Mining of Stress-related Genes in Pigmented and Non-pigmented Rice using Gene Co-expression Network and Clustering Approaches

Pigmented rice has been associated with stress-resistant traits, such as resistance to abiotic and biotic stresses, but the genes network that is responsible for traits remains limited. Hence, this study aims identify stress-related in pigmented using computational approaches. The gene co-expression was constructed Pearson Correlation Coefficient (PCC) ≥ 0.9 among differentially expressed (DEGs) of trancriptomes between non-pigmented rice. clustered Markov Cluster algorithm (MCL) functional modules hub each module were determined. analyses performed determine related ontology (GO) pathway. Protein-protein interaction (PPI) from STRING database used validate analyses. A total 721 DEGs construct varieties. Of these, 614 15,259 edges identified by PCC. Using MCL, 10 clusters Three enriched seven GO terms (i.e., response stress, stimulus, transcription factor activity) are indicating highly correlated genes. Interestingly, nine found be drought tolerance, disease hormone biosynthesis. Validation revealed 48 also connected PPI network, suggesting their potential candidate proteins traits. This demonstrated molecular clustering approach efficient identifying genes, which could provide new insights understanding plant responses stresses.