Ferrante Laura, Rajpoot Kashif, Jeeves Mark, Ludwig Christian
School of Computer Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
University of Birmingham Dubai, Dubai International Academic City, United Arab Emirates.
Wellcome Open Res. 2023 May 19;7:262. doi: 10.12688/wellcomeopenres.18248.2. eCollection 2022.
Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- H, C-HSQC NMR spectra arising from C - C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- H, C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- H, C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.
新陈代谢对于细胞存活和增殖至关重要。深入了解代谢网络及其调控过程对于理解和攻克疾病往往至关重要。利用核磁共振（NMR）和质谱（MS）对新陈代谢进行稳定同位素示踪是获取代谢网络活动机制信息的强大工具。然而，为了获取有意义的信息，迫切需要自动化工具来分析这些复杂的光谱并消除手动分析引入的偏差。在此，我们提出一种数据驱动算法，用于自动注释和分析二维氢碳异核单量子相干（2D-H,C-HSQC）NMR光谱中由碳-碳标量耦合产生的NMR信号多重峰。该算法通过自动进行峰挑选和多重峰分析，将用户输入引导二维氢碳异核单量子相干（2D-H,C-HSQC）NMR光谱分析的需求降至最低。这使得非NMR专家也能使用这项技术。该算法已集成到现有的MetaboLab软件包中。为评估算法性能，测试了两个标准：峰是否被正确注释，其次算法对其分析的可信度如何。对于后者，引入了决定系数。使用了三个数据集进行测试。第一个用于测试三个生物学重复的可重复性，第二个测试算法对于表观J耦合常数不同缩放比例的稳健性，第三个关注不同的采样量。该算法正确注释了总体>90%的NMR信号，平均决定系数ρ分别为94.06±5.08%、95.47±7.20%和80.47±20.98%。我们的结果表明，所提出的算法能够准确识别和分析超高分辨率二维氢碳异核单量子相干（2D-H,C-HSQC）NMR光谱中的NMR信号多重峰。它对信号分裂增强和高达25%的非均匀采样具有稳健性。
