基于HBV病毒序列的突变位点挖掘与系统进化研究

发布时间：2018-06-21 13:07

本文选题：乙型肝炎病毒 + 最优风险与预防模式　；参考：《昆明理工大学》2013年硕士论文

【摘要】：乙型肝炎病毒(Hepatitis B virus,HBV)感染作为严重影响人类健康的疾病之一,是导致慢性肝脏疾病、肝硬化和肝癌的主要原因。HBV由于其自身复制的特殊性,具有高变异特性。据研究表明HBV基因变异是HBV持续感染的根本原因。同时在自然选择或随机演化的过程中,研究HBV进化演变,找出病毒起源也很重要。因此进行我国乙型肝炎病毒基因变异与系统进化演变的研究对于了解现今乙型肝炎病毒的发病机制和指导临床治疗提供了一定的参考依据。为了了解HBV的基因变异情况,检测HBV序列的SNP位点即单突变位点已广泛应用于大量的研究,所检测出的SNP位点对指导临床有重要意义。但是目前关于SNP位点检测的方法多因技术难度较高,费用大等不利因素而受到制约。因此,探讨一种基于计算机的SNP位点检测方法成为一种趋势。在本课题中,针对HBV序列的SNP位点的特点,我们提出了一种基于最优风险与预防模式的HBV序列的SNP位点检测方法。所提出的方法首次应用于HBV序列的SNP位点检测,实验结果表明：该方法不仅有效的检测出HBV序列的X基因片段和前C区基因片段中已经报道的SNP位点,而且还发现了一些新的位点。与硬件检测SNP位点不同的是,所提出的计算机方法具有操作简单和费用低的优点,而且普通实验室和医疗机构均可以承受。其次,HBV序列在进化过程中只有少量突变的位点,除此之外大都保留遗传给了后代或者在进化过程消失。因此建立系统进化树可以直观的反映出HBV序列进化的关系,有助于了解HBV进化历史和进化机制。基于此,本文提出了一种新的系统进化树构建方法。该方法首先采用MEME(Multiple EM for Motif Elicitation)算法来挖掘HBV模体(Motif),然后利用新的序列度量指标CI(Conservation Index,保守指数)构建HBV序列的系统进化树,最后利用进化距离对已构建的系统进化树进行可靠性评估。实验结果表明：新的序列度量标准CI可以有效的构建HBV序列系统进化树,分析HBV序列之间的进化关系。
[Abstract]:Hepatitis B virus (HBV) infection, as one of the serious diseases affecting human health, is the main cause of chronic liver disease, liver cirrhosis and liver cancer. Studies have shown that HBV gene mutation is the root cause of HBV persistent infection. In the process of natural selection or random evolution, it is also important to study the evolution of HBV and find out the origin of the virus. Therefore, the study on the genetic variation and phylogenetic evolution of hepatitis B virus in China provides a certain reference for understanding the pathogenesis of hepatitis B virus and guiding clinical treatment. In order to understand the variation of HBV gene, the single mutation site (SNP) of HBV sequence has been widely used in many researches. The detected SNP site is of great significance in guiding clinical practice. However, the current methods of SNP locus detection are restricted by some unfavorable factors, such as high technical difficulty and high cost. Therefore, it is a trend to explore a computer-based SNP site detection method. In this paper, according to the characteristics of SNP loci of HBV sequences, we propose a SNP locus detection method based on optimal risk and prevention mode. The proposed method is applied to the detection of SNP sites in HBV sequences for the first time. The experimental results show that the proposed method is not only effective in detecting the X gene fragments of HBV sequences and SNP sites reported in pre-C region gene fragments, but also in the detection of SNP sites in HBV sequences. Some new sites were also found. Different from hardware detection of SNP sites, the proposed computer method has the advantages of simple operation and low cost, and can be borne by both general laboratories and medical institutions. Secondly, there are only a few mutation sites in the evolution of HBV sequence. In addition, most of HBV sequences are inherited to offspring or disappeared during evolution. Therefore, the establishment of phylogenetic tree can directly reflect the evolution of HBV sequence, which is helpful to understand the history and mechanism of HBV evolution. Based on this, a new method of constructing phylogenetic tree is proposed. In this method, the MEME-Multiple EM for motif Elicitation algorithm is used to mine HBV motif motifs, and then a new sequence metric, CIGCA Conservation Index (conservative index), is used to construct the phylogenetic tree of HBV sequences. Finally, the evolutionary distance is used to evaluate the reliability of the constructed phylogenetic tree. The experimental results show that the new sequence metric CI can effectively construct the phylogenetic tree of HBV sequences and analyze the evolutionary relationship between HBV sequences.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：R512.62;TP311.13

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