A strategy for extracting and analyzing large-scale quantitative epistatic interaction data.
Collins SR, Schuldiner M, Krogan NJ, Weissman JS.
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. src.science@gmail.com
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 score and detection of alleviating interactions.
Publication Types:
PMID: 16859555 [PubMed - indexed for MEDLINE]PMCID: PMC1779568