title: Quality classification of tandem mass spectrometry data.
abstract: Peptide identification by tandem mass spectrometry is an important tool in proteomic research. Powerful identification programs exist, such as SEQUEST, ProICAT and Mascot, which can relate experimental spectra to the theoretical ones derived from protein databases, thus removing much of the manual input needed in the identification process. However, the time-consuming validation of the peptide identifications is still the bottleneck of many proteomic studies. One way to further streamline this process is to remove those spectra that are unlikely to provide a confident or valid peptide identification, and in this way to reduce the labour from the validation phase.
  
  
  RESULTS
  We propose a prefiltering scheme for evaluating the quality of spectra before the database search. The spectra are classified into two classes: spectra which contain valuable information for peptide identification and spectra that are not derived from peptides or contain insufficient information for interpretation. The different spectral features developed for the classification are tested on a real-life material originating from human lymphoblast samples and on a standard mixture of 9 proteins, both labelled with the ICAT-reagent. The results show that the prefiltering scheme efficiently separates the two spectra classes.
authors.affiliation: Department of Information Technology and Turku Centre for Computer Science, University of Turku, Finland. jussi.salmi@it.utu.fi
authors.forename: Jussi
authors.initials: J
authors.lastname: Salmi
authors.forename: Robert
authors.initials: R
authors.lastname: Moulder
authors.forename: Jan-Jonas
authors.initials: JJ
authors.lastname: Filén
authors.forename: Olli S
authors.initials: OS
authors.lastname: Nevalainen
authors.forename: Tuula A
authors.initials: TA
authors.lastname: Nyman
authors.forename: Riitta
authors.initials: R
authors.lastname: Lahesmaa
authors.forename: Tero
authors.initials: T
authors.lastname: Aittokallio
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D045424:Complex Mixtures / Q000032:analysis; D013058:Mass Spectrometry / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010449:Peptide Mapping / Q000379:methods*; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D045424:Complex Mixtures; D020543:Proteome