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SCNVSim: somatic copy number variation and structure variation simulator

Overview of attention for article published in BMC Bioinformatics, February 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

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11 X users

Citations

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34 Dimensions

Readers on

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87 Mendeley
citeulike
1 CiteULike
Title
SCNVSim: somatic copy number variation and structure variation simulator
Published in
BMC Bioinformatics, February 2015
DOI 10.1186/s12859-015-0502-7
Pubmed ID
Authors

Maochun Qin, Biao Liu, Jeffrey M Conroy, Carl D Morrison, Qiang Hu, Yubo Cheng, Mitsuko Murakami, Adekunle O Odunsi, Candace S Johnson, Lei Wei, Song Liu, Jianmin Wang

Abstract

Somatically acquired structure variations (SVs) and copy number variations (CNVs) can induce genetic changes that are directly related to tumor genesis. Somatic SV/CNV detection using next-generation sequencing (NGS) data still faces major challenges introduced by tumor sample characteristics, such as ploidy, heterogeneity, and purity. A simulated cancer genome with known SVs and CNVs can serve as a benchmark for evaluating the performance of existing somatic SV/CNV detection tools and developing new methods. SCNVSim is a tool for simulating somatic CNVs and structure variations SVs. Other than multiple types of SV and CNV events, the tool is capable of simulating important features related to tumor samples including aneuploidy, heterogeneity and purity. SCNVSim generates the genomes of a cancer cell population with detailed information of copy number status, loss of heterozygosity (LOH), and event break points, which is essential for developing and evaluating somatic CNV and SV detection methods in cancer genomics studies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
France 1 1%
Sweden 1 1%
China 1 1%
Spain 1 1%
Japan 1 1%
United States 1 1%
Unknown 80 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 28%
Student > Ph. D. Student 19 22%
Student > Master 13 15%
Student > Bachelor 8 9%
Student > Doctoral Student 7 8%
Other 10 11%
Unknown 6 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 28%
Biochemistry, Genetics and Molecular Biology 23 26%
Computer Science 17 20%
Engineering 6 7%
Neuroscience 3 3%
Other 6 7%
Unknown 8 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 19 August 2015.
All research outputs
#5,447,869
of 22,793,427 outputs
Outputs from BMC Bioinformatics
#1,951
of 7,280 outputs
Outputs of similar age
#60,966
of 255,870 outputs
Outputs of similar age from BMC Bioinformatics
#35
of 140 outputs
Altmetric has tracked 22,793,427 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,280 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 72% 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 255,870 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 76% of its contemporaries.
We're also able to compare this research output to 140 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.