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X Demographics
Mendeley readers
Attention Score in Context
| Title |
RVD: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation DNA resequencing
|
|---|---|
| Published in |
BMC Research Notes, May 2013
|
| DOI | 10.1186/1756-0500-6-206 |
| Pubmed ID | |
| Authors |
Anna Cushing, Patrick Flaherty, Erik Hopmans, John M Bell, Hanlee P Ji |
| Abstract |
Rare single nucleotide variants play an important role in genetic diversity and heterogeneity of specific human disease. For example, an individual clinical sample can harbor rare mutations at minor frequencies. Genetic diversity within an individual clinical sample is oftentimes reflected in rare mutations. Therefore, detecting rare variants prior to treatment may prove to be a useful predictor for therapeutic response. Current rare variant detection algorithms using next generation DNA sequencing are limited by inherent sequencing error rate and platform availability. |
X Demographics
The data shown below were collected from the profiles of 4 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 | 2 | 50% |
| Montenegro | 1 | 25% |
| Sweden | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 50% |
| Members of the public | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Germany | 1 | 3% |
| Unknown | 32 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 26% |
| Student > Ph. D. Student | 7 | 21% |
| Student > Bachelor | 3 | 9% |
| Professor | 3 | 9% |
| Professor > Associate Professor | 3 | 9% |
| Other | 6 | 18% |
| Unknown | 3 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 44% |
| Biochemistry, Genetics and Molecular Biology | 4 | 12% |
| Engineering | 3 | 9% |
| Mathematics | 3 | 9% |
| Computer Science | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 5 | 15% |
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 24 October 2023.
All research outputs
#5,932,635
of 24,083,187 outputs
Outputs from BMC Research Notes
#838
of 4,361 outputs
Outputs of similar age
#47,345
of 198,537 outputs
Outputs of similar age from BMC Research Notes
#16
of 60 outputs
Altmetric has tracked 24,083,187 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,361 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one has done well, scoring higher than 80% 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 198,537 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 76% of its contemporaries.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.