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Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching
BACKGROUND: The robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlapping patterns arising e.g. from post-translational modifications. As the classification...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3608065/ https://www.ncbi.nlm.nih.gov/pubmed/23137144 http://dx.doi.org/10.1186/1471-2105-13-291 |
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author | Slawski, Martin Hussong, Rene Tholey, Andreas Jakoby, Thomas Gregorius, Barbara Hildebrandt, Andreas Hein, Matthias |
author_facet | Slawski, Martin Hussong, Rene Tholey, Andreas Jakoby, Thomas Gregorius, Barbara Hildebrandt, Andreas Hein, Matthias |
author_sort | Slawski, Martin |
collection | PubMed |
description | BACKGROUND: The robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlapping patterns arising e.g. from post-translational modifications. As the classification of the recorded data points into either ‘noise’ or ‘signal’ lies at the very root of essentially every proteomic application, the quality of the automated processing of mass spectra can significantly influence the way the data might be interpreted within a given biological context. RESULTS: We propose non-negative least squares/non-negative least absolute deviation regression to fit a raw spectrum by templates imitating isotope patterns. In a carefully designed validation scheme, we show that the method exhibits excellent performance in pattern picking. It is demonstrated that the method is able to disentangle complicated overlaps of patterns. CONCLUSIONS: We find that regularization is not necessary to prevent overfitting and that thresholding is an effective and user-friendly way to perform feature selection. The proposed method avoids problems inherent in regularization-based approaches, comes with a set of well-interpretable parameters whose default configuration is shown to generalize well without the need for fine-tuning, and is applicable to spectra of different platforms. The R package IPPD implements the method and is available from the Bioconductor platform (http://bioconductor.fhcrc.org/help/bioc-views/devel/bioc/html/IPPD.html). |
format | Online Article Text |
id | pubmed-3608065 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-36080652013-04-01 Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching Slawski, Martin Hussong, Rene Tholey, Andreas Jakoby, Thomas Gregorius, Barbara Hildebrandt, Andreas Hein, Matthias BMC Bioinformatics Methodology Article BACKGROUND: The robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlapping patterns arising e.g. from post-translational modifications. As the classification of the recorded data points into either ‘noise’ or ‘signal’ lies at the very root of essentially every proteomic application, the quality of the automated processing of mass spectra can significantly influence the way the data might be interpreted within a given biological context. RESULTS: We propose non-negative least squares/non-negative least absolute deviation regression to fit a raw spectrum by templates imitating isotope patterns. In a carefully designed validation scheme, we show that the method exhibits excellent performance in pattern picking. It is demonstrated that the method is able to disentangle complicated overlaps of patterns. CONCLUSIONS: We find that regularization is not necessary to prevent overfitting and that thresholding is an effective and user-friendly way to perform feature selection. The proposed method avoids problems inherent in regularization-based approaches, comes with a set of well-interpretable parameters whose default configuration is shown to generalize well without the need for fine-tuning, and is applicable to spectra of different platforms. The R package IPPD implements the method and is available from the Bioconductor platform (http://bioconductor.fhcrc.org/help/bioc-views/devel/bioc/html/IPPD.html). BioMed Central 2012-11-08 /pmc/articles/PMC3608065/ /pubmed/23137144 http://dx.doi.org/10.1186/1471-2105-13-291 Text en Copyright ©2012 Slawski et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Slawski, Martin Hussong, Rene Tholey, Andreas Jakoby, Thomas Gregorius, Barbara Hildebrandt, Andreas Hein, Matthias Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching |
title | Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching |
title_full | Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching |
title_fullStr | Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching |
title_full_unstemmed | Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching |
title_short | Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching |
title_sort | isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3608065/ https://www.ncbi.nlm.nih.gov/pubmed/23137144 http://dx.doi.org/10.1186/1471-2105-13-291 |
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