The Zhao Bioinformatics Laboratory
PlantGRN: Modeling and Deciphering Plant Transcriptional Regulatory Networks   
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DeGNServer:Deciphering Genome-Scale Networks through High Performance Reverse Engineering Analysis

Analysis of genome-scale Gene Networks (GNs) from large-scale gene expression profiles opens the door to uncover new biological knowledge. However, inferring genome-scale Gene Network (GN) from large-scale gene expression data and subsequent functional module mining are very computational intense tasks; therefore it requires both efficient algorithms and parallel computing engineering in order to enable and empower Genome-scale Gene Network analysis. Context likelihood of relatedness (CLR) method based on the mutual information for scoring the similarity of gene pairs is one of the most accurate methods to infer gene networks. But it is computational unfeasibility to decipher a genome-wide network with large genomes, such as many plant genomes, with large-scale gene expression profiles, on a single computer due to limits on memory and CPU capacity. DeGNServer is a high performance web server that is capable of constructing genome-scale networks and further mining sub-networks from large genome-scale expression profiles for the species with large genomes/large number of genes.

The major feature includes:

  1. DeGNServer integrates six proven association methods(Spearman rank correlation,Pearson correlation,Mutual-information, Maximum information coefficient, Kendall rank correlation,Thei-Sen Estimator) for co-expression GN analysis and further utilize Context Likelihood of Relatedness approach for gene network analysis. In order to enable and empower genome-scale GN analysis, all algorithm have been implemented and deployed on our in-house parallel computing platform, namely BioGrid, which has over dedicated 700 CPU cores;
  2. Subnetwork identification and visualzation based on community structure mining methods;


   Funding by the National Science Foundation    Funding by the Oklahoma Center for the Advancement of Science & Technology    Additional funding by the Samuel Roberts Noble Foundation

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