Improved identification of metabolites in complex mixtures using HSQC NMR spectroscopy.

The automated and robust identification of metabolites in a complex biological sample remains one of the greatest challenges in metabolomics. In our experiments, HSQC carbon-proton correlation NMR data with a model that takes intensity information into account improves upon the identification of metabolites that was achieved using COSY proton-proton correlation NMR data with the binary model of [Y. Xi, J.

A new computer program (GlycoX) to determine simultaneously the glycosylation sites and oligosaccharide heterogeneity of glycoproteins.

A new computer program, GlycoX, was developed to aid in the determination of the glycosylation sites and oligosaccharide heterogeneity in glycoproteins. After digestion with the nonspecific protease, each glycan at a specific glycosylation site contains a small peptide tag that identifies the location of the glycan. GlycoX was developed in MATLAB requiring the entry of the exact masses of the glycopeptide and the glycan spectra in the form of a mass-intensity table and taking advantage of the accurate mass capability of the mass analyzer, in this case a Fourier transform ion cyclotron resonance (FT ICR) mass spectrometer.

Discrimination models using variance-stabilizing transformation of metabolomic NMR data.

After the extensive work that is being done in the areas of genomics, proteomics, and metabolomics, the study of metabolites has come of interest in its own right. Metabolites in biological systems give an understanding of the state of the system and provide a powerful tool for the study of disease and other maladies. Several analytical techniques such as mass spectrometry and high-resolution NMR spectroscopy have been used to study metabolites.