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Characterizing the genetic basis of bacterial phenotypes using genome-wide association studies: a new direction for bacteriology

Overview of attention for article published in Genome Medicine, November 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

twitter
16 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
86 Dimensions

Readers on

mendeley
284 Mendeley
citeulike
3 CiteULike
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Title
Characterizing the genetic basis of bacterial phenotypes using genome-wide association studies: a new direction for bacteriology
Published in
Genome Medicine, November 2014
DOI 10.1186/s13073-014-0109-z
Pubmed ID
Authors

Timothy D Read, Ruth C Massey

Abstract

Genome-wide association studies (GWASs) have become an increasingly important approach for eukaryotic geneticists, facilitating the identification of hundreds of genetic polymorphisms that are responsible for inherited diseases. Despite the relative simplicity of bacterial genomes, the application of GWASs to identify polymorphisms responsible for important bacterial phenotypes has only recently been made possible through advances in genome sequencing technologies. Bacterial GWASs are now about to come of age thanks to the availability of massive datasets, and because of the potential to bridge genomics and traditional genetic approaches that is provided by improving validation strategies. A small number of pioneering GWASs in bacteria have been published in the past 2 years, examining from 75 to more than 3,000 strains. The experimental designs have been diverse, taking advantage of different processes in bacteria for generating variation. Analysis of data from bacterial GWASs can, to some extent, be performed using software developed for eukaryotic systems, but there are important differences in genome evolution that must be considered. The greatest experimental advantage of bacterial GWASs is the potential to perform downstream validation of causality and dissection of mechanism. We review the recent advances and remaining challenges in this field and propose strategies to improve the validation of bacterial GWASs.

Twitter Demographics

The data shown below were collected from the profiles of 16 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 284 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 4 1%
United States 4 1%
Belgium 2 <1%
United Kingdom 2 <1%
Uganda 1 <1%
Norway 1 <1%
Australia 1 <1%
Brazil 1 <1%
Netherlands 1 <1%
Other 4 1%
Unknown 263 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 77 27%
Researcher 70 25%
Student > Master 26 9%
Student > Bachelor 21 7%
Student > Doctoral Student 13 5%
Other 45 16%
Unknown 32 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 113 40%
Biochemistry, Genetics and Molecular Biology 53 19%
Immunology and Microbiology 28 10%
Medicine and Dentistry 15 5%
Computer Science 13 5%
Other 20 7%
Unknown 42 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 14 November 2016.
All research outputs
#2,917,057
of 21,321,365 outputs
Outputs from Genome Medicine
#642
of 1,355 outputs
Outputs of similar age
#46,856
of 345,655 outputs
Outputs of similar age from Genome Medicine
#29
of 72 outputs
Altmetric has tracked 21,321,365 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,355 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.9. This one has gotten more attention than average, scoring higher than 52% 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 345,655 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 86% of its contemporaries.
We're also able to compare this research output to 72 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 61% of its contemporaries.