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

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Title:	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
Authors:	江國立;Kuo-Li Chiang
Contributors:	資訊工程研究所
Keywords:	長終端重覆序列;人類內生性反轉錄病毒;分類;long terminal repeats;human endogenous retrovirus;classification
Date:	2005-06-15
Issue Date:	2009-09-22 11:40:19 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	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.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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