MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics

Zhiqiang Pang, Lei Xu, Charles Viau, Yao Lu, Reza Salavati, Niladri Basu and Jianguo Xia ()
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Zhiqiang Pang: McGill University
Lei Xu: McGill University
Charles Viau: McGill University
Yao Lu: McGill University
Reza Salavati: McGill University
Niladri Basu: McGill University
Jianguo Xia: McGill University

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

Date: 2024
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DOI: 10.1038/s41467-024-48009-6

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