String Kernels of Imperfect Matches for Off-target Detection in RNA Interference

RNA interference (RNAi) is a posttranscriptional gene silencing mechanism frequently used to study gene functions and knock down viral genes. RNAi has been regarded as a highly effective means of gene repression. However, an “off-target effect” deteriorates its specificity and applicability. The complete off-target effects can only be characterized by examining all factors through systematic investigation of each gene in a genome. However, this complete investigation is too expensive to conduct experimentally which motivates a computational study. The sequence matching between an siRNA and its target mRNA allows for mismatches, G-U wobbles, and the secondary structure bulges, in addition to exact matches. To simulate these matching features, we propose string kernels measuring the similarity between two oligonucleotides and develop novel efficient implementations for RNAi off-target detection. We apply the algorithms for off-target errors in C. elegans and human.

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