Abstract
Identification and elimination of noise peaks in mass spectra from large proteomics data streams simultaneously improves the accuracy of peptide identification and significantly decreases the size of the data. There are a number of peak filtering strategies that can achieve this goal. Here we present a simple algorithm wherein the number of highest intensity peaks retained for further analysis is proportional to the mass of the precursor ion. We show that this technique provides an improvement over other intensity based strategies, especially for low mass precursors.
Keywords: LC-MS/MS, Mass spectrometry, noise filtering, peak filtering, proteomics.
Protein & Peptide Letters
Title:Precursor Mass Dependent Filtering of Mass Spectra for Proteomics Analysis
Volume: 21 Issue: 8
Author(s): Beata Reiz, Michael P. Myers, Sandor Pongor and Attila Kertesz-Farkas
Affiliation:
Keywords: LC-MS/MS, Mass spectrometry, noise filtering, peak filtering, proteomics.
Abstract: Identification and elimination of noise peaks in mass spectra from large proteomics data streams simultaneously improves the accuracy of peptide identification and significantly decreases the size of the data. There are a number of peak filtering strategies that can achieve this goal. Here we present a simple algorithm wherein the number of highest intensity peaks retained for further analysis is proportional to the mass of the precursor ion. We show that this technique provides an improvement over other intensity based strategies, especially for low mass precursors.
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Cite this article as:
Reiz Beata, Myers P. Michael, Pongor Sandor and Kertesz-Farkas Attila, Precursor Mass Dependent Filtering of Mass Spectra for Proteomics Analysis, Protein & Peptide Letters 2014; 21 (8) . https://dx.doi.org/10.2174/09298665113209990065
DOI https://dx.doi.org/10.2174/09298665113209990065 |
Print ISSN 0929-8665 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5305 |
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