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研究生:許承偉
研究生(外文):Chen-Wei Hsu
論文名稱:利用關聯圖及其展開貝氏網路建立轉錄啟動點和啟動子元素之模型
論文名稱(外文):Modeling Transcription Start Site and Promoter Elements with Dependency Graphs and Their Expanded Bayesian Networks
指導教授:呂忠津
指導教授(外文):Chung-Chin Lu
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
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:2004
畢業學年度:92
語文別:英文
論文頁數:39
中文關鍵詞:啟動子塔塔盒子
外文關鍵詞:TATApromoterpredictiontranscription
相關次數:
  • 被引用被引用:0
  • 點閱點閱:116
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  • 下載下載:15
  • 收藏至我的研究室書目清單書目收藏:0
感謝人類基因體計畫 (HGP) 中,定序人類DNA序列這部份的提前完成,因此我們現在能從網路上得到大量的人類原始的DNA序列。而至目前為止也有許多的程式被發展出來分析這些DNA序列。在基因的5’端有一區塊它包含了轉錄啟動點稱為啟動子,啟動子主要的功能為調控基因的表現,我們也可以藉由分析它來增進尋找基因的準確度。現在已有許多程式可以來做啟動子的和轉錄啟動點的預測,但是就目前為止,它們做出來的結果並不佳,主要的原因是因為錯誤的預測過多,造成太多假的訊號,而我們需要的是更準確更有效率的預測程式。

在這篇論文中,我們的方法首先是利用卡方分佈對轉錄啟動點做出關連圖當做基本的模型,再利用展開貝氏網路將關連圖做進一步處理,展開時我們不限定每個圖中節點出現的次數,以便能完全的抓到各節點間的關連性。而考慮到在啟動子內有許多不同的促進元素,例如塔塔盒子、菊西盒子和凱特盒子,於是我們對不同的啟動子元素用同樣的方法都做一個相似的模型,我們整合了這些不同的元素來做出最後對轉錄啟動點的預測。為了確認我們模型的效果,我們從網路上挑選了最有名中的四個程式來和我們的程式做比較,經過測試的結果,我們的程式可以在比其它的程式有較高的敏感度下,仍能有較高的專一性,證明我們的方法是最好的。
We have a large amount of raw genomic DNA sequence data now with the completion of the Human Genome
Project (HGP). There are hundreds of programs developed to analyze these DNA sequences. Promoter is
a region usually located at the 5' flanking end of a gene and encompasses the transcription start
site. The promoter plays an important role in gene regulation and the detection of the promoter
region could help to improve the accuracy of gene-finding. There are also several in silico
approaches to predict promoter region or transcription start site, but the performance of these
programs are usually unsatisfactory since the number of false positives is too high.

In this thesis, we first develop a dependency graph as the basic model for the transcription start
site by chi-square test and then expand this graph with a Bayesian network by allowing nucleotides
in each position to appear more than once to catch their inter-dependency but avoid overfitting. In
consideration of more than one signals within the promoter region, we also construct dependency
graph and it's expanded Bayesian network to model TATA box. The prediction of TATA box will be
integrated into the prediction of transcription start site in this thesis. The results show that
our method has the best performance comparing with four most famous programs available on the
Internet.
1 Introduction 1
1.1 Gene Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Promoter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 TSS Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 The Biology of Eukaryotic Promoter 5
2.1 DNA and Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 The RNA Polymerase II Machinery . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 The RNA Polymerase II Core Promoter . . . . . . . . . . . . . . . . . . . . 9
3 Method 11
3.1 Chi-Square Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3 Model and Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3.1 Dependency Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3.2 Expanded Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . 17
3.4 G+C Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.5 Detection of Promoter Elements . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 Datasets 25
4.1 TATA Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.2 Transcription Start Site Datasets . . . . . . . . . . . . . . . . . . . . . . . . 26
5 Results 28
5.1 Measures for Predictive Accuracy . . . . . . . . . . . . . . . . . . . . . . . . 28
5.2 Cross-validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
5.3 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
6 Conclusion 36
Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
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