| Title: | DiNAR: revealing hidden patterns of plant signalling dynamics using Diferential Network Analysis in R |
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| Authors: | ID Zagorščak, Maja (Author)
ID Blejec, Andrej (Author)
ID Ramšak, Živa (Author)
ID Petek, Marko (Author)
ID Stare, Tjaša (Author)
ID Gruden, Kristina (Author) |
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| Files: |  URL - Source URL, visit https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0345-0
 PDF - Presentation file, download (1,63 MB)
MD5: C8FAC8E31CDE82D606A42D8CD9CF3309
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  NIB - National Institute of Biology
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| Abstract: | Background
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.
Results
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.
Conclusions
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. |
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| Keywords: | biological networks, clustering, gene expression, time series, dynamic network analysis, dynamic data visualisation, web application, multi-conditional datasets, background knowledge |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 30.08.2018 |
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| Year of publishing: | 2018 |
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| Number of pages: | str. 1-9 |
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| Numbering: | Vol. 14 |
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| PID: | 20.500.12556/DiRROS-19649  |
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| UDC: | 577.2 |
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| ISSN on article: | 1746-4811 |
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| DOI: | 10.1186/s13007-018-0345-0 |
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| COBISS.SI-ID: | 4791631 |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 4. 9. 2018;
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| Publication date in DiRROS: | 24.07.2024 |
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| Views: | 344 |
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| Downloads: | 223 |
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