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Using the plurality of codon positions to identify deleterious variants in human exomes

Overview of attention for article published in Bioinformatics, October 2014
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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6 X users
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1 Facebook page

Readers on

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16 Mendeley
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Article details
Title
Using the plurality of codon positions to identify deleterious variants in human exomes
Published in
Bioinformatics, October 2014
DOI 10.1093/bioinformatics/btu653
Pubmed ID
Authors
Abstract

A codon position could perform different or multiple roles in alternative transcripts of a gene. For instance, a non-synonymous position in one transcript could be a synonymous site in another. Alternatively, a position could remain as non-synonymous in multiple transcripts. Here we examined the impact of codon position plurality on the frequency of deleterious single-nucleotide variations (SNVs) using data from 6500 human exomes.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 6%
United Kingdom 1 6%
Unknown 14 88%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 7 44%
Student > Ph. D. Student 5 31%
Lecturer > Senior Lecturer 1 6%
Student > Master 1 6%
Unknown 2 13%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 38%
Agricultural and Biological Sciences 5 31%
Computer Science 3 19%
Unknown 2 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 28 January 2015.
All research outputs
#14,387,227
of 25,371,288 outputs
Outputs from Bioinformatics
#8,393
of 12,808 outputs
Outputs of similar age
#124,540
of 266,273 outputs
Outputs of similar age from Bioinformatics
#161
of 258 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,808 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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 266,273 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 52% of its contemporaries.
We're also able to compare this research output to 258 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.