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.
Researchers at the Czech Academy of Sciences 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, the researchers 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.
Human inspection of the model fits in CyGenexpi
The user is shown the profile of the regulator (blue) and target (red) as well as the best profile found by Genexpi (green). The red ribbon is the error margin of the measured profile. The algorithm classified the first profile as a good fit, while the second was considered implausible to be regulated. The user may however modify the classification based on their knowledge of the data and organism
Availability – The latest version of the Genexpi software is freely available (LGPL v3) at http://github.com/cas-bioinf/genexpi/wiki/, including source code (Java + OpenCL, optionally R; platform independent).