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IRIDA’s Genomic Epidemiology Application Ontology (GenEpiO): Genomic, Clinical and Epidemiological Data Standardization and Integration Emma Griffiths Brinkman 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, Portugal March 11, 2016

IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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Page 1: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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

Page 2: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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Contextual Information is Crucial for Interpreting Genomics Data.

Microbial genomics is a high resolution tool for identification.

Page 3: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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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

Page 5: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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When Words Can Mean Different Things.

Semantic Ambiguity.

Page 6: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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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”

=

Page 7: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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

Page 10: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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Ontology Offers Faster, More Accurate Data Integration.

Page 11: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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The Mission: Developing an Ontology Resource for Genomic Epidemiology in Canada

Page 12: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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)

Page 13: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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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

Page 17: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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

Page 19: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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GenEpiO is Standardizing Terms for Reporting and Quality Control.

• Reproducibility• Reproducibility• Reproducibility• Reproducibility

Page 20: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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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)

Page 21: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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The Future Ontology Development Will Focus On Three Key Areas.

Food Antimicrobial Resistance

Epidemiology

Page 22: IRIDA's Genomic epidemiology application ontology (GenEpiO): Genomic, clinical and epidemiological data standardization and integration

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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