Transcriptomic Analysis Through Denovo Assembly, Mapping and Annotation for Identification of Salinity Stress Tolerant Genes in Rice (Oryza Sativa)

Abstract

Rice is predominant dietary energy source provides 20% of the world’s dietary energy supply. Plant growth and development is negatively affected by several environmental factors, such as drought, salinity, and extreme temperature which leads to yield losses. A vital role is performed by transcriptome analysis for characterization and identification of the genes conferring tolerance to salt stresses and map out signaling pathways and ultimately utilize this information for improving the salinity stress conditions in salinity susceptible rice variety IR-64. Denovo assembly performed with Omics-box provided total of 95,242 transcripts, 65,028 total genes with GC percentage 44.41 contigs with N50, Average contig length and median value of 1,726,986.39 and 542 respectively. BLAST analysis for 95,242 contigs revealed 89,595 top Blast hits. A total of 4815 protein families in IR- 64 rice variety with salinity stress treatment have been identified using InterProScan tool. The maximum no. of GOs are observed to be retrieved from InterPro database followed by Uniprot GO central ,TAIR, Ensemble plants, GOC. Total annotations found are 4,81,294 involved in 3 different process of molecular function (MF), cellular component(CC), and biological process(BP). Putative transcripts were screened against the KEGG database, which resulted in 148 different pathways. The maximum no. of enzymes working was observed in Transferases class of enzymes. These studies uncovered the recent advances in rice breeding, genomics and functional analysis of genes combined with high throughput sequencing technique have increased the changes to achieving multiple stress tolerance. The study aimed to have a better understanding of the genes expressed 105 during stress conditions which have successfully explained the whole scenario that what are the possible mechanisms occurring during salinity stress conditions in IR-64 Rice variety through In-Silico analysis.