RNA二级结构的若干计算生物学问题研究
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摘要
对于RNA分子结构与功能的研究是当今生物信息学领域的一个非常重要的课题。随着研究的不断深入,RNA正在从人们眼中简单的、线性的、功能单一的分子形象演变成今天种类多样,结构复杂,功能特异的新个体,特别是大量非编码RNA的发现以及对于其功能特性的分析,使得人们逐步认识到RNA世界的多样性和重要性。RNA已经不仅仅被认为是DNA到蛋白质之间的一种信息传递中介,它已经逐渐在中心法则中取得了与DNA和蛋白质同等重要的地位。RNA组学(Rnomics)或者核糖组学(Ribonomics)也成为继基因组学(Genomics),蛋白质组学(Proteomics)等一系列系统生物学概念之后的一个崭新的系统交叉学科。
RNA分子结构与功能的研究不光依赖于实验手段,同时也需要借助生物信息学的方法进行分析,特别是目前对于RNA分子的研究已经进入了一个大规模的,高通量的,系统分析的时代,为了更加深入探索RNA结构与功能的关系,了解RNA在生命活动中的各种工作机制,大力发展RNA研究方面的生物信息学方法和技术显得尤为重要。在这样一个热点研究背景之下,本论文从算法设计以及计算机软件平台构建的角度,对于RNA分子结构相关的计算生物学问题进行研究。论文内容涉及RNA分子结构的表示,RNA结构预测,RNA结构比对,RNA结构的压缩和信息度的衡量,RNA综合分析平台的构建,以及非编码RNA基因预测等若干问题,属于计算机,生物,医学交叉的前沿学科领域。
论文的主要内容与成果概括如下:
(1).对于RNA分子的结构表示理论进行了系统的分析,对于各种结构表示方法进行比较,同时提出了一种基于6-D编码的RNA分子二级结构表示方法,将RNA构象的二级结构转化为结构矩阵,提取矩阵奇异值向量作为其主要结构特征,从而从代数矩阵论角度给出了其分子结构的精确描述。
(2).对于RNA分子二级结构的预测算法进行了系统的阐述,基于图论极大独立集思想提出了一种基于Hopfield网络进行并行预测的有效算法,进一步提高了RNA二级结构预测的效率。
(3).对于RNA分子二级结构的各种相似度衡量算法进行了探讨和比较,采用6-D编码设计了一种利用矩阵奇异值分解进行结构比对的算法。
(4).基于上述相似度提出了一种新的模糊核聚类(Kernel Fuzzy C-means,KFCM)算法,应用于RNA二级结构构象的聚类分析中。结果表明该聚类算法对于RNA构象分析十分有效。
(5).构建了RNA结构比对以及结构构象聚类的整合软件平台(RNACluster),将基于最小生成树(Minimum Spanning Tree,MST)表示的聚类算法应用于RNA分子的构象聚类、RNA构象转换(RNA conformational switches)以及非编码RNA预测当中。
(6).首次提出了RNA二级结构压缩的概念,设计了一种利用上下文无关文法压缩RNA二级结构的算法,构建了相关软件(RNACompress),该软件可以有效的对于RNA一级序列及二级结构同时进行建模,并且进行无损压缩。
(7).首次引入基于压缩的Kolmogorov复杂度来衡量RNA结构的信息度。并将其应用于11种GTP-binding RNA核酸适体(aptamer)的结构信息度衡量及其绑定活性与结构信息复杂度关系的定量研究中。
(8).对于非编码RNA的相关概念及其基因预测算法进行了论述。系统总结了非编码RNA相关的网上平台与数据库资源。对于非编码RNA计算生物学领域的未来研究方向和研究热点提出了自己的看法,并作了相应展望。
The computational studies of RNA structure and function are one of the hotest research topics in bioinformatics. As more and more non-coding RNAs are identified recently, RNA has been viewed as a more complex molecule in its diversity of functions rathor than barly serving as the transmitter between DNA and protein. A view that is emerging now is that RNA world are much more complicated than our previously thought, and RNAs may play the same important roles in the central dogma as DNA and protein does. In addition, "Rnomics" and "Ribonomics" has currently become another two new interdisciplines after the notion of "Genomics" and "Proteomics" were presented.
Computational techniques can be served as an extremely helpful complementarity to the experimental study of RNA structure and function, expecially when this study is in its large scale, high throughput and systematic level. In order to faciliate this study, more efficient bioinformatic analysis approaches are needed. In our thesis, we performed some basic computational studies on RNA secondary structure as well as their functions from the perspective of algorithm designning and platform implementation. The content in this thesis includes: representation of RNA secondary structure; RNA secondary structure prediction; comparison of RNA secondary structure; compression of RNA secondary structure and informational complexity measurement; construction of integrated platform for RNA structure analysis; non-coding RNA prediction, etc.
The main contributes of this thesis are listed as following:
(1). Different representations of RNA secondary structure are compared. A 6-D encoding based RNA secondary structure representation method is presented. The RNA secondary structure can be represented as a structure matrix, and the corresponding singlar value vector of this matrix is calculated to extract the main information of the RNA secondary structure. This kind of representation has provided an accurate description of RNA secondary structure mathmatically.
(2). A thoroughly discussion of RNA secondary structure prediction approches is given. Based on the definitions of Discrete Hopfield Neural Network (DHNN) and Maximal Independent Set (MIS) in Graph theory, a heuristic algorithm is presented to select stems in RNA structure as well as its application in RNA secondary structure prediction. This method has substaintially improved the efficency of RNA secondary structure prediction.
(3). Different RNA secondary structure comparison methods is discussed. A novel method is presented to compare the similarity of RNA secondary structures using a matrix representation of the RNA structures. Relevant features of the RNA secondary structures can be easily extracted through singular value decomposition (SVD) of the representing matrices.
(4). A fuzzy kernel clustering method is applied, using the similarity metric defined above, to cluster the RNA secondary structure ensembles. Our application results suggest that our fuzzy kernel clustering method is highly promising for classifications of RNA structure ensembles, because of its low computational complexity and high clustering accuracy.
(5). An integrated platform (RNACluster) is constructured to calculate and compare different distances between RNA secondary structures, and to perform cluster identification to derive useful information of RNA structure ensembles, using a minimum spanning tree (MST) based clustering algorithm. RNACluster can be used in the analysis of RNA structure ensemble clustering、RNA conformational switches and non-coding RNA prediction.
(6). An universal algorithm for the compression of RNA secondary structure is discussed (RNACompress). RNACompress employs an efficient content-free-grammar based model to compress RNA sequences and their secondary structures simultaneously.
(7). A novel informational complexity measurment of RNA seondary structure is presented based on the notion of Kolmogorov comlexity. A test of the activities of 11 distinct GTP-binding RNAs (aptamers) compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity.
(8). A survey of the concept of non-coding RNA and its computational prediction is given. A complete list of web sources for non-coding RNA analysis is compiled. Several future research directions and topics in this field are discussed.
RNA分子结构与功能的研究不光依赖于实验手段,同时也需要借助生物信息学的方法进行分析,特别是目前对于RNA分子的研究已经进入了一个大规模的,高通量的,系统分析的时代,为了更加深入探索RNA结构与功能的关系,了解RNA在生命活动中的各种工作机制,大力发展RNA研究方面的生物信息学方法和技术显得尤为重要。在这样一个热点研究背景之下,本论文从算法设计以及计算机软件平台构建的角度,对于RNA分子结构相关的计算生物学问题进行研究。论文内容涉及RNA分子结构的表示,RNA结构预测,RNA结构比对,RNA结构的压缩和信息度的衡量,RNA综合分析平台的构建,以及非编码RNA基因预测等若干问题,属于计算机,生物,医学交叉的前沿学科领域。
论文的主要内容与成果概括如下:
(1).对于RNA分子的结构表示理论进行了系统的分析,对于各种结构表示方法进行比较,同时提出了一种基于6-D编码的RNA分子二级结构表示方法,将RNA构象的二级结构转化为结构矩阵,提取矩阵奇异值向量作为其主要结构特征,从而从代数矩阵论角度给出了其分子结构的精确描述。
(2).对于RNA分子二级结构的预测算法进行了系统的阐述,基于图论极大独立集思想提出了一种基于Hopfield网络进行并行预测的有效算法,进一步提高了RNA二级结构预测的效率。
(3).对于RNA分子二级结构的各种相似度衡量算法进行了探讨和比较,采用6-D编码设计了一种利用矩阵奇异值分解进行结构比对的算法。
(4).基于上述相似度提出了一种新的模糊核聚类(Kernel Fuzzy C-means,KFCM)算法,应用于RNA二级结构构象的聚类分析中。结果表明该聚类算法对于RNA构象分析十分有效。
(5).构建了RNA结构比对以及结构构象聚类的整合软件平台(RNACluster),将基于最小生成树(Minimum Spanning Tree,MST)表示的聚类算法应用于RNA分子的构象聚类、RNA构象转换(RNA conformational switches)以及非编码RNA预测当中。
(6).首次提出了RNA二级结构压缩的概念,设计了一种利用上下文无关文法压缩RNA二级结构的算法,构建了相关软件(RNACompress),该软件可以有效的对于RNA一级序列及二级结构同时进行建模,并且进行无损压缩。
(7).首次引入基于压缩的Kolmogorov复杂度来衡量RNA结构的信息度。并将其应用于11种GTP-binding RNA核酸适体(aptamer)的结构信息度衡量及其绑定活性与结构信息复杂度关系的定量研究中。
(8).对于非编码RNA的相关概念及其基因预测算法进行了论述。系统总结了非编码RNA相关的网上平台与数据库资源。对于非编码RNA计算生物学领域的未来研究方向和研究热点提出了自己的看法,并作了相应展望。
The computational studies of RNA structure and function are one of the hotest research topics in bioinformatics. As more and more non-coding RNAs are identified recently, RNA has been viewed as a more complex molecule in its diversity of functions rathor than barly serving as the transmitter between DNA and protein. A view that is emerging now is that RNA world are much more complicated than our previously thought, and RNAs may play the same important roles in the central dogma as DNA and protein does. In addition, "Rnomics" and "Ribonomics" has currently become another two new interdisciplines after the notion of "Genomics" and "Proteomics" were presented.
Computational techniques can be served as an extremely helpful complementarity to the experimental study of RNA structure and function, expecially when this study is in its large scale, high throughput and systematic level. In order to faciliate this study, more efficient bioinformatic analysis approaches are needed. In our thesis, we performed some basic computational studies on RNA secondary structure as well as their functions from the perspective of algorithm designning and platform implementation. The content in this thesis includes: representation of RNA secondary structure; RNA secondary structure prediction; comparison of RNA secondary structure; compression of RNA secondary structure and informational complexity measurement; construction of integrated platform for RNA structure analysis; non-coding RNA prediction, etc.
The main contributes of this thesis are listed as following:
(1). Different representations of RNA secondary structure are compared. A 6-D encoding based RNA secondary structure representation method is presented. The RNA secondary structure can be represented as a structure matrix, and the corresponding singlar value vector of this matrix is calculated to extract the main information of the RNA secondary structure. This kind of representation has provided an accurate description of RNA secondary structure mathmatically.
(2). A thoroughly discussion of RNA secondary structure prediction approches is given. Based on the definitions of Discrete Hopfield Neural Network (DHNN) and Maximal Independent Set (MIS) in Graph theory, a heuristic algorithm is presented to select stems in RNA structure as well as its application in RNA secondary structure prediction. This method has substaintially improved the efficency of RNA secondary structure prediction.
(3). Different RNA secondary structure comparison methods is discussed. A novel method is presented to compare the similarity of RNA secondary structures using a matrix representation of the RNA structures. Relevant features of the RNA secondary structures can be easily extracted through singular value decomposition (SVD) of the representing matrices.
(4). A fuzzy kernel clustering method is applied, using the similarity metric defined above, to cluster the RNA secondary structure ensembles. Our application results suggest that our fuzzy kernel clustering method is highly promising for classifications of RNA structure ensembles, because of its low computational complexity and high clustering accuracy.
(5). An integrated platform (RNACluster) is constructured to calculate and compare different distances between RNA secondary structures, and to perform cluster identification to derive useful information of RNA structure ensembles, using a minimum spanning tree (MST) based clustering algorithm. RNACluster can be used in the analysis of RNA structure ensemble clustering、RNA conformational switches and non-coding RNA prediction.
(6). An universal algorithm for the compression of RNA secondary structure is discussed (RNACompress). RNACompress employs an efficient content-free-grammar based model to compress RNA sequences and their secondary structures simultaneously.
(7). A novel informational complexity measurment of RNA seondary structure is presented based on the notion of Kolmogorov comlexity. A test of the activities of 11 distinct GTP-binding RNAs (aptamers) compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity.
(8). A survey of the concept of non-coding RNA and its computational prediction is given. A complete list of web sources for non-coding RNA analysis is compiled. Several future research directions and topics in this field are discussed.
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