Abstract
BackgroundIn 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.ResultsIn 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.ConclusionThe results of comparison show that the continuous wavelet-based algorithm provides the best average performance.
Highlights
In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis
In order to obtain a fair comparison, here we only focus on single spectrum based peak detection algorithms
We provide a comprehensive survey of existing peak detection methods
Summary
In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis. Mass Spectrometry (MS) is a common analytical tool in proteome research It can be used as a technique to measure masses of proteins/peptides in complex mixtures obtained from biological samples. The first step in proteomic data analysis is to extract peptide induced signals (i.e., peaks) from raw MS spectra. Various algorithms have been proposed to facilitate the identification of informative peaks that correspond to true peptide signals. These algorithms differ from each other in their principles, implementations and performance. In order to provide a comprehensive comparison of existing peak detection algorithms and extract reasonable criteria for (page number not for citation purposes)
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