The Gene Ontology (GO) provides structured controlled vocabularies for describing gene and gene product attributes across species. It includes three ontologies for molecular function, biological process, and cellular component. The GO is manually developed and electronically annotated to gene products to capture biological knowledge in a computable form. The GO Consortium aims to develop and maintain the GO through manual and computational methods, and to provide public GO annotation data and tools.

1. The Gene Ontology
and Gene Ontology Annotation resources
Mélanie Courtot, Ph.D.
EMBL-EBI
GO/GOA Project leader
SPOT/UniProt content teams
mcourtot@ebi.ac.uk
Industry workshop
March 17 2016

2. In 1999, collaboration
between 3 Model
Organism Databases
Ashburner et al., Nat Genet. 2000 May;25(1):25-9.

3. • A way to capture
biological knowledge for
individual gene products
in a written and
computable form
• A set of concepts
and their relationships
to each other arranged
as a hierarchy
http://www.ebi.ac.uk/QuickGO
Less specific concepts
More specific concepts
The Gene Ontology

4. 1. Molecular Function
An elemental activity or task or job
• protein kinase activity
• insulin receptor
activity
3. Cellular Component
Where a gene product is located
• mitochondrion
• mitochondrial matrix
• mitochondrial inner membrane
2. Biological Process
A commonly recognized series of events
• cell division

5. Provide a public resource
of data and tools
Annotate gene products
using ontology terms
Develop the ontology
Aims of the GO project

6. Develop the ontology
• An OWL ontology of >41,000 classes
• biological process, cellular component, molecular function
• > 14,000 imported classes (CL, Uberon, ChEBI, NCBI_tax)
• >136,000 logical axioms, including:
• ~72,000 subClassOf axioms between named GO classes
• ~41,000 simple existential restrictions (subClassOf R some C)
• EL expressivity => fast, scalable reasoning (with ELK)
https://www.cs.ox.ac.uk/isg/tools/ELK/

7. Building the GO
• The GO editorial team
• Submission via GitHub, https://github.com/geneontology/
• Submissions via TermGenie, http://go.termgenie.org
• ~80% terms are now created this way

8. Annotate gene products
gene ->
GO term
associated
genes
GO
Database
genome and protein
databases

10. …a statement that a gene product;
P00505
Accession Name GO ID GO term name Reference Evidence
code
IDAPMID:2731362aspartate transaminase activityGO:0004069GOT2
A GO annotation is …

11. …a statement that a gene product;
1. has a particular molecular function
or is involved in a particular biological process
or is located within a certain cellular component
A GO annotation is …
P00505
Accession Name GO ID GO term name Reference Evidence
code
IDAPMID:2731362aspartate transaminase activityGO:0004069GOT2

12. …a statement that a gene product;
1. has a particular molecular function
or is involved in a particular biological process
or is located within a certain cellular component
2. as described in a particular reference
A GO annotation is …
P00505
Accession Name GO ID GO term name Reference Evidence
code
IDAPMID:2731362aspartate transaminase activityGO:0004069GOT2

13. …a statement that a gene product;
1. has a particular molecular function
or is involved in a particular biological process
or is located within a certain cellular component
2. as described in a particular reference
3. as determined by a particular method
A GO annotation is …
P00505
Accession Name GO ID GO term name Reference Evidence
code
IDAPMID:2731362aspartate transaminase activityGO:0004069GOT2

14. Experimental
data
Computational
analysis
Author statements/
curator inference
(+ Inferred from electronic annotations)
http://www.evidenceontology.org/
Tracking provenance

15. Manual annotations
• Time-consuming process
producing lower numbers of
annotations (~2,800 taxons
covered)
• More specific GO terms
• Manual annotation is essential
for creating predictions
Aleksandra
Shypitsyna
Elena
Speretta
Alex
Holmes
Tony
Sawford

16. Electronic Annotations
• Quick way of producing large numbers of annotations
• Annotations use less-specific GO terms
• Only source of annotation for ~438,000 non-model
organism species
orthology taxon
constraints

17. * Includes manual annotations integrated from external model organism
and specialist groups
2,752,604Manual annotations*
269,207,317Electronic annotations
Provide a public resource of data and tools
Number of annotations in UniProt-GOA database (March
2016)
http://www.ebi.ac.uk/GOA
https://www.ebi.ac.uk/QuickGO/

19. Enrichment analysis
Sample Reference
40%
20%
20%
20%
=> The sample is over-enriched for

20. Spinocerebellar ataxia type 28
Paola
Roncaglia

21. Novel biomarkers of rectal radiotherapy

22. Biomarker for diagnosis and prognosis

23. Gene expression changes in diabetes

24. Improved network analysis

25. 25

26. Many gene products are associated with a
large number of descriptive, leaf GO nodes:
GO slims

27. …however annotations can be mapped up
to a smaller set of parent GO terms:
GO slims

28. Slim generation for industry
• Collaboration funded by Roche
• Need a custom GO slim for analysis of genesets of
interest
• Need to be descriptive enough
• Without redundancy
• Internal proprietary vocabulary – hard to maintain
• Desire to automatically map to GO
http://www.swat4ls.org/wp-content/uploads/2015/10/SWAT4LS_2015_paper_44.pdf

29. ROCHE CV
GSEA with full GO GSEA with Roche CV
Courtesy Laura Badi

30. • Mapping query: participant_OR_reg_participant some
cannabinoid
• Description: “A process in which a cannabinoid
participates, or that regulates a process in which a
cannabinoid participates.”

31. Results
• We have successfully mapped 84% of terms from RCV
(308/365) to OWL queries that can be used to replicate
some proportion of the original manual mapping.
• In addition, these queries find 1000s of terms that were
missed in the original mapping.
David
Osumi-Sutherland

32. GO SLIM (generic)

33. ROCHE CV – MANUAL ONLY

34. ROCHE CV MANUAL + AUTO

35. Acknowledgements
• GO editors and developers
• GO annotators
• The Gene Ontology (GO) Consortium
• Samples, Phenotype and Ontology team (Helen Parkinson)
• Protein Function Content team (Claire O’Donovan)
• Funding: EMBL-EBI, National Human Genome Research Institute
(NHGRI)

36. Useful links
• Ontology browser:
http://www.ebi.ac.uk/ols/beta/ontologies/go
• Browsing GO & annotations, GO slims:
https://www.ebi.ac.uk/QuickGO/
• GO Annotation: http://www.ebi.ac.uk/GOA
• EBI-Roche collaboration paper:
http://www.swat4ls.org/wp-
content/uploads/2015/10/SWAT4LS_2015_paper_44.pdf
• Contact: mcourtot@ebi.ac.uk