Given the capability for large-scale data-generation already realized in current ''omics'' and peptide microarray-based approaches, experimentalists will increasingly be confronted with extraordinary large data sets and the consequent problem of identifying and characterizing features common to subsets of the data. These are by no means trivial problems. Up to a certain level of size and complexity, data can be presented in simple tabular forms or in charts, however, larger and/or more complex bodies of data (e.g. in proteome databases) will need to be fed into bioinformatics data mining systems that can be used for automated interpretation and validation of the results, and eventually for in silico mapping of peptide targets. Moreover, such systems can conveniently be used to design next-generation experiments aimed at extending the description of target structures identified in previous analyses [9] .