次世代定序 (Next Generation Sequencing，簡稱NGS) 技術已經允許我們對許多有興趣的物種有效率地產生出他們的基因體(genomes)。然而，這些被定序出來的基因體都還只是一群contigs，他們在被定序基因體上的次序與方向是未知的。Scaffolding是用來決定這些contigs的次序與方向的程序，對於後續要取得一個定序基因體的完整序列的程序是重要且有幫助的。目前已有許多的scaffolding的工具被開發出來可以用一個完整或是不完整的參考基因體決定目標基因體草圖上contigs的前後次序與方向，例如OSLay、Mauve Aligner、MeDuSa、Ragout與CSAR。特別的是，CSAR是由我們實驗室所發展出來的，而且我們的實驗結果也已顯示出CSAR在大多數情況下的敏感度、準確度、F-score與基因體覆蓋率等的表現優於其他工具。但是CSAR做scaffolding的基因體必須只包含不重複的序列標記 (singleton sequence markers)。事實上，重複的序列標記 (du-plicate sequence markers) 在基因體上非常普遍。這促使我們尋找一個方法能讓CSAR同時考慮不重複與重複的序列標記使得它的準確度表現能夠進一步被改進。在本研究中，我們設計以下的啟發式方法來解決這個問題。首先，我們利用Shao和Moret提出的最多配對模式 (maximum-matching model) 用以下的步驟將重複的序列標記轉變成不重複的序列標記：(1) 在重複的序列標記間建立最多的匹配，(2) 捨棄不在匹配當中的複本，然後 (3) 將每個匹配配對視為一個新的標記family。在那之後，目標與參考基因體中的所有標記都是不重複的。接下來我們用CSAR來根據參考基因體將目標基因體的contigs做scaffold。最後，我們實驗兩種不同類型的資料組來驗證我們的scaffolding方法，包含模擬資料組與真實資料組。我們的實驗結果顯示在CSAR的scaffolding程序中考慮重複的序列標記確實可以得到比不考慮重複的序列標記以及CSAR本身利用MUMmer處理重複的序列標記更好的結果。

Next generation sequencing technologies have allowed us to efficiently produce genomes for many organisms of interest. However, most sequenced genomes are just collections of independent contigs, whose relative positions and orientations along the genome being sequenced are unknown. Scaffolding is a process to determine the orders and orientations of these contigs, which is critical and helpful for accomplishing the subsequent finishing process. Currently, several scaffolding tools have been developed to use a complete or incomplete reference genome to order and orient the contigs of target draft genomes, such as OSLay, Mauve Aligner, MeDuSa, Ragout and CSAR. In particular, CSAR was developed by our laboratory and our experimental results have shown that CSAR always outperforms than other tools in terms of sensitivity, precision, F-score and genome coverage. However, the genomes used by CSAR for scaffolding must be singleton sequence markers. In fact, duplicate sequence markers are very common in the genomes. This motivates us to find a method that allows CSAR to consider both singleton and duplicate sequence markers such that its accuracy performance can be further improved. In this study, we design the following heuristic approach to address this problem. First, we utilize the so-called maximum-matching model, which was proposed by Shao and Moret, to transform duplicate sequence markers into singleton ones by using the following steps: (1) build a maximum matching between duplicate sequence markers, (2) discard the copies not in the matching and (3) treat each matched pairs as a new marker family. After that, the markers are all singletons in the target and reference genomes. Next, we use CSAR to scaffold the contigs of the target genome based on the reference genome. Finally, we perform experiments on two different datasets to validate our scaffolding approach, including simulated datasets and real datasets. As a result, our experimental results show that considering duplicate sequence markers in the scaffolding process of CSAR indeed has better performance than that without considering duplicate sequence markers, as well as CSAR itself that utilizes MUMmer to deal with the duplicate sequence markers.

中文摘要 I
Abstract III
Acknowledgement V
Contents VI
List of figures VIII
List of tables XI
Chapter 1 Introduction 1
Chapter 2 Methods 4
2.1 Identified Conserved Markers 5
2.1.1 Method of MUMmer 5
2.1.2 Method of Sibelia 6
2.2 Method of Maximum-matching Model 7
2.2.1 Preliminaries 7
2.2.2 Main Concept 9
2.3 Method of CSAR 11
2.3.1. Preliminaries 11
2.3.2. Main Concept 13
Chapter 3 Experimental Results and Discussion 14
3.1 Evaluation 14
3.2 Testing on Simulated Datasets 17
3.2.1 Simulated Genomes 18
3.2.2 Setting of Parameters 19
3.2.3 Experimental Results Obtained Using Maximum-matching Model 19
3.2.4 Comparison of Results with and without Duplicate Markers 26
3.2.5 Comparison of Results Obtained Using Maximum-matching Model and Exemplar Model 30
3.3 Testing on Real Datasets 34
3.3.1 Datasets and Setting of Parameters 35
3.3.2 Comparison of Results Obtained Using Maximum-matching Model and Exemplar Model 37
Chapter 4 Conclusions 44
References 45

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