Literature DB >> 33462440

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

Elena Kuzmin1,2,3, Mahfuzur Rahman4, Benjamin VanderSluis4, Michael Costanzo5, Chad L Myers6, Brenda J Andrews7,8, Charles Boone9,10.   

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.

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Year:  2021        PMID: 33462440      PMCID: PMC9127509          DOI: 10.1038/s41596-020-00456-3

Source DB:  PubMed          Journal:  Nat Protoc        ISSN: 1750-2799            Impact factor:   17.021


  48 in total

1.  Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Authors:  Paul Shannon; Andrew Markiel; Owen Ozier; Nitin S Baliga; Jonathan T Wang; Daniel Ramage; Nada Amin; Benno Schwikowski; Trey Ideker
Journal:  Genome Res       Date:  2003-11       Impact factor: 9.043

2.  Synthetic genetic array analysis for global mapping of genetic networks in yeast.

Authors:  Elena Kuzmin; Sara Sharifpoor; Anastasia Baryshnikova; Michael Costanzo; Chad L Myers; Brenda J Andrews; Charles Boone
Journal:  Methods Mol Biol       Date:  2014

3.  Systematic genetic analysis with ordered arrays of yeast deletion mutants.

Authors:  A H Tong; M Evangelista; A B Parsons; H Xu; G D Bader; N Pagé; M Robinson; S Raghibizadeh; C W Hogue; H Bussey; B Andrews; M Tyers; C Boone
Journal:  Science       Date:  2001-12-14       Impact factor: 47.728

4.  Reporter-Based Synthetic Genetic Array Analysis: A Functional Genomics Approach for Investigating Transcript or Protein Abundance Using Fluorescent Proteins in Saccharomyces cerevisiae.

Authors:  Hendrikje Göttert; Mojca Mattiazzi Usaj; Adam P Rosebrock; Brenda J Andrews
Journal:  Methods Mol Biol       Date:  2018

5.  A vector system for efficient and economical switching of C-terminal epitope tags in Saccharomyces cerevisiae.

Authors:  Min-Kyung Sung; Cheol Woong Ha; Won-Ki Huh
Journal:  Yeast       Date:  2008-04       Impact factor: 3.239

6.  Phenotypic analysis of temperature-sensitive yeast actin mutants.

Authors:  P Novick; D Botstein
Journal:  Cell       Date:  1985-02       Impact factor: 41.582

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

Authors:  Sean R Collins; Maya Schuldiner; Nevan J Krogan; Jonathan S Weissman
Journal:  Genome Biol       Date:  2006       Impact factor: 13.583

8.  Pooled CRISPR screening with single-cell transcriptome readout.

Authors:  André F Rendeiro; Christian Schmidl; Paul Datlinger; Thomas Krausgruber; Peter Traxler; Johanna Klughammer; Linda C Schuster; Amelie Kuchler; Donat Alpar; Christoph Bock
Journal:  Nat Methods       Date:  2017-01-18       Impact factor: 28.547

9.  Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers.

Authors:  Peter C Fong; David S Boss; Timothy A Yap; Andrew Tutt; Peijun Wu; Marja Mergui-Roelvink; Peter Mortimer; Helen Swaisland; Alan Lau; Mark J O'Connor; Alan Ashworth; James Carmichael; Stan B Kaye; Jan H M Schellens; Johann S de Bono
Journal:  N Engl J Med       Date:  2009-06-24       Impact factor: 91.245

10.  Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing.

Authors:  Joseph M Replogle; Thomas M Norman; Albert Xu; Jeffrey A Hussmann; Jin Chen; J Zachery Cogan; Elliott J Meer; Jessica M Terry; Daniel P Riordan; Niranjan Srinivas; Ian T Fiddes; Joseph G Arthur; Luigi J Alvarado; Katherine A Pfeiffer; Tarjei S Mikkelsen; Jonathan S Weissman; Britt Adamson
Journal:  Nat Biotechnol       Date:  2020-03-30       Impact factor: 54.908

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  1 in total

1.  Epi-Decoder: Decoding the Local Proteome of a Genomic Locus by Massive Parallel Chromatin Immunoprecipitation Combined with DNA-Barcode Sequencing.

Authors:  Maria Elize van Breugel; Fred van Leeuwen
Journal:  Methods Mol Biol       Date:  2022
  1 in total

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