Genexpi: a toolset for identifying regulons and validating gene regulatory networks using time-course expression data

Abstract

BackgroundIdentifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks.ResultsWe introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package.ConclusionsGenexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2138-x) contains supplementary material, which is available to authorized users.

DNA mapping and kinetic modeling of the HrdB regulon inStreptomyces coelicolor

“…The number represents the maximal estimate of the possible false positives. The degree of accuracy corresponds to the degree of accuracy of the measured time series and is comparable with the results of similar analyses (28).…”

“…To model the possible regulatory effects of HrdB we used the Genexpi tool and associated workflow (28) with minor additions. In particular, the expression of all genes was first smoothed with a B-spline and the smoothed expression of putative targets was modeled as an ordinary differential equation (ODE).…”

“…Here, y is the smoothed expression of the regulator (HrdB) as a function of time, k 1 is related to the maximum possible synthesis, w is the regulatory weight, b is bias (inversely related to the regulatory influence that saturates the synthesis of the mRNA) and k 2 is mRNA decay rate constant. For further details on those two models and the optimization procedure, see (28).…”

Gene Regulatory Network Inference: Connecting Plant Biology and Mathematical Modeling

“…In addition to TuxNet, other tools are available to facilitate the use of BNs and DBNs for plant biologists, such as BNArray, a tool developed in R that creates small DBNs and combines them to predict regulatory subnetworks (Chen et al, 2006). Similarly, open source Cytoscape plugins are available for network inference: (i) NetworkBMA uses Bayesian Network Averaging to infer regulatory networks (Fraley et al, 2014); (ii) Cygenexpi is based on ODEs and uses known putative regulations and time-course data to assess regulatory interactions (Modrák and Vohradskı, 2018); and (iii) ARACNE can analyze and integrate high-throughput expression steady-state data and was already successfully used in identifying previously known and new transcriptional regulations in the Arabidopsis root (Margolin et al, 2006;Chávez Montes et al, 2014).…”

Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

“…Traditionally, the inference of GRNs has been performed with tools based on command-line or in the R programming language such as ARACNe (Margolin et al, 2006a), but current alternatives include more user-friendly approaches which are listed in Table 3 . These include an ARACNe implementation in geWorkbench , which was listed previously in the correlation network section, and also available are the Cytoscape plugins CyGenexpi (Modrák and Vohradský, 2018), CyNetworkBMA (Fronczuk et al, 2015), GRNCOP2 (Gallo et al, 2011), and iRegulon (Janky et al, 2014) ( Table 3 ).…”