1. 1 Ontology (Science) Barry Smith University at Buffalo http://ontology.buffalo.edu/smith

2. ] Buffalo, NY Tutorials and Classes: July 20-23, 2009 Conference: July 24-26, 2009 http://icbo.buffalo.edu International Conference on Biomedical Ontology 2

3. MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFES IPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVIS VMVGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVY TLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLER CHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKY GYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERL KRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRAC ALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVC KLRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDD NNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGI SLLAFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLK TLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPW MDVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEY ATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGS RFETDLYESATSELMANHSVQTGRNIYGVDFSLTSVSG TTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDV How to do biology across the genome?

4. MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDR KRSFEKVVISVMVGKNVKKFLTFVEDEPDFQGGPIPSKYLIPKKINLMVYTLFQVHTLKFNRKDYDTL SLFYLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYM FLLLHVDELSIFSAYQASLPGEKKVDTERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRA CALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSCAC TARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLLAFAGPQRNVYVDDTTR RIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMDVVGFEDP NQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGS RFETDLYESATSELMANHSVQTGRNIYGVDSFSLTSVSGTTATLLQERASERWIQWLGLESDYHCS FSSTRNAEDVVAGEAASSNHHQKISRVTRKRPREPKSTNDILVAGQKLFGSSFEFRDLHQLRLCYEI YMADTPSVAVQAPPGYGKTELFHLPLIALASKGDVEYVSFLFVPYTVLLANCMIRLGRRGCLNVAPV RNFIEEGYDGVTDLYVGIYDDLASTNFTDRIAAWENIVECTFRTNNVKLGYLIVDEFHNFETEVYRQS QFGGITNLDFDAFEKAIFLSGTAPEAVADAALQRIGLTGLAKKSMDINELKRSEDLSRGLSSYPTRMF NLIKEKSEVPLGHVHKIRKKVESQPEEALKLLLALFESEPESKAIVVASTTNEVEELACSWRKYFRVV WIHGKLGAAEKVSRTKEFVTDGSMQVLIGTKLVTEGIDIKQLMMVIMLDNRLNIIELIQGVGRLRDGG LCYLLSRKNSWAARNRKGELPPKEGCITEQVREFYGLESKKGKKGQHVGCCGSRTDLSADTVELIE RMDRLAEKQATASMSIVALPSSFQESNSSDRYRKYCSSDEDSNTCIHGSANASTNASTNAITTAST NVRTNATTNASTNATTNASTNASTNATTNASTNATTNSSTNATTTASTNVRTSATTTASINVRTSATT TESTNSSTNATTTESTNSSTNATTTESTNSNTSATTTASINVRTSATTTESTNSSTSATTTASINVRTS ATTTKSINSSTNATTTESTNSNTNATTTESTNSSTNATTTESTNSSTNATTTESTNSNTSAATTESTN SNTSATTTESTNASAKEDANKDGNAEDNRFHPVTDINKESYKRKGSQMVLLERKKLKAQFPNTSEN MNVLQFLGFRSDEIKHLFLYGIDIYFCPEGVFTQYGLCKGCQKMFELCVCWAGQKVSYRRIAWEAL AVERMLRNDEEYKEYLEDIEPYHGDPVGYLKYFSVKRREIYSQIQRNYAWYLAITRRRETISVLDSTR GKQGSQVFRMSGRQIKELYFKVWSNLRESKTEVLQYFLNWDEKKCQEEWEAKDDTVVVEALEKG GVFQRLRSMTSAGLQGPQYVKLQFSRHHRQLRSRYELSLGMHLRDQIALGVTPSKVPHWTAFLSM LIGLFYNKTFRQKLEYLLEQISEVWLLPHWLDLANVEVLAADDTRVPLYMLMVAVHKELDSDDVPDG RFDILLCRDSSREVGE 4

5. To successfully navigate through such data, biomedicine needs help from ontologies 5 See Smith, et al. “The OBO Foundry: Coordinated Evolution of Ontologies to Support Biomedical Data Integration”, Nature Biotechnology, 25 (11), November 2007. http://www.nature.com/nbt/journal/v25/n11/pdf/nbt1346.pdf

6. Uses of ‘ontology’ in PubMed abstracts 6

7. MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDR KRSFEKVVISVMVGKNVKKFLTFVEDEPDFQGGPIPSKYLIPKKINLMVYTLFQVHTLKFNRKDYDTL SLFYLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYM FLLLHVDELSIFSAYQASLPGEKKVDTERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRA CALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSCAC TARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLLAFAGPQRNVYVDDTTR RIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMDVVGFEDP NQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGS RFETDLYESATSELMANHSVQTGRNIYGVDSFSLTSVSGTTATLLQERASERWIQWLGLESDYHCS FSSTRNAEDVVAGEAASSNHHQKISRVTRKRPREPKSTNDILVAGQKLFGSSFEFRDLHQLRLCYEI YMADTPSVAVQAPPGYGKTELFHLPLIALASKGDVEYVSFLFVPYTVLLANCMIRLGRRGCLNVAPV RNFIEEGYDGVTDLYVGIYDDLASTNFTDRIAAWENIVECTFRTNNVKLGYLIVDEFHNFETEVYRQS QFGGITNLDFDAFEKAIFLSGTAPEAVADAALQRIGLTGLAKKSMDINELKRSEDLSRGLSSYPTRMF NLIKEKSEVPLGHVHKIRKKVESQPEEALKLLLALFESEPESKAIVVASTTNEVEELACSWRKYFRVV WIHGKLGAAEKVSRTKEFVTDGSMQVLIGTKLVTEGIDIKQLMMVIMLDNRLNIIELIQGVGRLRDGG LCYLLSRKNSWAARNRKGELPPKEGCITEQVREFYGLESKKGKKGQHVGCCGSRTDLSADTVELIE RMDRLAEKQATASMSIVALPSSFQESNSSDRYRKYCSSDEDSNTCIHGSANASTNASTNAITTAST NVRTNATTNASTNATTNASTNASTNATTNASTNATTNSSTNATTTASTNVRTSATTTASINVRTSATT TESTNSSTNATTTESTNSSTNATTTESTNSNTSATTTASINVRTSATTTESTNSSTSATTTASINVRTS ATTTKSINSSTNATTTESTNSNTNATTTESTNSSTNATTTESTNSSTNATTTESTNSNTSAATTESTN SNTSATTTESTNASAKEDANKDGNAEDNRFHPVTDINKESYKRKGSQMVLLERKKLKAQFPNTSEN MNVLQFLGFRSDEIKHLFLYGIDIYFCPEGVFTQYGLCKGCQKMFELCVCWAGQKVSYRRIAWEAL AVERMLRNDEEYKEYLEDIEPYHGDPVGYLKYFSVKRREIYSQIQRNYAWYLAITRRRETISVLDSTR GKQGSQVFRMSGRQIKELYFKVWSNLRESKTEVLQYFLNWDEKKCQEEWEAKDDTVVVEALEKG GVFQRLRSMTSAGLQGPQYVKLQFSRHHRQLRSRYELSLGMHLRDQIALGVTPSKVPHWTAFLSM LIGLFYNKTFRQKLEYLLEQISEVWLLPHWLDLANVEVLAADDTRVPLYMLMVAVHKELDSDDVPDG RFDILLCRDSSREVGE 7

8. 8 what cellular component? what molecular function? what biological process? Gene Ontology

9. 9 what cellular component? what molecular function? what biological process? GO aids information retrieval via curation of data and literature

10. 10 GO as Common Controlled Vocabulary MouseEcotope GlyProt DiabetInGene GluChem Holliday junction helicase complex

11. 11 GO promotes integration of data MouseEcotope GlyProt DiabetInGene GluChem sphingolipid transporter activity

12. 12

13. Ontology engineers: “LET’S GENERALIZE THESE BENEFITS BY BUILDING MANY MANY TINY ONTOLOGIES IN OTHER AREAS” 13

14. The standard engineering methodology Pragmatics (‘usefulness’) is everything Usefulness = we get to write software which runs on our machines 14

15. It’s easier to write useful software if we work with a simplified model (“…we can’t know what reality is like in any case; we only have our ‘concepts’…”) Engineer A: This looks like a useful model to me (One week later:) Engineer B: This other thing looks like a useful model to me The standard engineering methodology

16. Result: Data in Pittsburgh does not interoperate with data in Vancouver  Science is siloed 16

17. Scientific theories must be common resources 1.they cannot be bought or sold 2.they must use open publishing venues 3.they must constantly evolve to reflect results of scientific experiments (“evidence-based”) 4.must be synchronized –use common system of units –common terminologies 17

18. Why build scientific ontologies Multiple ontologies only make our data silo problems worse Just as bad scientific theories must die, so also bad ontologies must die Ontologies should be relatively independent of tools, implementations and applications* * Need to clearly separate the Science Domain Knowledge from the Software Programming Knowledge 18

19. Scientific ontologies must be constrained so that they converge Q: What is to serve as constraint in order to avoid silo creation ? A:Reality, as revealed, incrementally, by experimentally-based science 19

20. Ontological realism Find out what the world is like (= by doing science) Build representations adequate to this world, not to some simplified model in your laptop … this strategy is being realized by the Gene Ontology and an expanding community of biomedical scientists 20

21. The Open Biomedical Ontologies (OBO) Foundry Goal: to provide a suite of controlled structured vocabularies for the callibrated annotation of data to support integration and reasoning across the entire domain of biomedicine as biomedical science advances, these ontologies must be evolved in tandem 21

22. 22 OntologyScopeURLCustodians Cell Ontology (CL) cell types from prokaryotes to mammals obo.sourceforge.net/cgi- bin/detail.cgi?cell Jonathan Bard, Michael Ashburner, Oliver Hofman Chemical Entities of Bio- logical Interest (ChEBI) molecular entitiesebi.ac.uk/chebi Paula Dematos, Rafael Alcantara Common Anatomy Refer- ence Ontology (CARO) anatomical structures in human and model organisms (under development) Melissa Haendel, Terry Hayamizu, Cornelius Rosse, David Sutherland, Foundational Model of Anatomy (FMA) structure of the human body fma.biostr.washington. edu JLV Mejino Jr., Cornelius Rosse Functional Genomics Investigation Ontology (FuGO) design, protocol, data instrumentation, and analysis fugo.sf.netFuGO Working Group Gene Ontology (GO) cellular components, molecular functions, biological processes www.geneontology.orgGene Ontology Consortium Phenotypic Quality Ontology (PaTO) qualities of anatomical structures obo.sourceforge.net/cgi -bin/ detail.cgi? attribute_and_value Michael Ashburner, Suzanna Lewis, Georgios Gkoutos Protein Ontology (PrO) protein types and modifications (under development)Protein Ontology Consortium Relation Ontology (RO) relationsobo.sf.net/relationshipBarry Smith, Chris Mungall RNA Ontology (RnaO) three-dimensional RNA structures (under development)RNA Ontology Consortium Sequence Ontology (SO) properties and features of nucleic sequences song.sf.netKaren Eilbeck

23. Orthogonality one ontology for each domain no need for ‘mappings’ (too expensive, too fragile, too difficult to keep up-to-date as mapped ontologies change) http://obofoundry.org 23

24. Orthogonality is our best (perhaps our only) hope of solving the data silo problem ontologists need to be trained to seek orthogonality to seek reuse with a vengeance 24

25. Ontologies like the GO are part of science True, they must be associated with computer implementations (with engineering artifacts) But the ontologies are not themselves engineering artifacts The same ontology can be associated with multiple engineering artifacts 25

26. Benefits of orthogonality ensures that those new to ontology to find the common, tested resources they need and to find examplars of good practice ensures mutual consistency of ontologies (trivially) thereby ensures additivity of annotations 26

27. More benefits of orthogonality it rules out simplification and partiality brings an obligation on the part of ontology developers to commit to scientific accuracy and domain- completeness 27

28. More benefits of orthogonality helps to eliminate redundancy serves the division of ontological labor: allows experts to focus on their own domains of expertise makes possible the establishment of clear lines of authority 28

29. The goal of orthogonality is a basic goal of science it is a pillar of the scientific method that scientists should strive always to resolve conflicts between competing theories 29

30. Is there a problem with orthogonality? what if I need my own ontology of cellular membranes to meet my own special purposes? strategy of application ontologies should be developed from the start using terms whose definitions employ the resources of orthogonal ontologies like those within the Foundry any other approach creates silos 30

31. Better to have one consensus ontology serving multiple purposes imperfectly because multiple ontologies addressing the same domain, whether they are good ones or bad ones, create silos 31

32. For engineers, ontologie s 1.can be bought and sold 2.need have no well-demarcated scientific domains 3.need not be subject to further maintenance 4.can be stand-alone products 5.are typically tied to one specific implementation Ontology (engineering) thereby makes the silo problem worse 32

33. Ontologies created to serve scientific purposes 1.are developed to be common resources (thus they cannot be bought or sold) 2.for representation of well-demarcated scientific domains 3.subject to constant maintenance by domain experts 4.designed to be used in tandem with other, complementary ontologies 5.maximally independent of format and implementation 33

34. Some obvious truths Scientific hypotheses should be formulated by scientists Scientific experiments should be carried out by scientists Scientific databases should be developed and maintained by scientists Scientific textbooks and journal articles should be written by scientists 34

35. An obvious conclusion: Scientific ontologies should be built by scientists 35

36. Problems to be addressed How should ontologist-scientists be trained? How do we create a career path for scientific ontologists? How do we assign credit to those who contribute to ontology creation and maintenance? 36

37. Ontologies like the GO are comparable to – scientific theories – scientific databases – scientific journal publications 37

38. Ontologies like the GO are being used experimentally by scientific journal publishers – to provide more useful access to data and other sorts of content via controlled structured keyword lists – to provide a basis for creating formally structured versions of journal articles 38

39. The OBO Foundry is working with journal publishers to create a methodology for expert peer review of ontologies as articles are peer reviewed so keyword lists are peer reviewed so an author’s use of keyword lists is peer reviewed 39

40. Benefits of peer review 1.provides a gigantic impetus to the improvement of scientific knowledge over time 2.brings benefits to readers, since they need only absorb and collate vetted results (contrast what happens where vetting is not allowed e.g. on the Semantic Web) 40

41. Scientific ontology analogous to open source software S. Weber, The Success of Open Source, Cambridge, MA: Harvard University Press, 2004. Ontologies should be more like Linux and less like the Semantic Web 41

42. Weber’s six criteria for success 1.Disaggregated contributions can be derived from knowledge that is not proprietary. 2.The product is perceived as valuable to a critical mass of users. 3.The product benefits from widespread peer attention and review, and can improve through error correction. 4.There are strong positive network effects. 5.An individual or a small group can take the lead and generate a substantive core that promises to evolve into something truly useful. 6.A voluntary community of iterated interaction can develop around the process of building the product. 42

43. OBO Foundry peer review creates incentives for investment of effort in ontology work It gives career-related credit to both authors and reviewers (university promotions and funding are based on peer review credit) Supports creation of a professional career path for ontologists It gives credit to scientific experts for investment of scientific expertise in ontology development It allows measurement of citations of ontologies It magnifies the motivating potential of the factor of influence – scientists help to determine what ontology resources exist in their discipline 43

44. THE END 44