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Identification of Copy Number Variants Through Whole-Exome Sequencing in Autosomal Recessive Nonsyndromic Hearing Loss

Overview of attention for article published in Genetic Testing, July 2014
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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Title
Identification of Copy Number Variants Through Whole-Exome Sequencing in Autosomal Recessive Nonsyndromic Hearing Loss
Published in
Genetic Testing, July 2014
DOI 10.1089/gtmb.2014.0121
Pubmed ID
Authors

Guney Bademci, Oscar Diaz-Horta, Shengru Guo, Duygu Duman, Derek Van Booven, Joseph Foster, Filiz Basak Cengiz, Susan Blanton, Mustafa Tekin

Abstract

Genetic variants account for more than half of the cases with congenital or prelingual onset hearing loss. Autosomal recessive nonsyndromic hearing loss (ARNSHL) is the most common subgroup. Whole-exome sequencing (WES) has been shown to be effective detecting deafness-causing single-nucleotide variants (SNVs) and insertion/deletions (INDELs). After analyzing the WES data for causative SNVs or INDELs involving previously reported deafness genes in 78 families with ARNSHL, we searched for copy number variants (CNVs) through two different tools in 24 families that remained unresolved. We detected large homozygous deletions in STRC and OTOA in single families. Thus, causative CNVs in known deafness genes explain 2 out of 78 (2.6%) families in our sample set. We conclude that CNVs can be reliably detected through WES and should be the part of pipelines used to clarify genetic basis of hearing loss.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 2%
Belgium 1 2%
Unknown 39 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 24%
Researcher 9 22%
Student > Ph. D. Student 5 12%
Student > Bachelor 4 10%
Professor 2 5%
Other 5 12%
Unknown 6 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 27%
Agricultural and Biological Sciences 8 20%
Medicine and Dentistry 8 20%
Computer Science 2 5%
Chemistry 2 5%
Other 2 5%
Unknown 8 20%
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 22 October 2019.
All research outputs
#8,262,981
of 25,377,790 outputs
Outputs from Genetic Testing
#118
of 698 outputs
Outputs of similar age
#75,528
of 240,158 outputs
Outputs of similar age from Genetic Testing
#3
of 9 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 698 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 82% 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 240,158 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 67% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.