Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.

We apply our recently developed information-theoretic measures for the characterisation and comparison of protein-protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these mac...

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Autores principales:	Luis P Fernandes, Alessia Annibale, Jens Kleinjung, Anthony C C Coolen, Franca Fraternali
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f8aa387ddbe5429894aa85b2252044dc
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spelling	oai:doaj.org-article:f8aa387ddbe5429894aa85b2252044dc2021-12-02T20:11:51ZProtein networks reveal detection bias and species consistency when analysed by information-theoretic methods.1932-620310.1371/journal.pone.0012083https://doaj.org/article/f8aa387ddbe5429894aa85b2252044dc2010-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20805870/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203We apply our recently developed information-theoretic measures for the characterisation and comparison of protein-protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large-scale analysis of protein-protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast-two-hybrid methods are sufficiently consistent to allow for intra-species comparisons (between different experiments) and inter-species comparisons, while data from affinity-purification mass-spectrometry methods show large differences even within intra-species comparisons.Luis P FernandesAlessia AnnibaleJens KleinjungAnthony C C CoolenFranca FraternaliPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 8, p e12083 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Luis P Fernandes Alessia Annibale Jens Kleinjung Anthony C C Coolen Franca Fraternali Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.
description	We apply our recently developed information-theoretic measures for the characterisation and comparison of protein-protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large-scale analysis of protein-protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast-two-hybrid methods are sufficiently consistent to allow for intra-species comparisons (between different experiments) and inter-species comparisons, while data from affinity-purification mass-spectrometry methods show large differences even within intra-species comparisons.
format	article
author	Luis P Fernandes Alessia Annibale Jens Kleinjung Anthony C C Coolen Franca Fraternali
author_facet	Luis P Fernandes Alessia Annibale Jens Kleinjung Anthony C C Coolen Franca Fraternali
author_sort	Luis P Fernandes
title	Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.
title_short	Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.
title_full	Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.
title_fullStr	Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.
title_full_unstemmed	Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.
title_sort	protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/f8aa387ddbe5429894aa85b2252044dc
work_keys_str_mv	AT luispfernandes proteinnetworksrevealdetectionbiasandspeciesconsistencywhenanalysedbyinformationtheoreticmethods AT alessiaannibale proteinnetworksrevealdetectionbiasandspeciesconsistencywhenanalysedbyinformationtheoreticmethods AT jenskleinjung proteinnetworksrevealdetectionbiasandspeciesconsistencywhenanalysedbyinformationtheoreticmethods AT anthonycccoolen proteinnetworksrevealdetectionbiasandspeciesconsistencywhenanalysedbyinformationtheoreticmethods AT francafraternali proteinnetworksrevealdetectionbiasandspeciesconsistencywhenanalysedbyinformationtheoreticmethods
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Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods.

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