7.3 - Prototype discovery tool for mouse-human phenotype based on outputs of D7.1 and D7.2

Mouse models are a critical tool for translational researchers and access to rich information about phenotypes as well as genotype informs translational researchers in selecting a mouse strain for future experiments. However, different representation of human and mouse phenotypes, and understanding the relationships between measured phenotypes and disease presents a challenge in translation of mouse data to human researchers. Our chosen disease area is Type 2 Diabetes Mellitus (T2D) – previously referred to as non-insulin dependent diabetes or adult-onset diabetes, a common chronic disease affecting 9% of adults in 2014 that continues to increase in numbers and significance. We sought to improve the ontological representation of T2D and related phenotypes using both mouse and human phenotype ontologies and propose a rapid and lightweight process for ontology development enabling domain experts to contribute directly to ontology development without recourse to ontology editing tools. Our aim was to generate improved representation of disease associated phenotypes by providing an improved ontology as an input to a semantic matching service, PhenoDigm (http://www.sanger.ac.uk/resources/databases/phenodigm/), particularly for non-Mendelian diseases. PhenoDigm uses a semantic similarity measure to identify commonalities in phenotypes between strains of model species and calculates their overlap with clinical features of human disease. A phenotype score is given to capture how well a disease model recapitulates the phenotypes associated with a human disease population. In this it is limited by the available ontologies used for comparison. Deliverables 7.1 and 7.2 provided a workshop and an ontology (DIAB) for Type 2 Diabetes (T2D) and evaluated the use of the DIAB ontology annotation and query for TD2 data. Here we evaluate the ontology as an extension of PhenoDigm to test whether the ontology improves recall of mouse models of T2D using three gold standard T2D associated gene lists for the evaluation and comparing DIAB’s T2D associated phenotype list to DIAB subsets, and other available T2D associated phenotype datasets.