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Abstract

Climate change is predicted to have a grave threat to crop growth due to the damaging effects resulting abiotic stresses like drought, heat, cold, salinity, etc., which might lead to catastrophic loss of crop productivity and may end up with a wide spread famine. Rice, being the major staple food crop for more than half of the world�s population is seriously affected by these abiotic stresses. Rice is simultaneously exposed to these multiple stresses, which brings various changes in molecular mechanisms governed within the cell. The identification and characterization of molecular components underlying to various stress response mechanism in rice, is of great interest to crop breeding in improving grain yield. In this study, meta-analysis was performed on four different abiotic stresses in rice and Arabidopsis by analyzing 446 micro-array samples pertained to various micro-array experiments. For the identification of stress responsive genes, a gene selection technique called Bootstrap- Maximum Relevance and Minimum Redundancy in conjunction with non-parametric statistical testing procedure has been proposed. The selected genes are then assessed by quantitative trait loci, gene ontology analysis. The gene co-expression modules as well as networks are constructed by using weighted gene co-expression network analysis technique for four abiotic stresses in both the plant species. For each stress, the hub genes presented in the network are identified by using a proposed technique called bootstrap gene co-expression analysis. Based on the hub genes, we also predicted the three-dimensional structures of some crucial proteins related to the abiotic stress response in rice for understanding the roles of these proteins in these networks. This computational study puts some new light on the mechanism of abiotic stress tolerance in rice.

Biography

Samarendra Das has joined the Agricultural Research Service of ICAR in 2013. After completing the pre-requisite training programs, he has finally joined ICARIndian Agricultural Statistics Research Institute, New Delhi as a Scientist in October, 2013. Within the short period of time, he is being associated with three institute funded projects, as PI in two projects and Co-PI in one project. He has also published three research papers and also involved in teaching MSc and PhD students. His research interest lies in the field of Modeling of Gene Regulation, Gene Selection, etc.

Email: samarendra4849@gmail.com