Bayesian peptide peak detection for high resolution TOF mass spectrometry

Authors:
Jianqiu Zhang;Xiaobo Zhou;Honghui Wang;Anthony Suffredini;Lin Zhang;Yufei Huang;Stephen Wong
Affiliations:
Department of Electrical and Computer Engineering, University of Texas at San Antonio, TX;Texas Methodist Hospital Research Institute, Houston, TX;Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD;Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD;School of Information and Electric Engineering, China University of Mining Technology, XuZhou, China;Department of Electrical and Computer Engineering, University of Texas at San Antonio, and Greehey Children's Cancer Research Institute, Department of Epidemiology and Biostatistics, University Te ...;Texas Methodist Hospital Research Institute, Houston, TX
Venue:
IEEE Transactions on Signal Processing
Year:
2010

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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.