臺灣博碩士論文加值系統

轉錄組(Transcriptome)包含在特定組織或細胞類型，於特定發育階段和特定生理或病理條件下，由基因組轉錄的所有RNA所包含之所有信息。而轉錄組學(Transcriptomic)則是使用高通量技術大規模的研究RNA分子，而此透過高通量技術檢查細胞中轉錄組的組成與豐度的過程，便是轉錄組分析(Transcriptome profiling)。轉錄組分析能夠用於找出特定細胞狀態下的表現量模式，或是找出關鍵的基因。DNA微陣列分析(DNA microarray analysis)與次世代定序(next-generation sequencing, NGS)技術的RNA測序(RNA-Sequencing, RNA-Seq)在近年已被廣泛運用於轉錄組分析研究的領域。本論文將使用轉錄組分析(Transcriptome profiling)研究轉錄組學，將利用其探討癌症特性與精準醫療、疾病的非侵入性診斷(non-invasive diagnosis)、以及植物逆境與作物產量，本論文整體架構將依此分為三部分。
第一部分為使用轉錄組分析研究癌症特性與精準醫療，我們收集大規模的癌症RNA-Seq資料進行分析後，找出大量癌症的不同時期或與正常組織之間的差異表現轉錄體並建構了大型資料庫；此外我們亦整合大規模的癌症基因表現量與藥物處理後基因表現差異資料(drug treatment profiles)，透過演算法計算出藥物調控指數，而依此開發出一個能夠整合癌症患者基因表現量與突變資料的精準醫療預測方法。第二部分為使用轉錄組分析探討非侵入性診斷，血液中的循環microRNA (miRNA)雖然已被發現具有作為非侵入性診斷的生物標記(biomarker)潛力，然而尚未有大規模的分析並建構資料庫，因此在本研究中，我們收集並分析大規模的miRNA之DNA微陣列分析與small RNA-Seq資料，結合生物途徑與特徵選取(feature selection)的方法，建構一個循環miRNA表現量資料庫。第三部分則探討轉錄組學在植物逆境與作物產量的運用，我們收集大規模的各種植物物種在逆境環境下的RNA-Seq資料，分析後找出了各種植物在不同逆境條件下的RNA表現量差異並建構了大型資料庫；此外我們亦使用small RNA-Seq調查了129種台灣稻米栽培種的miRNA，並且調查了這些稻米的八種產量相關性狀，結合特徵選取與機器學習的方法，找出了與稻米穗數(panicle number)性狀密切相關的miRNA，並且能夠以少數miRNA的表現量便能夠有效預測稻米的穗數。
本論文涵蓋了多個層面的生物資訊技術，以透過轉錄體分析許多不同的領域。在癌症領域的研究能提供生物與醫學研究者一些研究癌症機制與治療的參考；而在以循環miRNA作為非侵入性診斷的生物標記的研究則有潛力能夠提供臨床上於例行檢測時便能夠診斷出特定疾病的可能性；而在植物的逆境與產量的研究成果，則能夠促進農業與生物研究上對抗全球氣候變遷而導致的作物危機，並且也能夠提供農業上對育種方便的一個新觀點。最後，我們建立了三個大型的資料庫，其能夠對於學術交流，幫助知識庫的建立，並且在相互驗證方面將提供具體的助益。

The transcriptome contains complete information of all RNAs that are transcribed by the genome at specific developmental stages and under specific physiological or pathological conditions in a particular tissue or cell type. Transcriptomics is a large-scale study of RNA molecules using high-throughput techniques. The process of examining the composition and abundance of transcriptomes in cells by high-throughput techniques is transcriptome profiling. It can be used to identify the expression patterns to classify cellular states or to identify genes with specific expression patterns. DNA microarray analysis and RNA Sequencing (RNA-Seq), a tool of next-generation sequencing (NGS) technologies, have been widely utilized for transcriptome profiling. In this dissertation, we collected, analyzed, and integrated large-scale datasets of transcriptome profiling to study cancer precision medicine, non-invasion diagnosis of disease, plant stress, and crop yield. The architecture of this dissertation is divided into three major components.
The first component is using transcriptome profiling to study cancer characteristics and precision medicine. After collecting and analyzing large-scale cancer RNA-Seq data, we identified a large number of differentially expressed transcripts between different cancer stages or between normal tissues and cancers, which resulted in a comprehensive cancer RNA-Seq database. Additionally, we integrated large-scale cancer expression profiles and drug treatment profiles to develop a novel precision medicine method. The second component is using transcriptome profiling to study the non-invasion diagnosis of disease. Circulating microRNAs (miRNAs) in the blood have been found to be a potential biomarker for non-invasive diagnostics. Although there have been several studies attempting to generate circulating miRNA database, they have not yet integrate the large-scale circulating miRNAs profiles and predict the potential biomarkers using machine learning methods. In this study, we collected and analyzed large-scale DNA microarray analysis of miRNAs and small RNA-Seq data, combined with biological pathways and feature selection methods to construct a circulating miRNA-expression database. The third component is utilizing transcriptome profiling to study the application of transcriptomics in plant stress and crop yield. We collected, processed, analyzed and visualized large-scale publically available plant stress RNA-seq data to construct a plant stress RNA-Seq database. In addition, we also used small RNA-Seq to investigate miRNAs of 129 Taiwanese rice cultivars and examine eight yield-related traits of these rice cultivars. In combination with feature selection and machine learning methods, we have identified certain miRNAs that are closely related to the panicle number traits in rice cultivars.
This study is a framework to provide integrated and comprehensive knowledge for bioinformatics and transcriptome profiling. The results of this study in cancer research provide a resource for research on cancer mechanisms and treatments by biological and medical researchers. The Study on circulating miRNAs will facilitate potential non-invasive biomarkers discovery for routine clinical examinations. The research findings on plant stress and yield may help to solve the food crisis caused by global climate change. Finally, we constructed three large databases, which may contribute to academic interconnection, knowledge base establishment, and mutual validation.

中文摘要 i
Abstract ii
Table of contents iv
List of tables vii
List of figures vii
Chapter 1 Preface 1
Transcriptome profiling 1
DNA microarrays technology 1
RNA Sequencing 2
Cancer characteristics and precision medicine 4
Non-invasive diagnosis of diseases 5
Plant stress and crop yield 5
Research objectives 6
Chapter 2 Construct a cancer RNA-Seq database of phenotype-specific transcriptome profiling in cancer cells 7
Abstract 7
Introduction 7
Materials and methods 10
RNA-seq datasets 10
Phenotype-specific differentially expressed transcripts (DETs) 11
Construction of mRNA-lncRNA co-expression networks 12
Results 13
Web interfaces 13
Example applications 15
Discussion 21
Chapter 3 Construction of a prediction system for precision medicine and drug repositioning of cancer using large-scale drug treatment profiles 23
Abstract 23
Introduction 23
Precision medicine 23
Drug repositioning 25
Purpose 26
Materials and methods 27
Cancer datasets collection and pre-treatment 27
Drug treatment profiles collection and pre-treatment 28
Calculation of Drug Regulatory Score 29
Analysis of precision medicine and drug repositioning 32
Results 33
Results of precision medicine 33
Results of drug repositioning 35
Discussion 37
Chapter 4 A comprehensive database for circulating microRNA biomarker identification 39
Abstract 39
Introduction 39
Materials and methods 42
Circulating miRNA datasets collections 42
Phenotype-specific differentially expressed circulating miRNAs identification and pathway enrichment analysis 42
Feature selection pipeline construction for circulating miRNAs 43
Results 45
Web interfaces 45
KEGG enrichment analysis 46
Feature selection 47
Example Applications 48
Discussion 53
Chapter 5 Construction of a plant stress transcriptome database using large-scale RNA-seq data 55
Abstract 55
Introduction 56
Materials and methods 58
Plant stress RNA-seq datasets collection 58
Stress-specific differentially expressed transcripts 58
Protein-coding transcript-lncRNA co-expression networks 59
Results 59
Utility and discussion 59
Web interface 60
Case study 63
Discussion 65
Future development 65
Conclusions 65
Chapter 6 Use feature selection to identify the crucial miRNAs that can regulate the agronomic traits of rice 67
Abstract 67
Introduction 67
Materials and methods 69
Rice cultivars collection, cultivation and phenotype investigation 69
Small RNA library preparation and sequencing 69
Identification of known miRNAs and prediction of novel miRNAs 69
Phenotype profiles and expression profiles pre-process 70
Feature selection 71
Classification 72
Results 72
Identification of known miRNAs and prediction of novel miRNAs 72
Feature selection and Classification across eight traits 73
Feature selection and Classification of Panicle Number 75
Discussion 83
Chapter 7 Conclusion 85
References 88

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