Investigating the metabolic fingerprint of term infants with normal and increased fetal growth. Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: An observational study of 17,345 persons. Computational and Structural Biotechnology Journal, 13, 131–144.
A new paradigm for known metabolite identification in metabonomics/metabolomics: Metabolite identification efficiency. eRah: A computational tool integrating spectral deconvolution and alignment with quantification and identification of metabolites in GC/MS-based metabolomics. doi: 10.1021/acs.analchem.6b00885.ĭomingo-Almenara, X., Brezmes, J., Vinaixa, M., Samino, S., Ramirez, N., Ramon-Krauel, M., et al.
Combining NMR and LC/MS using backward variable elimination: Metabolomics analysis of colorectal cancer, polyps, and healthy controls. NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Chemometrics and Intelligent Laboratory Systems, 85(1), 144–154. Adaptive binning: An improved binning method for metabolomics data using the undecimated wavelet transform. The CCPN metabolomics Project: A fast protocol for metabolite identification by 2D-NMR. H., Mannella, V., Boucher, W., & Musco, G. Gaussian binning: A new kernel-based method for processing NMR spectroscopic data for metabolomics. Dynamic adaptive binning: an improved quantification technique for NMR spectroscopic data. This new robust scheme accomplishes to automatically identify peak resonances in 1H-NMR spectra with high accuracy and less human intervention with a wide range of applications in metabolic profiling.Īnderson, P. The methodological scheme was compared against widely used software tools, exhibiting good performance in terms of correct assignment of the metabolites.
The proposed scheme has been tested on the 1D 1H NMR spectra of: (a) an amino acid mixture, (b) a serum sample spiked with the amino acid mixture, (c) 20 blood serum, (d) 20 human amniotic fluid samples, (e) 160 serum samples from publicly available database. The methodological scheme comprises of the sequential application of preprocessing, data reduction, metabolite screening and combination selection. This paper introduces a new, automated computational scheme for the identification of metabolites in 1D 1H NMR spectra based on the Human Metabolome Database. Metabolite identification in biological samples using Nuclear Magnetic Resonance (NMR) spectra is a challenging task due to the complexity of the biological matrices.