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


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

Similar works

Full text



Last time updated on 12/02/2018

This paper was published in FigShare.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.