title: Quality assessment of tandem mass spectra using support vector machine (SVM).
abstract: 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 tandem 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 TOV 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.
authors.affiliation: Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr, Saskatoon, SK, S7N 59A, Canada. anmin.zou@ia.ac.cn
authors.forename: An-Min
authors.initials: AM
authors.lastname: Zou
authors.forename: Fang-Xiang
authors.initials: FX
authors.lastname: Wu
authors.forename: Jia-Rui
authors.initials: JR
authors.lastname: Ding
authors.forename: Guy G
authors.initials: GG
authors.lastname: Poirier
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D011506:Proteins / Q000032:analysis* / Q000737:chemistry; D040901:Proteomics / Q000379:methods; D053719:Tandem Mass Spectrometry / Q000379:methods / Q000592:standards*
chemical_list: D011506:Proteins