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The National Center for Biomedical Ontology

The National Center for Biomedical Ontology. Stanford – Berkeley Mayo – Victoria – Buffalo UCSF – Oregon – Cambridge. Ontologies are essential to make sense of biomedical data. A biological ontology is:. A machine interpretable representation of some aspect of biological reality.

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The National Center for Biomedical Ontology

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  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 • what kinds of things exist? eye disc sense organ develops from is_a • what are the relationships between these things? eye part_of ommatidium

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

  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 disease models Animal models Mutant Gene Mutant or missing ProteinMutant Phenotype

  12. Animal disease models Mutant Gene Mutant or missing ProteinMutant Phenotype (disease) Humans Animal models Mutant Gene Mutant or missing ProteinMutant Phenotype (disease model)

  13. Animal disease models Mutant Gene Mutant or missing ProteinMutant Phenotype (disease) Humans Animal models Mutant Gene Mutant or missing ProteinMutant Phenotype (disease model)

  14. Animal disease models Mutant Gene Mutant or missing ProteinMutant Phenotype (disease) Humans Animal models Mutant Gene Mutant or missing ProteinMutant Phenotype (disease model)

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

  16. Phenotype (clinical sign) = entity + attribute

  17. Phenotype (clinical sign) = entity + attribute P1 = eye + hypoteloric

  18. Phenotype (clinical sign) = entity + attribute P1 = eye + hypoteloric P2 = midface + hypoplastic

  19. Phenotype (clinical sign) = entity + attribute P1 = eye + hypoteloric P2 = midface + hypoplastic P3 = kidney + hypertrophied

  20. Phenotype (clinical sign) = entity + attribute P1 = eye + hypoteloric P2 = midface + hypoplastic P3 = 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 P1 = eye + hypoteloric P2 = midface + hypoplastic P3 = kidney + hypertrophied Syndrome = P1 + P2 + P3 (disease) = holoprosencephaly

  23. Human holo- prosencephaly Zebrafish shh Zebrafish oep

  24. Human holo- prosencephaly Zebrafish shh Zebrafish oep

  25. ZFIN mutant genes

  26. OMIM genes ZFIN mutant genes

  27. OMIM genes ZFIN mutant genes FlyBase mutant genes

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

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

  30. cBio Bioinformatics Goals • Apply ontologies • Software toolkit for annotation • Manage data • Databases and interfaces to store and view annotations • Investigate and compare • Linking human diseases to genetic models • Maintain • Ongoing reconciliation of ontologies with annotations

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

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

  33. Phenotype from published evidence

  34. Ontologies enable users to describe assays

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

  36. Ontologies enable users to describe environments

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

  38. Ontologies enable users to describe genotypes

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

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

  41. 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

  42. 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?

  43. 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

  44. Core 1 Components

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

  46. 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

  47. 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

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