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Topology analysis and visualization of Potyvirus protein-protein interaction network

Overview of attention for article published in BMC Systems Biology, November 2014
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Title
Topology analysis and visualization of Potyvirus protein-protein interaction network
Published in
BMC Systems Biology, November 2014
DOI 10.1186/s12918-014-0129-8
Pubmed ID
Authors

Gabriel Bosque, Abel Folch-Fortuny, Jesús Picó, Alberto Ferrer, Santiago F Elena

Abstract

BackgroundOne of the central interests of Virology is the identification of host factors that contribute to virus infection. Despite tremendous efforts, the list of factors identified remains limited. With omics techniques, the focus has changed from identifying and thoroughly characterizing individual host factors to the simultaneous analysis of thousands of interactions, framing them on the context of protein-protein interaction networks and of transcriptional regulatory networks. This new perspective is allowing the identification of direct and indirect viral targets. Such information is available for several members of the Potyviridae family, one of the largest and more important families of plant viruses.ResultsAfter collecting information on virus protein-protein interactions from different potyviruses, we have processed it and used it for inferring a protein-protein interaction network. All proteins are connected into a single network component. Some proteins show a high degree and are highly connected while others are much less connected, with the network showing a significant degree of dissortativeness. We have attempted to integrate this virus protein-protein interaction network into the largest protein-protein interaction network of Arabidopsis thaliana, a susceptible laboratory host. To make the interpretation of data and results easier, we have developed a new approach for visualizing and analyzing the dynamic spread on the host network of the local perturbations induced by viral proteins. We found that local perturbations can reach the entire host protein-protein interaction network, although the efficiency of this spread depends on the particular viral proteins. By comparing the spread dynamics among viral proteins, we found that some proteins spread their effects fast and efficiently by attacking hubs in the host network while other proteins exert more local effects.ConclusionsOur findings confirm that potyvirus protein-protein interaction networks are highly connected, with some proteins playing the role of hubs. Several topological parameters depend linearly on the protein degree. Some viral proteins focus their effect in only host hubs while others diversify its effect among several proteins at the first step. Future new data will help to refine our model and to improve our predictions.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 2 4%
Unknown 54 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 27%
Student > Ph. D. Student 8 14%
Student > Bachelor 6 11%
Professor 5 9%
Student > Master 5 9%
Other 11 20%
Unknown 6 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 38%
Biochemistry, Genetics and Molecular Biology 18 32%
Immunology and Microbiology 2 4%
Computer Science 2 4%
Unspecified 1 2%
Other 4 7%
Unknown 8 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 16 October 2015.
All research outputs
#13,661,887
of 23,577,654 outputs
Outputs from BMC Systems Biology
#455
of 1,139 outputs
Outputs of similar age
#178,526
of 366,206 outputs
Outputs of similar age from BMC Systems Biology
#15
of 54 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,139 research outputs from this source. They receive a mean Attention Score of 3.6. This one has gotten more attention than average, scoring higher than 58% 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 366,206 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 50% of its contemporaries.
We're also able to compare this research output to 54 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 68% of its contemporaries.