Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/180153
Title: Alpsnmr: an r package for signal processing of fully untargeted nmr-based metabolomics
Author: Madrid Gambín, Francisco Javier
Oller Moreno, Sergio
Fernandez, Luis
Bartova, Simona
Giner, Maria Pilar
Joyce, Christopher
Ferraro, Francesco
Montoliu, Ivan
Moco, Sofia
Marco Colás, Santiago
Keywords: Metabòlits
Ressonància magnètica nuclear
Programari
Metabolites
Nuclear magnetic resonance
Computer software
Issue Date: 13-Jan-2020
Publisher: Oxford University Press
Abstract: 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.
Note: Versió postprint del document publicat a: https://doi.org/10.1093/bioinformatics/btaa022
It is part of: Bioinformatics, 2020, vol. 36, num. 9, p. 2943-2945
URI: http://hdl.handle.net/2445/180153
Related resource: https://doi.org/10.1093/bioinformatics/btaa022
ISSN: 1367-4803
Appears in Collections:Articles publicats en revistes (Enginyeria Electrònica i Biomèdica)

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