Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
+44 1223 790975
ISSN: 0974-276X
+44 1223 790975
Stefan Ascher
Austria
Research Article
Improving Phosphopeptide/Protein Identification Using a New Data Mining Framework for MS/MS Spectra Preprocessing
Author(s): Fabio R. Cerqueira, Sandra Morandell, Stefan Ascher, Karl Mechtler, Lukas A. Huber, Bernhard Pfeifer, Armin Graber, Bernhard Tilg and Christian BaumgartnerFabio R. Cerqueira, Sandra Morandell, Stefan Ascher, Karl Mechtler, Lukas A. Huber, Bernhard Pfeifer, Armin Graber, Bernhard Tilg and Christian Baumgartner
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 identifications 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 ch.. View More»
DOI:
10.4172/jpb.1000072