臺灣博碩士論文加值系統

試圖去了解基因與其功能之間的複雜關係 在生物研究中一直是主要的目標，，而近年來計算方法 (computational methods) 已經成為其中一種主要方法去解決這類問題 快速累積的基因表現量資料也促進了表現型特異性 (phenotype-specific)，資料的分析。在本論文中，我們利用 mRNA 與 microRNA 表現量資料發展了不同的方法，對表現型特異性調控進行研究。

第一部分是研究特定表現型調控程序，並且使用三種不同的分析方法建構出一個資料庫。在過去幾十年來，基因表現量之分析已被廣泛地研究，提供大量的表現量數據，然而，由於轉錄因子的調控機制是在後轉錄階段進行，通常是難以從基因表現量之變異直接地辨識出標靶基因，但近幾年因實驗技術的進步，使得科學家已經可以透過 ChIP-seq 實驗，有系統地找出轉錄因子之標靶基因。因此，我們建構了一個特定表現型調控程序之資料庫，稱作DPRP(http://syslab.nchu.edu.tw/DPRP/)，對於轉錄因子與基因表現量之資料，做有效率且整合性地分析，提供生物學家作為參考。DPRP提供了三種不同的演算法：Fisher’sExactTest、Kolmogorov–Smirnovtest、BASE(BindingAssociationwithSortedExpression)演算法，利用這些演算法對於基因表現量的分析應用，可以促進生物和臨床研究領域中，更多轉錄調控程序之新假說的建立。

第二部分是研究miRNA與標靶基因交互作用之資料，並且對miRTarBase資料庫進行功能提升。miRNA是一種非編碼(non-coding)的小分子RNA，它能夠對基因的表達進行負調控，藉此控制許多細胞之運作機制。而miRTarBase(http://mirtarbase.mbc.nctu.edu.tw/)是一個廣泛收集經實驗驗證之miRNA與標靶基因交互作用的資料庫。在此研究中，最重大之更新為提供miRNA-target網路關係圖及miRNA-target基因表現的功能。本研究對於miRTarBase進行幾個重大的更新，包括：（i）增加了10倍的miRNA-target相互作用資料（）提供miRNA-target網路關係圖（）提供miRNA-target之表現量資料（）提供miRNA-target相關疾病資料（v）其他附加功能，如升級訊息之提示和錯誤報告/用戶回饋系統。

Understanding how to map genes to their function is a challenge problem in biology research. In recent years, computational methods have become one of the promising research approaches to resolve this problem. Rapid accumulation of gene expression data with phenotype information facilitates the investigation of phenotype-specific regulatory programs. In this dissertation, I develop several approaches to study phenotype-specific regulatory programs using mRNA and microRNA (miRNA) expression data.

First, a database of phenotype-specific regulatory programs that uses three different methods to identify the regulatory programs underlying gene expression profiles was developed. In recent decades, gene expression has been extensively studied, providing a large amount of expression data in public databases. Because transcription factor (TF) activity is often regulated at the post-transcriptional level, identification of the TFs responsible for gene expression changes directly based on their own expression is challenging. Recently, advances in technology have made it possible to perform experiments through the system to determine TF target genes. Therefore, we built a database of phenotype-specific regulatory programs (DPRP, http://syslab.nchu.edu.tw/DPRP/) to identify the regulatory programs underlying gene expression profiles derived from the integrative analysis of TF binding data and gene expression data. DPRP uses three methods: the Fisher’s exact test, Kolmogorov– Smirnov test, and BASE (Binding Association with Sorted Expression) algorithm. These algorithms integrate the application of gene expression data to generate new transcriptional regulatory program hypotheses in biological and clinical research.

Second, data integration of miRNA-target interactions (MTIs) and miRTarBase functional improvements are discussed. miRNAs are a non-coding small RNAs that negatively regulate gene-expression profiles. The miRTarBase database (http://mirtarbase.mbc.nctu.edu.tw/) provides the most current and comprehensive information regarding experimentally validated miRNA-target interactions. The current status and recent improvements of the database include (i) a ten-fold increase in miRNA-target interaction entries, (ii) introduction of a miRNA-target network, (iii) expression profiles of miRNAs and their target genes, (iv) miRNA target-associated disease data, and (v) additional utilities such as an upgrade reminder and an error reporting/user feedback system are reported.

摘 要 ............i
ABSTRACT ............iii
CONTENTS ............v
LIST OF TABLES ............vii
LIST OF FIGURES ............viii
CHAPTER 1 Introduction ............1
1.1 Whole-cell computational model ............1
1.2 Research goal ............1
1.3 Major contributions ............2
1.4 Organization of the dissertation ............3
CHAPTER 2 Background ............4
2.1 Phenotype-Specific Regulatory Programs ............4
2.1.1 Transcriptional regulation ............4
2.1.2 Methods for inferring the regulatory activity of TFs ............5
2.1.3 ChIP-chip and ChIP-seq data ............5
2.2 miRNA-Target Interactions (MTIs) ............6
2.2.1 MicroRNAs (miRNAs) ............6
2.2.2 Databases related to miRNAs ............8
2.2.3 Experimentally confirmed miRNA-target interactions ............9
CHAPTER 3 Phenotype-Specific Regulatory Programs Derived from Transcription Factor Binding Data ............10
3.1 Transcription Factor Binding Data ............10
3.1.1 Gene expression data ............10
3.1.2 Phenotype annotation of gene expression data ............11
3.1.3 ChIP-seq data............13
3.2 Algorithms of Phenotype-Specific Regulatory Programs ............13
3.2.1 Fisher''s exact test ............13
3.2.2 Kolmogorov-Smirnov test ............14
3.2.3 BASE algorithm ............14
3.3 Results ............15
3.3.1 Web interfaces ............15
3.3.2 Example applications............19
3.4 Discussion ............22
CHAPTER 4 An Information Resource for Experimentally Validated miRNA-Target Interactions ............23
4.1 Updated Database Content ............23
4.1.1 Experimental validation method–addition of the ''NGS'' support type ............26
4.1.2 miRNA target-associated diseases ............28
4.1.3 miRNA-target network ............28
4.1.4 Expression profile of miRNA and its target gene ............28
4.2 Results ............33
4.2.1 Enhanced interface ............33
4.3 Discussion ............35
CHAPTER 5 Conclusions and Future Works ............39
5.1 The Key Results ............39
5.2 Suggestions for Future Research ............40
REFERENCES ............41

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