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.
Highlights
Progress in high-throughput molecular methods accompanied by more complex experimental designs demands novel data visualisation solutions
We validated the usability of software on the Arabidopsis thaliana and Solanum tuberosum datasets, but it is set to handle any biological instances
Condition-specific networks are dynamically constructed according to userdefined cut-off parameters: thresholds for the measure of statistical significance and the threshold for the node weight values (e.g. interpolated absolute values of logFC 0.5, here denoted with abs(n) and abs(m) )
Summary
Progress in high-throughput molecular methods accompanied by more complex experimental designs demands novel data visualisation solutions. The ideal in silico network should be concise and able to capture key features of the actual system This is difficult to achieve, with non-model organisms, network-based strategies have proven very useful for interpreting biological data [2]. DiNAR uses prior knowledge accompanied by differential network analysis to visualise complex experimental datasets. Nodes and edges tables with predefined structure for DiNAR input could be constructed manually (see the manual for more details), for larger networks this would be unnecessarily time-consuming. Pre-processing subApp reads the supplied nodes and edges in three formats: tables (tab, comma or semicolon separated), GraphML (standard graph structure data format) or GraphML combined with XGMML. Files generated by the subApp are available at https://github.com/NIB-SI/DiNAR/tree/master/subApps/clustering/examples
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