Bioinform Int | Volume 1, Issue 3 | Case Report | Open Access
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
Fulltext PDFRice (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..