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Variant calling in low-coverage whole genome sequencing of a Native American population sample

Overview of attention for article published in BMC Genomics, January 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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91 Mendeley
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2 CiteULike
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Title
Variant calling in low-coverage whole genome sequencing of a Native American population sample
Published in
BMC Genomics, January 2014
DOI 10.1186/1471-2164-15-85
Pubmed ID
Authors

Chris Bizon, Michael Spiegel, Scott A Chasse, Ian R Gizer, Yun Li, Ewa P Malc, Piotr A Mieczkowski, Josh K Sailsbery, Xiaoshu Wang, Cindy L Ehlers, Kirk C Wilhelmsen

Abstract

The reduction in the cost of sequencing a human genome has led to the use of genotype sampling strategies in order to impute and infer the presence of sequence variants that can then be tested for associations with traits of interest. Low-coverage Whole Genome Sequencing (WGS) is a sampling strategy that overcomes some of the deficiencies seen in fixed content SNP array studies. Linkage-disequilibrium (LD) aware variant callers, such as the program Thunder, may provide a calling rate and accuracy that makes a low-coverage sequencing strategy viable.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 5%
United Kingdom 1 1%
Netherlands 1 1%
Sweden 1 1%
Unknown 83 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 31%
Student > Ph. D. Student 22 24%
Student > Master 13 14%
Student > Bachelor 7 8%
Student > Doctoral Student 5 5%
Other 6 7%
Unknown 10 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 37%
Biochemistry, Genetics and Molecular Biology 15 16%
Computer Science 5 5%
Medicine and Dentistry 5 5%
Mathematics 3 3%
Other 14 15%
Unknown 15 16%
Attention Score in Context

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 13 October 2015.
All research outputs
#6,935,655
of 22,741,406 outputs
Outputs from BMC Genomics
#3,210
of 10,633 outputs
Outputs of similar age
#83,599
of 307,435 outputs
Outputs of similar age from BMC Genomics
#151
of 440 outputs
Altmetric has tracked 22,741,406 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 10,633 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 68% 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 307,435 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 71% of its contemporaries.
We're also able to compare this research output to 440 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 64% of its contemporaries.