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Identification of polymorphic inversions from genotypes

Overview of attention for article published in BMC Bioinformatics, February 2012
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3 tweeters

Citations

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44 Dimensions

Readers on

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99 Mendeley
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2 CiteULike
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Title
Identification of polymorphic inversions from genotypes
Published in
BMC Bioinformatics, February 2012
DOI 10.1186/1471-2105-13-28
Pubmed ID
Authors

Alejandro Cáceres, Suzanne S Sindi, Benjamin J Raphael, Mario Cáceres, Juan R González

Abstract

Polymorphic inversions are a source of genetic variability with a direct impact on recombination frequencies. Given the difficulty of their experimental study, computational methods have been developed to infer their existence in a large number of individuals using genome-wide data of nucleotide variation. Methods based on haplotype tagging of known inversions attempt to classify individuals as having a normal or inverted allele. Other methods that measure differences between linkage disequilibrium attempt to identify regions with inversions but unable to classify subjects accurately, an essential requirement for association studies.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 4 4%
Germany 1 1%
Sweden 1 1%
Netherlands 1 1%
Canada 1 1%
United States 1 1%
Unknown 90 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 30%
Researcher 27 27%
Student > Master 14 14%
Student > Bachelor 7 7%
Student > Postgraduate 3 3%
Other 9 9%
Unknown 9 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 53 54%
Biochemistry, Genetics and Molecular Biology 22 22%
Veterinary Science and Veterinary Medicine 2 2%
Computer Science 2 2%
Mathematics 2 2%
Other 8 8%
Unknown 10 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 08 March 2022.
All research outputs
#12,829,738
of 20,710,163 outputs
Outputs from BMC Bioinformatics
#4,406
of 6,821 outputs
Outputs of similar age
#144,110
of 244,512 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 20,710,163 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,821 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 31st percentile – i.e., 31% 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 244,512 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them