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Biological relevance of CNV calling methods using familial relatedness including monozygotic twins

Overview of attention for article published in BMC Bioinformatics, April 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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8 X users

Citations

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68 Mendeley
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Title
Biological relevance of CNV calling methods using familial relatedness including monozygotic twins
Published in
BMC Bioinformatics, April 2014
DOI 10.1186/1471-2105-15-114
Pubmed ID
Authors

Christina A Castellani, Melkaye G Melka, Andrea E Wishart, M Elizabeth O Locke, Zain Awamleh, Richard L O’Reilly, Shiva M Singh

Abstract

Studies involving the analysis of structural variation including Copy Number Variation (CNV) have recently exploded in the literature. Furthermore, CNVs have been associated with a number of complex diseases and neurodevelopmental disorders. Common methods for CNV detection use SNP, CNV, or CGH arrays, where the signal intensities of consecutive probes are used to define the number of copies associated with a given genomic region. These practices pose a number of challenges that interfere with the ability of available methods to accurately call CNVs. It has, therefore, become necessary to develop experimental protocols to test the reliability of CNV calling methods from microarray data so that researchers can properly discriminate biologically relevant data from noise.

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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Sweden 1 1%
Australia 1 1%
Unknown 65 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 28%
Researcher 17 25%
Student > Bachelor 8 12%
Student > Doctoral Student 4 6%
Other 3 4%
Other 10 15%
Unknown 7 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 34%
Agricultural and Biological Sciences 21 31%
Medicine and Dentistry 8 12%
Computer Science 4 6%
Engineering 2 3%
Other 1 1%
Unknown 9 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 March 2019.
All research outputs
#7,237,204
of 25,632,496 outputs
Outputs from BMC Bioinformatics
#2,569
of 7,732 outputs
Outputs of similar age
#64,612
of 241,636 outputs
Outputs of similar age from BMC Bioinformatics
#39
of 126 outputs
Altmetric has tracked 25,632,496 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 7,732 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 66% 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 241,636 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 126 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 67% of its contemporaries.