基于特征提取方法的p53家族基因信息表达及分析

发布时间：2018-05-13 03:00

本文选题：p53家族基因 + CGR　；参考：《江南大学》2017年硕士论文

【摘要】：p53基因是迄今为止发现与肿瘤相关性最高的基因,家族成员p63、p73在结构和功能上与其具有很高的同源性,因此如何使用有效的数学方法挖掘更准确的p53家族的生物信息,将对肿瘤的预防和控制具有重要意义。本文以p53家族的mRNA完整CDS序列为研究对象,采用特征提取方法对序列的表达信息进行识别,并运用层次聚类分析方法对p53家族基因序列进行分析。具体工作概括如下:(1)基因签名是一种新兴的基于特征提取的基因表达信息识别方法,能够有效的识别基因的一些生物信息。本文在原基于CGR方法的基因签名上,引入具有一定物理特性的核苷酸游离电子平均能量(EIIP),建立了一种新的E基因签名方法。同时,定义两序列间的E欧氏距离及e均方差公式,并对相关物种进行层次聚类分析。通过对16个物种的p53家族基因mRNA完整CDS序列进行E基因签名得出,在每种基因中均有物种关系越近,基因签名相似度越高的结论。(2)除了根据CGR图形构造原理进行基因签名外,本文还利用CGR方法构造一个12维特征向量对mRNA序列进行数值刻画,并定义了欧氏距离为序列间的距离。再依据此欧氏距离对16个物种的p53家族基因序列进行层次聚类分析,并将聚类结果与原来仅利用8维特征向量得到的聚类结果作对比,发现使用12维特征向量刻画基因序列的方法更合理。(3)为避免序列有效信息的丢失,综合序列CGR游走,平均功率谱,EIIP值和碱基实数化4个特征指标,建立一种多指标物种相似性分析方法,并对多个物种的p53家族基因的mRNA序列作出聚类谱系图进行层次聚类分析。聚类结果与实际相符,说明使用四重特征指标方法对基因序列进行刻画,能够较全面反映序列的生物信息。
[Abstract]:P53 gene is the gene most closely related to tumor so far. The family member p63 p73 has high homology in structure and function, so how to use effective mathematical method to mine more accurate biological information of p53 family. It will be of great significance to the prevention and control of tumor. In this paper, the mRNA complete CDS sequence of p53 family is taken as the research object, the expression information of the sequence is identified by feature extraction method, and the p53 family gene sequence is analyzed by hierarchical cluster analysis. The specific work is summarized as follows: (1) Gene signature is a new method of gene expression information recognition based on feature extraction, which can effectively recognize some biological information of gene. In this paper, based on the CGR method, a new E gene signature method is established by introducing the nucleotide free electron mean energy (EIIP) with certain physical characteristics. At the same time, the E Euclidean distance and the e mean square error formula between the two sequences are defined, and the related species are analyzed by hierarchical clustering. Based on the E gene signature of mRNA complete CDS sequence of p53 family gene from 16 species, it is concluded that the closer the species relationship is in each gene, the higher the similarity of gene signature is. In this paper, the CGR method is used to construct a 12-dimensional eigenvector to characterize the mRNA sequence, and the Euclidean distance is defined as the distance between the sequences. Based on the Euclidean distance, the p53 family gene sequences of 16 species were analyzed by hierarchical cluster analysis, and the results were compared with those obtained by using only 8-dimensional eigenvector. It is found that it is more reasonable to use 12-dimensional eigenvector to depict gene sequences. In order to avoid the loss of effective information of sequences, we found that the synthetic sequence CGR walks, the average power spectrum of EIIIP values and the real number of bases are converted into four characteristic indexes, so as to avoid the loss of effective information of the sequences. A multi-index species similarity analysis method was established, and the mRNA sequences of p53 family genes of multiple species were analyzed by hierarchical cluster analysis. The clustering results are consistent with the actual results, which shows that the method of quadruple characteristic index can reflect the biological information of the sequence completely.
【学位授予单位】：江南大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：Q811.4

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