%0 Journal Article
%T Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action
%A Maureen E Hillenmeyer
%A Elke Ericson
%A Ronald W Davis
%A Corey Nislow
%A Daphne Koller
%A Guri Giaever
%J Genome Biology
%D 2010
%I BioMed Central
%R 10.1186/gb-2010-11-3-r30
%X Yeast competitive fitness data constitute a unique, genome-wide assay of the cellular response to environmental and chemical perturbations [1-8]. Here, we systematically analyzed the largest fitness dataset available, comprising measurements of the growth rates of barcoded, pooled deletion strains in the presence of over 400 unique perturbations [1] and show that the dataset reveals novel aspects of cellular physiology and provides a valuable resource for systems biology. In the haploinsufficiency profiling (HIP) assay consisting of all 6,000 heterozygous deletions (where one copy of each gene is deleted), most strains (97%) grow at the rate of wild type [9] when assayed in parallel. In the presence of a drug, the strain deleted for the drug target is specifically sensitized (as measured by a decrease in growth rate) as a result of a further decrease in 'functional' gene dosage by the drug binding to the target protein. In this way, fitness data allow identification of the potential drug target [3,4,10]. In the homozygous profiling (HOP) assay (applied to non-essential genes), both copies of the gene are deleted in a diploid strain to produce a complete loss-of-function allele. This assay identifies genes required for growth in the presence of compound, often identifying functions that buffer the drug target pathway [5-8].The field of functional genomics aims to predict gene functions using high-throughput datasets that interrogate functional genetic relationships. To address the value of fitness data as a resource for functional genomics, we asked how well co-fitness (correlated growth of gene deletion strains in compounds) predicts gene function compared to other large-scale datasets, including co-expression, protein-protein interactions, and synthetic lethality [11-13]. Interestingly, co-fitness predicts cellular functions not evident in these other datasets. We also investigated the theory that genes are essential because they belong to essential complexes [14,1
%U http://genomebiology.com/2010/11/3/R30