RTExtract: Time-series NMR Spectra Quantification Based on 3D Surface Ridge Tracking

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2020 Jul 13

PMID 32653900

Citations 4

Authors

Yue Wu

Michael T Judge

Jonathan Arnold

Suchendra M Bhandarkar

Arthur S Edison

Affiliations

Soon will be listed here.

Abstract

Motivation: 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.

Results: 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.

Availability And Implementation: 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 Information: Supplementary data are available at Bioinformatics online.

Citing Articles

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NMR Metabolomics Methods for Investigating Disease.

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NMR and Metabolomics-A Roadmap for the Future.

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Uncovering in vivo biochemical patterns from time-series metabolic dynamics.

Wu Y, Judge M, Edison A, Arnold J PLoS One. 2022; 17(5):e0268394.

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