A wealth of methods has been developed to interpret quantitative peptide sequence data representing specific biological problems. By way of examples, SignalP, which identifies the presence of signal peptidase I cleavage sites, is a popular method for the prediction of signal peptides [10] ; LipoP, which identifies peptidase II cleavage sites, predicts lipoprotein signal peptides in Gram-negative bacteria [11] ; various prediction methods predict phosphorylation sites by identifying short amino acid sequence motifs surrounding a suitable acceptor residue [12, 13, 14, 15] etc. In general terms, these methods can be divided in two major groups depending on the structural properties of the biological receptor investigated, and of the nature of the peptides recognized. The simplest situation deals with interactions where a receptor binds peptides that are in register and of a known length. In this case, the peptide data is pre-aligned, and conventional fixed length, alignment-free pattern recognition methods like position specific weight matrices (PSSM), artificial neural networks (ANN), and support vector machines (SVM) can be used. Peptide-MHC class I binding is a prominent example of the successful use of such methods to characterize receptor-ligand interaction represented by pre-aligned data (reviewed in [16] ). Another more complex type of problems deals with interactions where either the motif lengths, and/or the binding registers, are unknown. In these cases, the peptide data must a priori be assumed to be unaligned and any bioinformatics method dealing with such data is faced with the challenge of simultaneously recognizing the binding register (i.e. performing an alignment) and identifying the binding motif (i.e. performing a specificity analysis). Peptide-MHC class II binding is a preeminent example of a receptor-ligand interaction represented by unaligned data. Several bioinformatics methods have been developed to identify binding motifs in such peptide data including Gibbs sampling [17] , hidden Markov models (HMM) [18] , stabilization matrix method (SMM) alignment [19] , and alignment using artificial neural networks [20] (for more references see [21] ). Another example of unaligned peptide data is that of antibodies interacting with linear peptide epitopes. Although B-cell epitopes frequently are conformational and three-dimensional in structure, some do contain linear components that can be represented by peptide interaction with the corresponding antibodies [22, 23, 24] .