PowerPoint Presentation

The rare disease data linkage planBoosting research by making data resources findable, accessible, interoperable and reusable togetherIRDiRC 2017, Paris, February 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, Jos 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.

#

In this presentation, I will give you a light-weight introduction to the rare disease data linkage plan to boost research on rare diseases.1

ELIXIR16 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.

#

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.2

To boost rare disease researchObjective16 February 2017

Our goal is to boost rare disease research

#

Our goal is to boost rare disease research3

> 6000 rare diseases

16 February 2017

But then we have to take into account that there are over 6000 rare diseases

#

But then we have to take into account that there are over 6000 rare diseases4

> 6000 rare diseasesbiobanks, registries, sequencing, OMICS,

16 February 2017

Collecting multiple types of materials and data, such as biobanks, registries, sequence data, omics, etcetera

#

For each disease we collect multiple types of materials and data, such as samples in biobanks, health information in registries, sequence data, omics data, etcetera5

> 6000 rare diseasesbiobanks, registries, sequencing, OMICS, across countries and institutes

16 February 2017

And across many countries and institutes

#

And across many countries and institutes6

What we want to achieve16 February 20177

Rare disease researcher

What we aim to achieve is the following; here is rare disease researcher Claudio

#

What we aim to achieve is the following; here is rare disease researcher Claudio7

Rare disease researcher asksWhich treatments for symptoms of other diseases may mitigate the same symptoms of my rare disease?

Claudio has questions like these

#

Claudio has questions like these: which treatments for symptoms of other diseases may mitigate the same symptoms in my disease of interest?

A rare disease researcher asksWhere can I obtain biosamples of donors with an abnormality in head or neck?In which biobanks can I find these samples?

Or theseWe have built a demonstration web tool to show how we can answer these questions with the technologies that we advocate.

#

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.

16 February 2017

10

Here, Claudio can select his question.

#

Here, Claudio can select his question.10

16 February 2017

11

Select the symptom that he is interested in

#

Select the symptom that he is interested in11

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.

#

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.12

The role of computational analysis in boosting health care and life science willDecreaseIncreaseStay the sameQuestions to you16 February 2017

Before I continue, think about this question

#

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?13

The role of computational analysis in boosting health care and life science willDecreaseIncreaseStay the sameQuestions to you16 February 2017

Most people tend to say it will increase.

#

Most people tend to say it will increase.14

to boost rare disease research

16 February 2017

If that is the case, then

#

If that is the case, then to boost rare disease research15

enable computational analysts to boost rare disease research

16 February 2017

We need to

#

We need to enable computational analysts to boost rare disease research16

The substrate for computational analystsDATA

And then we have to look at the substrate for computational analysts: data

#

And then we have to look at the substrate for computational analysts: data17

How to boost rare disease research?PATIENTDATA

In the rare disease domain, we have many different types of data, such as patient data

#

In the rare disease domain, we have many different types of data, such as patient data18

How to boost rare disease research?OMICSDATA

Omics data

#

Omics data19

How to boost rare disease research?SAMPLEDATA

Or biological samples

#

Or biosamples20

How to boost rare disease research?SAMPLEDATA

And in all shapes and sizes, different languages, different formats

#

And in all shapes and sizes, different languages, different formats21

How to boost rare disease research?PATIENTDATASAMPLEDATAOMICSDATAPATIENTDATA> 6000 rare diseasesacross countries and institutes

Remember that we have thousands of data sources in our domain.

#

Remember that we have thousands of data sources in our domain.22

How to boost rare disease research?PATIENTDATASAMPLEDATAOMICSDATA

PATIENTDATA

Data incompatibilities are an enormous bottleneck for data analysts: they spend months per data source to resolve them.

#

Data incompatibilities are an enormous bottleneck for data analysts: they spend months per data source to resolve them. 23

How to boost rare disease research?DATADATA

YouThem

A way to address this is by letting others make your data compatible: they transform the data to be more compatible.

#

A way to address this is by letting others make your data compatible: they transform the data to be more compatible. 24

How to boost rare disease research?DATAYouThem

DATA

They can do that with multiple data sources, and integrate them.

#

They can do that with multiple data sources, and integrate them.25

DATAHow to boost rare disease research?DATAYouThem

However, there is a big risk. When for whatever reason, they cannot maintain this anymore, for instance because the funding stops

#

However, there is a big risk. When for whatever reason, they cannot maintain this anymore, for instance because the funding stops.26

How to boost rare disease research?DATAYou

There is nothing left.

#

There is nothing left.27

How to boost rare disease research?DATAYou

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.

#

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.28

Inspiration: personal Health Train16 February 2017

https://www.youtube.com/watch?v=mktAtHmy-FM

#

G20 Endorsement of FAIR principlesNext to ELIXIR, EOSC, NIH Commons

We, the leaders of the G20facilitate 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.

#

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 G20.30

FAIR principles applied to rare disease dataRD DATA

FAIR

LinkableRD 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.

#

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.31

FAIR principles applied to rare disease dataRD DATA

FAIR

LinkableRD DATA

YouYouYou 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.

#

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. 32

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.

#

Data can be more easily combined. Each resource is an independently FAIR resource. This is a much more robust infrastructure.33

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.

#

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.34

Rare disease data linkage plan - 2017> 7 biobanks/registries FAIR at the sourceStudy FAIR pathways, Orphanet, mutation dataSupport: RD-Connect, ELIXIR, BBMRI (-NL), FAIRDict, ODEX4All, patient organisations

David van EnckevortTechnical leader

Our aims for 2017

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.35

Light-weight introduction to the rare disease data linkage planAccount for scale and sparsity of data in rare disease domainFederated infrastructure of local FAIR dataSummary16-Feb-1736

#

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 data36

InvitationContact us about making rare disease data FAIRTurn plan into long-running serviceLong term plans16 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.

#

I invite you to contact us about making rare disease data FAIRAnd 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.37

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 projectsErik 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, Jos 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.

Thank you for your attention. Here are some contact points.38