Joint presentation, Hans Constandt, CEO, ONTOFORCE and Chris Evelo, Ph.D., Maastricht University and ELIXIR Public data has different levels of FAIRness. The higher the FAIRness level of a data source, the easier it is to use this source for data integration and linking. One of the goals of the intergovernmental organization ELIXIR is to facilitate the improvement of finding and sharing data and exchange of expertise in life science. ONTOFORCE focusses on integrating and linking public and private data by bringing data to a higher level of FAIRness. In this joint presentation, we will discuss what ELIXIR is doing to make public data more FAIR and combine this with showing examples of what the direct benefits are for data searching, browsing and visual analytics on the DISQOVER platform by making and using more FAIR internal, private or third party data.

1. Chris Evelo
Hans Constandt
May 15, 2018
Easier Integration and
Enrichment of Data by
Making Data More FAIR
chris_evelo
hconstandt

2. Agenda
• Elixir: Interoperability services aiming at actual reuse
• Things we can do (and how we do it)
• Lessons from Open PHACTS
• Services for Findability
• Services for Interoperability
• Integration in end user tools for actual reuse
• FAIR Data @Work
• What do we want from a Data Set?
• How do we get there?
• Your internal linked data?
• Lift data to higher level of FAIRness?
• Linked Data federation?
• Thank You
• Q&A

3. www.elixir-europe.org
ELIXIR
Safeguarding the results of life science
research in Europe

4. From reproducibility to reusability

5. We can do things like this (diabetic liver)
Pihlajamäki et al. dataset
is from Gene Expression
Omnibus (accession
number GSE15653)
Pihlajamäki et al. J Clin
Endocrinol Metab. 2009,
94 (9): 3521-3529. DOI:
10.1210/jc.2009-0212.
Martina Kutmon et al.
BMC Genomics 2014,
15:971.
DOI: 10.1186/1471-2164-
15-971

6. Data predators

7. Data predators

8. Vitamin D-microRNA network
31 targets up-regulated (3 in pathways)
23 targets down-regulated (4 in pathways)
Targeted by multiple microRNAs:
CLSPN - cell cycle
FZD5 - receptor for Wnt proteins
CACNG4 - calcium channel
Data: Wang et al. 2011. in Gene
Expression Omnibus (GEO, accession
number: GSE17461).
Published paper: Effects of
1alpha,25 dihydroxyvitamin D3 and
testosterone on miRNA and mRNA
expression in LNCaP cells. WL Wang
et al. Mol Cancer 2011. 10.
doi:10.1186/1476-4598-10-58
This work: Integrative network-
based analysis of mRNA and
microRNA expression in 1, 25-
dihydroxyvitamin D 3-treated cancer
cells. M Kutmon et al. Genes &
nutrition 10 (5), 35
doi:10.1007/s12263-015-0484-0

9. Workflow

10. Internal &
external
data
repositories
e.g. dbNP,
Sage, Atlas
knowledge
resources &
(semantic web)
Integration
e.g. Open PHACTS
WikiPathways
study capturing
ISA
models
study
data
processing,
statistics,
storage
e.g. arrayanalysis.org
ontologies
modeling & data integration,
network biology (extension),
supervised statistics
curation,
simulation annotation &
provenance
Integrative Systems Biology
research
applications
mapping
BridgeDb
extraction,
SPARQLing
conversion

12. Nanopub
Db
VoID
Data Cache
(Virtuoso Triple Store)
Semantic Workflow Engine
Linked Data API (RDF/XML, TTL, JSON)
Domain
Specific
Services
Identity
Resolution
Service
Chemistry
Registration
Normalisation
& Q/C
Identifier
Management
Service
Indexing
CorePlatform
P12374
EC2.43.4
CS4532
“Adenosine
receptor 2a”
VoID
Db
Nanopub
Db
VoID
Db
VoID
Nanopub
VoID
Public Content Commercial
Public
Ontologies
User
Annotations
Apps

13. Nanopub
Db
VoID
Data Cache
(Virtuoso Triple Store)
Semantic Workflow Engine
Linked Data API (RDF/XML, TTL, JSON)
Domain
Specific
Services
Identity
Resolution
Service
Chemistry
Registration
Normalisation
& Q/C
Identifier
Management
Service
Indexing
CorePlatform
P12374
EC2.43.4
CS4532
“Adenosine
receptor 2a”
VoID
Db
Nanopub
Db
VoID
Db
VoID
Nanopub
VoID
Public Content Commercial
Public
Ontologies
User
Annotations
Apps

14. www.elixir-europe.org/excelerate
@ELIXIREurope /company/elixir-europe
ELIXIR-EXCELERATE is funded by the European Commission within the
Research Infrastructures programme of Horizon 2020, grant agreement number
676559.
Interoperability services aiming at actual reuse
ELIXIR

15. Three levels of activity to reuse FAIR data
1. FAIR data itself
2. Interoperability services
3. Integration in end user tools

16. Services for findability
- Bioschemas: annotate the web
- FAIRsharing: describing standards and data collection
- BioStudies/BioSamples: describing studies and
samples and (links to) data
- (Omics)data repositories: ELIXIR core data resources
- FAIR datapoints: the future?
- Nanopublication collections: RDF with explicit
provenance and evidence

17. Services for interoperability

18. Services for interoperability

19. Services for interoperability
• Database identifier mapping
• Mappings for
• Gene products (ENSEMBL)
• Metabolites (HMDB, ChEBI, WikiData)
• Reactions (RHEA)
• Gene Variants (ENSEMBL, dbSNP)
• Framework and web service
• Stackable
• Integrated in PathVisio and Cytoscape, available for R, SemWeb

20. 2c. Needed services for interoperability
- Dedicated mapping approaches (e.g. variants to
protein domains, Mutalyzer)
- Chemistry resolution services (not really there)
- Textual term resolution (also for queries) (sounds so
simple..)
- …

21. Don’t be afraid to reinvent wheels!

22. Integration in tools
- (Federated) SPARQL queries with integrated mapping
(this is what is behind the Open PHACTS API)
- Combine in R, Cytoscape, Galaxy, KNIME, CWLetc
Needs packages, plugins, tools
- Combine in knowledge discovery tools working with
your own data and additional on purpose mapping
e.g. Ontoforce’s DISQOVER

23. The Biggest Wins for Knowledge Creation
- Integrating various data sources
- Using a unified (semantic) logic
- Linking data points
hconstandt

24. Real-World Knowledge Creation Challenges
- Combining humans & domain
knowledge are unbeatable
- Maximize synergy between
human Creativity & machine power
In complex real-world knowledge creation challenges,
often the right questions themselves are not known.
hconstandt

25. What do we want from a Data Set?
• Potential users know it exists and where to find it
• Potential users know what it’s about
• Usable for different purposes
• Connected
• Future proof
• Usable for automation
hconstandt

26. Ultimately?
• Driving knowledge generation
• Saving time and money in research and business
doi:10.1038/nrd3681
PMID:22378269.
hconstandt

27. How do we get there?
• Integrate data
• Harmonize data
• Enrich data
• Interlink data
• And … never forget the end user…
hconstandt

28. Use golden sources & specialized niche sources to
• fill in the gaps
• make the links
• expand
• enrich
Create your internal Linked Data ecosystem
hconstandt

29. Lift data to higher level of FAIRness
• Keep in sync with public data
• Harmonize
• Interlink
• Uniform User Interface and API
Sync Harmonize
Link
Source
DISQOVER
SyncSource Harmonize
hconstandt

31. Linked Data federation
• Thank You
• Q&A

32. Linked Data federation
• Thank You
• Q&A

33. Linked Data federation
• Thank You
• Q&A

34. Linked Data federation
• Thank You
• Q&A

35. Linked Data federation
• Thank You
• Q&A

37. hconstandt

38. Special thanks to people who worked with us and inspired us
Michel Dumontier Helena Deus Bryn Robert MaryJo Zabarowski Phillips Kuhl
Tom Plasterer Wolfgang Colsman Anthony Rowe John Reynders Tim Berners Lee
Bernard Munos Lee Harland Dan Gshwend Sebastien Lefebvre Eric Prudhommeaux
Dean Allemang Robert Greenwood Wolfgang Hoeck Bhanu Bahl Ian Dix
Carole Goble Matthias Nolte Derek Marren Chris Evelo Mark Benioff
Ian Harrow Maryann Martone Rajan Dasai Theo Platt Renzo Constandt
Bart Van Leeuwen Arun Nayar Martin Leach Jay Bergeron … & many more
Thank you!
hconstandt

39. chris_evelo
hconstandt