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研究生:李權君
研究生(外文):Chuan-ChunLee
論文名稱:微核醣核酸活性預測方法之改進
論文名稱(外文):Improving the performance of a miRNA activity predicting method
指導教授:劉宗霖劉宗霖引用關係
指導教授(外文):Tsung-Lin Liu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:生物資訊與訊息傳遞研究所
學門:生命科學學門
學類:生物訊息學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:47
中文關鍵詞:microarraymiRNA活性miRNA target資料庫個體差異
外文關鍵詞:microarraymiRNA activitymiRNA target predictionindividual difference
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微核糖核酸(miRNAs)是小片段不會轉譯出蛋白質的核糖核酸(RNA),透過轉譯的抑制(translation inhibition)或傳訊核糖核酸(mRNA)的降解(degradation)來調控標的基因(target genes)的表現。在分析微陣列(microarray)資料中,我們發現當miRNA有表現量上的顯著改變時,並不是其所有targets都會有表現量改變,因此如何辨別miRNA的活性是我們的研究主題。在本論文中,當一個miRNA所有targets的表現量變化大於其非目標基因(non-targets)的表現量變化,我們定義該miRNA具有活性。過去已有許多關於如何利用mRNA表現量資料來辨別miRNA的活性研究,例如TREX及mirAct等。但我們測試了幾組資料後發現他們的預測結果不如預期。為了改善預測的結果,我們首先找到較適合用來預測miRNA活性的miRNA targets資料庫(miRNA target prediction)。利用同時具有mRNA與miRNA的表現量資料,來計算miRNA與target間表現量的相關係數(correlation)。我們發現相較於其他資料庫,利用Pictar可得到較強的負相關性,也就是Pictar是一個較適合用來預測miRNA活性的miRNA targets資料庫。另外我們也發現隨著樣品數目(sample size)的增加,miRNA與targets間的負相關性趨勢越來越顯著,然而儘管搭配Pictar與大樣品數兩種特性進行預測,其預測結果仍具有高偽陽率(high false positive rate)。故我們認為利用miRNA表現量資料來加強預測是必須的,透過miRNA表現量資料可以去除預測出具活性但實際上沒有表現量改變的miRNA,更加確認miRNA是否具活性。此外,我們發現利用個體差異的預測方法可以改善活性預測的結果,即當mRNA表現量資料可分為兩群,例:腫瘤組與對照組,我們將各別腫瘤組的樣品之表現量與所有對照組比較,可預測並得到該腫瘤組中具活性的miRNA。由於個體間差異變化之大,故此預測miRNA活性的方法未來或許可以應用於個人化醫療。
MicroRNAs (miRNAs) are small non-coding RNAs that regulate target genes’ expression through translation inhibition or mRNA degradation. In microarray data, we observed that not all the target genes of a miRNA are up- or down-regulated when the miRNA is differentially expressed. Therefore, to identify miRNA’s activity is an important topic. Here we defined that a miRNA is active if its target genes are more up- or down-regulated than its non-target genes. A number of computational approaches, e.g., TREX and mirAct, have been developed to predict miRNA’s activity using mRNA array data. However, we tested TREX and mirAct on many mRNA array data sets and found that the performances were not good. To enhance the performance, we set out to find the target prediction program that gives the strongest negative correlation in expression between microRNAs and the predicted targets. Using the data sets with both microRNA and mRNA expression, we found that the miRNA and target gene pairs predicted by Pictar were more negatively correlated compared with other target prediction programs. We also observed that the larger sample size, the stronger negative correlation between miRNAs and their target genes. Nevertheless, even with PicTar and a large sample size, the false positive rate is still not satisfactory. Thus, it is necessary that we incorporate microRNA’s expression data to filter away the microRNAs predicted as up- or down-regulated but in fact not differentially expressed. In addition, we found that microRNA activity prediction is better when we consider individual differences. When two groups of samples are involved, e.g., normal v.s. tumor, we obtained the differential expressions of genes by comparing the expressions between the two groups. We found that if we treated each tumor sample separately, i.e., by comparing the gene expression in one tumor sample to those of the normal samples, individual microRNA activity can be better predicted. As individual differences are relatively large, this approach provides personal information of microRNA activity, which could be useful for personalized medicine in the future.
中文摘要 I
Abstract II
誌謝 IV
目錄 V
表目錄 VII
圖目錄 VIII
第一章 緒論 1
第二章 相關研究 2
2-1. miRNA介紹 2
2-2. DNA微陣列(DNA microarray) 3
2-3. 預測miRNA活性的工具 4
2-3-1. TREX 4
2-3-2. mirAct 5
第三章 資料蒐集與方法 7
3-1. 基因表現量資料蒐集與處理 7
3-2. miRNA targets資料庫收集 9
3-3. miRNAs與targets表現量之相關性分析(correlation in expression analysis)方法 9
3-3-1. 相關係數(correlation coefficient) 9
3-3-2. 累積分布函數(cumulative distribution function, cdf) 10
3-3-3. D值分析 11
3-3-4. 樣品個數大小(sample size)分析 12
3-4. miRNA活性預測 13
3-4-1. TREX 13
3-4-2. 藉由個體差異(individual difference)的計算預測miRNA活性 14
3-4-3. 利用miRNA表現量資料濾掉沒有表現量改變之miRNA 15
3-5. miRNA活性預測評估 16
3-5-1. 敏感性(sensitivity) 16
3-5-2. 偽陽率(False positive rate) 17
第四章 實驗結果與分析 18
4-1. Pictar5是一個適合用來預測miRNA活性的資料庫 18
4-2. 其他預測miRNA活性方法之結果評估 22
4-3. 利用個體差異(individual difference)的方法進行預測 23
4-4. 搭配miRNA表現量資料的預測結果比較 26
4-5. 探討miRNA訊號強弱對預測結果之影響 31
4-5-1. D值越大其偽陽率有較低的趨勢 31
4-5-2. D值大小影響預測準確度 32
4-5-3. 隨著樣品數增加可以加強miRNA的訊號(signal) 35
第五章 討論 37
參考文獻 41
補充資料 44
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