A new algorithm for quantitative deciphering of pre-mature MiRNAs using some statistical parameters

It is now a prime research area to understand microRNA (miRNA) in a quantitative manner. The researchers across the globe have been working on quantitative estimation of microRNA (miRNA) from different angles. Our present study would be a new addition in the present scenario. The miRNAs are non-coding short ribonucleic acid (RNA) molecules, approximately ~25 nucleotides long. MiRNAs help in understanding the entire scope of post-transcriptional gene regulation. MiRNAs regulate numerous cellular processes and have roles in tissue remodeling. In the present work the authors made an exhaustive study on deciphering the inherent statistical behavior in pre-mature miRNA strings through few statistical parameters namely Hurst Exponent values, Variance, Poly String Mean and Poly String Standard Deviation. These four parameters not only quantify miRNA but also classify the miRNA strings. In the present work the authors have to tried to explore the quantification and classification based on statistical results on nucleotide strings of pre-mature miRNAs of the three organisms Homo sapiens (hsa), Macaca mulatta (mml) and Pan troglodytes (ptr).