Madrid Gambín, Francisco JavierOller Moreno, SergioFernández Romero, LuisBartova, SimonaGiner, Maria PilarJoyce, ChristopherFerraro, FrancescoMontoliu, IvanMoco, SofiaMarco Colás, Santiago2021-09-202021-09-202020-01-131367-4803https://hdl.handle.net/2445/180153Nuclear 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.3 p.application/pdfeng(c) Madrid Gambín, Francisco Javier et al., 2020MetabòlitsRessonància magnètica nuclearProgramariMetabolitesNuclear magnetic resonanceComputer softwareinfo:eu-repo/semantics/article6998282021-09-20info:eu-repo/semantics/openAccess