Representing and reasoning with biological knowledge
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The document discusses the mission and evolution of the Gene Ontology (GO) Consortium, which aims to create a comprehensive computational model of biological systems across various species. It highlights the development of the ontology and its applications, such as automated inference and reasoning using OWL, to aid in understanding gene functions through complex models called GO-CAMs. The challenges faced in reasoning and querying data are also addressed, as well as acknowledgments to contributors in the field.
Representing and reasoning with biological knowledge
- 1. Reasoning with Biological Knowledge Benjamin Good Gene Ontology Consortium Lawrence Berkeley National Labs @bgood U.S. Semantic Technologies Symposium Series March 11-13, 2019 at Duke University in Durham, NC
- 2. Biological Knowledge • There is a lot: PubMed grows by more than 1 million articles a year Human Gene
- 3. Hard to answer important questions What are the functions of Fibronectin? My experiment suggested that 238 genes are doing something important. What are they doing? What processes are they enabling?
- 4. Vital to Structure Biological Knowledge Knowledge Bases convert textual and or expert knowledge into a form suitable for question answering
- 6. Gene Ontology (GO) Mission “The mission of 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
- 7. The Gene Ontology Ontology - 45k terms - 106k edges Biological Process Cellular Component Molecular Function Gene s G1 G2 G3 G4 G5 G6 Gene “Annotations”
- 8. GO Knowledge Base 2018 • More than 140,000 published papers referenced in … • More than 750,000 experimentally supported annotations ... • Used to infer more than 7,000,000 functional annotations for more than 3000 organisms. • >22,000 citations of original paper. GO Consortium. (2018) The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. gky1055
- 9. of reasoning with the GO
- 10. GO evolution timeline 1998 - flatfile 2000 - SQL database 2006 - OBO format, OBO-Edit 2012 - Ontology editors start working in OWL 2017 - GO annotations done natively in OWL as “GO-CAMs”. OWL reasoning now used throughout. ● Most people involved are biologists by training and objective. ● Adoption of Semantic Web approaches purely driven by application need.
- 11. From unconnected gene annotations to models of function NEDD4 Ubiquitin-protein ligase activity NEDD4 Nucleus NEDD4 Ubiquitin-dependent protein catabolic process GO-CAM NEDD4
- 12. Causal Activity Models “GO-CAMs” OWL framework for developing models of gene function of arbitrary complexity
- 13. Ontology of a GO-CAM Classes from GO (and other ontologies) are used to describe the types of nodes (OWL instances) 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 the asserted relationships
- 14. OWL Representation <http://model.geneontology.org/R-HSA-112409/007> a <http://purl.obolibrary.org/obo/GO_0005634> , <http://www.w3.org/2002/07/owl#NamedIndividual> ; <http://purl.org/dc/elements/1.1/contributor> "http://orcid.org/0000-0002-7757-3347" ; <http://purl.org/dc/elements/1.1/date> "2019-03-05" ; <http://purl.org/pav/providedBy> "https://reactome.org" ; <http://model.geneontology.org/R-HSA-112409/011> Ubiquitin-protein ligase activity Ligase activity Nucleus Cell ComponentClasse s Individuals <http://purl.obolibrary.org/obo/BFO_0000066> Occurs in Object Property Propertie s
- 15. OWL 2: annotatable axioms ECO_0000313: imported information used in automatic assertion <http://geneontology.org/lego/evidence> <http://model.geneontology.org/R-HSA-112409/0088> ECO_000311: imported information ● All claims annotated with terms from the Evidence Code Ontology ● And provenance: contributor, date, source, provider <dc:contributor> "http://orcid.org/0000-0002-7757-3347" “2019-03-05” "https://reactome.org" <dc:date> <pav:providedBy>
- 16. Everything is OWL Makes it possible to use general purpose OWL reasoners with thousands of axioms encoded in GO, RO, and other ontologies to : a. Find errors b. Infer relationships
- 17. Uses of OWL Reasoning • Editorial interface • Very large and complex set of ontologies • Automated consistency checking helps keep distributed and diverse curation community using same basic OWL representation • Type inference reduces curator burden • Import pipelines • Quality control • Type inference increases automated integration • Export pipelines • Inferred relations facilitate query
- 18. Consistency Checking • Prevent invalid inferences • Helps keep a distributed curation community using the same patterns • QC for import code Interface Turns red when OWL inconsistency is detected.
- 19. Cellular Component Inference Translesion synthesis by POLI
- 20. OWL Inference example ?Y Protein Complex DNA-Directed Polymerase Activity 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
- 21. Molecular Function Inference Connecting to the Gene Ontology automatically Showing imported data without any type assignment. Type (and thus connection to the ontology) is inferred automatically based on properties of the instance
- 25. Reasoning challenges ● Many challenges rooted in the very large T-box ○ In 2017, no open-source OWL reasoner could provide fast enough inferences for the Noctua application. Jim Balhoff had to write Arachne for it to work ■ = very high barrier to entry. ○ Explanations ■ Challenging UX even when “just” OWL inferences. ● We reason in OWL, Query in RDF, share as CSV, JSON, Neo4J, GPAD, GAFF, … would be nice to have a unified platform for the kb. ● NLP, ML for knowledge acquisition, curator assist
- 26. Acknowledgements A very long list 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
- 29. Building monsters GO:0086094 positive regulation of ryanodine-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
- 30. Pathway to GO- CAMReactome GO-CAM (OWL) BioPAX Series of rule- based transformations