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NCBO, the OBO-Foundry, and you GO User’s meeting September 10 th , 2006

NCBO, the OBO-Foundry, and you GO User’s meeting September 10 th , 2006. Suzanna Lewis GO Consortium & National Center for Biomedical Ontology http://www.geneontology.org/ http://www.bioontology.org/. There is no requirement that ontology be done using any particular technology.

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NCBO, the OBO-Foundry, and you GO User’s meeting September 10 th , 2006

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  1. NCBO, the OBO-Foundry, and youGO User’s meetingSeptember 10th, 2006 Suzanna Lewis GO Consortium & National Center for Biomedical Ontology http://www.geneontology.org/ http://www.bioontology.org/

  2. There is no requirement that ontology be done using any particular technology.

  3. Three fundamental dichotomies • types vs. instances • continuants vs. occurrents • dependent vs. independent

  4. occurent dependent continuant independent For example, in the GO’s 3 ontologies molecular function biological process cellular component Molecules, cell components , organisms are independent continuants which have functions (these are dependent continuants), and these functions may be realized as an occurent process when “functioning”

  5. A Portion of the OBO Library

  6. Due Diligence is the 1st step! • We keep reinventing the wheel • We don’t even know what’s out there! • We need tools to help us compare and contrast ontologies • We need tools to keep track of ontology history and to compare versions • We need infrastructure for connecting ontologies

  7. Open Biomedical Ontologies: OBO Mark 1 • Initially side-project of the Gene Ontology • http://obo.sourceforge.net • ontology management and versioning • website • mailing lists • limitations due to lack of resources • lacking ontology development support • little in the way of integration • neither ‘nuts-n-bolts’ and semantic integration

  8. The National Center for Biomedical Ontology What is NCBO?

  9. NCBO’s 7 Cores • Core 1: Computer science • Core 2: Bioinformatics • Core 3: Driving biological projects • Core 4: Infrastructure • Core 5: Education and Training • Core 6: Dissemination • Core 7: Administration

  10. Who NCBO is • 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)

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

  12. Animal disease models Animal models Mutant Gene Mutant or missing ProteinMutant Phenotype

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

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

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

  16. SHH-/+ SHH-/- shh-/+ shh-/-

  17. Phenotype (clinical sign) = entity + quality

  18. Phenotype (clinical sign) = entity + quality P1 = eye + hypoteloric

  19. Phenotype (clinical sign) = entity + quality P1 = eye + hypoteloric P2 = midface + hypoplastic

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

  21. Phenotype (clinical sign) = entity + quality P1 = eye + hypoteloric P2 = midface + hypoplastic P3 = kidney + hypertrophied PATO: hypoteloric hypoplastic hypertrophied ZFIN: eye midface kidney +

  22. Phenotype (clinical sign) = entity + quality Anatomical ontology Cell & tissue ontology Developmental ontology Gene ontology biological process cellular component + PATO (phenotype and trait ontology)

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

  24. Human holo- prosencephaly Zebrafish shh Zebrafish oep

  25. What is Phenote? • A tool for annotating Phenotypes • Curator reads about a phenotype in the literature related to taxonomy or genotype • Curator enters genotype(or taxonomy) • Curator enters genetic context (optional) • Curator searches/enters Entity (e.g. Anatomy) • Curator searches/enters PATO attribute/value

  26. A Portion of the OBO Library

  27. OBO Mark II: Infrastructure • Integrated access to all OBO ontologies • Programmatic and user access • web interface • interface via tools (OBO-Edit, Protégé) • application programmer interfaces (APIs) • web services • Advanced search facilities • lexgrid • Visualization • Ontology metadata

  28. Return to GO (do not collect $200)

  29. Specific Aims of the GO 2006 • We will maintain comprehensive, logically rigorous and biologically accurate ontologies. • We will comprehensively annotate 9 reference genomes in as complete detail as possible. • We will support annotation across all organisms. • We will provide our annotations and tools to the research community.

  30. Weaving and untangling the GO • Missing relations • is_a completeness • Adding new relations within single GO ontology • Adding “regulates” to BP • Distinguishing different part_of relations • Adding Relations between GO axis • Linking between MF & BP & CC • Adding relations between GO & other ontologies • GO+Cell • GO+anatomy • GO+ChEBI

  31. Implicit ontologies within the GO: • cysteine biosynthesis (ChEBI) • myoblast fusion (Cell Type Ontology) • hydrogen ion transporter activity (ChEBI) • snoRNA catabolism (Sequence Ontology) • wing disc pattern formation (Drosophila anatomy) • epidermal cell differentiation (Cell Type Ontology) • regulation of flower development (Plant anatomy) • interleukin-18 receptor complex (not yet in OBO) • B-cell differentiation (Cell Type Ontology)

  32. Obol produces genus-differentia logical definitions GO editor OBO editor go.obo oboedit obol name parser cell.obo cell.obo cell.obo Ego.obo go ‘fixed’ obol report obol config reasoner cjm

  33. Relations to Other Ontologies CL GO blood cell cell differentiation lymphocyte differentiation lymphocyte B-cell activation B-cell is_a B-cell differentiation

  34. CELL Ontology [Term] id: CL:0000236 name: B-cell is_a: CL:0000542 ! lymphocyte develops_from: CL:0000231 ! B-lymphoblast Augmented GO [Term] id: GO:0030183 name: B-cell differentiation is_a: GO:0042113 ! B-cell activation is_a: GO:0030098 ! lymphocyte differentiation intersection_of: is_a GO:0030154 ! cell differentiation intersection_of: has_participant CL:0000236 ! B-cell

  35. There are many less than perfect ontologies

  36. Use the power of combination and collaboration • Ontologies are like telephones: they are valuable only to the degree that they are used and networked with other ontologies • But to work telephones must be connected • Like telephones, most ontologies were broken when the technology was first being developed

  37. The OBO-Foundry is: • foun·dry • An establishment where metal is melted and poured into molds • OBO-foun·dry • An establishment where scientific theory is formalized and represented in ontologies

  38. To create the conditions for a step-by-step evolution towards robust gold standard reference ontologies in the biomedical domain. • To introduce some of the features of scientific peer review into biomedical ontology development. • obofoundry.org

  39. The OBO Foundry OBO Foundry A subset of OBO ontologies whose developers agree in advance to accept a common set of principles designed to assure • intelligibility to biologist curators, annotators, users • formal robustness • stability • compatibility • interoperability • support for logic-based reasoning

  40. December 1st & 2nd Building out from the original GO

  41. CRITERIA • The ontology is OPENand available to be used by all. • The ontology is in, or can be instantiated in, a COMMON FORMAL LANGUAGE. • The developers of the ontology agree in advance to COLLABORATE with developers of other OBO Foundry ontology where domains overlap. The OBO Foundryhttp://obofoundry.org/

  42. CRITERIA • UPDATE: The developers of each ontology commit to its maintenance in light of scientific advance, and to soliciting community feedback for its improvement. • ORTHOGONALITY: They commit to working with other Foundry members to ensure that, for any particular domain, there is community convergence on a single controlled vocabulary. The OBO Foundryhttp://obofoundry.org/

  43. Orthogonality of ontologies implies additivity of annotations • If we annotate a database or body of literature with one high-quality biomedical ontology, we should be able to add annotations from a second such ontology without conflicts The OBO Foundryhttp://obofoundry.org/

  44. CRITERIA • IDENTIFIERS: The ontology possesses a unique identifier space within OBO. • VERSIONING: The ontology provider has procedures for identifying distinct successive versions to ensure BACKWARDS COMPATIBITY with annotation resources already in common use • The ontology includes TEXTUAL DEFINITIONS and where possible equivalent formal definitions of its terms. The OBO Foundryhttp://obofoundry.org/

  45. CRITERIA • CLEARLY BOUNDED: The ontology has a clearly specified and clearly delineated content. • DOCUMENTATION: The ontology is well-documented. • USERS: The ontology has a plurality of independent users. The OBO Foundryhttp://obofoundry.org/

  46. CRITERIA • AGREE ON RELATIONS: The ontology uses relations which are unambiguously defined following the pattern of definitions laid down in the OBO Relation Ontology.* • The success of ontology alignment demands that ontological relations (is_a, part_of, ...) have the same meanings in the different ontologies to be aligned. Genome Biology 6:R46, 2005.

  47. Elements for Success 1 • A Community with a common vision • A pool of talented and motivated developers/scientists • A mix of academic and commercial • An organized, light weight approach to product development • A leadership structure • Communication • A well-defined scope, (our “business”) Adopted from “Open Source Menu for Success”

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