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Generation and comparative genomics of synthetic dengue viruses

Overview of attention for article published in BMC Bioinformatics, May 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (68th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

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9 tweeters

Citations

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1 Dimensions

Readers on

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23 Mendeley
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Title
Generation and comparative genomics of synthetic dengue viruses
Published in
BMC Bioinformatics, May 2018
DOI 10.1186/s12859-018-2132-3
Pubmed ID
Authors

Eli Goz, Yael Tsalenchuck, Rony Oren Benaroya, Zohar Zafrir, Shimshi Atar, Tahel Altman, Justin Julander, Tamir Tuller

Abstract

Synthetic virology is an important multidisciplinary scientific field, with emerging applications in biotechnology and medicine, aiming at developing methods to generate and engineer synthetic viruses. In particular, many of the RNA viruses, including among others the Dengue and Zika, are widespread pathogens of significant importance to human health. The ability to design and synthesize such viruses may contribute to exploring novel approaches for developing vaccines and virus based therapies. Here we develop a full multidisciplinary pipeline for generation and analysis of synthetic RNA viruses and specifically apply it to Dengue virus serotype 2 (DENV-2). The major steps of the pipeline include comparative genomics of endogenous and synthetic viral strains. Specifically, we show that although the synthetic DENV-2 viruses were found to have lower nucleotide variability, their phenotype, as reflected in the study of the AG129 mouse model morbidity, RNA levels, and neutralization antibodies, is similar or even more pathogenic in comparison to the wildtype master strain. Additionally, the highly variable positions, identified in the analyzed DENV-2 population, were found to overlap with less conserved homologous positions in Zika virus and other Dengue serotypes. These results may suggest that synthetic DENV-2 could enhance virulence if the correct sequence is selected. The approach reported in this study can be used to generate and analyze synthetic RNA viruses both on genotypic and on phenotypic level. It could be applied for understanding the functionality and the fitness effects of any set of mutations in viral RNA and for editing RNA viruses for various target applications.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 22%
Student > Doctoral Student 3 13%
Student > Master 3 13%
Student > Bachelor 2 9%
Student > Ph. D. Student 2 9%
Other 3 13%
Unknown 5 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 22%
Medicine and Dentistry 3 13%
Social Sciences 3 13%
Agricultural and Biological Sciences 1 4%
Mathematics 1 4%
Other 3 13%
Unknown 7 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 June 2018.
All research outputs
#3,214,843
of 13,087,494 outputs
Outputs from BMC Bioinformatics
#1,378
of 4,907 outputs
Outputs of similar age
#84,099
of 269,584 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 21 outputs
Altmetric has tracked 13,087,494 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,907 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 71% 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 269,584 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 21 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 90% of its contemporaries.