Abstract
© 2020 The Author(s). Published by Oxford University Press. All rights reserved.Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines.
Original language | English |
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Pages (from-to) | 2943-2945 |
Journal | Bioinformatics |
Volume | 36 |
Issue number | 9 |
DOIs | |
Publication status | Published - 1 May 2020 |
Externally published | Yes |
Funding
This work was supported by Nestle Research. Additional financial support has been provided by the Institut de Bioenginyeria de Catalunya (IBEC). IBEC is a member of the CERCA Programme/Generalitat de Catalunya.
Funders | Funder number |
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Nestle Research | |
Institute for Bioengineering of Catalonia |