Gene Ontology Overview and Perspective Lung Development Ontology Workshop

2  what kinds of things exist?  what are the relationships between these things? eye part_of sclera is_a sense organ develops from Optic placode A biological ontology is: A (machine) interpretable representation of some aspect of biological reality

3 Gene Ontology (GO) Consortium Formed to develop a shared language adequate for the annotation of molecular characteristics across organisms; a common language to share knowledge. Seeks to achieve a mutual understanding of the definition and meaning of any word used; thus we are able to support cross- database queries. Members agree to contribute gene product annotations and associated sequences to GO database; thus facilitating data analysis and semantic interoperability.

4 Gene Ontology widely adopted AgBase

5 Molecular Function = elemental activity/task  the tasks performed by individual gene products; examples are carbohydrate binding and ATPase activity Biological Process = biological goal or objective  broad biological goals, such as mitosis or purine metabolism, that are accomplished by ordered assemblies of molecular functions Cellular Component = location or complex  subcellular structures, locations, and macromolecular complexes; examples include nucleus, telomere, and RNA polymerase II holoenzyme GO represents three biological domains

Terms are defined graphically relative to other terms

7 Build and maintain logically rigorous and biologically accurate ontologies Comprehensively annotate reference genomes Support genome annotation projects for all organisms Freely provide ontologies, annotations and tools to the research community The Gene Ontology (GO) 1. Build and maintain logically rigorous and biologically accurate ontologies 2. Comprehensively annotate reference genomes 3. Support genome annotation projects for all organisms 4. Freely provide ontologies, annotations and tools to the research community

8 The GO is still developing daily both in ontological structures and in domain knowledge Ontology development workshops focus on specific domains needing revision and bring together ontology developers and domain experts Currently running ~2 workshops / year 1. Metabolism and cell cycle (Aug, 2004) 2. Immunology and defense response (Nov05, Apr06) 3. Early CNS development (June, 2006) 4. Peripheral nervous system development (Feb, 2007) 5. Blood Pressure Regulation (June, 2007) 6. Muscle Development (July, 2007) Building the ontologies

9 725 new terms related to immunology 127 new terms added to cell type ontology Building the ontology: Immune System Process Red part_of Blue is_a Alex Diehl

10 P05147 PMID: GO: IDA P05147GO: IDA PMID: GO Term Reference Evidence Annotating Gene Products using GO Gene Product

11 Annotations are assertions There is evidence that this gene product can be best classified using this term The source of the evidence and other information is included There is agreement on the meaning of the term

12 Annotations are the connections between genomic information and the GO. Experiments provide the data that enables us to annotate gene products with terms from the ontologies. Annotations for App: amyloid beta (A4) precursor proteinApp Annotations are assertions

13 NO Direct Experiment Inferred from evidence Direct Experiment in organism We use evidence codes to describe the basis of the annotation IDA: Inferred from direct assay IPI: Inferred from physical interaction IMP: Inferred from mutant phenotype IGI: Inferred from genetic interaction IEP: Inferred from expression pattern IEA: Inferred from electronic annotation ISS: Inferred from sequence or structural similarity TAS: Traceable author statement NAS: Non-traceable author statement IC: Inferred by curator RCA: Reviewed Computational Analysis ND: no data available

14 GO Annotation Stats: I GO Annotations Total manual GO annotations - 388,633 Total proteins with manual annotations – 80,402 Contributing Groups (including MGI): - 19 Total Pub Med References – 346,002 Total number predicted annotations – 17,029,553 Total number taxa – 129,318 Total number distinct proteins – 2,971,374 April 24, 2007

15 Now we can query across all annotations based on shared biological activity. Annotations of gene products to GO are genome specific

16 GO is a functional annotation system of great utility to the data-driven biologist

17 GO enables genomic data analysis Microarrays allow biologists to record changes in gene function across entire genomes Result: Vast amounts of gene expression data desperately needing cataloging and tagging Many data analysis tools use GO graph structure to statistically evaluate clusters of co-expressed genes based on shared functional annotations  680 pub (of 1517) on GO list  46 microarray tools contributed

18 OCT 13, 2006 Cancer Genome Projects GO supports functional classifications

19 GO is wildly successful FIGURE 3. Representative cell-type-specific genes and corresponding molecular functions. Nature: January 2007

20 Comprehensively annotate Reference Genomes Saccharomyces cerevisiae Schizosaccharomyces pombe Arabidopsis thaliana Human Mouse Fly Rat Chicken Zebrafish Worm Dicty E.coli

21 Priority genes: those implicated in human diseases Determine orthologs/homologs in reference genomes For these genes, comprehensively curate biomedical literature Reference Genome Annotation Project Mary Dolan

22 Shared annotation focus = Coordinated attention to ontology structure Orthology/homology set across primary model organisms Reference ID mappings including associations of sequences, gene/proteins, and human diseases Ultimately, transparent access to comprehensive information about genes among the primary data providers Reference Genome Development Projects

23 1. Verifying and maintaining domain representations in the ontology that reflect best knowledge of the real world. - Depends on the involvement of biologists (domain experts) - Difficult to automate - Must accommodate continuing changes in what we think we understand about biological systems 2. Providing comprehensive annotations, where experimental evidence is available, for all genes - Dependant on the quality of annotations from experimental literature - Combines manual curation by highly-trained scientists supplemented by computational inference prediction annotations - Comprehensiveness may depend on changes in biomedical publishing Ongoing Challenges for the GO Consortium

24 acknowledgements MGI Carol Bult Janan Eppig Jim Kadin Joel Richardson Martin Ringwald Lois Maltais TBK Reddy Monica McAndrews-Hill Nancy Butler GO Michael Ashburner (Cambridge) J. Michael Cherry (Stanford) Suzanna Lewis (LBNL) Rex Chisholm (NWU) David Hill (Jackson Lab) Midori Harris (EBI) Chris Mungall (LBNL) Jane Lomax (EBI) Eurie Hong (Stanford) Jen Clark (EBI) MGI Alex Diehl Mary Dolan Harold Drabkin David Hill Li Ni Dmitry Sitnikov

25 Gene Ontology MGI projects are supported by NIH [NHGRI, NICH, and NCI]. Bar Harbor, Maine, USA Mouse Genome Informatics GO Consortium is supported by NIH-NHGRI and by the European Union RTD Programme PRO is supported by NIGMS Corpora is supported by NLM