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Identification of metabolites from 2D 1H-13C HSQC NMR using peak correlation plots

Overview of attention for article published in BMC Bioinformatics, December 2014
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Title
Identification of metabolites from 2D 1H-13C HSQC NMR using peak correlation plots
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0413-z
Pubmed ID
Authors

Tommy Öman, May-Britt Tessem, Tone F Bathen, Helena Bertilsson, Anders Angelsen, Mattias Hedenström, Trygve Andreassen

Abstract

BackgroundIdentification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent technique for analysis of mixtures of organic compounds and gives a detailed chemical fingerprint of most individual components above the detection limit. For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in 1H NMR spectra has previously been successfully employed. Similar correlation of 2D 1H-13C Heteronuclear Single Quantum Correlation spectra was recently applied to investigate the structure of heparine. In this paper, we demonstrate how a similar approach can be used to identify metabolites in human biofluids (post-prostatic palpation urine).ResultsFrom 50 1H-13C Heteronuclear Single Quantum Correlation spectra, 23 correlation plots resembling pure metabolites were constructed. The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database.ConclusionsCorrelation plots prepared by statistically correlating 1H-13C Heteronuclear Single Quantum Correlation spectra from human biofluids provide unambiguous identification of metabolites. The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 1%
Australia 1 1%
Unknown 80 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 22%
Student > Ph. D. Student 15 18%
Student > Bachelor 12 15%
Student > Master 10 12%
Student > Doctoral Student 6 7%
Other 9 11%
Unknown 12 15%
Readers by discipline Count As %
Chemistry 20 24%
Biochemistry, Genetics and Molecular Biology 15 18%
Agricultural and Biological Sciences 13 16%
Immunology and Microbiology 4 5%
Pharmacology, Toxicology and Pharmaceutical Science 4 5%
Other 12 15%
Unknown 14 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 December 2014.
All research outputs
#11,069,907
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#4,222
of 5,420 outputs
Outputs of similar age
#163,597
of 264,465 outputs
Outputs of similar age from BMC Bioinformatics
#258
of 321 outputs
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