1 How Ontologies Create Research Communities Barry Smith

2 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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4 where in the body ? what kind of disease process ? we need semantic annotations of data which human beings can understand and computers can reason with

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6 Institute for Formal Ontology and Medical Information Science (IFOMIS) BIRN: Bioinformatics Research Network

7 Institute for Formal Ontology and Medical Information Science (IFOMIS) BIRN: Bioinformatics Research Network Center for In Vivo Microscopy Brain Imaging and Analysis Center Neuropsychiatric Imaging Research Laboratory Yerkes National Primate Research Center Clinical Cognitive Neuroscience Laboratory Mallinkrodt Institute of Radiology Lineberger Comprehensive Cancer Center fMRI Research Center Surgical Planning Laboratory Center for Magnetic Resonance Research

Multiscale Systems Immunology for Adjuvant [Vaccine] Development Investigators Duke Center for Computional Immunology Thomas B Kepler Lindsay G Cowell Cliburn Chan Duke Center for Computational Sciences, Engineering and Medicine John Pormann Rachael Brady Bill Rankin Duke Mathematics Bill Allard Duke Institute of Statistics and Decision Sciences Mike West Duke Computer Science Jun Yang Duke Human Vaccine Institute Greg Sempowski Munir Alam Department of Pathology,Emory Bali Pulendran Department of Physiology & Biophysics, UC Irvine Michael Cahalan Department of Pediatrics, Vanderbilt Kathryn Edwards

9 how do we make different sorts of data combinable in ways useful to the human beings who carry out research?

10 how was this problem solved in the years BC? how did clinical researchers from different disciplines communicate? how did they learn to communicate?

11 through the basic biomedical sciences: anatomy, physiology, biochemistry, histology,...

12 create ontologies corresponding to the basic biomedical sciences clinical medicine relies on anatomy and molecular biology to provide integration across medical specialisms

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14 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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16 what makes GO so wildly successful ?

17 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:

18  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

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

20 This process leads to a slowly growing computer- interpretable map of biological reality within which major databases are automatically integrated in semantically searchable form

21 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

22 but also need to extend this methodology beyond the basic biomedical sciences, to clinical domains disease ontology immunology ontology symptom (phenotype) ontology neuron ontology brain (mal)function ontology...

23 the problem need to ensure consistency of the new clinical ontologies with the basic biomedical sciences need to find ways to ensure clinical data is annotated in terms of these new controlled vocabularies if we do not start now, the problem will only get worse

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

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26 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.

27 The OBO Foundry Third step (2006)

28 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

29 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.

30 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

31 already in good shape 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

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

33 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)

34 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

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

36 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

37 CRITERIA  The ontology is OPEN  The ontology employs a COMMON FORMAL LANGUAGE.  The developers agree to COLLABORATE  UPDATE in light of scientific advance  ORTHOGONALITY: one ontology per domain

38  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

39 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

40 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

41  to introduce some of the features of SCIENTIFIC PEER REVIEW into biomedical ontology development  KUDOS for early adopters of high quality ontologies / terminologies e.g. in reporting clinical trial results  establish ONTOLOGY CHAMPIONS to create EVIDENCE-BASED TERMINOLOGY RESEARCH GOALS The OBO Foundry

42  DATA 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

43 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). expand to all areas of biomedical experimentation The OBO Foundry

44  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

45 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

46 how to replicate the successes of the GO 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 clinical trial initiatives to foster the collection of clinical data annotated in their terms Fourth Step (the future)

Draft Ontology for Acute Respiratory Distress Syndrome

Draft Ontology for Muscular Sclerosis what data do we have? what data do the others have? what data do we not have?

Draft Ontology for Muscular Sclerosis to apprehend what is unknown requires a complete demarcation of the relevant space of alternatives