RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

• Main Authors: Arjun Krishnan, Chirag Gupta, Madana M. R. Ambavaram, Andy Pereira
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2017-09-01
• Series: Frontiers in Plant Science
• Subjects: rice; coexpression network; drought; abiotic stress; webserver; database
• Online Access: http://journal.frontiersin.org/article/10.3389/fpls.2017.01640/full

Transcriptional profiling is a prevalent and powerful approach for capturing the response of crop plants to environmental stresses, e.g., response of rice to drought. However, functionally interpreting the resulting genome-wide gene expression changes is severely hampered by the large gaps in our genomic knowledge about which genes work together in cellular pathways/processes in rice. Here, we present a new web resource – RECoN – that relies on a network-based approach to go beyond currently limited annotations in delineating functional and regulatory perturbations in new rice transcriptome datasets generated by a researcher. To build RECoN, we first enumerated 1,744 abiotic stress-specific gene modules covering 28,421 rice genes (>72% of the genes in the genome). Each module contains a group of genes tightly coexpressed across a large number of environmental conditions and, thus, is likely to be functionally coherent. When a user provides a new differential expression profile, RECoN identifies modules substantially perturbed in their experiment and further suggests deregulated functional and regulatory mechanisms based on the enrichment of current annotations within the predefined modules. We demonstrate the utility of this resource by analyzing new drought transcriptomes of rice in three developmental stages, which revealed large-scale insights into the cellular processes and regulatory mechanisms involved in common and stage-specific drought responses. RECoN enables biologists to functionally explore new data from all abiotic stresses on a genome-scale and to uncover gene candidates, including those that are currently functionally uncharacterized, for engineering stress tolerance.