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研究生： 張耀明
Yao-Ming Chang
論文名稱： 人類微核醣核酸、目標基因與同源體之預測
Prediction of Human miRNAs, Targets and Homologs
指導教授： 葉耀明
Yeh, Yao-Ming
施純傑
Shih, Chun-Chieh
學位類別： 博士
Doctor
系所名稱： 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2010
畢業學年度： 98
語文別： 英文
論文頁數： 93
中文關鍵詞： 微核醣核酸 、目標基因 、微核醣核酸預測 、目標基因預測 、同源微核醣核酸 、微核醣核酸同源序列預測 、組織特有基因 、頻繁序列模式
英文關鍵詞： microRNA, target gene, miRNA prediction, target prediction, homologous miRNA, miRNA homolog prediction, tissue-selective genes, frequent pattern
論文種類： 學術論文
相關次數： 點閱：275  下載：2 
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    • 微核醣核酸(microRNAs)是一種長度大約22個鹼基的核醣核酸物質。它可以透過鹼基序列互補的特性抑制特定的目標基因以達到減少蛋白質生成的目的。大部分的人類基因都被發現可能是微核醣核酸的目標基因。許多研究也發現微核醣核酸在生物中扮演非常重要的角色。我們的研究主要著眼於兩個主題：未知的微核醣核酸之預測與微核醣核酸同源序列(homolog)之預測。透過這兩個研究主題我們可以對於微核醣核酸之起源與演化過程有更多的了解進而對相關的問題提出解決方案。
    在第一部分的研究中，我們發展了一個新的微核醣核酸預測方法，有別於先前的研究，此方法不用跨物種保守的資訊。我們先找出在某些組織特有表現的基因(tissue-selective genes)，並於這些基因的3’UTR序列中找出頻繁出現的序列模式(frequent motif)。透過這些頻繁序列模式，我們找到許多已知微核醣核酸的目標基因。接著我們更利用這些頻繁序列預測出未知的微核醣核酸。在我們的預測結果中，有一部份也已經透過實驗的證實為真實的微核醣核酸。這樣高成功率的預測也大大地降低實驗所需的時間與成本。
    第二部分的研究中，我們提出了一個新的方法在其他物種當中發掘更多可能是人類微核醣核酸的同源序列。透過成熟微核醣核酸(mature miRNA)序列在其他物種基因組中的搜尋之後，我們接著利用一些微核醣核酸結構與系列上的特性當作過濾條件得到許多之前未知的同源序列。在我們的結果中發現，許多人類的微核醣核酸同源序列在動物的祖先基因組中可能就已經出現。

    MicroRNAs (miRNAs) are small endogenous RNA molecules ~22 nt that target specific mRNAs to reduce the expression or translation. A large proportion of human protein-coding genes have been found that are probably regulated by miRNAs, suggesting that miRNAs play a critical role in a wide variety of biological functions. In this dissertation, we focus on two issues related to miRNA research: novel miRNA prediction and miRNA homolog prediction. We study these two issues from thorough the understanding of miRNA biogenesis and evolutionary characteristics and then propose two effective new approaches to solve biological problems.
    In first work, we developed a method to predict novel human miRNAs and target genes without requiring cross-species conservation. We first identified lowly/moderately expressed tissue-selective genes using EST data and then identified overrepresented motifs of 7 nucleotides in the 3' UTRs of these genes. Using these motifs as potential target sites of miRNAs, we recovered more than two thirds of the known human miRNAs. We then used those motifs that did not match any known human miRNA seed region to infer novel miRNAs. We predicted 36 new human miRNA genes with 44 mature forms and 4 novel alternative mature forms of 2 known miRNA genes when a stringent criterion was used and many more novel miRNAs when a less stringent criterion was used. Some of our results have been experimentally verified with a highly successful rate (8 out of 11) which can definitely reduce much experimental cost and time.
    In second work, we proposed a new search method to discover as more as possible human miRNA homologs in distant species, such as worm, fruit fly, lancelet, and zebrafish. We first searched miRNA homologous candidates in genomes according to a given known mature miRNA. Then, the similar mature candidates were extended to be precursor candidates and checked by filters of both sequence and structural criterions. The precursor candidates that passed all filters were considered as the possible miRNA homologs. In our results, many of human miRNA homologs were found in all four genomes. So, we infer that most human miRNAs may share the common ancestors with worm and fruit fly.

    Chapter 1 Introduction 1 1.1 What are microRNAs 1 1.1.1 History of miRNAs 1 1.1.2 MiRNA biogenesis 2 1.1.3 Principles of miRNA Target Recognition 4 1.1.4 Database of Known miRNA 5 1.2 Motivation 5 1.3 Objectives 7 1.4 Organization of the dissertation 7 Chapter 2 Background 9 2.1 miRNA Prediction 9 2.1.1 Computational Methods 10 2.1.2 Biological Methods 12 2.2 miRNA Target Prediction 13 2.3 Limitations of Previous miRNA and Target Prediction Methods 15 2.4 Gene Expression Profile 16 2.4.1 Expressed Sequence Tag 16 2.4.2 Microarray 17 2.5 Clustering of Known miRNAs 17 2.6 Homologous miRNA Search 18 Chapter 3 miRNA and Target Gene Prediction Using Tissue-Selective Motif 21 3.1 Introduction 21 3.2 Methods 22 3.2.1 Collection of Human Gene Expression Data 22 3.2.2 Identification of Low-Key Tissue-Selective Genes 23 3.2.3 Identification of Tissue-Selective Motifs in 3’UTR 24 3.2.4 Motif Filtering 24 3.2.5 Matching Frequent Motifs to the Seed Region of Known Mature miRNAs 25 3.2.6 Secondary Structure of Potential Novel miRNAs 25 3.3 Results 26 3.3.1 Low-Key Tissue-Selective Genes in Tissues 26 3.3.2 Frequent Tissue-Selective Motifs 28 3.3.3 Predicted Targets of Know miRNAs 29 3.3.4 Predicted Novel miRNAs 32 3.4 Experimental validation of predicted novel miRNAs and their targets 45 3.4.1 Method Designed of Experimental Validation 45 3.4.2 Expression Tests of Predicted Novel miRNAs 46 3.4.3 Functional Validation of Novel miRNAs by Luciferase Assay and Immunoblotting. 48 3.5 Summary 49 Chapter 4 Prediction of Human miRNA Homologs in Distant Species 53 4.1 Introduction 53 4.1.1 Background 53 4.1.2 Difference of Homology Search Between Protein-Coding Genes and miRNAs 54 4.2 Materials and Methods 57 4.2.1. miRNA Reference sets, Genomic Sequences, and Annotations 57 4.2.2 Methods 57 4.3 Results 66 4.3.1 Homologous Candidates of Human miRNAs in Four Species 66 4.3.2 Comparison of Sequence Similarity and Structural Base-Pairing by BBQ Grid Representation 67 4.3.3 New Predicted Human miRNA Homologs in the Four Genomes 72 4.3.4 Biological Supporting Evidences 74 4.4 Discussions and Conclusions 78 4.4.1 Comparison of the Bi-swing Match Method and BLAST 78 4.4.2 Pseudo miRNA and miRNA Evolution 79 4.4.3 MiR-548 Family in Non-Primate Species 79 4.4.4 Conclusions 80 Chapter 5 Conclusions and future works 82 5.1 Contribution to Biology 82 5.2 Contribution to Computer Science 82 5.3 Future works 83 5.3.1 Atypical miRNA Target Site Prediction 83 5.3.2 Plant miRNA Site Prediction 84 Bibliography 87 List of Publications 93

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