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xenoGI: reconstructing the history of genomic island insertions in clades of closely related bacteria

Overview of attention for article published in BMC Bioinformatics, February 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

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1 blog
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9 X users

Citations

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14 Dimensions

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39 Mendeley
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1 CiteULike
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Title
xenoGI: reconstructing the history of genomic island insertions in clades of closely related bacteria
Published in
BMC Bioinformatics, February 2018
DOI 10.1186/s12859-018-2038-0
Pubmed ID
Authors

Eliot C. Bush, Anne E. Clark, Carissa A. DeRanek, Alexander Eng, Juliet Forman, Kevin Heath, Alexander B. Lee, Daniel M. Stoebel, Zunyan Wang, Matthew Wilber, Helen Wu

Abstract

Genomic islands play an important role in microbial genome evolution, providing a mechanism for strains to adapt to new ecological conditions. A variety of computational methods, both genome-composition based and comparative, have been developed to identify them. Some of these methods are explicitly designed to work in single strains, while others make use of multiple strains. In general, existing methods do not identify islands in the context of the phylogeny in which they evolved. Even multiple strain approaches are best suited to identifying genomic islands that are present in one strain but absent in others. They do not automatically recognize islands which are shared between some strains in the clade or determine the branch on which these islands inserted within the phylogenetic tree. We have developed a software package, xenoGI, that identifies genomic islands and maps their origin within a clade of closely related bacteria, determining which branch they inserted on. It takes as input a set of sequenced genomes and a tree specifying their phylogenetic relationships. Making heavy use of synteny information, the package builds gene families in a species-tree-aware way, and then attempts to combine into islands those families whose members are adjacent and whose most recent common ancestor is shared. The package provides a variety of text-based analysis functions, as well as the ability to export genomic islands into formats suitable for viewing in a genome browser. We demonstrate the capabilities of the package with several examples from enteric bacteria, including an examination of the evolution of the acid fitness island in the genus Escherichia. In addition we use output from simulations and a set of known genomic islands from the literature to show that xenoGI can accurately identify genomic islands and place them on a phylogenetic tree. xenoGI is an effective tool for studying the history of genomic island insertions in a clade of microbes. It identifies genomic islands, and determines which branch they inserted on within the phylogenetic tree for the clade. Such information is valuable because it helps us understand the adaptive path that has produced living species.

X Demographics

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

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 21%
Researcher 7 18%
Student > Bachelor 6 15%
Student > Master 4 10%
Student > Doctoral Student 2 5%
Other 5 13%
Unknown 7 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 33%
Biochemistry, Genetics and Molecular Biology 11 28%
Immunology and Microbiology 2 5%
Computer Science 2 5%
Veterinary Science and Veterinary Medicine 1 3%
Other 1 3%
Unknown 9 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 15 October 2018.
All research outputs
#2,891,033
of 25,663,438 outputs
Outputs from BMC Bioinformatics
#836
of 7,735 outputs
Outputs of similar age
#63,885
of 447,838 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 116 outputs
Altmetric has tracked 25,663,438 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,735 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 well, scoring higher than 89% 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 447,838 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.