The ongoing success of the proteomics endeavor is the result of a prolific symbiosis between experimental ingenuity [2, 3, 4] and efficient bioinformatics [5, 6, 7, 8, 9, 10, 11]. Without these, ground-breaking landmarks such as the human genome project [12, 13] or the HUPO initiative [14] would likely not have seen the light of day. But despite valuable contributions, the road to a better understanding of disease proteomics is still hurdled by significant difficulties in the extensive identification of post-translational modifications and in the sequencing of novel proteins like cancer fusion proteins or antibody chains. Recently, tandem mass spectrometry (MS/MS) based approaches seemed to be reaching the limit on the amount of information that could be extracted from MS/MS spectra [15, 16, 17]. However, a closer look reveals that a common limiting procedure is to analyze each spectrum in isolation, even though high throughput mass spectrometry regularly generates many spectra from related peptides. By capitalizing on this redundancy we have shown that, similarly to the alignment of protein sequences [5], unidentified MS/MS spectra can also be aligned for the identification of modified and unmodified variants of the same peptide. Moreover, this alignment procedure can be iterated for the accurate grouping of multiple peptide variants (Figure 1). The highly correlated peaks in spectra from variants of the same peptide allowed us to reliably identify all known and even some unknown modifications in a sample of cataractous lenses