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Open AccessArticle

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics

1
Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada
2
The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, Canada
3
Department of Animal Science, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada
*
Author to whom correspondence should be addressed.
Metabolites 2020, 10(5), 186; https://doi.org/10.3390/metabo10050186
Received: 16 April 2020 / Revised: 30 April 2020 / Accepted: 3 May 2020 / Published: 7 May 2020
(This article belongs to the Special Issue Metabolomics–Integration of Technology and Bioinformatics)
Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and (3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100× faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment. View Full-Text
Keywords: global metabolomics; peak detection; batch effects; pathway activity prediction global metabolomics; peak detection; batch effects; pathway activity prediction
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MDPI and ACS Style

Pang, Z.; Chong, J.; Li, S.; Xia, J. MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics. Metabolites 2020, 10, 186.

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