The results highlight that our classification task has an intrinsic symmetry: the fraction of correctly classified alignments for the "plus strand" of a ncRNA should be similar to the accuracy of the "minus strand". However, we observe a small but noticeable bias to predict that the ncRNA lies in same reading direction as the input alignment (Table 1). The SVM model was trained with different alignments in the positive and negative training sets, which results in an asymmetric model. If the same alignments, but in different directions, were taken for training, the SVM model would be exactly symmetric. But training data should be independent in the different classes, hence we refrained from enforcing this exact symmetry to avoid potential overtraining artifacts. Another possibility to avoid asymmetry would be to take the averaged SVM decision values of both reading directions as the final decision. But this has an unknown effect on the probability estimates.