Improving Phosphopeptide/Protein Identification Using a New Data Mining Framework for MS/MS Spectra Preprocessing |
| Fabio R. Cerqueira1,*, Sandra Morandell2, Stefan Ascher2, Karl Mechtler3, Lukas A. Huber2, Bernhard Pfeifer1, Armin Graber4, Bernhard Tilg1, Christian Baumgartner1,* |
| 1Research Group for Clinical Bioinformatics, Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Eduard Wallnoefer Zentrum 1, 6060, Hall in Tirol, Austria |
| 2Biocenter, Division of Cell Biology, Medical University of Innsbruck, Fritz-Pregl Str. 3, 6020, Innsbruck, Austria |
| 3Research Institute of Molecular Pathology (IMP), Dr Bohr-Gasse 7 and Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Dr Bohr Gasse 3, A1030 Vienna, Austria |
| 4Institute for Bioinformatics, University for Health Sciences, Medical
Informatics and Technology, Eduard Wallnoefer Zentrum 1, 6060, Hall in Tirol, Austria |
| *Corresponding author: |
Fabio R. Cerqueira, Research Group for Clinical Bioinformatics,
Institute of Biomedical Engineering,
University for Health Sciences,
Medical Informatics and Technology,
Eduard Wallnoefer Zentrum 1, 6060,
Hall in Tirol, Austria,
Phone: +43 50 8648 3827,
Fax: +43 50 8648 673827,
E-mail: fabio.cerqueira@umit.at
Christian Baumgartner, Research Group for Clinical Bioinformatics,
Institute of Biomedical Engineering, University for
Health Sciences,
Medical Informatics and Technology, Eduard Wallnoefer Zentrum 1, 6060,
Hall in Tirol, Austria,
E-mail: christian.baumgartner@umit.at |
|
| Received January 27, 2009; Accepted March 21, 2009; Published March 21, 2009 |
| Citation:Cerqueira FR, Morandell S, Ascher S, Mechtler K, Huber LA, et al. (2009) Improving Phosphopeptide/protein Identification
using a New Data Mining Framework for MS/MS Spectra Preprocessing. J Proteomics Bioinform 2: 150-164. doi:10.4172/jpb.1000072 |
| Copyright: © 2009 Cerqueira FR, et al. This is an open-access article distributed under the terms of the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author
and source are credited. |
| Abstract |
Phosphopeptide/protein Identification using tandem mass spectrometry (MS/MS) is a challenging issue in proteomics research. In particular, phosphopeptides typically exhibit low intensity peaks of b and y ions in spectra when serine or threonine is phosphorylated. Consequently, the existing algorithms for peptide and protein Identification generate a high false discovery rate when coping with phosphopeptide spectra. In order to increase the number of correct phosphopeptide Identification using database search, a new data mining approach for spectra preprocessing is proposed. A support vector machine classifier is used to calculate the probability of each peak representing a b or y ion. Next, low-probability peaks are removed from spectra, while remaining peaks have their intensities enhanced. As a result, a huge increase in signal-to-noise ratio is provided and the chances of detecting important peaks are significantly advanced. Experiments using MASCOT and SEQUEST along with Peptide/ProteinProphet and a decoy database approach showed a
significant
improvement in the sensitivity of phosphopeptide Identification without compromising specificity, demonstrating that our new strategy for MS/MS spectra preprocessing is a powerful proteomics tool for enhancing phosphopeptide Identification. |
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