1. FAIR Sample and Data Access
David van Enckevort
ISBER 2017

2. Introduction Project Manager
Genomics Coordination Centre at Dept. Genetics
Member of the group lead by Prof. M.A. Swertz
My focus: sharing of biobank data and material

3. Outline FAIR Principles Making your data and samples FAIR
MOLGENIS: how our tools can help you
Examples of FAIR samples and data

4. Make resources sustainable for reuse
FAIR Principles
Make resources sustainable for reuse
Findability
Accessibility
Interoperability
Reusability
doi:  /sdata

5. Findable
Resources are assigned a globally unique and persistent identifier
Resources are described with rich metadata
Metadata clearly and explicitly include the identifier of the resource it describes
Resource can by found through other systems

6. Accessible
Retrievable by their identifier using a standardized communications protocol
The protocol is open, free, and universally implementable
The protocol allows for an authentication and authorization procedure, where necessary
Metadata are accessible, even when the data are no longer available

7. Interoperable
Use a formal, accessible, shared, and broadly applicable language for knowledge representation.
Use vocabularies that follow FAIR principles
Include qualified references to other (meta)data

8. Reusable
Richly described with a plurality of accurate and relevant attributes
Released with a clear and accessible usage license
Associated with detailed provenance
Meet domain-relevant community standards

9. What is FAIR not? A standard A specific technology or format
A data management system or analysis tool
The same as Open Access
Trivial to implement
11 November November 2018

10. Make your samples & data FAIR
Step 1
Make knowledge explicit (do not assume people will know things that are obvious to you)
Include sufficient metadata (units, SOPs, access conditions, consent)
Provide the raw data (e.g. when you need BMI also provide length and weight)

11. Make your samples & data FAIR
Step 2
Use standards information models
Encode data using ontologies

12. Standard information models
Define the minimal information you should capture to make your data (re)usable
Provide structure to the information
Common information models: MIABIS, MIAPE, MIAME

13. Ontologies http://bioportal.bioontology.org/
Provide a well defined and unambiguous meaning to a term
Provide relations between terms, e.g. ’Breast Cancer’ is a ‘Cancer’ is a ‘Disease’
Common ontologies include: OBIB, HPO, OMIM

14. Information model defines what information to include
Ontologies define acceptable values
Improve interoperability and reusability

15. Make your samples & data FAIR
Step 3
Publish metadata about your samples and data collections
Make it available to others

16. How we help you to make your data FAIR
MOLGENIS
How we help you to make your data FAIR

17. Platform for scientific data
Data request
Find and request (biobank) data sets and items
Genome browser
Data sharing and integration DAS protocol
Upload format
Import data and meta data using EMX format
Model registry
Meta-data registry of models for biobanks and molecular data
Annotators
Data integration for diagnostics and personalized
medicine
Compute
Large scale computation on computational clusters, grids and clouds
Connect
Harmonisation tools
RNA pipeline
NGS data quantitation, structure, eQTL allele specific expression
Impute pipeline
GWAS harmonization and imputation
R statistics
Use R data API to up/download data and integrate graphics
Data explorer
Filter and download for further analysis
DNA pipeline
NGS data alignment, SNV/SV calling, QC, NIPT

18. MOLGENIS/connect toolbox
‘FAIRifier’ system for retrospective interoperability of data
Biobank
Connect
Make data attributes interoperable
<ID>
SORTA
Make data values interoperable

19. Problem solved with MOLGENIS/connect
Different data at the source
Sample
Material
Sex
Clinical Diagnosis 1
DNA F
Ring chromosome 14 2
Leukocyte 3 4
Lymphoblast ID
Type
Diagnose
Geslacht D7
dna
Ring 14
Vrouw D8
wbc
Man D9
D10
lbc

20. Code data to a common standard Identifiers, ontologies, codes

21. Make data values interoperable
SORTA
<ID>
SORTA
Make data values interoperable

22. SORTA Workflow Upload data using Excel
SORTA shortlists candidate codes
Lexical matching
Semantic matching
Human expert decides (and so trains SORTA)
SORTA automatically recodes when high matching score (e.g. 80%)
Use n-gram matching treshold (e.g 80%)

23. Expert curation of the matches

24. Expert curation of the matches
Original text

25. Expert curation of the matches
Candidate standardized terms

26. Expert curation of the matches
Confidence scores

27. Expert curation of the matches
Select the right match

28. Make data attributes interoperable
BiobankConnect
<ID>
Biobank
Connect
Make data attributes interoperable

29. Software generates mappings
Standard model to conform to

30. Software generates mappings
Mapping rules for your data

31. Software generates mappings
Mapping rules for your data
You can map multiple datasets

32. Software generates mappings
Colour indicates state of the mapping

33. Curation of mappings

34. Create rules for conversion
Curation of mappings
Create rules for conversion

35. Curation of mappings
On the fly validation

36. Curation of mappings
Mark mapping as:
Curated
To be discussed

37. Benefits Tools reduce the burden of harmonising data
Allowing expert curation to provide high quality data
Make data usable for pooling and aggregation

38. Examples how it solves problems
FAIR Sample and Data
Examples how it solves problems

39. Pooling heterogeneous data
CM ever had high blood pressure
516 data items
Are you taking medication for high blood pressure?
353 data items
Standard variable wanted:
‘History of hypertension’
Hypertension
6401 data items
Increased in blood pressure
224 data items
Have you ever been told that you have elevated or high blood pressure?
75 data items
PREVEND

40. How FAIR helps Common models standardize the data that you capture
Ontologies standardize the way you express the values

41. Finding samples or data
Descriptive
data
Aggregated data
Sample and donor data
Study name
Contact info
Age high, Age low, Sex, etc.
Sampled date, storage temp., Material type, Disease, Age
Biobank catalogues / directories

42. BBMRI-ERIC Directory https://directory.bbmri-eric.eu/
World largest biobank directory
Listing over 1000 collections
Millions of samples
Federation of BBMRI National Nodes
Part of the Common Services for IT
Negotiator
Locator

43. Directory federation model
push
BBMRI-ERIC directory
pull
biobank
network
Biobanks provide data to the national node
BBMRI-ERIC directory receives data from the BBMRI National Nodes

44. All biobanks describe their samples with the same metadata

45. Enabling structured search using common terms

46. Send a request to the biobank for access

47. How FAIR helps
Common structure and protocols allows the Directory to aggregate data from the national nodes
Common terms allows researchers to find the right data and samples
Specifying access conditions gives insight into the availability of samples and data
Unique identifiers facilitate requests for access

48. Summary FAIR enables better use of biobank samples and data
Making them findable and accessible and promote reuse
We offer tools to help you make your data FAIR

49. To learn more Software MOLGENIS - http://www.molgenis.org/ Reading
FAIR principles - DOI:  /sdata
BiobankConnect - DOI:  /amiajnl
SORTA - DOI:  /database/bav089
MOLGENIS/connect - DOI:  /bioinformatics/btw155
Movies
Upload -
SORTA -
BiobankConnect -

50. Thank you for your attention!