Davoud Naderi; Raheba Jami; Fazal UR Rehman
Recent Genomic research has significantly developed human knowledge on structural non-coding RNAs (ncRNAs) which folds into characteristic secondary structures and performs specific-structure ...
Recent Genomic research has significantly developed human knowledge on structural non-coding RNAs (ncRNAs) which folds into characteristic secondary structures and performs specific-structure dependent biological functions. Hence, RNA secondary structure prediction is among the most commonly evaluated issues in computational RNA biology. The aim of the present study is to introduce the key role of RNA motifs in biological processes. Such motifs are specifically effective in regulating gene expression, maintaining structure and strength of RNA molecule, splicing the early mRNA, and providing the most appropriate recognition site for protein binding. Doing research and analyzing RNA motifs requires using several methods and algorithms which can provide the structure and properties of these building blocks in organisms. Generally, the most successful computational methods used in organizing RNA include the QRNA, RNAz, and CMfinder algorithms, which correlate nucleotides and other features of RNA structure formation and maintenance. In plants, RNA Recognition Motif (RRM), an 80- amino acid protected motif, is one of the most abundant protein motifs in eukaryotes. This motif has various important roles like participating in growth processes and responding to stresses in plants, and accelerating different metabolic processes. In addition, further analysis of ORRM family members represented presence of ORRM2, ORRM3 and ORRM4 acting as RNA editing factors in mitochondria as well as ORRM6 which is a chloroplast RNA editing factor. Among the construction motifs, the Pseudoknot motif which contributes to several biological activities such as altering expression of pathogenic genes in some viruses and formation of telomerase and self-truncating introns is of great significance, since these are important breeding factors in biotechnology. Based on the results of the study, it can be proposed that further studies on bioinformatics analysis of plant motifs are required to be implemented to open new windows on controlling pathogens in plants.