Efficient randomization of biological networks while preserving functional characterization of individual nodes
Iorio, Francesco (European Bioinformatics Institute. European Molecular Biology Laboratory)
Bernardo-Faura, Martí (Centre de Recerca en Agrigenòmica)
Gobbi, Andrea (Fondazione Bruno Kessler)
Cokelaer, Thomas (Institut Pasteur. The Center of Bioinformatics, Biostatistics and Integrative Biology)
Jurman, Giuseppe. (Fondazione Bruno Kessler)
Saez-Rodriguez, Julio (RWTH Aachen University. Joint Research Centre for Computational Biomedicine)

Date:	2016
Abstract:	Background: Networks are popular and powerful tools to describe and model biological processes. Many computational methods have been developed to infer biological networks from literature, high-throughput experiments, and combinations of both. Additionally, a wide range of tools has been developed to map experimental data onto reference biological networks, in order to extract meaningful modules. Many of these methods assess results' significance against null distributions of randomized networks. However, these standard unconstrained randomizations do not preserve the functional characterization of the nodes in the reference networks (i. e. their degrees and connection signs), hence including potential biases in the assessment. - Results: Building on our previous work about rewiring bipartite networks, we propose a method for rewiring any type of unweighted networks. In particular we formally demonstrate that the problem of rewiring a signed and directed network preserving its functional connectivity (F-rewiring) reduces to the problem of rewiring two induced bipartite networks. Additionally, we reformulate the lower bound to the iterations' number of the switching-algorithm to make it suitable for the F-rewiring of networks of any size. Finally, we present BiRewire3, an open-source Bioconductor package enabling the F-rewiring of any type of unweighted network. We illustrate its application to a case study about the identification of modules from gene expression data mapped on protein interaction networks, and a second one focused on building logic models from more complex signed-directed reference signaling networks and phosphoproteomic data. - Conclusions: BiRewire3 it is freely available at https://www. bioconductor. org/packages/BiRewire/, and it should have a broad application as it allows an efficient and analytically derived statistical assessment of results from any network biology tool.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Networks ; Pathways ; Rewiring
Published in:	BMC bioinformatics, Vol. 17 (2016) , art. 542, ISSN 1471-2105

DOI: 10.1186/s12859-016-1402-1
PMID: 27998275

14 p, 1.6 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Experimental sciences > CRAG (Centre for Research in Agricultural Genomics)
Articles > Research articles
Articles > Published articles