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DensityMap: a genome viewer for illustrating the densities of features

Overview of attention for article published in BMC Bioinformatics, May 2016
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

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9 X users

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Title
DensityMap: a genome viewer for illustrating the densities of features
Published in
BMC Bioinformatics, May 2016
DOI 10.1186/s12859-016-1055-0
Pubmed ID
Authors

Sébastien Guizard, Benoît Piégu, Yves Bigot

Abstract

Several tools are available for visualizing genomic data. Some, such as Gbrowse and Jbrowse, are very efficient for small genomic regions, but they are not suitable for entire genomes. Others, like Phenogram and CViT, can be used to visualise whole genomes, but are not designed to display very dense genomic features (eg: interspersed repeats). We have therefore developed DensityMap, a lightweight Perl program that can display the densities of several features (genes, ncRNA, cpg, etc.) along chromosomes on the scale of the whole genome. A critical advantage of DensityMap is that it uses GFF annotation files directly to compute the densities of features without needing additional information from the user. The resulting picture is readily configurable, and the colour scales used can be customized for a best fit to the data plotted. DensityMap runs on Linux architecture with few requirements so that users can easily and quickly visualize the distributions and densities of genomic features for an entire genome. The input is GFF3-formated data representing chromosomes (linkage groups or pseudomolecules) and sets of features which are used to calculate representations in density maps. In practise, DensityMap uses a tilling window to compute the density of one or more features and the number of bases covered by these features along chromosomes. The densities are represented by colour scales that can be customized to highlight critical points. DensityMap can compare the distributions of features; it calculates several chromosomal density maps in a single image, each of which describes a different genomic feature. It can also use the genome nucleotide sequence to compute and plot a density map of the GC content along chromosomes. DensityMap is a compact, easily-used tool for displaying the distribution and density of all types of genomic features within a genome. It is flexible enough to visualize the densities of several types of features in a single representation. The images produced are readily configurable and their SVG format ensures that they can be edited.

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X Demographics

The data shown below were collected from the profiles of 9 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
Cuba 1 2%
Sweden 1 2%
United States 1 2%
Unknown 46 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 24%
Student > Ph. D. Student 10 20%
Student > Bachelor 4 8%
Student > Master 4 8%
Student > Doctoral Student 3 6%
Other 9 18%
Unknown 9 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 47%
Biochemistry, Genetics and Molecular Biology 9 18%
Computer Science 4 8%
Mathematics 1 2%
Immunology and Microbiology 1 2%
Other 1 2%
Unknown 11 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 24 February 2017.
All research outputs
#6,933,623
of 25,245,273 outputs
Outputs from BMC Bioinformatics
#2,457
of 7,663 outputs
Outputs of similar age
#90,130
of 305,159 outputs
Outputs of similar age from BMC Bioinformatics
#35
of 101 outputs
Altmetric has tracked 25,245,273 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,663 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 gotten more attention than average, scoring higher than 67% 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 305,159 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 70% of its contemporaries.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.