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Bioinformation. 2019 Jul 31;15(7):480-489. doi: 10.6026/97320630015480. eCollection 2019.

Identification of putative drought-responsive genes in rice using gene co-expression analysis.

Author information

1
Hunan Rice Research Institute, Changsha, 410125, China.
2
College of Pharmacy, Hubei University of Chinese Medicine, China.
3
Wuhan Benagen Tech Solutions Company Limited, Wuhan 430070, China.
4
State key laboratory of hybrid rice, Changsha, 410125, China.
5
Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture,Changsha, 410125, China.

Abstract

Drought is one of the major abiotic stresses causing yield losses and restricted growing area for several major crops. Rice being a major staple food crop and sensitive to water-deficit conditions bears heavy yield losses due to drought stress. To breed drought tolerant rice cultivars, it is of interest to understand the mechanisms of drought tolerance. In this regard, large amount of publicly available transcriptome datasets could be utilized. In this study, we used different transcriptome datasets obtained under drought stress in comparison to normal conditions (control) to identify novel drought responsive genes and their underlying molecular mechanisms. We found 517 core drought responsive differentially expressed genes (DEGs) and different modules using gene co-expression analysis to specifically predict their biological roles in drought tolerance. Gene ontology and KEGG analyses showed key biological processes and metabolic pathways involved in drought tolerance. Further, network analysis pinpointed important hub DEGs and major transcription factors regulating the expression of drought responsive genes in each module. These identified novel DEGs and transcription factors could be functionally characterized using systems biology approaches, which can significantly enhance our knowledge about the molecular mechanisms of drought tolerance in rice.

KEYWORDS:

WGCNA; co-expressed genes; drought; network analysis; transcriptome

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