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Model-driven data curation pipeline for LC–MS-based untargeted metabolomics

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A Correction to this article was published on 16 March 2023

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Abstract

Introduction

There is still no community consensus regarding strategies for data quality review in liquid chromatography mass spectrometry (LC–MS)-based untargeted metabolomics. Assessing the analytical robustness of data, which is relevant for inter-laboratory comparisons and reproducibility, remains a challenge despite the wide variety of tools available for data processing.

Objectives

The aim of this study was to provide a model to describe the sources of variation in LC–MS-based untargeted metabolomics measurements, to use it to build a comprehensive curation pipeline, and to provide quality assessment tools for data quality review.

Methods

Human serum samples (n=392) were analyzed by ultraperformance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) using an untargeted metabolomics approach. The pipeline and tools used to process this dataset were implemented as part of the open source, publicly available TidyMS Python-based package.

Results

The model was applied to understand data curation practices used by the metabolomics community. Sources of variation, which are often overlooked in untargeted metabolomic studies, were identified in the analysis. New tools were used to characterize certain types of variations.

Conclusion

The developed pipeline allowed confirming data robustness by comparing the experimental results with expected values predicted by the model. New quality control practices were introduced to assess the analytical quality of data.

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Acknowledgements

MEM acknowledges Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina, PUE 055 project) and the National Agency of Scientific and Technological Promotion (ANPCyT, PICT-2018-02137 and PICT-2020-01019 projects) for providing the funding. MEM is a research staff member from CONICET. We would also like to thank Dr. Christoph Bueschl for helpful discussions.

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Contributions

The manuscript was conceived and written through contributions of all authors. All authors have given approval to the final version of the manuscript.

Corresponding author

Correspondence to María Eugenia Monge.

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Conflict of interest

The authors have no disclosures of potential conflicts of interest related to the presented work.

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The original online version of this article was revised: the missing surname of the corresponding author and the incorrect email id has been updated appropriately.

Supplementary Information

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Riquelme, G., Bortolotto, E.E., Dombald, M. et al. Model-driven data curation pipeline for LC–MS-based untargeted metabolomics. Metabolomics 19, 15 (2023). https://doi.org/10.1007/s11306-023-01976-1

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  • DOI: https://doi.org/10.1007/s11306-023-01976-1

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