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Mentioned by

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5 X users

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mendeley
62 Mendeley
citeulike
2 CiteULike
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Article details
Title
DDIG-in: detecting disease-causing genetic variations due to frameshifting indels and nonsense mutations employing sequence and structural properties at nucleotide and protein levels
Published in
Bioinformatics, January 2015
DOI 10.1093/bioinformatics/btu862
Pubmed ID
Authors
Abstract

Frameshifting (FS) indels and nonsense (NS) variants disrupt the protein coding sequence downstream of the mutation site by changing the reading frame or introducing a premature termination codon, respectively. Despite such drastic changes to the protein sequence, FS indels and NS variants have been discovered in healthy individuals. How to discriminate disease-causing from neutral FS indels and NS variants is an understudied problem.

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Timeline Attention over time Attention Score history
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X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Netherlands 1 2%
Hong Kong 1 2%
United Kingdom 1 2%
Spain 1 2%
Unknown 58 94%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 12 19%
Researcher 10 16%
Student > Master 8 13%
Student > Doctoral Student 7 11%
Student > Bachelor 7 11%
Other 9 15%
Unknown 9 15%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 34%
Agricultural and Biological Sciences 17 27%
Computer Science 4 6%
Medicine and Dentistry 2 3%
Environmental Science 1 2%
Other 3 5%
Unknown 14 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 May 2015.
All research outputs
#15,861,352
of 26,378,648 outputs
Outputs from Bioinformatics
#9,379
of 13,072 outputs
Outputs of similar age
#192,185
of 362,948 outputs
Outputs of similar age from Bioinformatics
#149
of 202 outputs
Altmetric has tracked 26,378,648 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,072 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 26th percentile – i.e., 26% 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 362,948 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 202 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.