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Recurrent de novo mutations in neurodevelopmental disorders: properties and clinical implications

Overview of attention for article published in Genome Medicine, November 2017
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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 (92nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

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44 X users
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1 Facebook page
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1 Wikipedia page

Citations

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110 Dimensions

Readers on

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243 Mendeley
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Title
Recurrent de novo mutations in neurodevelopmental disorders: properties and clinical implications
Published in
Genome Medicine, November 2017
DOI 10.1186/s13073-017-0498-x
Pubmed ID
Authors

Amy B. Wilfert, Arvis Sulovari, Tychele N. Turner, Bradley P. Coe, Evan E. Eichler

Abstract

Next-generation sequencing (NGS) is now more accessible to clinicians and researchers. As a result, our understanding of the genetics of neurodevelopmental disorders (NDDs) has rapidly advanced over the past few years. NGS has led to the discovery of new NDD genes with an excess of recurrent de novo mutations (DNMs) when compared to controls. Development of large-scale databases of normal and disease variation has given rise to metrics exploring the relative tolerance of individual genes to human mutation. Genetic etiology and diagnosis rates have improved, which have led to the discovery of new pathways and tissue types relevant to NDDs. In this review, we highlight several key findings based on the discovery of recurrent DNMs ranging from copy number variants to point mutations. We explore biases and patterns of DNM enrichment and the role of mosaicism and secondary mutations in variable expressivity. We discuss the benefit of whole-genome sequencing (WGS) over whole-exome sequencing (WES) to understand more complex, multifactorial cases of NDD and explain how this improved understanding aids diagnosis and management of these disorders. Comprehensive assessment of the DNM landscape across the genome using WGS and other technologies will lead to the development of novel functional and bioinformatics approaches to interpret DNMs and drive new insights into NDD biology.

X Demographics

X Demographics

The data shown below were collected from the profiles of 44 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 243 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 243 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 41 17%
Student > Ph. D. Student 35 14%
Student > Bachelor 26 11%
Student > Master 18 7%
Other 14 6%
Other 50 21%
Unknown 59 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 73 30%
Medicine and Dentistry 34 14%
Agricultural and Biological Sciences 32 13%
Neuroscience 12 5%
Unspecified 11 5%
Other 18 7%
Unknown 63 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 11 March 2020.
All research outputs
#1,367,252
of 23,994,935 outputs
Outputs from Genome Medicine
#298
of 1,483 outputs
Outputs of similar age
#32,009
of 445,258 outputs
Outputs of similar age from Genome Medicine
#14
of 35 outputs
Altmetric has tracked 23,994,935 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,483 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one has done well, scoring higher than 79% 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 445,258 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 92% of its contemporaries.
We're also able to compare this research output to 35 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 62% of its contemporaries.