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Attention Score in Context
Title |
The distribution and mutagenesis of short coding INDELs from 1,128 whole exomes
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Published in |
BMC Genomics, February 2015
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DOI | 10.1186/s12864-015-1333-7 |
Pubmed ID | |
Authors |
Danny Challis, Lilian Antunes, Erik Garrison, Eric Banks, Uday S Evani, Donna Muzny, Ryan Poplin, Richard A Gibbs, Gabor Marth, Fuli Yu |
Abstract |
Identifying insertion/deletion polymorphisms (INDELs) with high confidence has been intrinsically challenging in short-read sequencing data. Here we report our approach for improving INDEL calling accuracy by using a machine learning algorithm to combine call sets generated with three independent methods, and by leveraging the strengths of each individual pipeline. Utilizing this approach, we generated a consensus exome INDEL call set from a large dataset generated by the 1000 Genomes Project (1000G), maximizing both the sensitivity and the specificity of the calls. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 27 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 5 | 19% |
Researcher | 5 | 19% |
Student > Doctoral Student | 4 | 15% |
Student > Bachelor | 3 | 11% |
Other | 2 | 7% |
Other | 4 | 15% |
Unknown | 4 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 11 | 41% |
Biochemistry, Genetics and Molecular Biology | 6 | 22% |
Computer Science | 3 | 11% |
Medicine and Dentistry | 2 | 7% |
Engineering | 1 | 4% |
Other | 0 | 0% |
Unknown | 4 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 15 March 2015.
All research outputs
#20,264,045
of 22,794,367 outputs
Outputs from BMC Genomics
#9,273
of 10,648 outputs
Outputs of similar age
#215,718
of 255,870 outputs
Outputs of similar age from BMC Genomics
#261
of 286 outputs
Altmetric has tracked 22,794,367 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,648 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% 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 255,870 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 286 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.