Automated screening for metabolites in complex mixtures using 2D COSY NMR spectroscopy View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2006-12

AUTHORS

Yuanxin Xi, Jeffrey S. de Ropp, Mark R. Viant, David L. Woodruff, Ping Yu

ABSTRACT

One of the greatest challenges in metabolomics is the rapid and unambiguous identification and quantification of metabolites in a biological sample. Although one-dimensional (1D) proton nuclear magnetic resonance (NMR) spectra can be acquired rapidly, they are complicated by severe peak overlap that can significantly hinder the automated identification and quantification of metabolites. Furthermore, it is currently not reasonable to assume that NMR spectra of pure metabolites are available a priori for every metabolite in a biological sample. In this paper we develop and report on tests of methods that assist in the automatic identification of metabolites using proton two-dimensional (2D) correlation spectroscopy (COSY) NMR. Given a database of 2D COSY spectra for the metabolites of interest, our methods provide a list sorted by a heuristic likelihood of the metabolites present in a sample that has been analyzed using 2D COSY NMR. Our models attempt to correct the displacement of the peaks that can occur from one sample to the next, due to pH, temperature and matrix effects, using a statistical and chemical model. The correction of one peak can result in an implied correction of others due to spin–spin coupling. Furthermore, these displacements are not independent: they depend on the relative position of functional groups in the molecule. We report experimental results using defined mixtures of amino acids as well as real complex biological samples that demonstrate that our methods can be very effective at automatically and rapidly identifying metabolites. More... »

PAGES

221-233

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11306-006-0036-0

DOI

http://dx.doi.org/10.1007/s11306-006-0036-0

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https://app.dimensions.ai/details/publication/pub.1024010976


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