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Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Broad network-based predictability of Saccharomyces cerevisiae gene loss-of-function phenotypes. | Method Open Access Broad network-based predictability of Saccharomyces cerevisiae gene loss-of-function phenotypes Kriston L McGary Insuk Lee and Edward M Marcotte Addresses Center for Systems and Synthetic Biology Institute for Cellular and Molecular Biology University of Texas at Austin 2500 Speedway Austin Texas 78712 USA. Department of Chemistry Biochemistry University of Texas at Austin 2500 Speedway Austin Texas 78712 uSa. Correspondence Edward M Marcotte. Email marcotte@icmb.utexas.edu Published 5 December 2007 Genome Biology 2007 8 R258 doi l0.ll86 gb-2007-8- l2-r258 The electronic version of this article is the complete one and can be found online at http genomebiology.com 2007 8 12 R258 Received 24 July 2007 Revised 16 October 2007 Accepted 5 December 2007 2007 McGary et al. licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract We demonstrate that loss-of-function yeast phenotypes are predictable by guilt-by-association in functional gene networks. Testing 1 102 loss-of-function phenotypes from genome-wide assays of yeast reveals predictability of diverse phenotypes spanning cellular morphology growth metabolism and quantitative cell shape features. We apply the method to extend a genome-wide screen by predicting then verifying genes whose disruption elongates yeast cells and to predict human disease genes. To facilitate network-guided screens a web server is available http www.yeastnet.org. Background Geneticists have long observed that mutations that lead to the same organismal phenotype are typically functionally related and have interpreted epistatic relationships between genes as genetic pathways and more recently as gene networks. In the post-genomic period an abundance of high-throughput data has .
