Abstract
<jats:p>Nuclear magnetic resonance (NMR) spectroscopy quantifies metabolites across many biological matrices, but in one-dimensional spectra of complex biofluids such as urine and plasma, extensive signal overlap obscures individual metabolite signals and complicates their quantification. Resolving these overlaps by deconvolution is only the first step: turning a set of spectra into a statistically analysable table also requires aligning corresponding signals across samples and condensing them into a feature matrix, a path that has typically been stitched together from several separate tools and is both cumbersome and time consuming. We present metabodeconplus, 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 as Lorentzian line shapes, automatically aligns the resulting peaks across samples, and condenses them into a data matrix of aligned signal integrals ready for built-in sample classification or downstream statistics. Automated parameter optimization removes manual tuning, and a Rust computational backend with parallelization reduces runtime substantially over the deconvolution-only predecessor MetaboDecon1D, from which the workflow is extended. The package is freely available as open source on GitHub and is currently under review at CRAN. metabodeconplus thus lowers the barrier from raw NMR spectra to reproducible metabolomic analysis.</jats:p>