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PanACEA: a bioinformatics tool for the exploration and visualization of bacterial pan-chromosomes

Overview of attention for article published in BMC Bioinformatics, June 2018
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  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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Title
PanACEA: a bioinformatics tool for the exploration and visualization of bacterial pan-chromosomes
Published in
BMC Bioinformatics, June 2018
DOI 10.1186/s12859-018-2250-y
Pubmed ID
Authors

Thomas H. Clarke, Lauren M. Brinkac, Jason M. Inman, Granger Sutton, Derrick E. Fouts

Abstract

Bacterial pan-genomes, comprised of conserved and variable genes across multiple sequenced bacterial genomes, allow for identification of genomic regions that are phylogenetically discriminating or functionally important. Pan-genomes consist of large amounts of data, which can restrict researchers ability to locate and analyze these regions. Multiple software packages are available to visualize pan-genomes, but currently their ability to address these concerns are limited by using only pre-computed data sets, prioritizing core over variable gene clusters, or by not accounting for pan-chromosome positioning in the viewer. We introduce PanACEA (Pan-genome Atlas with Chromosome Explorer and Analyzer), which utilizes locally-computed interactive web-pages to view ordered pan-genome data. It consists of multi-tiered, hierarchical display pages that extend from pan-chromosomes to both core and variable regions to single genes. Regions and genes are functionally annotated to allow for rapid searching and visual identification of regions of interest with the option that user-supplied genomic phylogenies and metadata can be incorporated. PanACEA's memory and time requirements are within the capacities of standard laptops. The capability of PanACEA as a research tool is demonstrated by highlighting a variable region important in differentiating strains of Enterobacter hormaechei. PanACEA can rapidly translate the results of pan-chromosome programs into an intuitive and interactive visual representation. It will empower researchers to visually explore and identify regions of the pan-chromosome that are most biologically interesting, and to obtain publication quality images of these regions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 26%
Researcher 8 19%
Student > Bachelor 4 9%
Student > Master 4 9%
Professor > Associate Professor 3 7%
Other 8 19%
Unknown 5 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 35%
Biochemistry, Genetics and Molecular Biology 10 23%
Computer Science 4 9%
Nursing and Health Professions 2 5%
Veterinary Science and Veterinary Medicine 1 2%
Other 2 5%
Unknown 9 21%
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 29 July 2018.
All research outputs
#6,982,866
of 25,392,205 outputs
Outputs from BMC Bioinformatics
#2,469
of 7,685 outputs
Outputs of similar age
#110,468
of 336,369 outputs
Outputs of similar age from BMC Bioinformatics
#38
of 96 outputs
Altmetric has tracked 25,392,205 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 7,685 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 336,369 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 66% of its contemporaries.
We're also able to compare this research output to 96 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 60% of its contemporaries.