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

ABSTRACT: 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.

SUBMITTER: Modrak M

PROVIDER: S-EPMC5899412 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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

Modrák Martin M Vohradský Jiří J

BMC bioinformatics 20180413 1

<h4>Background</h4>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.<h4>Results</h4>We introduce Genexpi: a tool to identify sig ...[more]

PMID: 29653518

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Project description:MotivationIdentifying gene regulatory networks (GRNs) which consist of a large number of interacting units has become a problem of paramount importance in systems biology. Situations exist extensively in which causal interacting relationships among these units are required to be reconstructed from measured expression data and other a priori information. Though numerous classical methods have been developed to unravel the interactions of GRNs, these methods either have higher computing complexities or have lower estimation accuracies. Note that great similarities exist between identification of genes that directly regulate a specific gene and a sparse vector reconstruction, which often relates to the determination of the number, location and magnitude of nonzero entries of an unknown vector by solving an underdetermined system of linear equations y = Φx. Based on these similarities, we propose a novel framework of sparse reconstruction to identify the structure of a GRN, so as to increase accuracy of causal regulation estimations, as well as to reduce their computational complexity.ResultsIn this paper, a sparse reconstruction framework is proposed on basis of steady-state experiment data to identify GRN structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the in silico networks of the DREAM challenges. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project. Actual results show that, with a lower computational cost, the proposed method can significantly enhance estimation accuracy and greatly reduce false positive and negative errors. Furthermore, numerical calculations demonstrate that the proposed algorithm may have faster convergence speed and smaller fluctuation than other methods when either estimate error or estimate bias is considered.

Project description:Recently, there has been interest in using the short message service (SMS or text messaging), to gather frequent information on the clinical course of individual patients. One possible role for identifying clinical course patterns is to assist in exploring clinically important subgroups in the outcomes of research studies. Two previous studies have investigated detailed clinical course patterns in SMS data obtained from people seeking care for low back pain. One used a visual analysis approach and the other performed a cluster analysis of SMS data that had first been transformed by spline analysis. However, cluster analysis of SMS data in its original untransformed form may be simpler and offer other advantages. Therefore, the aim of this study was to determine whether cluster analysis could be used for identifying clinical course patterns distinct from the pattern of the whole group, by including all SMS time points in their original form. It was a 'proof of concept' study to explore the potential, clinical relevance, strengths and weakness of such an approach.This was a secondary analysis of longitudinal SMS data collected in two randomised controlled trials conducted simultaneously from a single clinical population (n?=?322). Fortnightly SMS data collected over a year on 'days of problematic low back pain' and on 'days of sick leave' were analysed using Two-Step (probabilistic) Cluster Analysis.Clinical course patterns were identified that were clinically interpretable and different from those of the whole group. Similar patterns were obtained when the number of SMS time points was reduced to monthly. The advantages and disadvantages of this method were contrasted to that of first transforming SMS data by spline analysis.This study showed that clinical course patterns can be identified by cluster analysis using all SMS time points as cluster variables. This method is simple, intuitive and does not require a high level of statistical skill. However, there are alternative ways of managing SMS data and many different methods of cluster analysis. More research is needed, especially head-to-head studies, to identify which technique is best to use under what circumstances.

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

Publications

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

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