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

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VanBug Presentation of Genomic Epidemiology Application Ontology, GenEpiO (Sept 15 2016)

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

  1. 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 1
  2. 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. 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. 3
  4. 4. The of Contextual Information Isn’t STANDARDIZED •Free text, short hand, granularity, misspelling, paper format 4
  5. 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 5 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
  6. 6. 6 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. 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) 7
  8. 8. Current Ontology Development Focuses On 3 Key Areas Food Antimicrobial Resistance Disease Surveillance 8 (FoodON) (SurvO) (ARO) See draft version at https://github.com/GenEpiO/genepio/wiki
  9. 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: 9
  10. 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. 10
  11. 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 11 (>60 members from 15 countries) GenEpiO facilitates data sharing!
  12. 12. Context is everything in foodborne outbreak investigations. GenEpiO helps fit the data together. 12
  13. 13. 13 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

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