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Related Experiment Video

Updated: May 24, 2026

A Phenotyping Regimen for Genetically Modified Mice Used to Study Genes Implicated in Human Diseases of Aging

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Published on: July 14, 2016

MouseFinder: Candidate disease genes from mouse phenotype data.

Chao-Kung Chen¹, Christopher J Mungall, Georgios V Gkoutos

¹Vertebrate Genomics Team, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.

|February 15, 2012

Summary

Mouse phenotype data aids disease gene discovery. A semantic approach and the MouseFinder web application improve mapping between human and mouse traits, identifying candidate genes like ARTN for hereditary ptosis.

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Area of Science:

Genetics
Bioinformatics
Genomic Medicine

Background:

Mouse phenotype data is crucial for identifying disease-associated genes, particularly when the molecular basis is unknown.
Systematic use of this data has been hindered by challenges in mapping human clinical features to mouse phenotype annotations.
Bridging this gap is essential for advancing genetic research and understanding disease mechanisms.

Purpose of the Study:

To develop a semantic approach for effectively mapping human clinical features to mouse phenotype data.
To create a user-friendly web application for accessing and utilizing this integrated data.
To demonstrate the utility of this approach in identifying novel disease-gene associations.

Main Methods:

Development of a semantic framework to standardize and compare human and mouse phenotype descriptions.
Creation of the MouseFinder web application (www.mousemodels.org) for querying whole-phenome comparisons.
Validation of the approach by assessing recall of known disease-gene associations and orthology relationships.

Main Results:

The semantic approach significantly improved the mapping accuracy between human and mouse phenotypes.
The MouseFinder application provides a searchable interface for exploring cross-species phenotype data.
The study successfully identified ARTN as a strong candidate gene for a hereditary form of ptosis located at the 1p34.1-p32 locus.

Conclusions:

A semantic approach effectively overcomes challenges in integrating human and mouse phenotype data for gene discovery.
The MouseFinder web application is a valuable tool for researchers investigating genetic basis of diseases.
This methodology enhances the identification of candidate genes and understanding of orthology relationships in human disease.