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2010

BPDA - A Bayesian peptide detection algorithm for mass spectrometry

11 years 8 months ago
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...
Youting Sun, Jianqiu Zhang, Ulisses Braga-Neto, Ed
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where BMCBI
Authors Youting Sun, Jianqiu Zhang, Ulisses Braga-Neto, Edward R. Dougherty
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