Global Mapping of the Yeast Genetic Interaction Network

Science ◽  
2004 ◽  
Vol 303 (5659) ◽  
pp. 808-813 ◽  
Author(s):  
A. H. Y. Tong
Keyword(s):  
Genetic Interaction ◽  
Interaction Network ◽  
Genetic Interaction Network ◽  
Global Mapping ◽  
Yeast Genetic

scholarly journals TheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network

2017 ◽  
Vol 7 (5) ◽  
pp. 1539-1549 ◽  
Author(s):  
Matej Usaj ◽  
Yizhao Tan ◽  
Wen Wang ◽  
Benjamin VanderSluis ◽  
Albert Zou ◽  
...  
Keyword(s):  
Genetic Interaction ◽  
Interaction Network ◽  
Genetic Interaction Network ◽  
Yeast Genetic

scholarly journals Exploring a local genetic interaction network using evolutionary replay experiments

2021 ◽  
Author(s):  
Ryan C. Vignogna ◽  
Sean W. Buskirk ◽  
Gregory I. Lang
Keyword(s):  
Experimental Evolution ◽  
Gene Deletion ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Genetic Interactions ◽  
Loss Of Function ◽  
Positive Allele ◽  
Genetic Interaction Network ◽  
Allele Specific ◽  
Yeast Genetic

ABSTRACTUnderstanding how genes interact is a central challenge in biology. Experimental evolution provides a useful, but underutilized, tool for identifying genetic interactions, particularly those that involve non-loss-of-function mutations or mutations in essential genes. We previously identified a strong positive genetic interaction between specific mutations in KEL1 (P344T) and HSL7 (A695fs) that arose in an experimentally-evolved Saccharomyces cerevisiae population. Because this genetic interaction is not phenocopied by gene deletion, it was previously unknown. Using “evolutionary replay” experiments we identified additional mutations that have positive genetic interactions with the kel1-P344T mutation. We replayed the evolution of this population 672 times from six timepoints. We identified 30 populations where the kel1-P344T mutation reached high frequency. We performed whole-genome sequencing on these populations to identify genes in which mutations arose specifically in the kel1-P344T background. We reconstructed mutations in the ancestral and kel1-P344T backgrounds to validate positive genetic interactions. We identify several genetic interactors with KEL1, we validate these interactions by reconstruction experiments, and we show these interactions are not recapitulated by loss-of-function mutations. Our results demonstrate the power of experimental evolution to identify genetic interactions that are positive, allele specific, and not readily detected by other methods, and sheds light on a previously under-explored region of the yeast genetic interaction network.

scholarly journals Construction and application of a protein and genetic interaction network (yeast interactome)

2009 ◽  
Vol 37 (7) ◽  
pp. e54-e54 ◽  
Author(s):  
Gregory R. Stuart ◽  
William C. Copeland ◽  
Micheline K. Strand
Keyword(s):  
Genetic Interaction ◽  
Interaction Network ◽  
Genetic Interaction Network

scholarly journals Network motif analysis of a multi-mode genetic-interaction network

2007 ◽  
Vol 8 (8) ◽  
pp. R160 ◽  
Author(s):  
R James Taylor ◽  
Andrew F Siegel ◽  
Timothy Galitski
Keyword(s):  
Genetic Interaction ◽  
Network Motif ◽  
Interaction Network ◽  
Motif Analysis ◽  
Genetic Interaction Network ◽  
Multi Mode

scholarly journals mSphere of Influence: Comprehensive Genetic Analysis

mSphere ◽  
2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Jason M. Peters
Keyword(s):  
Antibiotic Resistance ◽  
Genetic Analysis ◽  
Gene Networks ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Cellular Function ◽  
Biofuel Production ◽  
Wiring Diagram ◽  
Genetic Interaction Network ◽  
New Biology

ABSTRACT Jason M. Peters works in the fields of antibiotic resistance and biofuel production. In this mSphere of Influence article, he reflects on how the paper “A global genetic interaction network maps a wiring diagram of cellular function” by Costanzo et al. (Science 353:aaf1420, 2016, https://doi.org/10.1126/science.aaf1420) has impacted his work by highlighting the power of gene networks to uncover new biology.

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