MetAssimulo 2.0: a web app for simulating realistic 1D & 2D Metabolomic 1H NMR spectra

Yan Yan; Beatriz Jiménez; Michael T Judge; Toby Athersuch; Maria De Iorio; Timothy M D Ebbels

doi:10.1093/bioinformatics/btaf045

MetAssimulo 2.0: a web app for simulating realistic 1D & 2D Metabolomic 1H NMR spectra

Bioinformatics. 2025 Jan 25:btaf045. doi: 10.1093/bioinformatics/btaf045. Online ahead of print.

Authors

Yan Yan¹, Beatriz Jiménez², Michael T Judge^{1

3}, Toby Athersuch¹, Maria De Iorio^{4

5}, Timothy M D Ebbels¹

Affiliations

¹ Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, W12 0NN, United Kingdom.
² National Phenome Centre & Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, IRDB Building, Hammersmith Campus, London, W12 0NN, United Kingdom.
³ Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland 20850, United States.
⁴ Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
⁵ STAR Institute for Human Development and Potential.

PMID: 39862393
DOI: 10.1093/bioinformatics/btaf045

Abstract

Metabolomics extensively utilizes Nuclear Magnetic Resonance (NMR) spectroscopy due to its excellent reproducibility and high throughput. Both one-dimensional (1D) and two-dimensional (2D) NMR spectra provide crucial information for metabolite annotation and quantification, yet present complex overlapping patterns which may require sophisticated machine learning algorithms to decipher. Unfortunately, the limited availability of labeled spectra can hamper application of machine learning, especially deep learning algorithms which require large amounts of labelled data. In this context, simulation of spectral data becomes a tractable solution for algorithm development.Here, we introduce MetAssimulo 2.0, a comprehensive upgrade of the MetAssimulo 1.0 metabolomic 1H NMR simulation tool, reimplemented as a Python-based web application. Where MetAssimulo 1.0 only simulated 1D 1H spectra of human urine, MetAssimulo 2.0 expands functionality to urine, blood, and cerebral spinal fluid (CSF), enhancing the realism of blood spectra by incorporating a broad protein background. This enhancement enables a closer approximation to real blood spectra, achieving a Pearson correlation of approximately 0.82. Moreover, this tool now includes simulation capabilities for 2D J-resolved (J-Res) and Correlation Spectroscopy (COSY) spectra, significantly broadening its utility in complex mixture analysis. MetAssimulo 2.0 simulates both single, and groups, of spectra with both discrete (case-control, e.g. heart transplant vs healthy) and continuous (e.g. BMI) outcomes and includes inter-metabolite correlations. It thus supports a range of experimental designs and demonstrating associations between metabolite profiles and biomedical responses.By enhancing NMR spectral simulations, MetAssimulo 2.0 is well positioned to support and enhance research at the intersection of deep learning and metabolomics.

Availability and implementation: The code and the detailed instruction/tutorial for MetAssimulo 2.0 is available at https://github.com/yanyan5420/MetAssimulo_2.git The relevant NMR spectra for metabolites are deposited in MetaboLights with accession number MTBLS12081.

Supplementary information: Supplementary data are available at Bioinformatics online.