Dataset Information

Quantitative analysis of fitness and genetic interactions in yeast on a genome scale.

ABSTRACT: Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.

SUBMITTER: Baryshnikova A

PROVIDER: S-EPMC3117325 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Quantitative analysis of fitness and genetic interactions in yeast on a genome scale.

Baryshnikova Anastasia A Costanzo Michael M Kim Yungil Y Ding Huiming H Koh Judice J Toufighi Kiana K Youn Ji-Young JY Ou Jiongwen J San Luis Bryan-Joseph BJ Bandyopadhyay Sunayan S Hibbs Matthew M Hess David D Gingras Anne-Claude AC Bader Gary D GD Troyanskaya Olga G OG Brown Grant W GW Andrews Brenda B Boone Charles C Myers Chad L CL

Nature methods 20101114 12

Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accur ...[more]

PMID: 21076421

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Project description:Recent technological developments in genetic screening approaches have offered the means to start exploring quantitative genotype-phenotype relationships on a large-scale. What remains unclear is the extent to which the quantitative genetic interaction datasets can distinguish the broad spectrum of interaction classes, as compared to existing information on mutation pairs associated with both positive and negative interactions, and whether the scoring of varying degrees of such epistatic effects could be improved by computational means. To address these questions, we introduce here a computational approach for improving the quantitative discrimination power encoded in the genetic interaction screening data. Our matrix approximation model decomposes the original double-mutant fitness matrix into separate components, representing variability across the array and query mutants, which can be utilized for estimating and correcting the single-mutant fitness effects, respectively. When applied to three large-scale quantitative interaction datasets in yeast, we could improve the accuracy of scoring various interaction classes beyond that obtained with the original fitness data, especially in synthetic genetic array (SGA) and in genetic interaction mapping (GIM) datasets. In addition to the known pairs of interactions used in the evaluation of the computational approach, a number of novel interaction pairs were also predicted, along with underlying biological mechanisms, which remained undetected by the original datasets. It was shown that the optimal choice of the scoring function depends heavily on the screening approach and on the interaction class under analysis. Moreover, a simple preprocessing of the fitness matrix could further enhance the discrimination power of the epistatic miniarray profiling (E-MAP) dataset. These systematic evaluation results provide in-depth information on the optimal analysis of the future, large-scale screening experiments. In general, the modeling framework, enabling accurate identification and classification of genetic interactions, provides a solid basis for completing and mining the genetic interaction networks in yeast and other organisms.

Dataset Information

Quantitative analysis of fitness and genetic interactions in yeast on a genome scale.

Publications

Quantitative analysis of fitness and genetic interactions in yeast on a genome scale.

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