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Title: RTExtract: Time-series NMR spectra quantification based on 3D surface ridge tracking
Abstract Motivation Time-series 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 RTExtract (Ridge Tracking based Extract), 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 two hours instead of ∼48 hours, and two instead of seven parameters need tuning. Multiple regions with overlapping and changing chemical shifts are accurately tracked. Availability 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.
Authors:
; ; ; ; ;
Award ID(s):
1713746
Publication Date:
NSF-PAR ID:
10156043
Journal Name:
Bioinformatics
ISSN:
1367-4803
Sponsoring Org:
National Science Foundation
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