Supplementary Information
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1 Supplementary Information Performance measures A binary classifier, such as SVM, assigns to predicted binding sequences the positive class label (+1) and to sequences predicted as non-binding the negative class label ( 1). Correct assignments to the positive or the negative class increase the numbers of the true positives (TP) or the true negatives (TN), respectively. When wrongly attributed, false negatives (FN) or false positives (FP) increase. Matthews correlation coefficient (MCC) is a balanced measure and indicates the correlation between observed and predicted classification. MCC = T P T N F P F N (T P + F P ) (T P + F N) (T N + F P ) (T N + F N) (1) The precision (Prec), also called positive predictive value, indicates the portion of positive classified examples that are really positive: P rec = T P T P + F P. (2) If each element in the data is assigned to the negative class then TP and FP are zero. In this case the precision is not defined any more. The sensitivity (Sens) indicates the fraction of right classified examples in the positive class: T P Sens = T P + F N. (3) The creation of the receiver operating characteristic (ROC) curve is a common way to visualize a model performance. The x-axis shows the false positive rate and the y-axis displays the true positive rate by varying a parameter, in our case the classification threshold. The true positive rate and false positive rate are defined as T P T P R = T P + F N (4) and F P F P R = F P + T N, (5) respectively. A similar visualization gives the Precision-Recall (PR) curve showing the precision on the y-axis and the recall on the x-axis: Recall = T P T P + F N. (6) By calculating the area under the ROC curve (AUC) the performance of a classifier can be reduced to a single value. A reasonable classifier has an AUC 0.5, a perfect one an AUC of 1. 1
2 Figure S1: Calculation of secondary structure features The secondary structure features are calculated in the following way: A) RNAfold is used to predict the secondary structure of the transcript. RNAfold also calculates the predicted folding energy, which is directly used as an independent feature. For each secondary structure we calculate the stem density as the ratio of the number of base paired nucleotides and the total RNA length. The feature number of stems is simply the count of stems in the sequence. B) The accessibility is computed by identifying subsequences with at least four consecutive nucleotides in single stranded form, which do not form part of a stem. If a tetranucleotide is accessible, the corresponding feature is set to 1. If at least one nucleotide of the tetranucleotide is paired, the corresponding feature is set to 0.
3 Table S1 - Confidence interval of the AUCs on the AURA dataset The table lists the RBP name, the AUC and its confidence interval (in square brackets) at a confidence level of α = 0.01, for each method on the AURA dataset. The confidence interval is calculated for each AUC by exchanging 3K- 10 times with other randomly selected non-overlapping transcripts from ENSEMBL. Name Oli OliMo OliMoSS AGO [0.94,0.96] 0.85 [0.94,0.95] 0.84 [0.93,0.95] AGO [0.90,0.94] 0.83 [0.90,0.95] 0.70 [0.80,0.85] AGO [0.92,0.94] 0.84 [0.92,0.95] 0.78 [0.94,0.96] AUF [0.68,0.71] 0.69 [0.68,0.71] 0.67 [0.65,0.67] CPEB [0.70,0.73] 0.67 [0.72,0.77] 0.59 [0.58,0.63] CPEB [0.31,0.43] 0.54 [0.39,0.51] 0.60 [0.39,0.51] CUGBP [0.75,0.80] 0.78 [0.73,0.81] 0.65 [0.60,0.62] ELAVL [0.75,0.77] 0.73 [0.73,0.76] 0.69 [0.66,0.69] PUM [0.71,0.76] 0.68 [0.70,0.78] 0.66 [0.60,0.63] PABP 0.57 [0.43,0.49] 0.58 [0.45,0.51] 0.52 [0.43,0.51] QKI 0.87 [0.97,0.98] 0.86 [0.97,0.98] 0.86 [0.97,0.98] TNRC6A 0.87 [0.94,0.97] 0.83 [0.93,0.97] 0.79 [0.90,0.95] TNRC6B 0.86 [0.90,0.92] 0.86 [0.94,0.96] 0.82 [0.89,0.92] TNRC6C 0.80 [0.85,0.89] 0.80 [0.89,0.92] 0.68 [0.80,0.85] U2AF [0.82,0.88] 0.73[0.83,0.86] 0.67 [0.80,0.83]
4 Table S2 - Performance of Oli, OliMo, OliMoSS and RNAcontext on the AURA dataset with a sequence identity of 30% The table lists RBPs, the number of sequences and the AUCs achieved using each method on the AURA dataset. The AUCs are calculated in 10-fold cross validations and at a sequence identity of 30%. The negatives are provided in all cases by 3K-. Data are reported with means ± standard deviation (sd). Name #(RBP+) Oli OliMo OliMoSS RNAcontext AGO AGO AGO AUF CPEB CPEB CUGBP ELAVL PUM PABP QKI TNRC6A TNRC6B TNRC6C U2AF Mean±sd 0.73± ± ± ±0.11
5 Table S3 - Wilcoxon signed-rank test The table shows the results of the Wilcoxon signed-rank test for each method over all AUCs. Oli OliMo OliMoSS RNAcontext RPISeq-SVM RPISeq-RF Oli X 0, OliMo X OliMoSS X RNAcontext X RPISeq-SVM X 0.009
6 Table S4 - Precision values for the AURA dataset The table contains the precision values calculated in a 10-fold cross validation for each RBP. The last row shows the mean and the standard deviation of the precision values for each method. Name Oli OliMo OliMoSS RNAcontext RPISeq-SVM RPISeq-RF AGO AGO AGO AUF CPEB CPEB CUGBP ELAVL PABP PUM QKI TNRC6A TNRC6B TNRC6C U2AF Mean±sd 0.34± ± ± ± ± ±0.11
7 Table S5 - Information gain ratio of tetranucleotides in the AURA dataset The information gain ratio is calculated on the tetranucleotides for each protein in the AURA dataset. The table shows the 18 most important tetranucleotides for each protein, i.e. tetranucleotides ranked by the information gain ratio.
8 Table S6 - Number of shared and overlapping binding partners for all RBP+ sets in the AURA dataset The table shows the number of shared target sequences for all RBP+ sets.
9 Table S7 - Sensitivity calculated on the overlapping sequences for each RBP+ set for AURA dataset. The sensitivities are shown for SVM models trained on RBP+ sets, shown in the columns, and applied on the RBP+ sets of the RBPs, shown in the rows. All the sequences in the RBP+ sets are considered binding partners and the sensitivity of the model is calculated.
10 Table S8 - Sensitivity calculated only on shared binding partners between each RBP+ set for AURA dataset The sensitivities are calculated for SVMs trained on RBP+ sets shown in the columns and applied only on the overlapping sequences of RBP+ sets, shown in the rows. The shared sequences in the RBP+ sets are considered positives and used for calculations.
11 Table S9 - Specificity calculated on each RBP+ set for AURA dataset The specificities are shown for SVMs trained on RBP+ sets (column) and applied on the non-overlapping sequences contained in the other RBP+ sets (row). All non-overlapping binding sequences in the RBP+ sets are considered negatives and the specificities are calculated.
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