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The Mass Distance Fingerprint: a statistical framework for de novo detection of predominant modifications using high-accuracy mass spectrometry


Potthast, F; Gerrits, B; Häkkinen, J; Rutishauser, D; Ahrens, C H; Roschitzki, B; Baerenfaller, K; Munton, R P; Walther, P; Gehrig, P; Seif, P; Seeberger, P H; Schlapbach, R (2007). The Mass Distance Fingerprint: a statistical framework for de novo detection of predominant modifications using high-accuracy mass spectrometry. Journal of Chromatography B, 854(1-2):173-182.

Abstract

We describe a statistical measure, Mass Distance Fingerprint, for automatic de novo detection of predominant peptide mass distances, i.e., putative protein modifications. The method's focus is to globally detect mass differences, not to assign peptide sequences or modifications to individual spectra. The Mass Distance Fingerprint is calculated from high accuracy measured peptide masses. For the data sets used in this study, known mass differences are detected at electron mass accuracy or better. The proposed method is novel because it works independently of protein sequence databases and without any prior knowledge about modifications. Both modified and unmodified peptides have to be present in the sample to be detected. The method can be used for automated detection of chemical/post-translational modifications, quality control of experiments and labeling approaches, and to control the modification settings of protein identification tools. The algorithm is implemented as a web application and is distributed as open source software.

Abstract

We describe a statistical measure, Mass Distance Fingerprint, for automatic de novo detection of predominant peptide mass distances, i.e., putative protein modifications. The method's focus is to globally detect mass differences, not to assign peptide sequences or modifications to individual spectra. The Mass Distance Fingerprint is calculated from high accuracy measured peptide masses. For the data sets used in this study, known mass differences are detected at electron mass accuracy or better. The proposed method is novel because it works independently of protein sequence databases and without any prior knowledge about modifications. Both modified and unmodified peptides have to be present in the sample to be detected. The method can be used for automated detection of chemical/post-translational modifications, quality control of experiments and labeling approaches, and to control the modification settings of protein identification tools. The algorithm is implemented as a web application and is distributed as open source software.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Functional Genomics Center Zurich
08 University Research Priority Programs > Systems Biology / Functional Genomics
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:1 July 2007
Deposited On:28 Dec 2009 06:03
Last Modified:05 Apr 2016 13:35
Publisher:Elsevier
ISSN:1570-0232
Publisher DOI:https://doi.org/10.1016/j.jchromb.2007.04.020
PubMed ID:17513179

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