Information-based methods for predicting gene function from systematic gene knock-downs |
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Authors: | Matthew T Weirauch Christopher K Wong Alexandra B Byrne Joshua M Stuart |
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Affiliation: | (1) Department of Biomolecular Engineering, University of California, 1156 High Street, Mail Stop: SOE2, Santa Cruz, CA 95064, USA;(2) Department of Molecular Genetics, The Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College St., Toronto, ON, M5S 3E1, Canada |
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Abstract: | Background The rapid annotation of genes on a genome-wide scale is now possible for several organisms using high-throughput RNA interference assays to knock down the expression of a specific gene. To date, dozens of RNA interference phenotypes have been recorded for the nematode Caenorhabditis elegans. Although previous studies have demonstrated the merit of using knock-down phenotypes to predict gene function, it is unclear how the data can be used most effectively. An open question is how to optimally make use of phenotypic observations, possibly in combination with other functional genomics datasets, to identify genes that share a common role. |
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