In this paper, a novel Bayesian peak detection algorithm is proposed for peptide peak detection in high resolution prOTOFtrade MALDI Mass Spectrometry(MS) data. A nonlinear parametric model is proposed for modeling the peptide signals, chemical noise, and thermal noise. A metropolized Gibbs sampling algorithm is derived for Bayesian peak detection. The proposed algorithm is compared with a popular wavelet-based algorithm and the results show a significant improvement in performance on simulated data. The algorithm is finally tested on real MS MALDI data and the results agree with visual inspection very well.
['Jianqiu Zhang', 'Honghui Wang', 'Anthony F. Suffredini', 'Denise A. Gonzales', 'Elias Gonzalez', 'Yufei Huang', 'Xiaobo Zhou']
Bayesian peak detection for Pro-TOF MS MALDI data