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Despoina Antonakaki, Dasha Zhernakova, Erik Roos,

Towards an intelligent framework to quickly find data from distributed heterogeneous biomedical resources. Despoina Antonakaki, Dasha Zhernakova, Erik Roos, K Joeri van der Velde, Mark Kiestra ,Tomasz Adamusiak, Niran Abeygunawardena, Helen Parkinson, Rolf Sijmons, Morris A. Swertz.

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Despoina Antonakaki, Dasha Zhernakova, Erik Roos,

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  1. Towards an intelligent framework to quickly find data from distributed heterogeneous biomedical resources. Despoina Antonakaki, Dasha Zhernakova, Erik Roos, K Joeri van der Velde, Mark Kiestra,Tomasz Adamusiak, Niran Abeygunawardena, Helen Parkinson, Rolf Sijmons, Morris A. Swertz

  2. Biologists challenges: A web of data • Find data • Many different resources • local, structured – array express, free text – pubmed • Type in many search boxes • Google, NCBI/Entrez, EBI/EB-eye, KEGG/DBGET • Merge and pool data • Big excel file (trying to make headers fit) • Size of data • Working for weeks (map and match) Major problem : “Using Microsoft Word as sequence annotation tool”

  3. Informatics challenges: Too many silos… • Differences in terminology • Need to reach “hidden”, structured data : DB encapsulated, legacy • Different conceptualization of information • Differences in formats and structure • Too many formats, specifying & describing biomedical entities: • no standard representation model • Automatic matching and merging • Difficult to merge into single query • Working for weeks (map & match) • Query across silos Format 1 Format 2 Format3 DB1 DB2 DB3 …

  4. Wanted:‘meta’ search infrastructure toFind me casesFind me cohorts/partners Connecting different ‘ biobanks’? Celiac Disease query Local? National? EU? Global? LifeLines PSI TweelingReg GenerationR

  5. Outline • Three challenges for biologists’ and the corresponding for the Informatics’: • Merge and pool data - Differences in formats and structure • Find data - Differences in terminology • Size of data - Automatic matching and merging • Across data sets – All above + distribution • Approaches • Integrate data into one ‘pheno’ model (MOLGENIS) • Use ontologies (OntoCAT) • Indexing (Lucene) • Query expansion (Lucene + OntoCAT) • Discussion • Federated data queries (molgenis & rdf)

  6. Data warehouse, put it all in one place? Loading … Pheno-OM

  7. Pheno-OM data model Observable feature Flexible: any feature, value, and target combination Protocol * * Height time * * Observation target Observed value Ind1 179cm Protocol application * * time time Panel/cohort/Biobanks Individual Observed Relation Inferred Value * * http://wwwdev.ebi.ac.uk/microarray-srv/pheno/doc/objectmodel.html

  8. An example of excel data • Or bbmri-nl

  9. Use ontologies HPO: Abnormally shaped ears Auricular malformation Deformed auricles Deformed ears Malformed auricles Malformed ears Malformed external ears MP: Abnormally shaped ears Auricular malformation Deformed auricles Deformed ears Malformed auricles Malformed ears Malformed external ears To overcome different terminologies, two approaches: • Use ontologies to annotate the source • Of course depends on other parties • Use ontologies for query expansion (synonyms, part of, subclasses) Deformed ears? Abnormale shaped ears Ontologies with mappings Ontologies with mappings Pheno-DB Index Ontologies with mappings

  10. Outline • Three challenges for biologists’ and the corresponding for the Informatics’: • Merge and pool data - Differences in formats and structure • Find data - Differences in terminology • Size of data - Automatic matching and merging • Across data sets – All above + distribution • Approaches • Integrate data into one ‘pheno’ model (MOLGENIS) • Use ontologies (OntoCAT) • Indexing (Lucene) • Query expansion (Lucene + OntoCAT) • Discussion • Federated data queries (molgenis & rdf)

  11. Complexity in Ontologies To search across different ontologies requires expert knowledge ..sometimes they change unpredictably .. ..or sometimes they become suddenly unavailable ..

  12. Some facts… OWL API • NCBO Bioportal : • 204 ontologies , 29 REST signatures … • BUT : Rest signature change/break without notice , • OLS: 79 OBO ontologies, 16 web service signatures - stable, open, local • BUT: not as rich , rudimentary documentation • Individual user’s ontologies created • Integration is hard … EFO Bioportal Import OntoAPI Ontology Browser

  13. OntoCAT hides the complexityontocat.org

  14. Generic Ontology Service interface • Implemented in Java 6, • Open Source (LGPL v3), • Simple and easy-to-use API for BioPortal , OLS web services, OWL API (BioportalOntologyService, OlsOntologyService and FileOntologyService ). HPO NCBO Bioportal OBO files OLS (EMBL-EBI) BBMRI ontology OWL API

  15. Use case diagram of OntoCAT • Use case of a simplified user interaction with existing ontology resources through OntoCAT . • Web applications can connect using REST or SOAP services • R connect with Ontocat bioconductor

  16. Common workflow to integrate ontology resources

  17. Ontocat example :Find “membrane” term in multiple ontologies

  18. More examples available

  19. OntoCAT & Zooma use cases • Updating Ontology properties: • EFO involves construction of mappings to multiple domain specific ontologies (Disease, Cell Type) • Multithreading the Ontocat requests allows to process & import extra information • from over 20,000 external ontology terms in less that 10 minutes • Annotate user experimental values with ontology terms • Array Express Archive & Gene Expression Atlas >1 million unique experiment annotated from EBI’s version EFO • Not existing ones have to be checked against publicly available ontologies • Previously manual process now with Zooma (local EFO, OWL, local DBs) ??? Array express archive Gene Expression Atlas > 1 million unique experiment annotations ??? Not available in EFO ? ??? Annotate (ontology terms) EBI (pre release version of the application ontology EFO)

  20. OntoCAT & Zooma use cases • Local ontology management • eXtensive Genotype And Phenotype data platform (XGAP - Molgenis) : search widget Interactive annotation of data with ontology terms • Allows search publically available ontologies & download terms for unambiguous annotation of QTL or GWAS data. • Data analysis & annotation • New Bioconductor ready to read & query OWL/OBO into R . • Build in offline support for EFO & Bioportal ontology queries

  21. OntoCAT characteristics & tools • OntoCAT provides synonym & definition lookup across two major implemented ontology services • Supports interoperability using RDF • Class combining multiple ontology resources including different repositories behind single entry point (CompositeOntologyService) • Cache • Ranking • Prioritization • Fallback mechanism if ontology resource unavailable

  22. Demo on Google App Engine framework • http://ontocat-web.appspot.com

  23. Ontocat browser retrieving OLS http://gbic.target.rug.nl:8080/ontocatbrowser/molgenis.do?__target=main&select=OntocatBrowser

  24. OntoCAT’s applications • OntoCAT ontology mapping application: • http://zooma.sourceforge.net • OntoCAT Bioconductor/R package: • http://bioconductor.org/help/bioc-views/2.7/bioc/html/ontoCAT.html

  25. Outline • Three challenges for biologists’ and the corresponding for the Informatics’: • Merge and pool data - Differences in formats and structure • Find data - Differences in terminology • Size of data - Automatic matching and merging • Across data sets – All above + distribution • Approaches • Integrate data into one ‘pheno’ model (MOLGENIS) • Use ontologies (OntoCAT) • Indexing (Lucene) • Query expansion (Lucene + OntoCAT) • Discussion • Federated data queries (molgenis & rdf)

  26. Indexing: general features • Data structure overcomes barriers in large DB • created by using DB tables as basis for search • Efficient access of ordered records & rapid random lookup • Less disk space for storage (key fields) • Open source java library (known in internet search engines) • Full text indexing & searching capability • Format independent (documents & fields) • Query Expansion: • Add additional terms related (synonyms & children) appended by OR operator, assigned lower weight • Changes document ranking  order of retrieved docs • Even if query expansion doesn’t improve search, query more precise DB

  27. Indexing: the approach • Overcome the barriers of searching in large data size • Optimize the in memory representation, e.g. as a tree • Steps: • Create a new index and add documents (fields from DB, ontology terms from Ontocat) • Analyzer: extract tokens out of text to be indexed and eliminates the rest • Parser: Select Fields (term/value) • Tokenized? Indexed? Case sensitive? • Collect results def: "Paired, cup-shaped cartilage that are dorsal to the septomaxillae and anterior to the oblique cartilage. The anterior, convex face of each alary cartilage is synchondrotically fused to the superior prenasal cartilage and the ventral edge is fused to the superior margin of the crista intermedia." [AAO:LAP] related_synonym: "alinasal cartilage" [] related_synonym: "cartilago alaris" []related_synonym: "cartilago alaris nasi" []related_synonym: "cartilago cupullaris" [] [Term] id: AAO:0000289name: Meckel's_cartilage def: "Paired, rod-shaped elements that extend the length of the mandible and lie between the dentaries and the angulosplenials." [AAO:LAP] relationship: part_of AAO:0000274 ! lower_jaw_skeleton [Term] id: CHEBI:24431 name: molecular structure def: "A description of the molecular entity or part thereof based on its composition and/or the connectivity between its constituent atoms." [] Output results Septomaxillae 1. Analyze Query 2. Parse Index 3. Collect Results cartilago cupullaris Oblique cartilage. Tokenized?? Tokenized?? angulosplenias Enters search term index

  28. Indexing DB: implementation

  29. Outline • Three challenges for biologists’ and the corresponding for the Informatics’: • Merge and pool data - Differences in formats and structure • Find data - Differences in terminology • Size of data - Automatic matching and merging • Across data sets – All above + distribution • Approaches • Integrate data into one ‘pheno’ model (MOLGENIS) • Use ontologies (OntoCAT) • Indexing (Lucene) • Query expansion (Lucene + OntoCAT) • Discussion • Federated data queries (molgenis & rdf)

  30. CWA Query expansion HPO: Abnormally shaped ears Auricular malformation Deformed auricles MP: Malformed auricles Malformed ears Malformed external ears etc 32 Local ontologies (OLW or OBO) Deformed ears? query expansion Pheno Warehouse Abnormally shaped ears  BioPortal Deformed ears  OntoCAT – Ontology common API tasks http://www.ontocat.org and http://precedings.nature.com/documents/4666 OLS

  31. Query expansion details & ontology selection Ontologies used

  32. The expanded query & the results

  33. query: lung diseasesearching WITHOUT query expansion: relevant irrelevant absolutely irrelevant

  34. Indexing: implementation (ontocat) Lucene scoring uses a combination of the Vector Space Model (VSM) of Information Retrieval and the Boolean model to determine how relevant a given Document is to a User's query.

  35. query: lung diseasesearching WITH query expansion: the same relevant results new partly relevant results

  36. Outline • Three challenges for biologists’ and the corresponding for the Informatics’: • Merge and pool data - Differences in formats and structure • Find data - Differences in terminology • Size of data - Automatic matching and merging • Across data sets – All above + distribution • Approaches • Integrate data into one ‘pheno’ model (MOLGENIS) • Use ontologies (OntoCAT) • Indexing (Lucene) • Query expansion (Lucene + OntoCAT) • Discussion • Federated data queries (molgenis & rdf)

  37. Distributed querying in BBMRI Deformed ears? query RDF + OWL? Twin Registry BBMRI-SE Generation R LifeLines OntoCAT – Ontology common API tasks http://www.ontocat.org and http://precedings.nature.com/documents/4666

  38. Federated data queries (molgenis & rdf) • How to make Molgenis data distributed via RDF/SPARQL ? HPO: Abnormally shaped ears Auricular malformation Deformed auricles Deformed ears Malformed auricles Malformed ears Malformed external ears MP: Abnormally shaped ears Auricular malformation Deformed auricles Deformed ears Malformed auricles Malformed ears Malformed external ears Deformed ears? Abnormale shapedears DB ? SPARQL RDF DB Ontologies with mappings Ontologies with mappings Ontologies with mappings DB

  39. Discussion & next steps : distributed querying? • How to map a database to RDF such that it helps querying? • Diversity : all data molgenis’ pheno model .(+ quick - working offline , - have to update all the time) • Map to all distributed sources “on the fly”. (RDF & SPARQL ) • Agree on distributed query mechanisms (+ always up to date • - slow, breaks if sources go offline) • Investigate other project like Open Data • Can molgenis be part of open data?

  40. NL NL

  41. Thank you for your attention. Questions?

  42. Ontocat http://www.ontocat.org/ , • http://precedings.nature.com/documents/4666/version/1 • http://www.biomedcentral.com/imedia/1627447285460829_article.pdf • Guide/ examples http://www.ontocat.org/wiki/OntocatGuide • Available from : • http://gbic.target.rug.nl:8080/ontocatbrowser/molgenis.do?__target=main&select=OntocatBrowser • Ontocat Demo on Google App Engine framework : http://ontocat-web.appspot.com • Molgenis Lucene Index & query expansion app : • http://www.molgenis.org/svn/molgenis_projects/molgenis4phenotype/handwritten/java/plugins/LuceneIndex/ • Pheno-OM datamodel : http://wwwdev.ebi.ac.uk/microarray-srv/pheno/doc/objectmodel.html • XGAP: http://www.xgap.org

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