Abstract
With the increasing amount of –omics data available, a particular effort has to be made to provide suitable analysis tools. A major challenge is that of unraveling the molecular regulatory networks from massive and heterogeneous datasets. Here we describe RulNet, a web-oriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative –omics data by means of rule discovery. Queries for rule discovery can be written in an extended form of the RQL query language, which has a syntax similar to SQL. RulNet also offers users interactive features that progressively adjust and refine the inferred networks. In this paper, we present a functional characterization of RulNet and compare inferred networks with correlation-based approaches. The performance of RulNet has been evaluated using the three benchmark datasets used for the transcriptional network inference challenge DREAM5. Overall, RulNet performed as well as the best methods that participated in this challenge and it was shown to behave more consistently when compared across the three datasets. Finally, we assessed the suitability of RulNet to analyze experimental –omics data and to infer regulatory networks involved in the response to nitrogen and sulfur supply in wheat (Triticum aestivum L.) grains. The results highlight putative actors governing the response to nitrogen and sulfur supply in wheat grains. We evaluate the main characteristics and features of RulNet as an all-in-one solution for RN inference, visualization and editing. Using simple yet powerful RulNet queries allowed RNs involved in the adaptation of wheat grain to N and S supply to be discovered. We demonstrate the effectiveness and suitability of RulNet as a platform for the analysis of RNs involving different types of –omics data. The results are promising since they are consistent with what was previously established by the scientific community.
Highlights
Regulation of gene expression is defined as the spatiotemporal control of the amount of gene products
transcription factors (TFs) are themselves encoded by genes and the complex interactions between genes and TFs put together are called Gene Regulatory Networks (GRNs)
To illustrate the results that can be obtained with RulNet, the platform was used to mine the results of an experiment carried out to better understand wheat grain development adaptive responses to N and S supply
Summary
Regulation of gene expression is defined as the spatiotemporal control of the amount of gene products. It governs cell differentiation and the adaptation of living organisms to their environment. TFs are themselves encoded by genes and the complex interactions between genes and TFs put together are called Gene Regulatory Networks (GRNs). A wider range of regulatory relationships between genes, their products and their interactions with the various metabolites and signaling molecules present in a cell are generally considered necessary to explain this complexity.
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