應用資料探採於核糖體核糖核酸二級結構之分析

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方淑芬(Shu-Fen Fang ) 查詢紙本館藏

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資訊工程研究所

論文名稱

應用資料探採於核糖體核糖核酸二級結構之分析
(Mining Common Structural Motifs in SSU 16 S Ribosomal RNA Secondary Structures)

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摘要(中)

核糖體核糖核酸是參與蛋白質合成的重要角色,而其穩定時所呈現的二次結構更是影響功能的重要因素；長度為4的U字型環狀結構,發現古細菌及真細菌在特定位置上會出現序列不同的U字型環狀結構,由於二級結構上的序列多而複雜,本文應用資料探採(Data Mining)技術於核糖體小次單元核糖核酸的二級結構之環狀結構的組合,從探採所得到的序列型樣中,可明顯看出不同物種間存在數個相似結構的組合,這些結構的組合可以幫助生物學家進行其他有關核糖體功能的研究。將得到的相似結構序列應用於決策樹(Decision Tree Induction)的分類技術,從結果得知,相似的結構序列確實是物種分類時的重要資訊。從我們進行的實驗中,包含分類及建演化樹的結果,得知這個研究是可行且富有價值的。

摘要(英)

Some structural motifs, like tetra-loops, in ribosomal RNA are known to functionally implicate in virtually every aspect of protein synthesis. Our aim in this study is to discover common structural motifs (CSMs), which possibly are related to specific domain or functions, within the secondary structures of ribosomal RNAs. After applying data mining techniques to mine the common structural motifs, a machine learning approach is used to find significant discriminating common structural motifs from groups of organisms. By applying to several data sets constructed in this study, it suggests that the CSMs can provide effective information to classify organisms and help biologists understand the functions of ribosomal RNA. From the experiments of the classification of organisms and the construction of phylogenetic trees by CSMs mined, we find our approach is promising.

關鍵字(中)

★ 二級結構
★ 核糖體
★ 資料探勘

關鍵字(英)

★ common motifs
★ data mining
★ rRNA
★ secondary structures

論文目次

Contents
Chapter 1 Introduction1
1.1 Problem Definition4
1.2 Brief Description of Our Method and Goal5
1.3 Organization of the Thesis5
Chapter 2 Related Work6
2.1 Ribosomal RNA Secondary Structures6
2.1.1 Prediction of RNA Secondary Structure8
2.1.2 Databases of Ribosomal RNA Secondary Structures8
2.2 Ribosomal RNA Structural Motifs9
2.2.1 Functional Sites9
2.2.2 Comparative Analysis10
2.2.3 Loop Sequence Analysis in Ribosomal RNA11
2.3 Phylogenetic Analysis Based on Ribosomal RNA Sequences12
2.3.1 Approaches for Reconstructing Phylogenetic Trees13
2.3.2 Structure Impact17
2.4 Data Mining17
2.4.1 Mining Sequential Patterns18
2.4.2 Decision Tree Induction22
Chapter 3 The Process of Our Approach24
3.1 Materials25
3.2 Approach26
3.2.1 Mining Common Structural Motifs26
3.2.2 Constructing Character Matrix27
3.2.3 CSMs Reduction and Classification by Decision Tree Induction28
3.2.4 Reconstructing Phylogenetic Tree Based on CSMs30
3.2.5 Implement of our apporch31
Chapter 4 Experiments and Results32
Chapter 5 Discussions and Summary43
References44
Appendix A48
Data Set48
Appendix B51
Statistics in primary sequences and secondary structures51
Appendix C55
Observation of loop and helix sequences55
Appendix D59
Investigate the frequencies of CSMs for each group of organisms59

參考文獻

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指導教授

洪炯宗(Jorng-Tzong Horng)

審核日期

2001-7-5

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