title: Efficient randomization of biological networks while preserving functional characterization of individual nodes.
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.
authors.affiliation: European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK. iorio@ebi.ac.uk.
authors.forename: Francesco
authors.identifier: http://orcid.org/0000-0001-7063-8913
authors.initials: F
authors.lastname: Iorio
authors.affiliation: European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.|Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, 08193, Spain.
authors.forename: Marti
authors.initials: M
authors.lastname: Bernardo-Faura
authors.affiliation: Fondazione Bruno Kessler, Povo, Trento, I-38122, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Gobbi
authors.affiliation: European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.|Institut Pasteur - Bioinformatics and Biostatistics Hub - C3BI, USR 3756 IP CNRS, Paris, France.
authors.forename: Thomas
authors.initials: T
authors.lastname: Cokelaer
authors.affiliation: Fondazione Bruno Kessler, Povo, Trento, I-38122, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Jurman
authors.affiliation: European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK. saezrodriguez@gmail.com.|RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), MTI2 Wendlingweg 2, Aachen, 52074, Germany. saezrodriguez@gmail.com.
authors.forename: Julio
authors.initials: J
authors.lastname: Saez-Rodriguez
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D053263:Gene Regulatory Networks; D006801:Humans; D008954:Models, Biological; D060066:Protein Interaction Maps; D011897:Random Allocation; D012984:Software
keywords: Networks; Pathways; Rewiring