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研究生:王建凱
研究生(外文):Jian-Kai Wang
論文名稱:DynaPho: 由磷酸化蛋白質體資料推論動態化生物訊息
論文名稱(外文):DynaPho: inferring signaling dynamics from phosphoproteomics data
指導教授:阮雪芬阮雪芬引用關係
指導教授(外文):Hsueh-Fen Juan
口試委員:黃宣誠王禹超林振慶
口試委員(外文):Hsuan-Cheng HuangYu-Chao WangChen-Ching Lin
口試日期:2015-07-27
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:基因體與系統生物學學位學程
學門:生命科學學門
學類:生物科技學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:70
中文關鍵詞:磷酸化蛋白質體動態化生物訊息
外文關鍵詞:DynaPhophosphoproteomicssignaling dynamics
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細胞中蛋白質的磷酸化不僅調控許多生理生化反應,更在許多病理狀況中扮演關鍵角色。近年來,研究磷酸蛋白質體技術的躍昇,例如製造更高精確辨析的質譜儀及發掘磷酸化胜肽鍊的技術提升,足夠研究能以磷酸化胺基酸位置為主的磷酸蛋白質體。透過許多改良的技術產生大量磷酸化蛋白體的數據便急需一個更新或精進的計算方法或分析流程將這些大量數據轉成可理解並有價值的資訊。DynaPho 為一個以網頁操作為基礎的分析工具,透過多種演算法分析磷酸化資料和包含磷酸化位點的序列,並整合各種資料庫註解及解析磷酸訊號的動態變化。DynaPho 的組成為一個前處理模組與五個分析模組,包含 (1) 敘述性統計分析;(2) 磷酸化數據分群分析;(3) 趨勢性、時間性功能豐富性分析;(4) 磷酸酵素活化時間分析;(5) 蛋白質交互作用的網路分析。我們透過分析人類子宮頸癌細胞在細胞週期各階段的磷酸蛋白質體巨量資料來說明 DynaPho 的分析功能與流程。透過分析磷酸化的胺基酸序列,不僅找出細胞週期中關鍵的 CDK 家族,更進一步分析出酵素活化的時間變化表,如 CDK1,在第一階段成長期、合成期與第二階段成長期有活化的現象。透過蛋白質交互網路更可以綜觀細胞在細胞週期中的連續性訊息的傳遞,如 RanBP2-ErbB2 的傳遞路徑等。因為 DynaPho 可運用於分析磷酸化蛋白質體動態訊息的變化,能使我們更加瞭解複雜的生物系統。DynaPho 可以透過網址 http://dynapho.hchuang.info/ 免費地連結使用。

Protein regulatory phosphorylation controls normal and pathophysiological signaling activities in cell. Recently, great advances in phosphorproteomics, including high-accuracy mass spectrometry (MS) and phosphopeptide-enrichment techniques, have allowed identifying site-specific phosphorylation. Development of computational analysis methods is required to transform large-scaled phosphoproteome data into valuable information of biological relevance. DynaPho is a web-based tool for analyzing temporal phosphoproteomes. It combines several algorithms to analyze the phosphorylation profiles as well as sequence-content of phosphosites and integrates various databases to annotate and uncover the dynamics of phosphosignaling. DynaPho consists of five major analysis modules: (1) description and summary of phosphoproteomics data; (2) clustering of phosphorylation profile; (3) temporal functional enrichment; (4) generation of kinase activation profile; and (5) temporal protein interaction network. We illustrate DynaPho via an analysis of massive phosphoproteomics dataset of cell cycle on HeLa cell. Based on the phosphorylation profiles, these data were divided into eight clusters corresponding to different cell cycle stages. The analysis of kinase activation profile revealed CDK family play a major role in cell cycle signaling. For instance, CDK1 is activated in G1, synthesis and G2 stage. The temporal protein interaction network discoveries RanBP2-ErbB2 signaling across mitosis and G1 stage. DynaPho can reveal the dynamics of temporal phosphoproteomics data contributing to improved understanding of complexity of biological systems. DynaPho is freely available at http://dynapho.hchuang.info/.

口試委員會審定書 ii
致謝 iii
CONTENT iv
FIGUREURE CONTENT vi
TABLE CONTENT vii
摘要 viii
ABSTRACT ix
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 MATERIALS AND METHODS 4
2.1 Position in analyzing MS data 4
2.2 Input data format 4
2.3 Phosphosites among six stages of cell cycle in HeLa cell as a case study 4
2.4 Collect databases 5
2.5 Architecture and Sequential analyzing flowchart 5
2.6 Filter and Fill data in data preprocessing 6
2.7 Workflow and methods in basic statistics module 8
2.8 Workflow and methods in profile clustering module 8
2.9 Workflow and methods in function annotation module 11
2.10 Workflow and methods in kinase activation profile module 12
2.11 Workflow and methods in interaction network module 14
2.12 DynaPho implementation 15
CHAPTER 3 RESULTS 17
3.1 Data quality status monitors changes over the cell cycle 17
3.2 Dynamic phosphorylation profiles reveal unified biological information 17
3.3 Cellular signaling in temporal function profiles 19
3.4 Regulated phosphoproteome by potential kinases 20
3.5 Phosphorylation signaling in cell cycle by protein interaction network 21
CHAPTER 4 DISCUSSION 23
CHAPTER 5 CONCLUSIONS 25
REFERENCES 26
FIGURES 31
TABLES 64
APPENDIX 70

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