Use of Fuzzy Logic in RNA-seq Data for Transcript Mining

Transcriptome profiling through next generation sequencing generates a massive list of transcripts from samples exposed to specific stimuli. This study aims to find differentially expressed genes from RNA-seq data that can be used to develop a molecular-based nitrogen sensor. The transcripts were screened using classifiers based on the expression of genes following exposure of plants to nitrogen deficiency and readaptation. Results revealed genes with antagonistic response to nitrogen availability with 135 and 67 from the leaf and root samples, respectively. Some of these genes were found associated with glyoxylate and dicarboxylate metabolism, RNA degradation, oxidative phosphorylation, carbon fixation degradation, and metabolism of amino acid, purine, fatty acid, and butanoate. Also, a number of transporters showed considerable response to nitrogen. The identification of DEGs using fuzzy logic show potential to generate a list of candidate genes for biosensor development.