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Real-life ontology development:

Real-life ontology development:. lessons from the Gene Ontology. What is GO? Evolution of GO Mechanisms of updating GO Tools for ontology development Lessons learned. Gene Ontology. Built for a very specific purpose: “annotation of genes and proteins in genomic and protein databases”

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Real-life ontology development:

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  1. Real-life ontology development: lessons from the Gene Ontology

  2. What is GO? • Evolution of GO • Mechanisms of updating GO • Tools for ontology development • Lessons learned

  3. Gene Ontology • Built for a very specific purpose: “annotation of genes and proteins in genomic and protein databases” • Applicable to all species

  4. Gene Ontology - scope • Three disjoint axes: • molecular function • molecular role e.g. catalytic activity, binding • biological process • broad biological phenomena e.g. mitosis, growth, digestion • cellular component • sub-cellular location e.g nucleus, ribosome, origin recognition complex

  5. Gene Ontology • Directed acyclic graph (DAG) • Terms connected by two transitive relations (edges): • is_a • part_of

  6. Gene Ontology • Developed by an international consortium • about 50 members • Editorial office, 4 full-time editors (ish) • Many other part-time editors at databases • Multiple changes made a day • made live immediately

  7. Gene Ontology • Main ontology format OBO flat file • Changes are live immediately • no releases • Propagated to GO database • monthly snapshots archived

  8. Evolution of GO • Original GO created in 2000 • Three databases involved: • FlyBase (Drosophila) • MGI (Mouse) • SGD (S. cerevisae) • Used immediately

  9. Evolution of GO • Later databases: • TAIR (Arabadopsis) • TIGR (microbes including prokaryotes) • SWISS-PROT (several thousand species inc. human) • PSU (P. falciparum) • Recent additions • ZFIN (zebrafish) • PAMGO (plant pathogens)

  10. Evolution of GO • GO development traditionally annotation-driven • development directed by use • Terms added as new species annotated • Terms added on as as-needed basis

  11. Evolution of GO • Resulted in ‘organic’ structure, little formality • Ontological formality added subsequently • philosophical and logical

  12. Growth of GO

  13. Modifying the graph: • Before:

  14. Modifying the graph: • But then I need to annotate VW Beetles, pre-1980 • The graph no longer works, because the engine is in the boot

  15. Modifying the graph: • After:

  16. Mechanisms for ontology change • Small incremental changes • Initially all changes to the ontologies made this way

  17. Mechanisms for ontology change • Suggested changes initially submitted by email • Moved to an online tracking system when this became unmanageable

  18. Requesting changes to GO - curator requests tracker • Web-based tracking system hosted at SourceForge.net • Public • Tracker item for each new request or question

  19. Curator requests tracker

  20. Mechanisms for ontology change • Problems: • Larger questions about the higher ontology structure remain unresolved • Makes some items impossible to close • No sense of the ‘big picture’ • Large areas of the ontologies missing or incomplete because no annotations • Massive volume • needed to increase the number of editors

  21. Mechanisms for ontology change • Larger-scale changes: • content meetings • interest groups

  22. Content meetings • Short meetings aimed at developing specific areas of GO ontology content • proposals refined and discussed before meeting • small number of people (10-15) • invited experts • specific topics

  23. Content meetings • Further refinements made following meeting by email • Changes are made once consensus reached • Large number of terms typically added (500+)

  24. Content meetings • Recent meetings: • immunology • interactions between organisms • CNS development

  25. Content meetings • Advantages • Allows a lot of detailed work to be done on a very specific area • Involves external expertise

  26. Content meetings • Problems: • Expensive - everyone has to be in the same location • Only works for very specific topics • Long lag time getting terms into ontologies

  27. Interest groups • Groups of experts for a specific topic • e.g. development, cell cycle, plants • Includes GO curators/annotators and external experts • Don’t typically meet face to face

  28. Interest groups • Communicate via email, desktop sharing etc • Transporters area of the ontology recently revised this way

  29. Interest groups • Advantages • Cheap, no travel required • Allows a lot of detailed work to be done on a very specific area • Involves external expertise

  30. Interest groups • Disadvantages • Harder to reach consensus when not face to face • Projects tend to drag on

  31. Mechanisms for ontology change • Systematic changes via small working groups

  32. Systematic changes • Projects not directly related to biological content • Systematic changes throughout ontology • Small group of GO consortium members • meets regularly by desktop sharing, voice over IP • Experts recruited to meetings as needed

  33. Systematic changes • Changes either • made on a branch of the ontology and merged in later • always have big problems merging branched file into main file • merged directly into live ontology after session • fast, but people get angry

  34. is_a complete • GO contains both is_a and part_of relations • Typically, graphs a mixture of incomplete is_a and part_of hierarchies • A result of ‘organic’ evolution of GO • All graphs now have complete is_a paths to root

  35. partial disjointness • Biological process terms organised by granularity: • cellular process • multicellular organism process • multi-organism process • To avoid massive increase in number of paths to root, these terms are disjoint • no is_a children in common

  36. sensu • sensu (meaning ‘in the sense of’) used to disambiguate, by taxonomic group, terms with identical strings but different meanings • e.g. sporulation (sensu Viridiplantae) v/s sporulation (sensu Bacteria)

  37. sensu • Current project to remove the sensu term strings • Replace with strings that represent the true differentiae • e.g. • cell wall (sensu Bacteria) -> peptidoglycan-based cell wall • cell wall (sensu Fungi) -> chitin- and beta-glucan-containing cell wall

  38. Systematic changes to GO • Advantages • Fast • Efficient • Small number of people required

  39. Systematic changes to GO • Disadvantages • Difficult to obtain wider consensus • Changes sometimes have to be undone

  40. Useful tools for ontology development • WebEx • desktop sharing, can control each others desktops • wiki • mainly internal • Skype • free international calls! • conference calls • not free

  41. Tracking changes to GO • General tracking • files stored in cvs, all differences trackable (in theory) • far from ideal - frequent discussion is should we history track, date-stamp terms?

  42. Tracking changes to GO • Obsolete terms • formerly stored within the ontology • in OBO format made a special kind of deprecated term (tag is_obsolete) • Soon to create ‘replaced_by’ and ‘consider’ tags to point to live terms

  43. Tracking changes to GO • Crediting experts • traditionally no mechanism for doing this • creating abstracts for content meetings, adding tag to term • as yet no mechanism for crediting individuals

  44. Useful tools for ontology development • OBO-Edit • ontology editor originally developed for GO • can be used for any OBO format ontology • developed by group of users

  45. Useful tools for ontology development • Reasoner integrated into OBO-Edit • based on OBOL • detects missing links, redundant links, • soon misplaced terms, automatic term creation • Validation system • typographical errors, is_a orphans, duplicate synonyms etc.

  46. Lessons learned • An ontology doesn’t have to be perfect or complete to be used • For domain ontologies, external experts should be involved • Communication is critical • You will never please everyone

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