‘What is in it for me?’ Semantic Support for Collaborative BioBank Research

1. ‘What is in it for me?’Semantic Support for Collaborative BioBank Research Marco Roos, Erik van Mulligen, Hailiang Mei, Linda Broer, Martijn Jansen, Reinout van Schouwen, MartijnSchuemie, Peter-Bram 't Hoen, GertJan van Ommen, Barend Mons, Christine Chichester Center for Medical Systems Biology, LUMC; Informatics Institute, University of Amsterdam, Dept of Medical Informatics, Erasmus Medical Center; Netherlands Bioinformatics Center; Genetic Epidemiology Unit, Erasmus Medical Center; Swiss Institute of Bioinformatics

2. Preface These slides were presented at the BBMRI conference ‘BioBanking for Science’ in Amsterdam, October 2010, Enabling Technologies track In some places the slides include feedback from the audience of this conference and the group of DorretBoomsma, Free University of Amsterdam. I hereby thank them for their contribution. The slides were presented on behalf of BioSemantics collaborators, in particular the BioSemantics teams at the Leiden University Medical Centre and the Erasmus Medical Centre, Rotterdam

3. The presenter: “in between” scientist Background inBiology Computer Science Factsheet: PhD Molecular Cytology, ‘minor’ computer science, e-Scientist Coordinator/scientist BioSemantics groupHuman Genetics Department Leiden University Medical Centre and Informatics Institute University of Amsterdam Project or Area Liaison (PAL) UK e-Science (OMII-UK/myGrid) Advisor to NBIC (Netherlands BioInformatics Centre) Member of Concept Web Alliance

4. Audience Survey • Are you a BioBanker? • Are you a BioBank PI? • Are you a BioBank researcher? • Can you name one BioBank you wish to use data/material/expertise from? • Can you name one type of data you wish existed in a BioBank? • How well do you think you know what others have? • How well do you think others know what you have?

5. BBMRI – ‘BioBanking for Science’ survey results • In the enabling Technologies track • 70% BioBankers • 30% Enabling Technologists • >90% would like to use data from colleagues • >90% colleagues have incomplete knowledge of my biobank • Requirements, Biological Psychologists say: • Discover who has what types of data • Who is willing to share • Get people into contact • Acknowledge ethical issues

6. How can Semantic Technologies support Collaboration in BioBanking? Examples

7. Example: Mining Abstracts Procedure: • Use conference abstracts to represent BioBankers’ interests • Mine key concepts from the abstracts • Cluster abstracts based on corresponding abstracts • Present best matching abstracts, hence biobankers who could work together • Present key concepts suggesting why they could work together • Disclaimer: these technologies provide suggestions

8. With whom could I collaborate? And why?Mining results from Biobanks for Science 2010 Abstracts Accepted Conference Abstract Titles Clusters of abstracts Hierarchy (different clusters at different levels of granularity) Discriminating concepts for a cluster

9. Anni: Making sense of genomic data Procedure (simplified) • Collect concepts associated with disambiguated gene names in biomedical literature, ranked conform frequency of collocation => gene concept profiles • Link profiles to differentially expressed genes • Compute key concepts associated with differentially expressed gene sets

10. http://biosemantics.org/anniMeaning of gene sets as derived by semantic miningJelieret al., Genome Biol. 2008;9(6):R96. Epub 2008 Jun 12, PMID: 18549479 Differentially expressed genes grouped by matching associations in literature(Multi Dimensional Scaling projection) Gene/concept set manipulations Browse Concept Sets

11. Some proofs of principle • Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease • Reveals ‘emergent meaning’ of gene clusters in and between omics experiments • Rob Jellier, Peter-Bram ‘t Hoenet al.BMC Bioinformatics. 2008 Jun 24;9:291.PMID: 18577208 • Novel protein-protein interactions inferred from literature context • Predicting relations never reported in medline before • Herman van Haagenet al.PLoS One. 2009 Nov 18;4(11):e7894.PMID: 19924298

12. Example: The fabulous JaneJelier and Schuemie et al. Genome Biol. 2008;9(6):R96. Epub 2008 Jun 12. Copy-paste the abstract that you wish to publish http://biosemantics.org/Jane Note: only works in domains that gives electronic access to literature ironically: not computer science

13. The fabulous JaneJelier and Schuemie et al. Genome Biol. 2008;9(6):R96. Epub 2008 Jun 12. Find which journal is most appropriate NB feature not shown: authors with most similar abstracts = most likely collaborators / competitors / reviewers

14. Concept mining for BioBanking (sneak preview) Procedure (simplified) • Mine a document representing a BioBanker’s research interest (a publication or description) for key concepts, independent of language • Match with BioBank specific profiles • Present most appropriate • BioBanks • BioBankers • Documents • Etcetera

15. Reading a page… Sneak preliminary preview of demo, 22-23 Novermber 2010http://snipurl.com/cwa-conference

16. Suggesting information BioBank Collaboration Suggestions BioBanks of interes Consortia of interest BioBankers with similar interests Documents of Interest Discriminating Concepts Sneak preliminary preview of demo, 22-23 Novermber 2010http://snipurl.com/cwa-conference

17. Language independent… BioBank Collaboration Suggestions BioBanks of interes Consortia of interest BioBankers with similar interests Documents of Interest Discriminating Concepts Sneak preliminary preview of demo, 22-23 Novermber 2010http://snipurl.com/cwa-conference

18. How to develop Semantic biobanking ‘beyond the obvious’? A pilot project

19. Requirements interviews • Help us compile information relevant to research projects • Present BioBanks suited for collaboration • Help us answer ‘Which BioBank or researcher has the right type of data for my experiment?’

20. Requirements interviews • Do not • Ask us to provide information about our biobank again and again • Decrease our control over exposing information about our biobank • Decrease our control over engaging in new collaborations

21. Privileged user-experts ‘in the loop’ Linda BroerPhD student Genetic epidemiology Erasmus MC Rotterdam, NL

22. User feedback Plan of action • Support collaboration process, including security and control over data access • Support the more sensitive process of data operations across biobanks, using lessons learned from collaboration support

23. Collaborative BioBanking workflow (1/2) Biobank researchers: ?‘With whom can we collaborate on Topic X?’ BioBank professor Potential collaborators (data-owners) E-mail to potential collaborators Topic of interest? Number of cases/controls? Type of data? Literature study: can we align measurements? Evaluation 1 Write analysis plan Analysis plan e.g. GWA on trait Xinclusions/exclusions Data requirements, deadlines

24. Collaborative BioBanking workflow (2/2) Analysis plan Fine tune data requirements Get agreement Evaluation 2 Analysis Sharespace Results Interpretation: new questions etcetera

25. BioSemantics Support in Pilot project Suggestions: Experts Relevant studies/data (past, present, future) Relevant documentation ‘Other relevant data’ Social Features (future): Access status of data (e.g. Public, ‘upon request’) Groupings of experts ‘Others who...’ BioBankFinder E-mail to potential collaborators Helpful information Investigate potential for future BioSemantics support and research Literature study: can we align measurements? Evaluation 1 Write analysis plan Analysis plan e.g. GWA on trait Xinclusions/exclusions Data requirements, deadlines

26. User scenario I am looking for peers and data to help me confirm my hypothesis about the association between KIF1B and Multiple Sclerosis BioBankinformation Experts Literature Founding papers Surveys Statistics Specimen

27. Bridging User interface Search ‘middleware’ Machine readable index BioBankinformation

28. Suggesting information BioBank Collaboration Suggestions BioBanks of interes Consortia of interest BioBankers with similar interests Documents of Interest Discriminating Concepts Sneak preliminary preview of demo, 22-23 Novermber 2010http://snipurl.com/cwa-conference

29. A vision myBioBank.orgA biobanker’s market place?

30. BBMRI – ‘BioBanking for Science’ survey results • Enabling Technologies track • 70% BioBankers • 30% Enabling Technologists • >90% would like to use data from colleagues • >90% colleagues have incomplete knowledge of my biobank • A professional ‘dating site’ for BioBankers?

31. my BioBank.org Powered by ConceptWiki.org Proposition • A web site for and by Biobank researchers • Search Biobank information, forge collaborations • Publish and share biobankinformation • BioBank information registered by biobankers & mined and integrated from public resources • Biobank researchers & consortia • Biobanksregistered by users & from partner sites(e.g. Biobankers.de, BBMRI-NL) • Relevant publications • Link-outs to external knowledge resources (e.g. dbGap)

32. myBioBank.org vision, example viewpoint 1 BioBank ownerregistering biobank information for new collaborations

33. my BioBank.org For those who wish to participate in collaborations with their own data (controlled sharing) Powered by ConceptWiki.org Search My BioBanks Open BioBanks My Research Register a biobank I manage this biobank I wish to participate in collaborations Type or copy-paste a private biobank description Upload FoundingPublication or web site Type or copy-paste a public biobank description Key Concepts public / private (click for more information) To alleviate the burdon of providing informationnext to (virtual) imports from partner registries Priority Concept Remove Reduce Add Increase BioBank information Guess BioBank Name: Web site: Founding documents: Investigators + Manager: Sharing + Key Concepts + Related Resources +

34. my BioBank.org Powered by ConceptWiki.org Search My BioBanks Open BioBanks My Research Register a biobank I manage this biobank I wish to participate in collaborations Type or copy-paste a private biobank description Brain Vol.133-8, pp. 2248-2263 Das Kompetenznetz Multiple Sklerose hat vom BMBF nicht nur den Auftrag, die vernetzte Forschung auf dem Gebiet der MS in Deutschland voranzubringen, sondern auch den schnellen Transfer von neuen Erkenntnissen in die Praxis zu gewährleisten. Adjust key concepts to alter search results Key ConceptsAutomatically extracted Key Concepts public / private (click for more information) Autoimmunity Priority Concept Ralf Gold Multiple Sclerosis Remove Reduce Mouse animal model Fred Lühder Add Increase Neurotryphic factor Automatically extract BioBank informationuse founding documents and/or existing resources BioBank information Guess BioBank Name: Kompetenznetz Multiple Sklerose Web site: http://www.kompetenznetz-multiplesklerose.de Founding documents: PMID:20826430, DOI: 10.1002/ana.22010 Investigators + Manager: Sharing + Related Resources +

35. my BioBank.org Powered by ConceptWiki.org Search My BioBanks Open BioBanks My Research Key Concepts (click for more information) Alzheimer’s Disease Priority Concept Cock van Duijn Multiple Sclerosis Remove Reduce Case/control study KIF1B DorretBoomsma Add Increase Isolated population Automatically generated suggestionsby matching key concepts with public information and registered information that is accessible to you BioBank information + Discoveries (click for more information) Investigators – Linda Broer Cock van Duijn BioBanks registered for Collaboration + Consortia + Biological predictions + Export

36. my BioBank.org Powered by ConceptWiki.org Search My BioBanks Open BioBanks My Research Availability Example from myExperiment.org

37. myBioBank.org vision, example viewpoint 2 Registered BioBank researcherlooking for relevant information for new collaborations

38. my BioBank.org Powered by ConceptWiki.org Search My BioBanks Open BioBanks My Research My Research Type or copy-paste a private description of your research Upload FoundingDocuments Type or copy-paste a public description of your research Key Concepts private/public + Discoveries (click for more information) Export

39. my BioBank.org Powered by ConceptWiki.org Search My BioBanks Open BioBanks My Research My Research Genetic associations in MS in a MS case/control study related to GRIP (genetic research in isolated populations) Upload FoundingDocuments Type or copy-paste a public description of your research Automatically generated suggestionsby matching key concepts with public information and registered information that is accessible to you Key Concepts private/public + Discoveries (click for more information) Investigators – Linda Broer ElineSlagboom BioBanks registered for Collaboration – Kompetenznet Multiple Sklerose Consortia + Biological predictions + Export

40. myBioBank.org vision, example viewpoint 3 Visiting BioBank researcherlooking for relevant information for new collaborations

41. my BioBank.org Powered by ConceptWiki.org Search My BioBanks Open BioBanks My Research Research Interest Type or copy-paste Research Description Upload RelevantPubcations Key Concepts (click for more information) Priority Concept Remove Reduce Add Increase Search results (click for more information) Export

42. my BioBank.org Powered by ConceptWiki.org Search My BioBanks Open BioBanks My Research Research Interest Case/control studies to investigate the genetic association between the gene KIF1B and MS. Upload RelevantPubcations Key Concepts (click for more information) Alzheimer’s Disease Priority Concept Cock van Duijn Multiple Sclerosis Remove Reduce Case/control study KIF1B DorretBoomsma Add Increase Isolated population Automatically generated suggestionsby matching key concepts with public information only Search results (click for more information) Investigators – Linda Broer ElineSlagboom BioBanks registered for Collaboration + Consortia + Export

43. Survey Biological Psychologists say • Parts of this have been done, but possibly not combined like this • Example: DBGap • Involvement of and support for PI’s should be considered • Are current procedures sufficient as they are? (e-mail, personal non-digital communication, confidentiality contracts, etc.)

44. Technologies under the hood (data not shown) • Concept Mining Technology • Concept Web and Semantic Web (Linked Data) • Web Services (wsdl) • Web 2.0 (“e-Laboratories”) • See Appendix

45. Acknowledgements The BioSemantics team (particularly Barend Mons, Martijn Schuemie, Erik van Mulligen, Kristina Hettne, Jan Kors, Herman van Haagen, Polina Reshetova, Erik Schultes, Peter-Bram ‘t Hoen), the AID team (Scott Marshall, Sophia Katrenko, Willem van Hage, Edgar Meij, Konstantinos Krommydas, Wibisono Adianto, Pieter Adriaans), Andrew Gibson, Piter de Boer, Adam Belloum, myGrid (particularly Katy Wolstencroft , Jiten Bhagat, Alan Williams, Jun Zhao, Carole Goble, Dave de Roure), OMII-UK (particular ly Malcolm Atkinson, Dave, Carole, Mario Antonioletti, Neil Chue Hong), and NBIC (in particular Bharat Singh, Rob Hooft, Christine Chichester), and Jano van Hemert, Jos Koetsier, Wendy Bickmore, Willeke van Roon-Mom, Morris Swertz, Antoine van Kampen, Bob Hertzberger, Johan den Dunnen, GertJan van Ommen

46. Appendix APPENDIX • Concept Mining Technology & Concept Web • Linked Data • Web 2.0

47. Semantic BioBanking ‘beyond the obvious’Technologies Concept Mining

48. Concept mining mini 101 Concept 1 Concept 2 Concept 3

49. Concept mining mini 101 Disambiguated concepts from the Concept Web Concept:295165264 known as Marco RoosMolecular cytologist and bioinformatician in Leiden i.e. not Marco Roos, the plant biologist in Leiden You Me Neighbour

50. Concept mining mini 101 You Me Neighbour