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Detecting non-allelic homologous recombination from high-throughput sequencing data

Overview of attention for article published in Genome Biology (Online Edition), April 2015
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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 (85th percentile)

Mentioned by

twitter
16 tweeters
patent
1 patent
facebook
1 Facebook page

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
96 Mendeley
citeulike
4 CiteULike
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Title
Detecting non-allelic homologous recombination from high-throughput sequencing data
Published in
Genome Biology (Online Edition), April 2015
DOI 10.1186/s13059-015-0633-1
Pubmed ID
Authors

Matthew M Parks, Charles E Lawrence, Benjamin J Raphael

Abstract

Non-allelic homologous recombination (NAHR) is a common mechanism for generating genome rearrangements and is implicated in numerous genetic disorders, but its detection in high-throughput sequencing data poses a serious challenge. We present a probabilistic model of NAHR and demonstrate its ability to find NAHR in low coverage sequencing data from 44 individuals. We identify NAHR-mediated deletions or duplications in 109 of 324 potential NAHR loci in at least one of the individuals. These calls segregate by ancestry, are more common in closely spaced repeats, often result in duplicated genes or pseudogenes, and affect highly studied genes such as GBA and CYP2E1.

Twitter Demographics

The data shown below were collected from the profiles of 16 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 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
France 1 1%
Unknown 94 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 23%
Student > Bachelor 18 19%
Researcher 9 9%
Student > Doctoral Student 9 9%
Student > Master 7 7%
Other 16 17%
Unknown 15 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 35 36%
Agricultural and Biological Sciences 26 27%
Computer Science 8 8%
Medicine and Dentistry 7 7%
Psychology 1 1%
Other 2 2%
Unknown 17 18%

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 12 September 2018.
All research outputs
#2,124,779
of 17,897,867 outputs
Outputs from Genome Biology (Online Edition)
#1,794
of 3,675 outputs
Outputs of similar age
#33,127
of 234,505 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 17,897,867 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 3,675 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one has gotten more attention than average, scoring higher than 51% 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 234,505 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 85% of its contemporaries.
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