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Unraveling Protein Networks with Power Graph Analysis

Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementa...

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Detalles Bibliográficos
Autores principales: Royer, Loïc, Reimann, Matthias, Andreopoulos, Bill, Schroeder, Michael
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2424176/
https://www.ncbi.nlm.nih.gov/pubmed/18617988
http://dx.doi.org/10.1371/journal.pcbi.1000108
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author Royer, Loïc
Reimann, Matthias
Andreopoulos, Bill
Schroeder, Michael
author_facet Royer, Loïc
Reimann, Matthias
Andreopoulos, Bill
Schroeder, Michael
author_sort Royer, Loïc
collection PubMed
description Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are spotted. Additionally, we demonstrate the generality of Power Graph Analysis by applying it to two other types of networks. We show how power graphs induce a clustering of both transcription factors and target genes in bipartite transcription networks, and how the erosion of a phosphatase domain in type 22 non-receptor tyrosine phosphatases is detected. We apply Power Graph Analysis to high-throughput protein interaction networks and show that up to 85% (56% on average) of the information is redundant. Experimental networks are more compressible than rewired ones of same degree distribution, indicating that experimental networks are rich in cliques and bicliques. Power Graphs are a novel representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Power Graphs compress up to 85% of the edges in protein interaction networks and are applicable to all types of networks such as protein interactions, regulatory networks, or homology networks.
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spelling pubmed-24241762008-07-11 Unraveling Protein Networks with Power Graph Analysis Royer, Loïc Reimann, Matthias Andreopoulos, Bill Schroeder, Michael PLoS Comput Biol Research Article Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are spotted. Additionally, we demonstrate the generality of Power Graph Analysis by applying it to two other types of networks. We show how power graphs induce a clustering of both transcription factors and target genes in bipartite transcription networks, and how the erosion of a phosphatase domain in type 22 non-receptor tyrosine phosphatases is detected. We apply Power Graph Analysis to high-throughput protein interaction networks and show that up to 85% (56% on average) of the information is redundant. Experimental networks are more compressible than rewired ones of same degree distribution, indicating that experimental networks are rich in cliques and bicliques. Power Graphs are a novel representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Power Graphs compress up to 85% of the edges in protein interaction networks and are applicable to all types of networks such as protein interactions, regulatory networks, or homology networks. Public Library of Science 2008-07-11 /pmc/articles/PMC2424176/ /pubmed/18617988 http://dx.doi.org/10.1371/journal.pcbi.1000108 Text en Royer et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Royer, Loïc
Reimann, Matthias
Andreopoulos, Bill
Schroeder, Michael
Unraveling Protein Networks with Power Graph Analysis
title Unraveling Protein Networks with Power Graph Analysis
title_full Unraveling Protein Networks with Power Graph Analysis
title_fullStr Unraveling Protein Networks with Power Graph Analysis
title_full_unstemmed Unraveling Protein Networks with Power Graph Analysis
title_short Unraveling Protein Networks with Power Graph Analysis
title_sort unraveling protein networks with power graph analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2424176/
https://www.ncbi.nlm.nih.gov/pubmed/18617988
http://dx.doi.org/10.1371/journal.pcbi.1000108
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