The Research Of RNA Secondary Structure Prediction Algorithm Based On Comparative Sequence Analysis

The investigation of RNA (ribonucleic acid) secondary structures is a challenging task in molecular biology. Secondary structure can be determined directly by x-ray diffraction or NMR (Nuclear Magnetic Resonance), but this is difficult, slow and expensive. Moreover, it is currently impossible to crystallize most RNAs. So it is very necessary to develop mathematic and computational methods to predict the secondary structure of RNA.The research of this paper puts its emphasis mainly on RNA secondary structure prediction problem. By fully taking advantages of the popular method, we combine the minimum free energy model and comparative sequence analysis. Two new methods are proposed in this paper to improve the accuracy of the RNA secondary structure prediction results. The creativities and contribution are discussed in detail as follows:Firstly, the methods to predict RNA secondary structure are introduced, including the mathematic models, free RNA secondary structure databases, main algorithms and softwares. Then seven popular softwares are compared detailedly to show their advantages, disadvantages and their scope of application.Secondly, Hopfield network is modified to predict RNA secondary structure. An algorithm for finding a near-maximum independent set in a circle graph is presented. The algorithm is modified for predicting the secondary structure in RNA. Because of the stochastic initialization, sometimes improper initialization will cause a large deviation from real structure. In this paper, we use the homologous sequence to guide the initialization for Hopfield network and modify the stem pool to improve the accuracy of the prediction results. Experiments prove that the modified algorithm performs better than the original algorithm.Thirdly, an algorithm based on forest representation and genetic algorithm is proposed to predict RNA secondary strutrue. When the predicted RNA is complex or the similarity the homologous RNA is low, the results from Hopfield network easily fall into local optimization. So we introduce an RNA Secondary Structure Prediction algorithm based on forest representation and genetic algorithm to escape from the local optimization. We use the result of Hopfield network to guide the initialization of genetic algorithm. And homologous RNA sequences have characteristic structures that are highly conserved in evolution. Therefore, so we choose the structure which has the highest similarity score with the structure of homologous sequence as the result. Experiments prove that the algorithm largely solves the problem of local optimization and the accuracy of the prediction results is further improved.Fourthly, based on the above algorithms, we developed an RNA secondary structure prediction system.Finally, this paper looks forward to RNA secondary structure prediction prospect and some vital aspects that may be conducted in the future investigations are discussed.