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研究生: 李英準
Ying-Chun Lee
論文名稱: 使用隱藏馬可夫模型偵測人類基因體序列上之長終端重覆序列結構
Detection of LTR Structures in Human Genomic Sequences Using Profile Hidden Markov Models
指導教授: 洪炯宗
Jorng-Tzong Horng
張猷忠
Yu-Chung Chang
口試委員:
學位類別: 碩士
Master
系所名稱: 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
畢業學年度: 92
語文別: 英文
論文頁數: 31
中文關鍵詞: 隱藏馬可夫模型跳躍子長終端重覆序列生物資訊
外文關鍵詞: bioinformatics, long terminal repeats, transposable elements, Hidden Markov Models
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  • 在人類基因體序列中,至少有45%的區域屬於跳躍子(Transposable Elements),造成如此大量的原因,與它們在基因體內移動的行為有關,而它們可能隱含了遺傳演化,及基因體增加適應性及變異性的原因。長終端重覆序列反轉錄跳躍子(long terminal repeat retrotransposons)是一個很重要的一類跳躍子,它們有完整的結構及功能,長終端重覆序列中包含有功能的調控區域,我們相信這些重要的區域會在演化過程中保留下來,因此我們在已知的長重覆終端序列上搜尋這些重要的區域,以隱藏馬可夫模型(Hidden Markov Model)代表這些區域,使用這些模型作為辨認基礎,我們可以辨認大部份已知的長重覆終端序列,並正確地分辨各族群,不但能處理結構完整的情況,也能偵測出新的長重覆終端序列,供遺傳演化上之研究。


    More then 45% of human genome was annotated as transposable elements (TEs). The expansion of the human genome is resulted from the mobilization of these TEs and they may increase the plasticity and variation in our genome. Long terminal repeat (LTR) retrotransposons are major components in TEs. There are regulatory sites in LTR and we believe that they could be conserved in evolution. Therefore, we search for these significant motifs in the sequence of LTRs and these motifs are used to train Hidden Markov Model (HMM). Using these models as fingerprints, we can detect most of the known LTRs detected by RepeatMasker and LTR instances are classified into families using the predictive models proposed. It could be helpful for evolution analysis.

    Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 3 1.3 Goal 4 Chapter 2 Related Works 5 Chapter 3 Material and Methods 7 3.1 Overview 7 3.2 Implementation 8 3.3 Materials 9 3.4 Generating Profile HMM of LTR motifs 9 3.5 LTR Detection 9 3.6 Model Evaluation 11 Chapter 4 Results 14 4.1 Motif Profiles 14 4.2 Detection Datasets 15 4.3 Comparison between LTR_STRUC and RepeatMasker 15 4.4 Comparison with RepeatMasker 17 4.5 Case Study: HERV15 Recombination 19 Chapter 5 Discussions and Conclusions 21 References 24 Appendix 26

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