MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra

Viktoria Dorfer, Peter Pichler, Thomas Stranzl, Johannes Stadlmann, Thomas Taus, Stephan Winkler, Karl Mechtler

Research output: Contribution to journalArticlepeer-review

312 Citations (Scopus)


Todays 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.

Original languageEnglish
Pages (from-to)3679-3684
Number of pages6
JournalJournal of Proteome Research
Issue number8
Publication statusPublished - 1 Aug 2014


  • MS/MS
  • database search algorithm
  • high mass accuracy
  • high-resolution spectra
  • peptide identification
  • proteomics
  • tandem mass spectrometry
  • Peptides/isolation & purification
  • Humans
  • Tandem Mass Spectrometry/methods
  • Algorithms
  • Databases, Protein
  • Proteomics/methods
  • Search Engine
  • HeLa Cells

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