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GenomeTrakr: Perspectives on linking internationally - Canada and IRIDA.ca

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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.

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GenomeTrakr: Perspectives on linking internationally - Canada and IRIDA.ca

  1. 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. 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
  3. 3.                           Genomics,  Epidemiology,  Clinical,  Lab  Data               Integrated  Rapid  Infec4ous  Disease  Analysis  informa4cs  pla;orm     suppor)ng  real-­‐)me  infec)ous  disease  outbreak  inves)ga)ons   Goal  
  4. 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. 5. National Public Health Agency Provincial Public Health Agency Academic/Public                         Will Hsiao Fiona Brinkman Gary Van Domselaar www.IRIDA.ca  
  6. 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. 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. 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. 9. Analy)cal   Tool   Quality  Control  Module   Quality   Metrics   Quality   Control   IRIDA’S  QA/QC  Model  
  10. 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. 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. 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. 13. Challenges     …  for  IRIDA   …  for  interna)onal  linkages     13  
  14. 14. Challenges     …  for  IRIDA   …  for  interna)onal  linkages     Biggest  challenge  is  NOT     bioinforma(cs/soPware  development     14  
  15. 15. Challenges     …  for  IRIDA   …  for  interna)onal  linkages     Biggest  challenge  is  NOT     bioinforma(cs/soPware  development     It’s  sharing     15  
  16. 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. 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. 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. 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. 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. 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. 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. 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. 24 Sharing  –  via  computers,  people     Its  all  about  communica(on     Computers  –  ontologies,  data  standards  are  key     Humans  –  gemng  them  together  is  key…      
  25. 25. 25 Sharing  –  via  computers,  people     Its  all  about  communica(on     Computers  –  ontologies,  data  standards  are  key     Humans  –  gemng  them  together  is  key…      
  26. 26. Brinkman  Lab  Kayaking  Trip                 Addi)onal  key  trainee  funding  and  compu)ng  infrastructure:   We’re  hiring!  

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