Mining the modular structure of protein interaction networks.

BACKGROUND:Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms...

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Main Authors: Ariel José Berenstein, Janet Piñero, Laura Inés Furlong, Ariel Chernomoretz
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4391834?pdf=render
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spelling doaj-c8a17290478f49e09bb55c1e7ecc81c42020-11-24T21:36:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01104e012247710.1371/journal.pone.0122477Mining the modular structure of protein interaction networks.Ariel José BerensteinJanet PiñeroLaura Inés FurlongAriel ChernomoretzBACKGROUND:Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. METHODOLOGY:We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera's cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. RESULTS:As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge.http://europepmc.org/articles/PMC4391834?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Ariel José Berenstein
Janet Piñero
Laura Inés Furlong
Ariel Chernomoretz
spellingShingle Ariel José Berenstein
Janet Piñero
Laura Inés Furlong
Ariel Chernomoretz
Mining the modular structure of protein interaction networks.
PLoS ONE
author_facet Ariel José Berenstein
Janet Piñero
Laura Inés Furlong
Ariel Chernomoretz
author_sort Ariel José Berenstein
title Mining the modular structure of protein interaction networks.
title_short Mining the modular structure of protein interaction networks.
title_full Mining the modular structure of protein interaction networks.
title_fullStr Mining the modular structure of protein interaction networks.
title_full_unstemmed Mining the modular structure of protein interaction networks.
title_sort mining the modular structure of protein interaction networks.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2015-01-01
description BACKGROUND:Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. METHODOLOGY:We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera's cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. RESULTS:As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge.
url http://europepmc.org/articles/PMC4391834?pdf=render
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