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Plasmid Classification in an Era of Whole-Genome Sequencing: Application in Studies of Antibiotic Resistance Epidemiology

Overview of attention for article published in Frontiers in Microbiology, February 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
57 X users
wikipedia
1 Wikipedia page

Citations

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171 Dimensions

Readers on

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537 Mendeley
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Title
Plasmid Classification in an Era of Whole-Genome Sequencing: Application in Studies of Antibiotic Resistance Epidemiology
Published in
Frontiers in Microbiology, February 2017
DOI 10.3389/fmicb.2017.00182
Pubmed ID
Authors

Alex Orlek, Nicole Stoesser, Muna F. Anjum, Michel Doumith, Matthew J. Ellington, Tim Peto, Derrick Crook, Neil Woodford, A. Sarah Walker, Hang Phan, Anna E. Sheppard

Abstract

Plasmids are extra-chromosomal genetic elements ubiquitous in bacteria, and commonly transmissible between host cells. Their genomes include variable repertoires of 'accessory genes,' such as antibiotic resistance genes, as well as 'backbone' loci which are largely conserved within plasmid families, and often involved in key plasmid-specific functions (e.g., replication, stable inheritance, mobility). Classifying plasmids into different types according to their phylogenetic relatedness provides insight into the epidemiology of plasmid-mediated antibiotic resistance. Current typing schemes exploit backbone loci associated with replication (replicon typing), or plasmid mobility (MOB typing). Conventional PCR-based methods for plasmid typing remain widely used. With the emergence of whole-genome sequencing (WGS), large datasets can be analyzed using in silico plasmid typing methods. However, short reads from popular high-throughput sequencers can be challenging to assemble, so complete plasmid sequences may not be accurately reconstructed. Therefore, localizing resistance genes to specific plasmids may be difficult, limiting epidemiological insight. Long-read sequencing will become increasingly popular as costs decline, especially when resolving accurate plasmid structures is the primary goal. This review discusses the application of plasmid classification in WGS-based studies of antibiotic resistance epidemiology; novel in silico plasmid analysis tools are highlighted. Due to the diverse and plastic nature of plasmid genomes, current typing schemes do not classify all plasmids, and identifying conserved, phylogenetically concordant genes for subtyping and phylogenetics is challenging. Analyzing plasmids as nodes in a network that represents gene-sharing relationships between plasmids provides a complementary way to assess plasmid diversity, and allows inferences about horizontal gene transfer to be made.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 537 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 <1%
Malaysia 1 <1%
Indonesia 1 <1%
Brazil 1 <1%
United Kingdom 1 <1%
Canada 1 <1%
New Zealand 1 <1%
Estonia 1 <1%
Unknown 529 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 99 18%
Researcher 96 18%
Student > Master 68 13%
Student > Bachelor 66 12%
Student > Doctoral Student 35 7%
Other 63 12%
Unknown 110 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 152 28%
Agricultural and Biological Sciences 102 19%
Immunology and Microbiology 65 12%
Medicine and Dentistry 22 4%
Veterinary Science and Veterinary Medicine 17 3%
Other 50 9%
Unknown 129 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 41. 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 19 January 2023.
All research outputs
#1,070,239
of 26,489,229 outputs
Outputs from Frontiers in Microbiology
#590
of 30,396 outputs
Outputs of similar age
#22,245
of 431,206 outputs
Outputs of similar age from Frontiers in Microbiology
#13
of 417 outputs
Altmetric has tracked 26,489,229 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 30,396 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 particularly well, scoring higher than 98% 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 431,206 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 417 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.