The Mass Distance Fingerprint: A statistical framework for de novo detection of predominant modifications using high-accuracy mass spectrometry

Research output: Contribution to journalArticle


We describe a statistical measure, Mass Distance Fingerprint, for automatic de novo detection of predominant peptide mass distances, i.e., putative protein modifications. The method's focus is to globally detect mass differences, not to assign peptide sequences or modifications to individual spectra. The Mass Distance Fingerprint is calculated from high accuracy measured peptide masses. For the data sets used in this study, known mass differences are detected at electron mass accuracy or better. The proposed method is novel because it works independently of protein sequence databases and without any prior knowledge about modifications. Both modified and unmodified peptides have to be present in the sample to be detected. The method can be used for automated detection of chemical/post-translational modifications, quality control of experiments and labeling approaches, and to control the modification settings of protein identification tools. The algorithm is implemented as a web application and is distributed as open source software.


  • Frank Potthast
  • Bertran Gerrits
  • Jari Häkkinen
  • Dorothea Rutishauser
  • Christian H. Ahrens
  • Bernd Roschitzki
  • Katja Baerenfaller
  • Richard P. Munton
  • Pascal Walther
  • Peter Gehrig
  • Philipp Seif
  • Peter H. Seebergerg
  • Ralph Schlapbach
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Biophysics


  • modification, Mass Distance Histogram, post-translational, protein identification, Mass Distance Fingerprint
Original languageEnglish
Pages (from-to)173-182
JournalJournal of Chromatography. B
Issue number1-2
Publication statusPublished - 2007
Publication categoryResearch