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Quantitative criteria for improving performance of buccal DNA for high-throughput genetic analysis

Overview of attention for article published in BMC Genetics, January 2012
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Mentioned by

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2 tweeters

Citations

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1 Dimensions

Readers on

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14 Mendeley
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Title
Quantitative criteria for improving performance of buccal DNA for high-throughput genetic analysis
Published in
BMC Genetics, January 2012
DOI 10.1186/1471-2156-13-75
Pubmed ID
Authors

Jessica G Woo, Lisa J Martin, Lili Ding, W 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).

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Professor 3 21%
Professor > Associate Professor 3 21%
Researcher 2 14%
Student > Ph. D. Student 2 14%
Lecturer > Senior Lecturer 1 7%
Other 2 14%
Unknown 1 7%
Readers by discipline Count As %
Medicine and Dentistry 7 50%
Agricultural and Biological Sciences 2 14%
Business, Management and Accounting 1 7%
Computer Science 1 7%
Biochemistry, Genetics and Molecular Biology 1 7%
Other 0 0%
Unknown 2 14%

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
#9,507,992
of 12,372,276 outputs
Outputs from BMC Genetics
#515
of 828 outputs
Outputs of similar age
#85,664
of 126,302 outputs
Outputs of similar age from BMC Genetics
#6
of 12 outputs
Altmetric has tracked 12,372,276 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 828 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 32nd percentile – i.e., 32% 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 126,302 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 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 50% of its contemporaries.