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DPA-RNAPredict: A dynamic programming algorithm for RNA secondary structure prediction

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(Thesis) M.Sc.
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RNA plays an important role within cellular life forms, and RNA secondary structure prediction is a significant area of study for many scientists seeking insights into potential drug interactions or innovative new treatment methodologies. By accurately predicting the structure of RNA, we can better determine its function, since function is largely determined by structure. Through this research, a software package, DPA-RNAPredict, is developed for RNA secondary structure prediction using energy minimization evolved from Dr. Wiese's lab. Through the use of a DPA, a substantial improvement is provided in terms of computational run time compared to RnaPredict and P-RnaPredict, both EAs, and SARNA-Predict, a SA algorithm for RNA folding. The prediction accuracy of DPA-RNAPredict is also compared against these algorithms using the same thermodynamic model. The DPA-RNAPredict INN-HB thermodynamic model outperforms the Nussinov DPA, and provides competitive results when compared against SA and EAs for RNA secondary structure prediction.
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