Introduction
 
Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, test, analyse and visualize GRNs that govern various biological processes. The web-server is pre-loaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, Medicago truncatula, Lotus japonicus and Glycine max. To allow a maximum of flexibility, users can also upload their own transcriptomic and annotation datasets from these or other species to analyse their in-house experiments. Users are able to select which experiments, genes and GRN algorithms they wish to perform for GRN analysis. To achieve this flexibility, we have implemented multiple mainstream GRN predication algorithms including co-expression, Graphical Gaussian Models (GGMs), CLR, parallelized version of TIGRESS, GENIE3 and our previous developed LPC algorithm. Moreover, we also provide tools to allow comparisons between predicted GRNs obtained from different algorithms, as well as comparisons between species. The web server integrates a web-based visualization for interactive interpretation of networks and a function to export data for visualization using software such as Cytoscape. Features such as identification of over-represented functional classes according to GO annotations will help users to interpret networks and to predict gene functions.
In conclusion, LegumeGRN provides a comprehensive, flexible and user-friendly web interface to build, test, analyse and visualize GRNs. It will help researchers to generate hypotheses about gene function and identify putative regulators of specific pathways for functional and comparative genomic studies.
Funding Support
The Legume Gene Regulatory Network (LegumeGRN) prediction server is supported by Oklahoma Plant Science Research (OPSR) award for project number PSB11-031, from the Oklahoma Center for the Advancement of Science & Technology (OCAST).
For questions and suggestions regarding the web server, please contact site administrator.
Citation
Mingyi Wang, Jerome Verdier, Vagner A. Benedito, Yuhong Tang, Jeremy D. Murray, Yinbing Ge, Jörg D. Becker, Helena Carvalho, Christian Rogers, Michael Udvardi, Ji He, LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies, PLoS ONE 8(7): e67434. doi:10.1371/journal.pone.0067434