↓ Skip to main content

CNV-seq, a new method to detect copy number variation using high-throughput sequencing

Overview of attention for article published in BMC Bioinformatics, March 2009
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 (91st percentile)

Mentioned by

patent
19 patents
wikipedia
1 Wikipedia page
q&a
1 Q&A thread

Citations

dimensions_citation
398 Dimensions

Readers on

mendeley
685 Mendeley
citeulike
47 CiteULike
connotea
11 Connotea
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
CNV-seq, a new method to detect copy number variation using high-throughput sequencing
Published in
BMC Bioinformatics, March 2009
DOI 10.1186/1471-2105-10-80
Pubmed ID
Authors

Chao Xie, Martti T Tammi

Abstract

DNA copy number variation (CNV) has been recognized as an important source of genetic variation. Array comparative genomic hybridization (aCGH) is commonly used for CNV detection, but the microarray platform has a number of inherent limitations.

Mendeley readers

The data shown below were compiled from readership statistics for 685 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 30 4%
United Kingdom 14 2%
Brazil 10 1%
Germany 8 1%
France 6 <1%
Netherlands 5 <1%
Belgium 4 <1%
Italy 3 <1%
Switzerland 3 <1%
Other 15 2%
Unknown 587 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 218 32%
Student > Ph. D. Student 164 24%
Student > Master 65 9%
Student > Bachelor 45 7%
Professor > Associate Professor 42 6%
Other 111 16%
Unknown 40 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 420 61%
Biochemistry, Genetics and Molecular Biology 103 15%
Computer Science 40 6%
Medicine and Dentistry 38 6%
Mathematics 10 1%
Other 23 3%
Unknown 51 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 14 January 2020.
All research outputs
#1,293,217
of 15,422,162 outputs
Outputs from BMC Bioinformatics
#396
of 5,636 outputs
Outputs of similar age
#7,934
of 96,824 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 15,422,162 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,636 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 92% 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 96,824 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them