本文關(guān)鍵詞： DNA序列 非序列比對 相似性分析 系統(tǒng)發(fā)育樹 離散傅里葉變換 拉馬努金傅里葉變換　出處：《山東大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著科學(xué)技術(shù)的迅猛發(fā)展,科研人員得到的生物基因及蛋白質(zhì)數(shù)據(jù)日益增長,生物信息學(xué)研究的重點從獲取并積累數(shù)據(jù)逐步轉(zhuǎn)變到如何分析并解釋這些數(shù)據(jù)。海量的生物數(shù)據(jù)中蘊含著極其豐碩的生物信息,從這些生物數(shù)據(jù)中獲取盡可能多的信息是一項十分有意義的工作,越來越多的生物學(xué)、醫(yī)學(xué)及藥學(xué)工作者已經(jīng)認(rèn)識到生物信息學(xué)的實用性和重要性,同時很多數(shù)學(xué)家和計算機科學(xué)家等許多跨領(lǐng)域的研究者也被吸引到生物信息學(xué)這個新興的交叉學(xué)科中。生物序列的相似性分析是其中最基本、最重要的內(nèi)容之一,比如,分子進化問題和基因識別問題等研究工作都是建立在生物序列相似性分析的基礎(chǔ)之上。序列比對(Sequence Alignment)方法是生物序列相似性分析的傳統(tǒng)方法,但由于其存在需要大量用戶定義參數(shù)、序列很長時計算量較大等問題,序列非比對(Alignment-free)方法作為其改進及補充而被提出并迅速發(fā)展成為相似性分析的研究熱點之一。本文以基因序列為研究對象,在Voss映射和拉馬努金傅里葉變換(Ramanujan Fourier Transform,RFT)的基礎(chǔ)上,改進原有方法并得到了新的非比對方法,構(gòu)建了系統(tǒng)發(fā)育樹并進行了相似性分析。本文展示了基于拉馬努金傅里葉變換功率譜的基因序列非比對方法來進行序列相似性分析。首先使用Voss映射將DNA序列轉(zhuǎn)換成四條指標(biāo)序列,并在其上應(yīng)用改進的拉馬努金傅里葉變換算法,得到一組RFT系數(shù),通過計算RFT系數(shù)間的歐幾里得距離(Euclidean metric)來進行相似性分析,利用非加權(quán)分組平均法(Unweighted Pair Group Method with Arithmetic Means,UPGMA)構(gòu)建系統(tǒng)發(fā)育樹。為了計算不同長度序列間RFT系數(shù)的歐幾里得距離,本文采用將較短的指標(biāo)序列后補零至與數(shù)據(jù)集內(nèi)最長序列長度相等的方法,在保持信息量的前提下,我們將對數(shù)據(jù)集中的基因序列在相同維度的空間中進行分析。得到的結(jié)果將與時下流行的離散傅里葉變換方法及多序列比對(Multiple Sequence Alignment,MSA)方法的結(jié)果進行比較,可以發(fā)現(xiàn),新方法有著較好的聚類結(jié)果并節(jié)約了計算量。
[Abstract]:With the rapid development of science and technology, biological gene and protein data researchers obtained increasing bioinformatics research focus from the acquisition and accumulation of data changing to how to analyze and interpret the data. Massive biological data contained in biological information is extremely rich, is a very meaningful work to get as much as possible more information from these biological data in biology, more and more, the practicality and importance of medical and pharmacy workers has been recognized by bioinformatics, and many research mathematicians and computer scientists and many other interdisciplinary researchers are attracted to bioinformatics in this emerging discipline. The similarity analysis of biological sequences is one of the most basic, one of the most important contents such as molecular evolution and gene identification problems research work are based on biological sequence. Like on the basis of the analysis. Sequence alignment (Sequence Alignment) method is similar to the traditional method of biological sequence analysis, but due to the existence of a large number of users need to define the parameters, the calculation problem of large amount of long sequences, sequence alignment (Alignment-free) method for its improvement and supplement has been proposed and become one of the research hotspots of similar analysis. Based on the gene sequence as the research object, in the Voss map and the La Manu Kim Fu Liye transform (Ramanujan Fourier Transform, RFT) on the basis of the improvement of the original method and get a new non alignment, phylogenetic tree construction and similarity analysis. This paper shows the non alignment method. The gene sequence La Manu Kim Fu Liye transform based on power spectrum of sequence similarity analysis. The DNA sequence into four index sequence using Voss mapping, and in The application of improved Ramanujan Fu Liye transform algorithm to get a set of RFT coefficients, by calculating the RFT coefficient between the Euclidean distance (Euclidean Metric) to similarity analysis using non weighted average method in groups (Unweighted Pair Group Method with Arithmetic Means, UPGMA) to construct phylogenetic tree. In order to calculate the length of the sequence between different RFT coefficients the Euclidean distance, the index will be shorter sequence after the repairing method and data set to zero the longest sequence of equal length, while keeping the amount of information, we will focus on the analysis of the gene sequence data in the same dimensions. The results obtained with the discrete Fu Liye transform method popular and multiple sequence alignment (Multiple Sequence, Alignment, MSA) method to compare the results, it can be found that the new method has a better clustering result and save The amount of calculation.

【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：Q811.4;TN911.7

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本文編號：1454207