Title :
Bayesian peak detection for Pro-TOF MS MALDI data
Author :
Zhang, Jianqiu ; Wang, Honghui ; Suffredini, Anthony ; Gonzales, Denise ; Gonzalez, Elias ; Huang, Yufei ; Zhou, Xiaobo
Author_Institution :
Dept. of ECE, Univ. of Texas-San Antonio, San Antonio, TX
fDate :
March 31 2008-April 4 2008
Abstract :
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.
Keywords :
Bayes methods; free energy; mass spectra; mass spectroscopy; molecular biophysics; peak detectors; thermal noise; Bayesian peak detection; chemical noise; high resolution mass spectrometry; metropolized Gibbs sampling; peptide peak detection; prO-TOF MALDI data; thermal noise; visual inspection; Bayesian methods; Chemicals; Detection algorithms; Inspection; Mass spectroscopy; Parametric statistics; Peptides; Sampling methods; Signal resolution; Testing; Bayesian methods; MALDI; Mass spectrometry; Peak detection; proteomics;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2008.4517696
