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Accurate indel prediction using paired-end short reads

Overview of attention for article published in BMC Genomics, February 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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7 X users

Citations

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32 Dimensions

Readers on

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97 Mendeley
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2 CiteULike
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Title
Accurate indel prediction using paired-end short reads
Published in
BMC Genomics, February 2013
DOI 10.1186/1471-2164-14-132
Pubmed ID
Authors

Dominik Grimm, Jörg Hagmann, Daniel Koenig, Detlef Weigel, Karsten Borgwardt

Abstract

One of the major open challenges in next generation sequencing (NGS) is the accurate identification of structural variants such as insertions and deletions (indels). Current methods for indel calling assign scores to different types of evidence or counter-evidence for the presence of an indel, such as the number of split read alignments spanning the boundaries of a deletion candidate or reads that map within a putative deletion. Candidates with a score above a manually defined threshold are then predicted to be true indels. As a consequence, structural variants detected in this manner contain many false positives.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Netherlands 1 1%
Sweden 1 1%
Saudi Arabia 1 1%
United Kingdom 1 1%
Spain 1 1%
Denmark 1 1%
Unknown 87 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 29%
Student > Ph. D. Student 19 20%
Other 8 8%
Student > Master 8 8%
Professor 7 7%
Other 19 20%
Unknown 8 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 43%
Biochemistry, Genetics and Molecular Biology 19 20%
Computer Science 9 9%
Medicine and Dentistry 4 4%
Engineering 3 3%
Other 6 6%
Unknown 14 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 27 November 2014.
All research outputs
#8,262,445
of 25,374,917 outputs
Outputs from BMC Genomics
#3,704
of 11,244 outputs
Outputs of similar age
#67,286
of 205,195 outputs
Outputs of similar age from BMC Genomics
#67
of 194 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 65% 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 205,195 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 66% of its contemporaries.
We're also able to compare this research output to 194 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.