1. Ontologies, data standards and controlled vocabularies

2. Why use standards and CVs? Very important in High-throughput biology to sort through the vast amounts of data To use the same data labels universally To enable quick retrieval of data To enable easy comparison of data To remove ambiguities

3. What’s in a name? What is a cell?

4. What’s in a name? What is a cell? OR

5. What’s in a name? What is a cell? OR

6. What’s in a name? What is a cell?

7. Ambiguities in naming The same name can be used to describe different concepts, e.g: –Glucose synthesis –Glucose biosynthesis –Glucose formation –Glucose anabolism –Gluconeogenesis All refer to the process of making glucose Makes it difficult to compare the information Solution: use Ontologies and Data Standards

8. Ontologies An ontology is a formal specification of terms and relationships between them – widely used in biology and boinformatics (e.g. taxonomy) The relationships are important and represented as graphs Ontology terms should have definitions Ontologies are machine-readable They are needed for ordering and comparing large data sets

9. Gene Ontology (GO) http://www.geneontology.org Many annotation systems are organism-specific or different levels of granularity GO introduced standard vocabulary first used for mouse, fly and yeast, but now generic Three ontologies: molecular function, biological process and cellular component

10. GO Ontologies Molecular function: tasks performed by gene product –e.g. G-protein coupled receptor Biological process: broad biological goals accomplished by one or more gene products –e.g. G- protein signaling pathway Cellular component: part(s) of a cell of which a gene product is a component; includes extracellular environment of cells –e.g nucleus, membrane etc.

11. GO hierarchy Relationships: “is-a” “part of”

12. How do gene products get GO terms? Electronic annotation: –Through mappings to other biological entities and then automatic inference to proteins Manual annotation: –Model organism databases –Gene Ontology Annotation (GOA) project Evidence codes –attached to all GO annotations to show the source

13. Evidence Codes IEAInferred from Electronic Annotation IDAInferred from Direct Assay IMPInferred from Mutant Phenotype IPIInferred from Protein Interaction IEPInferred from Expression Pattern IGIInferred from Genetic Interaction ISS*Inferred from Sequence or Structural Similarity IGCInferred from Genomic Context RCAReviewed Computational Analysis TASTraceable Author Statement NASNon-traceable Author Statement ICInferred from Curator Judgement NDNo Data available

14. Electronic annotation: GO mappings

15. Fatty acid biosynthesis (SwissProt keyword) EC:6.4.1.2 (EC number) IPR000438: Acetyl-CoA carboxylase carboxyl transferase beta subunit (InterPro entry) MF_00527: Putative 3- methyladenine DNA glycosylase (HAMAP) Camon et al. BMC Bioinformatics. 2005; 6 Suppl 1:S17 GO:fatty acid biosynthesis (GO:0006633) GO:DNA repair (GO:0006281) GO:acetyl-CoA carboxylase activity (GO:0003989) GO:acetyl-CoA carboxylase activity (GO:0003989)

16. UniProt entry

17. http://www.ensembl.org/info/data/compara Automatic transfer of annotations to orthologs Cow Dog Rat Dog Rat Mouse Ensembl GO term projection via gene homology Anopheles Mouse Chicken Cow Drosophila COMPARA Homologies between different species calculated GO terms projected from MANUAL annotation only (IDA, IEP, IGI, IMP, IPI) One-to-one and apparent one-to-one orthologies only used.

18. Manual annotation: GOA Project Largest open-source contributor of annotations to GO Member of the GO Consortium since 2001 Provides annotation for more than 130,000 species GOA’s priority is to annotate the human proteome GOA is responsible for human, chicken, bovine and many other annotations for the GO Consortium Annotation is done through reading of the literature

19. Reference Genomes Arabidopsis thaliana Caenorhabditis elegans Danio rerio (zebrafish) Dictyostelium discoideum Drosophila melanogaster Escherichia coli Homo sapiens Saccharomyces cerevisiae Mus musculus Schizosaccharomyces pombe Gallus gallus Rattus norvegicus Comprehensive annotation of a set of disease-related proteins in human Generate a reliable set of GO annotations for the 12 selected genomes Empowers comparative methods used in first pass annotation of other proteomes.

20. http://amigo.geneontology.org/cgi-bin/amigo/go.cgi Accessing GO data (1)

21. QuickGO browser http://www.ebi.ac.uk/quickgo Human Insulin Receptor (P06213) Accessing GO data (2)

22. Gene Association Files http://www.geneontology.org/GO.current.annotations.shtm Accessing GO data (3)

23. Gene Association File example Accessing GO data (3)

24. ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/ http://www.ebi.ac.uk/GOA/downloads.html Downloading GOA data

25. Functional annotation of proteins Uses of GO 1

26. Find functional information on interaction proteins (IntAct) Uses of GO 2

27. Microarray data analysis Proteomics data analysis Larkin JE et al, Physiol Genomics, 2004 Cunliffe HE et al, Cancer Res, 2003 GO classification Analysis of high-throughput data Uses of GOA Uses of GO 3

28. Other Ontologies: Open Biomedical Ontologies http://obo.sourceforge.net Central location for accessing well-structured controlled vocabularies and ontologies for use in the biological and medical sciences. Provides simple format for ontologies that can encode terms, relationships between terms and definitions of terms including those taken from external ontologies.

29. Scope of Open Biomedical Ontologies Anatomy Animal natural history and life history Chemical Development Ethology Evidence codes Experimental conditions Genomic and proteomic Metabolomics OBO relationship types Phenotype Taxonomic classification

30. Ontology Lookup Service (OLS) Single point of query for currently 47 ontologies. Ontologies are updated daily from CVS repositories, including the OBO CVS repository and the PRIDE CVS repository. A tool that offers interactive and programmatic interfaces for queries on term names, synonyms, relationships, annotations and database cross- references. Originally developed for using ontologies in PRIDE.

31. These relationships have consequences when querying a database annotated using the ontology. What happens when I ask for PRIDE experiments describing the proteome of brain tissue? The issue faced

32. Using Ontologies in PRIDE For an experiment you want to define: – Species: Newt / NCBI Taxonomy ID – Tissue / organ / cell type: BRENDA Tissue ontology, Cell Type ontology; – Sub-cellular component: Gene Ontology: GO; – Disease: Human Disease: DOID; – Genotype: GO; – Sample Processing: PSI Ontology; – Mass Spectrometry: PSI-MS Ontology; – Protein Modifications: PSI-MOD Ontology

33. OLS usage examples http://www.ebi.ac.uk/ontology-lookup/ What is the accession for “mitochondrion” in GO? In MeSH? –search by term name in a specific ontology or across all I’m looking for a term to annotate my protocol step but I’m not sure what term to use. –browse an ontology I’m looking for all the experiments done on liver tissue? –get all children term of liver and query on those as well My data set was annotated with GO version 123 but that was a long time ago? –get updated term names for the identifiers you have and see if any have been made obsolete

34. Standards for data exchange Systems Biology Markup Language (SBML) – computer-readable format for representing models of networks Biological Pathways Exchange (BioPAX) – format for representing pathways Proteomics Standards Initiative (PSI, MIAPE) Microarray standards –MIAME and MAGE

35. MIAPE/MIAME principles Enough information to: –Remove ambiguity in experiment –Allow easy interpretation of results –Allow experiment to be repeated –Enable comparison across similar experiments Use controlled vocabularies

36. Using ontologies and standards So much data in different places –need to organize and share it Used for data retrieval and comparison – easier to query Used for data integration and exchange – standard representation Used for evaluation –need “gold standard”