BPDA - A Bayesian peptide detection algorithm for mass spectrometry

Background: Mass spectrometry (MS) is an essential analytical tool in proteomics. Many existing algorithms for peptide detection are based on isotope template matching and usually work at different charge states separately, making them ineffective to detect overlapping peptides and low abundance peptides. Results: We present BPDA, a Bayesian approach for peptide detection in data produced by MS instruments with high enough resolution to baseline-resolve isotopic peaks, such as MALDI-TOF and LC-MS. We model the spectra as a mixture of candidate peptide signals, and the model is parameterized by MS physical properties. BPDA is based on a rigorous statistical framework and avoids problems, such as voting and ad-hoc thresholding, generally encountered in algorithms based on template matching. It systematically evaluates all possible combinations of possible peptide candidates to interpret a given spectrum, and iteratively finds the best fitting peptide signal in order to minimize the mean...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Youting Sun, Jianqiu Zhang, Ulisses Braga-Neto, Edward R. Dougherty
• Keywords: BMCBI 2010, Charge State, Peptide, Peptide Signals