Quantitative maps of genetic interactions in yeast - Comparative evaluation and integrative analysis

Abstract

BackgroundHigh-throughput genetic screening approaches have enabled systematic means to study how interactions among gene mutations contribute to quantitative fitness phenotypes, with the aim of providing insights into the functional wiring diagrams of genetic interaction networks on a global scale. However, it is poorly known how well these quantitative interaction measurements agree across the screening approaches, which hinders their integrated use toward improving the coverage and quality of the genetic interaction maps in yeast and other organisms.ResultsUsing large-scale data matrices from epistatic miniarray profiling (E-MAP), genetic interaction mapping (GIM), and synthetic genetic array (SGA) approaches, we carried out here a systematic comparative evaluation among these quantitative maps of genetic interactions in yeast. The relatively low association between the original interaction measurements or their customized scores could be improved using a matrix-based modelling framework, which enables the use of single- and double-mutant fitness estimates and measurements, respectively, when scoring genetic interactions. Toward an integrative analysis, we show how the detections from the different screening approaches can be combined to suggest novel positive and negative interactions which are complementary to those obtained using any single screening approach alone. The matrix approximation procedure has been made available to support the design and analysis of the future screening studies.ConclusionsWe have shown here that even if the correlation between the currently available quantitative genetic interaction maps in yeast is relatively low, their comparability can be improved by means of our computational matrix approximation procedure, which will enable integrative analysis and detection of a wider spectrum of genetic interactions using data from the complementary screening approaches.

The Notch signalling system: recent insights into the complexity of a conserved pathway

“…Such analyses have been carried out for individual screens 38,39 by incorporating different functional information available from the literature to establish connections and also to predict functional categories and modules among the genetic modifiers. The network analyses and integration of such maps have been pioneered in yeast, in which the proteomic and synthetic genetic interaction landscapes are well defined 107–111 . By comparison, genetic interactions at a genome-wide level are yet to be established in D. melanogaster , with the first large-scale synthetic genetic interaction analysis by RNAi being used recently to map the RAS–MAPK signalling networks in D. melanogaster 112 .…”

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

“…This indicates that genetic interaction modules encode also a wide range of functional cross-talk across multiple biological pathways. Such quantitative genetic networks are increasingly being mapped in model organisms to study many fundamental questions, such as genotype-phenotype relationships and buffering of genetic variation [ 25 , 28 , 58 ]. Genetic interactions are also involved in many human disease phenotypes, such as cancers and cardiovascular diseases.…”

Yeast genetic interaction screens in the age of CRISPR/Cas

“…SGAs were initially used to find synthetic lethal GIs (Tong et al 2001 , 2004 ; Davierwala et al 2005 ; Ooi et al 2006 ). Subsequent SGA screens and its variations analyzed more subtle effects of GIs on cellular fitness, including positive (suppression) as well as negative (enhancement) synthetic interactions (Schuldiner et al 2005 ; Collins et al 2010 ; Costanzo et al 2010 ; Baryshnikova et al 2010 ; Lindén et al 2011 ; Piening et al 2013 ; Stundon and Zakian 2015 ).…”