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

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dc.contributor.authorKrishnan, Arjunen
dc.contributor.authorGupta, Chiragen
dc.contributor.authorAmbavaram, Madana M. R.en
dc.contributor.authorPereira, Andyen
dc.date.accessioned2019-04-24T18:41:39Zen
dc.date.available2019-04-24T18:41:39Zen
dc.date.issued2017-09-20en
dc.description.abstractTranscriptional 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.en
dc.description.notesThe study was supported by National Science Foundation (DBI-0922747).en
dc.description.sponsorshipNational Science Foundation [DBI-0922747]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fpls.2017.01640en
dc.identifier.eissn1664-462Xen
dc.identifier.other1640en
dc.identifier.pmid28979289en
dc.identifier.urihttp://hdl.handle.net/10919/89109en
dc.identifier.volume8en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectriceen
dc.subjectcoexpression networken
dc.subjectdroughten
dc.subjectabiotic stressen
dc.subjectwebserveren
dc.subjectdatabaseen
dc.titleRECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Responseen
dc.title.serialFrontiers In Plant Scienceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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