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研究生: 洪哲倫
Hung, Che-Lun
論文名稱: Efficient Algorithms for Triple-wise Alignment and Its Applications
有效率的三條序列演算法及其應用
指導教授: 鍾葉青
Chung, Yeh-Ching
口試委員:
學位類別: 博士
Doctor
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 2010
畢業學年度: 98
語文別: 英文
論文頁數: 94
中文關鍵詞: 序列比對三條序列比對多重序列比對編碼區比對序列組比對平行序列比對特徵選取
外文關鍵詞: sequence alignment, three-way alignment, multiple sequence alignment, coding region alignment, profile alignment, parallel sequence alignment, feature selection
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  • 序列比對在生物序列的分析上是很重要的工具,對於生物學家而言,他們可以使用序列比對來分析基因的同源關係、物種之間的關係、以及蛋白質的結構等等。過去的幾十年裡,許多序列分析的研究都以雙序列比對的方法為基礎來開發。然而越來越多的研究顯示三條序列比對方法可以比雙序列比對方法提供更多的資訊或者更精確的比對結果。在本論文中,我們著重在三條序列比對的演算法與其應用。針對三條序列比對的演算法,我們提出了兩個有效的方法,一個是使用可變空格罰分的動態規劃演算法來比對蛋白質序列,一個是使用機率過濾模型的演算法來快速地比對基因序列。針對三條序列比對的應用,我們將三條序列比對的觀念移植到原來採用雙序列比對的方法以及應用上來取得更精確的分析結果。在本論文中首先提出的是一個結合雙序列與三條序列比對的漸進式多重序列比對的策略來提升多重序列比對的精確度。同樣地,在本論文中將三條序列比對的方法延伸到比對三個序列組,比對的結果可以提供與雙序列組比對不一樣的訊息。除此之外,我們還提出了三序列組的平行比對演算法來提升運算的效能。本論文更進一步提出了蛋白質功能預測的方法,藉由結合三序列組比對與投票演算法可以有效地預測蛋白質功能。本論文中包含上列所有提出的方法之理論分析與實驗。從實驗的結果得知,本論文中所提出來的方法與其對應的分析相呼應,並且所提出的方法得到了較佳的結果。


    Sequence alignment is a scientific method that contributes to DNA homology studies, phylogeny determinations, and identification of conserved motifs. In the past few decades, pair-wise alignment has become a methodological standard used in many MSA methods. However, an increasing number of studies indicated that the three-way alignment, which is the alignment of three sequences, is able to provide additional information or a more accurate alignment result than what pair-wise alignment is able to give. In this dissertation, we focused on the investigation and application of three-way alignment algorithm. For the investigation of three-way alignment algorithm, we proposed two efficient methods, a dynamic programming-based algorithm with the variable gap penalty strategy and a linear algorithm adopting a probabilistic filtration model, to align protein and DNA sequences, respectively. For the application of three-way alignment, we applied three-way alignment to the methods and applications that originally adopt pair-wise alignment approaches. We presented a new progressive multiple sequence alignment strategy that combines pair-wise and three-way alignments to compare multiple sequences accurately. Similarly, we extended the three-way alignment algorithm to align three profiles to provide the different insight to the profile-profile alignment method. In addition, we developed a parallel algorithm for three-profile alignment to reduce the computational cost. Further, we combined the three-profile alignment approach and a voting algorithm to select the functional sites of the target protein by comparing protein superfamilies. Theoretical analysis and extensive experimental tests of the proposed methods are conducted in this dissertation. From the conducted experimental results, we got some encouraged remarks regarding to the proposed methods for sequence analysis.

    Chapter 1 Introduction 1 1.1 Motivation of the Dissertation 1 1.2 Contribution of the Dissertation 2 1.3 Organization of the Dissertation 4 Chapter 2 Related Work 6 2.1 Pair-wise and three-way alignments 6 2.2 Multiple sequence alignment 11 2.3 Profile-profile alignment 12 2.4 Chapter Summary 13 Chapter 3 Efficient three-way alignment methods for Protein and DNA sequences 14 3.1 Three-way alignment with variable gap penalty 14 3.1.1 Definitions of Variable Gap Penalty Strategy 15 3.1.2 Dynamic programming algorithm 17 3.1.3 Time Complexity 26 3.1.4 Experimental Results 26 3.2 CORAL-T: Heuristic COding Region ALignment Method for Three Genome Sequences 34 3.2.1 Probabilistic filtration model 35 3.2.1 Shifting mutation 38 3.3 Experimental Results 39 3.3.1 Comparisons of the computing time 40 3.3.2 Comparisons of the performance 41 3.3 Chapter Summary 44 Chapter 4 Progressive multiple sequence alignment strategy: combining pair-wise and three-way alignments 45 4.1 Progressive multiple sequence alignment strategy: combining pair-wise and three-way alignments 45 4.1.1 Distance matrix and guide tree 46 4.1.2 Dynamic programming for pair-wise and three-way alignments 48 4.1.3 Progressive alignment 48 4.1.4 Complexity 51 4.2 Experimental Results 51 4.2.1 Alignment with ROSE package 52 4.2.2 Alignment with BRaliBASE2 53 4.2.3 Computation time 55 4.3 Chapter Summary 56 Chapter 5 Three-profile alignment and its parallelization 57 5.1 Dynamic programming-based Three-Profile Alignment algorithm and parallel algorithm 57 5.1.1 TPA algorithm 58 5.2 PTPA algorithm 60 5.2 Experiment Results 63 5.2.1 Case study: comparison of Three-profile alignment and Profile-Profile alignmnet in Enterovirus 63 5.2.2 Comparison of computation time 64 5.3 Chapter Summary 64 Chapter 6 Feature Amplified Voting Algorithm for Functional Analysis of Protein Superfamily 65 6.1 Method 65 6.1.1 Imidase and sequence clustering in the amidohydrolase superfamily 66 6.1.2 Observation and assumption 67 6.1.3 Algorithm 69 6.2 Experiment Results 74 6.2.1 Voting scores of imidase by FAVAT analysis 74 6.2.2 Comparison of FAVAT and MSA results 75 6.2.3 The corresponding locations of FAVAT-selected residues in 1GKQ and 1KCX 76 6.2 Chapter Summary 79 Chapter 7 Conclusions 81 Bibliography 83

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