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Estimation of allele frequency and association mapping using next-generation sequencing data

Overview of attention for article published in BMC Bioinformatics, June 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter
patent
1 patent

Citations

dimensions_citation
118 Dimensions

Readers on

mendeley
389 Mendeley
citeulike
10 CiteULike
connotea
3 Connotea
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Title
Estimation of allele frequency and association mapping using next-generation sequencing data
Published in
BMC Bioinformatics, June 2011
DOI 10.1186/1471-2105-12-231
Pubmed ID
Authors

Su Yeon Kim, Kirk E Lohmueller, Anders Albrechtsen, Yingrui Li, Thorfinn Korneliussen, Geng Tian, Niels Grarup, Tao Jiang, Gitte Andersen, Daniel Witte, Torben Jorgensen, Torben Hansen, Oluf Pedersen, Jun Wang, Rasmus Nielsen

Abstract

Estimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping. In most studies using next-generation sequencing, a cost effective approach is to use medium or low-coverage data (e.g., < 15X). However, SNP calling and allele frequency estimation in such studies is associated with substantial statistical uncertainty because of varying coverage and high error rates.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 389 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 3%
Brazil 6 2%
United Kingdom 6 2%
Canada 2 <1%
Japan 2 <1%
Italy 2 <1%
Sweden 1 <1%
Austria 1 <1%
Sudan 1 <1%
Other 9 2%
Unknown 346 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 119 31%
Student > Ph. D. Student 104 27%
Student > Master 43 11%
Professor > Associate Professor 21 5%
Student > Postgraduate 20 5%
Other 69 18%
Unknown 13 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 255 66%
Biochemistry, Genetics and Molecular Biology 51 13%
Computer Science 15 4%
Medicine and Dentistry 12 3%
Mathematics 9 2%
Other 26 7%
Unknown 21 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 December 2018.
All research outputs
#1,664,920
of 14,824,038 outputs
Outputs from BMC Bioinformatics
#645
of 5,498 outputs
Outputs of similar age
#1,591,462
of 13,964,444 outputs
Outputs of similar age from BMC Bioinformatics
#645
of 5,498 outputs
Altmetric has tracked 14,824,038 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,498 research outputs from this source. They receive a mean Attention Score of 5.0. 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 13,964,444 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 88% of its contemporaries.
We're also able to compare this research output to 5,498 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.