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研究生:潘偉傑
研究生(外文):Wei-Jie Pan
論文名稱:使用多種特徵方法預測siRNA之效率
論文名稱(外文):siPRED: siRNA efficacy prediction using various characteristic methods
指導教授:朱彥煒朱彥煒引用關係
指導教授(外文):Yen-Wei Chu
口試委員:洪振偉陳玉婷
口試日期:2011-06-24
學位類別:碩士
校院名稱:國立中興大學
系所名稱:基因體暨生物資訊學研究所
學門:生命科學學門
學類:生物學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:60
中文關鍵詞:基因沉默siRNA效率支持向量回歸類神經網路基因演算法
外文關鍵詞:gene silencingsiRNA efficacysupport vector regressionneural networksgenetic algorithms
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small interfering RAN (siRNA) 已知會在細胞中會廣泛地誘導基因沉默影響。因此,我們發展一套系統 (siPRED) 應用2層之演算法去預測siRNA效率。siPRED結闔第一層之特徵方法與第二層之整合機制。第一層中,根據序列編碼、特徵選擇與收集規則等3種類別進行分析,並應用支持向量回歸衍生各種特性方法預測siRNA效率,而為了改善第一層的方法,我們加入第二層之機制期望提升準確度。第二層之整合機制源自整合較佳之特性方法的結果,並分析支持向量回歸與類神經網路之整合機制預測siRNA效率。在使用整合機制之整合方法預測siRNA效率皆有較佳之效果,並提升第一層之訓練結果。同時,我們也利用基因演算法分析整合方法的特徵方法之權重,以觀察特徵方法的重要性。另外,在使用驗證之資料集中,siPRED相較於其他使用分數方法、類神經網路與線性回歸之系統確實有較佳之效能。另外,我們採用whole stacking energy (ΔG) -34.6 kcal/mol為臨界值篩選siRNA,其siRNA大於臨界稱為unstable siRNA,反之稱為stable siRNA,siPRED可在unstable siRNA中預測出更高準確度。

Small interfering RNA (siRNA) has been known widely that used for inducing gene silencing in cells. Therefore, we developed the predictive system, siPRED, that was applied the algorithms in two layers for predicting siRNA efficacy. siPRED was used individually the characteristic methods in first layer and fusion mechanisms in second layer. After the analysis of three categories, which are sequence, feature and rule characteristic respectively, the each characteristic method was derived from applying support vector regression (SVR) for predicting siRNA efficacy in first layer. The provenances of fusion mechanism were the outputs of the better training ability of characteristic methods, and then we analyzed the predicted siRNA efficacy, using fusion mechanisms with SVR and neural networks. The abilities in predicted siRNA efficacy through integrated methods in fusion mechanisms were the better performance than the result in first layer. Simultaneously, the weights of characteristic methods in integrated method were computed by genetic algorithms so that the importance of characteristic methods could be observed. Using validation dataset, siPRED was actually the better performance than other systems, which used scoring method, neural networks and linear regression. Finally, the threshold, whole stacking energy is -34.6 kcal/mol, was adopted for selecting siRNAs, whose whole stacking energy is bigger and equal than the threshold, and siPRED could predict more higher accuracy in those.

誌謝 i
摘要 ii
Abstract iii
Content iv
List of Tables vi
List of Figures viii
1 Introduction 1
1.1 RNA interference 1
1.2 Application of RNAi 2
1.3 Category of characteristic methods 3
1.4 siPRED 4
2 Related work 6
2.1 Pearson correlation coefficient 6
2.2 Support vector regression 7
2.3 Neural networks 9
2.4 Genetic algorithms 12
2.5 Evaluation 14
3 Materials and Methods 16
3.1 Dataset 16
3.2 Three categories of characteristic methods 16
3.2.1 Sequence characteristic 16
3.2.2 Feature characteristic 18
3.2.3 Rule characteristic 21
3.3 System construction of siPRED 22
4 Result 24
4.1 Training of characteristic methods in first layer 24
4.2 Training and comparison of fusion mechanism in second layer 25
4.3 Find the impact on each characteristic method in Hybrid+F65 28
4.4 Comparison of algorithms 30
4.5 Assess performance using evaluation 31
4.6 Using whole ∆G to improve correlation coefficient 32
5 Discussion 37
6 Conclusion 40
Reference 43
Supplementary data 48


[1].C. D. Novinaand P. A. Sharp, 「The RNAi revolution,」 Nature, 2004, pp. 161-4.
[2].C. Napoli, C. Lemieux and R. Jorgensen, 「Introduction of a Chimeric Chalcone Synthase Gene into Petunia Results in Reversible Co-suppression of Homologous Genes in trans,」 Plant Cell, 1990, pp. 279-289.
[3].A. R. van der Krol, L. A. Mur, M. Beld, J. N. Mol and A. R. Stuitje, 「Flavonoid genes in petunia: addition of a limited number of gene copies may lead to a suppression of gene expression,」 Plant Cell, 1990, pp. 291-9.
[4].A. Fire, M. K. Montgomery, S. A. Kostas, S. E. Driver and C. C. Mello, 「Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans,」 Nature, 1998, pp. 806-11.
[5].Daneholt, Bertil. 「Advanced Information: RNA interference,」 The Nobel Prize in Physiology or Medicine, 2006.
[6].S. M. Elbashir, W. Lendeckel and T. Tuschl, 「RNA interference is mediated 21- and 22-nucleotide RNAs,」 Genes & Development, 2001, pp. 188-200.
[7].E. Bernstein, A. A. Caudy, S. M. Hammond and G. J. Hannon, 「Role for a bidentate ribonuclease in the initation step of RNA interference,」 Nature, 2001, pp. 363-6.
[8].G. J. Hannon, 「RNA interference,」 Nature, 2002, pp. 244-51.
[9].M. Tijsterman and R. H. Plasterk, 「Dicers at RISC; the mechanism of RNAi,」 Cell, 2004, pp. 1-3.
[10].A. J. Hamilton and D. C. Baulcombe, 「A species of small antisense RNA in posttranscriptional gene silencing in plants,」 Science, 1999, pp. 950-2.
[11].S. M. Elbashir, J. Harborth, W. Lendeckel, A. Yalcin, K. Weber and T. Tuschl, 「Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells,」 Nature, 2001, pp. 494-8.
[12].P. Yin, Q. Xu and C. Duan, 「Paradoxical actions of endogenous and exogenous insulin-like growth factor-binding protein-5 revealed by RNA interference analysiss,」 The Journal of biological chemistry, 2004, pp. 32660-6.
[13].M. J. Jacque, K. Triques and M. Stevenson, 「Modulation of HIV-1 replication by RNA interference,」 Nature, 2002, pp. 435-8.
[14].M. A. Martinez, A. Gutierrez, M. Armand-Ugon, J. Blanco, M. Parera, J. Gomez, B. Clotet and J. Este, 「Suppression of chemokine receptor expression by RNA interference allows for inhibition of HIV-1 replication,」 AIDS, 2002, pp. 2385-90.
[15].G. Randall and C. M. Rice, 「Chronic hepatitis C virus infection,」 The journal of the American Medical Assocaition, 2003, pp. 2431-7.
[16].T. Holen, 「Efficient prediction of siRNAs with siRNArules 1.0: an open-source JAVA approach to siRNA algorithms,」 RNA, 2006, pp 1620-5.
[17].S. Takasaki, Y. Kawamura and A. Konagaya, 「Selecting effective siRNA sequences based on the self-organizing map and statistical techniques,」 Computational biology and chemistry, 2006, pp. 169-78.
[18].S. Takasaki, Y. Kawamura and A. Konagaya, 「Selecting effective siRNA sequences by using radial basis function network and decision tree learning,」 BMC Bioinformatics, 2006, p. S22.
[19].S. Takasaki, 「Selecting effective siRNA target sequences by using Bayes'' theorem,」 Computational biology and chemistry, 2009, pp. 368-72.
[20].W. Gong, Y. Ren, Q. Xu, Y. Wang, D. Lin, H. Zhou and T. Li, 「Integrated siRNA design based on surveying of features associated with high RNAi effectiveness,」 BMC Bioinformatics, 2006, p.516.
[21].J. P. Vert, N. Foveau, C. Lajaunie and Y. Vandenbrouck, 「An accurate and interpretable model for siRNA efficacy prediction,」 BMC Bioinformatics, 2006, p. 520.
[22].S. A. Shabalina, A. N. Spiridonov and A. Y. Ogurtsov, 「Computational models with thermodynamic and composition features improve siRNA design,」 BMC Bioinformatics, 2006, p. 65.
[23].G. Ge, G. W. Wong and B. Luo, 「Prediction of siRNA knockdown efficiency using artificial neural network models,」 Biochemical and biophysical research communications, 2005, pp. 723-8.
[24].Z. J. Lu and D. H. Mathews, 「Efficient siRNA selection using hybridization thermodynamics,」 Nucleic acids research, 2008, pp. 640-7.
[25].A. Reynolds, D. Leake, Q. Boese, S. Scaringe, W. S. Marshall and A. Khvorova, 「Rational siRNA design for RNA interference,」 Nature Biotechnology, 2004, pp. 326-330.
[26].M. Amarzguioui and H. Prydz, 「An algorithm for selection of functional siRNA sequences,」 Biochemical and biophysical research communications, 2004, pp. 1050-1058.
[27].K. Ui-Tei, Y. Naito, F. Takahashi, T. Haraguchi, H. Ohki-Hamazaki, A. Juni, R. Ueda and K. Saigo, 「Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference,」 Nucleic acids research, 2004, pp. 936-948.
[28].S. A. Shabalina, A. N. Spiridonov and A. Y. Ogurtsov, 「Computational models with thermodynamic and composition features improve siRNA design,」 BMC Bioinformatics, 2006, p. 65.
[29].P. Jia, T. Shi, Y, Cai and Y. Li, 「Demonstration of two novel methods for predicting functional siRNA efficiency,」 BMC Bioinformatics, 2006, p. 271.
[30].D. Huesken, J. Lange, C. Mickanin, J. Weiler, F. Asselbergs, J. Warner, B. Meloon, S. Engel, A. Rosenberg, D. Cohen, M. Labow, M. Reinhardt, F. Natt and J. Hall, 「Design of a genome-wide siRNA library using an artificial neural network,」 Nature Biotechnology, 2005, pp. 995-1001.
[31].M. Ichihara, Y. Murakumo, A. Masuda, T. Matsuura, N. Asai, M. Jijiwa, M. Ishida, J. Shinmi, H. Yatsuya, S. Qiao, M. Takahashi and K. Ohno, 「Thermodynamic instability of siRNA duplex is a prerequisite for dependable prediction of siRNA activities,」 Nucleic acids research, 2007, p. e123.
[32].T. A. Vickers, S. Koo, C. F. Bennett, S. T. Crooke, N. M. Dean and B. F. Baker, 「Efficient reduction of target RNAs by small interfering RNA and RNase H-dependent antisense agents. A comparative analysis,」 The Journal of biological chemistry, 2003, pp. 7108-18.
[33].A. Khvorova, A. Reynolds and S. D. Jayasena, 「Functional siRNAs and miRNAs exhibit strand bias,」 Cell, 2003, pp. 209-16.
[34].J. Harborth, S. M. Elbashir, K. Vandenburgh, H. Manninga, S. A. Scaringe, K. Weber and T. Tuschl, 「Sequence, chemical, and structural variation of small interfering RNAs and short hairpin RNAs and the effect on mammalian gene silencing,」 Antisense and nucleic acid drug development, 2003, pp. 83-105.
[35].P. Saetrom, 「Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming,」 Bioinformatics, 2004, pp. 3055-63.
[36].A. S. Peek, 「Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features,」 BMC Bioinformatics, 2007, p. 182.
[37].P. Muhonen, R. N. Parthasarathy, A. J. Janckila,K. G. Buki andH. K. Vaananen, 「Analysis by siRNA_profile program displays novel thermodynamic characteristics of highly functional siRNA molecules,」 Source code for biology and Medicine, 2008, p. 8.
[38].T. Xia, J. SantaLucia, Jr.,M. E. Burkard, R. Kierzek, S. J. Schroeder, X. Jiao, C. Cox and D. H. Turner, 「Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs,」 Biochemistry, 1998, pp. 14719-35.
[39].A. C. Hsieh, R. Bo, J. Manola, F. Vazquez, O. Bare, A. Khvorova, S. Scaringe and W. R. Sellers, 「A library of siRNA duplexes targeting the phosphoinositide 3-kinase pathway: determinants of gene silencing for use in cell-based screens,」 Nucleic acids research, 2004, pp. 893-901.
[40].S. Takasaki, S. Kotani and A. Konagaya, 「An effective method for selecting siRNA target sequences in mammalian cells,」 Cell Cycle, 2004, pp. 790-5.
[41].B. Jagla, N. Aulner, P. D. Kelly, D. Song, A. Volchuk, A. Zatorski, D. Shum, T. Mayer, D. A. De Angelis, O. Ouerfelli, U. Rutishauser and J. E. Rothman, 「Sequence characteristics of functional siRNAs,」 RNA, 2005, pp. 864-72.
[42].P. Jiang, H. Wu, Y. Da, F. Sang, J. Wei, X. Sun and Z. Lu, 「RFRCDB-siRNA: improved design of siRNAs by random forest regression model coupled with database searching,」 Computer methods and programs in biomedicine, 2007, pp. 230-8.
[43].O. Matveeva, Y. Nechipurenko, L. Rossi, B. Moore, P. Saetrom, A. Y. Ogurtsov, J. F. Atkins and S. A. Shabalina, 「Comparison of approaches for rational siRNA design leading to a new efficient and transparent method,」 Nucleic acids research, 2007, p. e63.
[44].B. E. Boser, I. M. Guyon, and V. N. Vapnik, 「A training algorithm for optimal margin classifiers,」 in D. Haussler, editor, 5th Annual ACM Workshop on COLT, 1992, pp. 144 – 152.
[45].http://kernelsvm.tripod.com/
[46].C. C. Chang and C. J. Lin, 「LIBSVM:a library for support vector machines,」 2001, http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[47].Michel N., Artificial intelligence: a guide to intelligent systems, 2004.
[48].M. Zuker, 「Mfold web server for nucleic acid folding and hybridization prediction,」 Nucleic acids research, 2003, pp. 3406–3415.
[49].Version 7.6 (R2008a) MATLAB Software


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