1. BI class 2010 Gene Ontology Overview and Perspective

2. What is Ontology? Dictionary: A branch of metaphysics concerned with the nature and relations of being 1606 1700s

3. What is the Gene Ontology? Allows biologists to make queries across large numbers of genes without researching each one individually

4. So what does that mean? From a practical view, ontology is the representation of something we know about. “Ontologies" consist of a representation of things, that are detectable or directly observable, and the relationships between those things.

5. Car? Ontology -definition

6. 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.

7. How does GO work? What does the gene product do? Where and does it act? Why does it perform these activities? What information might we want to capture about a gene product?

8. What is the Gene Ontology? Set of standard biological phrases (terms) which are applied to genes/proteins: –protein kinase –apoptosis –membrane

9. 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

10. The GO is Actually Three Ontologies Biological Process GO term: tricarboxylic acid cycle Synonym: Krebs cycle Synonym: citric acid cycle GO id:GO:0006099 Cellular Component GO term: mitochondrion GO id: GO:0005739 Definition: A semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. It is notably the site of tissue respiration. Molecular Function GO term: Malate dehydrogenase. GO id: GO:0030060 (S)-malate + NAD(+) = oxaloacetate + NADH.

11. Cellular Component where a gene product acts

12. Cellular Component

14. Enzyme complexes in the component ontology refer to places, not activities.

15. Molecular Function activities or “jobs” of a gene product glucose-6-phosphate isomerase activity

16. Molecular Function insulin binding insulin receptor activity

17. Molecular Function A gene product may have several functions Sets of functions make up a biological process.

18. Biological Process a commonly recognized series of events cell division

19. Biological Process transcription

20. Biological Process regulation of gluconeogenesis

21. Biological Process limb development

22. Ontology Structure Terms are linked by two relationships –is-a  –part-of 

23. Ontology Structure cell membrane chloroplast mitochondrial chloroplast membrane is-a part-of

24. Ontology Structure Ontologies are structured as a hierarchical directed acyclic graph (DAG) Terms can have more than one parent and zero, one or more children

25. Ontology Structure cell membrane chloroplast mitochondrial chloroplast membrane Directed Acyclic Graph (DAG) - multiple parentage allowed

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

27. GO Definitions: Each GO term has 2 Definitions A definition written by a biologist: necessary & sufficient conditions written definition (not computable) Graph structure: necessary conditions formal (computable)

28. Appropriate Relationships to Parents GO currently has 2 relationship types –Is_a An is_a child of a parent means that the child is a complete type of its parent, but can be discriminated in some way from other children of the parent. –Part_of A part_of child of a parent means that the child is always a constituent of the parent that in combination with other constituents of the parent make up the parent.

29. Placement in the Graph: Selecting Parents To make the most precise definitions, new terms should be placed as children of the parent that is closest in meaning to the term. To make the most complete definitions, terms should have all of the parents that are appropriate. In an ontology as complicated as the GO this is not as easy as it seems.

30. True Path Violations Create Incorrect Definitions..”the pathway from a child term all the way up to its top-level parent(s) must always be true". chromosome Part_of relationship nucleus

31. True Path Violations..”the pathway from a child term all the way up to its top-level parent(s) must always be true". chromosome Mitochondrial chromosome Is_a relationship

32. True Path Violations..”the pathway from a child term all the way up to its top-level parent(s) must always be true". chromosome Mitochondrial chromosome Is_a relationship Part_of relationship nucleus A mitochondrial chromosome is not part of a nucleus!

33. True Path Violations..”the pathway from a child term all the way up to its top-level parent(s) must always be true". nucleuschromosome Nuclear chromosome Mitochondrial chromosome Is_a relationships Part_of relationship mitochondrion Part_of relationship

34. The Development Node (some example for consistent definitions)

35. Cell level [i] y cell differentiation ---[p] y cell fate commitment ------[p] y cell fate specification ------[p] y cell fate determination ---[p] y cell development ------[p] y cellular morphogenesis during differentiation ------[p] y cell maturation

36. y cell differentiation The process whereby a relatively unspecialized cell acquires specialized features of a y cell. http://www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=cmed6.figgrp.41173 [i] y cell differentiation ---[p] y cell fate commitment ------[p] y cell fate specification ------[p] y cell fate determination ---[p] y cell development ------[p] y cellular morphogenesis during differentiation ------[p] y cell maturation

37. y cell fate commitment The process whereby the developmental fate of a cell becomes restricted such that it will develop into a y cell. [i] y cell differentiation ---[p] y cell fate commitment ------[p] y cell fate specification ------[p] y cell fate determination ---[p] y cell development ------[p] y cellular morphogenesis during differentiation ------[p] y cell maturation

38. y cell fate specification The process whereby a cell becomes capable of differentiating autonomously into a y cell in an environment that is neutral with respect to the developmental pathway. Upon specification, the cell fate can be reversed. [i] y cell differentiation ---[p] y cell fate commitment ------[p] y cell fate specification ------[p] y cell fate determination ---[p] y cell development ------[p] y cellular morphogenesis during differentiation ------[p] y cell maturation

39. y cell fate determination The process whereby a cell becomes capable of differentiating autonomously into a y cell regardless of its environment; upon determination, the cell fate cannot be reversed. [i] y cell differentiation ---[p] y cell fate commitment ------[p] y cell fate specification ------[p] y cell fate determination ---[p] y cell development ------[p] y cellular morphogenesis during differentiation ------[p] y cell maturation

40. Gene Ontology widely adopted AgBase

41. Terms are defined graphically relative to other terms

42. 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

43. The GO is still developing daily both in ontological structures and in domain knowledge Ontology development workshops focus on specific domains needing experts 2 workshops / year 1.Metabolism and cell cycle 2.Immunology and defense response 3.Early CNS development 4.Peripheral nervous system development 5.Blood Pressure Regulation 6.Muscle Development Building the ontologies

44. Mappings files 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 carboxylase activity ( GO:0003989 ) GO:acetyl-CoA carboxylase activity (GO:0003989)

45. 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

46. P05147 PMID: 2976880 GO:0047519 IDA P05147GO:0047519 IDA PMID:2976880 GO Term Reference Evidence Annotating Gene Products using GO Gene Product

47. Gene protein inherits GO term 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

48. Annotations for APP: amyloid beta (A4) precursor proteinAP Annotations are assertions

49. 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

50. GO structure GO isn’t just a flat list of biological terms terms are related within a hierarchy

51. GO structure gene A

52. GO structure This means genes can be grouped according to user- defined levels Allows broad overview of gene set or genome

53. GO Annotation Stats (2007) 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

54. gene -> GO term associated genes GO annotations GO database genome and protein databases

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

56. GO browser

57. Search on ‘mesoderm development’ mesoderm development

59. Definition of mesoderm development Gene products involved in mesoderm development

60. Traditional analysis Gene 1 Apoptosis Cell-cell signaling Protein phosphorylation Mitosis … Gene 2 Growth control Mitosis Oncogenesis Protein phosphorylation … Gene 3 Growth control Mitosis Oncogenesis Protein phosphorylation … Gene 4 Nervous system Pregnancy Oncogenesis Mitosis … Gene 100 Positive ctrl. of cell prolif Mitosis Oncogenesis Glucose transport …

61. Using GO annotations But by using GO annotations, this work has already been done GO:0006915 : apoptosis

62. Grouping by process Apoptosis Gene 1 Gene 53 Mitosis Gene 2 Gene 5 Gene45 Gene 7 Gene 35 … Positive ctrl. of cell prolif. Gene 7 Gene 3 Gene 12 … Growth Gene 5 Gene 2 Gene 6 … Glucose transport Gene 7 Gene 3 Gene 6 …

63. Anatomy of a GO term id: GO:0006094 name: gluconeogenesis namespace: process def: The formation of glucose from noncarbohydrate precursors, such as pyruvate, amino acids and glycerol. [http://cancerweb.ncl.ac.uk/omd/index.html] exact_synonym: glucose biosynthesis xref_analog: MetaCyc:GLUCONEO-PWY is_a: GO:0006006 is_a: GO:0006092 unique GO ID term name definition synonym database ref parentage ontology

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

65. 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

66. OCT 13, 2006 Cancer Genome Projects GO supports functional classifications

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

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

69. Species coverage All major eukaryotic model organism species Human via GOA group at UniProt Several bacterial and parasite species through TIGR and GeneDB at Sanger –many more in pipeline

70. Annotation coverage

71. GO tools GO resources are freely available to anyone to use without restriction –Includes the ontologies, gene associations and tools developed by GO Other groups have used GO to create tools for many purposes: http://www.geneontology.org/GO.tools

72. GO tools Affymetrix also provide a Gene Ontology Mining Tool as part of their NetAffx™ Analysis Center which returns GO terms for probe sets

73. GO tools Many tools exist that use GO to find common biological functions from a list of genes: http://www.geneontology.org/GO.tools.microarray.shtml

74. GO tools Most of these tools work in a similar way: –input a gene list and a subset of ‘interesting’ genes –tool shows which GO categories have most interesting genes associated with them i.e. which categories are ‘enriched’ for interesting genes –tool provides a statistical measure to determine whether enrichment is significant

75. GO for microarray analysis Annotations give ‘function’ label to genes Ask meaningful questions of microarray data e.g. –genes involved in the same process, same/different expression patterns?

76. Using GO in practice statistical measure –how likely your differentially regulated genes fall into that category by chance microarray 1000 genes experiment100 genes differentially regualted mitosis – 80/100 apoptosis – 40/100 p. ctrl. cell prol. – 30/100 glucose transp. – 20/100

77. Using GO in practice However, when you look at the distribution of all genes on the microarray: ProcessGenes on array # genes expected in occurred 100 random genes mitosis 800/100080 80 apoptosis 400/100040 40 p. ctrl. cell prol. 100/100010 30 glucose transp. 50/1000 5 20

78. Enrichment tools GO is developing its own enrichment tool as part of the GO browser AmiGO Currently in testing phase, should be released next month