FAIR Agronomy, where are we? The KnetMiner Use Case
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Marco Brandizi and Keywan Hassani-Pak, Rothamsted Research, Invited Presentation at SWAT4HCLS 2022. FAIR data principles are being a driving force in life sciences and other scientific domains, helping researchers to share their data and free all of their potential to integrate information and do novel discoveries. Knowledge graphs are an ever more popular paradigm to model data according to such principles, and technologies such as graph databases are emerging as complementary to approaches like linked data. All of this includes the agronomy, farming and food domains. How advanced the adoption of sound data management policies is in these life domains? How does that compare to other life sciences? In this presentation, we will talk about our practical experience, focusing on KnetMiner, a gene and molecular biology discovering platform, which is based on building and publishing knowledge graphs according to the FAIR principles, as well as using a mix of linked data standards for life sciences and recent graph database and API technologies. We will welcome questions and discussions from the audience about similar experience.
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FAIR Agronomy, where are we? The KnetMiner Use Case
- 1. FAIR Agronomy, where are we? The KnetMiner Use Case SWAT4LS 2022 Marco Brandizi marco.brandizi@rothamsted.ac.uk Keywan Hassani-Pak keywan.hassani-pak@rothamsted.ac.uk Find this presentation on SlideShare background source: https://www.flickr.com/photos/60091022@N08/22004495993/
- 2. Hello! https://knetminer.com • Rothamsted Research is a non-profit research centre, focused on agricultural science, including farming, plant biology, statistics and bioinformatics, livestock management, entomology • 300 people, founded in 1843, international collaborations, national capabilities • Our team develops the KnetMiner gene discovery platform, offering knowledge graph based services for plants, pests and their interactions • We are part of the Computational and Analytical Sciences department, a community of experts in a wide range of analytical technologies and data analysis tools
- 3. What is the future for SW4LS? What can Agronomy learn from Biomedical SW? Network knowledge is thriving There is more than Semantic Web and Linked Data now Lightweight modelling is replac… adding to OWL Agrifood is leveraging biomedics experience background source: https://wallpapersafari.com/w/yAIwLx
- 4. Network knowledge is thriving
- 5. Network Knowledge is Thriving
- 6. Network Knowledge is Thriving Good Reads: • Recent trends in knowledge graphs: theory and practice, https://link.springer.com/article/10.1007/s00500-021-05756-8 • Wikidata: A large-scale collaborative ontological medical database, https://www.sciencedirect.com/science/article/pii/S1532046419302114 • KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response, https://doi.org/10.1016/j.patter.2020.100155 • Constructing and Mining Web-Scale Knowledge Graphs, https://fdocuments.net/reader/full/kdd14-t2-bordes-gabrilovich-3 • Graph Embeddings: The Secret Ingredient for Relationship-Driven AI, https://www.youtube.com/watch?v=-CscGHDXrZY
- 7. The KnetMiner Knowledge Graphs
- 8. The KnetMiner Knowledge Graphs
- 9. The KnetMiner Knowledge Graphs
- 10. The KnetMiner Knowledge Graphs OXL format
- 11. There is more than Semantic Web and Linked Data now
- 12. More than Semantic Web and Linked Data
- 13. The Hybrid KnetMiner KnetMiner ontology (BioKNO) knetminer.org rdf2pg tool +RDF exporter1 1) Based on https://github.com/EBIBioSamples/java2rdf
- 14. Exploiting both RDF and Cypher
- 15. Lightweight modelling is adding to OWL
- 16. Lightweight Data Modelling source: https://tinyurl.com/y52c263s source: https://www.schemaapp.com
- 17. Lightweight Data Modelling in KnetMiner and Beyond AgriSchemas ontology (BioKNO) knetminer.com
- 18. AgriSchemas
- 19. Use case Data Types Data Sources Status Molecular Biology Gene, Protein, Pathway encodes, participates Via Knetminer: ENSEMBL, UniProt, TILLING, wheat-expression.com, KEGG Done. Ontology Annotations Ontology Term (schema:DefinedTerm) dc:type, schema:additionalType Via Knetminer: GO, PO, CROP- Onto Done. Experiments Study, agri:StudyFactor, PropertyValue EBI/GXA, GLTen, MIAPPE/BrAPI sources, ? GXA Done MIAPPE, much work done during ELIXIR BioHackathon, going on with monthly calls GLTen use case drafted Literature agri:ScholarlyPublication mentions Via Knetminer: PubMed Done Gene Expression bioschema:expressedIn, reified statements, agri:evidence, agri:pvalue, agri:baseCondition EBI/GXA, Via Knetminer: wheat- expression.com GXA Host-pathogen interaction Gene, Phenotype, agri:ScholarlyPublication agri:HostPathogenInteraction agri:evidence PHI-Base Use case drafted Weather ? ? TO DO Dataset metadata Dataset, DataCatalog license, distribution knetminer.org/data ongoing
- 20. AgriSchemas Progress Use case Data Types Data Sources Status Molecular Biology Gene, Protein, Pathway encodes, participates Via Knetminer: ENSEMBL, UniProt, TILLING, wheat-expression.com, KEGG Done. Ontology Annotations Ontology Term (schema:DefinedTerm) dc:type, schema:additionalType Via Knetminer: GO, PO, CROP-Onto Done. Experiments Study, agri:StudyFactor, PropertyValue EBI/GXA, GLTen, MIAPPE/BrAPI sources, ? GXA Done MIAPPE, much work done during ELIXIR BioHackathon, going on with monthly calls GLTen use case drafted Literature agri:ScholarlyPublication mentions Via Knetminer: PubMed Done Gene Expression bioschema:expressedIn, reified statements, agri:evidence, agri:pvalue, agri:baseCondition EBI/GXA, Via Knetminer: wheat- expression.com GXA Host-pathogen interaction Gene, Phenotype, agri:ScholarlyPublication agri:HostPathogenInteraction agri:evidence PHI-Base Use case drafted Weather ? ? TO DO Dataset metadata Dataset, DataCatalog license, distribution knetminer.org/data ongoing
- 21. KnetMiner & AgriSchemas SELECT ?gene ?geneAcc ?condLabel ?studyTitle ?study ?pub ?pubTitle ?pubYear ?condTerm { ?gene a bioschema:Gene; dcterms:identifier ?geneAcc. # ?gene bioschema:expressedIn ?condition also available ?expStatement a rdfs:Statement; rdf:subject ?gene; rdf:predicate bioschema:expressedIn; rdf:object ?condition; agri:evidence ?study. ?pub schema:mentions ?gene. ?pub a agri:ScholarlyPublication; dcterms:title ?pubTitle. OPTIONAL { ?pub schema:datePublished ?pubYear } ?condition schema:name ?condLabel. OPTIONAL { ?condition dc:type ?condTerm. } ?study dc:title ?studyTitle; }
- 23. Agrifood is leveraging biomedics experience
- 24. Agrifood is leveraging biomedics experience Biomedics Agrifood Resources NCBI, EBI, BioPortal, Bio2RDF. Many used in plant biology too (eg, ENSEMBL, PubMED). Smaller similar projects, eg, AgroLD, AgroPortal, CGIAR/GARDIAN, WheatIS, more data integration needed. Ontologies Integration efforts by OBO. MolBio ontologies (eg, GO, CL), medical ontologies (eg, UMLS, PATO), experimental reporting ontologies (eg, OBI, SIO, EFO), specie classification (NCBITax). Significant overlapping (eg, GO, NCBITax), good coverage of plant biology (eg, Crop Ontology, Plant Trait Onto). Food-related ontologies (eg, FoodOn) and taxonomies (eg, AGROVOC). Coverage of different aspects (eg, animals, forestry). Lack of coverage? Eg, weather. Schemas and formats Established formats (eg, FASTA, ISA-Tab, CDISC), established standardisations (HL7, FHIR) ongoing standardization efforts (BioSchemas). Significant overlapping (eg, FASTA, ISA-Tab). More heterogeneous landscape, eg, MIAPPE and COPO not widely adopted, simpler solutions preferred (eg, Frictionless in DFW) Data Access Heterogeneous APIs, much use of SPARQL, but still limited. A few established APIs (eg, BrAPI, WheatIS), more desirable. Data and AI Much traditional machine learning, a number of graph embedding projects (eg, https://tinyurl.com/yxbau649, https://tinyurl.com/yyq9rdh9) ML for precision farming (eg, https://tinyurl.com/y5zopcd5). Some graph embedding projects (eg, https://tinyurl.com/yxt9lgk7, https://tinyurl.com/y3mnr2n2)
- 25. References • AgriSchemas • https://github.com/Rothamsted/agri-schemas • Use cases: https://github.com/Rothamsted/agri-schemas/tree/master/drafts/201904-dfw- hackathon • Real data & ETL tools: https://github.com/Rothamsted/agri-schemas/tree/master/dfw-dataset • Knetminer • Web site: http://knetminer.org • Publication: https://doi.org/10.1111/pbi.13583 • Case study about FAIR data: • https://knetminer.com/cases/the-power-of-standardised-and-fair-knowledge-graphs.html • FAIR data infrastructure: https://doi.org/10.1515/jib-2018-0023 • Data endpoint: http://knetminer.org/data • DFW • AgriSchemas and DFW: • https://designingfuturewheat.org.uk/dfw-and-fair-agriculture-data-the-knetminer- experience/ • Me • https://www.slideshare.net/mbrandizi, https://marcobrandizi.info/about-me/
- 26. Acknowledgements Ajit Singh Software Engineer • Samiul Haque, Ed Eyles, IT admins • Joseph Hearnshaw, software engineer • Louis Timberlake, visiting student • Alice Minotto, Earlham Institute, hosting providers • Robert Davey, Earlham Institute, DFW WP4 coordinator • William Brown, Ricardo Gregorio, IT admins • Monika Mistry, master Student, data Curator • Sandeep Amberkar, bioinformatician, data curator • Richard Holland, ext contractors, developers Keywan Hassani-Pak KnetMiner Team Leader Chris Rawlings Head of Computational & Analytical Sciences Jeremy Parsons Bioinformatics Scientist
Editor's Notes
- Our application is an example of gene exploration app over KGs. Knowledge is matched to search input and ranked based on search input plus biological significance (semantic motifs, later)
- Initially, and still quite so, based on ad-hoc workflow system (KnetBuilder) and ad-hoc KG format (OXL). So, KGs come in many ways.
- Many different knowledge graph databases exist, and also several ETL + data exchange formats. SPARQL alternatives exist for graph-like data access.
- We support both SPARQL and Cypher, they’ve different sets of pros/cons. RDF is at the base of modelling and ETL. We have developed the rdf2pg tool for combining the two.
- An example of Cypher benefits: keyword-found entities are matched to genes via well-known paths (semantic motifs). Initially, SM were based on a limited transition machine syntax, now we have a Cypher traverser, more expressive syntax.
- Everyone knows schema.org. Here, two examples of how rather commercial apps are leveraging it. schema.org and bioschemas are “lightweight”, easier to use and practical for application development. Moreover, they’re complementary to “true” ontologies like GO.
- What we do with schema.org and bioschemas: our ontology mapped to the standards, more datasets integrated, a new project, AgriSchemas, to extend bioschemas to the agrifood domain.
- A prototype with real datasets is on line. Here, a query that combine specific KnetMiner data (with several mappings and URI reuse) with EBI/GXA data mapped to AgriSchemas.
- And here the results. As you see, Knetminer info (eg, gene/pub associations computed via text mining) can be linked to GXA info (ie, gene expression), including ontology annotations. If you want to collaborate, we have ideas on how to exploit this combination for novel insights in wheat.