RNA Secondary Structure Prediction Modeling And Application Study

In recent 20 years, biologists have achieved some significant breakthroughs and improvements in RNA research field. A batch of new RNAs with important functions such as ribozyme, antisense RNA, snoRNA, siRNA and MicroRNA are gradually discovered. These discoveries make people understand more about the diversity and complexity of RNA. It is revealed that RNA not only serves as carrier and intermediate of genetic information, but also plays an important role in catalysing RNA cleavage and splicing, modifying pre-RNA, regulating gene expression and organism development etc. The study of new RNAs expands the research field of RNA and results in an amount of significant and challenging subjects in life science. A brand-new branch of modern molecular biology RNAomics is fast growing up after functional genomics and proteomics.Like proteins, RNA's various functions are closely affiliated with theirs specific structures. In contrast to the diversity of RNA primary structure, the diversity of its secondary and high-class structure possesses rich biological significance. Therefore, the RNA structure is the key to probe into RNA complicated functions and characteristics. Because of fast decomposition and hard crystallization of RNA, it's not easy for experiment methods such as X-ray crystallography and NMR to determine RNA 3D-structures. Moreover, using this means is not only very time consuming and labor intensive but also costly. Therefore, the use of computational methods to predict RNA structure is highly desired, particularly now that the entire genomes of a variety of organisms have been sequenced.Early in 1981, Zuker proposed free energy minimization algorithms and with more than 20 years improvements it has been the most widely used method to predict RNA secondary structure. However, this method cannot meet the higher requests for current RNA structure prediction day by day. There are two reasons. On the one hand, its average prediction accuracy is not high, only 50~70%. On the other hand, it cannot predict pseudoknots and more complicated 3D-structures owing to its intrinsic algorithm limitation. In order to solve these problems, some new algorithms and modified algorithms are proposed one after another. So the prediction of RNA structure catches researchers' attention again and becomes a hot topic in bioinformatics. In this thesis, we review and summarize various existing RNA structure prediction methods as well as explore the developing direction of the leading RNA structure prediction methods of new generation. Besides putting forward some new thingkings, we have also contributed a little work to the application study on RNA secondary structure prediction.The thesis mainly includes four parts of following contents and conclusions:(1) Currently some important prediction methods of RNA secondary structure are introduced, and the existing problems among them are analyzed. Firstly, two models of the traditional comparative sequence analysis method—covariance model and stochastic context-free grammars model are roughly described here. Secondly, we introduce the famous free energy minimization algorithm and base pair maximization algorithm, and detailedly explain their realization with dynamic programming algorithm as well as RNA free energy...