1. The National Center for Biomedical Ontology Stanford – Berkeley Mayo – Victoria – Buffalo UCSF – Oregon – Cambridge

2. Ontologies are essential to make sense of biomedical data

3. A biological ontology is:  A machine interpretable representation of some aspect of biological reality eye  what kinds of things exist?  what are the relationships between these things? ommatidium sense organeye disc is_a part_of develops from

4. The Foundational Model of Anatomy

5. Knowledge workers seem trapped in a pre-industrial age  Most ontologies are  Of relatively small scale  Built by small groups working arduously in isolation  Success rests heavily on the particular talents of individual artisans, rather than on SOPs and best practices  There are few technologies available to make this process “faster, better, cheaper”

6. A Portion of the OBO Library

7. Open Biomedical Ontologies (OBO) Open Biomedical Data (OBD) BioPortal Capture and index experimental results Revise biomedical understanding Relate experimental data to results from other sources National Center for Biomedical Ontology

8.  Stanford: Tools for ontology alignment, indexing, and management (Cores 1, 4–7: Mark Musen)  Lawrence–Berkeley Labs: Tools to use ontologies for data annotation (Cores 2, 5–7: Suzanna Lewis)  Mayo Clinic: Tools for access to large controlled terminologies (Core 1: Chris Chute)  Victoria: Tools for ontology and data visualization (Cores 1 and 2: Margaret-Anne Story)  University at Buffalo: Dissemination of best practices for ontology engineering (Core 6: Barry Smith)

9. cBio Driving Biological Projects  Trial Bank: UCSF, Ida Sim  Flybase: Cambridge, Michael Ashburner  ZFIN: Oregon, Monte Westerfield

10. The National Center for Biomedical Ontology Core 3: Driving Biological Projects Monte Westerfield

11. Animal models Mutant Gene Mutant or missing Protein Mutant Phenotype Animal disease models

12. HumansAnimal models Mutant Gene Mutant or missing Protein Mutant Phenotype (disease) Mutant Gene Mutant or missing Protein Mutant Phenotype (disease model) Animal disease models

13. HumansAnimal models Mutant Gene Mutant or missing Protein Mutant Phenotype (disease) Mutant Gene Mutant or missing Protein Mutant Phenotype (disease model) Animal disease models

14. HumansAnimal models Mutant Gene Mutant or missing Protein Mutant Phenotype (disease) Mutant Gene Mutant or missing Protein Mutant Phenotype (disease model) Animal disease models

15. SHH -/+ SHH -/- shh -/+ shh -/-

16. Phenotype (clinical sign) = entity + attribute

17. Phenotype (clinical sign) = entity + attribute P 1 = eye + hypoteloric

18. Phenotype (clinical sign) = entity + attribute P 1 = eye + hypoteloric P 2 = midface + hypoplastic

19. Phenotype (clinical sign) = entity + attribute P 1 = eye + hypoteloric P 2 = midface + hypoplastic P 3 = kidney + hypertrophied

20. Phenotype (clinical sign) = entity + attribute P 1 = eye + hypoteloric P 2 = midface + hypoplastic P 3 = kidney + hypertrophied PATO: hypoteloric hypoplastic hypertrophied ZFIN: eye midface kidney +

21. Phenotype (clinical sign) = entity + attribute Anatomy ontology Cell & tissue ontology Developmental ontology Gene ontology biological process molecular function cellular component + PATO (phenotype and trait ontology)

22. Phenotype (clinical sign) = entity + attribute P 1 = eye + hypoteloric P 2 = midface + hypoplastic P 3 = kidney + hypertrophied Syndrome = P 1 + P 2 + P 3 (disease) = holoprosencephaly

23. Human holo- prosencephaly Zebrafish shh Zebrafish oep

24. Human holo- prosencephaly Zebrafish shh Zebrafish oep

25. ZFIN mutant genes

26. ZFIN mutant genes OMIM genes

27. OMIM genes ZFIN mutant genes FlyBase mutant genes

28. OMIM gene ZFIN gene FlyBase gene FlyBase mut pub ZFIN mut pub mouseratSNO MED OMIM disease LAMB1lamb1LanB151539 - FECHfechFerro- chelatase 25229Protoporphyria, Erythropoietic GLI2gli2aci3884122- SLC4A1slc4a1CG81777719Renal Tubular Acidosis, RTADR MYO7Amyo7ack8459316Deafness; DFNB2; DFNA11 ALAS2alas2Alas1714Anemia, Sideroblastic, X- Linked KCNH2kcnh2sei27312- MYH6myh6Mhc1663112Cardiomyopathy, Familial Hypertrophic; CMH TP53tp53p5364331911Breast Cancer ATP2A1atp2a1Ca-P60A326111Brody Myopathy EYA1eya1eya251546Branchiootorenal Dysplasia SOX10sox10Sox100B11744Waardenburg-Shah Syndrome

29. Open Biomedical Ontologies (OBO) Open Biomedical Data (OBD) BioPortal Capture and index experimental results Revise biomedical understanding Relate experimental data to results from other sources National Center for Biomedical Ontology

30. The National Center for Biomedical Ontology Core 2: Bioinformatics Suzanna Lewis

31. cBio Bioinformatics Goals 1.Apply ontologies  Software toolkit for annotation 2.Manage data  Databases and interfaces to store and view annotations 3.Investigate and compare  Linking human diseases to genetic models 4.Maintain  Ongoing reconciliation of ontologies with annotations

32. cBio Bioinformatics Goals 1.Apply ontologies  Software toolkit for annotation 2.Manage data  Databases and interfaces to store and view annotations 3.Investigate and compare  Linking human diseases to genetic models 4.Maintain  Ongoing reconciliation of ontologies with annotations

33. Elicitation of Requirements for Annotation Tools  Applications pull from pioneer users in Core 3  ZFIN  FlyBase  Trial Bank  Study these groups currently annotate data  Determine how our Core 2 tools can integrate with existing data flows and databases  Evaluate the commonalities and differences among approaches

34. Development of Data-Annotation Tool  Develop plug-in architecture  Default user interface for generic data-annotation tasks  Custom-tailored interfaces for particular biomedical domains  Enable interoperability with existing ontology- management platforms  Integrate ontology-annotation tool with BioPortal  Access ontologies for data annotation from OBO  Store data annotations in OBD

35. Phenotype as an observation context environment genetic The class of thing observed publication figures evidence assay sequence ID ontology

36. Phenotype from published evidence

37. Ontologies enable users to describe assays

38. Phenotype as an observation context environment genetic The class of thing observed publication figures evidence assay sequence ID ontology

39. Ontologies enable users to describe environments

40. Phenotype as an observation context environment genetic The class of thing observed publication figures evidence assay sequence ID ontology

41. Ontologies enable users to describe genotypes

42. Phenotypes as collections  Coincidence  Same organism, same time  Relative  Reduced, enhanced  Same focus of observation  All left hands  Differing levels of scale  Molecular, cellular, organismal  Recognizable patterns  Set of observations that describe a disease

44. Open Biomedical Ontologies (OBO) Open Biomedical Data (OBD) BioPortal Capture and index experimental results Revise biomedical understanding Relate experimental data to results from other sources National Center for Biomedical Ontology

45. The National Center for Biomedical Ontology Core 1: Computer Science Mark Musen

46. E-science needs technologies  To help build and extend ontologies  To locate ontologies and to relate them to one another  To visualize relationships and to aid understanding  To facilitate evaluation and annotation of ontologies

47. We need to relate ontologies to one another  We keep reinventing the wheel  We don’t even know what’s out there!  We need to make comparisons between ontologies automatically  We need to keep track of ontology history and to compare versions

48. We need to compute both similarities and differences  Similarities  Merging ontologies  Mapping ontologies  Differences  Versioning

49. Ontology engineering requires management of complexity  How can we  keep track of hundreds of relationships?  understand the implications of changes to a large ontology?  know where ontologies are underspecified? And where they are over constrained?

51. E-science needs technologies  To help build and extend ontologies  To locate ontologies and to relate them to one another  To visualize relationships and to aid understanding  To facilitate evaluation and annotation of ontologies

52. Core 1 Components

53. Core 1 Contributors  Stanford: Tools for ontology management, alignment, versioning, metadata management, automated critiquing, and peer review  Mayo: LexGrid technology for access to large controlled terminologies, ontology indexing, Soundex, search  Victoria: Technology for ontology visualization

54. Open Biomedical Ontologies (OBO) Open Biomedical Data (OBD) BioPortal Capture and index experimental results Revise biomedical understanding Relate experimental data to results from other sources National Center for Biomedical Ontology

55. Core 4: Infrastructure  Builds on existing IT infrastructure at Stanford and at our collaborating institutions  Adds  Online resources and technical support for the user community  Collaboration tools to link all participating sites

56. Core 5: Education and Training  Builds on existing, strong informatics training programs at Stanford, Berkeley, UCSF, Mayo/Minnesota, and Buffalo  New postdoctoral positions at Stanford, Berkeley, and Buffalo  New visiting scholars program

57. Core 6: Dissemination  Active relationships with relevant professional societies and agencies (e.g., HL7, IEEE, WHO, NIH)  Internet-based resources for discussing, critiquing, and annotating ontologies in OBO  Cooperation with other NCBCs to offer a library of open-source software tools  Training workshops to aid biomedical scientists in ontology development

58. Upcoming cBio Dissemination Workshops  Image Ontology Workshop Stanford CA, March 24–25, 2006  Training in Biomedical Ontology Schloss Dagstuhl, May 21–24, 2006  Training in Biomedical Ontology Baltimore, November 6–8, 2006 (in association with FOIS and AMIA conferences)

59. Core 7: Administration  Project management shared between Stanford and Berkeley  Executive committee (PI, co-PI, Center director, and Center associate director) provides day-to-day management and oversight  Council (All site PIs, including PIs of DBPs) provides guidance and coordination of work plans  Each Core has a designated “lead” selected from the Council

60. cBiO Organization Chart

61. Ontologies are essential to make sense of biomedical data