RATIONALE: Data analysis is a key step in mass spectrometry based untargeted 
metabolomics, starting with the generation of generic peak lists from raw liquid 
chromatography/mass spectrometry (LC/MS) data. Due to the use of various 
algorithms by different workflows, the results of different peak-picking 
strategies often differ widely.
METHODS: Raw LC/HRMS data from two types of biological samples (bile and urine), 
as well as a standard mixture of 84 metabolites, were processed with four 
peak-picking softwares: Peakview®, Markerview™, MetabolitePilot™ and XCMS 
Online. The overlaps between the results of each peak-generating method were 
then investigated. To gauge the relevance of peak lists, a database search using 
the METLIN online database was performed to determine which features had 
accurate masses matching known metabolites as well as a secondary filtering 
based on MS/MS spectral matching.
RESULTS: In this study, only a small proportion of all peaks (less than 10%) 
were common to all four software programs. Comparison of database searching 
results showed peaks found uniquely by one workflow have less chance of being 
found in the METLIN metabolomics database and are even less likely to be 
confirmed by MS/MS.
CONCLUSIONS: It was shown that the performance of peak-generating workflows has 
a direct impact on untargeted metabolomics results. As it was demonstrated that 
the peaks found in more than one peak detection workflow have higher potential 
to be identified by accurate mass as well as MS/MS spectrum matching, it is 
suggested to use the overlap of different peak-picking workflows as preliminary 
peak lists for more rugged statistical analysis in global metabolomics 
investigations.