A few example applications illustrating the power of the NNAlign method are presented in the following sections. First, the method is applied to examples of pre-aligned peptide data using examples of MHC class I binding. Next, the alignment problem is included using MHC class II binding data, showing the ability of the method to identify at the same time the correct length of the motif, the binding register, and the sequence motif itself. An important output from the NNAlign method is a sequence logo representing the identified binding motif. Such sequence logos provide a highly intuitive representation of single-receptor specificities (as is the case Figure 1 . Example of output from the NNAlign server trained on MHC class II binding data for allele HLA-DRB1*0101. Links on the results page (in pink) redirect to additional files and figures relevant for the analysis. Run ID is a sequential identifier for the current job, and Run Name a user-defined prefix that is added to all files of the run. The ''view data distribution'' link shows the transformation applied to the data in preprocessing, which can be either a linear or logarithmic transformation. In this case the method was trained with a motif length of 9, including a PFR of size 3 to both ends of the peptide, and encoding in the network input layer peptide length and PFR length. The hidden layer was made of a fixed number of 20 neurons. Peptides were presented to the networks using a Blosum encoding to account for amino acid similarity, for 500 hundred iterations per peptide without stopping on the best test set performance. At each cross-validation step, 10 networks were trained starting from 10 different initial configurations. The subsets for cross-validation were constructed using a Hobohm1 method that groups in the same subset sequences that align with more than 80% identity (thr = 0.8). The model can be downloaded to disk using the dedicated link, and can be resubmitted to NNAlign to find occurrences of the learned pattern in new data. The estimated performance of the trained method is expressed in terms of Root Mean Square Error, Pearson and Spearman correlation. A visual representation of the correlation can be obtained from the scatterplot of predicted versus observed values. The ''complete alignment core'' link allows downloading the prediction values in cross-validation for each peptide, and where the core was placed within the peptides. Next follows a section on the sequence logo, showing a logo representation of the binding motif learned by the network ensemble. If the relative option is selected, links to logos for the individual networks in the final ensemble are also listed here. Finally, if an evaluation set is uploaded, an additional section shows performance measures and core alignment for these data. doi:10.1371/journal.pone.0026781.g001