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 ± 2.20% and an AUC=0.827±0.025, 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.