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

Abstract

BackgroundProgress 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.ResultsWe 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.ConclusionsDiNAR 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.Electronic supplementary materialThe online version of this article (10.1186/s13007-018-0345-0) contains supplementary material, which is available to authorized users.

Transcriptional deregulation of stress-growth balance in Nicotiana benthamiana biofactories producing insect sex pheromones

“…Differentially expressed genes were visualised in the context of biological pathways and processes in MapMan v3.6.0 using the custom mapping file generated with Mercator 3.6. Differential network visualisations were performed using DiNAR ( Zagorščak et al., 2018 ) utilising two embedded prior knowledge networks: Plant Immune Signalling network and A. thaliana Comprehensive Knowledge Network ( Ramšak et al., 2018 ). To translate N. benthamiana v3.5 gene models to both A. thaliana and Solanum tuberosum gene nodes in either of the two networks, we used RBB translation of N. benthamiana genes to A. thaliana (see above).…”

“…Jasmonic acid signalling shows consistent upregulation with a stronger response in v1.0 compared to v1.2 high producers Following the results of pathway analyses, which pointed to an orchestrated transcriptional reprogramming of hormonal signalling networks in high-producing SxP, we decided to use knowledge networks of plant gene interactions to further explore active signalling connections and hubs. For that, we have generated homology-based translations of 33,372 N. benthamiana genes to A. thaliana genes (Supplementary Data Sheet S3), enabling usage of differential network analysis with DiNAR (Zagorsčǎk et al, 2018). We visualised differentially expressed genes in the context of two embedded knowledge networks-the Plant Immune Signalling network (PIS) and the Arabidopsis thaliana Comprehensive Knowledge Network (CKN) (Ramsǎk et al, 2018).…”

Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses

“…Instructions for the use of CKN or PSS as the prior knowledge network for integration and visualisation of multiple condition high-throughput data in the DiNAR application (Zagorščak et al ., 2018).…”

“…LGL Simple Interaction Format/Large Graph Format, compatible with Cytoscape (Shannon et al , 2003) and DiNAR (Zagorščak et al ., 2018).…”

DataSheet_1.pdf

“…The crop is highly useful as an organism for studying plant responses to environmental stress factors (Lukan et al, 2022;Bleker et al, 2024), such as herbivory (Petek et al, 2020a), viral diseases , transcriptional (Tomažet al, 2023) and small RNA regulation (Krizňik et al, 2020), single and combined abiotic stress responses (Demirel et al, 2020), and growth-defense trade-offs (Huot et al, 2014). Potato also serves as an excellent platform for transferring and testing vast amounts of knowledge garnered in Arabidopsis with an agriculturally relevant crop (Ramsǎk et al, 2018;Zagorsčǎk et al, 2018;Schwacke et al, 2019), aiding toward solving present-day food security issues (Cole et al, 2018).…”