Constructing the boundary between potent and ineffective siRNAs by MG-algorithm with C-features

Abstract

Background In siRNA based antiviral therapeutics, selection of potent siRNAs is an indispensable step, but these commonly used features are unable to construct the boundary between potent and ineffective siRNAs. Results Here, we select potent siRNAs by removing ineffective ones, where these conditions for removals are constructed by C-features of siRNAs, C-features are generated by MG-algorithm, Icc-cluster and the different combinations of some commonly used features, MG-algorithm and Icc-cluster are two different algorithms to search the nearest siRNA neighbors. For the ineffective siRNAs in test data, they are removed from test data by I-iteration, where I-iteration continually updates training data by adding these successively removed siRNAs. Furthermore, the efficacy of siRNAs of test data is predicted by their nearest neighbors of training data. Conclusions By siRNAs of Hencken dataset, results show that our algorithm removes almost ineffective siRNAs from test data, gives the clear boundary between potent and ineffective siRNAs, and accurately predicts the efficacy of siRNAs also. We suggest that our algorithm can provide new insights for selecting the potent siRNAs.

Cheminformatics Modeling of Gene Silencing for Both Natural and Chemically Modified siRNAs

“…These features work well for natural siRNAs and do not take the chemical structures into account. Jia et al employed C-features that are based on frequencies of different combinations of nucleotide letters [ 37 ]. As with similar features based on nucleotide single-letter representation, frequencies of single-letter combinations do not reflect the chemical nature of nucleotides involved, especially when dealing with chemically modified nucleotides.…”