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Computational Fitness Landscape for All Gene-Order Permutations of an RNA Virus

Overview of attention for article published in PLoS Computational Biology, February 2009
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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1 blog

Citations

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8 Dimensions

Readers on

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43 Mendeley
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1 CiteULike
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2 Connotea
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Title
Computational Fitness Landscape for All Gene-Order Permutations of an RNA Virus
Published in
PLoS Computational Biology, February 2009
DOI 10.1371/journal.pcbi.1000283
Pubmed ID
Authors

Kwang-il Lim, John Yin

Abstract

How does the growth of a virus depend on the linear arrangement of genes in its genome? Answering this question may enhance our basic understanding of virus evolution and advance applications of viruses as live attenuated vaccines, gene-therapy vectors, or anti-tumor therapeutics. We used a mathematical model for vesicular stomatitis virus (VSV), a prototype RNA virus that encodes five genes (N-P-M-G-L), to simulate the intracellular growth of all 120 possible gene-order variants. Simulated yields of virus infection varied by 6,000-fold and were found to be most sensitive to gene-order permutations that increased levels of the L gene transcript or reduced levels of the N gene transcript, the lowest and highest expressed genes of the wild-type virus, respectively. Effects of gene order on virus growth also depended upon the host-cell environment, reflecting different resources for protein synthesis and different cell susceptibilities to infection. Moreover, by computationally deleting intergenic attenuations, which define a key mechanism of transcriptional regulation in VSV, the variation in growth associated with the 120 gene-order variants was drastically narrowed from 6,000- to 20-fold, and many variants produced higher progeny yields than wild-type. These results suggest that regulation by intergenic attenuation preceded or co-evolved with the fixation of the wild type gene order in the evolution of VSV. In summary, our models have begun to reveal how gene functions, gene regulation, and genomic organization of viruses interact with their host environments to define processes of viral growth and evolution.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Spain 1 2%
Italy 1 2%
Unknown 39 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 30%
Student > Ph. D. Student 10 23%
Professor 8 19%
Student > Master 4 9%
Professor > Associate Professor 3 7%
Other 4 9%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 35%
Biochemistry, Genetics and Molecular Biology 4 9%
Computer Science 4 9%
Immunology and Microbiology 4 9%
Mathematics 3 7%
Other 11 26%
Unknown 2 5%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 27 October 2010.
All research outputs
#6,330,617
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#4,316
of 8,960 outputs
Outputs of similar age
#40,777
of 186,036 outputs
Outputs of similar age from PLoS Computational Biology
#13
of 37 outputs
Altmetric has tracked 25,374,647 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 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 51% 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 186,036 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 78% of its contemporaries.
We're also able to compare this research output to 37 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 64% of its contemporaries.