PT - JOURNAL ARTICLE AU - Francesco Iorio AU - Andrea Gobbi AU - Thomas Cokelaer AU - Marti Bernardo Faura AU - Giuseppe Jurman AU - Julio Saez-Rodriguez TI - Efficient randomization of biological networks while preserving functional characterization of individual nodes AID - 10.1101/069245 DP - 2016 Jan 01 TA - bioRxiv PG - 069245 4099 - http://biorxiv.org/content/early/2016/08/12/069245.short 4100 - http://biorxiv.org/content/early/2016/08/12/069245.full AB - 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 calibrate 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 and undirected-unweighted 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 software 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.