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Lecture Four: GO: The Gene Ontology ---- Infrastructure for Systems Biology

Lecture Four: GO: The Gene Ontology ---- Infrastructure for Systems Biology. S. cerevisiae. D. melanogaster. Cells that normally survive. CED-3 CED-4 OFF. CED-9 ON. Cells that normally die. CED-3 CED-4 ON. CED-9 OFF. C elegans. M. musculus.

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Lecture Four: GO: The Gene Ontology ---- Infrastructure for Systems Biology

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  1. Lecture Four: GO: The Gene Ontology----Infrastructure for Systems Biology

  2. S. cerevisiae

  3. D. melanogaster

  4. Cells that normally survive CED-3 CED-4 OFF CED-9 ON Cells that normally die CED-3 CED-4 ON CED-9 OFF C elegans

  5. M. musculus

  6. Comparison of sequences from 4 organisms MCM3 MCM2 CDC46/MCM5 CDC47/MCM7 CDC54/MCM4 MCM6 These proteins form a hexamer in the species that have been examined

  7. The Gene Ontologies A Common Language for Annotation of Genes from Yeast, Flies and Mice …and Plants and Worms …and Humans …and anything else!

  8. Gene Ontology - 1998 FlyBase Drosophila Cambridge, EBI, Harvard Berkeley & Bloomington. SGD Saccharomyces Stanford. MGI Mus Jackson Labs., Bar Harbor.

  9. Fruitfly - FlyBase Budding yeast - SaccharomycesGenome Database (SGD) Mouse - Mouse Genome Database (MGD & GXD) Rat - Rat Genome Database (RGD) Weed - TheArabidopsisInformation Resource (TAIR) Worm - WormBase Dictyostelium discoidem - Dictybase InterPro/UniProt at EBI - InterPro Fission yeast - Pombase Human - UniProt, Ensembl, NCBI, Incyte, Celera, Compugen Parasites - Plasmodium, Trypanosoma, Leishmania - GeneDB - Sanger Microbes - Vibrio, Shewanella, B. anthracus, … -TIGR Grasses - rice & maize - Gramene database zebra fish –Zfin ......... Gene Ontology -now

  10. To provide structured controlled vocabularies for the representation of biological knowledge in biological databases.

  11. Be open source • Use open standards • Make data & code available without constraint • Involve your community

  12. Gene Ontology Objectives • GO represents concepts used to classify specific parts of our biological knowledge: • Biological Process • Molecular Function • Cellular Component • GO develops a common language applicable to any organism • GO terms can be used to annotate gene products from any species, allowing comparison of information across species

  13. GO: Three ontologies What does it do? Molecular Function What processes is it involved in? Biological Process Where does it act? Cellular Component gene product

  14. Content of GO • Molecular Function 7,309 terms • Biological Process 10,041terms • Cellular Component1,629 terms • Total 18, 975 terms • Definitions: 94.9 % • Obsolete terms: 992

  15. What’s in a GO term? term: gluconeogenesis id: GO:0006094 definition: The formation of glucose from noncarbohydrate precursors, such as pyruvate, amino acids and glycerol.

  16. Annotation of gene products with GO terms Mitochondrial P450

  17. Cellular component: mitochondrial inner membrane GO:0005743 Biological process: Electron transport GO:0006118 Molecular function: monooxygenase activity GO:0004497 substrate + O2 = CO2 +H20 product

  18. Other gene products annotated to monooxygenase activity (GO:0004497) - monooxygenase, DBH-like 1 (mouse) - prostaglandin I2 (prostacyclin) synthase (mouse) - flavin-containing monooxygenase (yeast)    - ferulate-5-hydrolase 1 (arabidopsis)

  19. What’s in a name? • Glucose synthesis • Glucose biosynthesis • Glucose formation • Glucose anabolism • Gluconeogenesis • All refer to the process of making glucose from simpler components

  20. tree directed acyclic graph

  21. Parent-Child Relationships Nucleus Nuclear envelope Nucleoplasm Nucleolus Chromosome Perinuclear space A child is a subset of a parent’s elements The cell component term Nucleus has 5 children

  22. Ontology Relationships Directed Acyclic Graph

  23. Evidence Codes for GO Annotations http://www.geneontology.org/doc/GO.evidence.html

  24. IEAInferredfromElectronicAnnotation ISSInferred from Sequence Similarity IEPInferred from Expression Pattern IMPInferred from Mutant Phenotype IGIInferred from Genetic Interaction IPIInferred from Physical Interaction IDAInferred from Direct Assay RCA Inferred from Reviewed Computational Analysis TASTraceable Author Statement NASNon-traceable Author Statement ICInferred by Curator NDNo biological Data available

  25. Annotation summaries Meloidogyne incognita: McCarter et al. 2003

  26. Two types of GO Annotations:  Electronic Annotation  Manual Annotation • All annotations must: • be attributed to a source • indicate what evidence was found to support the GO term-gene/protein association

  27. Manual Annotations • High–quality, specific gene/gene product associations made, using: • Peer-reviewed papers • Evidence codes to grade evidence BUT – is very time consuming and requires trained biologists

  28. Manual Annotations: Methods • Extract information from published literature • Curators performs manual sequence similarity analyses to transfer annotations between highly similar gene products (BLAST, protein domain analysis)

  29. Finding GO terms 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-GFP 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

  30. Electronic Annotations • Provides large-coverage • High-quality • BUT–annotations tend to use high-level GO terms and provide little detail.

  31. Electronic Annotations: Methods • Database entries • Manual mapping of GO terms to concepts external to GO (‘translation tables’) • Proteins then electronically annotated with the relevant GO term(s) • Automatic sequence similarity analyses to transfer annotations between highly similar gene products

  32. Fatty acid biosynthesis (Swiss-Prot Keyword) EC:6.4.1.2 (EC number) IPR000438: Acetyl-CoA carboxylase carboxyl transferase beta subunit (InterPro entry) GO:Fatty acid biosynthesis (GO:0006633) GO:acetyl-CoA carboxylaseactivity (GO:0003989) GO:acetyl-CoA carboxylase activity (GO:0003989) Electronic Annotations

  33. Mappings of external concepts to GO EC:1.1.1.1 > GO:alcohol dehydrogenase activity ; GO:0004022 EC:1.1.1.10 > GO:L-xylulose reductase activity ; GO:0050038 EC:1.1.1.104 > GO:4-oxoproline reductase activity ; GO:0016617 EC:1.1.1.105 > GO:retinol dehydrogenase activity ; GO:0004745

  34. Additional points • A gene product can have several functions, cellular locations and be involved in many processes • Annotation of a gene product to one ontology is independent from its annotation to other ontologies • Annotations are only to terms reflecting a normal activity or location • Usage of ‘unknown’ GO terms

  35. Unknown v.s. Unannotated • “Unknown” is used when the curator has determined that there is no existing literature to support an annotation. • Biological process unknown GO:0000004 • Molecular function unknown GO:0005554 • Cellular component unknown GO:0008372 • NOT the same as having no annotation at all • No annotation means that no one has looked yet

  36. Annotation of a genome • GO annotations are always work in progress • Part of normal curation process • More specific information • Better evidence code • Replace obsolete terms • “Last reviewed” date

  37. How to access the Gene ontology and its annotations • 1. Downloads • Ontologies • Annotations : Gene association files • Ontologies and Annotations • 2. Web-based access • AmiGO • (http://www.godatabase.org) • QuickGO • (http://www.ebi.ac.uk/ego) • among others…

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