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Welcome to HRGRN
A Graph Search-Empowered Integrative Database of Arabidopsis Signaling Transduction, Metabolism and Gene Regulation Networks   
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HRGRN: A Graph Search-Empowered Integrative Database of Arabidopsis Signaling Transduction, Metabolism and Gene Regulation Networks

The biological networks controlling plant signal transduction, metabolism, and gene regulation are composed of not only tens of thousands of genes, compounds, proteins, and RNAs but also the complicated interactions and coordination among them. These networks play critical roles in many fundamental mechanisms, such as plant growth, development and environmental response. Although much is known about these complex interactions, the knowledge and data are currently scattered throughout published literatures, publicly available high-throughput datasets, and third-party databases. Many ���unknown��� yet important interactions among genes need to be mined and established through extensive computational analysis. However, exploring these complex biological interactions at network level from existing heterogeneous resources remains challenging and time-consuming for biologists.

Here, we introduce HRGRN, a graph search-empowered integrative database of Arabidopsis signal transduction, metabolism, and gene regulatory networks. HRGRN utilizes the highly scalable graph database, Neo4j, to host large-scale of biological interactions among genes, proteins, compounds and small RNAs that were either validated experimentally or predicted computationally. The associated biological pathway information were also specially marked for the interactions that are involved in the pathway to facilitate the investigation of cross-talks between pathways.

Furthermore, HRGRN integrates a series of graph path search algorithms to discover novel relationships among genes, compounds, RNAs and even pathways from heterogeneous biological interaction data that could be missed by traditional SQL database search methods. Users can also build sub-networks based on known interactions. The outcomes are visualized with both rich text, figures and interactive network graphs on web pages.

   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


Developed by The Zhao Lab, The Samuel Roberts Noble Foundation, Inc.