434 Nucleic Acids Research, 2007, Vol. 35, No. 2 Table 1. Overview of the 572 sequence, thermodynamic and accessibility features of the siRNAs Both global and positional features: DG, DH and DS during the transition from double-stranded to single-stranded state of the RNA (18); The ratio DH/DS as above; Average probabilities of target site positions to form secondary structures (mono-, di- and tetranucleotides); G + C content. Global features covering the complete antisense strand: DG during complex formation between the siRNA and the target mRNA; Relative frequencies of mono- or dinucleotides; Relative frequencies of homotri- and tetranucleotides; Maximal length of the G/C runs; Minimal free energy of the secondary structures at the mRNA target site; Melting temperature of the double-stranded siRNA; The probability and DG of forming a self-hairpin; Position of the target locus at the mRNA relative to the translation initiation site; Concentration of the siRNA. Features specific to each position of the antisense strand: Presence or absence of mono- and dinucleotides; Presence of G or C mononucleotides; Probability of the target site positions to form secondary structures; Change in free energy during complex formation between the siRNA and the target mRNA. Both global and positional features were used. SVM and constrained optimiza- tion methods performed the iterative selection of the most predictive features shown in Table 2 and Supplementary Table S1. to undisclosed sequence features. Shabalina et al. (14) neural network model generated position-dependent consensus pat- terns from a smaller number of molecules by using both sequence and thermodynamic features. Unfortunately, these patterns remain to be disclosed. Here we present a practical, freely accessible and transpar- ent tool for the identiﬁcation of target sites with over 90% knockdown activity. Our work is based on two postulates. First, we expected that optimal selection from a signiﬁcantly more comprehensive set of initial features may lead to the discovery of a complex and probabilistic signature. In turn, the signature(s) may lead to more sensitive and selective predictions. That Holen (15) needed to apply as many as 73 positional mononucleotide occurrence rules in order to achieve reliable predictions is evidence to support this postu- late. We have compiled the possible most comprehensive set of 572 sequence, thermodynamic and accessibility features as further direct evidence. Global and positional mono- and dinucleotide frequencies, the number of longer runs of each nucleotide, C or G, or A or U were computed. Global and positional values of DG and change in enthalpy (DH) and entropy (DS) as well as the DH/DS ratio were calculated. Mul- tiple predictors of the target site accessibility were computed (see Table 1; Supplementary Table S1 and Materials and Methods). Each of these individual features were correlated to the activities of the 2252 siRNAs in the Novartis dataset (1) (see Materials and Methods). No Pearson correlation coef- ﬁcient exceeded r ¼ 0.38 and only 15 features have r > 0.2 or r < 0.2 (Table 2). Several of these latter features repre- sent the same phenomenon. For example, the decreased stability at the 50 terminus of the antisense strand is repre- sented in free energy, enthalpy, mono- or dinucleotide fea- tures, such as selection against extreme negative free Table 2. The predictive performance of features Predictive performance Individuala Feature Position r Combinedb Feature Position Weight DG U G DH DS U DG UU G DH DDG DDG DS GG GC UA U C GG DDG DG DG DG GC CC UU CG A DH/DS CC 1–2 1 1 1–2 1–2 All All 1 All All 3–5 1–3 All 1 1 All 2 1 All 1–5 18 13 2 All All All 1 19 All 1 19–21 19–21 17–21 0.38 0.36 0.31 0.30 0.27 0.26 0.25 0.23 0.22 0.22 0.21 0.21 0.21 0.20 0.20 0.18 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.16 0.16 0.16 0.16 0.16 0.15 0.15 DG CC p3 U DH A G UU DH U A AU AA GG AA AU DG C GG CG AG G UG A UG CC GU A CC U All All All 1 18–19 19 1 18–19 20–21 2 2 6–7 17–18 20–21 18–19 9–10 3–4 1 16–17 1–2 20–21 14 4–5 20 20–21 13–14 5–6 1 20–21 7 0.146 0.134 0.128 0.109 0.107 0.099 0.094 0.086 0.084 0.068 0.066 0.063 0.059 0.058 0.056 0.055 0.055 0.054 0.053 0.052 0.052 0.050 0.049 0.047 0.046 0.044 0.040 0.039 0.036 0.035 Weights were optimized by an SVM with linear kernel. The absolute value of the weight indicates the contribution of that feature to the prediction in the linear kernel limited to 30 features. Note that the practical predictions use 142 features, shown in Supplementary Table S1 online. p3 is the probability of that each base of the tetranucleotide (i, i + 1, i + 2, i + 3) is paired as predicted by the sfold algorithm. aThe 30 features with the strongest correlations to siRNA activity in the Novartis dataset. bFeatures that in combination account for the most accurate predictions of the siRNA knockdown activity. energy, and GG, CC, GC and CG dinucleotides. The inferior performance of individual features is an even more serious issue. This performance is measured by the large overlaps in feature distributions between >90% and <80% active siR- NAs (Figure 1). Because previous machine learning methods (1,13,14,16) used considerably less representative sets of features, signiﬁcant improvements can be expected from their 86% prediction accuracy. This level is not satisfactory; even when applying multiple siRNA species, the risk of incomplete silencing remains substantial. However, to train a new method using 572 features over only 2252 siRNAs in the Novartis dataset would have led to overtraining; i.e. inferior performance on independent test sets. To avoid that, we applied constrained optimization models and SVMs for the optimal selection of a considerably smaller subset of features with the highest combined predictive value. We accomplished this objective by iteratively solving the models below with a stepwise elimination of the fea- ture(s) using different methods. The comparability of diverse features was ensured by standardization to zero mean and unit SD.