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IRIDA: Canada’s federated platform for genomic epidemiology
- 1. IRIDA: Canada’s federated platform for genomic epidemiology William Hsiao, Ph.D. William.hsiao@bccdc.ca @wlhsiao BC Public Health Microbiology and Reference Laboratory and University of British Columbia ABPHM 2015
- 2. Genome Canada Bioinformatics Competition: Large-Scale Project “A Federated Bioinformatics Platform for Public Health Microbial Genomics” Our Goal The IRIDA platform (Integrated Rapid Infectious Disease Analysis) An open source, standards compliant, high quality genomic epidemiology analysis platform to support real-time (food-borne) disease outbreak investigations 2 www.IRIDA.ca
- 3. 3 Each year, one in eight Canadians (or four million people) get sick with a domestically acquired food-borne illness.
- 4. Partnership among public health agencies and academic institutes to bridge the gaps between advancements in genomic epidemiology and application to real-life and real- time use cases in public health agencies - Project Team has direct access to state of the art research in academia - Project Team is directly embedded in user organization National Public Health Agency Provincial Public Health Agency Academic/Public
- 5. Interviews with key personnel to identify barriers to implement genomic epidemiology in public health agencies 5
- 6. GAP 1: PUBLIC HEALTH PERSONNEL LACK TRAINING IN GENOMICS
- 7. Solution 1a: Build a User Friendly, high quality analysis platform to process genomics data • Carefully designed and engineered software platform is just the starting point… User Interface Security File system Metadata Storage Application logic REST API Workflow Execution Manager Continuous Integration Documentation
- 8. • Easy to use interface hiding the technical details Solution 1a: Build a User Friendly, high quality analysis platform to process genomics data
- 9. Solution 1a: Build a User Friendly, high quality analysis platform to process genomics data
- 10. Solution 1b: Build Portable and Transparent Pipelines • Use Galaxy as workflow engine – large community support • Retools to address usability, security, and other limitations • Version Controlled Pipeline Templates • Input files, parameters, and workflow are sent to IRIDA-specific Galaxy for execution • Results and provenance information are copied from Galaxy 1. Input files 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. Solution 1c: Start the training NOW! • Canada’s National Microbiology Laboratory has hosted genomic workshops for partners and collaborators • IRIDA Project has dedicated funding for hosting workshops in 4Q of 2015 and 2016 • We would like to hear about other training initiatives and share experience and training material
- 12. GAP 2: INFORMATION SHARING IS INEFFICIENT AND AD-HOC
- 13. Many Players in surveillance and outbreak – ineffective information sharing Source: M. Taylor, BCCDC Provincial public health dept. National laboratory Local public health dept. Provincial laboratory Cases Physicians Frontline lab Information BioinformaticsandAnalyticalCapacities
- 14. Many Systems used in Reporting Diseases – require data re-entry and re-coding National Ministry of Health Provincial public health dept. National laboratory Local public health dept. Provincial laboratory Cases Physicians Local laboratory Fax/Electronic Fax Phone/Fax Electronic/Paper Electronic/Fax/Phone Mailing of Samples/Fax/Eelctroni c Source: M. Taylor, BCCDC
- 15. IRIDA is designed with these dilemma in mind • Solutions: – 2a: Localized Instance of federated databases – 2b: Permission Control – authentication /authorization for information sharing – 2c: User role-based display of information
- 16. Solution 2a: Local/Cloud Instances and Data Federation • Data processing capacity pushed to data generating labs • Allow data sharing securely for enhanced analysis • Eventually cultivating a culture of openness of data sharing and collaborative development of tools 16
- 17. Authorization Solution 2b: Security • Local authorization per instance. • Method-level authorization. • Object-level authorization. • Allow secure, fine grained and flexible information sharing
- 18. Solution 2c: Role-based Dynamic Display driven by Ontology • Ontologies often lack a content management system (CMS) • An Interface Model Ontology (IFM) can define a CMS for an ontology
- 19. IFM Interface View Permissions Detailed View Restricted View E.g. User role permissions control visibility and editing of content
- 20. GAP 3: INFORMATION REPRESENTATION IS INCONSISTENT
- 21. Solution 3a: Use Ontology • Ontology: a way to describe types of entities and relations between them • Why use ontology – Ontology is flexible and expandable – Lower levels of expressivity (e.g. controlled vocabulary, data dictionary) are heavy handed and show low level of compliance and adoption – Free text used as an alternative that are not computing friendly – Ontology and semantic web technologies may be a solution
- 22. Many Domains of Knowledge are needed to describe an outbreak investigation Build On, Work With: OBI TypON NGSOnto NIAID-GSC-BRC core metadata MIxS Ontology NCBI Biosample etc TRANS – Pathogen Transmission EPO Exposure Ontology Infectious Disease Ontology CARD, ARO for AMR USDA Nutrient DB EFSA Comp. Food Consump. DB Example gaps to be filled: Expand food ontology; expand CARD AMR data with others.
- 23. Lab Checklist/Ontology • Currently finishing a lab/genomics checklist and starting an epidemiology checklist • Metadata Domains: – Sample Collection – Sample Source – Environmental – Lab Analytics – Sequencing Process /QC – Sequencing Run /QC – Assembly Process / QC – Others overlapping with Epi: Demographic / Geographic / etc.
- 24. GAP 4: GENOMIC DATA INTERPRETATION IS COMPLEX AND TECHNOLOGY IS EVOLVING
- 25. Solution 4a: Use of QA/QC in IRIDA • Software Engineering – High quality software that meets regulatory guidelines – Open Source product to ensure “white box” testing – Ontology driven software development – Follow proper software development cycle • Data Quality – Built-in modules to check for input data quality – Warnings and Feedbacks during pipeline execution to laboratory technologists – Use of Ontology to check metadata (non-genomic) data quality • Analytic Tool Quality – Utilize validation datasets – Use of abstract pipeline description – with version control – Periodic analysis of exceptions and boundary cases to assess tool accuracy
- 26. Solution 4b: Generation of validation datasets To Participate, Contact Rene Hendriksen rshe@food.dtu.dk Or Errol Strain Errol.Strain@fda.hhs.gov http://www.globalmicrobialidentifier.org/Workgroups#work-group-4
- 27. Solution 4c: Exploratory tools can access certain data via REST API securely 27 http://pathogenomics.sfu.ca/islandviewer IslandViewer Dhillon and Laird et al. 2015, Nucleic Acids Research http://kiwi.cs.dal.ca/GenGIS Parks et al. 2013, PLoS One
- 28. Availability • Jun 1 2015: IRIDA 1.0 beta Internal Release – Release to collaborators for installation and full test • Jul 1 2015: IRIDA 1.0 beta1 – Announce Beta release, download, documentation available on website – www.irida.ca • Aug 1 2015: IRIDA 1.0 beta2 – Cloud installer, with documentation – Additional pipelines as available – Visualization as available
- 29. Acknowledgements Project Leaders Fiona Brinkman – SFU Will Hsiao – PHMRL Gary Van Domselaar – NML University of Lisbon Joᾶo Carriҫo 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 Morag Graham Chrystal Berry Lorelee Tschetter Aleisha Reimer Laboratory for Foodborne Zoonoses (LFZ) Eduardo Taboada 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 Patrick Tang Natalie Prystajecky Jennifer Gardy Damion Dooley Linda Hoang Kim MacDonald Yin Chang Eleni Galanis Marsha Taylor Cletus D’Souza Ana Paccagnella University of Maryland Lynn Schriml Canadian Food Inspection Agency (CFIA) Burton Blais Catherine Carrillo Dominic Lambert Dalhousie University Rob Beiko Alex Keddy 29 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
- 30. 30 30 IRIDA Annual General Meeting Winnipeg, April 8-9, 2015
- 31. The IRIDA platform (Integrated Rapid Infectious Disease Analysis) An open source, standards compliant, high quality genomic epidemiology analysis platform to support real-time (food-borne) disease outbreak investigations Contacts: William.hsiao@bccdc.ca @wlhsiao 31 www.IRIDA.ca
Editor's Notes
- Today, I’d like to tell you a bit about some of Canada’s effort on building a genomic epidemiology analysis platform
- IRIDA was conceived about 2 years ago through a Genome Canada Bioinformatics Grant. It is an effort to build an open source, standards compliant, high quality genomic epidemiology analysis platform to support real-time disease outbreak investigations, initially focused on food-borne illnesses
- 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
- IRIDA is partnership among provincial public health agencies, national public health agencies and academic institutes to bridge the gaps between advancements in genomic epidemiology and real-life and real-time use cases in public health agencies Project Team has direct access to state of the art research in academia Project Team is directly embedded in user organization
- Since we have access to the end users, we conducted interviews with these subject experts to identify what are the barriers for up-taking of genomics epidemiology in public health agencies. We interviewed epidemiologists, lab scientists and technologists, medical microbiologists and lab administrators. So for the rest of the presentation, I’ll talk about some of the gaps we identified and how IRIDA can meet the requirements.
- The first gap which should not be a surprise to this audience, is that public health workers are mostly unfamiliar with genomics and the bioinformatics analysis needed to process and interpret genomic data
- While we do believe in the long run, adequate training in genomics is needed to bridge this gap and in the short term, experts such as yourselves are needed as stopgaps, having high quality analysis platform to automate data processing and has consistent analysis protocols will help to ease the transition. However, carefully designed and engineered software platform is just the starting point and there will no doubt be many similar platforms to choose from. So I will touch on some of the more interesting design philosophies we have for IRIDA.
- We found that in the diagnostic testing world, complex procedures with lots of options lead to more human errors and more non-compliance. So, one design solution that we stress on is to have a simple user interface that hides the technical details. This solution of course can’t stand on its own and I’ll describe measures to ensure that flexibility and scientific rigors can be maintained
- We think a user interface should be like a joke… If you have to explain it , then it’s not good. That said, we do have extensive documentations for the administrators and accreditation auditors who don’t like jokes :P
- Next solution is to leverage Galaxy which has a large community support and user base as our pipeline engine. We had to retool Galaxy extensively to address usability, security and other limitations. To achieve this we build IRIDA platform on top of the Galaxy engine where input files, parameters and workflows are sent to Galaxy for execution and results and pipeline provenance information are copied back into the IRIDA database for
- To address the knowledge gaps in genomics, we have started training our public health lab workers on genomic analysis. We would like to hear about other training initiatives and will be happy to share our experience and training material
- The second gap that we identified is that sharing of information within and between organizations are highly inefficient and often involves sharing of Excel files with deleted columns to hide sensitive information
- There are many players involved in infectious disease surveillance and outbreak investigation. However, concerns with privacy and confidentiality (both founded and unfounded) means that information tend to be aggregated and lost as we move from the frontline labs to public health and reference labs. However the bioinformatics and analytical capacities are the most abundant in central labs and academia
- Moreover, different institutions have different software and often data is exported and printed, faxed, then re-imported to a new system by re-typing! This is a huge waste of time and source of errors
- IRIDA has a few designs to deal with these issues, and I’ll highlight 3 here.
- First we propose that we should push the data processing capacity to the periphery where data is the richest by encouraging local or private cloud instances of the IRIDA platform. This way our partners would not be obligated to give up their data. The different instances are connected via a federated database schema. Data can then be shared securely and easily to allow enhanced analysis to be done by genomic experts located centrally. The more we share successfully, the more likely people will realize the benefit in sharing and this can lead to a new culture of openness
- Second we have built-in mechanisms for authentication and authorization at different levels to allow secure and fine grained information sharing. This would allow parties to customize the data they share per material and data transfer agreements
- Third, we realized we need to have a flexible user interface to present the data. Therefore, we are in the process of developing an interface model ontology which defines a content management system.
- As an example, based on the user’s role, they will be able to see the content of the database displayed differently.
- The third gap we identified is that information representation is inconsistent across organizations
- Given the richness and complexity of genomic epidemiological data, we opt to use and develop ontologies compliant with OBO Foundry to describe the data; Currently, lower levels of expressivity such as controlled vocabularies and data dictionaries are used but they tend to be heavy handed and show low level of compliance and adoption. We believe ontology and semantic web technologies can make data sharing across heterogeneous systems and platforms more tractable.
- There are many domains of knowledge needed to describe an outbreak investigation and we strive to re-use existing standards as much as possible
- Currently we are finishing a lab/genomic checklist and will be starting an epidemiology checklist soon
- Lastly, Jon and others mentioned yesterday, genomic data interpretation is complex and the technology is still evolving, yet in the world of diagnostic lab, accreditation means standardized protocols need to be developed
- So we focus quite a bit of our energy on developing high quality software with build-in QA and QC components to assess data quality and analytic tool performance
- I also want to highlight GMI’s WG4’s effort in developing proficiency tests for wet lab and analysis pipelines. To participate you can contact Rene or Errol.
- To facilitate tool improvement and to allow exploratory analysis not part of IRIDA pipelines to be done, we would also allow pre-authorized tools to connect to IRIDA via a REST API securely. Currently we have two external tools for genomic island detection and phylogeography analysis.
- The software will be released to a few international collaborators for full testing by Jun 1. Then in Jul 1, we plan to release the beta version publicly so people can try it out. Of course the software will be free and we would love to collaborate with people on both the software and the ontology development.
- Large Group of People who contributed to this work
- We also have a wonderful group of advisors