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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Accurate single nucleotide variant detection in viral populations by combining probabilistic clustering with a statistical test of strand bias
|
|---|---|
| Published in |
BMC Genomics, July 2013
|
| DOI | 10.1186/1471-2164-14-501 |
| Pubmed ID | |
| Authors |
Kerensa McElroy, Osvaldo Zagordi, Rowena Bull, Fabio Luciani, Niko Beerenwinkel |
| Abstract |
Deep sequencing is a powerful tool for assessing viral genetic diversity. Such experiments harness the high coverage afforded by next generation sequencing protocols by treating sequencing reads as a population sample. Distinguishing true single nucleotide variants (SNVs) from sequencing errors remains challenging, however. Current protocols are characterised by high false positive rates, with results requiring time consuming manual checking. |
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 % |
|---|---|---|
| United States | 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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| Australia | 1 | 1% |
| Unknown | 80 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 23% |
| Student > Ph. D. Student | 16 | 19% |
| Student > Master | 15 | 18% |
| Student > Bachelor | 4 | 5% |
| Professor | 4 | 5% |
| Other | 15 | 18% |
| Unknown | 11 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 30 | 36% |
| Biochemistry, Genetics and Molecular Biology | 18 | 21% |
| Computer Science | 7 | 8% |
| Immunology and Microbiology | 5 | 6% |
| Medicine and Dentistry | 4 | 5% |
| Other | 5 | 6% |
| Unknown | 15 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 29 November 2022.
All research outputs
#3,621,629
of 25,371,288 outputs
Outputs from BMC Genomics
#1,268
of 11,244 outputs
Outputs of similar age
#30,137
of 209,577 outputs
Outputs of similar age from BMC Genomics
#30
of 179 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 88% 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 209,577 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 85% of its contemporaries.
We're also able to compare this research output to 179 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.