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This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB07)

Open Access Research

Quality assessment of peptide tandem mass spectra

Fang-Xiang Wu12*, Pierre Gagné3, Arnaud Droit3 and Guy G Poirier3

Author Affiliations

1 Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK Canada, S7N 5A9, Canada

2 Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK Canada, S7N 5A9, Canada

3 Health and Environment Unit, Eastern Quebec Proteomics Center, Laval University Medical Research Center (CHUL), Faculty of Medicine, 2705 Boul. Laurier, Ste-Foy, Quebec, Canada, G1V 4G2

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BMC Bioinformatics 2008, 9(Suppl 6):S13  doi:10.1186/1471-2105-9-S6-S13

Published: 28 May 2008

Abstract

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.