Event: Plant and Animal Genomes conference 2012 Speaker: Rachael Huntley The Gene Ontology (GO) is a well-established, structured vocabulary used in the functional annotation of gene products. GO terms are used to replace the multiple nomenclatures used by scientific databases that can hamper data integration. Currently, GO consists of more than 35,000 terms describing the molecular function, biological process and subcellular location of a gene product in a generic cell. The UniProt-Gene Ontology Annotation (UniProt-GOA) database1 provides high-quality manual and electronic GO annotations to proteins within UniProt. By annotating well-studied proteins with GO terms and transferring this knowledge to less well-studied and novel proteins that are highly similar, we offer a valuable contribution to the understanding of all proteomes. UniProt-GOA provides annotated entries for over 387,000 species and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. Annotation files for various proteomes are released each month, including human, mouse, rat, zebrafish, cow, chicken, dog, pig, Arabidopsis and Dictyostelium, as well as a file for the multiple species within UniProt. The UniProt-GOA dataset can be queried through our user-friendly QuickGO browser2 or downloaded in a parsable format via the EBI3 and GO Consortium FTP4 sites. The UniProt-GOA dataset has increasingly been integrated into tools that aid in the analysis of large datasets resulting from high-throughput experiments thus assisting researchers in biological interpretation of their results. The annotations produced by UniProt-GOA are additionally cross-referenced in databases such as Ensembl and NCBI Entrez Gene. 1 http://www.ebi.ac.uk/GOA 2 http://www.ebi.ac.uk/QuickGO 3 ftp://ftp.ebi.ac.uk/pub/databases/GO/goa 4 ftp://ftp.geneontology.org/pub/go/gene-associations

1. Introduction to the Gene Ontology
and GO annotation resources
Rachael Huntley
UniProt-GOA
EBI
PAGXX
January 2012

2. 2
Why do we need GO?

3. Reasons for the Gene Ontology
www.geneontology.org
• Inconsistency in English language

4. Inconsistency in English languauge
• Same name for different concepts
or
??
Cell

5.  Comparison is difficult – in particular across
species or across databases
Just one reason why the Gene Ontology (GO) is
is needed…
• Different names for the same concept
Eggplant
Aubergine
Brinjal
Melongene
Same for biological concepts

6. Reasons for the Gene Ontology
www.geneontology.org
• Inconsistency in English language
• Increasing amounts of biological data available
• Increasing amounts of biological data to come

7. Search on ‘DNA repair’...
get almost 65,000 results
Increasing amounts of biological data available
Expansion of sequence
information

8. Reasons for the Gene Ontology
www.geneontology.org
• Inconsistency in English language
• Large datasets need to be interpreted quickly
• Increasing amounts of biological data available
• Increasing amounts of biological data to come

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

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

11. Anatomy of a GO term
Unique identifierUnique identifier
Term nameTerm name
DefinitionDefinitionSynonymsSynonyms
Cross-referencesCross-references

12. Ontology structure
• Directed acyclic graph
Terms can have more than one parent
• Terms are linked by
relationships
is_a
part_of
regulates (and +/- regulates)
www.ebi.ac.uk/QuickGOoccurs_in
has_part
These relationships allow for complex analysis of large datasets

13. Reactome
http://www.geneontology.org

14. • Compile the ontologies
- currently over 35,000 terms
- constantly increasing and improving
• Annotate gene products using ontology terms
- around 30 groups provide annotations
• Provide a public resource of data and tools
- regular releases of annotations
- tools for browsing/querying annotations and editing the ontology
Aims of the GO project

15. GO Annotation

16. UniProt-Gene Ontology Annotation
(UniProt-GOA) database at the EBI
• Largest open-source contributor of annotations to GO
• Provides annotation for more than 397,000 species
• Our priority is to annotate the human proteome

17. A GO annotation is …
…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 determined by a particular method
3. as described in a particular reference
P00505
Accession Name GO ID GO term name Reference Evidence code
IDAPMID:2731362aspartate transaminase activityGO:0004069GOT2

18. Electronic Annotation
Manual Annotation
UniProt-GOA incorporates
annotations made using two methods
• Quick way of producing large numbers of annotations
• Annotations use less-specific GO terms
• Time-consuming process producing lower numbers
of annotations
• Annotations tend to use very specific GO terms
• Only source of annotation for many non-model organism species

19. 1. Mapping of external concepts to GO terms
e.g. InterPro2GO, UniProt Keyword2GO, Enzyme Commission2GO
Electronic annotation methods
GO:0004715 ; non-membrane spanning protein tyrosine kinase activity

20. Annotations are high-quality and have an explanation of the method (GO_REF)
Macaque
Mouse DogCow
Guinea PigChimpanzee Rat
Chicken
Ensembl compara
2. Automatic transfer of manual annotations to orthologs
...and more
e.g. Human
Arabidopsis
Rice
Brachypodium
Maize
Poplar
Grape
…and moreEnsembl compara
Electronic annotation methods

21. Manual annotation by GOA
High–quality, specific annotations made using:
• Full text peer-reviewed papers
• A range of evidence codes to categorise
the types of evidence found in a paper
e.g. IDA, IMP, IPI
http://www.ebi.ac.uk/GOA

22. * Includes manual annotations integrated from external model organism
and specialist groups
920,557Manual annotations*
110,247,289Electronic annotations
Dec 2011 Statistics
Number of annotations in UniProt-GOA
database

23. How to access and use
GO annotation data

24. Where can you find annotations?
UniProtKB
Ensembl
Entrez gene

25. GO Consortium website
Gene Association Files
17 column files containing all information for each annotation
http://www.ebi.ac.uk/GOA/downloads.html
UniProt-GOA website
Numerous species-specific files

26. GO browsers

27. http://www.ebi.ac.uk/QuickGO
The EBI's QuickGO browser
Search GO terms
or proteins
Find sets of
GO annotations

28. • Access gene product functional information
• Analyse high-throughput genomic or proteomic datasets
• Validation of experimental techniques
• Get a broad overview of a proteome
• Obtain functional information for novel gene products
How scientists use the GO
Some examples…

29. Term enrichment
• Most popular type of GO analysis
• Determines which GO terms are more often associated
with a specified list of genes/proteins compared with a
control list or rest of genome
• Many tools available to do this analysis
• User must decide which is best for their analysis

30. Numerous Third Party Tools
www.geneontology.org/GO.tools.shtml

31. pears on lw n3d ...
: Set_LW_n3d_5p_...
Colored by: Copy of Copy of C5_RMA (Defa...
Gene List: all genes (14010)
attacked
time
control
Puparial adhesion
Molting cycle
Hemocyanin
Defense response
Immune response
Response to stimulus
Toll regulated genes
JAK-STAT regulated genes
Immune response
Toll regulated genes
Amino acid catabolism
Lipid metobolism
Peptidase activity
Protein catabolism
Immune response
cted Gene Tree: pearson lw n3d ...
ch color classification: Set_LW_n3d_5p_...
Colored by: Copy of Copy of C5_RMA (Defa...
Gene List: all genes (14010)
Bregje Wertheim at the Centre for Evolutionary Genomics,
Department of Biology, UCL and Eugene Schuster Group, EBI.
MicroArray data analysis
Analysis of high-throughput genomic datasets

32. Validation of experimental techniques
(Cao et al., Journal of Proteome Research 2006)
Rat liver plasma membrane isolation

33. Annotating novel sequences
• Can use BLAST queries to find similar sequences with
GO annotation which can be transferred to the new sequence
• Two tools currently available;
AmiGO BLAST – searches the GO Consortium database
BLAST2GO – searches the NCBI database

34. Using the GO to provide a functional
overview for a large dataset
• Many GO analysis tools use GO slims to give a broad
overview of the dataset
• GO slims are cut-down versions of the GO and
contain a subset of the terms in the whole GO
• GO slims usually contain less-specialised GO terms

35. Slimming the GO using the ‘true path rule’
Many gene products are associated with a
large number of descriptive, leaf GO nodes:

36. Slimming the GO using the ‘true path rule’
…however annotations can be mapped up
to a smaller set of parent GO terms:

37. GO slims
or you can make your own using;
Custom slims are available for download;
http://www.geneontology.org/GO.slims.shtml
• AmiGO's GO slimmer
• QuickGO
http://www.ebi.ac.uk/QuickGO
http://amigo.geneontology.org/cgi-bin/amigo/slimmer

38. www.ebi.ac.uk/QuickGO
Map-up annotations
with GO slims
The EBI's QuickGO browser
Search GO terms
or proteins
Find sets of
GO annotations
Questions on how to use QuickGO?
Contact goa@ebi.ac.uk

39. The UniProt-GOA group
Curators:
Software developer:
Team leaders:
Emily Dimmer
Rachael Huntley
Rolf Apweiler
Email: goa@ebi.ac.uk
http://www.ebi.ac.uk/GOA
Claire O’Donovan
Tony Sawford
Yasmin Alam-Faruque
Prudence Mutowo

40. Acknowledgements
GO Consortium
Members of;
UniProtKB
InterPro
IntAct
HAMAP
Ensembl
Ensembl Genomes
Funding
National Human Genome Research Institute (NHGRI)
Kidney Research UK
British Heart Foundation
EMBL