You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 102 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 | 100 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 20 | 20% |
| Student > Ph. D. Student | 19 | 19% |
| Student > Master | 13 | 13% |
| Student > Bachelor | 12 | 12% |
| Student > Doctoral Student | 6 | 6% |
| Other | 9 | 9% |
| Unknown | 23 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 23 | 23% |
| Biochemistry, Genetics and Molecular Biology | 17 | 17% |
| Agricultural and Biological Sciences | 15 | 15% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 6% |
| Computer Science | 4 | 4% |
| Other | 12 | 12% |
| Unknown | 25 | 25% |
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
#17,734,890
of 22,774,233 outputs
Outputs from BMC Bioinformatics
#5,927
of 7,276 outputs
Outputs of similar age
#242,683
of 354,373 outputs
Outputs of similar age from BMC Bioinformatics
#120
of 150 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,276 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 354,373 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 150 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.