Causal reasoning using the Relation Ontology
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GREEKC Presentation on the OBO Relation Ontology and GO-CAMs http://obofoundry.org/ontology/ro.html
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Causal reasoning using the Relation Ontology
- 1. Causal Reasoning using the Relation Ontology Chris Mungall Lawrence Berkeley National Laboratory cjmungall@lbl.gov
- 2. Outline ● The need for an ontology of relations ● Tour of the Relation Ontology ● Use in GO Causal Inference ● Causal Relations for Diseases ● Integrating multiple knowledge graphs
- 3. Why we need relationship types
- 4. Why we need relationship types Melania Trump person Barack Obama person Michelle Obama person Vladimir Putin Russia Donald Trump person USA country country person
- 5. Why we need relationship types for biological data Gene C gene Gene B gene Disease X Gene A gene Disease Y disease disease
- 6. Why we need standardised relationship types for biological data B gene A gene INTERACTS_WITH B gene A gene physically interacts with B gene A gene binds database 1 database 2 database 3
- 7. Why we need standardised relationship types for biological data B gene A gene affects B gene A gene regulates B gene A gene PHOSPHORYLATES database 4 database 5 database 6
- 8. MeSH Why we need relationship types in biological ontologies
- 9. Application to linked data { “id”: “ENSEMBL:ENSG000000001”, “symbol”: “…” “type”: “gene”, “has_part” : [ { “id” : “ENSEMBL:ENST0000…”, “type” : “transcript”, “encodes” : { “id” : “ENSEMBL:ENSP....”, “type” : “protein”, …
- 10. Application to linked data { “id”: “ENSEMBL:ENSG000000001”, “symbol”: “…” “type”: “gene”, “has_part” : [ { “id” : “ENSEMBL:ENST0000…”, “type” : “transcript”, “encodes” : { “id” : “ENSEMBL:ENSP....”, “type” : “protein”, … {“@context”: { “symbol” : “rdfs:label”, “type” : “rdf:type”, “gene” : “http://purl.obolibrary.org/obo/SO_0000704”, “transcript” : “http://purl.obolibrary.org/obo/SO_0000673”, “has_part” : ???, “encodes” : ???, …
- 11. Relations are the glue for integration https://twitter.com/dhimmel/status/810996703901777920
- 12. OBO Relation Ontology ● An ontology of Relationship Types ◦ Hierarchically organized ● OWL provides mathematical-logical foundation ● Currently > 450 relations ◦ “Core” relations (e.g. part of) ◦ General purpose (e.g. has input) ◦ Domain-centric (e.g. phosphorylates) ● Originally used for relationships in ontologies ◦ Now used in Knowledge Graphs, Linked Data
- 17. Protégé View
- 18. Description Logics provide basis for logical reasoning ● TBox ◦ Classes and class axioms ⚫e.g. nucleus SubClassOf organelle, part_of some cytoplasm ◦ (Most ontologies are TBox-centric) ● ABox ◦ Instances and instance-level axioms ⚫e.g. patient123 has_sequence genome567 ◦ (Typically not asserted in ontologies) ● RBox ◦ Object Properties (aka Relations) ⚫e.g. part of is Transitive ◦ (RO is RBox-centric)
- 19. SubPropertyOf Axioms regulates positively regulates negatively regulates SubPropertyOfSubPropertyOf x positively regulates y ➔ x regulates y 606 SubPropertyOf Axioms
- 20. InverseOf Axioms regulates regulated by x regulates y ⬄ y regulated by x 105 InverseOf Axioms Note: relations often have an arbitrary canonical direction, properties of inverse is trivially inferred
- 21. Domain and Range expressed in material anatomical entity expressed in Domain: gene Range: material anatomical entity gene 221 Domain/Range Axioms BFO and OBO Core used for constraints
- 22. Characteristics ● Transitive ◦ x R y / y R z ➔ x R z ◦ Examples: part of, develops from ● Symmetric ◦ x R y ➔ y R x ◦ Examples: adjacent to ● Reflexive ● Anti-symmetric ● Functional 129 Axioms
- 23. SWRL (Semantic Web Rule Language) ● Examples: ◦ child_of(?x, ?y)∧brother_of(?y, ?z) ➔ has_uncle(?x, ?z) ◦ negatively_regulates(?x, ?y) ∧ negatively_regulates(?y, ?z) ➔ positively_regulates(?x, ?z) 18 SWRL Rules
- 24. Property Chains ● More compact way to write SWRL rules ◦ Uses function composition symbol ‘•’ ◦ Less expressive ◦ Examples: ⚫child_of • has_brother ➔ has uncle ⚫negatively regulates • negatively regulates ➔ positively regulates 139 Property Chain Axioms
- 25. RO Release Process ● All coordinated via GitHub ◦ Issues: https://github.com/oborel/obo-relations/isssues ◦ All changes proposed via Pull Requests https://github.com/oborel/obo-relations/pulls ◦ Validated by Travis-CI ◦ Merged by core editors ● All released are vetted ◦ Automatically ⚫ HermiT OWL Reasoner ⚫ ROBOT Release Tool ⚫ Ontology Development Kit Docker ◦ Manually https://github.com/INCATools/ontology-development-kit
- 26. RO Core ● Generic: apply across multiple domains ● E.g. ⚫every finger part of a hand ⚫every M phase part of a cell cycle ⚫Cambridge part of UK
- 27. General purpose and specific relations
- 28. RO for particular domains ● Ecology ◦ Biotic interaction relationships ● Anatomy ● Evolutionary Relationships ● Genome Features ● Causal activity models ● Disease causation
- 29. How RO is used ● Ontologies: ◦ Relationships between classes ◦ Widely used in OBO ● Knowledge Graphs: ⚫SPARQL endpoints ⚫Neo4J and other graph databases ⚫JSON-LD ⚫Relational Databases (e.g. GMOD/Chado)
- 30. Usage of RO in OBO ● Count of number of ontologies using each relation
- 31. Use of RO in Knowledge Graphs ● GO Causal Annotation Graphs ● Disease/Phenotype Graphs
- 32. GO’s initial attempts at causality GO:0086094 positive regulation of ryanodine-sensitive calcium-release channel activity by adrenergic receptor signaling pathway involved in positive regulation of cardiac muscle contraction Mungall’s law[??*]: an inexpressive bio-database schema will be abused to the maximum extent possible in order for curators to express complexities of biology [*] I have a feeling I’m not the first to express this GO:0086023 adenylate cyclase-activating adrenergic receptor signaling pathway involved in heart process subClassOf
- 34. GO Causal Activity Model (GO-CAM) RDF Graphs
- 37. RO axioms support inference across graphs of individuals grk-2 Cele#59dc728000000288 acts_upstream_of_positive_effect G-protein coupled serotonin receptor activity#59dc728000000347 Arachne Reasoner: https://github.com/balhoff/arachne
- 39. Reaction -> Activity BMP2 binds to the receptor complex (Reactome) GO:0005160 Mapping rules
- 40. Pathway -> Causal Activity Model
- 41. Causal Activity Flow Clathrin-mediated endocytosis
- 42. OWL Reasoning Infers more specific GO assignments using GO OWL axiomatization
- 43. Shortcut Relations and inference rules unify perspectives Any GO kinase activity Any GO activity GeneProduct1 GeneProduct2 directly regulates enabled by enabled by phosphorylates GO-CAM View (activity centric, semantics on nodes) entity- centric (SIF, CausalTab, ..)
- 45. Future Applications ● Boolean Modeling ● Causal Gene Set Enrichment
- 46. MONDO: Monarch Disease Ontology ● Unifies multiple disease resources ● Diseases as states ● Diseases have causal basis in ◦ disruption of a process ◦ dysfunction of a structure, causing disruption of a biological process ● Diseases have features ◦ also causally linked
- 49. Unifying multiple knowledge graphs ● KGs emerging as popular ML representation ◦ node embedding, NNs, link prediction ● Challenge ◦ combining different KGs together ● Different standards ◦ RO/OBO ◦ Wikidata ◦ SIO http://sio.semanticscience.org/ ◦ Many KGs have no standards, ad-hoc relations ⚫ e.g. SemMedDB
- 54. Conclusions ● Standardized relations required for ◦ ontologies ◦ knowledge graphs ◦ bioinformatics exchange formats ● RO provides ◦ Broad set of relations ◦ Different use cases ◦ OWL axiomatization enables inference ● Uses ◦ GO ◦ Disease and phenotype
- 55. Acknowledgments ● Relation Ontology ◦ Matt Brush ◦ David Osumi-Sutherland ◦ James Overton ◦ Jim Balhoff ◦ Suzanna Lewis ◦ Anne Thessen ◦ Mike Sinclair ◦ David Hill ◦ Kimberley Van Auken ◦ Larry Hunter ◦ Barry Smith ◦ Alan Ruttenberg ◦ Melissa Haendel ◦ Paul Thomas ● MONDO ◦ Nicole Vasilevsky ◦ Peter Robinson ◦ EBI curators ◦ GARD curators ◦ ClinGen curators ● BioLink ◦ Harold Solbrig ◦ Deepak Unni ◦ Seth Carbon ◦ Gregg Stuppe ◦ Laurent-Phillipe Albou ◦ Tim Putman ◦ Kent Shefchek ◦ Chris Bizon ◦ Michel Dumontier ◦ Lance Hannestad ◦ Richard Bruskiewich