Quality assessment of tandem mass spectra using support vector machine (SVM)
Background: Tandem mass spectrometry has become particularly useful for the rapid identification and characterization of protein components of complex biological mixtures. Powerful database search methods have been developed for the peptide identification, such as SEQUEST and MASCOT, which are implemented by comparing the mass spectra obtained from unknown proteins or peptides with theoretically predicted spectra derived from protein databases. However, the majority of spectra generated from a mass spectrometry experiment are of too poor quality to be interpreted while some of spectra with high quality cannot be interpreted by one method but perhaps by others. Hence a filtering algorithm that removes those spectra with poor quality prior to the database search is appealing.Results: This paper proposes a support vector machine (SVM) based approach to assess the quality of tandern mass spectra. Each mass spectrum is mapping into the 16 proposed features to describe its quality. Based the results from SEQUEST, four SVM classifiers with the input of the 16 features are trained and tested on ISB data and TOY data, respectively. The superior performance of the proposed SVM classifiers is illustrated both by the comparison with the existing classifiers and by the validation in terms of MASCOT search results.Conclusions: The proposed method can be employed to effectively remove the poor quality spectra before the spectral searching, and also to find the more peptides or post-translational peptides from spectra with high quality using different search engines or de novo method.
An-Min Zou Fang-Xiang Wu Jia-Rui Ding Guy G.Poirie
Departrnent of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK, S7N Departrnent of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK, S7N Health and Environment Unit, Laval University Medical Research Center (CHUL), Faculty of Medicine, 2
国际会议
The 7th Asia-Pacific Bioinformatics Conference(第七届亚太生物信息学大会)
北京
英文
556-567
2009-01-01（万方平台首次上网日期，不代表论文的发表时间）