↓ Skip to main content

iSVP: an integrated structural variant calling pipeline from high-throughput sequencing data

Overview of attention for article published in BMC Systems Biology, December 2013
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

twitter
8 X users

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
62 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
iSVP: an integrated structural variant calling pipeline from high-throughput sequencing data
Published in
BMC Systems Biology, December 2013
DOI 10.1186/1752-0509-7-s6-s8
Pubmed ID
Authors

Takahiro Mimori, Naoki Nariai, Kaname Kojima, Mamoru Takahashi, Akira Ono, Yukuto Sato, Yumi Yamaguchi-Kabata, Masao Nagasaki

Abstract

Structural variations (SVs), such as insertions, deletions, inversions, and duplications, are a common feature in human genomes, and a number of studies have reported that such SVs are associated with human diseases. Although the progress of next generation sequencing (NGS) technologies has led to the discovery of a large number of SVs, accurate and genome-wide detection of SVs remains challenging. Thus far, various calling algorithms based on NGS data have been proposed. However, their strategies are diverse and there is no tool able to detect a full range of SVs accurately.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
France 1 2%
Brazil 1 2%
Unknown 58 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 29%
Student > Ph. D. Student 15 24%
Other 6 10%
Student > Bachelor 4 6%
Student > Master 3 5%
Other 5 8%
Unknown 11 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 34%
Biochemistry, Genetics and Molecular Biology 11 18%
Computer Science 10 16%
Medicine and Dentistry 3 5%
Neuroscience 2 3%
Other 2 3%
Unknown 13 21%
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 24 June 2014.
All research outputs
#6,775,845
of 22,749,166 outputs
Outputs from BMC Systems Biology
#252
of 1,142 outputs
Outputs of similar age
#81,511
of 307,387 outputs
Outputs of similar age from BMC Systems Biology
#10
of 61 outputs
Altmetric has tracked 22,749,166 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 76% 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 307,387 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 73% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.