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New approaches for unravelling reassortment pathways

Overview of attention for article published in BMC Ecology and Evolution, January 2013
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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 (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
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6 X users

Citations

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151 Dimensions

Readers on

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54 Mendeley
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1 CiteULike
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Title
New approaches for unravelling reassortment pathways
Published in
BMC Ecology and Evolution, January 2013
DOI 10.1186/1471-2148-13-1
Pubmed ID
Authors

Victoria Svinti, James A Cotton, James O McInerney

Abstract

Every year the human population encounters epidemic outbreaks of influenza, and history reveals recurring pandemics that have had devastating consequences. The current work focuses on the development of a robust algorithm for detecting influenza strains that have a composite genomic architecture. These influenza subtypes can be generated through a reassortment process, whereby a virus can inherit gene segments from two different types of influenza particles during replication. Reassortant strains are often not immediately recognised by the adaptive immune system of the hosts and hence may be the source of pandemic outbreaks. Owing to their importance in public health and their infectious ability, it is essential to identify reassortant influenza strains in order to understand the evolution of this virus and describe reassortment pathways that may be biased towards particular viral segments. Phylogenetic methods have been used traditionally to identify reassortant viruses. In many studies up to now, the assumption has been that if two phylogenetic trees differ, it is because reassortment has caused them to be different. While phylogenetic incongruence may be caused by real differences in evolutionary history, it can also be the result of phylogenetic error. Therefore, we wish to develop a method for distinguishing between topological inconsistency that is due to confounding effects and topological inconsistency that is due to reassortment.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
Ireland 1 2%
Sweden 1 2%
United Kingdom 1 2%
Singapore 1 2%
Spain 1 2%
United States 1 2%
Unknown 46 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 26%
Student > Ph. D. Student 11 20%
Professor > Associate Professor 5 9%
Student > Master 5 9%
Student > Bachelor 4 7%
Other 6 11%
Unknown 9 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 41%
Biochemistry, Genetics and Molecular Biology 7 13%
Veterinary Science and Veterinary Medicine 3 6%
Computer Science 3 6%
Medicine and Dentistry 2 4%
Other 5 9%
Unknown 12 22%
Attention Score in Context

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 01 April 2014.
All research outputs
#3,561,561
of 25,374,917 outputs
Outputs from BMC Ecology and Evolution
#943
of 3,714 outputs
Outputs of similar age
#34,916
of 289,004 outputs
Outputs of similar age from BMC Ecology and Evolution
#16
of 73 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,714 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has gotten more attention than average, scoring higher than 74% 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 289,004 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 87% of its contemporaries.
We're also able to compare this research output to 73 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.