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研究生： 鄭吉宏
Cheng, Ji-Hong
論文名稱： 在病毒次世代定序資料中偵測缺失片段的精確位點與變異性之研究
A Study of Detecting Exact Breakpoints of Deletions with Diversity in Viral Next-Generation Sequencing Data
指導教授： 謝孫源
Hsieh, Sun-Yuan
共同指導: 曾新穆
Tseng, Vincent S.
學位類別： 博士
Doctor
系所名稱： 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2021
畢業學年度： 109
語文別： 英文
論文頁數： 74
中文關鍵詞： 次世代定序技術 、病毒基因體的缺失片段 、B型肝炎病毒 、新型冠狀病毒 、圖形處理器 、大數據
外文關鍵詞： Next-generation sequencing, Viral genomic deletion, Hepatitis B Virus, SARS-CoV-2, Graphics Processing Unit, Big data
相關次數： 點閱：125  下載：0 
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    • COVID-19的大流行已造成百萬人的死亡，並對於全世界人類的健康、生活與經濟產生嚴重的損害。根據最新研究的指出SARS-CoV-2基因體中特定區域的序列缺失片段與病毒的感染性有關聯。在B型肝炎病毒(hepatitis B virus, HBV)中也發現特定區域的序列缺失片段與肝癌(Hepatocellular carcinoma, HCC)有關聯。透過次世代定序技術(Next-generation sequencing, NGS)可以快速獲取生物基因體序列的資料，然而NGS技術產生短序列(read)的數量，經常是高達百萬條以上的大數據；除此之外，病毒的基因體序列資料又具有高演化率與變異性，因此從NGS的大數據資料中發掘病毒的序列缺失片段是一個挑戰。雖然有一些研究已致力於開發從NGS資料中發掘基因體的缺失片段的方法，但仍存有以下不足之處: (1)很少研究方法和實驗設計是在不同變異性的病毒NGS資料中偵測缺失片段的精確位點；(2)另外有一些方法使用近似計算的方式減少比對時間，但是找到位點不夠精確；(3)有一些方法藉由NGS的pair-end資料的特性縮小比對的範圍減少偵測缺失片段精確位點的時間，但是在single-end資料中就無法有效的減少時間。

    為了解決上述議題，本論文探討了三個主題：(1)在病毒的NGS資料中偵測缺失片段的精確位點；(2)提升在病毒的NGS資料中偵測缺失片段位點的正確性；(3)提升在病毒的NGS資料中偵測缺失片段位點的效率。

    在第一個研究主題中，本論文提出在病毒的NGS資料中偵測缺失片段精確位點的方法，VirDelect (Virus Deletion Detect)。我們證明VirDelect可以有效減少比對的次數，並且找到精確的遺失片段位點。實驗結果顯示VirDelect可以在不同變異度的模擬資料和真實資料獲得較高的分數。在第二個研究主題中，本論文提出增強型單鹼基比對演算法。我們提出新的相似度計算方式和新的資料結構來提升比對的正確性。實驗結果顯示，使用增強型單鹼基比對演算法的VirDelect其正確性在不同變異度的模擬資料和真實資料都高於第一個研究主題的結果。除此之外，增強型單鹼基比對演算法可偵測同一條參考序列中兩條缺失片段的精確位置。第三個研究主題中，本研究提出可並行化的缺失片段偵測演算法。我們讓並行化的VirDelect可以應用中央處理器(Central Processing Unit , CPU)和圖形處理器(Graphics Processing Unit, GPU)的多核心資源提升運算的效率。在CPU中，VirDelect使用多執行緒非同步地偵測多條NGS短序列的缺失片段。在GPU中，VirDelect的One-base alignment Plus使用陣列運算的方式並行比對序列產生分數，它可有效的應用GPU在陣列運算超高效能。實驗結果顯示，並行化的VirDelect確實在可以有效減少運算時間提升效率。

    上述的三個主題之共同目標是基於現今方法與研究不足之處，提出一系列可以在病毒的NGS資料中有效果並且有效率的偵測缺片段的精確位點之方法。我們將這三個主題有系統地整合在本文論當中，並經由實驗驗證我們提出的方法比代表性的方法有更好的表現。期盼本論文的方法可被實際應用在生物與醫學資訊領域，並且對人類的生活與健康有所貢獻。

    The COVID-19 pandemic has caused millions of deaths and has seriously impacted humans worldwide. According to the latest research, deletions in specific regions of the SARS-CoV-2 genome are related to viral infectivity. In hepatitis B virus (HBV), deletions in specific regions are associated with hepatocellular carcinoma (HCC). Next-generation sequencing (NGS) technology generates short read data at high speed; however, these are large datasets. In addition, viral genomes have a high mutation rate and diversity, it is therefore challenging to detect deletions in viral NGS data. Although approaches for the detection of deletions from NGS data have been developed, they have the following limitations: first, few methods have been designed for the detection of viral deletions with diversity in NGS data; second, some methods use approximate calculations to reduce time-cost, but the position of deletions are not exact; third, some methods are able to reduce the time-cost due to the characteristics of NGS paired-end data, but cannot effectively reduce the time cost in NGS single-end data.

    To resolve the aforementioned issues, this dissertation discusses three topics: (1) detecting the exact breakpoint of sequence deletions in viral NGS data, (2) improving the correctness of detecting deletions in viral NGS data, and (3) improving the efficiency of detecting sequence deletions in viral NGS data.

    To address the first research topic, we proposed a method, VirDelect, to detect the exact breakpoint of deletions in viral NGS data and proved that VirDelect effectively reduced the number of alignments and detected exact breakpoints. The results showed that VirDelect obtained higher scores in both simulated and real data. To address the second research topic, we proposed a method, One-base alignment plus (OAP), and defined a new similarity formula. Experiments showed that the correctness of VirDelect with OAP was higher than that of VirDelect without OAP in both simulated and real data. In addition, VirDelect with OAP can detect two deletions in one reference. To address the third research topic, we implemented VirDelect with multi-core Central Processing Unit (CPU) and Graphics Processing Unit (GPU) resources to improve computing efficiency based on a concurrent algorithm. VirDelect implements multiple threads of the CPU to detect deletions. OAP uses array operations that apply the GPU at high performance. The results showed that concurrent VirDelect can effectively reduce execution time-cost and improve computing efficiency.

    The aforementioned research topics are related to the major aim of developing a series of methods that can effectively and efficiently detect the exact breakpoints of deletions from viral NGS data based on current deficiencies in approach and research. Experimental results show that our proposed algorithm outperforms state-of-the-art algorithms. We hope that the approach described in this dissertation can be applied in the fields of biology and medical information, and will help to save lives and improve public health in general.

    摘 要 I ABSTRACT III 誌 謝 V Contents VI List of Figures IX List of Tables X Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Overview of the Dissertation 4 1.2.1 A Base Approach to Detect Exact Breakpoint of Deletions with Diversity in Viral NGS Data 5 1.2.2 An Enhanced Algorithm to Detect Exact Breakpoints of Deletions in Viral NGS Data 5 1.2.3 Efficient Algorithms to Detect Exact Breakpoints of Deletions in Viral NGS Data 6 1.3 Organization of the Dissertation 7 Chapter 2 Background and Related Work 8 2.1 Diversity of SARS-CoV-2 8 2.2 Diversity of Hepatitis B Virus 8 2.3 Hepatitis B Viral Whole Genome Cloning 11 2.4 Graphics Processing Unit 12 2.5 Next-generation sequencing 13 Chapter 3 A Base Approach to Detect Exact Breakpoint of Deletions with Diversity in Viral NGS Data 16 3.1 Introduction 16 3.2 Proposed Method 17 3.2.1 Definition of Diversity and Split Read Alignment 19 3.2.1.1 Definition of Diversity: 19 3.2.1.2 Split Read Alignment 20 3.2.2 Reducing Phases 22 3.2.2.1 Phase 1: Parallel Alignment 22 3.2.2.2 Phase 2: One-base Alignment 23 3.2.2.3 Phase 3: Vertical-checking and Cross-checking 23 3.2.2.4 Vertical-checking and Max_Global_Similarity 27 3.2.2.5 Cross-checking 27 3.3 Result and Discussion 28 3.3.1 Datasets and Environment 29 3.3.2 Scoring Function 30 3.3.3 Experimental results of VirDelect and Pindel 31 3.3.4 Analyzing Simulated Data for Different Lengths of Deletion and Higher Diversity 33 3.3.5 Analysis Results of Simulated and Real Data 34 3.4 Summary 36 Chapter 4 An Enhanced Algorithm to Detect Exact Breakpoints of Deletions in Viral NGS Data 37 4.1 Introduction 37 4.2 Proposed Method 38 4.2.1 Definition of One-base Alignment Plus 38 4.2.1.1 Similarity Score 38 4.2.1.2 Score Table and Hit Table 38 4.2.1.3 Initialize Table 39 4.2.2 Prefix of One-base Alignment Plus 40 4.2.2.1 Initial Table 40 4.2.2.2 Generate New Score Table 40 4.2.3 Suffix of One-base Alignment Plus 42 4.2.3.1 Initial Table 42 4.2.3.2 Generate New Score Table 43 4.2.3.3 Generate New Score Table in Zero Position 44 4.3 Result and Discussion 44 4.3.1 Dataset and Environment 44 4.3.1.1 Real Dataset and Environment: 44 4.3.1.2 Simulated Dataset 45 4.3.2 Scoring Function 46 4.3.3 Experimental Results of VirDelect with OAP 46 4.3.4 Discussion 49 4.4 Summary 50 Chapter 5 Efficient Algorithms to Detect Exact Breakpoints of Deletions in Viral NGS Data 51 5.1 Introduction 51 5.2 Proposed Method 53 5.2.1 VirDelect with Multiple Threads of CPU 53 5.2.2 Definition of GPU Function and Award Table 55 5.2.3 Prefix of One-base Alignment Plus on GPU 57 5.2.4 Suffix of One-base Alignment Plus on GPU 58 5.3 Result and Discussion 60 5.3.1 Dataset and Environment 60 5.3.2 Result and Discussion 61 5.4 Summary 62 Chapter 6 Conclusion and Future Works 63 6.1 Conclusion 63 6.2 Future Works 64 Bibliography 66

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