Genome-wide identification and analysis of GRF (growth-regulating factor) gene family in Camila sativa through in silico approaches

GRFs (growth-regulating factors) are transcription factors that significantly influence plant development and stress response. In the present study, genome-wide discovery and analysis of the CsGRF family and its significant roles in Camelina sativa development was done utilizing model GRF genes of Arabidopsis thaliana that are available in the public domain databases. Gene structure analysis, exon and intron structures, phylogenetic analysis, mapping of various GRF genes on the chromosome's distribution, conserved domain analysis, and synteny analysis will be systematically categorized. Investigation of cis -regulatory elements will also be carried out using various bioinformatic approaches. In the C. sativa genome, 19 GRF gene members and 4 GRF variants were found using publicly available genomic data. The encoding regions of GRF1, GRF2, GRF2A, and GRF8 were similar and maximal, i.e., 2046 bp, which encodes 555 amino acids, followed by GRF2. C. sativa has the most GRF gene representatives, scattered throughout six chromosomes, and appears to have 3 to 4 protein-coding regions. GRF is involved in biological processes (44.7%), molecular activities (50%), and cellular functions (24.6%). The molecular weights of GRF proteins range from 29.57 to 61.57 kDa. The majority of GRF proteins have a theoretical PI between 7.0 and 9.42. All CsGRFs have preserved QLQ and WRC (Trp, Arg, Cys) domains. All C. sativa proteins have SNH and QG (Gln, Gly) domains. The motif composition and gene structure of CsGRFs from the same sub-group were similar. In the analysis of conserved domains, the motifs of CsGRF genes were highly conserved. According to synteny investigations, large-scale duplications played a significant role in expanding the CsGRF family. Our findings will help to understand the functions of the GRF family in the evolutionary and physiological aspects of C. sativa and provide a future direction for novel work to improve crop productivity. Identifying single-gene families in multiple plant species is best to enhance crop productivity, growth, and development.