Causal reasoning using the Relation Ontology
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The document discusses the need for standardized relationship types in biological data and ontologies. It provides an overview of the Relation Ontology (RO), which defines over 450 standardized relationship types organized hierarchically. RO provides a foundation for integrating multiple knowledge graphs and represents relationships in ontologies, linked data, and knowledge bases. It enables logical reasoning and inference across graphs through properties like transitivity.
![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](https://image.slidesharecdn.com/causal-reasoning-using-ro-2018-181015031000/85/Causal-reasoning-using-the-Relation-Ontology-32-320.jpg)
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
- 51. https://biolink.github.io/biolink- model/docs/PairwiseGeneToGeneInteraction.html
- 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