A Computational Approach To Rna Sequencing For Secondary Structure Prediction

Abstract

RNA is not only a messenger of genetic codes from DNA to protein but also an active molecule in various biological functions. An RNA sequence and the structure formed determines the functions. Prediction of secondary structure of RNA is useful in determining its functions such as regulation of gene expressions, sensing of ligand, enzymatic features, translational control in mRNA, and replication in single-stranded RNA viruses. Also, RNA structures will provide insights into evolution, biology, and design of therapeutics. In this thesis research, I used hydrogen bond maximizing algorithm to predict RNA secondary structure, hydrogen bond maximizing program designed by Rex A. Dyer to create a Nussinov matrix, and a recursive or iterative algorithm to decode the Nussinov matrix, which gives RNA secondary structure. The Perl and Python programming languages have been used to solve the same problem in a recursive and iterative way. The comparison of execution time between an iterative and a recursive algorithm was done. The comparison between the programming languages Perl and Python gave an insight into speed, readability, and simplicity of these two programming languages, and comparison between recursive and iterative algorithms showed which one was faster in practice.