τ-SGA: synthetic genetic array analysis for systematically screening and quantifying trigenic interactions in yeast.

Abstract:

:Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic interactions from ordered arrays of yeast strains grown on agar plates as individual colonies. The protocol instructs users on the trigenic synthetic genetic array analysis technique, τ-SGA, for high-throughput screens. The steps describe construction of the double-mutant query strains and the corresponding single-mutant control query strains, which are screened in parallel in two replicates. The screening experimental set-up consists of sequential replica-pinning steps that enable automated mating, meiotic recombination and successive haploid selection steps for the generation of triple mutants, which are scored for colony size as a proxy for fitness, which enables the calculation of trigenic interactions. The procedure described here was used to conduct 422 trigenic interaction screens, which generated ~460,000 yeast triple mutants for trigenic interaction analysis. Users should be familiar with robotic equipment required for high-throughput genetic interaction screens and be proficient at the command line to execute the scoring pipeline. Large-scale screen computational analysis is achieved by using MATLAB pipelines that score raw colony size data to produce τ-SGA interaction scores. Additional recommendations are included for optimizing experimental design and analysis of smaller-scale trigenic interaction screens by using a web-based analysis system, SGAtools. This protocol provides a resource for those who would like to gain a deeper, more practical understanding of trigenic interaction screening and quantification methodology.

journal_name

Nat Protoc

journal_title

Nature protocols

authors

Kuzmin E,Rahman M,VanderSluis B,Costanzo M,Myers CL,Andrews BJ,Boone C

doi

10.1038/s41596-020-00456-3

subject

Has Abstract

pub_date

2021-01-18 00:00:00

eissn

1754-2189

issn

1750-2799

pii

10.1038/s41596-020-00456-3

pub_type

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