Efficient search algorithms for RNAi target detection

Abstract

RNA interference (RNAi) is a posttranscriptional gene silencing mechanism used to study gene functions, inhibit viral activities, and treat diseases therapeutically. However, RNAi has off-target effects--non-target genes can be unintentionally silenced. Therefore, target validation through target detection is crucial for the success of RNAi experiments. Effective target detection must examine each gene expressed by an organism, making computational efficiency a critical issue. We develop efficient sequential and parallel search algorithms using RNA string kernels, which model mismatches, G-U wobbles, bulges, and the seed region in the hybridization between an siRNA and its target mRNA. Empirical results demonstrate that our algorithms achieved speedups of six orders of magnitude over the alignment algorithm based on tests in the organisms of S. pombe, C. elegans, D. melanogaster, and human. Our design strategy also leads to a framework for efficient, flexible, and portable string search algorithms allowing for inexact matches.