↓ Skip to main content

Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

Overview of attention for article published in BMC Bioinformatics, September 2013
Altmetric Badge

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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
8 tweeters
patent
1 patent
facebook
1 Facebook page
wikipedia
1 Wikipedia page

Citations

dimensions_citation
344 Dimensions

Readers on

mendeley
956 Mendeley
citeulike
12 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
Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives
Published in
BMC Bioinformatics, September 2013
DOI 10.1186/1471-2105-14-s11-s1
Pubmed ID
Authors

Min Zhao, Qingguo Wang, Quan Wang, Peilin Jia, Zhongming Zhao

Abstract

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 956 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 15 2%
United Kingdom 10 1%
France 4 <1%
Germany 3 <1%
Italy 3 <1%
Norway 3 <1%
Brazil 3 <1%
Canada 2 <1%
Sweden 2 <1%
Other 15 2%
Unknown 896 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 255 27%
Researcher 221 23%
Student > Master 150 16%
Student > Bachelor 77 8%
Student > Doctoral Student 56 6%
Other 121 13%
Unknown 76 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 426 45%
Biochemistry, Genetics and Molecular Biology 250 26%
Computer Science 67 7%
Medicine and Dentistry 61 6%
Mathematics 12 1%
Other 48 5%
Unknown 92 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 May 2018.
All research outputs
#2,055,719
of 17,415,680 outputs
Outputs from BMC Bioinformatics
#762
of 6,159 outputs
Outputs of similar age
#21,767
of 175,518 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 28 outputs
Altmetric has tracked 17,415,680 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,159 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 87% 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 175,518 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 87% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.