Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/180153
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dc.contributor.authorMadrid Gambín, Francisco Javier-
dc.contributor.authorOller Moreno, Sergio-
dc.contributor.authorFernandez, Luis-
dc.contributor.authorBartova, Simona-
dc.contributor.authorGiner, Maria Pilar-
dc.contributor.authorJoyce, Christopher-
dc.contributor.authorFerraro, Francesco-
dc.contributor.authorMontoliu, Ivan-
dc.contributor.authorMoco, Sofia-
dc.contributor.authorMarco Colás, Santiago-
dc.date.accessioned2021-09-20T17:09:44Z-
dc.date.available2021-09-20T17:09:44Z-
dc.date.issued2020-01-13-
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/2445/180153-
dc.description.abstractNuclear 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.-
dc.format.extent3 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherOxford University Press-
dc.relation.isformatofVersió postprint del document publicat a: https://doi.org/10.1093/bioinformatics/btaa022-
dc.relation.ispartofBioinformatics, 2020, vol. 36, num. 9, p. 2943-2945-
dc.relation.urihttps://doi.org/10.1093/bioinformatics/btaa022-
dc.rights(c) Madrid Gambín, Francisco Javier et al., 2020-
dc.sourceArticles publicats en revistes (Enginyeria Electrònica i Biomèdica)-
dc.subject.classificationMetabòlits-
dc.subject.classificationRessonància magnètica nuclear-
dc.subject.classificationProgramari-
dc.subject.otherMetabolites-
dc.subject.otherNuclear magnetic resonance-
dc.subject.otherComputer software-
dc.titleAlpsnmr: an r package for signal processing of fully untargeted nmr-based metabolomics-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/acceptedVersion-
dc.identifier.idgrec699828-
dc.date.updated2021-09-20T17:09:44Z-
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
Appears in Collections:Articles publicats en revistes (Enginyeria Electrònica i Biomèdica)

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