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Integrating Pathway Databases with Gene Ontology Causal Activity Models
The document discusses the integration of pathway databases with Gene Ontology Causal Activity Models (GO-CAMs) to create a comprehensive computational model for biological systems. It highlights the challenges of managing the extensive Gene Ontology, which consists of over 45,000 terms, and how GO-CAMs can provide a structured approach to annotate gene functions and relationships in context. The presentation details current efforts to transition to GO-CAMs for improved biological knowledge representation and analysis.
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Integrating Pathway Databases with Gene Ontology Causal Activity Models
- 1. Integrating Pathway Databases withGene Ontology Causal Activity Models Benjamin Good Gene Ontology Consortium Lawrence Berkeley National Labs @bgood Rocky Bioinformatics Conference Aspen, CO, USA, Dec. 8, 2018
- 2. Gene Ontology GO Causal Activity Models Pathwaysto GO-CAMs Causal Inference Trail Talk Map
- 3. GO Mission “The missionof the GO Consortium is to develop a comprehensive, computational model of biological systems, ranging from the molecular to the organism level, across the multiplicity of species in the tree of life.” http://geneontology.org
- 4. The GO wascreated to meet the needs of the genome era 1998 birth of the GO Lathe, W., Williams, J., Mangan, M. & Karolchik, D. (2008) Genomic Data Resources: Challenges and Promises. Nature Education 1(3):2 Base Pairs in GenBank Databases in Nucleic Acids Research database issue 20081982
- 5. The Tower ofBabel by Pieter Bruegel the Elder 1563 (image from Wikipedia) Founders recognized that gene function is largely conserved across species Can use knowledge of one genome to help understand the next genome But you must speak a common language
- 6. Genes have the commonlanguage of DNA Fly Human
- 7. Gene Ontology: common languageof gene function Allows knowledge of gene function to be organized and re-used across databases
- 8. The Gene Ontologyis represented as a directed graph Image courtesy of Chris Mungall Ontology - 45k terms - 106k edges Biological Process Cellular Component Molecular Function
- 9. Annotations shared and managedin a flatfile NEDD4 Ubiquitin-protein ligase activity pmid:770007ECO:IDA
- 10. GO Knowledge Base 2018 Morethan 140,000 published papers used to create … More than 750,000 experimentally supported annotations ... Used to infer more than 7,000,000 functional annotations for more than 3000 organisms. 22K citations of original paper. GO Consortium. (2018) The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. gky1055
- 11. Use of theGO Gene Set analysis dominates usage. See next talk by Judy Blake! Experiment Ranked genes GO analysis Insight
- 12. Gene Ontology GO Causal Activity Models Pathwaysto GO-CAMs Causal Inference Trail Talk Map
- 13. 45,000 ontology terms... • Arehard to manage and hard to use. • One reason the GO has grown so large is the flatfile annotation model • In order indicate a new function, you need a new term • No way to say “Gene X has function Y in context C” • So you get a new term for each Y:C combination (and C itself is complex)
- 14. Building monsters GO:0086094 positive regulation ofryanodine-sensitive calcium-release channel activity by adrenergic receptor signaling pathway involved in positive regulation of cardiac muscle contraction GO:0010768 negative regulation of transcription from RNA polymerase II promoter in response to UV-induced DNA damage By Philippe Semeria
- 15. Relationship between geneand term unspecified NEDD4 GO:0006298 ?
- 16. GO Causal Activity Model(GO-CAM) • Activity Flow model of gene function • Meant to capture how gene products work together in specific contexts to enact biological processes • Annotations are expressed as OWL models • Look a lot like pathway representations
- 17. Ontology of aGO-CAM Classes from GO (and other OBO) are used to describe the types of nodes in the model. Relations from the Relation Ontology are used to describe the relations linking the nodes. Evidence classes from the Evidence Code Ontology justify and provide provenance for the asserted relationships
- 18. OWL • W3C standardWeb Ontology Language • https://www.w3.org/OWL/ • A Description Logic • General purpose tools for editing and reasoning with it. • Reasoning = automatic inference of unstated, entailed relationships Involved in creating Dr. McGuinness
- 19. From unconnected gene annotationsto models of function NEDD4 Ubiquitin-protein ligase activity NEDD4 Nucleus GO-CAM
- 20. Molecular Function Node (Folded View) NEDD4Ubiquitin-protein ligase activity NEDD4 Nucleus
- 21. From unconnected gene annotationsto models of function NEDD4 Ubiquitin-protein ligase activity NEDD4 Nucleus NEDD4 Ubiquitin-dependent protein catabolic process GO-CAM NEDD4
- 22. NEDD4 Ubiquitin-protein ligaseactivity NEDD4 Nucleus NEDD4 Ubiquitin-dependent protein catabolic process NEDD4 Cellular response to DNA damage stimulus NEDD4 Negative regulation of transcription from RNA ...
- 23. OWL Inference example ?Y Protein Complex DNA-Directed PolymeraseActivity RO: Enabled by?X DNA polymerase Complex ... OWL axioms GO-CAM model (OWL instances) RDF:type rdfs:subClassOf RDF:type RO: Capable of DNA Polymerase Activity RDF:type ?X type protein- containing complex ?X capable of ?Y ?Y type DNA polymerase activity ?X→ type DNA polymerase complex Arachne Reasoner https://github.com/balhoff/arachne
- 24. Classifier in Noctuaeditor Translesion synthesis by POLI
- 25. GO-CAM current status 2331GO-CAMs Curators actively building more Goal is to transition all GOC curators onto the use of GO-CAMs and the noctua editor. (WIP) http://geneontology.org/go-cam http://rdf.geneontology.org/ Browse Query
- 26. Gene Ontology GO Causal Activity Models Pathwaysto GO-CAMs Causal Inference Trail Talk Map
- 27. … comprehensive, computational model ofbiological systems... I think I’m going to need a little help...
- 28. Pathway Databases • Peoplehave been charting biochemical pathways for more than 60 years, mostly as images • A variety of computable pathway databases now exist • KEGG, Reactome, BioCyc and more. • These resources complement the efforts of the GO Roche Metabolic Pathways.
- 29. GO-CAM annotations relatedto gdf3 (by Sabrina Toro)
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- 36. Pathway Resources GO-CAMs Approach GO-CAMs asthe unifying structure Manual curation Automatic import
- 37. Automatic part 1.Rule basedtransformation from BioPAX (BioPAX is a standard exchange format for biochemical pathways) 1.OWL Inference
- 38. Pathway to GO- CAMReactome GO-CAM (OWL) BioPAX Series ofrule- based transformations
- 39. Reaction = Activity(molecular function) BMP2 binds to the receptor complex (Reactome) GO:0005160 Mapping rules
- 40. Pathway/Biological Process to CausalActivity Model
- 41. GO-CAM BioPAX Mapping rule: ‘Next Step’ RO:Providesdirect input for Reaction Reaction biopax:nextStep Molecular Function Molecular Function
- 42. Mapping rule: ‘negative regulationby sequestration’
- 43. Signaling by BMP (byReactome as a GO- CAM)
- 44. Reactome conversion • All 2244human Reactome pathways automatically converted into GO- CAMs • Doubling number of GO-CAMs • accessible for testing at http://noctua-dev.berkeleybop.org
- 45. Once GO-CAM generated, canapply OWL Reasoning Use axioms encoded in GO, RO, (more OBO) to : a. Find errors b. Infer new relationships
- 46. Function Classification Connecting tothe Gene Ontology automatically
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- 49. Gene Ontology GO Causal Activity Models Pathwaysto GO-CAMs Causal Inference Trail Talk Map
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- 53. Ongoing challenges ● Infermappings to GO when they are not provided. ● Merge pathways from multiple sources ● Incorporate existing knowledge from GO annotations ● Develop new applications ● Develop effective workflows for using pathway knowledge ○ Adapt external pathways into new GO-CAMs ? ○ Dynamically add new edges into extant GO- CAMs ? The manual part: supporting GO curators Direct kb creation
- 54. Open Issues inGitHub https://github.com/geneontology/ (62 repos..) Solve an issue, get a job! cjmungall@lbl.gov /noctua (GO-CAM editor): 138 /go-site (website, graph store): 271 /go-ontology : 486 /amigo (Web User UI): 134 /pathways2go: 13
- 55. Acknowledgements A very longlist of people that created and contributed to the Gene Ontology Consortium over the past 20 years Specifically: Chris Mungall, Paul Thomas, David Hill, Huaiyu Mi, Peter D’Eustachio, Kimberly Van Auken, Seth Carbon, Jim Balhoff, Laurent-Philippe Albou
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- 58. How to convertexisting GO annotations? GOC has not decided yet Ideal outcome (IMO) is one GO-CAM per biological process term Question centers on how much can be automatically done versus how much requires curators
- 59. Why Not justBioPAX? • Does not support automated inference • Does not enforce the use of ontologies to describe e.g. pathways, reactions • Does not have any concept of causal relationships • Actual implementations vary widely, forcing manual work to integrate • Does not align with GO model (OBO, OWL, etc.) • Happy to talk about a new BioPAX level 4...
- 60. What about mydb? Conversion code should in principle work on any BioPAX level 3 input. In practice different groups produce different BioPAX Successful tests on Pathway Commons for example pathways from KEGG, BioCyc, and WikiPathways
- 61. Causal edges providea scaffold for explanation that can be integrated and further explained using other relations, potentially from other knowledge bases that use the same ontologies Function Causes Function Function Causes Gene Product enables Gene Product enables Biological Process drug activates gene disease encodes Genetic association Has part
- 62. Automatic type:of Inference • ReactomeComplexes to GO Cellular Component terms from 0 to 2192 classified complexes - infinite improvement ! • When complete 2192/9476 • 23% complexes have some assignment to a GO term
- 63. GO-CAM element Ontologyor identifier source(s) Example Molecular activity GO molecular function ubiquitin-protein transferase activity (GO:0004842) Biological process GO biological process cellular response to UV (GO:0034644) Location GO cellular component nucleus (GO:0005634) Cell Type Ontology (CL) (8) retinal cell (CL: 0009004) anatomy ontologies, e.g. UBERON (9), C. elegans gross anatomy (10), EMAPA (11) eye (UBERON: 0000970) Active entity Gene, protein, RNA or complex identifier from a standard source, e.g. HGNC for a human gene NEDD4 (HGNC:7727) Biological phase GO biological phase (GO:0044848) mitotic G1 phase (GO:0000080) Developmental phase ontology, e.g. Mouse Developmental Stage Theiler stage 02 (MmusDv:0000005) Relations Relations Ontology occurs in (BFO:0000066)