Literature DB >> 12386004

Building and analysing genome-wide gene disruption networks.

J Rung1, T Schlitt, A Brazma, K Freivalds, J Vilo.   

Abstract

MOTIVATION: Microarray experiments comparing expression levels of all genes in yeast for hundreds of mutants allow us to examine properties of gene regulatory networks on a genomic scale. We can investigate questions such as network modularity, connectivity, and look for genes with particular roles in the network structure.
RESULTS: We have built genome-wide disruption networks for yeast, using a representation of gene expression data as directed labelled graphs. Nodes represent genes and arcs connect nodes if the disruption of the source gene significantly alters the expression of the target gene. We are interested in features of the resulting disruption networks that are robust over a range of significance cutoffs. The networks show a significant overlap with analogous networks constructed from scientific literature. In disruption networks the number of arcs adjacent to different nodes are distributed roughly according to a power-law, like in many complex systems where the robustness against perturbations is important. The networks are dominated by a single large component and do not have an obvious modular structure. Genes with the highest outdegrees often encode proteins with regulatory functions, whereas genes with the highest indegrees are predominantly involved in metabolism. The local structure of the networks is meaningful, genes involved in the same cellular processes are close together in the network. AVAILABILITY: http://www.ebi.ac.uk/microarray/networks

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Year:  2002        PMID: 12386004     DOI: 10.1093/bioinformatics/18.suppl_2.s202

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  18 in total

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2.  Quantifying modularity in the evolution of biomolecular systems.

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3.  Modeling the temporal interplay of molecular signaling and gene expression by using dynamic nested effects models.

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4.  A proof of the DBRF-MEGN method, an algorithm for deducing minimum equivalent gene networks.

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5.  From gene networks to gene function.

Authors:  Thomas Schlitt; Kimmo Palin; Johan Rung; Sabine Dietmann; Michael Lappe; Esko Ukkonen; Alvis Brazma
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6.  How to understand the cell by breaking it: network analysis of gene perturbation screens.

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Journal:  PLoS Comput Biol       Date:  2010-02-26       Impact factor: 4.475

Review 7.  Inferring cellular networks--a review.

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8.  Yeast glucose pathways converge on the transcriptional regulation of trehalose biosynthesis.

Authors:  Eva Apweiler; Katrin Sameith; Thanasis Margaritis; Nathalie Brabers; Loes van de Pasch; Linda V Bakker; Dik van Leenen; Frank Cp Holstege; Patrick Kemmeren
Journal:  BMC Genomics       Date:  2012-06-14       Impact factor: 3.969

9.  Evidence of highly regulated genes (in-Hubs) in gene networks of Saccharomyces cerevisiae.

Authors:  Jesper Lundström; Johan Björkegren; Jesper Tegnér
Journal:  Bioinform Biol Insights       Date:  2008-07-14

10.  Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions.

Authors:  Holger Fröhlich; Ozgür Sahin; Dorit Arlt; Christian Bender; Tim Beissbarth
Journal:  BMC Bioinformatics       Date:  2009-10-08       Impact factor: 3.169

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