TY - JOUR
T1 - AlpsNMR
T2 - An R package for signal processing of fully untargeted NMR-based metabolomics
AU - Madrid-Gambin, F.
AU - Oller-Moreno, S.
AU - Fernandez, L.
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.
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.
U2 - 10.1093/bioinformatics/btaa022
DO - 10.1093/bioinformatics/btaa022
M3 - Article
VL - 36
SP - 2943
EP - 2945
JO - Bioinformatics (Oxford, England)
JF - Bioinformatics (Oxford, England)
SN - 1367-4803
IS - 9
ER -