Welcome to the RulNet project

RulNet is a web-oriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative –omics data by means of rule discovery and statistical techniques.

In the post-genomic era, fast development of high throughput methods is accompanied by a dramatic increase of the amount of data produced in functional genomics projects. Retrieving biologically relevant information and knowledge on molecular regulations from such massive amounts of data is a major challenge for the scientific community. In this context, different approaches have been developed to infer regulatory networks from high throughput –omics data, providing researchers with conclusive results. Here we describe RulNet an all-in-one and efficient solution for rule inference and network visualization with output results also compatible with Cytoscape.

Partners and funding

Breedwheat

This project is partly funded as part of the BREEDWHEAT Project, a long-term public-private research initiative whose aim is to develop and use efficient genome sequence-based tools and new methodologies for breeding wheat varieties with improved quality, sustainability, and productivity.

INRA

The Database team from the LIMOS and the BIG team from the INRA of Clermont-Ferrand are curently engaged in a coordinated effort to develop RulNet.

LIMOS

This project was also initiated as part of a collaboration with Jean-Marc Petit from the database team of the LIRIS laboratory.

LIRIS

People involved

Marie PAILLOUX (LIMOS, Associate Professor, Project manager)

Carlos CEPEDA (LIMOS, Engineer)

Benjamin GOURIOU (LIMOS, Engineer)

Jean-Marc PETIT (LIRIS, Professor)

Jonathan VINCENT (INRA GDEC / LIMOS, PhD student)

Pierre MARTRE (INRA GDEC, Research Director)

Catherine RAVEL (INRA GDEC, Research Engineer)

Citing RulNet

Please cite the following article in any research that uses RulNet: J. Vincent, P. Martre, B. Gouriou, C. Ravel, Z. Dai, J-M. Petit, M. Pailloux. RulNet: A Web-Oriented Platform for Regulatory Network Inference, Application to Wheat -Omics Data. PLoS ONE 10(5), 2015.