臺灣博碩士論文加值系統

高通量轉錄體定序分析技術提供一種有效方法可以揭示蛋白質的生物功能與基因表現之間的關係。為鑑定不同實驗設計所產生基因表現量的差異資訊，首要面對的挑戰是如何選擇正確的正規化方法進行校正不同實驗資料集。本論文提出一個全新的正規化技術，藉由分析生物實驗關鍵字、基因本體論註解資訊、同源管家基因資訊及不同時期基因表現的穩定度等資訊，進行正確篩選一組適當的管家基因群以協助生物學家進行跨樣本資料集的正規化依據。主要概念是探索與實驗關鍵字距離最遠的基因本體論標籤，並從具有該標籤註解的候選管家基因群挑選一組在不同時期的基因表現量數據中仍可擁有較低變異係數的同源管家基因群。因此，本系統可以自動挑選一組與該實驗最無關連性的管家基因群做為正規化的重要依據。本論文使用標準轉錄體定序測試資料集進行系統分析，分析結果證明若採取不同管家基因群組或忽略不同發育時期基因表現的穩定度分析，該正規化的技術將會造成明顯不同的結論。本論文所提出的方法經由與傳統最受歡迎的正規化技術進行比較，不論在靈敏度或特異性都優於其他傳統的正規化方法。研究成果證明選擇合適管家基因群組作為跨資料組的正規化校正機制，確實能改善並解決基因表現量的差異性分析。

High throughput RNA-seq analysis provides a powerful tool for revealing relationships between gene expression level and biological function of proteins. To discover differentially expressed genes among various RNA-seq datasets obtained from different experimental designs, an appropriate normalization method for calibrating multiple experimental datasets is the first challenging problem. In this thesis, a novel normalization method to facilitate biologists in selecting a set of suitable housekeeping genes for inter-sample normalization is proposed. The approach is achieved by adopting user defined experimentally related keywords, gene ontology (GO) annotations, orthologous housekeeping genes, and stability of housekeeping genes at different time periods. By identifying the most distanced GO terms from query keywords and selecting housekeeping gene candidates with low coefficients of variation among different spatio-temporal datasets, the proposed method can automatically enumerate a set of functionally irrelevant housekeeping genes for practical normalization. By employing benchmark RNA-seq datasets to evaluate our developed system, the results showed that different selections of housekeeping gene set would lead to strong impact on differential gene expression analysis. The compared results have shown that our proposed method outperformed other traditional approaches in terms of both sensitivity and specificity. The proposed mechanism of selecting appropriate housekeeping genes for inter-dataset normalization is robust and accurate for differential expression analyses.

摘要 I
ABSTRACT II
CONTENTS III
LIST OF FIGURES IV
LIST OF TABLES IV
LIST OF ABBREVIATION V
1 INTRODUCTION 1
2 MATERIAL 3
3 METHOD 5
3.1 SYSTEM FLOWCHART 5
3.2 GO DISTANCE CALCULATION 7
3.3 HOUSEKEEPING GENE STABILITY 9
3.4 HOUSEKEEPING GENE SELECTION AND NORMALIZATION 10
3.5 HOUSEKEEPING CONTIG SELECTION AND NORMALIZATION FOR NONE-MODAL SPECIES 14
4 RESULTS 16
4.1 STATISTIC RESULTS 16
4.2 PUBLIC RNA-SEQ DATASET EVALUATION 18
5 AN ON-LINE WEB SYSTEM 24
6 CONCLUSIONS 26
REFERENCE 27

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