MouseFinder: Candidate disease genes from mouse phenotype data

C. K. Chen; C. J. Mungall; G. V. Gkoutos; S. C. Doelken; S. Kohler; B. J. Ruef; C. Smith; M. Westerfield; P. N. Robinson; S. E. Lewis; P. N. Schofield; D. Smedley

doi:10.1002/humu.22051

MouseFinder: Candidate disease genes from mouse phenotype data

C. K. Chen, C. J. Mungall, G. V. Gkoutos, S. C. Doelken, S. Kohler, B. J. Ruef, C. Smith, M. Westerfield, P. N. Robinson, S. E. Lewis, P. N. Schofield, D. Smedley

Cancer and Genomic Sciences

Research output: Contribution to journal › Article › peer-review

47 Citations (Scopus)

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.

Original language	English
Pages (from-to)	858-866
Number of pages	9
Journal	Human Mutation
Volume	33
Issue number	5
Early online date	16 Mar 2012
DOIs	https://doi.org/10.1002/humu.22051
Publication status	Published - 1 May 2012

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title = "MouseFinder: Candidate disease genes from mouse phenotype data",

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.",

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AU - Chen, C. K.

AU - Mungall, C. J.

AU - Gkoutos, G. V.

AU - Doelken, S. C.

AU - Kohler, S.

AU - Ruef, B. J.

AU - Smith, C.

AU - Westerfield, M.

AU - Robinson, P. N.

AU - Lewis, S. E.

AU - Schofield, P. N.

AU - Smedley, D.

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Y1 - 2012/5/1

N2 - 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.

AB - 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.

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DO - 10.1002/humu.22051

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JO - Human Mutation

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ER -

MouseFinder: Candidate disease genes from mouse phenotype data

Abstract

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