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Báo cáo y học: "A strategy for extracting and analyzing large-scale quantitative epistatic interaction data"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: A strategy for extracting and analyzing large-scale quantitative epistatic interaction data. | Method Open Access A strategy for extracting and analyzing large-scale quantitative epistatic interaction data Sean R Collins Maya Schuldiner Nevan J Krogan M and Jonathan S Weissman Addresses Howard Hughes Medical Institute Department of Cellular and Molecular Pharmacology University of California-San Francisco and California Institute for Quantitative Biomedical Research San Francisco California 94143 USA. ỶBanting and Best Department of Medical Research University of Toronto College Street Toronto Ontario Canada M5G 1L6. Department of Medical Genetics and Microbiology University of Toronto Kings College Circle Toronto ON Canada M5S 1A8. Department of Cellular and Molecular Pharmacology University of California San Francisco San Francisco CA 94143 USA. Correspondence Jonathan S Weissman. Email weissman@cmp.ucsf.edu Published 21 July 2006 Genome Biology 2006 7 R63 doi 10.1 186 gb-2006-7-7-r63 The electronic version of this article is the complete one and can be found online at http genomebiology.com 2006 7 7 R63 Received 9 December 2005 Revised 10 April 2006 Accepted 13 July 2006 2006 Collins 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 Recently approaches have been developed for high-throughput identification of synthetic sick lethal gene pairs. However these are only a specific example of the broader phenomenon of epistasis wherein the presence of one mutation modulates the phenotype of another. We present analysis techniques for generating high-confidence quantitative epistasis scores from measurements made using synthetic genetic array and epistatic miniarray profile E-MAP technology as well as several tools for higher-level analysis of the resulting data that are greatly enhanced by the quantitative