A bioinformatics pipeline for Screening Nodule-specific Cysteine-Rich (NCR) like peptides from Trigonella foenum-graecum and Medicago truncatula genomes

Legumes ( Fabaceae ) form symbiotic associations with rhizobia ( Rhizobiaceae ), but unchecked bacterial proliferation can lead to loss of host fitness. In the Inverted Repeat Lacking Clade (IRLC) legumes, Nodule-specific Cysteine-Rich (NCR) peptides regulate rhizobial differentiation, limiting their pathogenic potential and maintaining a balanced symbiosis that supports plant growth. In planta screening of NCRs is challenging for multiple reasons; hence, bioinformatics prediction from genomic and transcriptomic data offers a high-throughput alternative. In this study, an \textit{in silico} workflow was developed that incorporates established bioinformatics tools with R programming and local BLASTp to identify NCR-like peptides from genomic data. Various R libraries, including packages of the Bioconductor suite, ampir,and dplyr, were used to screen peptides based on specific criteria, such as a length of 20–180 amino acids and at least four cysteine residues. The identified peptides were further analyzed for motif patterns, sequence similarity with NCRs previously reported by Montiel et al. [1], and signal peptide cleavage sites. Using this approach, 284 and 497 NCR-like peptides with conserved cysteine positions were identified in Trigonella foenum-graecum and Medicago truncatula genome assemblies, respectively. The predicted physicochemical properties of the screened peptides from the two legumes were comparable because they are evolutionarily closely related. This suggests that the workflow can be applied to other IRLC legumes to screen for NCRs and explore their functions in rhizobial symbiosis and antimicrobial defense.