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Updated: Sep 17, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers.

Huaxu Yu¹, Jun Ding², Tong Shen¹

¹West Coast Metabolomics Center, University of California Davis, Davis, CA, USA.

Nature Communications

|July 2, 2025

Summary

This summary is machine-generated.

MassCube, a new open-source framework for mass spectrometry (MS) data processing, offers robust and efficient nontargeted peak detection in metabolomics. It outperforms existing tools in speed and accuracy, enabling advanced omics and biomedical research.

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Last Updated: Sep 17, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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Published on: May 27, 2014

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

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Published on: July 27, 2021

Area of Science:

Analytical Chemistry
Computational Biology
Metabolomics

Background:

Nontargeted peak detection in liquid chromatography-mass spectrometry (LC-MS) metabolomics requires robust and benchmarked methods.
Existing algorithms often lack comprehensive benchmarking and efficiency for large datasets.

Purpose of the Study:

To introduce MassCube, an open-source Python framework for MS data processing.
To systematically benchmark MassCube against other algorithms and data types.
To provide a tool for metabolome-informed phenotype prediction.

Main Methods:

Mass trace construction via signal clustering and Gaussian-filter edge detection for peak identification.
Grouping of peaks for adduct and in-source fragment detection.
Compound annotation using identity and fuzzy searches, followed by quality control.

Main Results:

MassCube achieves 100% signal coverage with comprehensive chromatographic metadata reporting.
Outperforms MS-DIAL, MZmine3, and XCMS in speed, isomer detection, and accuracy.
Efficiently processes large datasets (105 GB in 64 min on a laptop), significantly faster than alternatives.
Successfully identified age, sex, and regional differences in mouse brain metabolomics data despite batch effects.

Conclusions:

MassCube provides a robust, efficient, and accurate solution for LC-MS-based metabolomics peak detection.
Its performance and speed make it suitable for large-scale omics and biomedical research.
The framework is available for direct use or integration into larger research applications.