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ROMANCE: A new software tool to improve data robustness and feature identification in CE-MS metabolomics

Published in Electrophoresis. 2018, vol. 39, no. 9-10, p. 1222-1232
Abstract The use of capillary electrophoresis coupled to mass spectrometry (CE-MS) in metabolomics remains an oddity compared to the widely adopted use of liquid chromatography. This technique is traditionally regarded as lacking the reproducibility to adequately identify metabolites by their migration times. The major reason is the variability of the velocity of the background electrolyte, mainly coming from shifts in the magnitude of the electroosmotic flow and from the suction caused by electrospray interfaces. The use of the effective electrophoretic mobility is one solution to overcome this issue as it is a characteristic feature of each compound. To date, such an approach has not been applied to metabolomics due to the complexity and size of CE-MS data obtained in such studies. In this paper, ROMANCE (RObust Metabolomic Analysis with Normalized CE) is introduced as a new software for CE-MS-based metabolomics. It allows the automated conversion of batches of CE-MS files with minimal user intervention. ROMANCE converts the x-axis of eachMS file from the time into the effective mobility scale and the resulting files are already pseudo-aligned, present normalized peak areas and improved reproducibility, and can eventually follow existing metabolomic workflows. The software was developed in Scala, so it is multi-platform and computationally-efficient. It is available for download under a CC license. In this work, the versatility of ROMANCE was demonstrated by using data obtained in the same and in different laboratories, as well as its application to the analysis of human plasma samples.
Keywords Automated processingCapillary electrophoresisCE-MSElectrophoretic mobilityMetabolomics
PMID: 29292828
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Other version: http://doi.wiley.com/10.1002/elps.201700427
Research group Sciences analytiques
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GONZALEZ RUIZ, Victor et al. ROMANCE: A new software tool to improve data robustness and feature identification in CE-MS metabolomics. In: Electrophoresis, 2018, vol. 39, n° 9-10, p. 1222-1232. doi: 10.1002/elps.201700427 https://archive-ouverte.unige.ch/unige:115545

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Deposited on : 2019-04-01

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