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它們的基因體草圖 (draft genomes)。然而，大多數的基因體草圖都只是一群獨立的DNA片段 (contigs)，它們在被定序基因體上的相對位置與方向是未知的。目前有許多的scaffolding軟體工具，包括我們實驗室在2014年所設計出來的CAR，已經被發展出來可以利用一個完整的參考基因體去決定基因體草圖上DNA片段的前後順序與方向。大部分的scaffolding軟體工具只可以應用在單一完整的參考基因體。然而，這種做法會降低實務上的可應用性，因為目前公開的基因體資料庫中，基因體草圖比完整的基因體更容易取得。2015年我們實驗室發表一篇趨近線性時間的scaffolding演算法，其可以利用基因體草圖來做為scaffolding的參考。在本研究中，我們將此演算法實作成一個scaffolding軟體工具名叫CSAR (Contig Scaffolding using Algebraic Rearrangement distance)使得其可以根據單一完整的或草圖的參考基因體來決定基因體草圖上DNA片段的前後順序與方向。除此之外，我們的實驗結果也顯示出在大多數情況之下，我們的CSAR在敏感度、準確度、F-score與基因體覆蓋率等上的表現皆比其他的scaffolding軟體工具好。

Next generation sequencing technologies have allowed us to efficiently produce draft genomes for many organisms of interest. However, most draft genomes are just col-lections of independent contigs whose relative positions and orientations along the genome being sequenced are unknown. Currently, several scaffolding tools, including CAR that was designed by our laboratory in 2014, have been developed to order and orient the contigs of draft genomes using single complete reference genomes. How-ever, most of these scaffolding tools can apply only on a complete reference genome. This may reduce their usability in practice because in current public databases, the availability of draft genomes greatly exceeds that of completely sequenced ones. In 2015, our laboratory published a near-linear time scaffolding algorithm that can use a draft genome as a reference to order and orient the contigs. In this study, we imple-ment this algorithm into a scaffolding tool named CSAR (Contig Scaffolding using Algebraic Rearrangement Distance) such that it can order and orient the contigs in a target draft genome according to a complete or draft reference genome. In addition, our experimental results have shown that in most cases, our CSAR performs better than other scaffolding tools in terms of sensitivity, precision, F-score and coverage.

中文摘要 I
Abstract II
Acknowledgement III
Contents IV
List of figures V
List of figures VI
List of tables VII
Chapter 1 Introduction 1
Chapter 2 Preliminaries 3
Chapter 3 CSAR Algorithm 7
Chapter 4 Experimental Results 12
Chapter 5 Conclusion 20
References 21

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