RTExtract: time-series NMR spectra quantification based on 3D surface ridge tracking.

Area of Science:

• Metabolomics
• Analytical Chemistry
• Computational Biology

Background:

• Time-series nuclear magnetic resonance (NMR) spectroscopy is crucial for understanding metabolic dynamics.
• Quantifying chemical features in NMR spectra is challenging due to peak overlap and shifting.
• Existing protocols for NMR spectral analysis are often time-consuming and may be infeasible for certain spectral regions.

Purpose of the Study:

• To develop and introduce a novel algorithm for accurate quantification of time-series NMR spectra.
• To overcome limitations of existing methods in handling spectral complexity and time constraints.
• To provide a more efficient and robust tool for metabolic research.

Main Methods:

• Developed Ridge Tracking-based Extract (RTExtract), a computer vision algorithm.
• Formulated time-series NMR spectra as a 3D surface for analysis.
• Utilized local curvature, optima filtering, and a greedy algorithm for ridge detection and connection.
• Incorporated interactive steps for result refinement.

Main Results:

• RTExtract successfully tracked 115 out of 173 simulated ridges with high accuracy (RMSD < 0.001).
• Analysis time was drastically reduced from approximately 48 hours to under 2 hours.
• The number of parameters requiring tuning was reduced from seven to two.
• Accurate tracking was achieved in spectral regions with overlapping and shifting chemical shifts.

Conclusions:

• RTExtract offers a significant advancement in the efficient and accurate quantification of time-series NMR spectra.
• The algorithm provides a robust solution for analyzing complex metabolic data, overcoming limitations of traditional methods.
• The open-source availability of RTExtract facilitates its adoption in the metabolomics community.