1. Getting Started: a user’s guide to the GO GO Workshop 3-6 August 2010

2. 1. Provides structural annotation for agriculturally important genomes 2. Provides functional annotation (GO) 3. Provides tools for functional modeling 4. Provides bioinformatics & modeling support for research community Avian Gene Nomenclature

3. Introduction to GO  Anatomy of a GO term: a GO annotation example  GO evidence codes  Making annotations: literature biocuration & computation analysis  ND vs no GO  Using the GO GO tools Functional modeling considerations

4. Gene Ontology (GO)  Not about genes! Gene products: genes, transcripts, ncRNA, proteins The GO describes gene product function  Not a single ontology Biological Process (BP or P) Molecular Function (MF or F) Cellular Component (CC or C)

5. What is the Gene Ontology?  assign functions to gene products at different levels, depending on how much is known about a gene product  is used for a diverse range of species  structured to be queried at different levels, eg: find all the chicken gene products in the genome that are involved in signal transduction zoom in on all the receptor tyrosine kinases  human readable GO function has a digital tag to allow computational analysis of large datasets COMPUTATIONALLY AMENABLE ENCYCLOPEDIA OF GENE FUNCTIONS AND THEIR RELATIONSHIPS “a controlled vocabulary that can be applied to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing”

6. Ontologies digital identifier (computers) description (humans) relationships between terms As of ontology version 1.1348 (27/07/2010): 32,091 terms, 99.3% defined * 19169 biological process * 2745 cellular component * 8736 molecular function 1441 obsolete terms (not included in figures above)

7. GO annotation example NDUFAB1 (UniProt P52505) Bovine NADH dehydrogenase (ubiquinone) 1, alpha/beta subcomplex, 1, 8kDa Biological Process (BP or P) GO:0006633 fatty acid biosynthetic process TAS GO:0006120 mitochondrial electron transport, NADH to ubiquinone TAS GO:0008610 lipid biosynthetic process IEA Cellular Component (CC or C) GO:0005759 mitochondrial matrix IDA GO:0005747 mitochondrial respiratory chain complex I IDA GO:0005739 mitochondrion IEA NDUFAB1 Molecular Function (MF or F) GO:0005504 fatty acid binding IDA GO:0008137 NADH dehydrogenase (ubiquinone) activity TAS GO:0016491 oxidoreductase activity TAS GO:0000036 acyl carrier activity IEA

8. GO annotation example NDUFAB1 (UniProt P52505) Bovine NADH dehydrogenase (ubiquinone) 1, alpha/beta subcomplex, 1, 8kDa aspect or ontology GO:ID (unique) GO term name GO evidence code

9. GO EVIDENCE CODES Direct Evidence Codes IDA - inferred from direct assay IEP - inferred from expression pattern IGI - inferred from genetic interaction IMP - inferred from mutant phenotype IPI - inferred from physical interaction Indirect Evidence Codes inferred from literature IGC - inferred from genomic context TAS - traceable author statement NAS - non-traceable author statement IC - inferred by curator inferred by sequence analysis RCA - inferred from reviewed computational analysis IS* - inferred from sequence* IEA - inferred from electronic annotation Other NR - not recorded (historical) ND - no biological data available ISS - inferred from sequence or structural similarity ISA - inferred from sequence alignment ISO - inferred from sequence orthology ISM - inferred from sequence model Guide to GO Evidence Codes http://www.geneontol ogy.org/GO.evidence.s html

10. GO Mapping Example NDUFAB1 GO EVIDENCE CODES Direct Evidence Codes IDA - inferred from direct assay IEP - inferred from expression pattern IGI - inferred from genetic interaction IMP - inferred from mutant phenotype IPI - inferred from physical interaction Indirect Evidence Codes inferred from literature IGC - inferred from genomic context TAS - traceable author statement NAS - non-traceable author statement IC - inferred by curator inferred by sequence analysis RCA - inferred from reviewed computational analysis IS* - inferred from sequence* IEA - inferred from electronic annotation Other NR - not recorded (historical) ND - no biological data available Biocuration of literature detailed function “depth” slower (manual)

11. P05147 PMID: 2976880 Find a paper about the protein. Biocuration of Literature: detailed gene function

12. Read paper to get experimental evidence of function Use most specific term possible experiment assayed kinase activity: use IDA evidence code

13. GO Mapping Example NDUFAB1 GO EVIDENCE CODES Direct Evidence Codes IDA - inferred from direct assay IEP - inferred from expression pattern IGI - inferred from genetic interaction IMP - inferred from mutant phenotype IPI - inferred from physical interaction Indirect Evidence Codes inferred from literature IGC - inferred from genomic context TAS - traceable author statement NAS - non-traceable author statement IC - inferred by curator inferred by sequence analysis RCA - inferred from reviewed computational analysis IS* - inferred from sequence* IEA - inferred from electronic annotation Other NR - not recorded (historical) ND - no biological data available ISS - inferred from sequence or structural similarity ISA - inferred from sequence alignment ISO - inferred from sequence orthology ISM - inferred from sequence model Biocuration of literature detailed function “depth” slower (manual) Sequence analysis rapid (computational) “breadth” of coverage less detailed

14. Unknown Function vs No GO  ND – no data Biocurators have tried to add GO but there is no functional data available Previously: “process_unknown”, “function_unknown”, “component_unknown” Now: “biological process”, “molecular function”, “cellular component”  No annotations (including no “ND”): biocurators have not annotated this is important for your dataset: what % has GO?

15. Using the GO

16.  Decide on GO analysis tool  How much GO is available for your species?  Getting GO for you data set  Adding GO for your data

17. http://www.geneontology.org/

19. However….  many of these tools do not support non-model organisms  the tools have different computing requirements  may be difficult to determine how up-to-date the GO annotations are… Need to evaluate tools for your system.

20. Evaluating GO tools Some criteria for evaluating GO Tools: 1. Does it include my species of interest (or do I have to “humanize” my list)? 2. What does it require to set up (computer usage/online) 3. What was the source for the GO (primary or secondary) and when was it last updated? 4. Does it report the GO evidence codes (and is IEA included)? 5. Does it report which of my gene products has no GO? 6. Does it report both over/under represented GO groups and how does it evaluate this? 7. Does it allow me to add my own GO annotations? 8. Does it represent my results in a way that facilitates discovery?

21. Some useful expression analysis tools:  Database for Annotation, Visualization and Integrated Discovery (DAVID) http://david.abcc.ncifcrf.gov/  AgriGO -- GO Analysis Toolkit and Database for Agricultural Community http://bioinfo.cau.edu.cn/agriGO/ used to be EasyGO chicken, cow, pig, mouse, cereals, dicots includes Plant Ontology (PO) analysis  Onto-Express http://vortex.cs.wayne.edu/projects.htm#Onto-Express can provide your own gene association file  Funcassociate 2.0: The Gene Set Functionator http://llama.med.harvard.edu/funcassociate/ can provide your own gene association file

22. Functional Modeling Considerations  Should I add my own GO? use GOProfiler to see how much GO is available for your species use GORetriever to find existing GO for your dataset Does analysis tool allow me to add my own GO?  Should I do GO analysis and pathway analysis and network analysis? different functional modeling methods show different aspects about your data (complementary) is this type of data available for your species (or a close ortholog)?  What tools should I use? which tools have data for your species of interest? what type of accessions are accepted? availability (commercial and freely available)

23. Protein/Gene identifiers GORetriever GO annotations Genes/Proteins with no GO annotations GOanna Pathways and network analysis GO Enrichment analysis ArrayIDer Microarray Ids GOSlimViewer Yellow boxes represent AgBase tools Green/Purple boxes are non-AgBase resources Ingenuity Pathways Analysis (IPA) Pathway Studio Cytoscape DAVID Ingenuity Pathways Analysis (IPA) Pathway Studio Cytoscape DAVID EasyGO/AgriGO Onto-Express Onto-Express-to-go (OE2GO) Overview of Functional Modeling Strategy summarizes GO function GOModeler hypothesis testing

24. For more information about GO  GO Evidence Codes: http://www.geneontology.org/GO.evidence.shtml  gene association file information: http://www.geneontology.org/GO.format.annotation.shtml  tools that use the GO: http://www.geneontology.org/GO.tools.shtml  GO Consortium wiki: http://wiki.geneontology.org/index.php/Main_Page All websites are listed on the AgBase workshop website.