Bioinform Int | Volume 1, Issue 3 | Case Report | Open Access

A Computational Network Biology Approach to Understand Salinity Stress Response in Rice (Oryza sativa L.)

Samarendra Das1,2,3, Swarnaprabha Chhuria4,5, Eric C Rouchka3,6 and Shesh N Rai2,3,7,8,9*

1Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, India 2Biostatistics and Bioinformatics Facility, JG Brown Cancer Center, University of Louisville, USA
3School of Interdisciplinary and Graduate Studies, University of Louisville, USA 4Department of Horticulture, Institute of Agricultural Sciences, SOA University, India
5Department of Horticulture, College of Agriculture, Odisha University of Agriculture and Technology, India
6Computer Engineering and Computer Science, Speed School of Engineering and Bioinformatics and Biomedical 
Computing Laboratory, University of Louisville, USA
7Alcohol Research Center, University of Louisville, USA 
8Hepatobiology and Toxicology Center, University of Louisville, USA 
9Department of Bioinformatics and Biostatistics, University of Louisville, USA

*Correspondance to: Shesh N Rai 

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Rice  (Oryza  sativa  L.),  the  major  staple  food  for  more  than  half  of  world’s  population,  is  being 
seriously affected by salinity stress worldwide. Salinity tolerance in rice is governed by many genes, 
identification of these stress responsive key genes as well as understanding the underlying cellular 
mechanisms  is  of  paramount  importance  for  developing  salt  tolerant  varieties.  In  this  study, 
meta-analysis was performed to combine gene expression gene expression datasets related to the 
identification of salinity stress responsive genes. A two-stage filtering approach was used to initially 
identify relevant genes. Then, a weighted gene co-expression network analysis was performed to 
detect the various gene modules associated with salinity stress in rice followed by DHGA approach 
to detect hub genes and unique hub genes. Moreover, other bioinformatics tools and techniques like 
Gene Ontology, motif analysis, protein structure prediction and protein-protein interactions were 
used to understand the salinity stress response mechanism in rice. Through the hub gene detection 
approach, 167 and 178 hub genes were identified in salinity stress and normal condition respectively, 
where 121 hub genes were common to both the conditions and 46 were unique to salinity stress 
condition. The functional enrichment analysis of hub genes further revealed their involvement in 
various processes linked with the salinity stress in rice. The 46 salinity stress genes were further 
analyzed with QTL, protein-protein interaction, gene ontology and motif analysis. These identified 
genes and mechanisms will add to the understanding of salinity response and its regulation in rice.


Gene; Gene co-expression Network; Salinity; Hub gene; Rice


Das S, Chhuria S, Rouchka EC, Rai SN. A Computational Network Biology Approach to Understand Salinity Stress Response in Rice (Oryza Sativa L.). Bioinform Int. 2020;1(1):1003..

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