MouseFinder: Candidate disease genes from mouse phenotype data

Authors

  • Chao-Kung Chen,

    1. Vertebrate Genomics Team, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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    • Both authors contributed equally to this work.

  • Christopher J. Mungall,

    1. Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California
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    • Both authors contributed equally to this work.

  • Georgios V. Gkoutos,

    1. Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom
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  • Sandra C. Doelken,

    1. Computational Biology Group, Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
    2. Research Group Mundlos, Max Planck Institute for Molecular Genetics, Berlin, Germany
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  • Sebastian Köhler,

    1. Computational Biology Group, Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
    2. Bioinformatics, Berlin Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Berlin, Germany
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  • Barbara J. Ruef,

    1. ZFIN, Institute of Neuroscience, University of Oregon, Eugene, Oregon
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  • Cynthia Smith,

    1. The Jackson Laboratory, Bar Harbor, Maine
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  • Monte Westerfield,

    1. ZFIN, Institute of Neuroscience, University of Oregon, Eugene, Oregon
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  • Peter N. Robinson,

    1. Computational Biology Group, Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
    2. Research Group Mundlos, Max Planck Institute for Molecular Genetics, Berlin, Germany
    3. Bioinformatics, Berlin Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Berlin, Germany
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  • Suzanna E. Lewis,

    1. Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California
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  • Paul N. Schofield,

    1. The Jackson Laboratory, Bar Harbor, Maine
    2. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
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  • Damian Smedley

    Corresponding author
    1. Vertebrate Genomics Team, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
    • European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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  • For the Deep Phenotyping Special Issue

Abstract

Mouse phenotype data represents a valuable resource for the identification of disease-associated genes, especially where the molecular basis is unknown and there is no clue to the candidate gene's function, pathway involvement or expression pattern. However, until recently these data have not been systematically used due to difficulties in mapping between clinical features observed in humans and mouse phenotype annotations. Here, we describe a semantic approach to solve this problem and demonstrate highly significant recall of known disease–gene associations and orthology relationships. A Web application (MouseFinder; www.mousemodels.org) has been developed to allow users to search the results of our whole-phenome comparison of human and mouse. We demonstrate its use in identifying ARTN as a strong candidate gene within the 1p34.1-p32 mapped locus for a hereditary form of ptosis. Hum Mutat 33:858–866, 2012. © 2012 Wiley Periodicals, Inc.

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