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RulNet: A Web-Oriented Platform for Regulatory Network Inference, Application to Wheat -Omics Data.

Jonathan Vincent1, Pierre Martre2, Benjamin Gouriou3

  • 1Blaise Pascal University, UMR6158 CNRS LIMOS Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes, Aubière, F-63 173, France; INRA, UMR1095 Genetics, Diversity and Ecophysiology of Cereals, Clermont-Ferrand, F-63 039, France; Blaise Pascal University, UMR1095 Genetics, Diversity and Ecophysiology of Cereals, Aubière, F-63 177, France.

Plos One
|May 21, 2015
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Summary
This summary is machine-generated.

RulNet is a new platform for analyzing -omics data to uncover molecular regulatory networks. It uses rule discovery to infer these networks, performing comparably to top methods and aiding in understanding wheat grain responses to nutrients.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • The increasing volume of -omics data necessitates advanced analysis tools.
  • Unraveling molecular regulatory networks from complex, heterogeneous datasets is a significant challenge.

Purpose of the Study:

  • To introduce RulNet, a web-based platform for inferring and analyzing regulatory networks.
  • To evaluate RulNet's performance against existing methods and its utility in experimental data analysis.

Main Methods:

  • RulNet employs rule discovery from qualitative and quantitative -omics data.
  • A query language similar to SQL (RQL) is used for rule discovery.
  • Interactive features allow for progressive refinement of inferred networks.

Main Results:

  • RulNet demonstrated performance comparable to leading methods in the DREAM5 network inference challenge.
  • The platform showed consistent performance across multiple benchmark datasets.
  • RulNet successfully inferred regulatory networks involved in wheat grain response to nitrogen and sulfur.

Conclusions:

  • RulNet is an effective all-in-one solution for regulatory network inference, visualization, and editing.
  • The platform facilitates the discovery of regulatory networks from diverse -omics data.
  • RulNet's application to wheat grain data identified key factors in nutrient response, aligning with existing knowledge.