全基因體分析與基因網絡資料庫平台之建構與應用

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研究生：	李季青 Lee, Chi-Ching
論文名稱：	全基因體分析與基因網絡資料庫平台之建構與應用 Establishment and Application of Whole Genome Analytic Platform and Gene Interaction Database
指導教授：	唐傳義 Tang, Chuan Yi 呂平江 Lyu, Ping-Chiang
口試委員:	唐傳義 Tang, Chuan Yi 呂平江 Lyu, Ping-Chiang 廖崇碩 Liao, Chung-Shou 劉志俊 Liu, Chih-Chin 鄧致剛 Tang, Petrus
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2012
畢業學年度：	101
語文別：	英文
論文頁數：	133
中文關鍵詞：	微生物、基因體、基因交互作用網絡、基因島、果蠅
外文關鍵詞：	microorganism, genome, gene interaction network, genomic island, fly
相關次數：	點閱：3 下載：0
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在後基因體時代，隨著基因定序技術的進步與成本的降低。目前計有約莫四千個基因體被定序完成，更有約一萬五千個進行中之基因體定序計畫，有效處理與分析這些資料與從中擷取資訊，形成一個有趣的課題。本研究從三個方向切入，包含全基因體、未完成基因體與基因調控系統，建構相關的分析工具，並探討其應用性與未來的發展方向。首先，基因體提供了許多以往需要藉助生化與分生實驗才能得到的資訊，例如代謝資訊可以透過尋找酵素基因而做出預測。然而，全基因體的分析方法目前仍有所限制，在速度與準確度上都還有改進的空間。為此，我們發展了一個能從全基因體中抽資訊的方法（稱為Probe-set method），除了具備高度的容錯性之外，也能在短時間得到基因組的分析結果。並可根據該結果建構生物體的代謝關係分類樹。為了能夠大規模比較與驗證我們的結果，我們還開發了一個快速且簡便的分類樹比較方法，該方法亦能提供日後其他類相關的分類樹與演化關係樹比較之參考。除了全基因體的分析之外，針對未完成基因體我們也嘗試擷取其中的資料。臨床研究與致病機轉分析研究會集中在尋找一種稱為基因島（Genomic island）的外來遺傳物質片段，該片段所夾帶的基因群對於細菌的生化與制定及抗藥性表現有著很大的影響。我們發展了一個針對進行中的基因體計畫所設計的基因島預測工具，其最大的特色是能夠處理未完成的基因組，協助分析人員從未完成的基因片斷中抽取資訊。同時，我們還針對基因交互作用發展了首個以HTML5實作成的果蠅基因資訊呈現與網路分析工具及資料庫，提供簡單易用的使用介面與網路分析工具。我們期望我們所建構的平台與分析方法能夠提供基因組分析以至於小範圍基因網絡比較的分析能力。

Nowadays, new technique reduces the cost and time of sequencing projects. There are around four thousand complete genomes and fifteen thousand ongoing sequencing projects in the world. Dealing with this huge data becomes an interesting topic. This dissertation focuses on three topics including the usage of complete genomes, incomplete genomes and comparisons of gene interactions. On the complete genome analysis, information can be retrieved directly from genome analysis instead of functional and metabolic experiments. For example, the possible metabolic pathways of an organism can be identified by homologous search from enzymatic database. However, the genomic analytic methods have limitations, such as the computational time causes and predicting accuracies. For this reason, we developed a strategy called probe-set method extracted information from whole genomes. It has high sequence-error tolerances and it also costs little computation time to analyze hundreds of bacterial genomes. Classification tree rebuilt based on the result reflects the metabolic characteristics. Furthermore, to evaluate our classification result, we developed a simple tree comparing method based on topological similarities of queried trees. This method provides tree comparing standard for further tree reconstruction methods. For incomplete genomes, except for the whole genome analysis, the specific regions within the chromosomes called genomic islands bring external functionalities into bacteria, such as antibiotic resistant capabilities and pathogenic abilities. Discovering of genomic island (GI) sequences is important in most bacterial genome projects. We built a Web server that offers GI prediction for incomplete genomic sequences. It provides pre-analytic information that assist researchers to finish their genome projects. For more specific analysis, such as systematic comparison of signaling transduction mechanisms, we developed the comprehensive gene-gene interaction network database and user-friendly graphic analytic platform, called PCmap.
To sum up, we developed methods from global genomic point of view into gene cluster and gene interaction analysis. We believe these methods and Web servers can be useful for understanding and discovering the metabolic and functional relationships of organisms.

中文摘要    2
Abstract    3
Contents    4
Chapter 1 Introduction    7
Chapter 2 On the perspective of genomic information    13
2.1 Background    14
2.2 Results    17
2.2.1 Probe-set method: definition and evaluation    17
2.2.2 Global proteomic tree    22
2.2.3 Metabolic classifier for closely related species    24
2.2.4 Phylogenetic classifier for metabolic related species    25
2.2.5 Influences of horizontal gene transfer on classification quality of the proposed method    25
2.2.6 Simulated testing of organisms with incomplete genomes    27
2.2.7 An example of incomplete genome: Leptospirillum ferrodiazotrophum    28
2.3 Discussion    29
2.3.1 Advantages of the probe-set method    29
2.3.2 Possible applications    31
2.3.3 Future work    33
2.4 Material and Methods    34
2.4.1 Data preparation    34
2.4.2 Random truncation of proteomes    35
2.4.3 Computation of probe-set frequencies and the similarities of probe-set frequency patterns    36
2.4.4 Visualization of probe-sets    36
2.4.5 Construction of classification trees    37
2.4.6 Comparison of classification trees    38
2.5 Acknowledgement    38
2.6 Figures and Tables    40
Figure 2-1. Frequency patterns of six enzyme categories and five metabolic pathways    41
Figure 2-2. Probe frequency patterns and clustering result of 29 free/host-living microorganisms    42
Figure 2-3. Proteomic tree of 23 class Pseudomonadales species    43
Figure 2-4. Large-scale proteomic tree    44
Figure 2-5. Proteomic tree of ten genus Lactobacillus species    45
Figure 2-6. Proteomic tree of species with similar metabolic capacities    46
Figure 2-7. Measurement of the similarity between classification trees    47
Figure 2-8. Performance of the probe-set method in classifying incomplete genomes    48
Figure 2-9. Proteomic tree of 33 bacteria with nitrogen fixation capabilities    49
Chapter 3 On the perspective of gene clusters and the local region of chromosomes    50
3.1 Background    51
3.2 Results    54
3.2.1 GI-POP: The annotation platform with GI detecting modules    54
3.2.2 GI-GPS, the combinational GI-predicting method    55
3.2.3 Composition of Genome Profile    56
3.2.4 Basic assessment of the SVM classifier    57
3.2.5 The performance of GI detection on two synthetic draft genomes    59
3.2.6 User interface    59
3.3 Discussion    60
3.3.1 The combinational indices    61
3.3.2 The evolutionary insight    62
3.3.3 The boundary problem    62
3.3.4 Compositional and homologous approaches working together    63
3.3.5 The completed or in-complete genome: the perspective on genomic island detecting    64
3.4 Material and Methods    65
3.4.1 Data preparation    65
3.4.2 Databases and software packages used for annotation pipeline    65
3.4.3 Components of genome profiles    66
3.4.4 SVM training and testing    67
3.4.5 Evaluation of the predictive performance of the SVM classifier    68
3.4.6 Generate tRNA and direct repeating sequences    68
3.4.7 Sliding window mechanism    69
3.5 Acknowledgement    70
3.6 Figures and Tables    71
Figure 3-1. The flowchart of GI-POP    72
Figure 3-2. The flowchart of the GI-GPS; a combinational GI-predicting method    73
Figure 3-3. The profile variations of GI and host genomes and the flowchart used to build the SVM classifier    74
Figure 3-4. All the possible boundaries of given GIs    75
Figure 3-5. The PE score of the SVM classifier    76
Figure 3-6. The Web interface of GI-POP    77
Table 1. The performance evaluation of synthetic draft genomes    78
Chapter 4 On the perspective of systematic comparison of gene interaction networks    79
4.1 Background    80
4.2 Features and Interfaces    81
4.3 Discussion and Future work    84
4.4 Acknowledgement    84
4.5 Figures and Tables    85
Figure 4-1. The gene discovery procedure    86
Figure 4-2. The Web interface of PCmap    87
Figure 4-3. The Integrated Dashboard    88
Table 1. The comparison of usabilities of the widely-used gene interaction databases and tools    89
Chapter 5 Conclusions    90
5.1 Summary and perspectives    91
5.2 Acknowledgement    93
References    95
Appendices    104
Appendix 1.1 Large-scale proteomic tree    105
Appendix 1.2 The HGT+/– proteomic trees    106
Appendix 2.1 The ROC curve of 5-fold cross validations.    107
Appendix 2.2 The 43 selected published GIs from PAIDB.    108
Appendix 3.1 An example of the depth-first search (DFS) algorithm used in Path Tracker    110
Appendix 3.2 Handbook of PCmap    112

                                

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