人類長終端重覆序列之分類與預測使用隱藏馬可夫模型

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研究生：	江國立 Kuo-Li Chiang
論文名稱：	人類長終端重覆序列之分類與預測使用隱藏馬可夫模型 Human LTR classification and prediction using Profile Hidden Markov Models
指導教授：	洪炯宗 Jorng-Tzong Horng
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：	93
語文別：	英文
論文頁數：	61
中文關鍵詞：	長終端重覆序列、人類內生性反轉錄病毒、分類
外文關鍵詞：	long terminal repeats, human endogenous retrovirus, classification
相關次數：	點閱：7 下載：0
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在人類基因體上，大約8% 屬於長終端重覆序列反轉錄跳躍子(LTR elements)，長
終端重覆序列是跳躍子(transposable elements;TEs)中變異最大的一部分。大多數的
人類長終端重覆序列反轉錄跳躍子來是於人類內生性反轉錄病毒(HERVs)，人類內
生性反轉錄病毒的分類學是一個難解的問題，因為反轉錄病毒本身的多變異性。
在人體基因體上，逆向轉移(retrotranslocation)造成單現長終端重覆序列(Solitary
LTRs)與不完整的反轉錄病毒序列。在長終端重覆序列的調控區域中，啟動子
(promoter)和加強子(enhancer)在移動演化的過程中會被保留下來，因此，我們擷
取這些保留區域當作特徵來建立隱藏馬可夫模型(Hidden Markov Model)，使用隱
藏馬可夫模型，我們可以偵測並分類長終端重覆序列。在我們設計實驗中，我們
找到了大部分RepeatMasker 找到的長終端重覆序列，這篇論文中，我們討論使
用我們方法分類的效能與現象。

About 8 % of human genome was annotated as LTR elements. The long terminal
repeats (LTRs) in LTR elements are most divergent part of transposable elements (TEs).
Most human LTR elements come from human endogenous retrovirus (HERVs).
Taxonomy of HERVs is an unresolved problem since the diversity of retrovirus.
Solitary LTRs and partial retroviral sequences are the result of retrotranslocation in
human genomes. There are promoter and enhancer as regulatory sites in LTR and they
could be conserved in mobilization of LTR elements. Therefore, we capture the
conserved regions as fingerprints of LTR and build them into profile Hidden Markov
Models. We classify and predict LTRs using those profiles. From the experimental
results, we find most known LTRs detected by RepeatMasker are also found by our
approach. The performance and appearance in our LTR classifier are discussed.

Table of content......................................................................................................... I
List of Figures ........................................................................................................ III
List of Table ............................................................................................................ V
Chapter 1 Introduction.......................................................................................... 1
1.1 Background..........................................................................................1
1.2 Motivation............................................................................................5
1.3 Goal......................................................................................................5
Chapter 2 Related works....................................................................................... 6
2.1 Profile Hidden Markov Models ........................................................... 6
2.2 MEME – motif discovering ................................................................. 6
2.3 RepBase Update and RepeatMasker.................................................... 7
2.4 Tandem repeats finder.......................................................................... 7
2.5 BlastClust.............................................................................................8
2.6 CompareACE.......................................................................................8
Chapter 3 Material and Methods .......................................................................... 9
3.1 Overview..............................................................................................9
3.2 Material..............................................................................................12
3.3 Data preprocessing.............................................................................12
3.4 Motif extraction and building profile HMMs .................................... 14
3.5 Motif scanning and LTR detection..................................................... 15
3.6 Model evaluation ...............................................................................16
3.7 Implementation ..................................................................................20
II
Chapter 4 Experiment and Result....................................................................... 21
4.1 Performance of classification............................................................. 21
4.2 Family to family specificity of classification..................................... 25
4.3 Case study: LTR prediction in human chromosome 22..................... 31
Chapter 5 Discussion and Conclusion ................................................................ 34
Reference ............................................................................................................... 37
Appendix................................................................................................................ 39

                                

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