# MetAssimulo 2.0: a web app for simulating realistic 1D and 2D metabolomic 1H NMR spectra

## TL;DR

MetAssimulo 2.0 is a web app that simulates realistic 1D and 2D NMR spectra for metabolomics, supporting machine learning and biomedical research.

## Contribution

Expands simulation to blood and CSF, adds 2D NMR capabilities, and improves realism with protein background and inter-metabolite correlations.

## Key findings

- Achieves a Pearson correlation of ~0.82 with real blood spectra.
- Supports simulation of both 1D and 2D NMR spectra for diverse biological fluids.
- Enables case–control and continuous outcome simulations for experimental design.

## Abstract

Metabolomics extensively utilizes nuclear magnetic resonance (NMR) spectroscopy due to its excellent reproducibility and high throughput. Both 1D and 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 labeled 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.b 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, 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 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 versus healthy) and continuous (e.g. body mass index) 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.

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.