基因序列与结构的信息分析及应用算法研究
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摘要
随着人类基因组研究的重点向功能基因组转化,“海量”的生物数据为生命科学研究提供了广阔前景,同时也对现有的生物数据处理能力提出了严峻挑战。如何从浩如烟海的生物序列数据中挖掘出有价值的生物信息,以获取基因、蛋白质结构、功能和进化等理性知识是生物信息学研究的主要目的。因此基因序列与结构的信息分析是生物信息学的一个非常重要的研究课题。
基因序列与结构信息的获取是通过序列和结构的比较来实现的,序列或结构比对是序列或结构比较的基础。序列或结构信息最终是为获取基因组功能以及进化关系服务的。基因表达的产物是蛋白质,蛋白质也是生命活动的执行体,而蛋白质亚细胞定位与蛋白质功能是密切相关的,蛋白质亚细胞定位信息可以为蛋白质功能的研究提供有用线索。在蛋白质亚细胞定位预测研究中,如何获取更完整的序列特征信息是关键。本文将围绕基因序列或结构特征信息分析这一主题,将从以下三个方面进行深入研究：(1)新型序列和结构比对方法,以提高分歧较大序列的多序列比对准确率；(2)基于图形表示的全基因组系统发育分析方法；(3)基于复合特征的蛋白质亚细胞定位预测方法。论文的主要研究成果如下：
(1)基于最小编辑距离的序列比对算法中,针对动态规划过程中不是所有的过程都需要进行,提出了更有效的非动态规划算法,其复杂度分别为O(n.L)时间和O(n)空间,其他最快算法是由Pevzner和Waterman提出来,其复杂度分别为O(l+Ln)时间和O(l+Ln)空间。
(2)针对多序列比对计算的高复杂性,采用一种平面图表示来描述多序列比对进程,既能考虑到每种可能的比对,也定义了空格插入、每种可选路径上迭代信息值和打分规则,引入蚁群遗传算法搜索和探索解空间中的最优近似解,提高了找到可行解的能力和避免过早收敛,能有效提高相同列指标。
(3)针对现有RNA二级结构表示法存在高复杂性、退化和不同结构可能会对应相同表示的问题,提出了RNA二级结构的三位和四位编码表示方法,利用二进制的异或运算对RNA二级结构进行了比对分析。结构编码方式简单直接地展示了结构信息,有助于更好地实现突变分析可视化,从而推断疾病发生的机理。结构的编码方式也为结构比较提供了一种很好的数学模型,易于发现结构间的相似性和差异性,便于基因的检测和基因功能区的预测。该方法既能很好地区分自由基和基对及其它们的位置,也能区分含假结在内的不同子结构类。
(4)针对系统发育分析需要构建指导树,且指导树生成方面存在近似程度不高的问题,运用图形表示生物序列的思想,提出了一种新的DNA序列的二维图形表示,给出了一种基于全基因组序列的二维图形表示来分析基因组进化关系的新方法,该方法通过对二维曲线之间的差异测量来得到进化距离。通过冠状病毒DNA序列的相似性／相异性比较实验,利用PHILIP软件包构建系统发育树,结果与实际进化树相吻合。该方法用全基因组的相似矩阵代替了进化距离矩阵,不需要多序列比对。既很好地体现了物种之间的关系,也大大降低了计算复杂性和时间复杂度。
(5)引入一个基于距离频率的蛋白质序列编码方法,将一个原始序列定义为220维复合特征向量来表示一个蛋白质,包含20个氨基酸成分和200个相同氨基酸的距离频率。然后,我们用支持向量机算法进行蛋白质亚细胞定位预测,实验结果证明了该方法的有效性。
With as the focus of human gene transformed to functional genomics, The accumulation of biology sequence data has offered a bright future to life sciences research, but also a severe challenge to the capacity of contemporary biological data processing. It is the main goal of bioinformatics how to mine valuable biology information from the vast biology sequence data, to understanding the structure, function and evolution of genes and protein. It is very important to research on information analysis of gene sequence and structure.
The information of gene sequence and structure can be obtained by contrast of them which bases on the alignment of them. It serves for the achievement of gene group structure and Phylogenesis. Protein is the product of gene expression and the undertaker of physical activities. Protein subcellular location has close relation with the function of protein that the information of the former can provide valuable clues for the research of the later. In the protein subcellular localization prediction, how to obtain more complete information of sequence features is key. This essay will focus on the gene sequence or structure information of the subject, from the depth of the following three aspects.1) a new way of contrast of sequence and structure to improve the veracity of the multiple sequence alignment with great diversity.2) Phylogenetic analysis based on the illustrated whole gene group.3) protein subcellular location predication based on the comb characters
The main work is summarized as follows:
(1) For not all processes are need in dynamic planning process, this essay put forward a more effective non-dynamic programming algorithms-the minimum edit distance based on sequence alignment algorithm, its execution time complexity is O(nL), space complexity is O(n), Other fastest algorithm is proposed by the Pevzner and Waterman, and its complexity are O (l+Ln) time and O (l+Ln) space
(2)For multiple sequence alignment (MSA) calculations of high complexity, this paper introdices a ichnography to describe the MSA progress, by which that can take into account of every possible alignment, defines the space insert, iterative information value and scoring rules of each optional path, induct ant colony genetic algorithm to explore the solution space to solve the MSA problem. This method of representation integrates the advantages of both genetic algorithms, improves the ability to find feasible solutions and avoid premature convergence.
(3) the presence representation of the RNA secondary structure has high complexity, degradation, and different structures which may correspond to the same problem that was proposed RNA secondary structure.by the three or four encoding methods, using the binary OR operation of the RNA to analysis the RNA secondary structure. Structure encoding can display simple and direct structural information to help better realize the visualization of mutation analysis to infer the mechanism of disease. Structure encoding for structural comparison provides a good model, it is easy to find similarities between the structure and differences, to facilitate detection of genes and gene function prediction area.The method can not only well distinguish freebase and base pair on their location but also distinguish different sub-structures objects including Pseudoknot.
(4) For a phylogenetic analysis needs the guidance of the tree, and the guide tree-level exists the problem of poor similarity, this essay puts forward a new method of analysis genome evolutionary relationship which is represented by two-dimensional graph based on complete genome sequences of a new two-dimensional graphical with the thought of graphical representation of biological sequences and proposed a two-dimensional graphical representation of DNA sequence. The new method gets the evolutionary distance by measuring the difference between two-dimensional curves. The result is consistent with the actual evolutionary tree when experimentally compare the similarity/dissimilarity of Coronavirus DNA sequences and use PHILIP package phylogenetic tree. The method uses the similar matrix of the whole genome instead of the evolutionary distance matrix and does not need multiple sequence alignment. It not only well embodies the relationship between species, but also greatly reduces the complexity in time and in space.
(5) This essay introduces a protein sequence coding method based on distance frequency, which defines an original sequence as the220-dimensional feature vector to represent a complex protein that contains20amino acids and distance frequency of200same amino acids. Then, we use support vector machine for protein subcellular localization prediction. The experimental results show the effectiveness of the method
基因序列与结构信息的获取是通过序列和结构的比较来实现的,序列或结构比对是序列或结构比较的基础。序列或结构信息最终是为获取基因组功能以及进化关系服务的。基因表达的产物是蛋白质,蛋白质也是生命活动的执行体,而蛋白质亚细胞定位与蛋白质功能是密切相关的,蛋白质亚细胞定位信息可以为蛋白质功能的研究提供有用线索。在蛋白质亚细胞定位预测研究中,如何获取更完整的序列特征信息是关键。本文将围绕基因序列或结构特征信息分析这一主题,将从以下三个方面进行深入研究：(1)新型序列和结构比对方法,以提高分歧较大序列的多序列比对准确率；(2)基于图形表示的全基因组系统发育分析方法；(3)基于复合特征的蛋白质亚细胞定位预测方法。论文的主要研究成果如下：
(1)基于最小编辑距离的序列比对算法中,针对动态规划过程中不是所有的过程都需要进行,提出了更有效的非动态规划算法,其复杂度分别为O(n.L)时间和O(n)空间,其他最快算法是由Pevzner和Waterman提出来,其复杂度分别为O(l+Ln)时间和O(l+Ln)空间。
(2)针对多序列比对计算的高复杂性,采用一种平面图表示来描述多序列比对进程,既能考虑到每种可能的比对,也定义了空格插入、每种可选路径上迭代信息值和打分规则,引入蚁群遗传算法搜索和探索解空间中的最优近似解,提高了找到可行解的能力和避免过早收敛,能有效提高相同列指标。
(3)针对现有RNA二级结构表示法存在高复杂性、退化和不同结构可能会对应相同表示的问题,提出了RNA二级结构的三位和四位编码表示方法,利用二进制的异或运算对RNA二级结构进行了比对分析。结构编码方式简单直接地展示了结构信息,有助于更好地实现突变分析可视化,从而推断疾病发生的机理。结构的编码方式也为结构比较提供了一种很好的数学模型,易于发现结构间的相似性和差异性,便于基因的检测和基因功能区的预测。该方法既能很好地区分自由基和基对及其它们的位置,也能区分含假结在内的不同子结构类。
(4)针对系统发育分析需要构建指导树,且指导树生成方面存在近似程度不高的问题,运用图形表示生物序列的思想,提出了一种新的DNA序列的二维图形表示,给出了一种基于全基因组序列的二维图形表示来分析基因组进化关系的新方法,该方法通过对二维曲线之间的差异测量来得到进化距离。通过冠状病毒DNA序列的相似性／相异性比较实验,利用PHILIP软件包构建系统发育树,结果与实际进化树相吻合。该方法用全基因组的相似矩阵代替了进化距离矩阵,不需要多序列比对。既很好地体现了物种之间的关系,也大大降低了计算复杂性和时间复杂度。
(5)引入一个基于距离频率的蛋白质序列编码方法,将一个原始序列定义为220维复合特征向量来表示一个蛋白质,包含20个氨基酸成分和200个相同氨基酸的距离频率。然后,我们用支持向量机算法进行蛋白质亚细胞定位预测,实验结果证明了该方法的有效性。
With as the focus of human gene transformed to functional genomics, The accumulation of biology sequence data has offered a bright future to life sciences research, but also a severe challenge to the capacity of contemporary biological data processing. It is the main goal of bioinformatics how to mine valuable biology information from the vast biology sequence data, to understanding the structure, function and evolution of genes and protein. It is very important to research on information analysis of gene sequence and structure.
The information of gene sequence and structure can be obtained by contrast of them which bases on the alignment of them. It serves for the achievement of gene group structure and Phylogenesis. Protein is the product of gene expression and the undertaker of physical activities. Protein subcellular location has close relation with the function of protein that the information of the former can provide valuable clues for the research of the later. In the protein subcellular localization prediction, how to obtain more complete information of sequence features is key. This essay will focus on the gene sequence or structure information of the subject, from the depth of the following three aspects.1) a new way of contrast of sequence and structure to improve the veracity of the multiple sequence alignment with great diversity.2) Phylogenetic analysis based on the illustrated whole gene group.3) protein subcellular location predication based on the comb characters
The main work is summarized as follows:
(1) For not all processes are need in dynamic planning process, this essay put forward a more effective non-dynamic programming algorithms-the minimum edit distance based on sequence alignment algorithm, its execution time complexity is O(nL), space complexity is O(n), Other fastest algorithm is proposed by the Pevzner and Waterman, and its complexity are O (l+Ln) time and O (l+Ln) space
(2)For multiple sequence alignment (MSA) calculations of high complexity, this paper introdices a ichnography to describe the MSA progress, by which that can take into account of every possible alignment, defines the space insert, iterative information value and scoring rules of each optional path, induct ant colony genetic algorithm to explore the solution space to solve the MSA problem. This method of representation integrates the advantages of both genetic algorithms, improves the ability to find feasible solutions and avoid premature convergence.
(3) the presence representation of the RNA secondary structure has high complexity, degradation, and different structures which may correspond to the same problem that was proposed RNA secondary structure.by the three or four encoding methods, using the binary OR operation of the RNA to analysis the RNA secondary structure. Structure encoding can display simple and direct structural information to help better realize the visualization of mutation analysis to infer the mechanism of disease. Structure encoding for structural comparison provides a good model, it is easy to find similarities between the structure and differences, to facilitate detection of genes and gene function prediction area.The method can not only well distinguish freebase and base pair on their location but also distinguish different sub-structures objects including Pseudoknot.
(4) For a phylogenetic analysis needs the guidance of the tree, and the guide tree-level exists the problem of poor similarity, this essay puts forward a new method of analysis genome evolutionary relationship which is represented by two-dimensional graph based on complete genome sequences of a new two-dimensional graphical with the thought of graphical representation of biological sequences and proposed a two-dimensional graphical representation of DNA sequence. The new method gets the evolutionary distance by measuring the difference between two-dimensional curves. The result is consistent with the actual evolutionary tree when experimentally compare the similarity/dissimilarity of Coronavirus DNA sequences and use PHILIP package phylogenetic tree. The method uses the similar matrix of the whole genome instead of the evolutionary distance matrix and does not need multiple sequence alignment. It not only well embodies the relationship between species, but also greatly reduces the complexity in time and in space.
(5) This essay introduces a protein sequence coding method based on distance frequency, which defines an original sequence as the220-dimensional feature vector to represent a complex protein that contains20amino acids and distance frequency of200same amino acids. Then, we use support vector machine for protein subcellular localization prediction. The experimental results show the effectiveness of the method
引文
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