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研究生:陳建瑋
研究生(外文):Jian-wei Chen
論文名稱:針對大規模基因序列進行原生微型核糖核酸之偵測
論文名稱(外文):Identification of MicroRNA Precursors from Large-scale Genomic Sequences
指導教授:張天豪
指導教授(外文):Tien-Hao Chang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:50
中文關鍵詞:大規模微型核糖核酸
外文關鍵詞:microRNAlarge-scale
相關次數:
  • 被引用被引用:0
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  • 收藏至我的研究室書目清單書目收藏:1
微型核糖核酸(microRNA)是存在於非編碼核糖核酸(non-coding RNA)上一段非常短的核糖核酸,長度約為21~23核苷酸,它在基因的調控及生物的發育中扮演了關鍵的角色,因此,偵測未知的原生微型核糖核酸(microRNA precursors)已然成為當今熱門的研究方向。而透過生物資訊的計算方法,能夠幫助我們快速的發現新的原生微型核糖核酸,所以如何利用生物資訊的計算方法有效的預測原生微型核糖核酸也越來越重要。
由於大部分的原生微型核醣核酸預測工具都無法直接從長度數千萬至數億核苷酸的基因序列中偵測微型核醣核酸,所以,本論文提出一個能自動化處理大型序列的原生微型核糖核酸預測工具,該工具以一套ab initio方法,miR-KDE,為基礎,並搭配二級結構特徵的篩選機制及同源性分析,幫助使用者從大規模的基因序列中預測原生微型核糖核酸。其中,miR-KDE能有效的預測原生微型核糖核酸(整體的正確率為94.7%),本論文增加了二級結構的篩選機制及同源性的分析,使其能處理長度為上限為一千萬核苷酸的序列,並在合理的時間內(~20秒/十萬核苷酸)得到預測結果。相較於其他許多工具,不僅在輸入長度上的限制及處理所需的時間上,本篇論文所提出的工具都具有相當高的實用價值。
MicroRNA are short RNA about 21~23nt which exist in non-coding region. MiRNA play important rules in post-transcriptional regulation, reveals that RNA is not only a carrier of gene information, but also a mediator of gene expression . Detecting unknown miRNAs get more attentions. Using computational method can help us find new miRNAs effectively. So how to use these computational method to detect new miRNAs is more important.
However, most advanced miRNA prediction algorithms on miRNA discovering can not process large-scale genomic sequence as input. This article presents a tool that can predict pre-miRNAs automatically form large-scale genomic sequence. Our tool isa based on a ab initio tool, miR-KDE, and combine a secondary structure filter and homology analysis to help users to find pre-miRNAs from large-scale sequence. MiR-KDE can efficiently predict pre-miRNAs (overall accuracy 94.7%). In our tool, we add secondary structure filter and homology analysis that make our can process length of 10M nt sequence and report result in reasonable time (~20sec / 100k nt). Both the limit of input sequence and process time, our tool shows batter performance then other large-scale pre-miRNA predicting tools.
摘要 IV
ABSTRACT V
CHAPTER 1 緒論 1
CHAPTER 2 相關研究 4
2.1 微型核糖核酸 4
2.1.1 微型核糖核酸的作用機制 4
2.1.2 微型核糖核酸的特徵 5
2.1.3 微型核糖核酸的資料庫miRBase 8
2.2 預測微型核糖核酸方法 9
2.2.1 基於序列相似度的分析方法 9
2.2.2 序列特徵結合分類器的方法 10
CHAPTER 3 資料集與研究方法 12
3.1 資料集 12
3.2 從大規模基因序列中偵測原生微型核糖核酸之流程 14
3.3 基因序列的截取與篩選 15
3.4 特徵集 18
3.4.1 核糖核酸初級序列特微擷取 19
3.4.2 初級序列折疊成二級結構的測量值 19
3.4.3 初級序列折疊成二級結構測量值的正規化 24
3.4.4 莖–環結構特徵擷取 25
3.5 分類工具 26
3.6 同源性分析 27
CHAPTER 4 實驗結果和討論 31
4.1以7個物種的9條基因序列評估篩選序列片段二級結構特徵之效能 31
4.2以人類基因的16、17、18、19號染色體進行用於同源性分析比對資料集的建構及效果的評估 37
4.3以已知原生微型核糖核酸前後兩萬個核苷酸作為輸入,預測不同物種基因序列中的原生微型核糖核酸 40
CHAPTER 5 結論 45
5.1 結論 45
5.2 未來展望 45
參考文獻 47
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