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研究生： 孫怡明
Yi-Ming Sun
論文名稱： 以共調控人類基因的上游調控區預測轉錄調控模組之系統
A System for Prediction of Transcriptional Regulatory Modules in Human Upstream Control Regions of Coregulated Genes
指導教授： 洪炯宗
Jorng-Tzong Hong
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
畢業學年度： 91
語文別： 英文
論文頁數： 53
中文關鍵詞： 共調控 、調控區 、調控模組
外文關鍵詞： regulatory module, coregulate, regulation
相關次數： 點閱：14  下載：0 
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    • 基因晶片技術提供了大量顯現基因表現的方法，針對基因表現的狀況可以找出有相同表現結果的群組，基因調控機制在研究分子生物運作上是非常重要的議題，而找出基因調控點則是全面了解基因調控機制的重點之一。本研究之貢獻在於提出一個整合各種方法並能自動化進行調控點分析的系統。
    本研究發展了一個調控點預測系統，由使用者提供在調控機制上有相關性的基因群組，系統提供多種己被實際運用的預測程式(如Meme、gibbs、..)以及特殊序列(過度出現的重複序列、己知調控點)找尋各種可能的調控點，並提供相關的統計數值來協助過濾出比較重要的結果，接著利用找出的調控點資訊進行相關性分析來找出調控模組。經由整合在此系統的各個資料庫(TRANSFAC、HomoloDB、…)所提供的資訊，還可以提供使用者在進行分析時，能很方便地取得相關資訊來調整分析參數，來得到較佳的結果。系統同時提供個幾個不同的方法來呈現預測的結果，可以讓使用者更容易理解與決策。最後提供了實際分析的案例，來說明本系統的成效。

    The microarray technology provides a method to reveal expression profiles of huge number genes. By using gene expression profiles, gene groups with the same expression pattern can be found. The mechanisms to regulate gene expression are an important subject in studying molecular biology. Finding transcriptional regulatory binding sites is one key point to totally understand gene expression regulation mechanisms. Therefore, an automatic system integrates various methods to analyze regulatory sites is the contribution of this study.
    In this study we develop a system to predict transcriptional regulatory binding sites. Using the gene group, which is correlated in regulatory mechanisms, is submitted by users. The system provides the followings to discover every possible regulatory site. 1. Various prediction programs (e.g. Meme, Gibbs, etc.) that are applied in many real cases. 2. Special sequences (e.g. over-represented repeat and known-site). It also provides useful statistical values to filter significant results. Then, by analyzing the association, we can use the information of candidate sites to find regulatory modules. The information in each databases integrated in this system can provide users to get some related information. This helps to adjust parameters efficiently in analyzing process for better results. The system also provides a lot of methods to represent prediction results. We will get more understanding and assistance to make decisions. At the end, we propose an analyzed real cases to describe the result of this system.

    Chapter 1 Introduction 1 1.1 Backgrounds 1 The Central Dogma 2 Gene Expression 3 Regulation of gene expression 3 Regulation of gene expression 4 1.2 Motivation 5 1.3 Goals 5 Chapter 2 Related Works 7 2.1 Gene Expression Clustering 7 2.2 Regulatory Site Prediction 7 2.3 Consensus pattern and Motifs 8 2.4 Regulatory Site Co-occurrence 9 Chapter 3 Materials and Methods 10 3.1 Materials 10 3.1.1 Human Genome Sequence 10 3.1.2 Known TF Binding Site 11 3.2 Methods 11 3.2.1 DNA Motif Discovery 12 MEME 12 Gibbs Sampler. 13 AlignACE 14 3.2.2 Eliminate Redundant Motifs 15 3.2.3 Over-Represented Repeats Discovery 15 Z-Score 16 Whole genome index 17 3.2.4 Association Rule 17 X2 Test 18 P-Value 19 Chapter 4 Implementation 20 4.1 System Flow 20 4.2 Data Preprocessing 22 4.3 Data Storage 23 4.4 Web Interface 25 Chapter 5 Case Study 28 5.1 Predicted Candidate Sites 29 5.2 Significant co-occurrence combinations 30 Chapter 6 Summary 35 6.1 Discussion 35 6.2 Future Work 37 Referrences 39 Appendix 42

    1. Crick, F., Central dogma of molecular biology. Nature, 1970. 227(258): p. 561-3.
    2. Zhu, J. and M.Q. Zhang, Cluster, function and promoter: analysis of yeast expression array. Pac Symp Biocomput, 2000: p. 479-90.
    3. Yeung, K.Y. and W.L. Ruzzo, Principal component analysis for clustering gene expression data. Bioinformatics, 2001. 17(9): p. 763-74.
    4. Sharan, R., R. Elkon, and R. Shamir, Cluster analysis and its applications to gene expression data. Ernst Schering Res Found Workshop, 2002(38): p. 83-108.
    5. van Helden, J., B. Andre, and J. Collado-Vides, Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J Mol Biol, 1998. 281(5): p. 827-42.
    6. Vilo, J., et al., Mining for putative regulatory elements in the yeast genome using gene expression data. Proc Int Conf Intell Syst Mol Biol, 2000. 8: p. 384-94.
    7. Vilo, J. and K. Kivinen, Regulatory sequence analysis: application to the interpretation of gene expression. Eur Neuropsychopharmacol, 2001. 11(6): p. 399-411.
    8. Horng, J.T., et al., The repetitive sequence database and mining putative regulatory elements in gene promoter regions. J Comput Biol, 2002. 9(4): p. 621-40.
    9. Pevzner, P.A. and S.H. Sze, Combinatorial approaches to finding subtle signals in DNA sequences. Proc Int Conf Intell Syst Mol Biol, 2000. 8: p. 269-78.
    10. Tompa, M., An exact method for finding short motifs in sequences, with application to the ribosome binding site problem. Proc Int Conf Intell Syst Mol Biol, 1999: p. 262-71.
    11. Buhler, J. and M. Tompa, Finding motifs using random projections. J Comput Biol, 2002. 9(2): p. 225-42.
    12. Liu, X., D.L. Brutlag, and J.S. Liu, BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. Pac Symp Biocomput, 2001: p. 127-38.
    13. Wagner, A., Genes regulated cooperatively by one or more transcription factors and their identification in whole eukaryotic genomes. Bioinformatics, 1999. 15(10): p. 776-84.
    14. Birnbaum, K., P.N. Benfey, and D.E. Shasha, cis element/transcription factor analysis (cis/TF): a method for discovering transcription factor/cis element relationships. Genome Res, 2001. 11(9): p. 1567-73.
    15. Bing Liu, W.H.a.Y.M., Pruning and Sumarizing the Discovered Associations. 1999.
    16. Benson, D.A., et al., GenBank. Nucleic Acids Res, 2002. 30(1): p. 17-20.
    17. Wingender, E., et al., TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Res, 1996. 24(1): p. 238-41.
    18. Bailey, T.L. and C. Elkan, Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol, 1994. 2: p. 28-36.
    19. Bailey, T.L. and C. Elkan, The value of prior knowledge in discovering motifs with MEME. Proc Int Conf Intell Syst Mol Biol, 1995. 3: p. 21-9.
    20. Lawrence CE, A.S., Boguski MS, Liu JS, Neuwald AF, Wootton JC., Detecting subtle sequence signals : A gibbs sampling strategy for multiple alignment. Science, 1993. 262: p. 208-214.
    21. Thijs, G., et al., A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes. J Comput Biol, 2002. 9(2): p. 447-64.
    22. Thijs, G., et al., INCLUSive: integrated clustering, upstream sequence retrieval and motif sampling. Bioinformatics, 2002. 18(2): p. 331-2.
    23. Hughes, J.D., et al., Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J Mol Biol, 2000. 296(5): p. 1205-14.
    24. Manson McGuire, A. and G.M. Church, Predicting regulons and their cis-regulatory motifs by comparative genomics. Nucleic Acids Res, 2000. 28(22): p. 4523-30.
    25. Liu, J.S., Neuwald, A. F. & Lawrence, C. E., Bayesian models for multiple local sequaence alignment and Gibbs sampling strategies. J. Am. Statistics Association, 1995. 90: p. 1156-1170.
    26. Levy, S., S. Hannenhalli, and C. Workman, Enrichment of regulatory signals in conserved non-coding genomic sequence. Bioinformatics, 2001. 17(10): p. 871-7.
    27. Agrawal, R., T. Imielinski, and A. Swami. Mining Associations between Sets of Items in Large Databases. in Proc. of the ACM SIGMOD Int''l Conference on Management of Data. 1993. Washington D.C.
    28. Gusfield, D., Algorithm in strings, trees and sequences. Cambridge University Press, NY, 1997.
    29. Mohamed Ibrahim Abouelhoda, S.k., and Enno Ohlebusch, The Enhanced Suffix Array and Its Applications to Genome Analysis. WABI 2002, LNCS 2452, 2002: p. 449-463.
    30. Liu, B., Hsu, W., and Ma, Y., Pruning and Summarizing the Discovered Associations. Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego,, 1999: p. 125-134.
    31. R. Strikant, a.R.A., Mining Generalized Association Rules. Proc. of the 21st Int''1 conference on Very Large Database., 1995.
    32. Jensen, L.J. and S. Knudsen, Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation. Bioinformatics, 2000. 16(4): p. 326-33.
    33. Sudarsanam, P., Y. Pilpel, and G.M. Church, Genome-wide co-occurrence of promoter elements reveals a cis-regulatory cassette of rRNA transcription motifs in Saccharomyces cerevisiae. Genome Res, 2002. 12(11): p. 1723-31.
    34. Whitfield, M.L., et al., Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol Biol Cell, 2002. 13(6): p. 1977-2000.
    35. van Helden, J., B. Andre, and J. Collado-Vides, A web site for the computational analysis of yeast regulatory sequences. Yeast, 2000. 16(2): p. 177-87.
    36. Hertz, G.Z. and G.D. Stormo, Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics, 1999. 15(7-8): p. 563-77.
    37. van Helden, J., A.F. Rios, and J. Collado-Vides, Discovering regulatory elements in non-coding sequences by analysis of spaced dyads. Nucleic Acids Res, 2000. 28(8): p. 1808-18.
    38. Lawrence, C.E., et al., Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science, 1993. 262(5131): p. 208-14.
    39. Horng, J.T., et al., Mining putative regulatory elements in promoter regions of Saccharomyces cerevisiae. In Silico Biol, 2002. 2(3): p. 263-73.

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