A Lattice Path Algorithm for RNA Secondary Structure Prediction: an Application to a Yellow Fever Virus Molecule
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Date
2018
Department
Computer Science and Bioinformatics Program
Program
Master of Science
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This item is made available by Morgan State University for personal, educational, and research purposes in accordance with Title 17 of the U.S. Copyright Law. Other uses may require permission from the copyright owner.
Abstract
Bioinformatics algorithms and tools have been established within explicit contexts for experimental modeling and data analysis. In this thesis, a lattice path algorithm based on RNA combinatorics is proposed for RNA secondary structure prediction. The developed lattice path algorithm graphically represents a certain subset of lattice path, a type of combinatorical objects enumerated by the RNA numbers, that are related to RNA secondary sequences which are composed of the RNA alphabet {A,U,G,C}. The algorithm supports visualizing all possible predicted foldings of an RNA sequence. The algorithm is applied to an RNA subsequence of a conserved structure of the 3' untranslated region of the yellow fever virus. The thermodynamic algorithm, RNAfold, is utilized to obtain minimum free energy and base pair probabilities of the predicted folds. Constraints are applied to enforce particular RNA secondary structure foldings. By applying the lattice path algorithm and RNAfold, the results show additional possible folds of the target RNA sequence. Results illustrate new predicted secondary sequences for the conserved structure of the 3' untranslated region of the yellow fever virus. The lattice path algorithm correctly predicted the secondary structure of an RNA subsequence derived from the peptidyl transferase center region of a ribosomal RNA. Subject to further validation, the algorithm could be applied to the prediction of mucleic acids aptamers for different medical applications. With a precise computational approach and a rigorous model of bioinformatics algorithms, RNA secondary structure prediction can be achieved with this approach, which can be valuable for understanding RNA structure and function.