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Quality assessment of peptide tandem mass spectra

BACKGROUND: Tandem mass spectrometry has emerged as a cornerstone of high throughput proteomic studies owing in part to various high throughput search engines which are used to interpret these tandem mass spectra. However, majority of experimental tandem mass spectra cannot be interpreted by any exi...

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Autores principales: Wu, Fang-Xiang, Gagné, Pierre, Droit, Arnaud, Poirier, Guy G
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2423436/
https://www.ncbi.nlm.nih.gov/pubmed/18541048
http://dx.doi.org/10.1186/1471-2105-9-S6-S13
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author Wu, Fang-Xiang
Gagné, Pierre
Droit, Arnaud
Poirier, Guy G
author_facet Wu, Fang-Xiang
Gagné, Pierre
Droit, Arnaud
Poirier, Guy G
author_sort Wu, Fang-Xiang
collection PubMed
description BACKGROUND: Tandem mass spectrometry has emerged as a cornerstone of high throughput proteomic studies owing in part to various high throughput search engines which are used to interpret these tandem mass spectra. However, majority of experimental tandem mass spectra cannot be interpreted by any existing methods. There are many reasons why this happens. However, one of the most important reasons is that majority of experimental spectra are of too poor quality to be interpretable. It wastes time to interpret these "uninterpretable" spectra by any methods. On the other hand, some spectra of high quality are not able to get a score high enough to be interpreted by existing search engines because there are many similar peptides in the searched database. However, such spectra may be good enough to be interpreted by de novo methods or manually verifying methods. Therefore, it is worth in developing a method for assessing spectral quality, which can used for filtering the spectra of poor quality before any interpretation attempts or for finding the most potential candidates for de novo methods or manually verifying methods. RESULTS: This paper develops a novel method to assess the quality of tandem mass spectra, which can eliminate majority of poor quality spectra while losing very minority of high quality spectra. First, a number of features are proposed to describe the quality of tandem mass spectra. The proposed method maps each tandem spectrum into a feature vector. Then Fisher linear discriminant analysis (FLDA) is employed to construct the classifier (the filter) which discriminates the high quality spectra from the poor quality ones. The proposed method has been tested on two tandem mass spectra datasets acquired by ion trap mass spectrometers. CONCLUSION: Computational experiments illustrate that the proposed method outperforms the existing ones. The proposed method is generic, and is expected to be applicable to assessing the quality of spectra acquired by instruments other than ion trap mass spectrometers.
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spelling pubmed-24234362008-06-11 Quality assessment of peptide tandem mass spectra Wu, Fang-Xiang Gagné, Pierre Droit, Arnaud Poirier, Guy G BMC Bioinformatics Research BACKGROUND: Tandem mass spectrometry has emerged as a cornerstone of high throughput proteomic studies owing in part to various high throughput search engines which are used to interpret these tandem mass spectra. However, majority of experimental tandem mass spectra cannot be interpreted by any existing methods. There are many reasons why this happens. However, one of the most important reasons is that majority of experimental spectra are of too poor quality to be interpretable. It wastes time to interpret these "uninterpretable" spectra by any methods. On the other hand, some spectra of high quality are not able to get a score high enough to be interpreted by existing search engines because there are many similar peptides in the searched database. However, such spectra may be good enough to be interpreted by de novo methods or manually verifying methods. Therefore, it is worth in developing a method for assessing spectral quality, which can used for filtering the spectra of poor quality before any interpretation attempts or for finding the most potential candidates for de novo methods or manually verifying methods. RESULTS: This paper develops a novel method to assess the quality of tandem mass spectra, which can eliminate majority of poor quality spectra while losing very minority of high quality spectra. First, a number of features are proposed to describe the quality of tandem mass spectra. The proposed method maps each tandem spectrum into a feature vector. Then Fisher linear discriminant analysis (FLDA) is employed to construct the classifier (the filter) which discriminates the high quality spectra from the poor quality ones. The proposed method has been tested on two tandem mass spectra datasets acquired by ion trap mass spectrometers. CONCLUSION: Computational experiments illustrate that the proposed method outperforms the existing ones. The proposed method is generic, and is expected to be applicable to assessing the quality of spectra acquired by instruments other than ion trap mass spectrometers. BioMed Central 2008-05-28 /pmc/articles/PMC2423436/ /pubmed/18541048 http://dx.doi.org/10.1186/1471-2105-9-S6-S13 Text en Copyright © 2008 Wu 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 Research
Wu, Fang-Xiang
Gagné, Pierre
Droit, Arnaud
Poirier, Guy G
Quality assessment of peptide tandem mass spectra
title Quality assessment of peptide tandem mass spectra
title_full Quality assessment of peptide tandem mass spectra
title_fullStr Quality assessment of peptide tandem mass spectra
title_full_unstemmed Quality assessment of peptide tandem mass spectra
title_short Quality assessment of peptide tandem mass spectra
title_sort quality assessment of peptide tandem mass spectra
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2423436/
https://www.ncbi.nlm.nih.gov/pubmed/18541048
http://dx.doi.org/10.1186/1471-2105-9-S6-S13
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