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MouseFinder: Candidate disease genes from mouse phenotype data
Author(s) -
Chen ChaoKung,
Mungall Christopher J.,
Gkoutos Georgios V.,
Doelken Sandra C.,
Köhler Sebastian,
Ruef Barbara J.,
Smith Cynthia,
Westerfield Monte,
Robinson Peter N.,
Lewis Suzanna E.,
Schofield Paul N.,
Smedley Damian
Publication year - 2012
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.22051
Subject(s) - biology , phenome , candidate gene , phenotype , gene , locus (genetics) , genetics , computational biology , disease , identification (biology) , pathology , medicine , botany
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.