臺灣博碩士論文加值系統

微陣列實驗技術是一個具有能夠同時偵測數千甚至上萬基因表現的特性且被廣泛的運用於生物領域上。微陣列基因體比較雜合法則是一種用來偵測在單一實驗中，DNA序列拷貝數目改變的技術。技術的原理是透過比較經由螢光標定後的螢光強度比值，藉以偵測大片段基因區域改變的幅度，並將這些變異的程度藉由圖形對應的方式表現出來。在統計方法的應用上，是為了要能夠有效的辨別出染色體的擴增與缺失現象。因此，將焦點置於分析染色體的擴增與缺失現象。沿用Lai et al. (2005)文中所模擬與比較的過程，透過整合既有的統計方析方法，如循環二元分割法、適應性權重平滑法與CGH分割法等方法。並使用PERL、PHP與Apache等網頁服務程式，開發出一套以R語言為後端演算的統計分析平台，提供資料的正規化、統計分析與染色體區域擴增與缺失圖形，最後藉由UCSC Genome Browser與ID Converter的註解，供使用者針對感興趣的生物問題來做探討。

The DNA microarray is widely used to investigate gene expression profiles of many thousands of genes simultaneously. And it has become a common tool for exploring various questions in many areas of biological and medical sciences. Specifically, array-based comparative genomic hybridization (Array CGH) is applied to screen alteration of DNA copy numbers genomewide. The main purpose of such application is to detect the altered DNA segments among genome sequences from a control (reference) treatment to a test treatment. Typically, efficient statistical tools are developed to compare the intensity ratios of spots representing the competitive hybridization between the control mRNA sample and the test mRNA sample, which are separately labeled with red (Cy5) and green (Cy3) fluorescence dyes. Users usually focus on the gain region and the loss region on each chromosome. In consequence, the differentially altered regions are displayed by graphical plots.
From the simulation results presented in Lai et al. (2005), several competing statistical methods are selected for analysis of Array CGH data, including Adaptive Weights Smoothing method, Circular Binary Segmentation method and CGH Segmentation method. Furthermore, we use Perl, PHP programming language and Apache web server to integrate the chosen statistical methods into an analysis platform under R language environment. The proposed platform offers normalization, identification of the differentially altered regions and plotting of the gain and loss regions genomewide. In addition, users can annotate information through UCSC Genome Browser and ID Converter for advanced analyses.

目錄
第一章 緒論…………………………………………………………1
第一節 研究背景與動機………………………………………1
第二節 研究目的與架構………………………………………1
第二章 晶片簡介與資料說明………………………………………4
第一節 背景描述………………………………………………4
第二節 微陣列基因體比較雜合晶片簡介……………………8
第三節 實驗資料來源說明…………………………………14
第三章 文獻回顧…………………………………………………18
第一節 循環二元分割法……………………………………19
第二節 CGH分割法……………………………………………23
第三節 適應性權重平滑法…………………………………26
第四節 移動平均法…………………………………………30
第五節 分量平滑法…………………………………………32
第六節 凌波法………………………………………………34
第七節 染色體叢集法………………………………………38
第八節 最大事後機率估計法………………………………39
第九節 斷點偵測法…………………………………………41
第十節 染色體變異區域探勘法……………………………43
第十一節 隱藏式馬可夫模型…………………………………47
第四章 實驗數據分析與方法比較………………………………51
第一節 實驗數據分析………………………………………51
第二節 資料模擬與方法比較………………………………55
第五章 系統建置與架構…………………………………………62
第一節 系統建置架構………………………………………62
第二節 系統流程說明………………………………………65
第六章 結論與建議………………………………………………76
第一節 結論…………………………………………………76
第二節 未來研究方向與建議………………………………77
參考文獻………………………………………………………………79

附錄
附錄一 比較分析實驗資料的差異………………………………86
附錄二 系統使用說明手冊………………………………………95

圖目錄
圖1-1 研究架構……………………………………………………3
圖2-1 分子生物學的中心教條……………………………………5
圖2-2 三種染色體構造上的異常情形……………………………8
圖2-3 基因體比較雜合法原理……………………………………11
圖2-4 晶片影像……………………………………………………12
圖2-5 染色體區域圖形……………………………………………13
圖3-1 移動平均法…………………………………………………32
圖3-2 硬式臨界值演算法與軟式臨界值演算法的比較…………37
圖3-3 階層式分群樹狀結構………………………………………39
圖3-4 三階段異倍體偵測的過程…………………………………43
圖3-5 邊緣偵測過濾器的想法……………………………………44
圖4-1 個案四位於一號染色體長臂上增幅的情形………………54
圖4-2 個案四位於九號染色體上缺失的情形……………………54
圖4-3 個案五位於一號染色體短臂上缺失的情形………………55
圖4-4 ROC曲線的表示方法………………………………………57
圖4-5 資料模擬與統計方法………………………………………58
圖4-6 11種統計方法的比較………………………………………59
圖5-1 系統畫面……………………………………………………63
圖5-2 系統建置架構………………………………………………64
圖5-3 使用者介面架構……………………………………………65
圖5-4 資料分析流程………………………………………………66
圖5-5 cDNA資料格式範例…………………………………………67
圖5-6 BAC資料格式範例…………………………………………68
圖5-7 資料分析影像………………………………………………70
圖5-9 UCSC Genome Browser資料庫查詢頁面…………………73
圖5-9 ID Converter資料庫………………………………………73

表目錄
表2-1 實驗個案描述與結果………………………………………17
表4-1 統計方法來源與描述………………………………………56
表5-1 系統執行效能………………………………………………74

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