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Bioinformatics Advance Access originally published online on December 13, 2005
Bioinformatics 2006 22(4):400-406; doi:10.1093/bioinformatics/bti829
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Quality classification of tandem mass spectrometry data

Jussi Salmi 1,*, Robert Moulder 2, Jan-Jonas Filén 2,3, Olli S. Nevalainen 1, Tuula A. Nyman 4, Riitta Lahesmaa 2 and Tero Aittokallio 2,5

1Department of Information Technology and Turku Centre for Computer Science, University of Turku Finland
2Turku Centre for Biotechnology, University of Turku and Åbo Akademi University Finland
3The National Graduate School in Informational and Structural Biology Finland
4Finnish Institute of Occupational Health Helsinki, Finland
5Department of Mathematics, University of Turku Finland

*To whom correspondence should be addressed.

Motivation: 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.

Availability: The software tools are available on request from the authors.

Contact: jussi.salmi{at}it.utu.fi

Supplementary information: The Mascot ion score distributions and the C4.5 classification rules can be found at address http://staff.cs.utu.fi/staff/jussi.salmi/Supplementary_material.pdf

Received on July 21, 2005; revised on December 7, 2005; accepted on December 8, 2005

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