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Related Experiment Video

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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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PhenoHM: human-mouse comparative phenome-genome server.

Divya Sardana1, Suresh Vasa, Nishanth Vepachedu

  • 1Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA.

Nucleic Acids Research
|May 29, 2010
PubMed
Summary

PhenoHM enables cross-species gene discovery by linking mouse and human phenotypes. This comparative phenome-genome approach aids in identifying genes associated with similar traits and potential human disease causes.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Comparative Biology

Background:

  • Understanding gene function across species is crucial for comparative genomics.
  • The Mammalian Phenotype Ontology (MPO) and Unified Medical Language System (UMLS) are key resources for mouse and human phenotype data, respectively.

Purpose of the Study:

  • To develop a resource for cross-species identification of genes associated with orthologous phenotypes.
  • To facilitate the discovery of novel disease-causing genes by comparing human and mouse data.

Main Methods:

  • Reciprocal mapping of terms from the Mammalian Phenotype Ontology (MPO) to human disease concepts in the Unified Medical Language System (UMLS).
  • Cross-mapping of mouse-human phenotype terms to identify implicated genes.
  • Extrapolation of phenotype-gene associations between species using the PhenoHM server.

Main Results:

  • PhenoHM provides a server for human-mouse comparative phenome-genome analysis.
  • The resource enables rapid identification of genes associated with similar phenotypes in both species.
  • Facilitates the identification of potential novel disease causal genes.

Conclusions:

  • PhenoHM serves as a valuable resource for comparative genomics and understanding gene function.
  • Cross-species phenotype analysis aids in identifying conserved genetic underpinnings of diseases.
  • The PhenoHM server supports the discovery of novel disease genes through integrated human and mouse data.