Robust Prediction of the MASCOT Score for an Improved Quality Assessment in Mass Spectrometric Proteomics

Protein identification by tandem mass spectrometry is based on the reliable processing of the acquired data. Unfortunately, the generation of a large number of poor quality spectra is commonly observed in LC-MS/MS, and the processing of these mostly noninformative spectra with its associated costs s...

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Published inJournal of proteome research Vol. 7; no. 9; pp. 3708 - 3717
Main Authors Koenig, Thomas, Menze, Bjoern H, Kirchner, Marc, Monigatti, Flavio, Parker, Kenneth C, Patterson, Thomas, Steen, Judith Jebanathirajah, Hamprecht, Fred A, Steen, Hanno
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 01.09.2008
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Abstract Protein identification by tandem mass spectrometry is based on the reliable processing of the acquired data. Unfortunately, the generation of a large number of poor quality spectra is commonly observed in LC-MS/MS, and the processing of these mostly noninformative spectra with its associated costs should be avoided. We present a continuous quality score that can be computed very quickly and that can be considered an approximation of the MASCOT score in case of a correct identification. This score can be used to reject low quality spectra prior to database identification, or to draw attention to those spectra that exhibit a (supposedly) high information content, but could not be identified. The proposed quality score can be calibrated automatically on site without the need for a manually generated training set. When this score is turned into a classifier and when features are used that are independent of the instrument, the proposed approach performs equally to previously published classifiers and feature sets and also gives insights into the behavior of the MASCOT score.
AbstractList Protein identification by tandem mass spectrometry is based on the reliable processing of the acquired data. Unfortunately, the generation of a large number of poor quality spectra is commonly observed in LC-MS/MS, and the processing of these mostly noninformative spectra with its associated costs should be avoided. We present a continuous quality score that can be computed very quickly and that can be considered an approximation of the MASCOT score in case of a correct identification. This score can be used to reject low quality spectra prior to database identification, or to draw attention to those spectra that exhibit a (supposedly) high information content, but could not be identified. The proposed quality score can be calibrated automatically on site without the need for a manually generated training set. When this score is turned into a classifier and when features are used that are independent of the instrument, the proposed approach performs equally to previously published classifiers and feature sets and also gives insights into the behavior of the MASCOT score.
Author Kirchner, Marc
Steen, Judith Jebanathirajah
Steen, Hanno
Monigatti, Flavio
Patterson, Thomas
Menze, Bjoern H
Hamprecht, Fred A
Koenig, Thomas
Parker, Kenneth C
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/18707158$$D View this record in MEDLINE/PubMed
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Snippet Protein identification by tandem mass spectrometry is based on the reliable processing of the acquired data. Unfortunately, the generation of a large number of...
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SubjectTerms Proteomics
Tandem Mass Spectrometry - standards
Title Robust Prediction of the MASCOT Score for an Improved Quality Assessment in Mass Spectrometric Proteomics
URI http://dx.doi.org/10.1021/pr700859x
https://www.ncbi.nlm.nih.gov/pubmed/18707158
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