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

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

Motivation: 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.

Results: 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.

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.

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MetAssimulo 2.0:一个web应用程序,用于模拟现实的1D和2D代谢组学1H NMR光谱。
代谢组学由于其良好的重现性和高通量而广泛应用于核磁共振(NMR)光谱。一维(1D)和二维(2D)核磁共振波谱为代谢物注释和量化提供了重要信息,但存在复杂的重叠模式,可能需要复杂的机器学习算法来破译。不幸的是,标记光谱的有限可用性可能会阻碍机器学习的应用,特别是需要大量标记数据的深度学习算法。在这种情况下,光谱数据的模拟成为算法开发的一个易于处理的解决方案。在这里,我们介绍MetAssimulo 2.0,这是MetAssimulo 1.0代谢组学1H NMR模拟工具的全面升级,重新实现为基于python的web应用程序。MetAssimulo 1.0仅模拟人体尿液的1D 1H光谱,而MetAssimulo 2.0将功能扩展到尿液、血液和脑脊液(CSF),通过结合广泛的蛋白质背景增强了血液光谱的真实感。这种增强使得更接近真实的血液光谱,实现大约0.82的Pearson相关性。此外,该工具现在还包括二维j分辨(J-Res)和相关光谱(COSY)光谱的模拟功能,大大拓宽了其在复杂混合物分析中的应用范围。MetAssimulo 2.0模拟了离散(病例对照,例如心脏移植与健康)和连续(例如BMI)结果的单一和组谱,并包括代谢物间的相关性。因此,它支持一系列实验设计,并证明代谢物谱和生物医学反应之间的关联。通过增强核磁共振谱模拟,MetAssimulo 2.0很好地定位于支持和加强深度学习和代谢组学交叉的研究。可用性和实现:MetAssimulo 2.0的代码和详细的说明/教程可在https://github.com/yanyan5420/MetAssimulo_2.git上获得。代谢物的相关NMR谱存储在MetaboLights中,登录号为MTBLS12081。补充信息:补充数据可在生物信息学在线获取。
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