Introduction to the Gene Ontology and GO annotation resources

1. Rachael Huntley UniProtKB-GOA Curator EBI Introduction to the Gene Ontologyand GO annotation resources

2. Talk Overview • Intro to GO and GO terms • Annotating to GO • Accessing GO annotations • Practical use of GO • GO analysis tools • Precautions

3. Reasons for the Gene Ontology • Increasing amounts of biological data available • Large datasets need to be interpreted quickly • Inconsistency in naming of biological concepts www.geneontology.org

4. Increasing amounts of biological data available Search on ‘DNA repair’... get over 60,000 results Expansion of sequence information

5. Reasons for the Gene Ontology • Increasing amounts of biological data available • Large datasets need to be interpreted quickly • Inconsistency in naming of biological concepts

6. Large datasets need to be interpreted quickly • Need to organise the data, analyse it, share it in a timely manner to benefit other researchers • Inconsistencies in analyses make cross- • species or cross-database comparison difficult

7. Reasons for the Gene Ontology • Increasing amounts of biological data available • Large datasets need to be interpreted quickly • Inconsistency in naming of biological concepts

8. Inconsistency in naming of biological concepts English is not a very precise language • Same name for different concepts • Different names for the same concept An example … Taction Tactition Tactile sense ? Sensory perception of touch ; GO:0050975

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

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

11. Anatomy of a GO term Unique identifier Term name Definition Synonyms Cross-references

12. Ontology structure • Directed acyclic graph Terms can have more than one parent • Terms are linked by • relationships is_a part_of regulates +ve regulates -ve regulates www.ebi.ac.uk/QuickGO

13. Searching for GO terms http://www.ebi.ac.uk/QuickGO http://amigo.geneontology.org/cgi-bin/amigo/go.cgi … there are more browsers available on the GO Consortium Tools page: http://www.geneontology.org/GO.tools.shtml The latest OBO format Gene Ontology file can be downloaded from: http://www.geneontology.org/ontology/gene_ontology.obo

14. The EBI's QuickGO browser Search GO terms or proteins http://www.ebi.ac.uk/QuickGO

15. GO Annotation

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

17. Aims of the GO project • Compile the ontologies - currently over 34,000 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

18. Gene Ontology Annotation (UniProtKB-GOA) database at the EBI • Largest open-source contributor of annotations to GO • Member of the GO Consortium since 2001 • Provides annotation for more than 360,000 species • UniProtKB-GOA’s priority is to annotate the human proteome • UniProtKB-GOA is responsible for human, chicken and bovine annotations in the GO Consortium

19. Accession Name GO ID GO term name Reference Evidence code P00505 GOT2 GO:0004069 aspartate transaminase activity PMID:2731362 IDA 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

20. GOA makes annotations using two methods; Electronic • Quick way of producing large numbers of annotations • Annotations use less-specific GO terms Manual • Time-consuming process producing lower numbers • of annotations • Annotations are very detailed and accurate

21. Evidence Codes Some examples… Electronic Inferred from Electronic Annotation (IEA) – e.g. UniProtKB keyword mapping Manual Inferred from Direct Assay (IDA) – e.g. enzyme assay Inferred from Physical Interaction (IPI) – e.g. yeast 2-hybrid See the full list on the GO Consortium website http://www.geneontology.org/GO.evidence.shtml

22. Electronic annotation by UniProtKB-GOA 1. Mapping of external concepts to GO terms e.g. InterPro2GO, Swiss-Prot Keyword2GO, Enzyme Commission2GO 2. Automatic transfer of annotations to orthologs Macaque Chimpanzee Ensembl compara Guinea Pig Rat e.g. Human Mouse Dog Chicken Cow Annotations are high-quality and have an explanation of the method (GO_REF)

23. Mapping of concepts from UniProtKB files GO:0005856: cytoskeleton GO:0004715 ; non-membrane spanning protein tyrosine kinase activity GO:0007155 ; cell adhesion

24. Electronic annotation by UniProtKB-GOA 1. Mapping of external concepts to GO terms e.g. InterPro2GO, Swiss-Prot Keyword2GO, Enzyme Commission2GO 2. Automatic transfer of annotations to orthologs Macaque Chimpanzee Ensembl compara Guinea Pig Rat ...and more e.g. Human Mouse Dog Chicken Cow Annotations are high-quality and have an explanation of the method (GO_REF)

25. Manual annotation by UniProtKB-GOA High–quality, specific annotations made using: • Peer-reviewed papers • A range of evidence codes to categorise the types of evidence found in a paper http://www.ebi.ac.uk/GOA

26. Finding annotations in a paper …for B. napus PERK1 protein (Q9ARH1) In this study, we report the isolation and molecular characterization of the B. napus PERK1 cDNA, that is predicted to encode a novel receptor-like kinase. We have shown that like other plant RLKs, the kinase domain of PERK1 has serine/threonine kinase activity, In addition, the location of a PERK1-GTP fusion protein to the plasma membrane supports the prediction that PERK1 is an integral membrane protein…these kinases have been implicated in early stages of wound response… serine/threonine kinase activity, integral membrane protein wound response PubMed ID: 12374299 Function: protein serine/threonine kinase activity GO:0004674 Component: integral to plasma membrane GO:0005887 Process: response to wounding GO:0009611

27. Additional information • Qualifiers Modify the interpretation of an annotation • NOT(protein is not associated with the GO term) • colocalizes_with (protein associates with complex but is not a bona fide member) • contributes_to (describes action of a complex of proteins) • 'With' column Can include further information on the method being referenced e.g. the protein accession of an interacting protein

28. UniProtKB-GOA integrates manual annotations from external groups Human Protein Atlas GO:0005739 Mitochondrion also; LifeDB (subcellular locations) Reactome (pathways)

29. Status of UniProtKB-GOA Annotation Evidence Source Annotations Proteins UniProt Coverage 11,261,382 Electronic annotations 98,037,844 66% 910,536 Manual annotations* 159,630 0.9% Aug 2011 Statistics * Includes manual annotations integrated from external model organism and multi-species databases

30. How to access and use GO annotation data

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

32. Gene Association Files 17 column files containing all information for each annotation GO Consortium website UniProtKB-GOA website

33. GO browsers

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

35. How scientists use the GO • 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 informationfor novel gene products Some examples…

36. time Defense response Immune response Response to stimulus Toll regulated genes JAK-STAT regulated genes Puparial adhesion Molting cycle Hemocyanin Amino acid catabolism Lipid metobolism Peptidase activity Protein catabolism Immune response Immune response Toll regulated genes control attacked Analysis of high-throughput genomic datasets MicroArray data analysis Bregje Wertheim at the Centre for Evolutionary Genomics, Department of Biology, UCL and Eugene Schuster Group, EBI.

37. Analysis of high-throughput proteomic datasets Characterisation of proteins interacting with ribosomal protein S19 (Orrù et al., Molecular and Cellular Proteomics 2007)

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

39. MPYVSQSQHIDRVRGAIEGRLPAPGNSSRLVSSWQRSYEQYRLDPGSVIGPRVLTS SELR DVQGKEEAFLRASGQCLARLHDMIRMADYCVMLTDAHGVTIDYRIDRDRRGD FKHAGLYI GSCWSEREEGTCGIASVLTDLAPITVHKTDHFRAAFTTLTCSASPIFAPTG ELIGVLDAS AVQSPDNRDSQRLVFQLVRQSAALIEDGYFLNQTAQHWMIFGHASRN FVEAQPEVLIAFD ECGNIAASNRKAQECIAGLNGPRHVDEIFDTSAVHLHDVARTDTI MPLRLRATGAVLYAR IRAPLKRVSRSACAVSPSHSGQGTHDAHNDTNLDAISRFLHS RDSRIARNAEVALRIAGK HLPILILGETGVGKEVFAQALHASGARRAKPFVAVNCGAIP DSLIESELFGYAPGAFTGA RSRGARGKIAQAHGGTLFLDEIGDMPLNLQTRLLRVLA EGEVLPLGGDAPVRVDIDVICA THRDLARMVEEGTFREDLYYRLSGATLHMPPLRER ADILDVVHAVFDEEAQSAGHVLTLD GRLAERLARFSWPGNIRQLRNVLRYACAVCDS TRVELRHVSPDVAALLAPDEAALRPALA LENDERARIVDALTRHHWRPNAAAEALGM Obtain functional informationfor novel gene products  InterProScan www.ebi.ac.uk/InterProScan

40. 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 (from GO Consortium) • http://amigo.geneontology.org/cgi-bin/amigo/blast.cgi • – searches the GO Consortium database • BLAST2GO (from Babelomics) • http://www.blast2go.org/ • – searches the NCBI database

41. AmiGO BLAST Exportin-T from Pongo abelii (Sumatran orangutan) amigo.geneontology.org/cgi-bin/amigo/blast.cgi

42. Analysis of large datasets using GO

43. Using the GO to provide a functional overview for a large dataset • Many GO analysistools 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

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

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

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

47. The EBI's QuickGO browser Search GO terms or proteins Find sets of GO annotations Map-up annotations with GO slims www.ebi.ac.uk/QuickGO

48. GO analysis tools

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

50. 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