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ClustAGE: a tool for clustering and distribution analysis of bacterial accessory genomic elements

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

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Title
ClustAGE: a tool for clustering and distribution analysis of bacterial accessory genomic elements
Published in
BMC Bioinformatics, April 2018
DOI 10.1186/s12859-018-2154-x
Pubmed ID
Authors

Egon A. Ozer

Abstract

The non-conserved accessory genome of bacteria can be associated with important adaptive characteristics that can contribute to niche specificity or pathogenicity of strains. High degrees of structural and compositional diversity in genomic islands and other elements of the accessory genome can complicate characterization of accessory genome contents among populations of strains. Methods for easily and effectively defining the distributions of discrete elements of the accessory genome among bacterial strains in a population are needed to explore the relationships between the flexible genome and bacterial adaptive traits. We have developed the open-source software package ClustAGE. This program, written in Perl, uses BLAST to cluster nucleotide accessory genomic elements from the genomes of multiple bacterial strains and to identify their distribution within the study population. The program output can be used in combination with strain phenotype data or other characteristics to detect associations. Optional graphical output is available for visualizing accessory genome gene content and distribution patterns. The capabilities of the software are demonstrated on a collection of 14 Pseudomonas aeruginosa genome sequences. The ClustAGE software and utilities are effective for identifying characteristics and distributions of accessory genomic elements among groups of bacterial genomes. The ability to easily and effectively characterize the accessory genome of a sequence collection may provide a better understanding of the accessory genome's contribution to a species' adaptation and pathogenesis. The ClustAGE source code can be downloaded from https://clustage.sourceforge.io and a limited web-based implementation is available at http://vfsmspineagent.fsm.northwestern.edu/cgi-bin/clustage.cgi .

X Demographics

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The data shown below were collected from the profiles of 8 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 24%
Student > Ph. D. Student 13 22%
Professor 5 9%
Student > Master 5 9%
Other 4 7%
Other 8 14%
Unknown 9 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 22%
Biochemistry, Genetics and Molecular Biology 12 21%
Immunology and Microbiology 5 9%
Computer Science 4 7%
Chemical Engineering 3 5%
Other 10 17%
Unknown 11 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 30 April 2018.
All research outputs
#7,501,669
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#2,928
of 7,418 outputs
Outputs of similar age
#127,849
of 328,275 outputs
Outputs of similar age from BMC Bioinformatics
#40
of 103 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 58% 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 328,275 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 60% of its contemporaries.
We're also able to compare this research output to 103 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.