A network-based method to assess the statistical significance of mild co-regulation effects.

Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network b...

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Autores principales:	Emőke-Ágnes Horvát, Jitao David Zhang, Stefan Uhlmann, Özgür Sahin, Katharina Anna Zweig
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a9069f4c06754e05ad22cd5e21710d6b
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spelling	oai:doaj.org-article:a9069f4c06754e05ad22cd5e21710d6b2021-11-18T08:56:18ZA network-based method to assess the statistical significance of mild co-regulation effects.1932-620310.1371/journal.pone.0073413https://doaj.org/article/a9069f4c06754e05ad22cd5e21710d6b2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24039936/?tool=EBIhttps://doaj.org/toc/1932-6203Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis.Emőke-Ágnes HorvátJitao David ZhangStefan UhlmannÖzgür SahinKatharina Anna ZweigPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e73413 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Emőke-Ágnes Horvát Jitao David Zhang Stefan Uhlmann Özgür Sahin Katharina Anna Zweig A network-based method to assess the statistical significance of mild co-regulation effects.
description	Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis.
format	article
author	Emőke-Ágnes Horvát Jitao David Zhang Stefan Uhlmann Özgür Sahin Katharina Anna Zweig
author_facet	Emőke-Ágnes Horvát Jitao David Zhang Stefan Uhlmann Özgür Sahin Katharina Anna Zweig
author_sort	Emőke-Ágnes Horvát
title	A network-based method to assess the statistical significance of mild co-regulation effects.
title_short	A network-based method to assess the statistical significance of mild co-regulation effects.
title_full	A network-based method to assess the statistical significance of mild co-regulation effects.
title_fullStr	A network-based method to assess the statistical significance of mild co-regulation effects.
title_full_unstemmed	A network-based method to assess the statistical significance of mild co-regulation effects.
title_sort	network-based method to assess the statistical significance of mild co-regulation effects.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/a9069f4c06754e05ad22cd5e21710d6b
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A network-based method to assess the statistical significance of mild co-regulation effects.

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