438 Nucleic Acids Research, 2007, Vol. 35, No. 2 Figure 4. Three examples of aligned feature (dinucleotide) combinations selected by RFE and/or WFE with common sequence motifs. All of these features decrease siRNA activity. The selection against the dinucleotide CC at position 9 is expressed by disfavoring cytosines at position 9 in RFE and the dinucleotide CC at position 10 in both methods. In WFE, the selection against CC at 9 is expressed both directly (CC at 9) and indirectly by disfavoring AC and CC at 8, and CC at positions 8, 9, and 10 (see text). a more accurate prediction than the G + C content. To test this postulate, we complemented the feature set selected by WFE with G + C. As expected, adding G + C did not increase prediction accuracy, even with polynomial kernels. However, the position of the target site was important for RFE but eliminated by WFE. We believe that the polynomial kernel uses this feature better since loci too close to or too far from the translation initiation site appear to decrease activity. To improve predictions, we overruled WFE and manually complemented it by the target site feature. The accessibility of the target site as measured by the sfold p3 feature is one of the heaviest weighted features of WFE both in MVF and SVM with a linear kernel. However, RFE with a polynomial kernel eliminated p3. Although WFE outperformed RFE with a small margin in our study, this does not substantiate far-reaching conclusions. WFE with a linear kernel is more robust and better in han- dling a high number of features. However, RFE can identify features that have highly nonlinear effects on silencing activ- ity. An example would be the distance of the target from the translation initiation site. Such features may be missed by WFE. DISCUSSION Highly active siRNA molecules, although diverse in sequences, appear to conform to a widespread dinucleotide, thermodynamic and accessibility signature. This signature is highly probabilistic, meaning that there are numerous excep- tions to each ‘rule.’ Fortunately, appropriate methods allow accurate prediction, which in turn lets us identify the most active siRNAs for the gene to be silenced. A total of 92.3% accuracy was achieved in weight-based feature elimination. The most accurate predictions in cross- validation experiments required as many as 142 features (Supplementary Table S1). For brevity, Table 2 shows the linear kernel that was limited to 30 features. Further indications include the need for 150 features and the lack of high weights (over 5% of the sum of the absolute values). RFE on polynomial kernels was somewhat less accurate (89.4%) than the weight-based feature elimination. However, this accuracy was achieved using as few as 68 features (Sup- plementary Table S1). Of these, 30 features are shared with the 142 obtained with weight-based feature elimination. The lack of absolute criteria may be due to sequence diversity. Since a large number of genes are subject to posttranscriptional regulation, a wide spectrum of mRNA segments is sensitive to RNA interference. This diversity requirement can still accommodate probabilistic criteria spe- ciﬁc for the RISC complex (see below). Silencing activity appears to be determined by a wide-range of ﬂexible combi- nations of weighted sequence, thermodynamic and accessibil- ity features. A wide spectrum of sequences can ﬁt this thermodynamic proﬁle (40), which can provide a (partial) solution for the paradox of sequence diversity versus RISC-speciﬁc criteria. Accurate and rigorous analysis and prediction of RNAi in free energy terms may be a real possibility, akin to structural predictions of RNA (41) or proteins (42). Machine learning is also facilitated by the 16-fold reduction in dimensionality of DG proﬁle as compared to dinucleotides. Several key features are related to the change in free energy, enthalpy or entropy related to duplex formation. Glo- bal DG is assigned the highest weight by SVMs. For the 500 most active siRNAs, the average of DG is 164.43 kJ/mol, whereas for the 500 least active siRNAs it is 180.20 kJ/ mol. In siRNAs with >90% activity, preference for lower stability is also indicated by the selection against CC and GG dinucleotides at the whole antisense strand. On the contrary to the expected antisense frequency of 0.0625, CC dinucleotides occur with a frequency of 0.0489 and GG with a frequency of 0.0540. CC was assigned a weight of 0.04503 and GG received a weight of 0.03433. The gen- eral preference for less negative global DG is ﬁne-tuned by a preference at the 50-terminus of the antisense for A and U and selection against G, C, CG and UG. The 30 end shows a preference for C, G, GG, AG, UG, GU and a negative selec- tion against A, UU, AA and CC. The putative cleavage site for the Argonaute-2 (43) or similar endonuclease at around position 7 is rich in U, but GU is preferred to AU. These results complement the thermodynamic proﬁle reported earl- ier (7) and the proposition that the lower terminal stability is supposed to facilitate duplex unwinding by the topoisomerase enzyme (44). Using WFE, the accessibility of the target site emerges as the most predictive of the 142 features (Supplementary Table S1) and the third most important feature among the 30 shown in Table 2. Extreme negative weight is assigned to p3, the probability that all bases of a tetranucleotide are involved in secondary structures. p3 is estimated by a Bayesian sampling from the Boltzmann probability distribution of conformations as implemented in the sfold algorithm (20). Therefore, it is not surprising that p3 consistently received more signiﬁcant weights than the DG of the single most stable structure. How- ever, for BC between <60% and >70% active siRNAs, all accessibility features receive zero weights (data not shown). This indicates that most structured target sites can be silenced by <70% efﬁcacy. Whereas the correlation between activity and p3 is low (r ¼ 0.0584), this is signiﬁcant at the