1 How Ontologies Create Research Communities Barry Smith

2 how do we know what data we have ? how do I know what data you have ? how do we know what data we don’t have ? how do we make different sorts of data combinable, as we need to do in large domains such as neurodevelopment, immunology, cancer...? we are accumulating huge amounts of sequence data, image data, pharma data,...

3 genomic medicine, molecular medicine, translational medicine, personalized medicine... need methods for data integration to enable reasoning across data at multiple granularities to identify biomedically relevant relations on the side of the entities themselves

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5 where in the body ? what kind of disease process ? = we need ontologies we need semantic annotation of data

6 Semantic Web, Moby, wikis, etc. let a million flowers (and weeds) bloom to create integration rely on (automatically generated?) post hoc mappings how create broad-coverage semantic annotation systems for biomedicine?

7 most successful, thus far: UMLS built by trained experts massively useful for information retrieval and information integration UMLS Metathesaurus a system of post hoc mappings between source vocabularies separately built

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9 UMLS-based mappings fall short of creating interoperability because local usage is respected regimentation frowned upon, no concern for cross- framework consistency UMLS terminologies have different grades of formal rigor, different degrees of completeness, different update policies

10 with UMLS-based annotations we can know what data we have (via term searches), but it is noisy we can map between data at single granularities (via ‘synonyms’), but synonymy information is noisy how do we know what data we don’t have ? how do we reason with data (as at the molecular level), when no common logical backbone ?

11 to develop high quality annotation resources in a collaborative, community effort? create an evolutionary path towards improvement of terminologies, of the sort we find elsewhere in science find ways to reward early adopters of the results what is to be done?

12 science works out from a consensus core, and strives to isolate and resolve inconsistencies as it extends at the fringes we need to create a consensus core start with what for human beings are trivialities (low hanging fruit) and work out from there for science, consistency is a sine qua non

13 Pleural Cavity Pleural Cavity Interlobar recess Interlobar recess Mesothelium of Pleura Mesothelium of Pleura Pleura(Wall of Sac) Pleura(Wall of Sac) Visceral Pleura Visceral Pleura Pleural Sac Parietal Pleura Parietal Pleura Anatomical Space Organ Cavity Organ Cavity Serous Sac Cavity Serous Sac Cavity Anatomical Structure Anatomical Structure Organ Serous Sac Mediastinal Pleura Mediastinal Pleura Tissue Organ Part Organ Subdivision Organ Subdivision Organ Component Organ Component Organ Cavity Subdivision Organ Cavity Subdivision Serous Sac Cavity Subdivision Serous Sac Cavity Subdivision part_of is_a Foundational Model of Anatomy

14 include ontologies corresponding to the basic biomedical sciences in the core clinical medicine relies on anatomy and molecular biology to provide integration across medical specialisms

15 where do we find scientifically validated information linking gene products and other entities represented in biochemical databases to semantically meaningful terms pertaining to disease, anatomy, development, histology in different model organisms? but we need more

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17 what makes GO so wildly successful ?

18 science basis of the GO: trained experts curating peer-reviewed literature different model organism databases employ scientific curators who use the experimental observations reported in the biomedical literature to associate GO terms with gene products in a coordinated way The methodology of annotations

19  cellular locations  molecular functions  biological processes used to annotate the entities represented in the major biochemical databases thereby creating integration across these databases and making them available to semantic search A set of standardized textual descriptions of

20 what cellular component? what molecular function? what biological process?

21 This process leads to improvements and extensions of the ontology which in turn leads to better annotations  a virtuous cycle of improvement in the quality and reach of both future annotations and the ontology itself RESULT: a slowly growing computer-interpretable map of biological reality within which major databases are automatically integrated in semantically searchable form

22 Five bangs for your GO buck science base cross-species database integration cross-granularity database integration through links to the things which are of biomedical relevance  semantic searchability links people to software

23 but now need to improve the quality of GO to support more rigorous logic-based reasoning across the data annotated in its terms need to extend the GO by engaging ever broader community support for the addition of new terms and for the correction of errors

24 but also need to extend the methodology to other domains, including clinical domains  need for disease ontology immunology ontology symptom (phenotype) ontology clinical trial ontology...

25 the problem existing clinical vocabularies are of variable quality and low mutual consistency need for prospective standards to ensure mutual consistency and high quality of clinical counterparts of GO need to ensure consistency of the new clinical ontologies with the basic biomedical sciences if we do not start now, the problem will only get worse

26 the solution establish common rules governing best practices for creating ontologies and for using these in annotations apply these rules to create a complete suite of orthogonal interoperable biomedical reference ontologies this solution is already being implemented

27 a shared portal for (so far) 58 ontologies (low regimentation)  NCBO BioPortal First step (2003)

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29 Second step (2004) Second step (2004) reform efforts initiated, e.g. linking GO to other OBO ontologies to ensure orthogonality id: CL: name: osteoblast def: "A bone-forming cell which secretes an extracellular matrix. Hydroxyapatite crystals are then deposited into the matrix to form bone." is_a: CL: relationship: develops_from CL: relationship: develops_from CL: GO Cell type New Definition + = Osteoblast differentiation: Processes whereby an osteoprogenitor cell or a cranial neural crest cell acquires the specialized features of an osteoblast, a bone-forming cell which secretes extracellular matrix.

30 The OBO Foundry Third step (2006)

31 a family of interoperable gold standard biomedical reference ontologies to serve the annotation of inter alia scientific literature model organism databases clinical trial data The OBO Foundry The OBO Foundry

32 A prospective standard designed to guarantee interoperability of ontologies from the very start (contrast to: post hoc mapping) established March initial candidate OBO ontologies – focused primarily on basic science domains several being constructed ab initio by influential consortia who have the authority to impose their use on large parts of the relevant communities.

33 undergoing rigorous reform new GO Gene Ontology ChEBI Chemical Ontology CL Cell Ontology FMA Foundational Model of Anatomy PaTO Phenotype Quality Ontology SO Sequence Ontology CARO Common Anatomy Reference Ontology CTO Clinical Trial Ontology FuGO Functional Genomics Investigation Ontology PrO Protein Ontology RnaO RNA Ontology RO Relation Ontology The OBO Foundry

34 RELATION TO TIME GRANULARITY CONTINUANTOCCURRENT INDEPENDENTDEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy?) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Organism-Level Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) Cellular Process (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO) Annotations plus ontologies yield an ever-growing computer-interpretable map of biological reality.

35 RELATION TO TIME GRANULARITY CONTINUANTOCCURRENT INDEPENDENTDEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy?) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO) Building out from the original GO

36 Disease Ontology (DO) Biomedical Image Ontology (BIO) Upper Biomedical Ontology (OBO UBO) Environment Ontology (EnvO) Systems Biology Ontology (SBO) Under consideration: The OBO Foundry

37 OBO Foundry = a subset of OBO ontologies, whose developers have agreed in advance to accept a common set of principles reflecting best practice in ontology development designed to ensure tight connection to the biomedical basic sciences compatibility interoperability, common relations formal robustness support for logic-based reasoning The OBO Foundry

38 CRITERIA  The ontology is OPEN and 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 Foundry

39 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 Foundry

40 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 orthogonality of ontologies implies additivity of annotations The OBO Foundry

41  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. CRITERIA

42  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. CRITERIA The OBO Foundry

43  COMMON ARCHITECTURE: The ontology uses relations which are unambiguously defined following the pattern of definitions laid down in the OBO Relation Ontology.* * Smith et al., Genome Biology 2005, 6:R46 CRITERIA The OBO Foundry

44 Foundational is_a part_of Spatial located_in contained_in adjacent_to Temporal transformation_of derives_from preceded_by Participation has_participant has_agent OBO Relation Ontology The OBO Foundry

45 Further criteria will be added over time in light of lessons learned in order to bring about a gradual improvement in the quality of Foundry ontologies ALL FOUNDRY ONTOLOGIES WILL BE SUBJECT TO CONSTANT UPDATE IN LIGHT OF SCIENTIFIC ADVANCE IT WILL GET HARDER The OBO Foundry

46 But not everyone needs to join The Foundry is not seeking to serve as a check on flexibility or creativity ALL FOUNDRY ONTOLOGIES WILL ENCOURAGE COMMUNITY CRITICISM, CORRECTION AND EXTENSION WITH NEW TERMS IT WILL GET HARDER The OBO Foundry

47  to introduce some of the features of SCIENTIFIC PEER REVIEW into biomedical ontology development  CREDIT for high quality ontology development work  KUDOS for early adopters of high quality ontologies / terminologies e.g. in reporting clinical trial results GOALS The OBO Foundry

48  to providing a FRAMEWORK OF RULES to counteract the current policy of ad hoc creation of new annotation schemas by each clinical research group by  REUSABILITY: if data-schemas are formulated using a single well-integrated framework ontology system in widespread use, then this data will be to this degree itself become more widely accessible and usable GOALS The OBO Foundry

49  to serve as BENCHMARK FOR IMPROVEMENTS in discipline-focused terminology resources  once a system of interoperable reference ontologies is there, it will make sense to calibrate existing terminologies in its terms in order to achieve more robust alignment and greater domain coverage  exploit the avenue of EVIDENCE-BASED MEDICINE (NIH CLINICAL RESEARCH NETWORKS) to foster their use by clinicians GOALS The OBO Foundry

50 June 2006: establishment of MICheck: reflects growing need for prescriptive checklists specifying the key information to include when reporting experimental results (concerning methods, data, analyses and results). the vision is spreading The OBO Foundry

51  MICheck: ‘a common resource for minimum information checklists’ analogous to OBO / NCBO BioPortal  MICheck Foundry: will create ‘a suite of self- consistent, clearly bounded, orthogonal, integrable checklist modules’ * * Taylor CF, et al. Nature Biotech, in press MICheck Foundry The OBO Foundry

52 Transcriptomics (MIAME Working Group) Proteomics (Proteomics Standards Initiative) Metabolomics (Metabolomics Standards Initiative) Genomics and Metagenomics (Genomic Standards Consortium) In Situ Hybridization and Immunohistochemistry (MISFISHIE Working Group) Phylogenetics (Phylogenetics Community) RNA Interference (RNAi Community) Toxicogenomics (Toxicogenomics WG) Environmental Genomics (Environmental Genomics WG) Nutrigenomics (Nutrigenomics WG) Flow Cytometry (Flow Cytometry Community) MICheck/Foundry communities

53 how to replicate the successes of the GO in other areas For example in clinical medicine: choose two or three representative disease domains work out reasoning challenges for those domains work with specialists to create ontologies interoperable with OBO Foundry basic science ontologies to address these reasoning challenges work with leaders of professional associations and of clinical trial initiatives to foster the collection of clinical data annotated in their terms Fourth Step (the future)