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Updated: Jul 11, 2025

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
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G-Aligner: a graph-based feature alignment method for untargeted LC-MS-based metabolomics.

Ruimin Wang1,2,3, Miaoshan Lu2,3,4, Shaowei An1,3,5

  • 1Fudan University, Shanghai, 200433, Shanghai, China.

BMC Bioinformatics
|November 14, 2023
PubMed
Summary
This summary is machine-generated.

G-Aligner improves feature matching in untargeted LC-MS metabolomics by using a graph-based approach. This method enhances the accuracy of aligning features across multiple runs for better data analysis.

Keywords:
Combinatorial optimizationFeature alignmentLC–MSMultidimensional assignment problem

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Area of Science:

  • Analytical Chemistry
  • Bioinformatics
  • Metabolomics

Background:

  • Untargeted liquid chromatography-mass spectrometry (LC-MS) is crucial for metabolomics.
  • Feature alignment across multiple runs is essential for analyzing intensity variations.
  • Existing methods often prioritize retention time correction over comprehensive feature matching.

Purpose of the Study:

  • To develop an advanced feature alignment method for untargeted LC-MS data.
  • To improve the accuracy of feature matching across multiple analytical runs.
  • To address limitations in existing feature alignment algorithms.

Main Methods:

  • Proposed G-Aligner, a graph-based feature alignment method.
  • Modeled feature matching as an unbalanced multidimensional assignment problem.
  • Utilized three combinatorial optimization algorithms for optimal matching.

Main Results:

  • G-Aligner demonstrated superior performance on three public metabolomics datasets.
  • Achieved up to 9.8% increase in accurately aligned features and 26.6% in analytes.
  • Improved accuracy of other alignment software when integrated.

Conclusions:

  • G-Aligner significantly enhances feature matching accuracy in untargeted metabolomics LC-MS.
  • The graph-based approach effectively solves the multidimensional assignment problem for feature correspondences.
  • G-Aligner proves effective and robust for analyzing complex metabolomics data.