Figure 5. Contributions of the individual antisense sequence positions to the predictions in MVR, BC, SVM/WFE with linear kernel, and SVM/RFE with a polynomial kernel. For the first four methods, we show the sum of weights (absolute values) for the features at that position. For RFE (magenta line), we display the total decrease in the prediction accuracy when features specific to a given position are eliminated. p ¼ 0.0035 level. The considerable weight assigned to p3 indicates that the target sites of siRNAs with >90% activity are either highly accessible or other features must compensate for limited accessibility. The formation of self-hairpins within a single strand may inhibit silencing action (45). SVMs with over 100 features (Supplementary Table S1), BC, and MLR assigned strong negative weights to this feature, which was estimated by the RNAup package (19). While self-hairpin probability received zero weights in the SVM models with <50 features, it was strongly penalized indirectly by p3 from the sfold pre- dictions and sequence patterns that decrease the chances for Watson–Crick base pairing between the 50 and the 30 ends. Interestingly, while the 50–30 thermodynamic differential was eliminated during feature selection, high weights were assigned to sequence features that express the same thermo- dynamic differential. These include a preference for U and A at positions 1 and 2 but selection against these nucleotides at position 19. AG and UG are preferred at positions 20–21, whereas AA at 17–18, AA and UU at 18–19 and U at 20 are- less frequent than expected on a random basis. Contrary to some earlier rules (2), we found 12 siRNA molecules with >90% knockdown that contain GGGG tetranucleotide(s), which may form highly stable tetraplexes. Ten other highly active siRNAs contained overly stable runs of 7 or more G or C bases. The distribution of weights along the sequence follows a consistent pattern across SVMs, BC and MVR with widely varying numbers of features (Figure 5). The ﬁrst and second antisense positions dominate the predictions with the excep- tion of BC and MVR. SVMs had another major peak at position 19, in line with the hypothesis that loose termini facilitate duplex unwinding by the topoisomerase enzyme (7). The importance of the possible Argonaute-2 (43) cleavage site at position 7 was pronounced only with BC and SVM Nucleic Acids Research, 2007, Vol. 35, No. 2 439 with 60 features. The most accurate models speciﬁed prefer- ences for all positions. However, when the number of features was limited to 30, all features at positions 8, 11, 12 and 15 were eliminated. The accuracy of predictions dropped at such a low number of features (Figure 2). Cross-validation experiments and blind tests on untrained data show the robustness (stable high-performance over new data) of the biophysical signature and the predictions. Dinucleotide preferences form a marked pattern that cannot be attributed purely to energetic or entropic factors. We pos- tulate that these patterns are related to at least three sets of criteria. First, siRNAs need to be integrated into the RISC complex and have to facilitate helix unwinding by the topoisomerase and cleavage by Argonaute-2 enzymes. Second, accessible target sites are preferred or other features should compensate for reduced accessibility. Third, there is a selection against strands that can form self-hairpin structures. Availability: fast siRNA activity predictions can be performed on our web server at http://optirna.unl.edu/. SUPPLEMENTARY DATA Supplementary Data are available at NAR online. ACKNOWLEDGEMENTS The author is grateful to Drs M. E. Fromm, W. W. Stroup and J. J. M. Riethoven and J. Gardner for comments and suggestions and Dr F. Ma for systems administration. The web page was implemented by M. Eirich, E. Moss and A. Guru. Special thanks to Drs T. Holen, A. Khvorova and P. Sætrom for their siRNA collections. Support from the National Science Foundation, Tobacco Settlement Fund, and a Cyberinfrastructure Development Grant from the University of Nebraska–Lincoln are gratefully acknowledged. Funding to pay the Open Access publication charges for this article was provided by the National Science Foundation EPS-0346476. Conflict of interest statement. None declared. REFERENCES 1. Huesken,D., Lange,J., Mickanin,C., Weiler,J., Asselbergs,F., Warner,J., Meloon,B., Engel,S., Rosenberg,A., Cohen,D. et al. (2005) Design of a genome-wide siRNA library using an artificial neural network. Nat. Biotechnol., 23, 995–1001. 2. Elbashir,S.M., Martinez,J., Patkaniowska,A., Lendeckel,W. and Tuschl,T. 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