Community Ontology Development Lessons from the Gene Ontology.

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Presentation transcript:

Community Ontology Development Lessons from the Gene Ontology

Ontology: Sets of classes (terms) with relationships between them that describe a given domain

Annotation Associating some object (e.g. protein, gene, experiment) with ontology terms with some evidence

Gene Ontology Consortium

OBO Foundry “a suite of orthogonal interoperable reference ontologies in the biomedical domain”

Overview 1.Scope 2.Users 3.Development mechanism 4.Standards 5.Community input 6.Publicize 7.Feedback cycle 8.Document

1. Define your scope

Related efforts Make contact Know what’s out there

2. Have a user community

3. Decide on a mechanism for development

Editors Who can edit the ontology?

How Versioning system or database Critical that you know what others are working on

Remember – no system is a replacement for communication between developers!

Meet regularly

Developing GO Core editors – 5-10 editors – Communicate extensively – Distributed globally – Only these people are direct editors

Developing GO Per term requests Major overhauls Systematic changes

4. Define and use standards

Naming conventions

Relationships Use standard where possible Define where not

Good ontology design rubbish in = rubbish out modularity pragmatism v/s perfectionism

5. Use your community

Community input to GO Public tracker, discussion lists Involvement in specific development projects Direct term submission Community annotation tools

6. Publicize

Make sure people know you’re there OBO Foundry, Ontology Lookup Service, BioPortal Publish Advertise

7. Development cycle

Ontologies should be developed interatively – need mechanism to communicate changes to users – Static ontologies are not useful – Don’t wait until it’s finished before you start using it

8. Document

It’s boring, but you’ll thank yourself in the long run Document how and why you made decisions, how you think terms should be used

9. Other considerations post- v/s pre-composition

Adding logical constraints to terms Adds valuable reasoning power Automatic term placement, definition generation etc. Requires more thought per term