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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Autores principales:	Loïc Royer, Matthias Reimann, Bill Andreopoulos, Michael Schroeder
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/8d8f9ce79a254ea3af4dc65a563a389c
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spelling	oai:doaj.org-article:8d8f9ce79a254ea3af4dc65a563a389c2021-11-25T05:41:14ZUnraveling protein networks with power graph analysis.1553-734X1553-735810.1371/journal.pcbi.1000108https://doaj.org/article/8d8f9ce79a254ea3af4dc65a563a389c2008-07-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18617988/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Networks 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.Loïc RoyerMatthias ReimannBill AndreopoulosMichael SchroederPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 7, p e1000108 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Loïc Royer Matthias Reimann Bill Andreopoulos Michael Schroeder Unraveling protein networks with power graph analysis.
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.
format	article
author	Loïc Royer Matthias Reimann Bill Andreopoulos Michael Schroeder
author_facet	Loïc Royer Matthias Reimann Bill Andreopoulos Michael Schroeder
author_sort	Loïc Royer
title	Unraveling protein networks with power graph analysis.
title_short	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_sort	unraveling protein networks with power graph analysis.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/8d8f9ce79a254ea3af4dc65a563a389c
work_keys_str_mv	AT loicroyer unravelingproteinnetworkswithpowergraphanalysis AT matthiasreimann unravelingproteinnetworkswithpowergraphanalysis AT billandreopoulos unravelingproteinnetworkswithpowergraphanalysis AT michaelschroeder unravelingproteinnetworkswithpowergraphanalysis
_version_	1718414529592819712

Unraveling protein networks with power graph analysis.

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