BPDA2d — A 2D global optimization based Bayesian peptide detection algorithm for LC-MS

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Type: Journal Publication

Web: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3278754/


Abstract: Motivation: Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides. Results: BPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together—especially by deriving information across charge states—low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography–mass spectrometry data, according to sensitivity and detection accuracy.


Cited as: Y. Sun, J. Zhang, U.M. Braga-Neto and E.R. Dougherty,, "BPDA2d — A 2D global optimization based Bayesian peptide detection algorithm for LC-MS", Bioinformatics, Vol. 28, No. 4, February 2012, pp. 564-572

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