Publication Date
2008
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
Abstract
This work explores a new approach in using genetic algorithm to predict RNA secondary structures with pseudoknots. Since only a small portion of most RNA structures is comprised of pseudoknots, the majority of structural elements from an optimal pseudoknot-free structure are likely to be part of the true structure. Thus seeding the genetic algorithm with optimal pseudoknot-free structures will more likely lead it to the true structure than a randomly generated population. The genetic algorithm uses the known energy models with an additional augmentation to allow complex pseudoknots. The nearest-neighbor energy model is used in conjunction with Turner’s thermodynamic parameters for pseudoknot-free structures, and the H-type pseudoknot energy estimation for simple pseudoknots. Testing with known pseudoknot sequences from PseudoBase shows that it out performs some of the current popular algorithms.
Recommended Citation
Pham, Ryan, "A Seeded Genetic Algorithm for RNA Secondary Structural Prediction with Pseudoknots" (2008). Master's Projects. 105.
DOI: https://doi.org/10.31979/etd.tn97-fmrj
https://scholarworks.sjsu.edu/etd_projects/105
