Introducing BCCDC and Public Health Microbiology (PHM) Current State of PHM Sequence Technology Advancement -> revolution of PHM Genomic Epidemiology Amount of Sequence Data Produced Need to Process the data – Introduction to IRIDA Need of Metadata and Ontology Software to improve data sharing How research microbiology and PHM can joint effort

1. IRIDA: A federated bioinformatics
platform enabling richer genomic
epidemiology analysis in public health
William Hsiao, Ph.D.
William.hsiao@bccdc.ca
@wlhsiao
BC Centre for Disease Control Public Health Laboratory
and University of British Columbia
March 21 2016, UT San Antonio

3. Roles of Public Health Agencies
• Public Health (PH) agencies around the world track and intervene the
spread of diseases to improve health of the population
• PH agencies also come up with policies and strategies to prevent
diseases from occurring
• PH laboratories test patient and environmental samples and
determine the cause of diseases
• At the BC Public Health Lab, we process on average, 3,000 samples a
day or about 1 million samples a year.

4. Dual Arms of a Public Health Agency
What did you
eat? Where
did you eat
that? When?
What strain of
Salmonella
Enteritidis is it?
Epidemiological
Investigation
Laboratory
Investigation
Identify common exposure
Identify the culprit pathogen
Confirmed
by Epi
Confirmed
by Lab

5. Current State of Clinical Microbiology Laboratory
Didelot et al. 2012. doi:10.1038/nrg3226.
• Culture to isolate
organisms using
different media
• Different
diagnostic tests
and typing and
subtyping
methods
• Different drug
sensitivity tests

6. Current Methods of Characterizing Foodborne
Pathogens in a Public Health Laboratory
• Growth characteristics
• Phenotypic panels
• Agglutination reactions
• Enzyme immuno assays (EIAs)
• PCR
• DNA arrays (hybridization)
• Sanger sequencing of marker genes
• DNA restriction
• Electrophoresis (PFGE, capillary)
Each pathogen is characterized by methods that are specific to that pathogen in
multiple workflows (separate workflows for each pathogen) TAT: 5 min – weeks
(months)
Source: Rebecca Lindsey

7. • Many incompatible
systems
• Paper and Fax
communication
common
• Rich case
information
conveyed verbally
or in free text
• Require data re-
entry and re-coding
National Ministry of
Health
Provincial / State
public health dept.
National laboratory
Local public
health dept.
Provincial /
State laboratory
Cases
Physicians Local laboratory
Fax/Electronic
Fax
Phone/Fax
Electronic/Paper
Electronic/Fax/Phone
Mailing of
Samples/Fax/Eelctronic
Source: M. Taylor, BCCDC
Current State of Public Health Epidemiology

8. The Era of Molecular Epidemiology
• Molecular test results are often more specific and sensitive than
traditional phenotypical or biochemical tests
• These biomarkers can be correlated to epidemiological investigations
(People, Place, Time)
• Provides linkage based on common exposure to the same pathogen
at the molecular level
BUT….
• Most tests detect one or a few of specific biomarkers, representing a
fraction of the pathogens’ genetic information
• As pathogens evolve, targeted tests can lose their specificity

9. Era of Whole Genome Sequencing (WGS) =
lots of High Quality Data
• Capture the pathogen’s entire genetic makeup
• Unbiased (~97-99+% of the genome captured using common sequencing approaches)
• Significantly more data than traditional methods
• Allow higher resolution and higher sensitivity analysis to be applied
• Allow value-added
evolutionary & Functional
study of the pathogens
• Virulence factors
• AMR genes
• These genomics data can be useful
for downstream research use (e.g.
comparative genomics)

10. NGS Reduces Sequencing Cost allowing PHM Sequencing
$10K per human genome or $10
per bacterial genome
$100M per human genome

11. Whole Genome Sequencing of Foodborne Pathogens
• UK Public Health England committed to sequence all the Salmonella
isolates submitted to PH Lab
• US FDA and CDC (supported by National Center for Biotechnology
Information) created a distributed network of labs to utilize WGS for
pathogen identification
https://publichealthmatters.blog.gov.uk/2014/01/20/innovations-in-genomic-sequencing/
http://www.fda.gov/Food/FoodScienceResearch/WholeGenomeSequencingProgramWGS/ucm363134.htm

12. PulseNet Canada
• Part of PulseNet International
• a global laboratory surveillance network of enteric pathogens
• based on Pulse Field Gel Electrophoresis (PFGE) fingerprint technology
• Originally developed at CDC Atlanta for E. coli O157:H7 Outbreak investigation
in 1993
• PulseNet Canada formed in 2000 and shares fingerprint data with
other PulseNet partners including direct database linkage with the
CDC
• PulseNet is transitioning from PFGE to WGS within 3 years
• Sequencing facilities are being setup in PH labs across Canada this
year

13. Whole Genome Sequenced Based Workflow
Didelot et al. 2012. doi:10.1038/nrg3226.

14. 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

15. Each year, one in six American (or 48 million people)
get sick with a domestically acquired food-borne illness.
http://www.cdc.gov/foodborneburden/

16. Improve Public Health Microbiology using
Genomic Epidemiology
• Genomic Epidemiology Definition: Using whole genome sequencing
data from pathogens and epidemiological investigations to track
spread of an infectious disease
• Lead to faster and simpler test menu and more actionable
information (virulence factors, AMR, source tracking)
• However, there are a few hurdles to overcome….

17. Many Players in surveillance and outbreak – ineffective
information sharing
Provincial public
health dept.
National laboratory
Local public
health dept.
Provincial
laboratory
Cases
Physicians Frontline lab
Information
BioinformaticsandAnalyticalCapacities

18. Sequencing Improvement outpaces Computing
Improvements
Cloud
Computing
Cluster Computing
Algorithm
improvement

19. IRIDA Platform Overview
• IRIDA= Integrated Rapid Infectious Disease Analysis
• A free, open source, standards compliant, high quality genomic
epidemiology analysis platform to support real-time disease
outbreak investigations
Core Functions:
• Management of strain and genomic sequence data
• Rapid processing and analysis of genomic data
• Informative display of genomic results
• Sample, Case, and aggregate data (“metadata”) Management
Target audience:
• Public health agencies who need a platform to manage and
process genomic data
• Public health agencies who need a platform to use genomics for
outbreak investigations
IRIDA
Sequencing
Instruments
Web
Application
Data
management
Built-in
Analytical
Tools
External
Galaxy
Command-
line Tools

20. IRIDA is a Partnership
National
Public Health Agency
Provincial
Public Health Agency
Academic/Public
- Project Team has direct access to state of the
art research in academia
- Project Team is directly embedded in user
organization

21. IRIDA Has A Simple User Interface
Line List View (under testing)
Timeline View (Conceptualization)
Selectable fields
Travel
Symptoms and Onset
Exposure Types
Hospitalization
Launch a pipeline

22. IRIDA is a Robust, Extensible Platform
• IRIDA uses Galaxy to
manage workflows
• Adding additional
pipelines is relatively
easy
• Using a standard
API to allow 3rd party
tools to obtain data
from IRIDA (e.g.
IslandViewer and
GenGIS)
IRIDA
ServletContainer
REST API
Central File
Storage
Web
Interface
ApplicationLogic
Compute
Cluster
Galaxy
$ ~ >_ Galaxy

23. IRIDA is Built to Enable Collaboration
• Be able to compare pipelines
• Pipeline implemented using Galaxy –
transparent and shareable
• Define QC criteria using ontology to compare
the different pipelines of the same purpose
• Be able to share data to minimize data re-
entry from one platform to another
• Federation of platforms using standard API
to share data and analysis results

24. Distributed in Multiple, Flexible Access Options
• IRIDA is available in several different flavours.
• Download latest version at https://github.com/phac-nml/irida
Local Install Virtual Machine Cloud Instance Public Version
Advantages Full control of the
system; your data
never leaves your
centre
Full control of the
system; Easy to setup
Full control of the
system; does not
require local
computing
infrastructure
No setup required,
upload your data and
have it processed
using Compute
Canada Resource
Disadvantages Computing
infrastructure and IT
support needed to
main the resource
Not really scalable if
run on your own
desktop; some
performance loss
Data goes into a
cloud environment;
uploading to cloud
environment can be
slow
Data goes into a
public instance (data
remain private to
your account);
upload can be slow

25. Contextual Information is Crucial for Interpreting Genomics
Data.
Sequence
+ =
Contextual Info Find the Pathogenic Culprit!
Source: Emma Griffiths

26. Contextual Information Needs to be Shared…..
So Keep the Next User in Mind.
International Partners Intervention Partners
Source: Emma Griffiths

27. The
of Contextual Information
Isn’t
STANDARDIZED
Source: Emma Griffiths

28. When Words Can Mean Different Things.
Semantic Ambiguity.
http://www.neurolang.com/wp-content/uploads/2013/05/RhymesAmbiguity.png

29. “Ontologies are for the digital age what dictionaries were in the age of print.”
Logic
Vocabulary
Hierarchy
Knowledge Extraction
Ontology
Ontology, A Way of Structuring Information.
• Standardized, well-defined hierarchy terms
• interconnected with logical relationships
• “knowledge-generation engine”
=
Source: Emma Griffiths

30. Ontologies Standardize Vocabulary and Enable Complex Querying
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
Source: Emma Griffiths

31. 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
Source: Emma Griffiths

32. 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
Source: Emma Griffiths

33. 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)
Source: Emma Griffiths

34. Use computers to
identify common
exposures, symptoms
etc among genomics
clusters
Example: Automating Case Definition generation
Correlate 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
XXXXXXXXXXXXXX
GenEpiO Will Help Integrate Genomics and Epidemiological
Data
Source: Emma Griffiths

35. Public Health
Surveillance
Case Cluster
Analysis
Result
Reporting
Infectious Disease Epidemiology (from case to Intervention)Lab Surveillance (from sample to strain typing results)
Evidence Collection
& Outbreak
Investigation
Sample Collection
& Processing
Sequence Data
Generation &
Processing
Bioinformatics
Analysis
Result
Reporting
Whole Genome
Sequencing (SO, ERO, OBI etc)
Quality Control (OBI, ERO)
Legend
GenEpiO
OBO
Other
Anatomy
(FMA)
Environment (Envo)
Food (FoodOn)
Clinical Sampling (OBI)
Custom LIMS
Quality Control (OBI, ERO)
AMR (ARO)
Virulence (PATO)
Phylogenetic Clustering (EDAM)
Mobile Elements (MobiO)
Quality Control (OBI, ERO)
Nomenclature & Taxonomy
(NCBItaxon)
AMR (ARO) LOINC
Surveillance (SurvO)
Demographics (SIO)
Patient History (SIO)
Symptoms (SYMP)
Exposures (ExO)
Source Attribution (IDO)
Travel (IDO)
Transmission (TRANS)
Food (FoodOn)
Geography (OMRSE)
Outbreak Protocols
Surveillance (SurvO)
Food (FoodOn)
Surveillance (SurvO)
Mobile Elements (MobiO)
Infectious Disease (IDO)
Typing (TypON)

36. Genomic Epidemiology Ontology: Using a Common Language to Get
Ahead of the Epidemiological Curve
Fewer
cases…faster
resolution!
Source: Emma Griffiths

37. Whole Genome Sequencing
Salmonella Enteritidis

38. 39
Higher Salmonellosis Incidents in BC
 Higher salmonellosis rate than Canada national rate since 2007:
 S. Enteritidis most commonly isolated serotype since 2006 (accounts for 30-50% of all Salmonella
isolates in BC)
BC
Canada
Source: http://www.bccdc.ca/NR/rdonlyres/B24C1DFD-3996-493F-BEC7-0C9316E57721/0/2011_CD_Annual_Report_Final.pdf

39. • PFGE: Over half of isolates tested are 1 of 2 XbaI patterns
• Phagetyping (PT): ~half of isolates are 1 PT.
• So a better method of subtyping is needed for discrimination between cases of Enteritidis…
– OR is a very large outbreak (no supporting data for this)
Enteritidis Xba Patterns 1998-2012
SENXAI.0003
SENXAI.0001
SENXAI.0038
SENXAI.0006
SENXAI.0036
SENXAI.0004
SENXAI.0007
SENXAI.0008
SENXAI.0062
SENXAI.0041
SENXAI.0077
SENXAI.0002
SENXAI.0025
SENXAI.0060
SENXAI.0009
Enteritidis PT distribution 1998-2012
8
13a
13
Atypical
6a
1
4
51
5b
41
Untypable
1b
Untypeable
21
14b
6
2
All have been PFGE’d but not all PT’d
S. Enteritidis subtyping in BC
Source: Kim Macdonald

40. Isolates and Methods
• 36 isolates from 9 confirmed food-borne outbreaks
• Collected over 9 years – many more isolates in the freezer waiting to be organized
• Subtyping data by PFGE and PT available
• Isolates from epi-linked sources available for 2 of the outbreaks
• Isolate Picking Criteria:
• believed to be single source outbreak (common food, common food handler or common
ingredients)
• clear epidemiological linkage through enhanced interviews
• majority of the clusters have the same PT and/or PFGE. Some have one PFGE band difference
• Sequencing library prepared using Nextera or Nextera XT
• Sequenced on Illumina MiSeq 150bp or 250bp paired-end
• Minimal depth cover 30X per genome (average coverage 50x)

41. SNP Analysis
• What is a SNP?
• A SNP (single nucleotide polymorphism) is DNA sequence variation occurring
when a single nucleotide differs between two or more genomes
ATCGCGATATCATACGG
ATCGCAATATCATACGG
ATCGCGATATCATACGG
ATCGCGATATCATACGG
ATCGCAATATCATACGG
• SNP can be created from point mutation but can also be created from
insertion and deletion of one nucleotide

42. Why are SNPs useful
• Silent mutations that do not change protein sequences happen quite
frequently due to DNA replication errors => High Resolution
• SNPs occurs across the whole genome and can be detected from
whole genome sequencing => Unbiased markers
• SNPs can be used to infer phylogeny of organisms
• More shared SNPs = more closely related

43. Minimal Spanning Tree – colored by PT
PT8
PT4
PT13a
PT52
Note: for PT13a, 3 isolates have identical SNVs and collapsed into a single
node; edges are not drawn to scale

44. Minimal Spanning Tree – Coloured by outbreak
Created using PhyloViz Online:
http://online.phyloviz.net/

45. Whole Genome Sequencing
Giardia lamblia (duodenalis)

46. Giardia
• Giardia is a primitive, eukaryote protozoa belonging to Diplomonads
• Its representatives are differentiated into 8 lineages (A-H) with 2 lineages
(A & B) infecting human. Genomes (A, B, E) of 3 lineages are available.
• G. duodenalis (lineage A & B) causes gastrointestinal disease (giardiasis) in
human and is spread by drinking water.
• There is over 1 billion cases/ year worldwide.
• In BC, various waterborne outbreaks have been reported (Isaac-Renton et
al. 1992, Safaris and Isaac-Renton 1992).
• The infection may be transmitted by drinking water or food.
• Giardia is often associated with an animal host (beaver, Castor
canadiensis), and giardiasis is called “beaver fever”.

47. Study Overview
 For the present study, 89 samples from 4 major
outbreaks (Creston, Kitimat, Revelstoke and
Barriere), as well as other events were included.
 Trophozoites were retrieved from -80C freezer,
and DNA were extracted from Giardia strains from
surface water, human and beaver using a QIAamp
DNA mini kit.
 The identity of isolates was confirmed by 18S
rRNA but 18S doesn’t differentiate subtypes
 Paired-end (PE) DNA libraries were constructed
with Nextera® XT DNA kit, and whole genome re-
sequencing was conducted by Illumina MiSeq.
Aldergrove
Dawson Creek
Kamloops
Terrace
Mission Creek
Source: Clement Tsui

48. Bioinformatics Pipelines
Genome Sequencing
(MiSeq)
Quality checking
(Fastqc,
Trim Galore)
Reference Mapping
(Bowtie)
Variant calling
(GATK or DiscoSNP)
SNPs analysis
De novo Assembly
(SPades)
Gene calling
(MAKER)
Comparative
Genomics
Source: Clement Tsui

49. Both A and B are present in outbreaks
0 2 4 6 8 10
Barriere
Kitimat
Creston
B
A2
A1
 Outbreaks could have multiple sources.
Source: Clement Tsui

50. VANC/89/UBC/33, Vancouver, Canada
VANC/87/UBC/28, Aldergrove, Canada
B5/19, Calgary, Canada
VANC/90/UBC/43, Creston, Canada 
VANC/87/UBC/29, Aldergrove, Canada
VANC/85/UBC/5, Coquitlam, Canada
HAMILTON84/76, Hamilton, New Zealand
VANC/91/UBC/73, Kamloops, Canada
VANC/90/UBC/64, Barriere, Canada Δ
BE/1/IP/0482/1/15, Banff, Canada
VANC/89/UBC/37, Kitimat, Canada 
ATCC50170/93, Madison, USA
BTW/109, Botwood, Canada
VANC/92/UBC/101, Mission Creek, Canada
VANC/93/UBC/70, Barriere, Canada
VANC/92/UBC/104, Mission Creek, Canada
VANC/89/UBC/36, Oliver, Canada
VANC/90/UBC/71, Creston, Canada 
VANC/96/UBC/126/Major, Revelstoke, Canada 
HAMILTON7/75, Hamilton, New Zealand
CB2/108, Cornerbrook, Canada
VANC/85/UBC/1, Hornby Island, Canada
VANC/93/UBC/106/major, Mission Creek, Canada
VANC/88/UBC/35, Vancouver, Canda
VANC/88/UBC/34, Vancouver, Canada
SI/16, Strathmore, Canada
BE/2/IPO583/1/14, Banff, Canada
VANC/94/UBC/121, Chilliwack, Canada
MONASTASHE/6, Monastashe River, Canada
D3/18, Calgary, Canada
VANC/87/UBC/27/major, Aldergrove, Canada
WHANGAREI8/79, Whangarei, New Zealand
VANC/90/UBC/52, Creston, Canada 
A1
Panglobal, zoonotic
Creston 
Revestoke 
Barriere Δ
Kitimat 
Surface Water
Humans
Veterinary
0.09
1
1
1
1
0.995
1
1
1
0.793
VANC/90/UBC/55/minor, Goat River beaver lodge, Canada 
VANC/92/UBC/107, Vancouver, Canada
VANC/90/UBC/57, Bella Coola, Canada
VANC/86/UBC/3, Ashcroft, Canada (Mexico)
VANC/87/UBC/22, North Vancouver, Canada
VANC/85/UBC/2, Smithers, Canada
VANC/93/UBC/39, Campbell River, Canada (Kenya/Sudan)
VANC/87/UBC/23, Prince George, Canada
VANC/90/UBC/42, Creston, Canada 
A2
ATCC50803, Bethesda, USA (Afghanistan)
ATCC30888/13, Portland, USA
VANC/90/UBC/62, Barriere, Canada Δ
ATCC50163/89, Philadelphia, USA
VANC/85/UBC/7, Quesnel, Canada
Source: Clement Tsui

51. 0.06
VANC/96/UBC/126/minor, Revelstoke, Canada
VANC/91/UBC/68/2, Terrace, Canada
VANC/94/UBC/122, Mission Creek, Canada
VANC/92/UBC/102, Mission Creek, Canada
VANC/87/UBC/25, Kelowna, Canada
VANC/91/UBC/74, Mission Creek, Canada
VANC/90/UBC/63, Barriere, Canada
VANC/87/UBC/26, Slocan River, Canada
VANC/90/UBC/54,
Goat River beaver lodge, Canada
VANC/92/UBC/103, Mission Creek, Canada
VANC/94/UBC/125, Mission Creek, Canada
VANC/90/UBC/47, Kitimat, Canada
VANC/94/UBC/124, Mission Creek, Canada
VANC/89/UBC/48, Kitimat Canada
VANC/90/UBC/41, Creston, Canada
VANC/90/UBC/45, Creston, Canada
VANC/90/UBC/44, Creston, Canada
VANC/91/UBC/85, Mission Creek, Canada
VANC/91/UBC/72, Thompson River, Kamloops, Canada
VANC/90/UBC/49, Creston, Canada
VANC/89/UBC/59, Nanaimo, Canada
VANC/91/UBC/67, Terrace, Canada
VANC/91/UBC/68/1, Terrace, Canada
VANC/93/UBC/105, Mission Creek, Canada
VANC/87/UBC/27/minor, Aldergrove, BC
VANC/90/UBC/55/major, Goat River beaver lodge, Canada
VANC/90/UBC/46, Creston, Canada
VANC/92/UBC/84, Mission Creek, Canada
VANC/90/UBC/53, Goat River beaver lodge, Canada
VANC/92/UBC/99, Mission Creek, Canada
VANC/91/UBC/65, Barriere, Canada
VANC/90/UBC/56, Goat River beaver lodge, Canada
VANC/87/UBC/8,
North Vancouver, Canada
VANC/92/UBC/98, Mission Creek, Canada
VANC/96/UBC/127, Revelstoke, Canada
VANC/90/UBC/60, Creston, Canada
VANC/90/UBC/51, Kitimat, Canada
VANC/90/UBC/61, Barriere, Canada
VANC/93/UBC/106/minor, Mission Creek, Canada
VANC/96/UBC/129, Revelstoke, Canada
VANC/90/UBC/58, Mission Creek, Canada
VANC/91/UBC/69, Muskwa River, Dawson Creek, Canada
VANC/85/UBC/9, Terrace, Canada
VANC/90/UBC/40, Creston, Canada
VANC/90/UBC/50/2, Creston, Canada
VANC/96/UBC/128, Revelstoke, Canada
1
1
1
1
0.999
1
0.968
0.975
1
1
1
0.978
0.99
1
1
1
0.818
Creston Outbreak
Revelstoke Outbreak
Barriere Outbreak
Kitimat Outbreak
Kelowna,
Mission Creek
Surface Water
Humans
Veterinary
Source: Clement Tsui

52. Microbial genomics has been a valuable research tool
• Help us understand:
• microbial evolution
• pathogenesis
• create novel industrial processes
• create new laboratory tests
• Use historical isolates – not real time
• Use of laboratory strains – no associated rich clinical and
epidemiological metadata

53. Cultural and Practical Differences
Genomics Research Laboratory Genomics Diagnostic Laboratory
Curiosity driven Production / Case driven
Exploratory analysis tolerated Exploratory analysis discouraged
Reproducibility = other labs’ problem Reproducibility critical
Tweaking protocols desirable Stability in protocols desirable
Protocols don’t need to be validated Protocols need to be validated
Novelty justifies the high cost of
experiment
Conscious of cost per unit test; tests need
to be scalable
By working together, we can bridge the cultural differences

54. Diagnostic tests + Molecular Typing
Sample Processing + maintaining biobank
Sequence Generation
Data Processing and initial analyses (Bioinformatics)
Microbial Genomics Analysis
(Pathogen Evolution)
Epidemiological Analysis
(Molecular epidemiology)
use epi data to
improve typing
accuracy
metadata
typing data
value-added
data
use genomic data
to improve
diagnostic tests
and Molecular
Typing
genomic data
Insights into outbreak progression (short term) and
pathogen evolution (long term)
engage
microbiology
expert groups
engage math/
stats/comp. sc.
expert groups
known strains
Feedback
Genomics
Existing
novel strains

55. Acknowledgements
IRDA Project Principle Investigators
Fiona Brinkman – SFU
Will Hsiao – PHMRL
Gary Van Domselaar – NML
Rob Beiko – Dalhousie U.
Joᾶo Carriҫo – U. of Lisboa
Morag Graham – NML
Eduardo Taboada - NML
Lynn Schriml – U. of Maryland
National Microbiology Laboratory (NML)
Franklin Bristow
Aaron Petkau
Thomas Matthews
Josh Adam
Adam Olson
Tarah Lynch
Shaun Tyler
Philip Mabon
Philip Au
Celine Nadon
Matthew Stuart-Edwards
Chrystal Berry
Lorelee Tschetter
Aleisha Reimer
Peter Kruczkiewicz
Chad Laing
Vic Gannon
Matthew Whiteside
Ross Duncan
Steven Mutschall
Simon Fraser University (SFU)
Melanie Courtot
Emma Griffiths
Geoff Winsor
Julie Shay
Matthew Laird
Bhav Dhillon
Raymond Lo
BC Public Health Microbiology &
Reference Laboratory (PHMRL) and BC
Centre for Disease Control (BCCDC)
Judy Isaac-Renton
Natalie Prystajecky
Jennifer Gardy
Damion Dooley
Linda Hoang
Kim MacDonald
Yin Chang
Eleni Galanis
Marsha Taylor
Cletus D’Souza
Ana Paccagnella
Canadian Food Inspection Agency (CFIA)
Burton Blais
Catherine Carrillo
Dominic Lambert
Dalhousie University
Alex Keddy
McMaster University
Andrew McArthur
Daim Sardar
European Nucleotide Archive
Guy Cochrane
Petra ten Hoopen
Clara Amid
European Food Safety Agency
Leibana Criado Ernesto
Vernazza Francesco
Rizzi Valentina
Sidra Medical Center
Patrick Tang
Salmonella Project
Kim Macdonald
Matthew Croxen
Linda Hoang
Ana Paccagnella
Mark McCabe
Diane Eisler
Brian Auk
Natalie Prystajecky
Marsha Taylor
Eleni Galanis
Giardia Project
Clement Tsui
Ruth Miller
Anamaria Crisan
Damion Dooley
Kirby Cronin
Sara Tan
Justin Dirk
Mark McCabe
Sunny Mak
Brian Auk
Anna Li
C.P. Fung
Lorraine McIntyre
Renata Zanchettin
Natalie Prystajecky
Judy Isaac-Renton

56. 57
IRIDA Annual General Meeting
Winnipeg, April 8-9, 2015