臺灣博碩士論文加值系統

隨著生物定序技術的進步，已完成定序的蛋白質序列數目快速地大量增加，發展一套計算演算法找尋蛋白質磷酸化鄰近區域之特定樣本（以下均稱motif），應用於生物上成為相當重要的議題。本研究中，我們提出一個新的非監督式方法(unsupervised)從大量資料中萃取出磷酸化motif，我們稱作為motif快速探測器（Quick Motif Finder，簡稱Q-Motif）。Q-Motif採用刪減叢集法，從大量磷酸化資料中發現隱藏其中有意義的統計資訊，以及找出motif。透過刪減叢集法能夠將相似的磷酸化資料群聚成一個小組，並分析和辨識可能磷酸化的motif；而這些可能磷酸化的motif會藉由算分機制過濾出可能性較顯著的磷酸化motif。
我們運用Q-Motif測試幾組既有和新的資料，跟一個有名且常被使用的方法Motif-X (Schwartz and Gygi, 2005)並應用其算分方法將結果做比較。大約有80%的案例能夠辨識和Motif-X完全相同且統計上顯著的motif，並且Q-Motif還能額外發掘新的motif；而這些額外的新發現，我們幾乎都能夠從文獻中驗證它們確實是磷酸化motif。由於我們認為motif-x的單一位置給分和資料移除方式，無法完整確認整個motif在磷酸化資料中有夠強烈的表現，因此我們也針對計算分數方面重新改良。比較兩種算分結果，某些原本給分較高的motif，透過新的計分法計算分數變低；可以發現資料移除再做分數計算的概念，很明顯影響最終motif的呈現。從結果而言，我們認為此研究清楚地建立和改良一個相當不錯尋找motif的新方法。
我們的研究中提出採用疊代運算方法，對磷酸化數據資料做探索式數據分析。透過幾組實際磷酸化資料和二組人工合成資料測試，能夠證實Q-Motif找尋motif的效能。此外，此方法相當具有普遍性，因此還能夠應用在發掘除了磷酸化外，其他不同型式的motif。

Development of computational algorithms to discover biologically relevant phosphorylation motifs is becoming more and more important with the rapid increase in the proteomic sequences. Here we present a novel unsupervised method, called Quick Motif Finder, (in short, Q-Motif) to extract phosphorylation motifs. Q-Motif adopts the subtractive clustering algorithm to discover motifs exploiting statistical information hidden in phosphorylated data. The phosphorylated data is clustered into homogeneous groups, which are analyzed to identify candidate motifs. These candidate motifs are then filtered to find actual motifs with statistically significant motif scores.
We have applied Q-Motif on several new and existing data sets and compared its performance with a well known state-of-the-art method, Motif-X (Schwartz, 2005). In 80% cases Q-Motif could identify all statistically significant motifs extracted by the state-of-the-art method. In addition to this, Q-Motif uncovers several novel motifs. For most of these additional motifs, we have verified their existence using evidences from the literature. Because motif scores were calculated by Motif-X scoring method, we consider it could not represent the statistical significant of every motif extracted in original phosphorylated data. Therefore, we re-define the definition of scoring method and compare the results with different scoring. These clearly establish the excellent motif discovery ability of our algorithm.
An iterative algorithm proposed here uses exploratory data analysis to discover motifs from phosphorylated data. The effectiveness of Q-Motif has been demonstrated using several real data sets as well as using a synthetic data set. The method is quite general in nature and can be used to find other type of motifs also.

口試委員會審定書 #
中文摘要 i
ABSTRACT ii
CONTENTS iii
LIST OF FIGURES v
LIST OF TABLES vi
Chapter 1 緒論 1
1.1 研究背景…………………. 1
1.2 相關研究 3
1.3 研究動機 6
Chapter 2 材料與方法 9
2.1 資料來源………………………. 9
2.2 前景資料 (Foreground data) 11
2.2.1 F-Human centered on serine (“〖FH〗^S”) 11
2.2.2 F-Human centered on serine/threonine (“〖FH〗^ST”) 11
2.2.3 F-motif-x offered (“FM”) 11
2.2.4 F-Synthetic (“FS”) 12
2.2.5 F-Mass Spectrometry (“FMS”) 12
2.3 背景資料 (Background data) 13
2.3.1 B-Human centered on serine (“〖BH〗^S”) 13
2.3.2 B-Human centered on serine/threonine (“〖BH〗^ST”) 13
2.3.3 B-Mouse (“BM”) 13
2.4 Q-Motif流程 15
2.4.1 步驟一：建造分數矩陣將序列資料編碼 18
2.4.2 步驟二：藉由群聚尋找有潛力的motif並決定候選motif清單 19
2.4.3 步驟三：透過給分評估最後的motif清單 26
Chapter 3 研究結果 29
3.1 實驗一：分析特定激酶的人類前景資料和背景資料 (〖FH〗^S 和 〖BH〗^S) 30
3.1.1 以Serine為中心，個別分析PKA 、PKC、CK2、CDK激酶(〖FH〗_PKA^S 、〖FH〗_PKC^S 、〖FH〗_CK2^S 、〖FH〗_CDK^S 和 〖BH〗^S) 31
3.1.2 以Serine/Threonine為中心，個別分析PKA 、PKC、CK2、CDK激酶 (〖FH〗_PKA^ST 、〖FH〗_PKC^ST 、〖FH〗_CK2^ST 、〖FH〗_CDK^ST 和 〖BH〗^ST) 32
3.2 實驗二：分析Motif-X文獻中測試的實驗資料 (FM 和 〖BH〗^S) 36
3.3 實驗三：分析老鼠蛋白質質譜儀資料 (FMS 和 BM) 38
3.4 實驗四：分析Motif-X文獻中提供的合成資料 (FS 和 〖BH〗^S) 43
Chapter 4 討論 47
4.1 Q-Motif和F-Motif結果比較 47
4.2 利用文獻註解驗證磷酸化Motif 53
4.3 分析Motif在生物體內的意義 55
4.4 分析Motif在生物功能和註解的專一性 65
Chapter 5 結論 68
參考文獻 69
附錄 72

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