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

Gabriel Riquelme1,2, Emmanuel Ezequiel Bortolotto3, Matías Dombald3

  • 1Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina.

Metabolomics : Official Journal of the Metabolomic Society
|March 1, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new model and pipeline for data quality review in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS). It enhances data robustness and reproducibility for reliable inter-laboratory comparisons.

Keywords:
Data curationLiquid chromatographyMass spectrometryQuality control practices

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Area of Science:

  • Analytical Chemistry
  • Metabolomics
  • Biotechnology

Background:

  • Data quality review in untargeted metabolomics using LC-MS lacks community consensus.
  • Assessing analytical robustness for reproducibility and inter-laboratory comparisons is challenging.

Purpose of the Study:

  • To model sources of variation in LC-MS untargeted metabolomics.
  • To build a comprehensive curation pipeline for data quality review.
  • To provide tools for assessing data quality.

Main Methods:

  • Applied a model to understand data curation practices in metabolomics.
  • Analyzed 392 human serum samples using UPLC-HRMS.
  • Implemented a pipeline and tools within the open-source TidyMS Python package.

Main Results:

  • Identified sources of variation often overlooked in untargeted metabolomic studies.
  • Characterized variations using new analytical tools.
  • Confirmed data robustness by comparing experimental results with model predictions.

Conclusions:

  • The developed pipeline validates data robustness against predicted values.
  • Introduced new quality control practices for assessing analytical data quality.
  • Aimed to improve reproducibility and inter-laboratory comparisons in metabolomics research.