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Population genetic analysis of bi-allelic structural variants from low-coverage sequence data with an expectation-maximization algorithm

Overview of attention for article published in BMC Bioinformatics, May 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
4 tweeters
patent
1 patent

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
37 Mendeley
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Title
Population genetic analysis of bi-allelic structural variants from low-coverage sequence data with an expectation-maximization algorithm
Published in
BMC Bioinformatics, May 2014
DOI 10.1186/1471-2105-15-163
Pubmed ID
Authors

José Ignacio Lucas-Lledó, David Vicente-Salvador, Cristina Aguado, Mario Cáceres

Abstract

Population genetics and association studies usually rely on a set of known variable sites that are then genotyped in subsequent samples, because it is easier to genotype than to discover the variation. This is also true for structural variation detected from sequence data. However, the genotypes at known variable sites can only be inferred with uncertainty from low coverage data. Thus, statistical approaches that infer genotype likelihoods, test hypotheses, and estimate population parameters without requiring accurate genotypes are becoming popular. Unfortunately, the current implementations of these methods are intended to analyse only single nucleotide and short indel variation, and they usually assume that the two alleles in a heterozygous individual are sampled with equal probability. This is generally false for structural variants detected with paired ends or split reads. Therefore, the population genetics of structural variants cannot be studied, unless a painstaking and potentially biased genotyping is performed first.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 5%
United States 1 3%
Singapore 1 3%
Unknown 33 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 27%
Researcher 8 22%
Student > Master 4 11%
Student > Bachelor 3 8%
Student > Postgraduate 3 8%
Other 6 16%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 43%
Biochemistry, Genetics and Molecular Biology 10 27%
Computer Science 4 11%
Environmental Science 1 3%
Immunology and Microbiology 1 3%
Other 2 5%
Unknown 3 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 April 2022.
All research outputs
#5,821,171
of 22,215,881 outputs
Outputs from BMC Bioinformatics
#2,202
of 7,130 outputs
Outputs of similar age
#52,127
of 206,107 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 21 outputs
Altmetric has tracked 22,215,881 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,130 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 206,107 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 74% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.