
Sample MetadataIsolation Source (Food, Host
Body Product, Environmental), BioSample
Epidemiology InvestigationExposures
Clinical DataPatient demographics, Medical
History, Comorbidities, Symptoms, Health Status
ReportingCase/Investigation Status
GenEpiO(Genomic Epidemiology Application Ontology)
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Use computers to identify common exposures, symptoms etc among genomics clusters
Example: Automating Case Definition generationCorrelate Genomics Salmonella Cluster A cases between 01 Mar 2015- 15 Mar 2015 with High-Risk Food Types Spinach Leafy Greens and Geographical Location of Vancouver
XXXXXXXXXXXXXXGenEpiO Will Help Integrate Genomics and Epidemiological Data
in the IRIDA Platform.
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Integrated Rapid Infectious Disease Analysis Platform
Find out more about IRIDA from Will Hsiao (BC Public Health Lab) on Sat Mar 12 in the Molecular Epidemiology and Public Health session!
Website: IRIDA.ca
Email: [email protected]
GitHub: https://github.com/phac-nml/irida
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GenEpiO has been Implemented in Different IRIDA Interfaces.
• Creates BioSample-Compliant Genome Submission Forms.
Metadata Manager: Data entry portal
• Implements GenEpiO terms• Facilitates descriptive metadata• Secure environment• Selective sharing
IRIDA Offers Line List Visualizations of Selectable Data Based on GenEpiO Fields.
1. Line List View
2. Timeline View
Hideable cases
Selectable fields
Travel
Symptoms and Onset
Exposure Types
Hospitalization
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GenEpiO
Testing Has Made GenEpiO More Robust.
• FWS Datasets
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GenEpiO is Standardizing Terms for Reporting and Quality Control.
• Reproducibility• Reproducibility• Reproducibility• Reproducibility
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A Genomic Epidemiology Ontology has Advantages for Public Health.
Improved Public Health
Investigation power!
1. Eliminates semantic ambiguity
2. Term-mapping allows customization
3. Faster data integration
4. Standardized quality control and result reporting trigger actionable events in same way
5. Reproducibility (accreditation, validation)
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The Future Ontology Development Will Focus On Three Key Areas.
Food Antimicrobial Resistance
Epidemiology
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Genomic Epidemiology Ontology is Like Instrumentation for Your Contextual Information…it Needs Maintenance and
Improvements.
We’re forming a Genomic Epidemiology Ontology Consortium.Join us!
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E-mail: [email protected] https://github.com/Public-Health-Bioinformatics/IRIDA_ontology
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Acknowledgements
Integrated Rapid Infectious Disease Analysis Projectwww.IRIDA.ca
Primary InvestigatorsFiona Brinkman – SFUWill Hsiao – PHMRLGary Van Domselaar – NML
Co-InvestigatorsDr. Rob Beiko - DalhousieDr. Eduardo Taboada - LFZDr. Morag Graham - NMLDr. Joᾶo Andre Carrico – University of Lisbon
National Microbiology Laboratory (NML)Franklin BristowAaron PetkauThomas MatthewsJosh AdamAdam OlsenTara LynchShaun TylerPhilip MabonPhilip AuCeline NadonMatthew Stuart-EdwardsChrystal BerryLorelee TschetterAleisha Reimer
Laboratory for Foodborne Zoonoses (LFZ)Eduardo ToboadaPeter KruczkiewiczChad LaingVic GannonMatthew WhitesideRoss DuncanSteven Mutschall
Simon Fraser University (SFU)Emma GriffithsGeoff WinsorJulie ShayBhav DhillonClaire Bertelli
BC Public Health Microbiology & Reference Laboratory (PHMRL) and BC Centre for Disease Control (BCCDC)Natalie PrystajeckyJennifer GardyLinda HoangKim MacDonaldYin ChangEleni GalanisMarsha TaylorDamion DooleyCletus D’Souza
University of MarylandLynn Schriml
Canadian Food Inspection Agency (CFIA)Adam KoziolBurton BlaisCatherine Carrillo
Dalhousie UniversityAlex Keddy