HIPBI-RD Harmonising phenomics information for a better interoperability in the RD field

HIPBI-RDHarmonising phenomics information for a better interoperability in the RD field

Ana Rath, Annie Olry, Marc Hanauer, Peter Robinson, Sebastian Khler, Michael Brudno, Helen Parkinson, Simon Jupp,Tudor Grozaana.rath@inserm.fr Human Variome Project 6th Bienal meeting UNESCO, ParisJune 2016

www.orpha.net

HIPBI-RDAims:Contribute to harmonisation of phenotype annotationsContribute to interoperability between patient-centered resources and genetic-centered resourcesWe aim to provide the community with an integrated, RD-specific informatics ecosystem that will harmonize the way phenomics information is stored in databases and in patient files worldwide, and thereby contribute to interoperability. This ecosystem will consist of a suite of tools and ontologies, optimized to work together, and available to clinicians and scientists through commonly used software repositories. Additionally, the ecosystem will contribute to the interpretation of variants identified through exome and full genome sequencing by harmonizing the way phenotypic information is collected, thus improving diagnostics.

Application submitted; 2 succesful rounds of evaluation.Building a bioinformatics ecosystem allowing for refining phenotype-diseases annotations and curation, improvement of ontological resources via semantic interpretation of literature and crowdsourcing. Integration to genes in ORDO allowing for genotype-phenotype correlations. Re-use in the Exomizer, the Phenomizer and available for re-use in databases, matchmakers, etcThe Hipbi-RD ecosystem will consist of a suite of tools and ontologies, optimized to work together, and available to clinicians and scientists through commonly used software repositories, such as GitHub. It builds on our two existing ontologies: ORDO and HPO, which are promoted as reference ontologies in the field of RD by the IRDiRC (International Rare Diseases Research Consortium), the broadly used PhenoTips tool used for collecting structured patient data and the PhenomeCentral matchmaker portal, and expands tools developed by Bio-Lark and the European Bioinformatics Institute (EBI).This proposal therefore aims to provide the community with an integrated, RD-specific informatics ecosystem that will harmonize the way phenomics information is stored in databases and in patient files worldwide, and thereby contributing to interoperability between databases, registries and biobanks. 2

Specific objectivesIntegrate Human Phenotype Ontology (HPO) and Orphanet Rare Disease Ontology (ORDO). Have ORDO & HPO translated in 7 languages.Enrich rare disorder annotations via automated concept recognition (literature, records). Enrich rare disorder annotations via Crowdsourcing. Improve algorithms to allow clinical and genetic differential diagnosis. Improve Phenomizer and Genomizer

Orphanet scientific contentUniProtReactome

OMIMICD10ICD11UMLSMedDRASNOMED CTgeneOMIMHGNCIUPHARensemblGenatlas

SAAAAAORPHA

ICF-CY derivedtermsA

A. Rath, A. Olry, M. Hanauer, Inserm, US14, Paris France

Orphanet Rare Diseases Onntology (ORDO)Orphanet & European Bioinformatics Institute (EBI)

Helen Parkinson, Simon Jupp, EMBL-EBI, Hinxton, UK

Peter Robinson, Sebastian Khler, La Charit, Berlin, Germany

HPO controlled vocabulary and the Phenomizerhttps://compbio.charite.de/phenomizer/

HPO annotations of Orphanet Rare Disorders

Around 6,000 rare disorders

HPO phenotypesFrequenciesDiagnostic criteriaObligate (100%)Pathognomonic signVery frequent (99-80%)Diagnostic criterionFrequent (79-30%)Occasional (295%)Very rare (1-4%)Absent 0%

Annotated by OrphanetDisseminated by Orphanet and HPO

2,340 rare disorders

5,750 phenotypes49,600 annotations

ORDOHPOOrphanet HPO

Ontologies integratedBio-named entity recognitionPhenomeCentral

PhenomizerExomizer

Hipbi-RD ecosystemLiteratureRe-usePhenoTipsCrowdsourcingOKMProjet HIPBI-RD

Application submitted; 2 succesful rounds of evaluation.Building a bioinformatics ecosystem allowing for refining phenotype-diseases annotations and curation, improvement of ontological resources via semantic interpretation of literature and crowdsourcing. Integration to genes in ORDO allowing for genotype-phenotype correlations. Re-use in the Exomizer, the Phenomizer and available for re-use in databases, matchmakers, etc10

Bio-named recognitionBio-LarK CR

Tudor Groza , Garvan Institute, Australia

https://phenotips.org/

Michael Brudno, SickKids Hospital, Toronto, Canada

In conclusionHaving structured data in databases (but also in the healthcare setting) is a cornerstone to foster research by allowing for agregation of interoperable data.

Providing an integrated, computable ontologies ecosystem will help to standardisation and harmonisation of phenotypic and clinical annotations.

The ecosystem will contribute to the interpretation of variants identified through exome and full genome sequencing by harmonizing the way phenotypic information is collected, thus improving diagnostics.

Thank you for your attention!