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Mendeley readers
Attention Score in Context
| Title |
Accurate proteome-wide protein quantification from high-resolution 15N mass spectra
|
|---|---|
| Published in |
Genome Biology, December 2011
|
| DOI | 10.1186/gb-2011-12-12-r122 |
| Pubmed ID | |
| Authors |
Zia Khan, Sasan Amini, Joshua S Bloom, Cristian Ruse, Amy A Caudy, Leonid Kruglyak, Mona Singh, David H Perlman, Saeed Tavazoie |
| Abstract |
In quantitative mass spectrometry-based proteomics, the metabolic incorporation of a single source of 15N-labeled nitrogen has many advantages over using stable isotope-labeled amino acids. However, the lack of a robust computational framework for analyzing the resulting spectra has impeded wide use of this approach. We have addressed this challenge by introducing a new computational methodology for analyzing 15N spectra in which quantification is integrated with identification. Application of this method to an Escherichia coli growth transition reveals significant improvement in quantification accuracy over previous methods. |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| Japan | 1 | 3% |
| United States | 1 | 3% |
| Switzerland | 1 | 3% |
| Unknown | 34 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 42% |
| Researcher | 7 | 18% |
| Student > Master | 4 | 11% |
| Student > Bachelor | 2 | 5% |
| Student > Postgraduate | 2 | 5% |
| Other | 6 | 16% |
| Unknown | 1 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 19 | 50% |
| Biochemistry, Genetics and Molecular Biology | 10 | 26% |
| Unspecified | 2 | 5% |
| Chemistry | 2 | 5% |
| Computer Science | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 1 | 3% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 22 December 2011.
All research outputs
#6,494,647
of 25,373,627 outputs
Outputs from Genome Biology
#3,103
of 4,467 outputs
Outputs of similar age
#52,812
of 248,707 outputs
Outputs of similar age from Genome Biology
#27
of 41 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 30th percentile – i.e., 30% 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 248,707 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.