Context is Everything: Integrating Genomics, Epidemiological and Clinical Data Using GenEpiO
1. 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
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2. 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
3. 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 1
Bacterial Genome 2
Bacterial Genome 3
Bacterial Genome 4
Bacterial Genome 5
Bacterial Genome 6
Bacterial Genome 7
Bacterial Genome 8
Bacterial Genome 9
OUTBREAK?
Whole genome sequencing is increasingly used for diagnostics in
foodborne outbreaks and surveillance.
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5. • 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
7. GenEpiO: Combining Different Epi, Lab, Genomics and Clinical Data Fields
Lab Analytics
Genomics, PFGE
Serotyping, Phage typing
MLST, AMR
Sample Metadata
Isolation Source (Food, Host
Body Product,
Environmental), BioSample
Epidemiology Investigation
Exposures
Clinical Data
Patient demographics, Medical
History, Comorbidities,
Symptoms, Health Status
Reporting
Case/Investigation Status
GenEpiO
(Genomic Epidemiology
Application Ontology)
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8. 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
9. 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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10. Use ontology to
identify common
exposures, symptoms
etc among genomics
clusters
Example: Automating Case Definition generation
Correlate Genomics Salmonella Cluster A cases between 01 Mar 2014- 15 Mar 2014 with
High-Risk Food Types Chicken Nuggets and Geographical Location of Vancouver
XXXXXXXXXXXXXX
GenEpiO Will Help Integrate Genomics and
Epidemiological Data.
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11. 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: IRIDA-mail@sfu.ca
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(>60 members from 15 countries)
GenEpiO facilitates
data sharing!
12. Context is everything in foodborne outbreak investigations.
GenEpiO helps fit the data together.
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Project Leaders
Fiona Brinkman – SFU
Will Hsiao – PHMRL
Gary Van Domselaar – NML
Simon Fraser University (SFU)
Emma Griffiths
Geoff Winsor
Julie Shay
Matthew Laird
Bhav Dhillon
McMaster University
Andrew McArthur
Daim Sardar
European Food Safety Agency
Leibana Criado Ernesto
Vernazza Francesco
Rizzi Valentina
IRIDA-mail@sfu.ca
National Microbiology Laboratory (NML)
Franklin Bristow
Aaron Petkau
Thomas Matthews
Josh Adam
Adam Olsen
Tara Lynch
Shaun Tyler
Philip Mabon
Philip Au
Celine Nadon
Matthew Stuart-Edwards
Morag Graham
Chrystal Berry
Lorelee Tschetter
Eduardo Toboada
Peter Kruczkiewicz
Chad Laing
Vic Gannon
Matthew Whiteside
Ross Duncan
Steven Mutschall
University of Lisbon
Joᾶo Carriҫo
European Bioinformatics Institute
Melanie Courtot
Helen Parkinson
BC Public Laboratory and
BC Centre for Disease Control
(BCCDC)
Damion Dooley
Judy Isaac-Renton
Patrick Tang
Natalie Prystajecky
Jennifer Gardy
Linda Hoang
Kim MacDonald
Yin Chang
Eleni Galanis
Marsha Taylor
Jennifer Law
University of Maryland
Lynn Schriml
Canadian Food Inspection Agency
(CFIA)
Adam Koziol
Burton Blais
Catherine Carrillo
Dalhousie University
Rob Beiko
Alex Keddy
www.irida.ca
Editor's Notes
So why do we care about foodborne diseases?
Despite our high standard in food safety, each year 1 in eight Canadian get food poisoning, costing the economy $4 billion dollars. It is important to track the source and spread of the disease to prevent further sickness
Consortium key to help develop consensus and wide adoption, international input and expertise
“Person, place, time”
Exposure, food items, geographical information, symptoms, onset of symptoms
Created (manually in excel) on ad hoc basis per investigation
Need to be shared between stakeholders, but data governance is an issue
Create a smaller core (Lab, Epi exposure, and Food) ontology for line-list testing
Create a consortium for group to take on different domains of Genomic Epidemiology Application Ontology
Pursuing longer term funding for ontology
