Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30576
Computational Model for Predicting Effective siRNA Sequences Using Whole Stacking Energy (% G) for Gene Silencing

Authors: Reena Murali, David Peter S.

Abstract:

The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies show that upregulation of mRNA because serious diseases like cancer. So designing effective siRNA with good knockdown effects plays an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (%G), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.

Keywords: RNA Interference, Artificial Neural Network, double stranded RNA, short interfering RNA

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1326828

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2276

References:


[1] T. Holen, M. Amarzguioui, M.T. Wiiger, E. Babaie, and H. Prydz, "Positional effects of short interfering RNAs targeting the human coagulation trigger tissue factor", in Nucleic Acids Res, vol. 30(8), 2002, pp. 1757 - 1766.
[2] A. Fire, S. Xu, M.K. Montgomery, S.A. Kostas, S.E. Driver, and C.C. Mello, "Potent and specific genetic interference by double stranded RNA in c. elegans", in Nature, vol. 391, 1998, pp. 806 - 811.
[3] C. Cogoni, and G. Macino, "Post-transcriptional gene silencing across kingdoms", in Genes Dev, vol. 10, 2000, pp. 638 – 643.
[4] H. Shi, A. Djikeng, T. Mark, E. Wirtz, C. Tschudi, and E. Ullu, "Genetic interference in trypanosoma brucei by heritable and in-ducible doublestranded RNA", in RNA, vol. 6(7), 2000, pp. 1069 - 1076.
[5] M.A. Martinez, A. Gutierrez, M. Armand-Ugon, J. Blanco, M. Parera, J. Gomez, B. Clotet, and J.A. Este, "Suppression of chemokine receptor expression by RNA interference allows for inhibition of HIV-1 replication ", in AIDS, vol. 16(18), 2002, pp. 2385 - 2390.
[6] H. Xia, Q. Mao, S.L. Eliason, S.Q. Harper, I.H. Martins, H.T. Orr, H.L. Paulson, L. Yang, R.M. Kotin, and B.L. Davidson, "Rnai suppresses polyglutamine induced neurodegeneration in a model of spinocerebellar ataxia", in Nature Medicine, vol. 10, 2004, pp. 816 - 820.
[7] J. Soutschek, A. Akinc, B. Bramlage, K. Charisse, R. Constien, M. Donoghue, S. El-bashir, A. Geick, P. Hadwiger, J. Harborth, M. John, V. Kesavan, G. Lavine, R.K. Pandey, T. Racie, K.G. Rajeev, I. Rohl, I. Toudjarska, G. Wang, S. Wuschko, D. Bumcrot, V. Koteliansky, S. Limmer, M. Manoharan, and H.P. Vornlocher, "Therapeutic silencing of an endogenous gene by systemic ad¬ministration of modified sirnas ", in Nature, vol. 432, 2004, pp. 173 -178.
[8] A. Borkhardt, "Blocking oncogenes in malignant cells by RNA interference new hope for a highly specific cancer treatment? ", in Cancer Cell, vol. 2(3), 2002, pp. 167 – 168.
[9] M. Amarzguioui, and H. Prydz, “An algorithm, for selection of functional sirna sequences”, in Biochem Biophys Res Commun, vol. 316(4), 2004, pp. 1050 - 1058.
[10] T.Tuschl, “RNA interference and small interfering RNAs”, in Chembiochem, . vol. 2(4), 2001, pp. 239 - 241.
[11] J. Martinez, A. Patkaniowska, H. Urlaub, R. Luhrmann, and T. Tuschl, “Single-stranded antisense sirnas guide target rna cleavage in rnai”, in Cell, vol. 110(5), 2002, pp. 563 - 574.
[12] S.M. Elbashir, W. Lendeckel, and T. Tuschl, “RNA interfernce is mediated by 21 and 22nucleotide RNAs”, in Genes and Development, vol. 15, 2001, pp. 188 - 200.
[13] A. Reynolds, D. Leake, Q. Boese, S. Scaring, W. Marshall, and A. Khvorova, “Rational siRNA design for RNA interference”, in Nature Biotechnology, vol. 22(3), 2004, pp. 326 – 330.
[14] A.M. Chalk, C. Wahlestedt, and E.L. Sonnhammer, “ Improved and automated prediction of effective sirna”,. in Biochem. Biophys.Res. Commun, vol. 319, 2004, pp. 264 - 274.
[15] K. Ui-Tei, Y. Naito, F. Takahashi, T. Haraguchi, H. Ohki-Hamazaki, A. Juni, R. Ueda, R. and K Saigo, “Guidelines for the selection of highly effective sirna sequences for mammalian and chick rna interference”, in Nucleic Acids Res., vol. 32, 2004, pp. 936 -948.
[16] 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”, in Nucleic Acids Res., vol. 32(3), 2004, pp.893 - 901.
[17] Y.Ren, W. Gong, Q. Xu, and X. Zheng, “siRecords: an extensive database of mammalian siRNAs with efficacy ratings”, in Access, vol. 22(8), 2006, pp. 1–10
[18] D. Huesken, J. Lange, C. Mickanin, J. Weiler, F. Asselbergs, J. Warner, B. Meloon, S. Enge, 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”, in Nat Biotechnology, vol. 23, 2006, pp. :995–1002.
[19] J.P. Vert, N. Foveau, C. Lajaunie, and Y. Vandenbrouck, “An accurate and interpretable model for siRNA efficacy prediction”, in BMC Bioinformatics, vol. 7(520), 2006, pp. 1-17.
[20] S.A. Shabalina, A.N. Spiridonov, and A.Y. Ogurtsov, “ Computational models with thermodynamic and composition features improve siRNA design”, in BMC Bioinformatics, vol. 7(65), 2006, pp.1-16
[21] 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”, in Nucleic Acids Research, vol. 35(18), 2007, pp. 1–10.
[22] O.Matveeva, Y. Nechipurenko, L. Rossi, B. Moore, A.Y. Ogurtsov, J.F. Atkins, P.Saetrom and S.A. Shabalina, “Comparison of approaches for rational siRNA design leading to a new efficient and transparent method”, in Access, vol. 35, 2007, pp.1–10.
[23] M.Mysara, J.M. Garibaldi, and M. Elhefnawi, “MysiRNA-Designer a workflow for efficient siRNA design”, in PLoS One, vol. 6(10), 2011, pp 1-10.
[24] M.Mysara, M. Elhefnawi, and J. M. Garibaldi., “MysiRNA: Improving siRNA efficacy prediction using a machine-learning model combining multi-tools and whole stacking energy (DG)”, in Journal of Biomedical Informatics, vol. 45, 2012, pp. 528–534
[25] A. Khvorova, A. Reynolds, and S.D. Jayasena, “ Functional siRNAs and miRNAs exhibit strand bias”, in Cell, vol. 115, 2003, pp. 209–216..
[26] 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”, in Antisense Nucleic Acid Drug Dev, vol. 13, 2003, pp. 83–105.
[27] 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”, in Journal of Biology and Chemistry, vol. 278, 2003, pp. 7108–7118,
[28] M. Reena, S. P. David, “Computational Model for Predicting Effective siRNA Sequences for Gene Silencing”, Eight International Conference on Data Mining and Warehousing (ICDMW-2014),2014,pp138-142.