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

Overview of attention for article published in Genome Biology, 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 (88th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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16 X users
patent
5 patents
facebook
1 Facebook page

Citations

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46 Dimensions

Readers on

mendeley
111 Mendeley
citeulike
4 CiteULike
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Title
Detecting non-allelic homologous recombination from high-throughput sequencing data
Published in
Genome Biology, 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 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 111 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 109 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 22%
Student > Bachelor 21 19%
Researcher 10 9%
Student > Doctoral Student 9 8%
Student > Master 9 8%
Other 16 14%
Unknown 22 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 40 36%
Agricultural and Biological Sciences 27 24%
Computer Science 9 8%
Medicine and Dentistry 8 7%
Mathematics 1 <1%
Other 2 2%
Unknown 24 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 07 November 2023.
All research outputs
#2,450,102
of 25,373,627 outputs
Outputs from Genome Biology
#1,992
of 4,467 outputs
Outputs of similar age
#30,903
of 279,935 outputs
Outputs of similar age from Genome Biology
#37
of 66 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 55% 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 279,935 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 88% of its contemporaries.
We're also able to compare this research output to 66 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.