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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/8929

    Title: 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
    Authors: 李英準;Ying-Chun Lee
    Contributors: 資訊工程研究所
    Keywords: 隱藏馬可夫模型;跳躍子;長終端重覆序列;生物資訊;bioinformatics;long terminal repeats;transposable elements;Hidden Markov Models
    Date: 2004-06-21
    Issue Date: 2009-09-22 11:37:51 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 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.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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