【作者】 李阳；

【导师】 祝烈煌； 宋丹丹；

【作者基本信息】 北京理工大学 ， 生物医学工程， 2011， 硕士

【摘要】 随着越来越多的非编码基因及功能被识别和揭示,人们逐渐认识到非编码RNA的重要性。由于其结构决定功能,研究RNA的二级结构有着非常重要的意义。非编码RNA基因的数目的庞大以及生物实验的局限性,使得二级结构预测成为非编码RNA识别及其功能研究的重要途径。RNA序列的研究方法主要有两个大的方向,分别是基于多序列比较分析方法的RNA二级结构预测方法和基于单序列的RNA二级结构预测方法。本论文重点介绍单序列的处理方法:最大碱基配对数算法和基于最小自由能算法,它们是确定性动态规划类算法,但未能很好的解决伪结和准确性问题;同样介绍了基于概率论的隐马尔可夫模型(HMM)和随机上下文无关文法(SCFG),这两个算法则存在着数据归纳偏置的问题,并且存在较高的计算复杂度。条件随机场(CRFs)在图像标注、文本标注等领域应用良好,并在预测同源RNA序列的共有结构时处理的结果较好。本论文研究就是在已知一条RNA的序列编码信息之后,通过条件随机场模型方法来计算出该RNA序列的二级结构。本研究针对单个RNA序列二级结构预测中传统算法的不足,结合CRFs模型,通过改变传统的基于概率模型中严格的条件独立性假设,并在其中加入通过长期研究得到的先验知识,可以很好的解决数据归纳偏置及准确性的问题,得到理想的RNA二级结构。更多还原

【Abstract】 When more and more non-coding genes and their functions have been identified and revealed, the researchers come to realize the importance of non-coding RNAs. Because the structure determines the function, it is very important for the studying of RNA secondary structures. Because of the large number of non-coding RNAs and the limitations of biological experiments, RNA secondary structure prediction becomes a important way for researching the function of non-coding RNAs.There are two major directions for RNA sequence research methods. They are the RNA secondary structure prediction methods based on multiple comparative sequence analysis and RNA secondary structure prediction methods based on a single sequence. This paper focuses on the single-sequence secondary structure methods. The base pair maximization algorithm and minimum free energy algorithm are used to deal with the prediction of RNA secondary structure, and they are deterministic dynamic programming algorithm, but they are not good solution result from the accuracy issues. In this paper, we also introduced Hidden Markov model (HMM) and random context-free grammar (SCFG), and they are based on probability theory, but these two algorithms has a problem of inductive bias data as well as the problem of the higher computational complexity.Conditions Random Fields takes a good performance on the fields of image labeling and text marking. Besides this, CRFs also has a good performance on predicting the structure of homologous RNA sequences. This study is on the condition of a known sequence of RNA, and uses a conditional random field based method to predict the sequence of the RNA secondary structure. In this study, by researching on the weak points of traditional algorithms, we used the CRFs model and loosed the strict conditional independence assumption of the traditional probabilistic models to deal with the problem of RNA secondary structure prediction. In the algorithm we also adopted some priori knowledge. The problem of data bias is solved, and a RNA secondary structure is predicted much more precisely.更多还原