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PLoS Comput Biol. 2017 Apr 13;13(4):e1005496. doi: 10.1371/journal.pcbi.1005496. eCollection 2017 Apr.

Inferring modulators of genetic interactions with epistatic nested effects models.

Author information

1
ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland.
2
SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
3
University of Amsterdam, Amsterdam, The Netherlands.
4
Bonn-Aachen International Center for IT (B-IT), University of Bonn, Bonn, Germany.
5
UCB Biosciences GmbH, Monheim, Germany.
6
University of Cambridge, Cancer Research UK Cambridge Institute, Cambridge, United Kingdom.

Abstract

Maps of genetic interactions can dissect functional redundancies in cellular networks. Gene expression profiles as high-dimensional molecular readouts of combinatorial perturbations provide a detailed view of genetic interactions, but can be hard to interpret if different gene sets respond in different ways (called mixed epistasis). Here we test the hypothesis that mixed epistasis between a gene pair can be explained by the action of a third gene that modulates the interaction. We have extended the framework of Nested Effects Models (NEMs), a type of graphical model specifically tailored to analyze high-dimensional gene perturbation data, to incorporate logical functions that describe interactions between regulators on downstream genes and proteins. We benchmark our approach in the controlled setting of a simulation study and show high accuracy in inferring the correct model. In an application to data from deletion mutants of kinases and phosphatases in S. cerevisiae we show that epistatic NEMs can point to modulators of genetic interactions. Our approach is implemented in the R-package 'epiNEM' available from https://github.com/cbg-ethz/epiNEM and https://bioconductor.org/packages/epiNEM/.

PMID:
28406896
PMCID:
PMC5407847
DOI:
10.1371/journal.pcbi.1005496
[Indexed for MEDLINE]
Free PMC Article

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