The biggest hurdle of future ''omics'' research may easily become that of making sense of such large-scale biologic sequence data [37] . Presently, the ''omics'' experimentalist requires assistance from specialized and highly trained bioinformaticians capable of large-scale data handling and interpretation. Ideally, however, he or she should not only be armed with highthroughput data-generation technologies, but also with reasonably easy and robust bioinformatics methods allowing the experimentalist to analyze his or her own data. This would permit an immediate analysis of experimental results and assist in rational designs of next generation experiments aimed at extending the original analysis e.g. providing in silico tools for searches that potentially could encompass entire proteomes. Enabling the same person to do large-scale experiments and analysis should result in a better integration between design, experiment, and interpretation -and eventually support the development of new hypotheses. Unfortunately, suitable bioinformatics resources aimed at the nonexpert user are currently scarce, and rarely web-based. In our experience, open source software packages such as Weka [38] are not capable of performing concurrent alignment and motif identification, and are not suited for treating large-scale data sets. A widely used method for motif discovery, MEME [39] , can perform searches for un-gapped sequence patterns in DNA or protein sequences, and offers a user-friendly online server to the untrained user. However, this method is not designed for use in quantitative data, such as peptide-MHC binding or peptide microarray data.