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Context is Everything: Integrating Genomics, Epidemiological and Clinical Data Using GenEpiO Emma Griffiths Brinkman Lab Simon Fraser University, Burnaby, Canada On behalf of the GenEpiO Development Team Will Hsiao & Damion Dooley (BC Public Health Lab), Emma Griffiths & Fiona Brinkman (SFU) Sept 15, 2016 1

Context is Everything: Integrating Genomics, Epidemiological and Clinical Data Using GenEpiO

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Page 1: Context is Everything: Integrating Genomics, Epidemiological and Clinical Data Using GenEpiO

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Context is Everything: Integrating Genomics, Epidemiological and Clinical

Data Using GenEpiO

Emma GriffithsBrinkman Lab

Simon Fraser University, Burnaby, Canada

On behalf of the GenEpiO Development TeamWill Hsiao & Damion Dooley (BC Public Health Lab),

Emma Griffiths & Fiona Brinkman (SFU)

Sept 15, 2016

Page 2: Context is Everything: Integrating Genomics, Epidemiological and Clinical Data Using GenEpiO

Each year, one in eight Canadians (or 4 million people)

get sick with a domestically acquired food-borne illness.http://www.phac-aspc.gc.ca/efwd-emoha/efbi-emoa-eng.php

Slide courtesy of Will Hsiao

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Genomic sequences don’t mean much without contextual information.

• Must be combined with contextual information to make sense of the data… FAST!

Contextual info = Lab, Clinical, Epi Data

What is the pathogen?

Where did the exposures occur?

When were the exposures?

Who is at risk?

What virulence factors or drug resistance

determinants are present?

How severe is the disease?

Why did the contamination happen?

Bacterial Genome 1Bacterial Genome 2Bacterial Genome 3

Bacterial Genome 4Bacterial Genome 5Bacterial Genome 6Bacterial Genome 7Bacterial Genome 8Bacterial Genome 9

OUTBREAK?

Whole genome sequencing is increasingly used for diagnostics in foodborne outbreaks and surveillance.

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The

of Contextual InformationIsn’t

STANDARDIZED

•Free text, short hand, granularity, misspelling, paper format

Page 5: Context is Everything: Integrating Genomics, Epidemiological and Clinical Data Using GenEpiO

• Ontologies help resolve issues of taxonomy, granularity and specificity

• Reduces time consuming manual processing and mining

Leafy Greens “has_disposition” Transmission Vehicle (E.coli)

Spinach Lettuce

Endive “is_a” Lettuce TypeIcebergSpinacia oleracea

Amaranthus hybridus

S. Africa

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Ontology, A Way of Structuring Information

• Standardized, well-defined hierarchy of terms

• Interconnected with logical relationships e.g. Endive “is_a” Lettuce type

Integrates Epi Exposure data

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GenEpiO: Genomic Epidemiology Application Ontology

Standardize fields/terms used to describe genomics, lab, clinical and epidemiological data critical for food-borne pathogen

outbreak investigations Provincial National Public Health Agency Public Health Agency (Hsiao, co-PI) (Van Domselaar, co-PI)

Academic/Public Brinkman (PI)

www.irida.ca

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GenEpiO: Combining 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)

Page 8: Context is Everything: Integrating Genomics, Epidemiological and Clinical Data Using GenEpiO

Current Ontology Development Focuses On 3 Key Areas

Food Antimicrobial

Resistance

Disease Surveillance

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(FoodON)(SurvO) (ARO)

See draft version at https://github.com/GenEpiO/genepio/wiki

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Ontologies are commonly encoded using OWL (Web Ontology Language).

• Markup language for sharing ontologies on the web • Machine and human-readable• OWL statements written in RDF • Expressed in XML syntax

• Protégé (editor)• Ontology lookup services:

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Use ontology to identify common exposures, symptoms etc among genomics clusters

Example: Automating Case Definition generationCorrelate Genomics Salmonella Cluster A cases between 01 Mar 2014- 15 Mar 2014 with High-Risk Food Types Chicken Nuggets and Geographical Location of Vancouver

XXXXXXXXXXXXXXGenEpiO Will Help Integrate Genomics and

Epidemiological Data.

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Genomic Epidemiology Ontology is like instrumentation for your contextual

information…it needs maintenance and continual

improvement

To achieve consensus and uptake International GenEpiO Consortium

Join us!E-mail: [email protected] 11

(>60 members from 15 countries)

GenEpiO facilitates data sharing!

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Context is everything in foodborne outbreak investigations.

GenEpiO helps fit the data together.

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Project LeadersFiona Brinkman – SFUWill Hsiao – PHMRLGary Van Domselaar – NML

Simon Fraser University (SFU)Emma GriffithsGeoff WinsorJulie ShayMatthew LairdBhav Dhillon

McMaster UniversityAndrew McArthurDaim Sardar

European Food Safety AgencyLeibana Criado ErnestoVernazza FrancescoRizzi Valentina

[email protected]

National Microbiology Laboratory (NML)Franklin BristowAaron PetkauThomas MatthewsJosh AdamAdam OlsenTara LynchShaun TylerPhilip MabonPhilip AuCeline NadonMatthew Stuart-EdwardsMorag GrahamChrystal BerryLorelee TschetterEduardo ToboadaPeter KruczkiewiczChad LaingVic GannonMatthew WhitesideRoss DuncanSteven Mutschall

University of LisbonJoᾶo Carriҫo

European Bioinformatics InstituteMelanie CourtotHelen Parkinson

BC Public Laboratory and BC Centre for Disease Control (BCCDC)Damion DooleyJudy Isaac-RentonPatrick TangNatalie PrystajeckyJennifer GardyLinda HoangKim MacDonaldYin ChangEleni GalanisMarsha TaylorJennifer Law

University of MarylandLynn Schriml

Canadian Food Inspection Agency (CFIA)Adam KoziolBurton BlaisCatherine Carrillo

Dalhousie UniversityRob BeikoAlex Keddy www.irida.ca