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XCAVATOR: accurate detection and genotyping of copy number variants from second and third generation whole-genome sequencing experiments

Overview of attention for article published in BMC Genomics, September 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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5 X users
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60 Mendeley
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Title
XCAVATOR: accurate detection and genotyping of copy number variants from second and third generation whole-genome sequencing experiments
Published in
BMC Genomics, September 2017
DOI 10.1186/s12864-017-4137-0
Pubmed ID
Authors

Alberto Magi, Tommaso Pippucci, Carlo Sidore

Abstract

We developed a novel software package, XCAVATOR, for the identification of genomic regions involved in copy number variants/alterations (CNVs/CNAs) from short and long reads whole-genome sequencing experiments. By using simulated and real datasets we showed that our tool, based on read count approach, is capable to predict the boundaries and the absolute number of DNA copies CNVs/CNAs with high resolutions. To demonstrate the power of our software we applied it to the analysis Illumina and Pacific Bioscencies data and we compared its performance to other ten state of the art tools. All the analyses we performed demonstrate that XCAVATOR is capable to detect germline and somatic CNVs/CNAs outperforming all the other tools we compared. XCAVATOR is freely available at http://sourceforge.net/projects/xcavator/ .

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 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 30%
Researcher 7 12%
Student > Master 7 12%
Professor 5 8%
Student > Doctoral Student 4 7%
Other 10 17%
Unknown 9 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 38%
Agricultural and Biological Sciences 13 22%
Medicine and Dentistry 5 8%
Computer Science 2 3%
Engineering 2 3%
Other 4 7%
Unknown 11 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 26 October 2023.
All research outputs
#5,559,004
of 25,765,370 outputs
Outputs from BMC Genomics
#2,183
of 11,320 outputs
Outputs of similar age
#86,996
of 326,792 outputs
Outputs of similar age from BMC Genomics
#39
of 219 outputs
Altmetric has tracked 25,765,370 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,320 research outputs from this source. They receive a mean Attention Score of 4.8. 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 326,792 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 72% of its contemporaries.
We're also able to compare this research output to 219 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.