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dc.creatorWoods, John O.en
dc.creatorSingh-Blom, Ulf Martinen
dc.creatorLaurent, Jon M.en
dc.creatorMcGary, Kriston L.en
dc.creatorMarcotte, Edward M.en
dc.date.accessioned2014-12-15T17:10:58Zen
dc.date.available2014-12-15T17:10:58Zen
dc.date.issued2013-06-21en
dc.identifier.citationWoods, John O., Ulf M. Singh-Blom, Jon M. Laurent, Kriston L. McGary, and Edward M. Marcotte. “Prediction of Gene–phenotype Associations in Humans, Mice, and Plants Using Phenologs.” BMC Bioinformatics 14, no. 1 (June 21, 2013): 203. doi:10.1186/1471-2105-14-203.en
dc.identifier.urihttp://hdl.handle.net/2152/27959en
dc.descriptionAll authors are with the Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA. -- Ulf Martin Singh-Blom is with the Program in Computational and Applied Mathematics, The University of Texas at Austin, Austin, TX 78712, USA, and th Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, Stockholm 171 76, Sweden. -- Kriston L. McGary is with the Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA.en
dc.description.abstractBackground: Phenotypes and diseases may be related to seemingly dissimilar phenotypes in other species by means of the orthology of underlying genes. Such “orthologous phenotypes,” or “phenologs,” are examples of deep homology, and may be used to predict additional candidate disease genes. Results: In this work, we develop an unsupervised algorithm for ranking phenolog-based candidate disease genes through the integration of predictions from the k nearest neighbor phenologs, comparing classifiers and weighting functions by cross-validation. We also improve upon the original method by extending the theory to paralogous phenotypes. Our algorithm makes use of additional phenotype data — from chicken, zebrafish, and E. coli, as well as new datasets for C. elegans — establishing that several types of annotations may be treated as phenotypes. We demonstrate the use of our algorithm to predict novel candidate genes for human atrial fibrillation (such as HRH2, ATP4A, ATP4B, and HOPX) and epilepsy (e.g., PAX6 and NKX2-1). We suggest gene candidates for pharmacologically-induced seizures in mouse, solely based on orthologous phenotypes from E. coli. We also explore the prediction of plant gene–phenotype associations, as for the Arabidopsis response to vernalization phenotype. Conclusions: We are able to rank gene predictions for a significant portion of the diseases in the Online Mendelian Inheritance in Man database. Additionally, our method suggests candidate genes for mammalian seizures based only on bacterial phenotypes and gene orthology. We demonstrate that phenotype information may come from diverse sources, including drug sensitivities, gene ontology biological processes, and in situ hybridization annotations. Finally, we offer testable candidates for a variety of human diseases, plant traits, and other classes of phenotypes across a wide array of species.en
dc.description.sponsorshipen
dc.language.isoEnglishen
dc.publisherBMC Bioinformaticsen
dc.rightsAdministrative deposit of works to UT Digital Repository: This works author(s) is or was a University faculty member, student or staff member; this article is already available through open access at http://www.biomedcentral.com. The public license is specified as CC-BY: http://creativecommons.org/licenses/by/4.0/. The library makes the deposit as a matter of fair use (for scholarly, educational, and research purposes), and to preserve the work and further secure public access to the works of the University.en
dc.subjectphenotypesen
dc.subjectorthologyen
dc.subjectgenesen
dc.subjectorthologous phenotypesen
dc.subjectphenologsen
dc.titlePrediction of gene–phenotype associations in humans, mice, and plants using phenologsen
dc.typeArticleen
dc.description.departmentCenter for Systems and Synthetic Biologyen
dc.description.departmentInstitute for Cellular and Molecular Biologyen
dc.description.departmenten
dc.description.catalogingnotemarcotte@icmb.utexas.eduen
dc.identifier.Filename1471-2105-14-203.pdfen
dc.identifier.doidoi:10.1186/1471-2105-14-203en


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