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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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WNetAlign: fast and accurate spectra alignment using truncated Wasserstein distance and network simplex.

Justyna Król1, Maria Bochenek1, Sylwia Jopa2

  • 1Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland.

Briefings in Bioinformatics
|May 25, 2026
PubMed
Summary

A new algorithm aligns spectra from liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. This method accurately matches features across experiments, improving compound identification and analysis.

Keywords:
Wasserstein distanceliquid chromatography–mass spectrometrynetwork simplex algorithmnuclear magnetic resonance spectroscopyoptimal transportspectra alignment

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Computational Biology

Background:

  • Liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy are vital for analyzing complex biological samples.
  • Overlapping peaks and spectral shifts in LC-MS and NMR data complicate feature matching and data interpretation.
  • Accurate alignment of spectral data across multiple runs is essential for reliable compound identification, quantification, and structural elucidation.

Purpose of the Study:

  • To develop a robust and scalable algorithm for aligning spectra from LC-MS and NMR experiments.
  • To address challenges posed by retention time shifts in LC-MS and frequency shifts in NMR data.
  • To enhance the accuracy and efficiency of spectral data analysis in omics and structural biology.

Main Methods:

  • A truncated Wasserstein distance-based algorithm was developed for spectral alignment.
  • Alignment was formulated as a minimum-cost flow problem solved using the Network Simplex algorithm.
  • The algorithm incorporates constraints on maximum transport distance to improve computational speed and accuracy.

Main Results:

  • The algorithm achieved high precision (0.97) and recall (0.96) on benchmark LC-MS datasets with a rapid 0.6-s runtime.
  • It outperformed existing tools like OpenMS and DeepRTAlign in LC-MS data alignment.
  • The method demonstrated effectiveness across various dimensions of NMR data, including 2D, 4D, and 7D analyses.

Conclusions:

  • The proposed Wasserstein distance-based algorithm provides a powerful and efficient solution for aligning LC-MS and NMR spectra.
  • This method significantly improves the reliability of compound identification and structural analysis in omics studies.
  • The open-source implementation (wnetalign) facilitates its adoption in proteomics, metabolomics, and structural biology research.