Based On Genetic Algorithm For Rna Secondary Structure Prediction

Ribonucleic Acid (RNA) is essential for the functioning of a cell and is the product of gene transcription. RNA is involved in the building of proteins in the process which is known as translation. The structure of RNA molecule determines its function. This can be used for understanding RNA-Protein interactions which are relevant in pharmacology as well as genetic diseases. The primary structure, secondary structure and tertiary structure constitute the molecular structure of RNA. The secondary structure is located between the primary structure and the tertiary structure, which can store more high-level structural information than the other kind of structure. Hence, RNA secondary structure prediction is one of the important problems in the research field of bioinformatics. As it is restricted by the experiment conditions, most of RNA secondary structures are difficult to predict through experiment ways, so predicting RNA secondary structure by computer is an efficient way.Attempts to predict automatically the RNA secondary structure can be divided in essentially two general approaches. The second type of approach is sequence comparison technology which is more empirical and it involves searching for the RNA structure from database, so this technology not easy to popularize. The first involves the overall free energy minimization by adding contributions from each base pair, bulged base, loop, and other elements. Genetic algorithms are found to be suitable for this purpose. With the parallel search genetic algorithms can effectively reduce the negative impact from the operation of certain irreversible. So, the GA is considered more suitable for the RNA secondary structure prediction. When adopting GA approaches, two important issues––selection pressure and population diversity––must be considered. Emphasis on selective pressure accelerates the optimization convergence but potentially causes premature convergence because of hastened loss of diversity. On the contrary, maintaining diversity can yield a better solution quality, but often slows down the convergence speed due to the lack of selection pressure. Therefore, a good GA scheme must simultaneously address these two issues. GA should pursue a good balance between selection pressure and population diversity. In this dissertation, a new predicting method called Tabu Genetic Algorithm based RNA secondary structure prediction (TGARNA) is developed. In the TGARNA algorithm, a new method for testing the compatibility of stems is given in order to improve the performance of the population. In addition, tabu search is integrated into genetic operations in order to prevent inbreeding and maintain a high level of population diversity.The main contents of the dissertation are listed as follows:First, we introduced some basic knowledge of RNA secondary structure, such as the development of the method, cardinal principle, limitations and so on. Then introduced some typical Genetic Algorithm based RNA secondary structure prediction.Then, we realize the Genetic Algorithm based RNA secondary structure prediction (GARNA), and make the discussion of relevant parameters.Finally, we realize the Tabu Genetic Algorithm based RNA secondary structure prediction (TGARNA), compared with the GARNA, TGARNA is an effective method for predicting RNA secondary structure.