Semantics as a service at EMBL-EBI
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This document discusses using ontologies to make biological and biomedical data more interoperable and FAIR (Findable, Accessible, Interoperable, Reusable). It describes several ontology services and tools provided by EMBL-EBI to help with tasks like annotating data, mapping data to ontologies, searching and accessing ontologies, and publishing structured data. It also uses the example of the BioSamples database to illustrate challenges in working with large, heterogeneous datasets and how ontologies can help address issues like normalizing descriptions and attributes to enable better searching and data integration.
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Semantics as a service at EMBL-EBI
- 1. Simon Jupp Samples, Phenotypes and Ontologies EMBL-EBI Semantic services for data interoperability Elixir all hands meeting Interoperability workshop March 2017
- 2. Ontology services as building blocks for FAIR • You need standards (ontologies and controlled vocabularies) to make data interpretable • Interpretable data is more readily interoperable • We can use interoperable data to build integrated systems that make the data more findable by user • The data become reusable when we use common standards • But, • There are a lot of standards • Doing this at scale for different domains is hard
- 3. Improving Findability by greater InteroperabilitySmarter searching Data analysis Data integration Data visualisation
- 4. BioSamples case study • description of material of biological interest • may be linked to assay data • sequencing, microarray, • proteomics • also imaging, etc • We’ve been making this data FAIR for many years
- 5. The challenge - thousands of data attributes… • BioSamples is an example of real world experimental metadata • We see all the variability – warts and all • Good play ground for building tooling to cleanup and add values to this data • If we can build tooling that works for BioSamples – they’ll work anywhere!
- 6. What are the disease attributes? diseaseState hostDisease clinicallyAffectedStatus diagnosis Infection diseaseStatus healthState disease clinicalInformation hostHealthState affectedBy causeOfDeath NOT: diseaseStage: info about the stage of a disease e.g. "48 hai”, “stage”, “terminal” diseasestage tumorStatus:"non-tumor",120, "Tumor",100,"CSL +/+ Xenograft Tumor 1st", healthStatus: "normal","Allergic","stressed”,"NA(Not immunized)"
- 7. Makes finding the right data hard
- 8. Normalising sample descriptions through annotation with ontologies CL:CL_0000071 (blood vessel endothelial cell) obo:CHEBI_39867 (valproic acid) NCBITaxon:NCBITa xon_9606 (Homo Sapiens) Curation
- 9. Ontology challenges • How do I access ontologies? • How do I map data to ontologies? • Which ontologies should I use? • What about data that doesn’t map? • How can I translate from one ontology to another? • How can I extend an ontology? • How do I build “ontology aware” search applications? • How do I publish this data?
- 10. SPOT team - Adding value with ontologies Data Exploration and Cleanup Data structuring Ontology Annotatio n Data cleaning and mapping Ontology building FAIRified data
- 11. Data Enrichment Services • Building an interoperability toolkit for Europe (Elixir) • Integrated (linked) APIs • Plumbing for data curation systems and workflows • Lowering the barrier of entry to ontologies for data stewards New ontology lookup service!
- 12. The Ontology Toolkit Search/Visualise ontologies Annotate data Ontology cross mapping Create new ontology content Webulous
- 13. Ontology Lookup Service • Ontology search engine • Ontology term history tracking • Ontology visualisation • Powerful RESTful API Repository of over 160 pre-selected biomedical ontologies (4.5 million terms) http://www.ebi.ac.uk/ols • Provides unified mechanism to access multiple ontologies • Large community of users, 10s of millions of hits per month • Open source and dockerised
- 14. Zooma • Optimal mappings based on data we have seen previously • Favours precision over recall • Captures annotations + context – context is v. important • Currently contains over 92,000 annotations from 7 resources • ClinVar, Cellular Phenotype Database, ExpressionAtlas, UniProt, GWAS, EBiSC, OpenTargets • Used to improve and share their mappings across resources Repository of curated ontology mappings http://www.ebi.ac.uk/spot/zooma “Heart” UBERON:0000948 A Zooma Mapping + Context (where, when, why?)
- 15. New for 2017 – Ontology Cross Mapping • Cross-references are a powerful tool for integrating data • A lot curator effort in building ontology cross-references • Currently hard to find/explore Ontology Mapping space Datasource 1 Datasource 2 Human Phenotype Ontology SNOMED-CTMappings
- 16. Ontology Mapping Service (OxO) • UI and API to expose known mappings from OBO, UMLS and manually curated mappings sets (e.g. GWAS, OpenTargets) • Normlaised CURIE prefixes using identifiers.org • SNOMED-CT: / SNOMEDCT: / SNOMED: / SNOMEDCT_ • Provides a “silver standard” to support predictive mapping algorithms * Going live March 2017 http://www.ebi.ac.uk/spot/oxo *
- 17. Common questions • How do I access ontologies? • How do I map data to ontologies? • Which ontologies should I use? • What about data that doesn’t map? • How can I translate from one ontology to another? • How can I extend an ontology? • How do I build “ontology aware” search applications? • How do I publish this data?
- 18. Data Get the application ontology from OLS Building a search index with BioSolr Publishing structured data as RDF Yes No Yes No Yes No Webulous OBO foundry Create a new term Add mappings back to Zooma No Is the data annotated to ontologies? Is there unmapped data? Can you find terms in OLS? Is it the ontology want? Yes Data annotation workflow Search Zooma Search OLS Search OxO
- 19. Summary • Part of FAIR process will be alignment with standards • Already many standards and ontologies in use • We build tools and services that help get you there • You will have to do some curation • But our tooling can capture that so we can share the burden • How FAIR is FAIR enough? • We’ll never FAIRify all of BioSamples • Decide what your application is and optimise for that
- 20. Ontology team Helen ParkinsonTony Burdett Sira SarntivijaiOlga Vrousgou Thomas Liener Funding • EMBL • CORBEL This project receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654248. • EXCELERATE ELIXIR-EXCELERATE is funded by the European Commission within the Research Infrastructures programme of Horizon 2020, grant agreement number 676559.