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![data integration [ˈdeɪtə ˌɪntəˈgreɪʃən]:
(computational) means to access, retrieve
and analyse data sets from different
sources in order to exploit them, i.e., gain
new knowledge, and share that new
knowledge](https://image.slidesharecdn.com/tgaccourtot-160919073248/75/Ontologies-for-life-sciences-examples-from-the-gene-ontology-13-2048.jpg)
![data integration [ˈdeɪtə ˌɪntəˈgreɪʃən]:
(computational) means to access, retrieve
and analyse data sets from different
sources in order to exploit them, i.e., gain
new knowledge, and share that new
knowledge](https://image.slidesharecdn.com/tgaccourtot-160919073248/75/Ontologies-for-life-sciences-examples-from-the-gene-ontology-14-2048.jpg)
![FIG. 2. Human Nbp35 is a cytosolic
protein. (A) EGFP fluorescence of a HeLa
cell transiently transfected with a vector
encoding a huNbp35-EGFP fusion protein
(right) in comparison to the endogenous
autofluorescence (AFL) of control cells
(left).
(C) Sub-cellular localization of huNbp35 by cell fractionation. […]HuNbp35
exclusively colocalizes with tubulin in the cytosolic fraction, but not with
mitochondrial aconitase (mtAconitase) present in the membrane fraction.](https://image.slidesharecdn.com/tgaccourtot-160919073248/75/Ontologies-for-life-sciences-examples-from-the-gene-ontology-90-2048.jpg)
![GO evidence codes [small excerpt]
TAS, Traceable author statement
NAS, Non-traceable author statement
IDA, Inferred from Direct Assay
IMP, Inferred from Mutant Phenotype
IPI, Inferred from Physical Interaction
Experimental
evidence,
Methods &
Results
Abstract &
Introduction](https://image.slidesharecdn.com/tgaccourtot-160919073248/75/Ontologies-for-life-sciences-examples-from-the-gene-ontology-99-2048.jpg)
Uploaded byMelanie Courtot
Ontologies for life sciences: examples from the gene ontology
Ontologies for life sciences: examples from the Gene Ontology The document discusses ontologies for life sciences, using the Gene Ontology (GO) as an example. It provides an overview of GO, describing it as a way to capture biological knowledge for gene products in a written and computable form using a set of concepts and relationships arranged hierarchically. GO allows consistent descriptions of genes/gene products across databases. Model organism databases provide annotations connecting genes to GO terms. The GO is a collaborative effort to address the need for consistent descriptions of genes.
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Ontologies for life sciences: examples from the gene ontology
- 1. Ontologies for lifesciences: examples from the Gene Ontology Melanie Courtot GO/GOA project lead mcourtot@ebi.ac.uk @mcourtot
- 2. Ontologies for lifesciences
- 3. Cross dom ain resources . C ro ss d o m a in re s o u rc e s d g P b s y Data resourcesat EMBL-EBI Genes, genomes & variation RNA Central ArrayExpress Expression Atlas Metabolights PRIDE InterPro Pfam UniProt ChEMBL SureChEMBL ChEBI Molecular structures Protein Data Bank in Europe Electron Microscopy Data Bank European Nucleotide Archive European Variation Archive European Genome-phenome Archive Gene, protein & metabolite expression Protein sequences, families & motifs Chemical biology Reactions, interactions & pathways IntAct Reactome MetaboLights Systems BioModels Enzyme Portal BioSamples Ensembl Ensembl Genomes GWAS Catalog Metagenomics portal Europe PubMed Central BioStudies Gene Ontology Experimental Factor Ontology Literature & ontologies
- 4. Different words sameconcept: example of Dyschromatopsia
- 5. Search PubMed for“color blindness”
- 6. Search PubMed for“Dyschromatopsia”
- 7. Search PubMed for"abnormality of the eye"
- 8. Thousands of sampleattributes…
- 9. genomics transcriptomics proteomicsmetabolomics transcriptomics metabolomics individual experiments genomics transcriptomics proteomics metabolomics transcriptomics metabolomics individual experiments genomics transcriptomics proteomics metabolomics transcriptomics metabolomics individual experiments Data integration in times of ‘omics’ genomics transcriptomics proteomics metabolomics transcriptomics metabolomics individual experiments conducted at different times by different researchers using different equipment/approaches reporting same type of results differently
- 10. Data growth isfast 12 month doubling 18 month doubling 4 month doubling 3 month doubling 100000000 1E+09 1E+10 1E+11 1E+12 1E+13 1E+14 1E+15 1E+16 2002 2004 2006 2008 2010 2012 2014 2016 bytes date EGA ENA PRIDE MetaboLights ArrayExpress Slide credit: Paul Flicek
- 11. Data growth isfast 12 month doubling 18 month doubling 4 month doubling 3 month doubling 100000000 1E+09 1E+10 1E+11 1E+12 1E+13 1E+14 1E+15 1E+16 2002 2004 2006 2008 2010 2012 2014 2016 bytes date EGA ENA PRIDE MetaboLights ArrayExpress Slide credit: Paul Flicek Vast amount of data generated means vast amount of data submitted to repositories
- 12. Curation - Dirtydata and the long tail 200100 sex:female gender:female disease:breast cancer frequency=2285 frequency=1288
- 13. data integration [ˈdeɪtəˌɪntəˈgreɪʃən]: (computational) means to access, retrieve and analyse data sets from different sources in order to exploit them, i.e., gain new knowledge, and share that new knowledge
- 14. data integration [ˈdeɪtəˌɪntəˈgreɪʃən]: (computational) means to access, retrieve and analyse data sets from different sources in order to exploit them, i.e., gain new knowledge, and share that new knowledge
- 15. Standards What do theyoffer? • uniformity and consistency in reporting data • effective reuse, integration and mining of data • creation of SOPs, benchmarks, quality assessment • community cohesion
- 16. What constitutes astandard? 1. Establish your community 2. Define community needs 3. Define minimal information which needs to be gathered and exchanged by that community 4. Design* an interchange format 5. Design* domain-specific controlled vocabularies *Design = review, reuse and fill the gaps
- 17.
- 18. http://www.biosharing.org • Many “Minimuminformation about a…..” papers now published.
- 19. Standards – XMLinterchange formats http://www.sbml.org
- 20. Adding semantics tothe data formats
- 21. • Same namefor different concepts • Different names for the same concept Inconsistency in naming of biological concepts ? An example … Tactition Tactile sense Taction perception of touch ; GO:0050975
- 22. Sample description withsemantic markup CL:CL_0000071 (blood vessel endothelial cell) obo:CHEBI_39867 (valproic acid) NCBITaxon:NCBITa xon_9606 (Homo Sapiens) Curation
- 23. Ontologies • Representation ofimportant things in a specific domain • Describes types of entities (e.g. cells) and relations between them • An active, formal computational artifact • A mathematical model based on a subset of first order logic • Tools can automatically process ontologies • A communication tool • Provides a dictionary for collaborators, a shared understanding • Allows data sharing
- 25. Reasoning is critical • Prokaryotic and Eukaryotic cell are declared disjoints • Fungal cell is a Eukaryotic cell • Spore is a Fungal cell and a Prokaryotic cell ⇒ Unsatisfiability ⇒ Solution: clarify spore (sensu Mycetozoa) AND actinomycete-type spore http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022006
- 26. Different words sameconcept: example of Dyschromatopsia We searched earlier for : - Dyschromatopsia - Colorblindness - Abnormality of the eye
- 27. The ontology ofcolor blindness HP:0011518 (Dichromacy )HP:0011518 (Eye) HP:0000551 (Abnormality of color vision ) HP:0007641 (Dyschromatopsia) Is-a Is-a Disease-location
- 28. The ontology ofcolor blindness HP:0011518 (Dichromacy )HP:0011518 (Eye) HP:0000551 (Abnormality of color vision ) HP:0007641 (Dyschromatopsia) Is-a Is-a Disease-location “Colorblindness” “A form of colorblindness in which only two of the three fundamental colors can be distinguished due to a lack of one of the retinal cone pigments.” synonym definition
- 29. Building ontologies • Putthings into categories • Helps organise the data • Allows us to generalise over data • Capture the relations between things • Anatomical parts Biopolymer Nucleic Acid Polypeptide EnzymeDNA RNA tRNA mRNA smRNA
- 30. Ontologies add value Smartersearching Data visualisation Data analysis Data integration
- 31. CMPO term: graped micronucleus CMPO_0000156 CMPOterm: graped micronucleus CMPO_0000156 Integrate file formats Integrate metadata Apply phenotype ontology Predict disease gene/biomarkers Human Disease Cell Gene knockdown 31
- 32. 32 Genotype Phenotype Sequence Proteins Gene productsTranscript Pathways Cell type BRENDA tissue / enzyme source Development Anatomy Phenotype Plasmodium life cycle - Sequence types and features - Genetic Context - Molecule role - Molecular Function - Biological process - Cellular component - Protein covalent bond - Protein domain - UniProt taxonomy -Pathway ontology -Event (INOH pathway ontology) -Systems Biology -Protein-protein interaction -Arabidopsis development -Cereal plant development -Plant growth and developmental stage -C. elegans development -Drosophila development FBdv fly development.obo OBO yes yes -Human developmental anatomy, abstract version -Human developmental anatomy, timed version -Mosquito gross anatomy -Mouse adult gross anatomy -Mouse gross anatomy and development -C. elegans gross anatomy -Arabidopsis gross anatomy -Cereal plant gross anatomy -Drosophila gross anatomy -Dictyostelium discoideum anatomy -Fungal gross anatomy FAO -Plant structure -Maize gross anatomy -Medaka fish anatomy and development -Zebrafish anatomy and development -NCI Thesaurus -Mouse pathology -Human disease -Cereal plant trait -PATO PATO attribute and value.obo -Mammalian phenotype - Human phenotype -Habronattus courtship -Loggerhead nesting -Animal natural history and life history eVOC (Expressed Sequence Annotation for Humans) Ontologies for life sciences
- 34. Open Biological andBiomedical Ontologies (OBO) A subset of biological and biomedical ontologies whose developers have agreed in advance to accept a common set of principles reflecting best practice in ontology development designed to ensure … • tight connection to the biomedical basic sciences • compatibility • interoperability, common relations • formal robustness • support for logic-based reasoning
- 35.
- 36. Building metadata (&ontology) rich resources • We build tools for semantic enrichment and alignment • Interoperability toolkit • Microservices based architecture • Technology-agnostic • Pushing boundaries of ontology “embedding”
- 37. Raw Data toExplicit Knowledge Data Exploration and Cleanup Data structuring Ontology Annotation Data cleaning and mapping Ontology building Webulous OxO mapping service
- 38. Searching for ontologyterms: the EBI Ontology Lookup Service • for searching and visualizing >140 ontologies from the biomedical domain • includes (among others): • Gene Ontology • OBO Relations ontology • Evidence ontology • Pathogen Transmission Ontology • Symptom Ontology • Basic Formal Ontology
- 39. Ontology Lookup Service • Ontology search engine • Ontology visualisation • Powerful RESTful API • Open source project • Generic infrastructure (can load any ontology represented in OWL) https://github.com/EBISPOT/OLS Repository of over 150 biomedical ontologies (4.5 million terms, 11 million relations) http://www.ebi.ac.uk/ols
- 40.
- 41. • Sample attributesand variables are mapped to EFO ontology Sample attribute Mapping data to ontology terms
- 42. • Zooma automatically annotatessample attributes and variables with ontology classes Mapping data to ontology terms
- 43. Mapping data toontology terms Information supplied as part of a search The source of this mapping ZOOMA contains a linked data repository of annotation knowledge and highly annotated data
- 44. Expression Atlas: sourceof mappings • Atlas automated pipeline runs against Zooma, then curators: • Check that the automatic mappings are all correct • Create a list of new mappings that should be added to Zooma
- 45. • Webulous GoogleAdd-On • Connect to the Webulous server from Google Spreadsheets • Load templates from the Webulous server • Submit populated templates back to the server for processing Expression Atlas: curation What happens when we need a term that is not in EFO?
- 46. Adding diseases toEFO using • Design pattern templates can be loaded into Google Sheets
- 47. • A Webuloustemplate specifies a series of fields (columns) for the input data Some fields only allow values from a list of ontology terms Adding diseases to EFO using This data validation provides user with convenient term autocomplete when entering data into a cell
- 48. Adding diseases toEFO using
- 49. Raw Data toExplicit Knowledge Data Exploration and Cleanup Data structuring Ontology Annotation Data cleaning and mapping Ontology building Webulous OxO mapping service
- 50. BioSolr “BioSolr aims tosignificantly advance the state of the art with regards to indexing and querying biomedical data with freely available open source software” flaxsearch/BioSolr Solr documents with ontology annotation Enriched Solr with ontology content (synonyms, structure, relations) Solr/Elastic plugin Query expansion and hierarchical faceting
- 51. Which other diseasesare associated with PDE4D? View diseases grouped in therapeutic areas or organised in a tree View more information about PDE4D Filter by therapeutic area
- 52.
- 53. Publishing biological dataas Linked Open Data • The EBI RDF platform • Released Nov 2013 • Currently over 16 billion RDF triples • Datasets updated ~ quarterly LOD diagram August 2014 Jupp et al (2013). The EBI RDF Platform: Linked Open Data for the Life Sciences. Bioinformatics.
- 54. RDF Platform Integrationpoints Gene (via identifiers. org/ensembl) RNA transcript (via identifiers.org/ensembl) uniprot:Protein rdfs:seeAlso (not currently linking to identifiers.org but soon) discretized differential gene expression ratio (sio: SIO_001078) Gene Expression Atlas Ensembl sio:'is attribute of' (sio:SIO_000011) Uniprot Gene Ontology GO BP GO MF GO CC uniprot:classifiedWith bq:occursIn Organisms Organism/taxon ChEMBL Assay (?) chem bl:hasTarget ? bq:isVersionOf uniprot:organism rdfs:seeAlso 1 1 1 * 1 * * * 1 1 BioModels SBMLModel Reaction Species Compartment bq:is bq:isVersionOf bq:isVersionOf bq:is bq:isVersionOf bq:isHomologTo bq:hasPart ChEBI Reactome Pathway bq:isVersionOf bq:isVersionOf SBO bq:is Relationships within Biomodels can be found at https://github. com/sarala/ricordo- rdfconverter/wiki/SBML- RDF-Schema rdfs:seeAlso Structure PDB 1 rdfs:seeAlso Target (?) uniprot:transcribedFrom Protein (via identifiers. org/ensembl) uniprot:translatedTo bq:isVersionOf
- 55. RDF Platform –lessons learned Successes • Novel queries possible over EBI datasets • Production quality RDF releases • Community of users • Highly available public SPARQL endpoints • 500+ users (10-50 million hits per month) • Lots of interest • Catalyst for new RDF efforts Lessons ● Public SPARQL endpoints problematic ● Query federation not performant ● Inference support limited ● Not scalable for all EBI data e.g. Variation, ENA ● Lack of expertise in service teams ● Too much overhead to get started quickly in this space
- 56. An example: TheGene Ontology and Gene Ontology Annotation
- 57.
- 58. • A way tocapture biological knowledge for individual gene products in a written and computable form The Gene Ontology • A set of concepts and their relationships to each other arranged as a hierarchy www.ebi.ac.uk/QuickGO Less specific concepts More specific concepts
- 59. The Gene Ontology http://geneontology.org/ • Collaborative effort to address the need for consistent descriptions of genes/gene products across databases • Use of GO terms by collaborating databases facilitates uniform queries across all of them
- 60. Aims of theGO project • compile the ontologies • >40000 terms • constantly increasing and improving • annotate gene products using the terms • provide public resource of data and tools • regular releases of annotations • tools for browsing/querying annotations and editing the GO
- 61. The GO editorialoffice at EMBL-EBI • Part of the Sample, Phenotypes and Ontology team (SPOT) • Contributes to development of the Gene Ontology • Specific areas of interest: autophagy, synapse… • Answers user requests • New terms, modifications, updates • Help support • Curator requests GO editorial office at the EBI: Paola Roncaglia David Osumi-Sutherland
- 62. Develop the ontology • An OWL ontology of >41,000 classes • biological process, cellular component, molecular function • > 14,000 imported classes (CL, Uberon, ChEBI, NCBI_tax) • >136,000 logical axioms, including: • ~72,000 subClassOf axioms between named GO classes • ~41,000 simple existential restrictions (subClassOf R some C) • EL expressivity => fast, scalable reasoning (with ELK) https://www.cs.ox.ac.uk/isg/tools/ELK/
- 63. Ontology structure • Hierarchical Terms canhave more than one parent • Terms are linked by relationships is_a part_of regulates (and +/- regulates) www.ebi.ac.uk/QuickGOoccurs_in has_part These relationships allow for complex analysis of large datasets Terms can have more than one child
- 64. Biological Process what doesa gene product do? cell division transcription A commonly recognised series of events
- 65. Molecular Function how doesa gene product act? • insulin binding • insulin receptor activity • glucose-6-phosphate isomerase activity
- 66. Cellular Component where isa gene product located? plasma membrane • mitochondrion • mitochondrial membrane • mitochondrial matrix • mitochondrial lumen • ribosome • large ribosomal subunit • small ribosomal subunit
- 67. Example GO annotation– cytochrome c cellular components molecular functions biological processes Electron carrier activity GO:0009055 oxidation-reduction process GO:0055114 Mitochondrion GO:0005739 https://www.ebi.ac.uk/QuickGO/GProtein?ac=P99999
- 68. Anatomy of aGO term Unique identifier Term name Definition Synonyms Cross-references
- 69.
- 70. What is theGO ID for the term mitochondrial chromosome
- 71. What is theGO ID for the term mitochondrial chromosome GO:0000262
- 72. What are thefour direct parents of the term nucleosome?
- 73. What are thefour direct parents of the term nucleosome? Chromatin Chromosomal part DNA packaging complex Protein-DNA complex
- 74. What types ofrelationships are there between the term nucleosome and its direct parents?
- 75. What types ofrelationships are there between the term nucleosome and its direct parents? Part of chromatin Is a for the others
- 76. Building the GO • The GO editorial team • Submission via GitHub, https://github.com/geneontology/ • Submissions via TermGenie, http://go.termgenie.org • ~80% terms are now created this way
- 77. Annotate gene products GOA Database externalannotation groups (25) manual annotation by curators (125) electronic prediction methods (11)
- 79. Making annotations available GOA Database GOA& GOC ftp sites QuickGO
- 80. Manual annotations • Time-consumingprocess producing lower numbers of annotations (~2,800 taxons covered) • More specific GO terms • Manual annotation is essential for creating predictions
- 81. • Part of theProtein Function content team • Largest open-source contributor of annotations to GO • Focuses on human, but provide annotations for more than 441,000 species • Human curators, and collate manual and electronic annotations across community UniProt-Gene Ontology Annotation (UniProt- GOA) project at the EMBL-EBI http://www.ebi.ac.uk/GOA Aleksandra Shypitsyna Elena Speretta Penelope Garmiri Tony Sawford UniProt-GOA project at the EBI:
- 82. …a statement thata gene product; P00505 Accession Name GO ID GO term name Reference Evidence code IDAPMID:2731362aspartate transaminase activityGO:0004069GOT2 A GO annotation is …
- 83. …a statement thata gene product; 1. has a particular molecular function or is involved in a particular biological process or is located within a certain cellular component A GO annotation is … P00505 Accession Name GO ID GO term name Reference Evidence code IDAPMID:2731362aspartate transaminase activityGO:0004069GOT2
- 84. …a statement thata gene product; 1. has a particular molecular function or is involved in a particular biological process or is located within a certain cellular component 2. as described in a particular reference A GO annotation is … P00505 Accession Name GO ID GO term name Reference Evidence code IDAPMID:2731362aspartate transaminase activityGO:0004069GOT2
- 85. …a statement thata gene product; 1. has a particular molecular function or is involved in a particular biological process or is located within a certain cellular component 2. as described in a particular reference 3. as determined by a particular method A GO annotation is … P00505 Accession Name GO ID GO term name Reference Evidence code IDAPMID:2731362aspartate transaminase activityGO:0004069GOT2
- 86. Experimental data Computational analysis Author statements/ curator inference (+Inferred from electronic annotations) http://www.evidenceontology.org/ Tracking provenance
- 87.
- 88.
- 89.
- 90. FIG. 2. HumanNbp35 is a cytosolic protein. (A) EGFP fluorescence of a HeLa cell transiently transfected with a vector encoding a huNbp35-EGFP fusion protein (right) in comparison to the endogenous autofluorescence (AFL) of control cells (left). (C) Sub-cellular localization of huNbp35 by cell fractionation. […]HuNbp35 exclusively colocalizes with tubulin in the cytosolic fraction, but not with mitochondrial aconitase (mtAconitase) present in the membrane fraction.
- 91. Human Nbp35 isa cytosolic protein. Protein GO term Supporting evidence
- 92. Human Nbp35 isa cytosolic protein. • Find the correct UniProt entry http://www.uniprot.org
- 93. Human Nbp35 isa cytosolic protein.
- 94. Human Nbp35 isa cytosolic protein. Protein GO term Supporting evidence NUBP1
- 95. Human Nbp35 isa cytosolic protein. • Find the right GO term https://www.ebi.ac.uk/QuickGO/
- 96. Human Nbp35 isa cytosolic protein.
- 97. Human Nbp35 isa cytosolic protein. Protein GO term Supporting evidence NUBP1 GO:0005829
- 98. Human Nbp35 isa cytosolic protein. • Evidence: • Fig 2A Immunofluorescence and/or • Fig 2C subcellular fractionation
- 99. GO evidence codes[small excerpt] TAS, Traceable author statement NAS, Non-traceable author statement IDA, Inferred from Direct Assay IMP, Inferred from Mutant Phenotype IPI, Inferred from Physical Interaction Experimental evidence, Methods & Results Abstract & Introduction
- 100. Human Nbp35 isa cytosolic protein. Protein GO term Supporting evidence NUBP1 GO:0005829 IDA
- 102. Electronic Annotations • Quickway of producing large numbers of annotations • Annotations use less-specific GO terms Only source of annotation for ~438,000 non-model organism species
- 103. Electronic Annotations • Quickway of producing large numbers of annotations • Annotations use less-specific GO terms • Only source of annotation for ~438,000 non-model organism species orthology taxon constraints
- 104. Broad taxonomic coverage …aswell as less well-studied species that have; • Complete proteome • >25% GO annotation coverage We provide annotation files for well-studied species… We have annotations for species that may not have a dedicated curation effort; e.g. for 1,400 Solanacae species’ we have ~360,000 annotations for ~64,000 proteins
- 105. 1. Mapping ofexternal concepts to GO terms e.g. InterPro2GO, UniProt Keyword2GO, Enzyme Commission2GO Electronic annotation methods GO:0004715 ; non-membrane spanning protein tyrosine kinase activity
- 106. Annotations are high-qualityand have an explanation of the method (GO_REF) Macaque Mouse DogCow Guinea PigChimpanzee Rat Chicken Ensembl compara 2. Automatic transfer of manual annotations to orthologs ...and more e.g. Human Arabidopsis Rice Brachypodium Maize Poplar Grape …and moreEnsembl compara Electronic annotation methods http://www.geneontology.org/cgi-bin/references.cgi
- 107. An example ACCESSION GO ID GO ASPECT GO TERM P04637 GO:0047485 F protein N-terminus binding P04637 GO:0051087 F chaperone binding P04637 GO:0051721 F protein phosphatase 2A binding P04637 GO:0000733 P DNA strand renaturation P04637 GO:0006289 P nucleotide-excision repair P04637 GO:0006355 P regulation of transcription, DNA-templated P04637 GO:0006461 P protein complex assembly ACCESSION GO ID GO ASPECT GO TERM Q549C9 GO:0047485 F protein N-terminus binding Q549C9 GO:0051087 F chaperone binding Q549C9 GO:0051721 F protein phosphatase 2A binding Q549C9 GO:0000733 P DNA strand renaturation Q549C9 GO:0006289 P nucleotide-excision repair Q549C9 GO:0006355 P regulation of transcription, DNA-templated Q549C9 GO:0006461 P protein complex assembly Annotations from the source… …are projected on to the target
- 108. InterPro Source of~93 million GO mappings for ~30 million distinct UniProtKB sequences (Oct 30 2015 release) 3. Propagation of GO annotations to protein groups
- 109. GO mapping todomains: Function of domain may not be function of protein Family members can be experimentally characterised as lacking function: P14210 - a serine protease homologue with no proteolytic activity (proteins are reported to GOA to be blacklisted) Broad families that are functionally diverse: The GHMP kinase superfamily includes - Galactokinases (EC=2.7.1.6) - Homoserine kinases (EC=2.7.1.39) - Mevalonate kinases (EC=2.7.1.36) - Diphosphomevalonate decarboxylases (EC 4.1.1.33) Considerations for mapping GO terms
- 110. * Includes manualannotations integrated from external model organism and specialist groups 2,811,622Manual annotations* 280,313,749Electronic annotations Number of annotations in UniProt-GOA database (June 2016)
- 111. Many ways toaccess GO annotation data
- 112. http://www.ebi.ac.uk/QuickGO Map-up annotations with GOslims Search GO terms or proteins Find sets of GO annotations Questions on how to use QuickGO? Contact goa@ebi.ac.uk One example: the QuickGO browser
- 113.
- 115. GO term enrichmentanalysis • What is it? • What can you use it for? • How does it actually work? • How can I actually do it? • When is it NOT a good idea to do it?
- 116. Enrichment analysis –basic principle Sample 40% 20%
- 117.
- 118. => The sampleis over-enriched for Enrichment analysis Sample 40% 20% Reference 20% 20%
- 119. GO term enrichmentanalysis • What is it? • Most popular type of GO analysis • Determines which GO terms are more often associated with a specified list of genes/proteins compared with a control list or rest of genome
- 120. GO term enrichmentanalysis • What can you use it for?
- 121. GO term enrichmentanalysis “Our gene list contains targets for GATA1 (orange balls) and SP1 (blue balls) transcription factors (TFs). For each TF, we extract the proportion of targets in the gene list and in the genome to construct the contingency table. Fisher's exact test is used to determine if there is a nonrandom association between the gene list and the specific regulation of a TF.” • http://bioinfo.cipf.es/docs/renato/simple_enrichment_analysis
- 122. GO term enrichmentanalysis
- 123. GO term enrichmentanalysis • How does it actually work? • http://geneontology.org/page/go-enrichment-analysis • http://geneontology.org/faq/what-minimum-information- include-functional-analysis-paper • Also useful for GO analysis in general:
- 124. GO term enrichmentanalysis • How can I actually do it? • Many tools available to do this analysis • User must decide which is best for their analysis • We’ll focus on the tool provided by the GO Consortium • Be aware that there are numerous third-party tools and that they do not all use up-to-date GO data
- 126. GO term enrichmentanalysis • How do you get to the GO TE tool? • From front page of GO website • From AmiGO
- 127.
- 128.
- 129.
- 130.
- 131. Spinocerebellar ataxia type28 Paola Roncaglia
- 132. Novel biomarkers ofrectal radiotherapy
- 133. Biomarker for diagnosisand prognosis
- 134. Gene expression changesin diabetes
- 135.
- 136. Hands on -Dataset • Download http://tinyurl.com/IDs-for-enrichment • Go to http://geneontology.org • Run the enrichment analysis
- 137. Caveats • When canyou NOT do an enrichment analysis? • Too few target genes/proteins • Genes/proteins of interest are not present in your background set (e.g. array) • Genes/proteins of interest are not expressed/translated in your sample(s)
- 138.
- 139. Many gene productsare associated with a large number of descriptive, leaf GO nodes: GO slims
- 140. …however annotations canbe mapped up to a smaller set of parent GO terms: GO slims
- 141. Slim generation forindustry • Collaboration funded by Roche • Need a custom GO slim for analysis of genesets of interest • Need to be descriptive enough • Without redundancy • Internal proprietary vocabulary – hard to maintain • Desire to automatically map to GO http://www.swat4ls.org/wp-content/uploads/2015/10/SWAT4LS_2015_paper_44.pdf
- 142. ROCHE CV GSEA withfull GO GSEA with Roche CV Courtesy Laura Badi
- 143. • Mapping query:participant_OR_reg_participant some cannabinoid • Description: “A process in which a cannabinoid participates, or that regulates a process in which a cannabinoid participates.”
- 144. Results • We havesuccessfully mapped 84% of terms from RCV (308/365) to OWL queries that can be used to replicate some proportion of the original manual mapping. • In addition, these queries find 1000s of terms that were missed in the original mapping. David Osumi-Sutherland
- 145.
- 146. ROCHE CV –MANUAL ONLY
- 147. ROCHE CV MANUAL+ AUTO
- 148. Go slims formetagenomics functional analysis
- 149.
- 150.
- 151.
- 152.
- 153. Acknowledgements • GO editorsand developers • GO annotators • The Gene Ontology (GO) Consortium • Samples, Phenotype and Ontology team (Helen Parkinson) • Protein Function Content team (Claire O’Donovan) • Funding: EMBL-EBI, National Human Genome Research Institute (NHGRI)
- 154. Thank you foryour attention! Contact Gene Ontology Annotation: goa@ebi.ac.uk Contact Gene Ontology: http://geneontology.org/form/contact-go