BACKGROUND: Identifying 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. RESULTS: We 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. CONCLUSIONS: Genexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.

• Keywords
• Cytoscape, Gene network inference, Time series, Transcription regulation,
• MeSH
• Bacteria genetics   MeSH
• Time Factors MeSH
• Databases, Genetic * MeSH
• Eukaryota genetics   MeSH
• Gene Regulatory Networks * MeSH
• Humans MeSH
• Gene Expression Regulation * MeSH
• Regulon genetics   MeSH
• Reproducibility of Results MeSH
• Saccharomyces cerevisiae genetics   MeSH
• Software * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

A computational model of gene expression was applied to a novel test set of microarray time series measurements to reveal regulatory interactions between transcriptional regulators represented by 45 sigma factors and the genes expressed during germination of a prokaryote Streptomyces coelicolor. Using microarrays, the first 5.5 h of the process was recorded in 13 time points, which provided a database of gene expression time series on genome-wide scale. The computational modeling of the kinetic relations between the sigma factors, individual genes and genes clustered according to the similarity of their expression kinetics identified kinetically plausible sigma factor-controlled networks. Using genome sequence annotations, functional groups of genes that were predominantly controlled by specific sigma factors were identified. Using external binding data complementing the modeling approach, specific genes involved in the control of the studied process were identified and their function suggested.

• MeSH
• Transcription, Genetic MeSH
• Gene Regulatory Networks * MeSH
• Kinetics MeSH
• Models, Genetic * MeSH
• Computer Simulation MeSH
• Gene Expression Regulation, Bacterial * MeSH
• Oligonucleotide Array Sequence Analysis MeSH
• Sigma Factor metabolism   MeSH
• Spores, Bacterial genetics   growth & development   metabolism   MeSH
• Gene Expression Profiling * MeSH
• Streptomyces coelicolor genetics   metabolism   physiology   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Sigma Factor MeSH