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

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Abstract

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.

Detaljer

Författare
  • 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
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Biofysik

Nyckelord

Originalspråkengelska
Sidor (från-till)173-182
TidskriftJournal of Chromatography. B
Volym854
Utgåva nummer1-2
StatusPublished - 2007
PublikationskategoriForskning
Peer review utfördJa