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Improved genome annotation through untargeted detection of pathway-specific metabolites

Overview of attention for article published in BMC Genomics, June 2011
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57 Mendeley
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1 CiteULike
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Title
Improved genome annotation through untargeted detection of pathway-specific metabolites
Published in
BMC Genomics, June 2011
DOI 10.1186/1471-2164-12-s1-s6
Pubmed ID
Authors

Benjamin P Bowen, Curt R Fischer, Richard Baran, Jillian F Banfield, Trent Northen

Abstract

Mass spectrometry-based metabolomics analyses have the potential to complement sequence-based methods of genome annotation, but only if raw mass spectral data can be linked to specific metabolic pathways. In untargeted metabolomics, the measured mass of a detected compound is used to define the location of the compound in chemical space, but uncertainties in mass measurements lead to "degeneracies" in chemical space since multiple chemical formulae correspond to the same measured mass. We compare two methods to eliminate these degeneracies. One method relies on natural isotopic abundances, and the other relies on the use of stable-isotope labeling (SIL) to directly determine C and N atom counts. Both depend on combinatorial explorations of the "chemical space" comprised of all possible chemical formulae comprised of biologically relevant chemical elements.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 7%
Brazil 1 2%
Switzerland 1 2%
Denmark 1 2%
United Kingdom 1 2%
Unknown 49 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 39%
Student > Ph. D. Student 15 26%
Other 2 4%
Professor > Associate Professor 2 4%
Student > Master 2 4%
Other 4 7%
Unknown 10 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 46%
Chemistry 6 11%
Biochemistry, Genetics and Molecular Biology 6 11%
Engineering 2 4%
Nursing and Health Professions 1 2%
Other 5 9%
Unknown 11 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 04 November 2011.
All research outputs
#15,092,197
of 25,374,917 outputs
Outputs from BMC Genomics
#5,351
of 11,244 outputs
Outputs of similar age
#87,177
of 126,309 outputs
Outputs of similar age from BMC Genomics
#45
of 85 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 52% of its peers.
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 126,309 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 85 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.