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Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data
Author(s) -
Alejandro HerediaLangner,
William R. Can,
Kenneth D. Jarman,
Kristin H. Jarman
Publication year - 2004
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bth242
Subject(s) - tandem mass spectrometry , computer science , heuristic , sequence database , sequence (biology) , mass spectrometry , identification (biology) , algorithm , tandem , field (mathematics) , missing data , key (lock) , computational biology , data mining , artificial intelligence , chemistry , mathematics , biology , machine learning , genetics , chromatography , botany , materials science , composite material , gene , computer security , pure mathematics
Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the peptide under investigation belongs to an available database. Our objective is to develop and test the performance of a heuristic optimization algorithm capable of dealing with some features commonly found in actual MS/MS spectra that tend to stop simpler deterministic solution approaches.

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