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AnnotSV: an integrated tool for structural variations annotation

Overview of attention for article published in Bioinformatics, April 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (58th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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6 tweeters

Citations

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24 Dimensions

Readers on

mendeley
66 Mendeley
Title
AnnotSV: an integrated tool for structural variations annotation
Published in
Bioinformatics, April 2018
DOI 10.1093/bioinformatics/bty304
Pubmed ID
Authors

Véronique Geoffroy, Yvan Herenger, Arnaud Kress, Corinne Stoetzel, Amélie Piton, Hélène Dollfus, Jean Muller

Abstract

Structural Variations (SV) are a major source of variability in the human genome that shaped its actual structure during evolution. Moreover, many human diseases are caused by SV, highlighting the need to accurately detect those genomic events but also to annotate them and assist their biological interpretation. Therefore, we developed AnnotSV that compiles functionally, regulatory and clinically relevant information and aims at providing annotations useful to i) interpret SV potential pathogenicity and ii) filter out SV potential false positive. In particular, AnnotSV reports heterozygous and homozygous counts of single nucleotide variations and small insertions/deletions called within each SV for the analyzed patients, this genomic information being extremely useful to support or question the existence of an SV. We also report the computed allelic frequency relative to overlapping variants from DGV (MacDonald, et al., 2014), that is especially powerful to filter out common SV. To delineate the strength of AnnotSV, we annotated the 4,751 SV from one sample of the 1000 Genomes Project, integrating the sample information of 4 million of SNV/indel, in less than 60 seconds. AnnotSV is implemented in Tcl and runs in command line on all platforms. The source code is available under the GNU GPL license. Source code, README and Supplementary data are available at http://lbgi.fr/AnnotSV/. veronique.geoffroy@inserm.fr. In order to provide a ready to start installation of AnnotSV, each annotation source (that do not require a commercial license) is already provided with the AnnotSV sources. Supplementary data are available at Bioinformatics online.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 66 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 27%
Researcher 18 27%
Student > Master 7 11%
Student > Bachelor 5 8%
Student > Doctoral Student 4 6%
Other 7 11%
Unknown 7 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 25 38%
Agricultural and Biological Sciences 13 20%
Medicine and Dentistry 8 12%
Computer Science 5 8%
Neuroscience 3 5%
Other 3 5%
Unknown 9 14%

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 15 October 2018.
All research outputs
#7,394,533
of 14,571,953 outputs
Outputs from Bioinformatics
#5,949
of 9,411 outputs
Outputs of similar age
#113,185
of 278,246 outputs
Outputs of similar age from Bioinformatics
#123
of 200 outputs
Altmetric has tracked 14,571,953 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,411 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one is in the 35th percentile – i.e., 35% 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 278,246 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 58% of its contemporaries.
We're also able to compare this research output to 200 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.