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Probability‐based protein identification by searching sequence databases using mass spectrometry data
Author(s) -
Perkins David N.,
Pappin Darryl J. C.,
Creasy David M.,
Cottrell John S.
Publication year - 1999
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/(sici)1522-2683(19991201)20:18<3551::aid-elps3551>3.0.co;2-2
Subject(s) - mascot , computer science , false positive paradox , database search engine , tandem mass spectrometry , mass spectrometry , data mining , identification (biology) , context (archaeology) , sequence database , tandem mass tag , protein sequencing , sequence (biology) , peptide sequence , search engine , proteomics , chemistry , artificial intelligence , information retrieval , quantitative proteomics , chromatography , biology , paleontology , biochemistry , botany , political science , gene , law
Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be com pared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability‐based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.