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Updated: Jul 15, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Published on: May 27, 2014
Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass
Masahiro Sugimoto1, Shinichi Kikuchi, Masanori Arita
1Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan.
Abstract:
We developed a computational technique to assist in the large-scale identification of charged metabolites. The electrophoretic mobility of metabolites in capillary electrophoresis-mass spectrometry (CE-MS) was predicted from their structure, using an ensemble of artificial neural networks (ANNs). Comparison between relative migration times of 241 various cations measured by CE-MS and predicted by a trained ANN ensemble produced a correlation coefficient of 0.931. When we used our technique to characterize all metabolites listed in the KEGG ligand database, the correct compounds among the top three candidates were predicted in 78.0% of cases. We suggest that this approach can be used for the prediction of the migration time of any cation and that it represents a powerful method for the identification of uncharacterized CE-MS peaks in metabolome analysis.
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