Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis.
BACKGROUND
In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis. Recently, there has been significant progress in the development of various peak detection algorithms. However, neither a comprehensive survey nor an experimental comparison of these algorithms is yet available. The main objective of this paper is to provide such a survey and to compare the performance of single spectrum based peak detection methods.


RESULTS
In general, we can decompose a peak detection procedure into three consequent parts: smoothing, baseline correction and peak finding. We first categorize existing peak detection algorithms according to the techniques used in different phases. Such a categorization reveals the differences and similarities among existing peak detection algorithms. Then, we choose five typical peak detection algorithms to conduct a comprehensive experimental study using both simulation data and real MALDI MS data.


CONCLUSIONS
The results of comparison show that the continuous wavelet-based algorithm provides the best average performance.
Yang
Chao
C
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, PR China. yorkey@ust.hk
He
Zengyou
Z
Yu
Weichuan
W
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D010455:Peptides / Q000737:chemistry; D040901:Proteomics / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D012984:Software; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods
D010455:Peptides