MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra

J Proteome Res. 2014 Aug 1;13(8):3679-84. doi: 10.1021/pr500202e. Epub 2014 Jun 26.


Today's highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the analysis of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide identification algorithm. While most widely used search engines were developed when high-resolution mass spectrometry data were not readily available for fragment ion masses, we have designed a scoring algorithm particularly suitable for high mass accuracy. Our algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examined high mass accuracy data sets, with excellent overlap and identical peptide sequence identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available at , is provided free of charge both as standalone version for integration into custom workflows and as a plugin for the Proteome Discoverer platform.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Databases, Protein
  • HeLa Cells
  • Humans
  • Peptides / isolation & purification*
  • Proteomics / methods*
  • Search Engine*
  • Tandem Mass Spectrometry / methods*


  • Peptides