Using radial basis function networks and significance testing to select effective siRNA sequences

Abstract

Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are only a few consistencies among the recently reported design rules/guidelines for selecting siRNA sequences effective for mammalian genes. We propose a method for selecting effective siRNA target sequences by using a radial basis function (RBF) network and statistical significance analysis for a large number of known effective and ineffective siRNAs. The siRNA classification is first carried out by using the RBF network and then the preferred and unpreferred nucleotides for effective siRNAs at individual positions are chosen by significance testing. The gene degradation measure is defined as a score based on the preferred and unpreferred nucleotides. The effectiveness for the proposed method was confirmed by evaluating effective and ineffective siRNAs for the recently reported genes (15 genes, 196 sequences) and comparing the scores thus obtained with those obtained using other scoring methods. Since the score is closely correlated with the degree of gene degradation, it can easily be used for selecting high-potential siRNA candidates. The evaluation results indicate that the proposed method may be applicable for many other genes. It will therefore be useful for selecting siRNA sequences in mammalian genes.