Browsing by Subject "phenologs"
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Item Approaches to discover new human disease models through Boolean relationships of orthologous phenotypes(2012-04-26) Tien, Matthew; Marcotte, EdwardIn the development of genome-wide databases of model organisms, non-traditional approaches to study genomic networks have emerged to model molecular interactions. Past approaches to model such systems have used the homology of genes in model organisms to study human diseases and conditions. Combining the homology of genes between human model organisms with information in genomic databases, the Marcotte laboratory has discovered a systematic approach to predict new candidate genes for human diseases. In characterizing phenotypes of model organisms with homologous genes, it is possible to reveal similar genetic interactions of homologous genes in human diseases. These phenotypes are characterized by the presence and absence of all orthologous genes between species, these binary data structures are called phenologs. The project below examined the potential of Boolean relationships of phenologs within one or multiple species to optimize the identified set of genes for a human disease and to predict more candidate genes involved in human disease.Item Prediction of gene–phenotype associations in humans, mice, and plants using phenologs(BMC Bioinformatics, 2013-06-21) Woods, John O.; Singh-Blom, Ulf Martin; Laurent, Jon M.; McGary, Kriston L.; Marcotte, Edward M.Background: 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.