Phenobridge WP 7. Crossing the species bridge between mouse and human. 17 February 2015, Helmholtz Zentrum München

Transcription

1 Phenobridge WP 7 Crossing the species bridge between mouse and human 17 February 2015, Helmholtz Zentrum München Michael Raess on behalf of the WP7 collaborators

2 Who is WP7? Helen Parkinson, Nathalie Conte, Terry Meehan, Gautier Koscielny - European Bioinformatics Insitute Michael Raess - INFRAFRONTIER GmbH Frauke Neff, Philipp Gormanns, Christoph Lengger, Manuala Östereicher, Elida Schneltzer, Christine Schütt, Martin Hrabě de Angelis - Helmholtz Zentrum München Heimo Müller, Natalie Bordag, Kurt Zatloukal Medical University Graz Lucia Banci, Claudia Andreini, Leonardo Tenori, Claudio Luchinat CIRMMP Harald Staiger, Andreas Fritsche - Diabetes Experts, Medical University of Tübingen

3 Work package goal WP7-Phenobridge aims to bridge the gap between the phenotype information available from mouse model studies and from human clinical data in the field of obesity and diabetes, enabling a new level of interspecies analysis of disease datasets Linking BBMRI, ELXIR, INFRAFRONTIER, INSTRUCT

4 What are the Phenobridge objectives? Identify and develop a set of annotations, necessary terminologies, and mappings between terminologies for human and mouse models of diabetes and obesity Identify and group related interacting parameters in human and mouse which are involved in the development of clinical and molecular phenotypes Formalise rules for phenotypic annotation in human and mouse to work towards automation of phenotypic discovery and develop a related prototype service

5 How are we meeting our objectives? Identify and develop a set of annotations, necessary terminologies, and mappings Manual review of DO, HPO, OMIM, EFO by diabetes and ontology experts Development of a merged and extended component of DO, HP and Orphanet to incorporate diabetes phenotypes, frequency of symptoms and disease progression Identification of type 2 diabetes phenotype associations using data mining of diabetes journal abstracts, leading to a list of >570 potentially relevant MP and HP terms

6 How are we meeting our objectives? Identify and group related interacting parameters Two reviews of the terms obtained by the text mining approach, involving diabetes experts at the Helmholtz Zentrum München and the University of Tübingen Exclusion of clinically irrelevant terms Organisation of relevant terms into disease stage categories (IFG/IGT=Prediabetes, Manifest Diabetes, Symptoms, Consequences/ Complications, Type 1 / Type 2 diabetes, Associations with other diseases)

7 Expert curated list of HP and MP terms relevant to diabetes

8 HP MP New Total terms terms terms terms Diabetes Cause IFG/IGT (Prediabetes) Manifest Diabetes Diabetes Symptom Consequences/ Complications Type 1 Diabetes Type 2 Diabetes Assoc. w/ other diseases too Total (any temporal stage)

9 How are we meeting our objectives? Identify and group related interacting parameters Transforming the classification into the DIAB ontology using OWL Co-annotation of mouse and human datasets with the DIAB ontology BMB partners sample datasets Metabolights dataset Biosamples dataset

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11 How are we meeting our objectives? Identify and group related interacting parameters Transforming the classification into the DIAB ontology using OWL Co-annotation of mouse and human datasets with the DIAB ontology BMB partners sample datasets Metabolights dataset Biosamples dataset

12 Partner sample datasets maping to DIAB ontology results Ontology ID Term Pre-Diabetic Manifest Diabetes Consequences Complications Associate with other disease (and diabetes) Diabetes Cause Diabetes Symptom Type 1 Diabetes Type 2 Diabetes WP7 Data HP_ cirrhosis x x x x x Graz Mouse MP_ carcinoma x x x x x x Graz Mouse HP_ cholestasis x x x x Graz Mouse HP_ hepatic steatosis x x x x x Graz Mouse HP_ cirrhosis x x x x x Graz Human MP_ carcinoma x x x x x x Graz Human HP_ cholestasis x x x x Graz Human HP_ hepatic steatosis x x x x x Graz Human HP_ hepatitis x x x x x Graz Human MP_ obese x x x x x x Florence Human HP_ obesity x x x x x x Florence Human HP_ hypertension x x x x x x x Florence Human MP_ diabetes x x x x x x x Florence Human

13 Biosample data mapping to DIAB ontology statistical summary Type of Mapping Human Mouse % of each Average (true or false) sample % per type sample % per type type mapping number per type Type 2 True Type 1 True Diabetes True False pos False

14 How are we meeting our objectives? Work towards automation of phenotypic discovery (D7.3) Tool development: M3: Mining mouse models Tool development: Bridging genomes systematic mapping between syntnic regions in mouse and humans Test DIAB ontology with PhenoDigm

15 M3 Mining Mouse Models Intuitive access to mouse phenotyping data Various entry points (e.g diseases, genes, interactions) Integration with EBI RDF resources (together with WP4)

16 Bridging Genomes systematic mapping between syntenic regions Synteny: two or more genomic regions are derived from a single ancestral region Mapping between syntenic regions in mouse and humans allows discovery of funtional conservation and helps prioritise candidate genes / regions Provides integrated genetic and phenotypic data from different resources (Ensembl, GWAS catalogue, MGI, IMPC) Retrieves syntenic regions, associated overlapping or nearest genes, regulatory elements, phenotypic annotations

17 Bridging Genomes systematic mapping between syntenic regions

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19 Impact Comprehensive diabetes specific DIAB ontology for capturing mouse and human phenotypes Mapping different datasets to DIAB shows that relevant datasets are being captured Publication on DIAB development process is in preparation Tool development to access and integrate genotypic and phenotypic data in mouse and human

20 Sustainability DIAB Ontology RI to carry the tool/service/resource forward: INFRAFRONTIER / ELIXIR-EBI DIAB ontology will be integrated in the INFRAFRONTIER and IMPC resource databases INFRAFRONTIER / ELIXIR-EBI will continue DIAB development M3 tool will be maintained by INFRAFRONTIER Bridging genomes tool will be maintained by ELIXIR-EBI

21 Lessons learned Small focused workshops with domain experts work well to receive concise feedback on existing ontologies and their coverage of particular disease areas The methodology applied for building the DIAB ontology (data mining > expert curation of terms > ontology building) can be transferred to other disease areas