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Efficient haplotype block recognition of very long and dense genetic sequences

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

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
4 tweeters
patent
1 patent

Citations

dimensions_citation
35 Dimensions

Readers on

mendeley
77 Mendeley
citeulike
1 CiteULike
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Title
Efficient haplotype block recognition of very long and dense genetic sequences
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-10
Pubmed ID
Authors

Daniel Taliun, Johann Gamper, Cristian Pattaro

Abstract

The new sequencing technologies enable to scan very long and dense genetic sequences, obtaining datasets of genetic markers that are an order of magnitude larger than previously available. Such genetic sequences are characterized by common alleles interspersed with multiple rarer alleles. This situation has renewed the interest for the identification of haplotypes carrying the rare risk alleles. However, large scale explorations of the linkage-disequilibrium (LD) pattern to identify haplotype blocks are not easy to perform, because traditional algorithms have at least Θ(n2) time and memory complexity.

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 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 1%
Colombia 1 1%
Germany 1 1%
Uruguay 1 1%
Brazil 1 1%
Sweden 1 1%
United Kingdom 1 1%
Spain 1 1%
United States 1 1%
Other 0 0%
Unknown 68 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 34%
Student > Ph. D. Student 19 25%
Student > Master 8 10%
Student > Bachelor 5 6%
Student > Doctoral Student 4 5%
Other 9 12%
Unknown 6 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 42%
Biochemistry, Genetics and Molecular Biology 19 25%
Computer Science 9 12%
Medicine and Dentistry 5 6%
Nursing and Health Professions 1 1%
Other 4 5%
Unknown 7 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 17 November 2016.
All research outputs
#5,486,857
of 22,739,983 outputs
Outputs from BMC Bioinformatics
#1,984
of 7,266 outputs
Outputs of similar age
#63,889
of 306,547 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 101 outputs
Altmetric has tracked 22,739,983 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,266 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 72% 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 306,547 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 79% of its contemporaries.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.