Title :
Bayesian Peptide Peak Detection for High Resolution TOF Mass Spectrometry
Author :
Zhang, Jianqiu ; Zhou, Xiaobo ; Wang, Honghui ; Suffredini, Anthony ; Zhang, Lin ; Huang, Yufei ; Wong, Stephen
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Texas at San Antonio, San Antonio, TX, USA
Abstract :
In this paper, we address the issue of peptide ion peak detection for high resolution time-of-flight (TOF) mass spectrometry (MS) data. A novel Bayesian peptide ion peak detection method is proposed for TOF data with resolution of 10 000-15 000 full width at half-maximum (FWHW). MS spectra exhibit distinct characteristics at this resolution, which are captured in a novel parametric model. Based on the proposed parametric model, a Bayesian peak detection algorithm based on Markov chain Monte Carlo (MCMC) sampling is developed. The proposed algorithm is tested on both simulated and real datasets. The results show a significant improvement in detection performance over a commonly employed method. The results also agree with expert´s visual inspection. Moreover, better detection consistency is achieved across MS datasets from patients with identical pathological condition.
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; medical signal detection; molecular biophysics; patient diagnosis; proteins; time of flight mass spectra; time of flight mass spectroscopy; Bayesian peak detection algorithm; Markov chain Monte Carlo sampling; high resolution TOF mass spectrometry; high resolution time-of-flight mass spectrometry; parametric model; peptide ion peak detection; Bayesian methods; Chemicals; Detection algorithms; Instruments; Ions; Mass spectroscopy; Noise; Parametric statistics; Peptides; Permission; Protein engineering; Sampling methods; Signal resolution; Testing; Bayesian methods; Markov chain Monte Carlo; mass spectrometry; peptide peak detection; time-of-flight;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2010.2065226
