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研究生: 洪蔚奇
Hing, Wei-Chi
論文名稱: Genomic analysis for function and species-associated sites for influenza A virus
利用基因組比較方法在流感甲型病毒上預測功能及物種限制位點
指導教授: 唐傳義
Tang, Chuan-Yi
口試委員:
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊系統與應用研究所
Institute of Information Systems and Applications
論文出版年: 2010
畢業學年度: 99
語文別: 英文
論文頁數: 45
中文關鍵詞: 核糖核酸病毒流感序列比對物種限制位點
外文關鍵詞: RNA Virus, Influenza A virus, Multiple Sequence Alignment, Function Sites, Species-Associated Sites
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  • 近年來由於核糖核酸病毒的變異速率快,常演化出不同的病毒株,造成人們初次遇到時常措手不及造成大規模的流行甚至死亡,如禽流感。因此針對核糖核酸病毒來探索其序列、功能與演化的關係是目前一個相當重要的研究課題。在這篇論文中我們藉由序列比對工具的技術針對流感病毒甲型的十一段蛋白質序列做分析。研究對象主要有兩個1.流感病毒上的重要功能位置2.流感病毒上物種限制的位置。針對功能位點我們收集人類史上嚴重的1918年西班牙流感、2009年新流感去做分析。針對物種限制位點我們收集人、鳥禽類、豬三群去分析。最後我們將功能及物種限制位點作交互比對,希望能找出真正屬於功能以及物種限制的位點。


    In recent years, since the mutation rate of RNA virus is faster than that of DNA virus, it induces that there are many new viruses evolved from the evolution. These viruses may cause calamities at various countries and seasons, such as the avian influenza H5N1. It is an important topic to analyze the relationships among sequences, function, and evolution of RNA virus. In this thesis, we used multiple sequence alignment tools to analyze eleven protein segments of influenza A virus. Our research had two major goals, one is to find important function sites for influenza A virus, and the other one is to find species-associated sites for influenza A virus. Thus, we collected the 1918 Spanish influenza virus which was the devastating pandemic disease in the history and the pandemic disease 2009 to analyze for important function sites. We divided human, avian, and swine into three groups to find species-associated sites. Finally, we wanted to distinguish function and species-associated sites, and find the real meaning of each position in the sequence for influenza A virus.

    中文摘要 iii ABSTRACT iv ACKNOWLDGEMENTS v TABLE OF CONTENTS vi Chapter 1 - Introduction 7 Chapter 2 - Material and Method 14 2.1 Work flow 14 2.2 Sequences collection 16 2.3 Feature Amplified Voting Algorithm with Three-profile alignment (FAVAT) 19 2.4 Build consensus sequence 20 2.5 Protein modeling analysis 24 Chapter 3 - Result and Discussion 25 Chapter 4 - Conclusion and Future work 41 References 44

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