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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Ultra-deep mutant spectrum profiling: improving sequencing accuracy using overlapping read pairs
|
|---|---|
| Published in |
BMC Genomics, February 2013
|
| DOI | 10.1186/1471-2164-14-96 |
| Pubmed ID | |
| Authors |
Haiyin Chen-Harris, Monica K Borucki, Clinton Torres, Tom R Slezak, Jonathan E Allen |
| Abstract |
High throughput sequencing is beginning to make a transformative impact in the area of viral evolution. Deep sequencing has the potential to reveal the mutant spectrum within a viral sample at high resolution, thus enabling the close examination of viral mutational dynamics both within- and between-hosts. The challenge however, is to accurately model the errors in the sequencing data and differentiate real viral mutations, particularly those that exist at low frequencies, from sequencing errors. |
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 % |
|---|---|---|
| United States | 1 | 50% |
| Germany | 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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 4% |
| Australia | 2 | 2% |
| Germany | 1 | 1% |
| France | 1 | 1% |
| Canada | 1 | 1% |
| Sweden | 1 | 1% |
| Unknown | 88 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 40 | 41% |
| Student > Master | 13 | 13% |
| Student > Ph. D. Student | 12 | 12% |
| Student > Doctoral Student | 6 | 6% |
| Other | 6 | 6% |
| Other | 13 | 13% |
| Unknown | 8 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 42 | 43% |
| Biochemistry, Genetics and Molecular Biology | 20 | 20% |
| Medicine and Dentistry | 9 | 9% |
| Computer Science | 7 | 7% |
| Immunology and Microbiology | 3 | 3% |
| Other | 8 | 8% |
| Unknown | 9 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 February 2020.
All research outputs
#7,355,930
of 25,373,627 outputs
Outputs from BMC Genomics
#3,109
of 11,244 outputs
Outputs of similar age
#75,193
of 296,590 outputs
Outputs of similar age from BMC Genomics
#46
of 162 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 69th percentile.
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 70% 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 296,590 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 162 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.