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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Filtering of MS/MS data for peptide identification
|
|---|---|
| Published in |
BMC Genomics, November 2013
|
| DOI | 10.1186/1471-2164-14-s7-s2 |
| Pubmed ID | |
| Authors |
Jason Gallia, Katelyn Lavrich, Anna Tan-Wilson, Patrick H Madden |
| Abstract |
The identification of proteins based on analysis of tandem mass spectrometry (MS/MS) data is a valuable tool that is not fully realized because of the difficulty in carrying out automated analysis of large numbers of spectra. MS/MS spectra consist of peaks that represent each peptide fragment, usually b and y ions, with experimentally determined mass to charge ratios. Whether the strategy employed is database matching or De Novo sequencing, a major obstacle is distinguishing signal from noise. Improved ability to distinguish signal peaks of low intensity from background noise increases the likelihood of correctly identifying the peptide, as valuable information is preserved while extraneous information is not left to mislead. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 34 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 31% |
| Researcher | 6 | 17% |
| Student > Master | 4 | 11% |
| Student > Doctoral Student | 3 | 9% |
| Student > Bachelor | 3 | 9% |
| Other | 2 | 6% |
| Unknown | 6 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 31% |
| Chemistry | 6 | 17% |
| Biochemistry, Genetics and Molecular Biology | 4 | 11% |
| Immunology and Microbiology | 2 | 6% |
| Computer Science | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 8 | 23% |
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 31 October 2014.
All research outputs
#20,241,019
of 22,768,097 outputs
Outputs from BMC Genomics
#9,265
of 10,639 outputs
Outputs of similar age
#187,736
of 215,480 outputs
Outputs of similar age from BMC Genomics
#122
of 161 outputs
Altmetric has tracked 22,768,097 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,639 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% 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 215,480 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 161 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.