Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis
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In metabolomics, the rapid identification of quantitative differences between multiple biological samples remains a major challenge. While capillary electrophoresis–mass spectrometry (CE–MS) is a powerful tool to simultaneously quantify charged metabolites, reliable and easy-to-use software that is well suited to analyze CE–MS metabolic profiles is still lacking. Optimized software tools for CE–MS are needed because of the sometimes large variation in migration time between runs and the wider variety of peak shapes in CE–MS data compared with LC–MS or GC–MS. Therefore, we implemented a stand-alone application named JDAMP (Java application for Differential Analysis of Metabolite Profiles), which allows users to identify the metabolites that vary between two groups. The main features include fast calculation modules and a file converter using an original compact file format, baseline subtraction, dataset normalization and alignment, visualization on 2D plots (m/z and time axis) with matching metabolite standards, and the detection of significant differences between metabolite profiles. Moreover, it features an easy-to-use graphical user interface that requires only a few mouse-actions to complete the analysis. The interface also enables the analyst to evaluate the semiautomatic processes and interactively tune options and parameters depending on the input datasets. The confirmation of findings is available as a list of overlaid electropherograms, which is ranked using a novel difference-evaluation function that accounts for peak size and distortion as well as statistical criteria for accurate difference-detection. Overall, the JDAMP software complements other metabolomics data processing tools and permits easy and rapid detection of significant differences between multiple complex CE–MS profiles.
KeywordsCapillary electrophoresis–mass spectrometry Metabolome Data analysis Software
We thank Dr. Yusuke Tanigawara and Dr. Akito Nishimuta of the School of Medicine, Keio University, Dr. Satoshi Yoshida and Dr. Hideki Koizumi of Kirin Holdings, Dr. Akira Oikawa of Riken, and Dr. Eri Shimizu and Dr. Tadahiro Ozawa of Kao Corporation, for valuable discussions. We also thank Maki Sugawara, Hiroko Ueda, Shinobu Abe, and Kazuki Sugisaki of IAB for measurement, data analyses, and programming, and Dr. Ursula Petralia for editing the manuscript. This work was supported by research grants from the Yamagata Prefectural Government and the City of Tsuruoka.
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