Transcript mining using fuzzy rough set theory in Oryza sativa grown under N-limited condition

In rice cultivation, crop yield and productivity rely on the efficient supply of nitrogen. Mismanagement of nitrogen, such as insufficient and oversupply, lowers the plant vigor and stability. Using next-generation sequencing, transcriptome mining can discover nitrogen-responsive genes, regulators, and markers that can be used to improve the nitrogen use efficiency in rice plants. Here, we present an extension of the RNA Sequencing pipeline to generate a list of candidate genes that have the potential for molecular-based sensor development in Oryza sativa for nitrogen monitoring. The RNA Sequencing data used in this study represents 4714 genes from O. sativa seedlings that showed a dynamic response toward nitrogen availability. To generate a pool of candidate genes, we designed an algorithm for a two-step screening process that evaluates the expression of each gene across different sampling points using fuzzy logic. On the first screening, the genes were clustered based on their expression pattern, wherein a total of 135 genes from leaf tissues were found to exhibit an antagonistic response to nitrogen starvation and adaptation. These were further evaluated for the second screening where 27 genes showed at least 72% change in expression. Most of the genes were found associated with the plant’s response to stress, biotic and abiotic stimulus, transport, protein modification, and metabolic processes. This set of genes will serve as the target transcripts for nitrogen sensor development.