Subsets for cross-validation. In a n-fold cross-validation, n subsets are created from the complete dataset, and at each step n-1 subsets are used for training and 1 subset for testing. NNAlign offers three alternatives to create the subsets: i) random, splits the data into n subsets randomly; ii) homology clustering, uses a Hobohm 1 algorithm [44] to identify sequences that share an ungapped alignment with more than a specified fraction of matches; iii) common motif clustering, looks for stretches of identical amino acid between pairs of sequences as described by Nielsen et al. [19] . For both methods ii) and iii) similar sequences are grouped together in the same subset, but it is possible to choose to only include one representative for each group and disregard the other sequences from training. In this phase, if the input data contains repeated flanks (as might be the case in peptide array experiments, where linker sequences can be attached at the extremities of all peptides), these flanks are discarded, as they would affect the overlap estimation. If the user reckons that the repeated flanks might contain meaningful biological signal, an option allows retaining them in the training data. Note that in common motif clustering, the motif length is taken as the smallest in the interval of length given by the user. Thus, depending on the selected interval the subsets might be constructed in a different way and that could influence the cross-validated performance.