Published online 14 December 2006 Nucleic Acids Research, 2007, Vol. 35, No. 2 433–440 doi:10.1093/nar/gkl1065 More complete gene silencing by fewer siRNAs: transparent optimized design and biophysical signature Istvan Ladunga* Center for Biotechnology and Department of Statistics, University of Nebraska–Lincoln, Lincoln, NE 68588-0665, USA Received October 5, 2006; Revised November 14, 2006; Accepted November 20, 2006 ABSTRACT Highly accurate knockdown functional analyses based on RNA interference (RNAi) require the possible most complete hydrolysis of the targeted mRNA while avoiding the degradation of untarge- ted genes (off-target effects). This in turn requires significant improvements to target selection for two reasons. First, the average silencing activity of randomly selected siRNAs is as low as 62%. Second, applying more than five different siRNAs may lead to saturation of the RNA-induced silencing complex (RISC) and to the degradation of untar- geted genes. Therefore, selecting a small number of highly active siRNAs is critical for maximizing knockdown and minimizing off-target effects. To satisfy these needs, a publicly available and trans- parent machine learning tool is presented that ranks all possible siRNAs for each targeted gene. Support vector machines (SVMs) with polynomial kernels and constrained optimization models select and utilize the most predictive effective combinations from 572 sequence, thermodynamic, accessibility and self-hairpin features over 2200 published siRNAs. This tool reaches an accuracy of 92.3% in cross- validation experiments. We fully present the under- lying biophysical signature that involves free energy, accessibility and dinucleotide characteris- tics. We show that while complete silencing is possible at certain structured target sites, accessi- bility information improves the prediction of the 90% active siRNA target sites. Fast siRNA activity predictions can be performed on our web server at http://optirna.unl.edu/. INTRODUCTION It is a major challenge to select those target sites where a gene can be silenced most completely. Posttranscriptional *Tel: +1 402 472 6074; Email: sladunga@unl.edu regulation can silence tens of thousands of genes to different degrees (1). This indicates that whereas a wide spectrum of target sites responds to RNA interference, the knockdown remains incomplete for most of the sites. Opposing this diversity criterion, active siRNAs have to conform to require- ments speciﬁc for the RNA-induced silencing complex (RISC) complex (2). As indicated by the 62% average activ- ity of randomly selected siRNAs (3), these criteria are poorly satisﬁed by the majority of target sites. This paradox has inspired a number of researchers to capture these criteria in heuristic rules, statistical formulations or machine learning algorithms. Tuschl and his coworkers’ rules (2,4) (http:// www.rockefeller.edu/labheads/tuschl/sirna.html) specify a pattern of UU(N19)AA, limit the G + C content to a range of 30–70%, and suggest avoiding four or more consecutive A’s or U’s that act as terminator signals in vectors that utilize RNA polymerase III. Ui-Tei et al. (5) expressed preference for siRNAs with A/U at the 50 end, G/C at the 30 terminus at least 5 A/U nucleotides in the 50 third of the antisense strand, and the absence of any G/C runs of 9 or more nucleotides. Amarzguioui and Prydz (6) propose an A/U differential between the 50 and 30 trinucleotides, C/G at position 1, A at 6 and A/U at 19, while associating the motifs U1 and G19 with lack of functionality. Translating these sequence patterns to changes in Gibbs free energy (DG) shows that most sequence rules correlate highly with thermodynamic proﬁles (7). In contrast to the wider accep- tance of the above rules, the effects of secondary structures at the target site remain debated (2). While certain structures like stable hairpins have been shown to decrease or abolish silencing efﬁciency (8–10), many other structures do not seem to attenuate RNAi. Machine learning methods select the best targets more accurately than the heuristic rules. Key to this success is rigorous optimization over high numbers of features. Support vector machines (SVMs) (11) perform accurate binary classi- ﬁcations (BCs) between low- and high-activity molecules and regression analyses (12) and helped to formulate the Stockholm rules (12). Long and degenerate sequence patterns are revealed by the GPboost genetic algorithm (13). Among the artiﬁcial neural networks, BIOPREDsi (1) was trained on the largest number of siRNAs, but the method was limited 2006 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.