Institute of Bioinformatics.
Department of Genetics.
Bioinformatics. 2020 Dec 22;36(20):5068-5075. doi: 10.1093/bioinformatics/btaa631.
Time-series nuclear magnetic resonance (NMR) has advanced our knowledge about metabolic dynamics. Before analyzing compounds through modeling or statistical methods, chemical features need to be tracked and quantified. However, because of peak overlap and peak shifting, the available protocols are time consuming at best or even impossible for some regions in NMR spectra.
We introduce Ridge Tracking-based Extract (RTExtract), a computer vision-based algorithm, to quantify time-series NMR spectra. The NMR spectra of multiple time points were formulated as a 3D surface. Candidate points were first filtered using local curvature and optima, then connected into ridges by a greedy algorithm. Interactive steps were implemented to refine results. Among 173 simulated ridges, 115 can be tracked (RMSD < 0.001). For reproducing previous results, RTExtract took less than 2 h instead of ∼48 h, and two instead of seven parameters need tuning. Multiple regions with overlapping and changing chemical shifts are accurately tracked.
Source code is freely available within Metabolomics toolbox GitHub repository (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA/tree/master/metabolomics_toolbox/code/ridge_tracking) and is implemented in MATLAB and R.
Supplementary data are available at Bioinformatics online.
时间序列核磁共振（NMR）技术提高了我们对代谢动力学的认识。在通过建模或统计方法分析化合物之前，需要跟踪和量化化学特征。然而，由于峰重叠和峰移动，现有的协议在最好的情况下也是耗时的，对于 NMR 光谱的某些区域甚至是不可能的。
我们引入了基于脊跟踪的提取（RTExtract），这是一种基于计算机视觉的算法，用于定量时间序列 NMR 光谱。多个时间点的 NMR 光谱被表示为一个 3D 表面。首先使用局部曲率和最优值过滤候选点，然后通过贪婪算法将它们连接成脊。实现了交互步骤来优化结果。在 173 条模拟脊中，有 115 条可以被跟踪（均方根误差 < 0.001）。为了重现以前的结果，RTExtract 只需要不到 2 小时，而不是大约 48 小时，并且只需要调整两个参数，而不是七个。可以准确地跟踪具有重叠和变化化学位移的多个区域。
源代码可在代谢组学工具包 GitHub 存储库（https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA/tree/master/metabolomics_toolbox/code/ridge_tracking）中免费获得，并在 MATLAB 和 R 中实现。
补充数据可在生物信息学在线获得。
