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GenomeTrakr: Perspectives on linking internationally - Canada and IRIDA.ca
Talk at GenomeTrakr network meeting Sept 23 2015 in Washington DC. On Canada's open source Integrated Rapid Infectious Disease Analysis (IRIDA) bioinformatics platform - aiding genomic epidemiology analysis for public health agencies with planned open data release and linkage to GenomeTrakr. Discussed perspectives, challenges, solutions for getting more GenomeTrakr participation internationally.
Talk at GenomeTrakr network meeting Sept 23 2015 in Washington DC. On Canada's open source Integrated Rapid Infectious Disease Analysis (IRIDA) bioinformatics platform - aiding genomic epidemiology analysis for public health agencies with planned open data release and linkage to GenomeTrakr. Discussed perspectives, challenges, solutions for getting more GenomeTrakr participation internationally.
GenomeTrakr: Perspectives on linking internationally - Canada and IRIDA.ca
1.
1
Perspec)ves
on
Linking
Interna)onally
–
Canada
and
IRIDA.ca
Fiona
Brinkman
Professor, Department of Biochemistry and Molecular Biology
Adjunct, School of Compu>ng Science,
and Faculty of Health Sciences
Simon Fraser University
Greater Vancouver, BC, Canada
brinkman@sfu.ca GenomeTrakr -‐ Sept 23 2015 @fionabrinkman
Canada’s Integrated Rapid Infec>ous Disease Analysis
PlaTorm for Genomic Epidemiology
2.
Interac(ng
with,
complemen(ng
others
Interna(onal
resources
Integrated
Rapid
Infec4ous
Disease
Analysis
informa4cs
pla;orm
suppor)ng
real-‐)me
infec)ous
disease
outbreak
inves)ga)ons
Goal
Rich
genomic
epi
analysis
Public
health
agencies
Rapid,
open
genomic
data
release
Academia/Public
4.
User-‐friendly,
web-‐accessible
User
access
control
(e.g.
public
health
workers
vs
public)
Automated
assembly
pipelines,
Data
analysis
and
visualiza(on
Standards
compliant,
rich
ontologies
Open
source
Integrated
Rapid
Infec4ous
Disease
Analysis
informa4cs
pla;orm
suppor)ng
real-‐)me
infec)ous
disease
outbreak
inves)ga)ons
Goal
5.
National
Public Health Agency
Provincial
Public Health Agency
Academic/Public
Will Hsiao Fiona Brinkman
Gary Van Domselaar www.IRIDA.ca
6.
6
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 Health Microbiology &
Reference Laboratory (PHMRL) and
BC Centre for Disease Control
(BCCDC)
Judy Isaac-Renton
Patrick Tang
Natalie Prystajecky
Jennifer Gardy
Linda Hoang
Kim MacDonald
Yin Chang
Eleni Galanis
Marsha Taylor
Damion Dooley
Jennifer Law
University of Maryland
Lynn Schriml
Canadian Food Inspection Agency
(CFIA)
Adam Koziol
Burton Blais
Catherine Carrillo
Dalhousie University
Rob Beiko
Alex Keddy
7.
IRIDA
Design:
Carefully
designed
and
engineered
soHware
plaIorm
is
just
the
star)ng
point…
User
Interface
Security
File
system
Metadata
Storage
Applica)on
logic
REST
API
Workflow
Execu)on
Manager
Con)nuous
Integra)on
Documenta)on
Federated database model
8.
Addressing
ontology
gaps
Build
On,
Work
With:
OBI
TypON
NGSOnto
NIAID-‐GSC-‐BRC
core
metadata
MIxS
Ontology
NCBI
Biosample
etc
TRANS
–
Pathogen
Transmission
EPO
Exposure
Ontology
Infec)ous
Disease
Ontology
CARD,
ARO
for
AMR
USDA
Nutrient
DB
EFSA
Comp.
Food
Consump.
DB
Example
gaps
to
fill:
Improve
Food
ontologies,
AMR
data
Ontology:
Describes
types
of
en((es
and
rela(ons
between
them
9.
Analy)cal
Tool
Quality
Control
Module
Quality
Metrics
Quality
Control
IRIDA’S
QA/QC
Model
10.
IRIDA
Workflows:
Portable
and
Transparent
Pipelines
Use
Galaxy
as
workflow
engine
Version
Controlled
Pipeline
Templates
1.
Input
files,
parameters
sent
to
Galaxy
3.
Results
downloaded
from
Galaxy
IRIDA
UI/DB
Galaxy
Assembly
Tools
Variant
Calling
Tools
…
REST
API
Shared
File
System
Worker
Worker
2.
Tools
executed
on
Galaxy
workers
11.
Example
data
analysis,
visualiza)on
tools
IslandViewer/GenomeD3Plot
–
more
flexible
GI/VF/AMR
visualiza)on
Dhillon
BK
et
al
2015
Nucl
Acids
Res
PMID:
25916842
Laird
MR
et
al
2015
Bioinforma(cs
PMID:
26093150
www.pathogenomics.sfu.ca/islandviewer/
github.com/brinkmanlab/GenomeD3Plot/
12.
“SNVPhyl”
SNV
analysis
Integra)ng
genomics,
geographic
data
(led
by
NML,
Rob
Beiko,
Dalhousie
U)
http://kiwi.cs.dal.ca/GenGIS
SNVPhyl Software Demo by Aaron Petkau and
IRIDA poster by Emma Griffiths at #ASMNGS meeting
Example
data analysis,
visualization tools
13.
Challenges
…
for
IRIDA
…
for
interna)onal
linkages
13
14.
Challenges
…
for
IRIDA
…
for
interna)onal
linkages
Biggest
challenge
is
NOT
bioinforma(cs/soPware
development
14
15.
Challenges
…
for
IRIDA
…
for
interna)onal
linkages
Biggest
challenge
is
NOT
bioinforma(cs/soPware
development
It’s
sharing
15
16.
Canada’s
Public
Health
System
Challenges
Provincial public
health dept.
National laboratory
Local public
health dept.
Provincial
laboratory
Cases
Physicians Frontline lab
Informa)on
Bioinforma)cs
and
Analy)cal
Capaci)es
Info lost as aggregate data from Frontline lab to national PH labs
17.
$ Disease
Reporting
Informa)on
Sharing
is
Highly
Complex
• Variety
of
agreements,
legisla)on
• Lack
of
standards
(metadata,
legal
requirements)
• Fears
of
data
release
during
ongoing
inves)ga)ons,
and
IP
concerns
make
provinces
“risk
averse”
18.
Impact
• Impacts
data
sharing
na)onally,
interna)onally
• Also:
Lack
of
rich
example
data
impacts
ontology,
data
standards
development
(ability
for
computers
to
share)
Must
communicate
to
countries:
Get
data
sharing
arranged
early
–
both
na(onally
and
interna(onally
18
19.
Interna)onal
data
sharing
-‐ Get
data
sharing
arranged
early
-‐ Ensure
alloca(on
of
adequate
resources
to
set
it
up
-‐ Share
bioinforma(cs
resources,
code,
parameters
-‐ Share
data
examples
-‐ Start
simple:
Agree
on
minimal
genome
metadata
for
rapid
release
19
20.
Interna)onal
data
sharing
Pt.
2
-‐
Develop
harmonized
metadata
for
further
data
release
-‐
Interoperable
systems
-‐
Current
and
well-‐maintained
repositories
-‐
Valida4on
datasets
for
pipeline
calibra)on
-‐
User
access
control
Open
access
à
Opens
opportuni)es,
discoveries
20
21.
A new hope…
MLISA (Multilateral Information Sharing Agreement)
• Canadian multi-jurisdictional legal agreement
• Establishes standards re sharing, usage, disclosure and
protection of PH info for infectious diseases and PH events
• Technical annexes (for example for WGS) can be developed to
clarify specifically data to be exchanged
PulseNet as a model for sharing (in part)
22.
Can
USA–Canada
sharing
be
developed
as
a
model?
Flight paths across North America. Outbreaks follow flight
paths more closely than simple geographic distance.
23.
23
IRIDA’s Role in International Data Sharing
1. Application ontology for genomic epidemiology
2. Metadata standardization
3. Interoperability
4. Sensitive field sharing secured via authorization
5. Privacy protection and data security
6. Compatible with International Health Regulations (2005)
7. Aims to support federated design, plus open data sharing
24.
24
Sharing
–
via
computers,
people
Its
all
about
communica(on
Computers
–
ontologies,
data
standards
are
key
Humans
–
gemng
them
together
is
key…
25.
25
Sharing
–
via
computers,
people
Its
all
about
communica(on
Computers
–
ontologies,
data
standards
are
key
Humans
–
gemng
them
together
is
key…