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

Elena Kuzmin1,2,3, Mahfuzur Rahman4, Benjamin VanderSluis4

  • 1The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. kuzmin.elena@gmail.com.

Nature Protocols
|January 19, 2021
PubMed
Summary
This summary is machine-generated.

We present a new method, trigenic synthetic genetic array analysis (τ-SGA), for high-throughput screening of complex genetic interactions in yeast. This technique efficiently quantifies trigenic interactions, revealing deeper insights into cellular genetic networks.

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Area of Science:

  • Genetics
  • Systems Biology
  • Molecular Biology

Background:

  • Complex genetic interactions are crucial for understanding cellular functions and genetic network architecture.
  • Previous methods were limited in their ability to systematically analyze high-order genetic interactions.

Purpose of the Study:

  • To describe a novel, high-throughput method for screening and quantifying trigenic genetic interactions in yeast.
  • To provide a detailed protocol for the trigenic synthetic genetic array analysis (τ-SGA) technique.

Main Methods:

  • Development and application of the τ-SGA technique using ordered arrays of yeast strains.
  • Automated mating, meiotic recombination, and haploid selection to generate triple mutants.
  • Colony size scoring as a proxy for fitness to calculate trigenic interactions.

Main Results:

  • Successfully conducted 422 trigenic interaction screens, generating approximately 460,000 yeast triple mutants.
  • Established computational pipelines using MATLAB for scoring and analyzing large-scale τ-SGA data.
  • Provided recommendations for optimizing experimental design and analysis using SGAtools for smaller screens.

Conclusions:

  • The τ-SGA method offers a powerful, high-throughput approach for analyzing complex genetic interactions.
  • This protocol facilitates a deeper understanding of trigenic interactions and their role in cellular systems.
  • The methodology is applicable for large-scale genetic screens and provides tools for data analysis.