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Intervene: a tool for intersection and visualization of multiple gene or genomic region sets

Overview of attention for article published in BMC Bioinformatics, May 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

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1 blog
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45 X users
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1 Facebook page

Citations

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398 Dimensions

Readers on

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290 Mendeley
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Title
Intervene: a tool for intersection and visualization of multiple gene or genomic region sets
Published in
BMC Bioinformatics, May 2017
DOI 10.1186/s12859-017-1708-7
Pubmed ID
Authors

Aziz Khan, Anthony Mathelier

Abstract

A common task for scientists relies on comparing lists of genes or genomic regions derived from high-throughput sequencing experiments. While several tools exist to intersect and visualize sets of genes, similar tools dedicated to the visualization of genomic region sets are currently limited. To address this gap, we have developed the Intervene tool, which provides an easy and automated interface for the effective intersection and visualization of genomic region or list sets, thus facilitating their analysis and interpretation. Intervene contains three modules: venn to generate Venn diagrams of up to six sets, upset to generate UpSet plots of multiple sets, and pairwise to compute and visualize intersections of multiple sets as clustered heat maps. Intervene, and its interactive web ShinyApp companion, generate publication-quality figures for the interpretation of genomic region and list sets. Intervene and its web application companion provide an easy command line and an interactive web interface to compute intersections of multiple genomic and list sets. They have the capacity to plot intersections using easy-to-interpret visual approaches. Intervene is developed and designed to meet the needs of both computer scientists and biologists. The source code is freely available at https://bitbucket.org/CBGR/intervene , with the web application available at https://asntech.shinyapps.io/intervene .

X Demographics

X Demographics

The data shown below were collected from the profiles of 45 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 290 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
China 1 <1%
Unknown 288 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 53 18%
Researcher 49 17%
Student > Master 47 16%
Student > Bachelor 32 11%
Student > Postgraduate 10 3%
Other 31 11%
Unknown 68 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 88 30%
Agricultural and Biological Sciences 52 18%
Computer Science 12 4%
Medicine and Dentistry 9 3%
Neuroscience 8 3%
Other 40 14%
Unknown 81 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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 01 March 2022.
All research outputs
#1,265,477
of 25,401,784 outputs
Outputs from BMC Bioinformatics
#131
of 7,701 outputs
Outputs of similar age
#25,002
of 330,312 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 108 outputs
Altmetric has tracked 25,401,784 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,701 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 98% 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 330,312 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.