Rare Disease Data Linkage plan 2017 - IRDiRC 2017 presentation
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In this presentation I present the plan to make rare disease data resources findable, accessible, interoperable, and reusable for humans and computers (FAIR). The presentation was made for the IRDiRC conference 2017 in Paris.
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Rare Disease Data Linkage plan 2017 - IRDiRC 2017 presentation
- 1. THE RARE DISEASE DATA LINKAGE PLAN BOOSTING RESEARCH BY MAKING DATA RESOURCES FINDABLE, ACCESSIBLE, INTEROPERABLE AND REUSABLE TOGETHER I RDiRC 2017, Paris, Februar y 9, 2017 Marco Roos, David van Enckevort Acknowledging patient representatives, ELIXIR(-EXCELERATE), RD-CONNECT, BBMRI(-NL), ODEX4ALL, FAIRDict, the rare disease linked data and ontology task force, Mark Thompson, Robert Reihs, Rajaram Kaliyaperumal, Pedro Sernadela, Marc Hanauer, Mark Wilkinson, Claudio Carta, Rachel Thompson, Estrella Lopes, Lorena Casareto, Frederique Ehrhart, Roxana Merino, Luiz Bonino & team, Ronald Cornet, Peter Robinson, Mathias Brochhausen, Simon Jupp, Sira Sarntivijai, Helen Parkinson, Ana Rath, Heimo Muller, Lucia Monaco, Domenica Taruscio, Manuel Posada, Luca Sangiorgi, Morris Swertz, José Oliveira, Peter-Bram ‘t Hoen, Hanns Lochmuller, Larry Hunter, participants and organisers of rare disease Bring Your Own Data workshops In this presentation, I will give you a light-weight introduction to the rare disease data linkage plan to boost research on rare diseases.
- 2. 2 ELIXIR 16 February 2017 A distributed infrastructure for life science information The plan is supported by several projects and infrastructures. Here, I highlight ELIXIR, the European infrastructure for life science information. Ivo Gut, Sergi Beltran and Marco Roos co-lead the case for rare diseases in this infrastructure.
- 3. 3 To boost rare disease research Objective 16 February 2017 Our goal is to boost rare disease research
- 4. 4 > 6000 rare diseases 16 February 2017 But then we have to take into account that there are over 6000 rare diseases
- 5. 5 > 6000 rare diseases biobanks, registries, sequencing, OMICS, … 16 February 2017 Collecting multiple types of materials and data, such as biobanks, registries, sequence data, omics, etcetera
- 6. 6 > 6000 rare diseases biobanks, registries, sequencing, OMICS, … across countries and institutes 16 February 2017 And across many countries and institutes
- 7. 7 What we want to achieve 16 February 2017 7 Rare disease researcher What we aim to achieve is the following; here is rare disease researcher Claudio
- 8. 8 Rare disease researcher asks Which treatments for symptoms of other diseases may mitigate the same symptoms of my rare disease? Claudio has questions like these
- 9. 9 A rare disease researcher asks… Where can I obtain biosamples of donors with an abnormality in head or neck? In which biobanks can I find these samples? Or these… We have built a demonstration web tool to show how we can answer these questions with the technologies that we advocate.
- 10. 16 February 2017 10 Here, Claudio can select his question.
- 11. 16 February 2017 11 Select the symptom that he is interested in
- 12. And obtain this table with the answers. Note that all items are blue; that means you can click on them to get more information about them. Note that the phenotypes are all subtypes of abnormality of head or neck. Also note that there are multiple diseases, multiple biobanks, and multiple registries in the list. This shows that the information came from different sources.
- 13. 13 The role of computational analysis in boosting health care and life science will Decrease Increase Stay the same Questions to you 16 February 2017 Before I continue, think about this question
- 14. 14 The role of computational analysis in boosting health care and life science will Decrease Increase Stay the same Questions to you 16 February 2017 Most people tend to say it will increase.
- 15. 15 to boost rare disease research 16 February 2017 If that is the case, then…
- 16. 16 enable computational analysts to boost rare disease research 16 February 2017 We need to…
- 17. 17 The substrate for computational analysts DATA And then we have to look at the substrate for computational analysts: data
- 18. 18 How to boost rare disease research? PATIENT DATA In the rare disease domain, we have many different types of data, such as patient data
- 19. 19 How to boost rare disease research? OMICS DATA Omics data
- 20. 20 How to boost rare disease research? SAMPLE DATA Or biological samples
- 21. 21 How to boost rare disease research? SAMPLE DATA And in all shapes and sizes, different languages, different formats
- 22. 22 How to boost rare disease research? PATIENT DATA > 6000 rare diseases across countries and institutes Remember that we have thousands of data sources in our domain.
- 23. 23 How to boost rare disease research? PATIENT DATA Data incompatibilities are an enormous bottleneck for data analysts: they spend months per data source to resolve them.
- 24. 24 How to boost rare disease research? DA TA DATA You Them A way to address this is by letting others make your data compatible: ‘they’ transform the data to be more compatible.
- 25. 25 How to boost rare disease research? DA TA You Them DATA They can do that with multiple data sources, and integrate them.
- 26. 26 DATA How to boost rare disease research? DA TA You Them However, there is a big risk. When for whatever reason, ‘they’ cannot maintain this anymore, for instance because the funding stops…
- 27. 27 How to boost rare disease research? DA TA You There is nothing left.
- 28. 28 How to boost rare disease research? DA TA You We are back to square one: incompatible data. This is not good enough for data infrastructure. International leading data experts have defined an approach for this that I cannot explain better than is done in the following video.
- 29. 29 Inspiration: personal Health Train 16 February 2017 https://www.youtube.com/watch?v=mktAtHmy-FM
- 30. 30 G20 Endorsement of FAIR principles Next to ELIXIR, EOSC, NIH Commons We, the leaders of the G20… facilitate appropriate access to publicly funded research results on findable, accessible, interoperable and reusable (FAIR) principles … The FAIR principles are highly endorsed, by ELIXIR, the Open European Science Cloud, NIH via its ‘commons’ program, and since 2016 also the G20.
- 31. 31 FAIR principles applied to rare disease data RD DATA FAIR Linkable RD DATA You How do we apply them for rare diseases? At a high level, the steps are more-or-less the same: your data, a transformation, but now we have FAIR, linkable data on the right.
- 32. 32 FAIR principles applied to rare disease data RD DATA FAIR Linkable RD DATA You YouYou and them together Knowledge exchange But there are major differences: instead of ‘you’ and ‘them’ data experts and FAIR data experts do the transformation together. This involves substantial knowledge exchange. Another major difference is that instead of ‘them’ there is ‘you’ on the right: data owners stay in control.
- 33. 33 FAIR principles applied to rare disease data Data can be more easily combined. Each resource is an independently FAIR resource. This is a much more robust infrastructure.
- 34. 34 Creating substrate to boost rare disease research In our rare disease data linkage plan we go through this process, one at a time. Each time we improve our methods, each time we do this faster.
- 35. Rare disease data linkage plan - 2017 • > 7 biobanks/registries FAIR at the source • Study FAIR pathways, Orphanet, mutation data • Support: RD-Connect, ELIXIR, BBMRI (-NL), FAIRDict, ODEX4All, patient organisations David van Enckevort Technical leader Our aims for 2017
- 36. 36 Light-weight introduction to the rare disease data linkage plan Account for scale and sparsity of data in rare disease domain Federated infrastructure of local FAIR data Summary 16-Feb-17 36
- 37. 37 Invitation Contact us about making rare disease data FAIR Turn plan into long- running service Long term plans 16 February 2017 Contact us about making rare disease data FAIR. Let us know if you would like to help turning the plan for 2017 into a long running service for the rare disease community. We envision a role for patient organisations in that.
- 38. • Mascha Jansen: FAIR data projects and Bring Your Own Data workshops (mascha.jansen@dtls.nl) • David van Enckevort, Marco Roos: Rare disease data linkage plan & FAIR RD data projects • Erik Schultes: FAIR data (awareness) training; for Elixir/RD: Brane Leskosek (DTL: Celia van Gelder) Thank you Acknowledging patient representatives, ELIXIR(- EXCELERATE), RD-CONNECT, BBMRI(-NL), ODEX4ALL, FAIRDict, the rare disease linked data and ontology task force, David van Enckevort, Mark Thompson, Robert Reihs, Rajaram Kaliyaperumal, Pedro Sernadela, Marc Hanauer, Mark Wilkinson, Claudio Carta, Rachel Thompson, Estrella Lopes, Lorena Casareto, Frederique Ehrhart, Roxana Merino, Luiz Bonino & team, Ronald Cornet, Peter Robinson, Mathias Brochhausen, Simon Jupp, Sira Sarntivijai, Helen Parkinson, Ana Rath, Heimo Muller, Lucia Monaco, Domenica Taruscio, Manuel Posada, Luca Sangiorgi, Morris Swertz, José Oliveira, Peter-Bram ‘t Hoen, Hanns Lochmuller, Larry Hunter, participants and organisers of rare disease Bring Your Own Data workshops Thank you for your attention.
Editor's Notes
- In this presentation, I will give you a light-weight introduction to the rare disease data linkage plan to boost research on rare diseases.
- The plan is supported by several projects and infrastructures. Here, I highlight ELIXIR, the European infrastructure for life science information. Ivo Gut and Sergi Beltran from Barcelona and myself from Leiden are carrying the flag for rare diseases in this infrastructure.
- Our goal is to boost rare disease research
- But then we have to take into account that there are over 6000 rare diseases
- For each disease we collect multiple types of materials and data, such as samples in biobanks, health information in registries, sequence data, omics data, etcetera
- And across many countries and institutes
- What we aim to achieve is the following; here is rare disease researcher Claudio
- Claudio has questions like these: which treatments for symptoms of other diseases may mitigate the same symptoms in my disease of interest?
- Or, where can I obtain biosamples of donors with an abnormality in head or neck? And in which biobanks can I find these samples? We have built a demonstration web tool to show how we can answer these questions with the technologies that we advocate.
- Here, Claudio can select his question.
- Select the symptom that he is interested in
- And obtain this table with the answers. Note that all items are blue; that means you can click on them to get more information about them. Note that the phenotypes are all subtypes of abnormality of head or neck. Also note that there are multiple diseases, multiple biobanks, and multiple registries in the list. This shows that the information came from different sources.
- Before I continue, think about this question: do you think that the role of computational analysis will increase in health care and life science in the future?
- Most people tend to say it will increase.
- If that is the case, then to boost rare disease research
- We need to enable computational analysts to boost rare disease research
- And then we have to look at the substrate for computational analysts: data
- In the rare disease domain, we have many different types of data, such as patient data
- Omics data
- Or biosamples
- And in all shapes and sizes, different languages, different formats
- Remember that we have thousands of data sources in our domain.
- Data incompatibilities are an enormous bottleneck for data analysts: they spend months per data source to resolve them.
- A way to address this is by letting others make your data compatible: ‘they’ transform the data to be more compatible.
- They can do that with multiple data sources, and integrate them.
- However, there is a big risk. When for whatever reason, ‘they’ cannot maintain this anymore, for instance because the funding stops.
- There is nothing left.
- We are back to square one: incompatible data. This is not good enough for data infrastructure. International leading data experts have defined an approach for this that I cannot explain better than is done in the following video.
- The FAIR principles are highly endorsed, such as by ELIXIR, a European initiative called the Open European Science Cloud, NIH via its ‘commons’ program, and since 2016 also the G20.
- How do we apply them for rare diseases? At a high level, the steps are more-or-less the same: your data, a transformation, but now we have FAIR, linkable data on the right.
- But there are major differences: instead of ‘you’ and ‘them’ data experts and FAIR data experts do the transformation together. This involves substantial knowledge exchange. Another major difference is that instead of ‘them’ there is ‘you’ on the right: data owners stay in control of their data.
- Data can be more easily combined. Each resource is an independently FAIR resource. This is a much more robust infrastructure.
- In our rare disease data linkage plan we go through this process, one at a time. Each time we improve our methods, each time we do this faster.
- In 2017 we aim at our first seven biobanks/registries, we will study pathways, orphaned and mutation data. We have support from multiple projects, including patient organisations who we ask to also invest into the collaboration.
- In summary: I have given a light-weight introduction to the rare disease data linkage pan. I have shown how we account for the scale and sparsity of data in the rare disease domain, By federated infrastructure of local FAIR data
- I invite you to contact us about making rare disease data FAIR And I invite you to let us know if you would like to help us turn this plan into a long running service. We envision a role for patient organisations in that.
- Thank you for your attention. Here are some contact points.