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Quality Classification of Tandem Mass Spectrometry Data

Jussi Salmi, Robert Moulder, Jan-Jonas Filén, Olli S. Nevalainen, Tuula A. Nyman, Riitta Lahesmaa, Tero Aittokallio, Quality Classification of Tandem Mass Spectrometry Data. Bioinformatics 22(4), 400–406, 2006.

Abstract:

Motivation: Peptide identification by tandem mass spectrometry is an important tool in proteomic research. Powerful identification programs exist, such as SEQUEST, ProICATand 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 fromhumanlymphoblast 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.

BibTeX entry:

@ARTICLE{jSaMoFiNeNyLaAi06a,
  title = {Quality Classification of Tandem Mass Spectrometry Data},
  author = {Salmi, Jussi and Moulder, Robert and Filén, Jan-Jonas and Nevalainen, Olli S. and Nyman, Tuula A. and Lahesmaa, Riitta and Aittokallio, Tero},
  journal = {Bioinformatics},
  volume = {22},
  number = {4},
  publisher = {Oxford University Press},
  pages = {400–406},
  year = {2006},
  keywords = {protein identification, prefilter, Random Forest},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI), Biomathematics Research Unit (BIOMATH)

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