博碩士論文 952211002 詳細資訊




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姓名 林俊宏(Jun-Hong Lin)  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 細菌物種基因體中非編碼小片段核糖核酸之預測
(Prediction of Small Non-Coding RNA in Bacterial Genomes)
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摘要(中) 非編碼小片段核糖核酸 (sRNAs) 在許多細胞中扮演著重要調控功能的角色,由於非編碼小片段核糖核酸所具有特性使然: 非編碼小片段核糖核酸長度較短、 不會轉譯成蛋白質,以及穩定性會隨著不同條件而改變,因此現今以實驗及計算方法預測非編碼小片段核糖核酸皆相當困難。而目前大多數已知的非編碼小片段核糖核酸是在大腸桿菌上被發現,且高度保留於相近物種的基因體中。
因此我們發展出一項整合方法,藉由分析已知的非編碼小片段核糖核酸具有特徵,進而搜尋相近物種基因體上位於基因之間非編碼區內的高度保留區域,再以支持向量機(SVM)判定,以求發現新的非編碼小片段核糖核酸基因。
摘要(英) Small non-coding RNA genes have been shown to play important regulatory roles in a variety of cellular processes, but prediction of non-coding RNA genes is a great challenge to both experimental and computational approach due to the characteristics of sRNAs: small size, not translated into proteins, and varied stability under different conditions. Most known sRNAs have been identified in Escherichia coli and conserved in closely related organisms.
We hope to develop an integrative approach to search highly conserved intergenic regions among related bacterial genomes for combination of various characteristics extracting from known sRNAs genes on Escherichia coli using support vector machines (SVM) to predict novel sRNA genes.
關鍵字(中) ★ 非編碼小片段核糖核酸
★ 支持向量機
★ 高度保留區段
★ 細菌
★ 預測
關鍵字(英) ★ prediction
★ bacteria
★ small non-coding RNA (sRNA)
★ ncRNA
★ support vector machine (SVM)
★ conservation
★ sigma70 promoter
★ Rho-independent terminator
★ attenuator
★ Hfq protein
論文目次 Table of Contents
誌謝............................................................................................................................... V
TABLE OF CONTENTS .......................................................................................... VI
LIST OF FIGURES ............................................................................................... VIII
LIST OF TABLES ....................................................................................................... X
CHAPTER 1. INTRODUCTION............................................................................ 1
1.1. BACKGROUND ................................................................................................. 1
1.2. MOTIVATION ................................................................................................... 1
1.3. GOAL .............................................................................................................. 2
CHAPTER 2. RELATED WORKS ........................................................................ 3
2.1. QRNA ............................................................................................................ 3
2.2. SRNAPREDICT ................................................................................................ 3
2.3. PSOL .............................................................................................................. 3
2.4. SRNAFINDER ................................................................................................. 4
2.5. SUMMARY ....................................................................................................... 4
CHAPTER 3. MATERIAL AND METHODS ....................................................... 5
3.1. MATERIALS ..................................................................................................... 5
3.1.1. GENOME SEQUENCE .................................................................................... 5
3.1.2. INTERGENIC REGIONS .................................................................................. 5
3.1.3. SRNAS GENES ............................................................................................. 5
3.2. SYSTEM ORGANIZATION ................................................................................. 8
3.3. INTERGENIC SEQUENCES EXTRACTION ............................................................ 8
3.4. FINDING CONSERVED REGION OF INTERGENIC REGIONS ................................... 9
3.5. CONSERVED REGIONS FILTRATION ................................................................. 12
3.6. BUILDING OF SUPPORT VECTOR MACHINE MODEL ......................................... 12
3.6.1. SUPPORT VECTOR MACHINE ...................................................................... 12
3.6.2. BUILDING OF TRAINING AND TESTING SET ................................................. 13
3.7. FEATURES TRANSFORMATION ........................................................................ 13
3.7.1. SEQUENCE COMPOSITION .......................................................................... 13
3.7.2. STRUCTURAL MOTIF .................................................................................. 14
3.7.3. SEQUENCE CONSERVATION IN RELATED SPECIES ........................................ 14
3.7.4. OVER-REPRESENTED SEQUENCE PATTERNS ............................................... 14
3.7.5. MINIMUM FREE ENERGY ........................................................................... 14
3.8. FEATURES SELECTION ................................................................................... 14
CHAPTER 4. RESULT .......................................................................................... 16
4.1. SUPPORT VECTOR MACHINE MODEL PERFORMANCE ...................................... 16
4.2. VALIDATION WITH IDENTIFIED SRNAS IN NCRNADB .................................... 16
4.3. VALIDATION WITH SRNAS CANDIDATES PREDICTED BY PSOL TOOL ............. 18
4.4. PERFORMANCE COMPARISON WITH SRNAFINDER ........................................ 19
4.5. CRITERIA FOR SELECTING PUTATIVE SRNAS ................................................. 22
4.6. PAIRWISE OVERLAP BETWEEN SRNAS PREDICTION METHODS ....................... 23
4.7. CASE STUDY I: SMALL RNA ISTR ................................................................. 24
4.8. SRNA CANDIDATES PREDICTED BY OUR APPROACH ....................................... 26
CHAPTER 5. DISCUSSION ................................................................................. 32
5.1. TRANSCRIPTION FACTOR BINDING SITES ........................................................ 32
5.2. RHO-INDEPENDENT TERMINATOR PREDICTION .............................................. 32
5.3. ATTENUATOR ................................................................................................ 33
5.4. HFQ PROTEIN ................................................................................................ 36
REFERENCES ........................................................................................................... 40
參考文獻 References
1. Rivas, E., Klein, R.J., Jones, T.A. and Eddy, S.R. (2001) Computational identification of noncoding RNAs in E. coli by comparative genomics. Curr Biol, 11, 1369-1373. 2. Storz, G., Opdyke, J.A. and Zhang, A. (2004) Controlling mRNA stability and translation with small, noncoding RNAs. Curr Opin Microbiol, 7, 140-144. 3. Gottesman, S. (2004) The small RNA regulators of Escherichia coli: roles and mechanisms*. Annu Rev Microbiol, 58, 303-328. 4. Wang, C., Ding, C., Meraz, R.F. and Holbrook, S.R. (2006) PSoL: a positive sample only learning algorithm for finding non-coding RNA genes. Bioinformatics, 22, 2590-2596. 5. Tjaden, B., Goodwin, S.S., Opdyke, J.A., Guillier, M., Fu, D.X., Gottesman, S. and Storz, G. (2006) Target prediction for small, noncoding RNAs in bacteria. Nucleic Acids Res, 34, 2791-2802. 6. Wassarman, K.M., Repoila, F., Rosenow, C., Storz, G. and Gottesman, S. (2001) Identification of novel small RNAs using comparative genomics and microarrays. Genes Dev., 15, 1637-1651. 7. Argaman, L., Hershberg, R., Vogel, J., Bejerano, G., Wagner, E.G.H., Margalit, H. and Altuvia, S. (2001) Novel small RNA-encoding genes in the intergenic regions of Escherichia coli. Current Biology, 11, 941-950. 8. Chen, S., Lesnik, E.A., Hall, T.A., Sampath, R., Griffey, R.H., Ecker, D.J. and Blyn, L.B. (2002) A bioinformatics based approach to discover small RNA genes in the Escherichia coli genome. Biosystems, 65, 157-177. 9. Zhang, A., Wassarman, K.M., Rosenow, C., Tjaden, B.C., Storz, G. and Gottesman, S. (2003) Global analysis of small RNA and mRNA targets of Hfq. Mol Microbiol, 50, 1111-1124. 10. Moller, T., Franch, T., Hojrup, P., Keene, D.R., Bachinger, H.P., Brennan, R.G. and Valentin-Hansen, P. (2002) Hfq: a bacterial Sm-like protein that mediates RNA-RNA interaction. Mol Cell, 9, 23-30. 11. Geissmann, T.A. and Touati, D. (2004) Hfq, a new chaperoning role: binding to messenger RNA determines access for small RNA regulator. EMBO J, 23, 396-405.
12. Valentin-Hansen, P., Eriksen, M. and Udesen, C. (2004) The bacterial Sm-like protein Hfq: a key player in RNA transactions. Mol Microbiol, 51, 1525-1533. 13. Aiba, H. (2007) Mechanism of RNA silencing by Hfq-binding small RNAs. Curr Opin Microbiol, 10, 134-139. 14. Carter, R.J., Dubchak, I. and Holbrook, S.R. (2001) A computational approach to identify genes for functional RNAs in genomic sequences. Nucleic Acids Res, 29, 3928-3938. 15. Saetrom, P., Sneve, R., Kristiansen, K.I., Snove, O., Jr., Grunfeld, T., Rognes, T. and Seeberg, E. (2005) Predicting non-coding RNA genes in Escherichia coli with boosted genetic programming. Nucleic Acids Res, 33, 3263-3270. 16. Vogel, J. and Sharma, C.M. (2005) How to find small non-coding RNAs in bacteria. Biol Chem, 386, 1219-1238. 17. Kulkarni, R.V. and Kulkarni, P.R. (2007) Computational approaches for the discovery of bacterial small RNAs. Methods, 43, 131-139. 18. Livny, J., Fogel, M.A., Davis, B.M. and Waldor, M.K. (2005) sRNAPredict: an integrative computational approach to identify sRNAs in bacterial genomes. Nucleic Acids Res, 33, 4096-4105. 19. Tjaden, B. (2008) Prediction of small, noncoding RNAs in bacteria using heterogeneous data. Journal of Mathematical Biology, 56, 183-200. 20. Hershberg, R., Altuvia, S. and Margalit, H. (2003) A survey of small RNA-encoding genes in Escherichia coli. Nucleic Acids Res, 31, 1813-1820. 21. Luban, S. and Kihara, D. (2007) Comparative genomics of small RNAs in bacterial genomes. OMICS, 11, 58-73. 22. Gottesman, S. (2005) Micros for microbes: non-coding regulatory RNAs in bacteria. Trends Genet, 21, 399-404. 23. van Helden, J. (2003) Regulatory Sequence Analysis Tools. Nucleic Acids Research, 31, 3593. 24. Kingsford, C.L., Ayanbule, K. and Salzberg, S.L. (2007) Rapid, accurate, computational discovery of Rho-independent transcription terminators illuminates their relationship to DNA uptake. Genome Biol, 8, R22. 25. Szymanski, M., Erdmann, V.A. and Barciszewski, J. (2007) Noncoding RNAs database (ncRNAdb). Nucleic Acids Research, 35, D162.
26. Vogel, J., Argaman, L., Wagner, E.G.H. and Altuvia, S. (2004) The Small RNA IstR Inhibits Synthesis of an SOS-Induced Toxic Peptide. Current Biology, 14, 2271-2276. 27. Chang, T.H., Horng, J.T. and Huang, H.D. (2008) RNALogo: a new approach to display structural RNA alignment. Nucleic Acids Res. 28. Yachie, N., Numata, K., Saito, R., Kanai, A. and Tomita, M. (2006) Prediction of non-coding and antisense RNA genes in Escherichia coli with Gapped Markov Model. Gene, 372, 171-181. 29. Rivas, E. and Eddy, S.R. (2004) Noncoding RNA gene detection using comparative sequence analysis. feedback. 30. Henkin, T.M. and Yanofsky, C. (2002) Regulation by transcription attenuation in bacteria: how RNA provides instructions for transcription termination/antitermination decisions. Bioessays, 24, 700-707. 31. Merino, E. and Yanofsky, C. (2005) Transcription attenuation: a highly conserved regulatory strategy used by bacteria. Trends in Genetics, 21, 260-264. 32. Gama-Castro, S., Jimenez-Jacinto, V., Peralta-Gil, M., Santos-Zavaleta, A., Penaloza-Spinola, M.I., Contreras-Moreira, B., Segura-Salazar, J., Muniz-Rascado, L., Martinez-Flores, I. and Salgado, H. (2008) RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation. Nucleic Acids Research, 36, D120. 33. Brown, T.A. (2002) Genomes 2. Oxfordshire: BIOS Scientific Publishers. 34. Zhang, Y., Sun, S., Wu, T., Wang, J., Liu, C., Chen, L., Zhu, X., Zhao, Y., Zhang, Z., Shi, B. et al. (2006) Identifying Hfq-binding small RNA targets in Escherichia coli. Biochem Biophys Res Commun, 343, 950-955.
指導教授 吳立青、洪炯宗
(Li-Ching Wu、Jorng-Tzong Horng)
審核日期 2008-7-23
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