MagMet: A fully automated web server for targeted nuclear magnetic resonance metabolomics of plasma and serum.
Magn Reson Chem
; 61(12): 681-704, 2023 12.
Article
en En
| MEDLINE
| ID: mdl-37265034
ABSTRACT
Nuclear magnetic resonance (NMR) spectral analysis of biofluids can be a time-consuming process, requiring the expertise of a trained operator. With NMR becoming increasingly popular in the field of metabolomics, there is a growing need to change this paradigm and to automate the process. Here we introduce MagMet, an online web server, that automates the processing and quantification of 1D 1 H NMR spectra from biofluids-specifically, human serum/plasma metabolites, including those associated with inborn errors of metabolism (IEM). MagMet uses a highly efficient data processing procedure that performs automatic Fourier Transformation, phase correction, baseline optimization, chemical shift referencing, water signal removal, and peak picking/peak alignment. MagMet then uses the peak positions, linewidth information, and J-couplings from its own specially prepared standard metabolite reference spectral NMR library of 85 serum/plasma compounds to identify and quantify compounds from experimentally acquired NMR spectra of serum/plasma. MagMet employs linewidth adjustment for more consistent quantification of metabolites from higher field instruments and incorporates a highly efficient data processing procedure for more rapid and accurate detection and quantification of metabolites. This optimized algorithm allows the MagMet webserver to quickly detect and quantify 58 serum/plasma metabolites in 2.6 min per spectrum (when processing a dataset of 50-100 spectra). MagMet's performance was also assessed using spectra collected from defined mixtures (simulating other biofluids), with >100 previously measured plasma spectra, and from spiked serum/plasma samples simulating known IEMs. In all cases, MagMet performed with precision and accuracy matching the performance of human spectral profiling experts. MagMet is available at http//magmet.ca.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Imagen por Resonancia Magnética
/
Metabolómica
Límite:
Humans
Idioma:
En
Revista:
Magn Reson Chem
Asunto de la revista:
QUIMICA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Canadá