AlpsNMR: An R package for signal processing of fully untargeted NMR-based metabolomics

F. Madrid-Gambin; S. Oller-Moreno; L. Fernandez; L. Fernandez; S. Bartova; M.P. Giner; C. Joyce; F. Ferraro; I. Montoliu; S. Moco; S. Marco; S. Marco

doi:10.1093/bioinformatics/btaa022

AlpsNMR: An R package for signal processing of fully untargeted NMR-based metabolomics

F. Madrid-Gambin, S. Oller-Moreno, L. Fernandez, L. Fernandez, S. Bartova, M.P. Giner, C. Joyce, F. Ferraro, I. Montoliu, S. Moco, S. Marco, S. Marco

Research output: Contribution to Journal › Article › Academic › peer-review

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
Pages (from-to)	2943-2945
Journal	Bioinformatics
Volume	36
Issue number	9
DOIs	https://doi.org/10.1093/bioinformatics/btaa022
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
Nestle Research
Institute for Bioengineering of Catalonia

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title = "AlpsNMR: An R package for signal processing of fully untargeted NMR-based metabolomics",

abstract = "{\textcopyright} 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.",

author = "F. Madrid-Gambin and S. Oller-Moreno and L. Fernandez and L. Fernandez and S. Bartova and M.P. Giner and C. Joyce and F. Ferraro and I. Montoliu and S. Moco and S. Marco and S. Marco",

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AU - Madrid-Gambin, F.

AU - Oller-Moreno, S.

AU - Fernandez, L.

AU - Bartova, S.

AU - Giner, M.P.

AU - Joyce, C.

AU - Ferraro, F.

AU - Montoliu, I.

AU - Moco, S.

AU - Marco, S.

PY - 2020/5/1

Y1 - 2020/5/1

N2 - © 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.

AB - © 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.

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AlpsNMR: An R package for signal processing of fully untargeted NMR-based metabolomics

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