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HaploShare: identification of extended haplotypes shared by cases and evaluation against controls

Overview of attention for article published in Genome Biology, May 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)

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9 X users
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1 Facebook page

Citations

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43 Mendeley
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Title
HaploShare: identification of extended haplotypes shared by cases and evaluation against controls
Published in
Genome Biology, May 2015
DOI 10.1186/s13059-015-0662-9
Pubmed ID
Authors

Dingge Ying, Pak Chung Sham, David Keith Smith, Lu Zhang, Yu Lung Lau, Wanling Yang

Abstract

Recent founder mutations may play important roles in complex diseases and Mendelian disorders. Detecting shared haplotypes that are identical by descent (IBD) could facilitate discovery of these mutations. Several programs address this, but are usually limited to detecting pair-wise shared haplotypes and not providing a comparison of cases and controls. We present a novel algorithm and software package, HaploShare, which detects extended haplotypes that are shared by multiple individuals, and allows comparisons between cases and controls. Testing on simulated and real cases demonstrated significant improvements in detection power and reduction of false positive rate by HaploShare relative to other programs.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
United States 1 2%
Canada 1 2%
Unknown 40 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 26%
Researcher 6 14%
Student > Bachelor 5 12%
Student > Master 5 12%
Student > Doctoral Student 4 9%
Other 6 14%
Unknown 6 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 35%
Agricultural and Biological Sciences 13 30%
Computer Science 4 9%
Business, Management and Accounting 1 2%
Psychology 1 2%
Other 1 2%
Unknown 8 19%
Attention Score in Context

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 21 January 2016.
All research outputs
#7,047,002
of 25,371,288 outputs
Outputs from Genome Biology
#3,232
of 4,467 outputs
Outputs of similar age
#77,412
of 278,734 outputs
Outputs of similar age from Genome Biology
#68
of 77 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
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 is in the 27th percentile – i.e., 27% 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 278,734 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 71% of its contemporaries.
We're also able to compare this research output to 77 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.