A Descriptive Method for Generating siRNA Design Rules

  • Bui Thang Ngoc
  • Tu Bao Ho
  • Kawasaki Saori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7803)

Abstract

Short-interfering RNAs (siRNAs) suppress gene expression through a process called RNA interference (RNAi). Current research focuses on finding design principles or rules for siRNAs and using them to artificially generate siRNAs with high efficiency of gene knockdown ability. Design rules have been reported by analyzing biology experiments and applying learning methods. However, possible good design rules or hidden characteristics remain undetected. In contribution to computational methods for finding design rules which are mostly employed by discriminative learning techniques, in this paper we propose a novel descriptive method to discover two design rules for effective siRNA sequences with 19 nucleotides (nt) and 21 nt in length that have important characteristics of previous design rules and contain new characteristics of highly effective siRNA. The key idea of the method is first to transform siRNAs to transactions then apply an Apriori adaptation with automatic min_support values to detect descriptive rules for effective and ineffective siRNAs. Rational design rules are created by analyzing graphical representations of descriptive rules. Experimental evaluation on the two siRNA data sets including 5737 siRNA sequences shown that our design rules are promising to design siRNAs effectively.

Keywords

RNAi siRNA Apriori algorithm discriminative learning techniques descriptive method design rules 

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References

  1. 1.
    Elbashir, S.M., Lendeckel, W., Tuschl, T.: RNA interference is mediated by 21– and 22–nucleotide RNAs. Genes Dev. 15, 188–200 (2001)CrossRefGoogle Scholar
  2. 2.
    Elbashir, S.M., Martinez, J., Patkaniowska, A., Lendeckel, W., Tuschl, T.: Functional anatomy of siRNAs for mediating efficient RNAi in Drosophila melanogaster embryo lysate. EMBO J. 20, 6877–6888 (2001)CrossRefGoogle Scholar
  3. 3.
    Elbashir, S.M., Harborth, J., Weber, K., Tuschl, T.: Analysis of gene function in somatic mammalian cells using small interfering RNAs. Methods 26, 199–213 (2002)CrossRefGoogle Scholar
  4. 4.
    Scherer, L.J., Rossi, J.J.: Approaches for the sequence-specific knockdown of mRNA. Nat. Biotechnol. 21, 1457–1465 (2003)CrossRefGoogle Scholar
  5. 5.
    Reynolds, A., Leake, D., Boese, Q., Scaringe, S., Marshall, W.S., Khvorova, A.: Rational siRNA design for RNA interference. Nat. Biotechnol. 22(3), 326–330 (2004)CrossRefGoogle Scholar
  6. 6.
    Ui-Tei, K., Naito, Y., Takahashi, F., Haraguchi, T., Ohki-Hamazaki, H., Juni, A., Ueda, R., Saigo, K.: Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference. Nucleic Acids Res. 32, 936–948 (2004)CrossRefGoogle Scholar
  7. 7.
    Amarzguioui, M., Prydz, H.: An algorithm for selection of functional siRNA sequences. Biochem. Biophys. Res. Commun. 316(4), 1050–1058 (2004)CrossRefGoogle Scholar
  8. 8.
    Hsieh, A.C., Bo, R., Manola, J., Vazquez, F., Bare, O., Khvorova, A., Scaringe, S., Sellers, W.R.: A library of siRNA duplexes targeting the phosphoinositide 3-kinase pathway: determinants of gene silencing for use in cell-based screens. Nucleic Acids Res. 32(3), 893–901 (2004)CrossRefGoogle Scholar
  9. 9.
    Jagla, B., Aulner, N., Kelly, P.D., Song, D., Volchuk, A., Zatorski, A., Shum, D., Mayer, T., De Angelis, D.A., Ouerfelli, O., Rutishauser, U., Rothman, J.E.: Sequence characteristics of functional siRNAs. Rna 2005 11(6), 864–872 (2005)Google Scholar
  10. 10.
    Chalk, A.M., Wahlestedt, C., Sonnhammer, E.L.L.: Improved and automated prediction of effective siRNA. Biochem. Biophys. Res. Commun. 319, 264–274 (2004)CrossRefGoogle Scholar
  11. 11.
    Teramoto, R., Aoki, M., Kimura, T., Kanaoka, M.: Prediction of siRNA functionality using generalized string kernel and support vector machine. FEBS Lett. 579, 2878–2882 (2005)CrossRefGoogle Scholar
  12. 12.
    Ladunga, I.: More complete gene silencing by fewer siRNAs: Transparent optimized design and biophysical signature. Nucleic Acids Res. 35, 433–440 (2007)CrossRefGoogle Scholar
  13. 13.
    Huesken, D., Lange, J., Mickanin, C., Weiler, J., Asselbergs, F., Warner, J., Mellon, B., Engel, S., Rosenberg, A., Cohen, D., Labow, M., Reinhardt, M., Natt, F., Hall, J.: Design of a Genome-Wide siRNA Library Using an Artificial Neural Network. Nature Biotechnology 23(8), 955–1001 (2005)CrossRefGoogle Scholar
  14. 14.
    Pei, Y., Tuschl, T.: On the art of identifying effective and specific siRNAs. Nat. Methods 3, 670–676 (2006)CrossRefGoogle Scholar
  15. 15.
    Takasaki, S.: Efficient prediction methods for selecting effective siRNA sequences. Comput. Biol. Med. 40, 149–158 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bui Thang Ngoc
    • 1
  • Tu Bao Ho
    • 1
  • Kawasaki Saori
    • 1
  1. 1.Japan Advanced Institute of Science and TechnologyNomi CityJapan

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