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研究生：	賴福柔 Lai, Fu-Jou
論文名稱：	用新演算法預測合作轉錄因子對並開發效能指數和網站工具以加速合作轉錄因子對的預測效能評估和比較 A new algorithm to identify cooperative transcription factor pairs and developing performance indices and web tools to expedite prediction performance evaluation and comparison
指導教授：	黃悅民 Huang, Yueh-Min
共同指導教授:	吳謂勝 Wu, Wei-Sheng
學位類別：	博士 Doctor
系所名稱：	工學院 - 工程科學系 Department of Engineering Science
論文出版年：	2015
畢業學年度：	103
語文別：	英文
論文頁數：	76
中文關鍵詞：	核小體佔據、合作轉錄因子對、預測效能指數、預測效能比較、網站工具、網站資料庫
外文關鍵詞：	nucleosome occupancy, cooperative transcription factor pair, prediction performance index, prediction performance comparison, web tool, web database
相關次數：	點閱：148 下載：7
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從酵母菌全基因組的轉錄因子結合位置分析，人們已知基因的轉錄調控和合作轉錄因子網路有密切關係。少數幾個合作轉錄因子可以同時或在不同的條件下建立複雜的基因表現形態，以達成對基因的合作調控。所以，測定轉錄因子之間的合作度有助於了解它們彼此間的生物性關聯。近幾年來，在基因的調控上不論是正或負的協同效應，轉錄因子間的交互作用經過研究已有多種模式被提出，同時許多預測合作轉錄因子的演算法也陸續發表。這些演算法使用單一或多種公開的實驗數據，諸如:高通量染色質免疫沉澱微陣列、轉錄因子結合位、基因表現、轉錄因子剔除、蛋白質交互作用等資料。然而，另一種稱作核小體佔據的實驗數據卻還沒有被用於預測合作轉錄因子這類相關的研究，雖然一些研究顯示核小體和轉錄因子結合位有密切關係。此外，即使許多預測合作轉錄因子的演算法已經發表，但因為缺乏足夠的預測效能指數和適當的綜合效能評分，要針對這許多演算法的預測效能進行全面的比較，仍有困難和挑戰。在本論文的第一部份，我們提出一種嶄新的演算法，整合三種數據:(一)轉錄因子與基因調控(二)轉錄因子結合位(三)核小體佔據，來推測兩個轉錄因子之間的合作度。結果顯示許多預測出來的高合作轉錄因子對都有文獻支持，而且整體預測效能優於其它十一種演算法，可見這個方法用於測定酵母菌合作轉錄因子對是有效的。在本論文的第二部份，我們提出八種預測效能指數和設計兩種綜合效能評分法，用來比較現有的各種合作轉錄因子對演算法的預測效能。利用這個分析架構，藉由跟其他演算法的比較，可以全面又客觀地評估一個新的演算法的效能。但是要使用這個分析架構，研究者必須事先耗時費力地把它建立起來，包括收集整理各種全基因組的數據、收集各種演算法的預測轉錄因子對、和撰寫許多程式來執行八種效能指數的計算。為了節省研究者的時間和力氣，在本論文的第三部份，我們進一步開發了一個網站工具，把這個用來作預測效能比較的架構實現出來，它具有快速資料處理、整全效能比較、和容易使用介面等特性。同時，我們也建立了一個資料庫網站，當輸入一個或多個轉錄因子，每個轉錄因子的所有合作轉錄因子就會從已知的文獻中搜尋出來，每個輸出的合作轉錄因子分別鏈結五種資訊:(一)轉錄因子對佐證文獻(二)轉錄因子對蛋白質交互作用證據(三)轉錄因子對共同基因引用文獻(四)轉錄因子對共同基因體註解(五)轉錄因子對共同結合基因。利用這個完整的分析架構，還有網站的資料庫以及計算工具，從事合作轉錄因子對預測的研究者可以提早檢視文獻支持的資料和評估自己演算法的預測效能，加速在這個領域的研究進度。

Transcriptional regulation of gene expression is known to be highly connected through the networks of cooperative transcription factors (TFs) based on genome-wide location analysis in yeast. A small number of cooperative TFs can set up very complex spatial and temporal patterns of gene expression to accomplish combinatorial regulation of a gene simultaneously or under different conditions. Identifying cooperativity among transcription factors helps understand the biological relevance of the TFs under investigation. In the recent decade, various types of TF-TF interactions which contribute to positive or negative synergy in regulating gene expression have been studied and modeled, and many algorithms have been proposed to identify cooperative TF pairs using one or several experimental data including ChIP-chip, TF binding site (TFBS), gene expression, TF knockout and protein-protein interaction (PPI). However, the nucleosome occupancy data has not yet been used for this research topic despite that several researches have revealed the association between nucleosomes and TFBSs. Furthermore, although many algorithms have been proposed, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms due to lack of sufficient performance indices and adequate overall performance scores. In the first part of the dissertation, we develop a novel method to infer the cooperativity between two TFs by integrating the TF-gene documented regulation, TFBS and nucleosome occupancy data. The results show that many of our predictions are validated by the literature and the method outperforms 11 existing methods, suggesting that the method is effective in identifying cooperative TF pairs in yeast. In the second part of the dissertation, we adopt/propose eight performance indices and design two overall performance scores to compare the performance of the existing algorithms for predicting cooperative TF pairs. The performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. Nevertheless, to use the framework, researchers have to put a lot of efforts to construct it first, including collecting and processing multiple genome-wide datasets from the public domain, collecting the lists of the predicted cooperative TF pairs from existing algorithms in the literature, and writing a lot of codes to implement the eight performance indices. In order to save researchers time and effort, in the third part of the dissertation, we further develop a web tool to implement the performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface. Besides, we also construct a database web site at that given a TF, its cooperative TFs documented in the literature can be obtained and each cooperative TF is provided with validating information retrieved from public databases. These support data for the TF-TF pair include literature supports, physical or genetic protein-protein interactions, gene co-citations, common gene ontology (GO) terms and common target genes. With the help of the framework, the web tool and the web database we develop, researchers who conduct cooperative TF pair prediction can expedite the research progress by early investigating literature-support data and evaluating the prediction performance.

Table of Contents

摘要 …………………………………………………………………………………I
Abstract ……………………………………………………………………III
Acknowledgments ……………………………………………………V
Table of Contents ………………………………………………VI
List of Tables ………………………………………………………X
List of Figures ………………………………………………………XI
Chapter 1 Introduction ……………………………………………………………1

1.1 Biological background and literature review ……………1
1.1.1 Transcription factor ………………………………………………………2
1.1.2 Cooperative transcription factors ………………………4
1.1.3 Algorithms to identify cooperative TF pairs ………4
1.2 Motivation …………………………………………………………7
1.3 Contribution of the dissertation ……………………………………8
1.4 Organization of the dissertation …………………………………9

Chapter 2 A new algorithm to identify cooperative transcription factors using multiple data sources ………11

2.1 Background ……………………………………………………………11
2.2 Methods …………………………………………………………………12
2.2.1 Data sources …………………………………………………12
2.2.2 The proposed method …………………………………13
2.3 Results …………………………………………………………………17
2.3.1 Detailed investigation of the 27 predicted cooperative TF pairs …………17
2.3.2 Performance comparison with 11 existing methods ………………………19
2.4 Discussion ……………………………………………………24
2.4.1 Our method is robust against different thresholds of the cooperativity score …………………………………………………24
2.4.2 Our method is robust against different qualities of TFBS data …………25
2.4.3 Our method outperforms existing methods in the precision and recall when using a benchmark set of 27 known cooperative TF pairs ……26                            
2.4.4 The nucleosome occupancy data contributes to the overall improved  prediction ………………………………………………26
2.4.5 Issue of applying our method to other model organisms …………………27
2.4.6 A cooperative TF Network ……………………………………………28
2.5 Summary ……………………………………………………………30

Chapter 3 A framework to make comprehensive performance evaluation and comparison on predicting cooperative transcription factors …………………………32

3.1 Background …………………………………………………32
3.2 Methods ……………………………………………………………33
3.2.1 TF-based performance index 1 …………………………………34
3.2.2 TF-based performance index 2 ……………………………………35
3.2.3 TF-based performance index 3 ……………………………………36
3.2.4 TF-based performance index 4 ……………………………………36
3.2.5 TG-based performance index 1 ……………………………………37
3.2.6 TG-based performance index 2 ……………………………………38
3.2.7 TG-based performance index 3 ……………………………………38
3.2.8 TG-based performance index 4 ……………………………………39
3.3 Results and discussion ……………………………………………………40
3.3.1 Categorization of 14 sets of PCTFPs under evaluation based on the data sources utilized ……………………………………………………40
3.3.2 Performance comparison using four TF-based performance indices ………………………40
3.3.3 Performance comparison using four TG-based performance indices …………………………………41
3.3.4 Comprehensive performance comparison ………………………42
3.3.5 Robustness check on using mean or median in each performance index ………………………………44
3.3.6 Robustness check on using the sum of ranking scores or the sum of  normalized scores to summarize evaluation …………………………………45
3.4 Summary ………………………………………………………46

Chapter 4 A web tool to implement the performance comparison framework and a web database to provide literature-support data for a cooperative transcription factor ……………………………………47

4.1 Background …………………………………………………………47
4.2 Implementation …………………………………………………………50
4.2.1	Fifteen existing algorithms used for performance comparison ……………………………50
4.2.2 Eight existing performance indices used for performance evaluation …………………………………50
4.2.3 Two existing overall performance scores used for representing the comprehensive …………………………………………………51
4.2.4 Cooperative transcription factor pair dataset compilation …………………………………52
4.2.5 Construction of validating information for each PCTFP …………………………………52
4.3 Results and discussion ……………………………………………54
4.3.1 Web tool usage ………………………………………………………54
4.3.2 Web tool case study ………………………………………………55
4.3.3 Web database interface and case study …………………58
4.4 Summary ……………………………………………………64
4.5 Availability and requirements ………………………………64
4.5.1 Web tool …………………………………………………………………64
4.5.2 Web database ……………………………………………………………65

Chapter 5 Conclusions ……………………………………………………66

5.1 Conclusions ……………………………………………………66
References …………………………………………………………………69
Publication List ………………………………………………………………………76
 
List of Tables

Table 2.1 The 27 predicted cooperative TF pairs …………18
Table 2.2 The 11 compared existing methods ………………20
Table 2.3 The benchmark set of 27 known cooperative TF pairs …………………………………22
Table 3.1 Categorization of 14 sets of PCTFPs based on data sources utilized ………………………………40
Table 3.2 Ranking scores given to each performance index for each study …………………………………43
Table 3.3 Normalized scores given to each performance index for each study ………………………………43
Table 4.1 The numbers of the compared algorithms, the performance indices, and the predicted cooperative TF pairs (PCTFPs) for each of the 15 existing algorithms …………………………………………………48
Table 4.2 The eight performance indices implemented in our tool …………………………………51

List of Figures

Figure 2.1 Flowchart of our method ……………………………………15
Figure 2.2 Comparison of our method with 11 existing methods based on three performance indices …………23
Figure 2.3 The performance of our method when using different thresholds of the cooperativity score …………24
Figure 2.4 The performance of our method when using the TFBS data with different qualities ………………………………25
Figure 2.5 Comparison of our method with 11 existing methods based on the precision and the recall ………………26
Figure 2.6 The performance of our method with/without using nucleosome occupancy data ………………………………………27
Figure 2.7 A TF cooperativity network ………………………30
Figure 3.1 Performance evaluation and comparison using TF-based performance indices ……………………………………………41
Figure 3.2 Performance evaluation and comparison using TG-based performance indices ……………………………………………………42
Figure 3.3 Robustness against using mean or median of the scores in each performance index ………………………………………………44
Figure 3.4 Robustness against using two different comprehensive ranking measures …………………………………………46
Figure 4.1 The conceptual flowchart of our tool ……………54
Figure 4.2 The input and three settings of our tool ……56
Figure 4.3 The output of our tool ……………………………………………58
Figure 4.4 The input and filter settings for Search ……59
Figure 4.5 The first layer of the output ……………………………61
Figure 4.6 The second and third layer of the output ……63
Figure 4.7 The first layer and the second layer of browse ……………63
                                    

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