{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T05:19:39Z","timestamp":1787721579529,"version":"build-2784847793"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"crossref","award":["TRR374"],"award-info":[{"award-number":["TRR374"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]},{"award":["TRR374"],"award-info":[{"award-number":["TRR374"]}],"id":[{"id":"https:\/\/ror.org\/018mejw64","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Metabolites"],"abstract":"<jats:p>Background: In one-dimensional NMR spectra of complex biofluids such as urine and plasma, extensive signal overlap obscures individual metabolite signals. Resolving this overlap by deconvolution is only the first step: turning a set of spectra into a table for subsequent statistical analysis also requires the alignment of signals across samples and their integration into a single feature matrix. Methods: Here, metabodeconplus is presented, an R package that unifies this entire path into a single reproducible end-to-end workflow. From raw one-dimensional spectra, it deconvolutes overlapping signals, aligns resulting signals across samples, and integrates them into a data matrix for built-in sample classification or downstream statistical analysis. Automated parameter optimization removes manual tuning, and a Rust computational backend with parallelization leads to fast runtimes. Results: On the simulated Sim3 spectra, a combined score of correctly identified signals and reconstruction accuracy (maximum 1) rose from 0.712 for the predecessor package to 0.801 for metabodeconplus. For the urinary AKI dataset, metabodeconplus reached a classification accuracy of 73.7 \u00b1 2.20% and an AUC=0.827\u00b10.025, which is comparable to the binning baseline. An advantage is the potential unambiguous metabolite assignment of the deconvoluted signals. Conclusions: The package is freely available as open source on GitHub and on CRAN.<\/jats:p>","DOI":"10.3390\/metabo16090604","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T11:47:19Z","timestamp":1787572039000},"page":"604","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Metabodeconplus\u2014An R Package for Automated Deconvolution and Alignment of 1D NMR Metabolomics Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9681-9253","authenticated-orcid":false,"given":"Tobias","family":"Schmidt","sequence":"first","affiliation":[{"name":"Institute of Statistical Bioinformatics, University of Regensburg, 93053 Regensburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"Sombke","sequence":"additional","affiliation":[{"name":"Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3633-1330","authenticated-orcid":false,"given":"Helena U.","family":"Zacharias","sequence":"additional","affiliation":[{"name":"Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, 30625 Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter J.","family":"Oefner","sequence":"additional","affiliation":[{"name":"Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1326-4297","authenticated-orcid":false,"given":"Rainer","family":"Spang","sequence":"additional","affiliation":[{"name":"Institute of Statistical Bioinformatics, University of Regensburg, 93053 Regensburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3646-0060","authenticated-orcid":false,"given":"Wolfram","family":"Gronwald","sequence":"additional","affiliation":[{"name":"Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101564","DOI":"10.1016\/j.pnmrs.2025.101564","article-title":"NMR-based metabolomics: Where are we now and where are we going?","volume":"150\u2013151","author":"Gowda","year":"2025","journal-title":"Prog. 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