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Deep learning-assisted peak curation for large-scale LC-MS metabolomics

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Item Type:Article
Title:Deep learning-assisted peak curation for large-scale LC-MS metabolomics
Creators Name:Gloaguen, Y., Kirwan, J.A. and Beule, D.
Abstract:Available automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS, which uses machine learning based on a convoluted neural network to reduce the number and fraction of false peaks. NeatMS comes with a pre-trained model representing expert knowledge in the differentiation of true chemical signal from noise. Furthermore, it provides all necessary functions to easily train new models or improve existing ones by transfer learning. Thus, the tool improves peak curation and contributes to the robust and scalable analysis of large-scale experiments. We show how to integrate it into different liquid chromatography–mass spectrometry (LC-MS) analysis workflows, quantify its performance, and compare it to various other approaches. NeatMS software is available as open source on github under permissive MIT license and is also provided as easy-to-install PyPi and Bioconda packages.
Keywords:Liquid Chromatography, Deep Learning, Metabolomics, Software, Tandem Mass Spectrometry
Source:Analytical Chemistry
ISSN:0003-2700
Publisher:American Chemical Society
Volume:94
Number:12
Page Range:4930-4937
Date:29 March 2022
Official Publication:https://doi.org/10.1021/acs.analchem.1c02220
PubMed:View item in PubMed

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