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DiNAR: revealing hidden patterns of plant signalling dynamics using Differential Network Analysis in R

Overview of attention for article published in Plant Methods, August 2018
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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)

Mentioned by

27 tweeters
1 Google+ user


3 Dimensions

Readers on

46 Mendeley
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DiNAR: revealing hidden patterns of plant signalling dynamics using Differential Network Analysis in R
Published in
Plant Methods, August 2018
DOI 10.1186/s13007-018-0345-0
Pubmed ID

Maja Zagorščak, Andrej Blejec, Živa Ramšak, Marko Petek, Tjaša Stare, Kristina Gruden


Progress in high-throughput molecular methods accompanied by more complex experimental designs demands novel data visualisation solutions. To specifically answer the question which parts of the specifical biological system are responding in particular perturbation, integrative approach in which experimental data are superimposed on a prior knowledge network is shown to be advantageous. We have developed DiNAR, Differential Network Analysis in R, a user-friendly application with dynamic visualisation that integrates multiple condition high-throughput data and extensive biological prior knowledge. Implemented differential network approach and embedded network analysis allow users to analyse condition-specific responses in the context of topology of interest (e.g. immune signalling network) and extract knowledge concerning patterns of signalling dynamics (i.e. rewiring in network structure between two or more biological conditions). We validated the usability of software on the Arabidopsis thaliana and Solanum tuberosum datasets, but it is set to handle any biological instances. DiNAR facilitates detection of network-rewiring events, gene prioritisation for future experimental design and allows capturing dynamics of complex biological system. The fully cross-platform Shiny App is hosted and freely available at https://nib-si.shinyapps.io/DiNAR. The most recent version of the source code is available at https://github.com/NIB-SI/DiNAR/ with a DOI 10.5281/zenodo.1230523 of the archived version in Zenodo.

Twitter Demographics

The data shown below were collected from the profiles of 27 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 30%
Student > Ph. D. Student 11 24%
Student > Master 7 15%
Student > Bachelor 4 9%
Professor > Associate Professor 2 4%
Other 2 4%
Unknown 6 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 43%
Biochemistry, Genetics and Molecular Biology 5 11%
Computer Science 3 7%
Mathematics 2 4%
Medicine and Dentistry 2 4%
Other 5 11%
Unknown 9 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 12 January 2019.
All research outputs
of 21,422,252 outputs
Outputs from Plant Methods
of 1,025 outputs
Outputs of similar age
of 296,563 outputs
Outputs of similar age from Plant Methods
of 1 outputs
Altmetric has tracked 21,422,252 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,025 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done particularly well, scoring higher than 92% 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 296,563 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them