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Detecting rare gene transfer events in bacterial populations

Overview of attention for article published in Frontiers in Microbiology, January 2014
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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 (85th percentile)

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15 X users

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Title
Detecting rare gene transfer events in bacterial populations
Published in
Frontiers in Microbiology, January 2014
DOI 10.3389/fmicb.2013.00415
Pubmed ID
Authors

Kaare M. Nielsen, Thomas Bøhn, Jeffrey P. Townsend

Abstract

Horizontal gene transfer (HGT) enables bacteria to access, share, and recombine genetic variation, resulting in genetic diversity that cannot be obtained through mutational processes alone. In most cases, the observation of evolutionary successful HGT events relies on the outcome of initially rare events that lead to novel functions in the new host, and that exhibit a positive effect on host fitness. Conversely, the large majority of HGT events occurring in bacterial populations will go undetected due to lack of replication success of transformants. Moreover, other HGT events that would be highly beneficial to new hosts can fail to ensue due to lack of physical proximity to the donor organism, lack of a suitable gene transfer mechanism, genetic compatibility, and stochasticity in tempo-spatial occurrence. Experimental attempts to detect HGT events in bacterial populations have typically focused on the transformed cells or their immediate offspring. However, rare HGT events occurring in large and structured populations are unlikely to reach relative population sizes that will allow their immediate identification; the exception being the unusually strong positive selection conferred by antibiotics. Most HGT events are not expected to alter the likelihood of host survival to such an extreme extent, and will confer only minor changes in host fitness. Due to the large population sizes of bacteria and the time scales involved, the process and outcome of HGT are often not amenable to experimental investigation. Population genetic modeling of the growth dynamics of bacteria with differing HGT rates and resulting fitness changes is therefore necessary to guide sampling design and predict realistic time frames for detection of HGT, as it occurs in laboratory or natural settings. Here we review the key population genetic parameters, consider their complexity and highlight knowledge gaps for further research.

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X Demographics

The data shown below were collected from the profiles of 15 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 196 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 4 2%
Spain 3 2%
United States 3 2%
United Kingdom 2 1%
Denmark 1 <1%
Netherlands 1 <1%
Japan 1 <1%
Indonesia 1 <1%
Unknown 180 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 59 30%
Researcher 38 19%
Student > Bachelor 14 7%
Student > Master 13 7%
Student > Doctoral Student 12 6%
Other 39 20%
Unknown 21 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 87 44%
Biochemistry, Genetics and Molecular Biology 32 16%
Immunology and Microbiology 15 8%
Environmental Science 13 7%
Computer Science 4 2%
Other 16 8%
Unknown 29 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 April 2014.
All research outputs
#4,286,449
of 25,408,670 outputs
Outputs from Frontiers in Microbiology
#4,023
of 29,341 outputs
Outputs of similar age
#47,323
of 319,395 outputs
Outputs of similar age from Frontiers in Microbiology
#14
of 87 outputs
Altmetric has tracked 25,408,670 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 29,341 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done well, scoring higher than 86% 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 319,395 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 87 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.