Huesken, D., Lange, J., Mickanin, C., Weiler, J., Asselbergs, F., Warner, J., Meloon, B., Engel, S., Rosenberg, A., Cohen, D., Labow, M., Reinhardt, M., Natt, F. and Hall, J. (2005) Design of a genome-wide siRNA library using an artificial neural network. Nature Biotechnology, 23, 995-1001. doi:10.1038/nbt1005-1315a

has been cited by the following article:

• TITLE: Selection of highly efficient small interference RNA (SiRNA) targeting mammalian genes

  AUTHORS: Ali El-lakkani, Wissam H. Abd Elgawad, Eman A. Sayed

  KEYWORDS: RNAi; SiRNA; SiRNA Design

  JOURNAL NAME: Journal of Biophysical Chemistry, Vol.4 No.2, May 24, 2013

  ABSTRACT: RNAi is the method of silencing the expression of targeted genes. RNAi applications include gene function analysis and target validation. Designing highly efficient small interference RNA (siRNA) sequence with maximum target specificity for mammalian RNAi is one of important topics in recent years. In this work, a statistical analysis of the information for a large number (3734) of siRNA presented in the database available on the internet is done. This is to improve the design of efficient siRNA molecules. The (3734) siRNAs are classified according to their efficiency to three groups (high efficient, moderate efficient and low efficient). Thirteen properties (positional and thermodynamics) are identified in the high efficient group in the primary statistical study. In the final statistical study, the average weight of each identified property is calculated. A very good linear correlation was found between the average percentage efficiency and the weighted score of siRNA properties. It is found that the most important feature of highly efficient siRNA is the difference in binding energy between the 5’ end and the 3’ end of the anti-sense strand. The (RISC) activation step is a critical step in RNAi process where the efficiency of this process depends on the instability of the 5’ end of the anti-sense strand.