In order to enhance predictive performance, the NNAlign method exploits an ensemble of neural networks [20, 33] , which have been trained on different subsets of the data, and/or from alternative configurations of the network architecture (i.e. different number of hidden neurons and/or encoding schemes). As a consequence of different architectures and starting conditions, individual networks might disagree on the exact boundaries of the motif. This disagreement would complicate the visualization of the motif if this was represented as a simple overlay of the individual motifs as exemplified in Figure 3 , where sequence logos for four different networks from the ensemble trained on HLA*DRB1-04:01 binding data are shown in panels A through D. The individual networks agree on identifying the same strong primary anchor residues and positions, however, each single network identifies different ends (i.e. suggests different registers of the same motif; in casu starting at positions 1, 2, 2 and 3 of the predicted nonamer peptide). The weak C-terminal primary anchor residue of HLA*DRB1-04:01 probably explains why the boundaries are difficult to determine. A simple overlay of the predictions from individual networks would result in a muddled motif as depicted in Figure 3 , panel E. Implementing a Gibbs sampler approach, where matrix representations of the core motifs of different networks are aligned, we introduced an off-set correction for each network aiming at maximizing the information content of a combined logo representation of the motif. This approach led to a considerable improvement in the visual logo representation of the binding motif (Figure 3, panel F) . Offset correction is included as an integral part of the method to enhance motif visualization.