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A Python-Based Pipeline for Preprocessing LC-MS Data for Untargeted Metabolomics Workflows.

Gabriel Riquelme1,2, Nicolás Zabalegui1,2, Pablo Marchi3

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

Metabolites
|October 21, 2020
PubMed
Summary
This summary is machine-generated.

TidyMS is a new Python package that simplifies preprocessing of liquid chromatography-mass spectrometry (LC-MS) data for untargeted metabolomics. It ensures reproducible quality control and data curation for reliable analysis.

Keywords:
Pythondata cleaningdata curationpreprocessingquality controlreference materialssignal driftsystem suitabilityuntargeted metabolomics

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Reproducible data preprocessing is a challenge in untargeted metabolomics.
  • Liquid chromatography-mass spectrometry (LC-MS) data curation requires removing non-relevant features for high-quality analysis.

Purpose of the Study:

  • Introduce TidyMS, a Python package for preprocessing LC-MS data.
  • Enable robust quality control (QC) procedures in untargeted metabolomics workflows.
  • Provide a versatile and customizable strategy for LC-MS data preprocessing.

Main Methods:

  • Developed TidyMS, a Python package for LC-MS data preprocessing.
  • Implemented pipelines for system suitability, conditioning, signal drift evaluation, and data curation.
  • Utilized TidyMS with NIST plasma reference materials for validation.

Main Results:

  • TidyMS facilitates accurate and reliable LC-MS measurements.
  • The package enables the creation of cleaned data matrices for statistical analysis.
  • Demonstrated TidyMS capabilities with various QC applications and reference materials.

Conclusions:

  • TidyMS offers a rapid, reproducible, and customizable workflow for LC-MS data preprocessing.
  • The open-source package supports automated or semi-automated data curation in metabolomics.
  • TidyMS enhances the quality and reliability of untargeted metabolomics studies.