Abstract
RNA interference (RNAi) is a posttranscriptional gene silencing mechanism used to study gene functions, inhibit viral activities, and treat diseases. Due to the nonspecific effects of RNAi, target validation through target detection is crucial for the success of RNAi experiments. Since target detection involves large amounts of transcriptome-wide searches, computational efficiency is critical. To efficiently detect targets for RNAi design, we develop both sequential and parallel search algorithms using RNA string kernels, which model mismatches, G-U wobbles, and bulges between siRNAs and target mRNAs. Based on tests in S. pombe, C. elegans, and human, our algorithms achieved speedups of 6 orders of magnitude over a baseline implementation. Our design strategy also leads to a framework for efficient, flexible, and portable string search algorithms.
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