Comparison Algorithms Of RNA Secondary Structures Based On Image Processing

In recent years, RNA is very important in bioinformatics research. For the biochemical function of RNA, more and more people take part in researches and analysis of RNA. We know RNA which can be used to synthesis protein, transfer genetic information and regular gene expression. Moreover, people realize that RNA contains various types, biology functions and complexity structures. But in the past, RNA is only the transmission of DNA and protein.We can understand the functional relationship of two RNA secondary structures by computing the distance between them. RNA structures and functions are related, therefore it is very import to understand the structure of RNA in order to discovery its many functions. For structure RNA or non-coding RNA, structure conservatism is greater than sequence conservatism. Only starting from the sequence, we can not dig out its meaningful biological knowledge. It is important to understand its structure in order to explore its functions. So we should find out RNA structures, it is vital to understand the mechanism of various types of RNA in cell processes. RNA secondary structure provides a peculiar computational modeling for structural biology and evolutionary biology.RNA sequence structure comparison, which is one of the basic research contents in bioinformatics. By comparing the similarity of RNA sequences and structures, many people are able to find the functional and evolutional information hiding in RNA sequences. This has vital researching significance in the classification of RNA sequences and the prediction of RNA secondary structures. In the past, the methods of computing the distance between RNA secondary structures, which are achieved based on denotation of strings or trees. In view of RNA secondary structure is important in computational biology and RNA research. It is vital to study comparison method of RNA secondary structure, so we have developed a new and objective comparison algorithm of RNA structure. The algorithm based on image processing for computing the distance between RNA secondary structure, which is used to analyzing mutation of RNA.In this article, firstly, we introduce the background and significance of its topics, and then introduce the basic knowledge of RNA secondary structure prediction and comparison. It includes RNA secondary, prediction method and the tools, in the end. it describes the comparison algorithms of RNA structure simply; then gives another comparison method of RNA secondary structure which based on the image processing method, which contains the image representation of RNA secondary and feature vector extraction method, RNA structure entropy based on images, mutual information and two-dimensional correlation comparison algorithm. In this article, we use the minimum free energy to predict RNA secondary structure, then describe it as two-dimensional image forms, and extract the projection feature vector according to the image, based on these things, then give two-dimensional image correlation, entropy, and RNA secondary structure comparison algorithm of mutual information. For getting better evaluation about this method, using the RNA structure distance of this new method to measure RNA mutation, which locate the specific point mutations, also named the sequence of single point mutations, it is used to analyze the mutation of the given RNA sequence secondary structure, after analyzing the mutation, we can obtain the biggest distance comparing with the original structure, through the single point mutation, we can find the most significant RNA secondary structure from all of the mutation sequences. It describes the details about how to use the structure measure to analyze the RNA secondary structure mutation. Through the distribution maps of different distances, we can grasp the whole situation about the forward and backward of mutation in the structure differences, and the general number of the mutation on the structure differences. In order to provide more candidates and more reasonable structure measure to biologists, we select and output four most significant mutation sequences and structures which have the biggest structural differences, the numerical experiment results prove that our method are effective.Finally concluding the remark, we found some problems about mutation analysis of RNA secondary structure and look forward to the future of RNA research.