A strategy for extracting and analyzing large-scale quantitative epistatic interaction data

Sean R Collins¹ , Maya Schuldiner¹ , Nevan J Krogan²^,3^,4 and Jonathan S Weissman¹

¹Howard 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

²Banting and Best Department of Medical Research, University of Toronto, College Street, Toronto, Ontario, Canada M5G 1L6

³Department of Medical Genetics and Microbiology, University of Toronto, Kings College Circle, Toronto ON, Canada M5S 1A8

⁴Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94143, USA

author email corresponding author email

Genome Biology 2006, 7:R63doi:10.1186/gb-2006-7-7-r63


Published:	21 July 2006

Subject areas: Methods, Genetics, Model organisms, Bioinformatics

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 score and detection of alleviating interactions.