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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Quantitative criteria for improving performance of buccal DNA for high-throughput genetic analysis
|
|---|---|
| Published in |
BMC Genomic Data, August 2012
|
| DOI | 10.1186/1471-2156-13-75 |
| Pubmed ID | |
| Authors |
Jessica G Woo, Lisa J Martin, Lili Ding, W Mark Brown, Timothy D Howard, Carl D Langefeld, Charles J Moomaw, Mary Haverbusch, Guangyun Sun, Subba R Indugula, Hong Cheng, Ranjan Deka, Daniel Woo |
| Abstract |
DNA from buccal brush samples is being used for high-throughput analyses in a variety of applications, but the impact of sample type on genotyping success and downstream statistical analysis remains unclear. The objective of the current study was to determine laboratory predictors of genotyping failure among buccal DNA samples, and to evaluate the successfully genotyped results with respect to analytic quality control metrics. Sample and genotyping characteristics were compared between buccal and blood samples collected in the population-based Genetic and Environmental Risk Factors for Hemorrhagic Stroke (GERFHS) study (https://gerfhs.phs.wfubmc.edu/public/index.cfm). |
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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 3 | 18% |
| Professor > Associate Professor | 3 | 18% |
| Unspecified | 2 | 12% |
| Researcher | 2 | 12% |
| Student > Ph. D. Student | 2 | 12% |
| Other | 3 | 18% |
| Unknown | 2 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 41% |
| Unspecified | 2 | 12% |
| Agricultural and Biological Sciences | 2 | 12% |
| Business, Management and Accounting | 1 | 6% |
| Computer Science | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 3 | 18% |
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 27 August 2012.
All research outputs
#19,945,185
of 25,374,917 outputs
Outputs from BMC Genomic Data
#786
of 1,204 outputs
Outputs of similar age
#141,912
of 186,850 outputs
Outputs of similar age from BMC Genomic Data
#9
of 15 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 28th percentile – i.e., 28% 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 186,850 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.