Mouse models can aid the development and testing of hypotheses in various scientific fields, and they are a critical tool for translational researchers for example in drug development. This data is made even more impactful due to its rich annotations which allow it to be mapped to annotated human phenotype data (via HPO mappings, as shown in the DIAB ontology).
Access to rich information about phenotypes as well as genotype supports the selection of suitable mouse strains for experiments. However, differences in the representation of human and mouse phenotypes and the need to understand the relationships between phenotypes and disease presents a challenge in translation of mouse data to human researchers.
Focussed on our chosen disease area, Type 2 Diabetes Mellitus (T2D, previously referred to as non-insulin dependent diabetes or adult-onset diabetes), a common chronic disease, Phenomap provides an interface that allows users unfamiliar with mouse models to filter and display mouse phenotyping information according to their specific research interests.
Phenomap employs semantic web technologies to enable the integration of systemic phenotype mouse data from IMPC, MGI together with data from single mouse clinics. For example, researchers can ask questions such as “Which alleles are related to phenotypic alteration in the Diabetes relevant IPGTT procedure and has been validated by mouse phenotyping experts and statistical analysis?”.
These user interfaces may be enhanced in the future to leverage the semantic richness made possible by the work done in BioMedBridges deliverable 4.6.