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
Context is Everything: Integrating Genomics, Epidemiological and Clinical Data Using GenEpiO
Context is Everything: Integrating
Genomics, Epidemiological and Clinical
Data Using GenEpiO
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
Each year, one in eight Canadians (or 4 million people)
get sick with a domestically acquired food-borne illness.
Slide courtesy of Will Hsiao
Genomic sequences don’t mean much without contextual information.
• Must be combined with contextual
information to make sense of the data…
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
determinants are present?
How severe is the disease?
Why did the
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
Whole genome sequencing is increasingly used for diagnostics in
foodborne outbreaks and surveillance.
of Contextual Information
•Free text, short hand, granularity, misspelling, paper format 4
• Ontologies help resolve issues of
taxonomy, granularity and specificity
• Reduces time consuming manual
processing and mining
Leafy Greens “has_disposition” Transmission Vehicle (E.coli)
Endive “is_a” Lettuce TypeIcebergSpinacia
Ontology, A Way of Structuring Information
• Standardized, well-defined hierarchy
• Interconnected with logical
relationships e.g. Endive “is_a”
Integrates Epi Exposure data
GenEpiO: Genomic Epidemiology Application Ontology
Standardize fields/terms used to describe genomics, lab, clinical and
epidemiological data critical for food-borne pathogen outbreak investigations
Public Health Agency Public Health Agency
(Hsiao, co-PI) (Van Domselaar, co-PI)
GenEpiO: Combining Different Epi, Lab, Genomics and Clinical Data Fields
Serotyping, Phage typing
Isolation Source (Food, Host
Patient demographics, Medical
Symptoms, Health Status
Current Ontology Development Focuses On 3 Key Areas
See draft version at https://github.com/GenEpiO/genepio/wiki
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:
Use ontology to
etc among genomics
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
GenEpiO Will Help Integrate Genomics and
Genomic Epidemiology Ontology is like instrumentation for
your contextual information…
it needs maintenance and continual improvement
To achieve consensus and uptake International GenEpiO Consortium
(>60 members from 15 countries)
Context is everything in foodborne outbreak investigations.
GenEpiO helps fit the data together.
Fiona Brinkman – SFU
Will Hsiao – PHMRL
Gary Van Domselaar – NML
Simon Fraser University (SFU)
European Food Safety Agency
Leibana Criado Ernesto
National Microbiology Laboratory (NML)
University of Lisbon
European Bioinformatics Institute
BC Public Laboratory and
BC Centre for Disease Control
University of Maryland
Canadian Food Inspection Agency