利用赫伯特-黃轉換法辨識酵母菌在呼吸/還原週期中的震盪基因群

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：97

、訪客IP：3.145.76.12

姓名

邱義涵(Yi-han Chiou) 查詢紙本館藏

畢業系所

系統生物與生物資訊研究所

論文名稱

利用赫伯特-黃轉換法辨識酵母菌在呼吸/還原週期中的震盪基因群
(Identifying oscillation gene groups in Saccharomyces cerevisiae respiratory/reductive cycle using Hilbert-Huang Transform)

相關論文

★ 細菌物種基因體中非編碼小片段核糖核酸之預測	★ 從年齡動態網路探討疾病盛行率
★ 藉由比較基因表現資料研究次世代定序與晶片技術分析差異	★ 啟動子甲基化與對應之基因表現微陣列資訊整合分析
★ 乾燥綜合症與非病毒型肝炎之相關因子分析	★ 氣候變遷對人類疾病網路造成衝擊
★ 台北和中壢地區不孕症分佈與共病探討	★ 探討台灣的門診疾病與環境空氣品質的濃度變化之相關性
★ 以地區醫院病例探討桃園之地域族群與疾病之差別	★ 桃園地區之區域與疾病盛行率之關聯
★ CyTOF之生物標記篩選與分析	★ 透明細胞腎細胞癌質譜流式細胞儀資料分析與視覺化
★ 使用支持向量機預測蛋白質醣基化位置	★ 使用基因表現資料預測基因轉錄調控網路
★ RNA Riboswitch搜尋系統之設計與實作	★ 人類疾病差異表現基因與調控網路之整合系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

21世紀以來，傳統生物學已經慢慢邁向系統生物學的層次，近年來許多研究整合了不同的微陣列資料來分析細胞在某個時期的基因調控模組，並且輔以已經研究之結果，或是利用實驗方法來證實，同時，也有許多資料是為了研究微生物中的基因震盪情形所產生的。然而，利用數據分析的方法來分析從細胞中獲得充滿雜訊的資料，其分析出來的結果不能排除含有雜訊的可能性。台灣中央研究院黃鍔院士在1998年所發表的赫伯特-黃轉換法，主要的方法是利用簡單的代數方法將波型拆解成各個IMF，再利用赫伯特轉換法將IMF轉換成瞬時頻譜，如此的方法近年來被廣泛的運用在許多科學研究上，其去雜訊的效能倍受肯定。因此我們先將研究酵母菌基因震盪情形且具有48個時間點的微陣列資料利用赫伯特-黃轉換法轉換成瞬時頻譜之後，再將yeastract上的調控關係與MIT研究單位的ChIP-chip資料整合成一個可信度較高的調控關係矩陣，再對各個轉錄調控因子所調控的所有基因的瞬時頻譜，利用歐氏距離進行階層式的叢集分析，得到許多以基因為群組的基因調控模組，結果發現許多群的基因都具有某種程度的生物意義。

摘要(英)

The turn of 21st century, traditional biology is evolutes to systems biology, much study has integrate many type of data generate from high-throughput biological techniques, or validation with experiment results, and there are many data are focus on the oscillation of genes in microbes. However, use methods of numerical analysis to analyze data from a cell may have much noise, the results it analyzed also may contain much noise. The Hilbert-Huang transform (HHT) has been published in 1998, the content of this method include empirical model decomposition (EMD) and Hilbert transform. EMD can decompose a wave into several intrinsic mode functions (IMFs), then IMF can transform into frequency domain via Hilbert transform, the performance of denoising of this method has been use in variety study. We transform time-series microarray data into frequency domain with Hilbert-Huang transform, then construct a high-confidence TF-gene regulatory matrix use ChIP-chip data and documented relation from yeastract website, based on this matrix, we can use hierarchical clustering to cluster genes regulated by each TF, then got a lot of gene cluster as regulatory module which has certain biological meaning.

關鍵字(中)

★ 赫伯特-黃轉換法
★ 酵母菌
★ 呼吸/還原週期
★ 震盪基因群

關鍵字(英)

★ oscillation gene
★ oscillation gene groups
★ Saccharomyces cerevisiae
★ respiratory/reductive cycle
★ Hilbert-Huang Transform

論文目次

摘要 i
Abstract ii
誌謝 iii
Contents iv
List of Figures v
List of Tables vi
Chapter 1 INTRODUCTION 1
1.1 Biological Background 1
1.1.1Central Dogma 1
1.1.2 High Throughput Biological Techniques 5
1.2 Related Works 10
1.3 Problem 11
1.4 Computational Background 11
1.4.1 Hilbert Huang Transform 12
1.4.2 Cluster Analysis 14
1.4.3 Distances 19
1.5 Goal of Our Work 20
Chapter 2 MATERIALS 22
2.1 Time-Series Microarray 22
2.2 ChIP-Chip Data 23
2.3 Documented Relation 24
Chapter 3 METHODS 25
3.1 Construct a TF– Gene Regulatory Relationship Matrix 25
3.2 Apply Hilbert-Huang transform to Time-Series Expression Array 26
3.3 Determine Whether Distance is Better For Our Cluster Analysis 27
3.4 Clustering Genes 31
Chapter 4 RESULTS 32
References 44

參考文獻

1.Schmoldt, A., et al., Exploring Differential Gene Expression in Zebrafish to Teach Basic Molecular Biology Skills. Zebrafish, 2009. 6(2): p. 187-199.
2.Mallery, C.; Available from:http://www.bio.miami.edu/~cmallery/150/gene/chargaff.htm.
3.Wu, W.S. and W.H. Li, Systematic identification of yeast cell cycle transcription factors using multiple data sources. BMC Bioinformatics, 2008. 9(522): p.1471 - 2105.
4.Aparicio, O., et al., Chromatin immunoprecipitation for determining the association of proteins with specific genomic sequences in vivo. Curr Protoc Mol Biol, 2005. Chapter 21: p. Unit 21 3.
5.Paul T. Spellman, G.S., Michael Q. Zhang, Vishwanath R. Iyer, Kirk Anders, Michael B. Eisen, Patrick O. Brown, David Botstein, and Bruce Futcher, Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell, 1998. 9(12): p. 3273-3297.
6.Zhao, W., E. Serpedin, and E.R. Dougherty, Spectral preprocessing for clustering time-series gene expressions. EURASIP J Bioinform Syst Biol, 2009: p. 713248.
7.Huang, N.E., et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 1998. 454(1971): p. 903-995.
8.Davis, J.A., D.E. McNamara, and D.M. Cottrell, Analysis of the fractional hilbert transform. Appl Opt, 1998. 37(29): p. 6911-3.
9.Reinke, H. and D. Gatfield, Genome-wide oscillation of transcription in yeast. Trends Biochem Sci, 2006. 31(4): p. 189-91.
10.Li, C.M. and R.R. Klevecz, A rapid genome-scale response of the transcriptional oscillator to perturbation reveals a period-doubling path to phenotypic change. Proc Natl Acad Sci U S A, 2006. 103(44): p. 16254-9.
11.Johnsson, A., et al., Period lengthening of human circadian rhythms by lithium carbonate, a prophylactic for depressive disorders. Int J Chronobiol, 1983. 8(3): p. 129-47.
12.Johnsson, A., et al., Influence of lithium ions on human circadian rhythms. Z Naturforsch C, 1980. 35(5-6): p. 503-7.
13.Lee, T.I., et al., Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 2002. 298(5594): p. 799-804.
14.Monteiro, P.T., et al., YEASTRACT-DISCOVERER: new tools to improve the analysis of transcriptional regulatory associations in Saccharomyces cerevisiae. Nucleic Acids Res, 2008. 36(Database issue): p. D132-6.
15.Harbison, C.T., et al., Transcriptional regulatory code of a eukaryotic genome. Nature, 2004. 431(7004): p. 99-104.
16.Leza, M.A. and E.A. Elion, POG1, a novel yeast gene, promotes recovery from pheromone arrest via the G1 cyclin CLN2. Genetics, 1999. 151(2): p. 531-43.
17.Altermann, E. and T.R. Klaenhammer, PathwayVoyager: pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. BMC Genomics, 2005. 6(1): p. 60.
18.Kanehisa, M., et al., KEGG for linking genomes to life and the environment. Nucleic Acids Res, 2008. 36(Database issue): p. D480-4.
19.Kanehisa, M. and S. Goto, KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 2000. 28(1): p. 27-30.
20.Kanehisa, M., et al., From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res, 2006. 34(Database issue): p. D354-7.
21.Ogata, H., et al., KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res, 1999. 27(1): p. 29-34.
22.Wixon, J. and D. Kell, The Kyoto encyclopedia of genes and genomes--KEGG. Yeast, 2000. 17(1): p. 48-55.
23.Garner, M.M. and A. Revzin, A gel electrophoresis method for quantifying the binding of proteins to specific DNA regions: application to components of the Escherichia coli lactose operon regulatory system. Nucleic Acids Res, 1981. 9(13): p. 3047-60.
24.Van Weemen, B.K. and A.H. Schuurs, Immunoassay using antigen-enzyme conjugates. FEBS Lett, 1971. 15(3): p. 232-236.
25.Fields, S. and O. Song, A novel genetic system to detect protein-protein interactions. Nature, 1989. 340(6230): p. 245-6.
26.Jia, Y., et al., Current in vitro kinase assay technologies: the quest for a universal format. Curr Drug Discov Technol, 2008. 5(1): p. 59-69.
27.Sinha, S.; Available from: http://www.cs.uiuc.edu/homes/sinhas/work.html.
28.Affymetrix; Available from: http://www.affymetrix.com/technology/index.affx
29.Hentrich, T.; Available from: http://en.wikipedia.org/wiki/File:ChIP-on-chip_wet-lab.png

指導教授

吳立青(Li-Ching Wu)

審核日期

2009-8-5

推文