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Automated classification of tailed bacteriophages according to their neck organization

Overview of attention for article published in BMC Genomics, November 2014
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Title
Automated classification of tailed bacteriophages according to their neck organization
Published in
BMC Genomics, November 2014
DOI 10.1186/1471-2164-15-1027
Pubmed ID
Authors

Anne Lopes, Paulo Tavares, Marie-Agnès Petit, Raphaël Guérois, Sophie Zinn-Justin

Abstract

The genetic diversity observed among bacteriophages remains a major obstacle for the identification of homologs and the comparison of their functional modules. In the structural module, although several classes of homologous proteins contributing to the head and tail structure can be detected, proteins of the head-to-tail connection (or neck) are generally more divergent. Yet, molecular analyses of a few tailed phages belonging to different morphological classes suggested that only a limited number of structural solutions are used in order to produce a functional virion. To challenge this hypothesis and analyze proteins diversity at the virion neck, we developed a specific computational strategy to cope with sequence divergence in phage proteins. We searched for homologs of a set of proteins encoded in the structural module using a phage learning database.

X Demographics

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The data shown below were collected from the profile of 1 X user 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 190 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 2%
Nepal 1 <1%
Unknown 186 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 47 25%
Student > Ph. D. Student 31 16%
Student > Master 24 13%
Student > Bachelor 16 8%
Student > Doctoral Student 12 6%
Other 16 8%
Unknown 44 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 25%
Biochemistry, Genetics and Molecular Biology 47 25%
Immunology and Microbiology 22 12%
Engineering 4 2%
Chemistry 4 2%
Other 13 7%
Unknown 52 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 November 2014.
All research outputs
#15,821,622
of 23,498,099 outputs
Outputs from BMC Genomics
#6,810
of 10,787 outputs
Outputs of similar age
#218,424
of 365,783 outputs
Outputs of similar age from BMC Genomics
#150
of 243 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,787 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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 365,783 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 243 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.