DiNAR

DiNAR integrates multiple condition high-throughput data with biological prior knowledge networks to reveal hidden patterns of signaling dynamics through differential network analysis.

Key Features:

• Dynamic Visualisation: Provides dynamic visualisation of complex multi-conditional experiments.
• Differential Interaction Identification: Identifies strong differential interactions between biological conditions.
• Latent Effect Recall: Recalls latent effects that are present in multi-conditional experiments.
• Multi-omics Integration: Manages background knowledge networks in combination with transcriptomics, proteomics, and metabolomics experimental datasets.
• Condition-specific Response Analysis: Analyses condition-specific responses in the context of network topology and extracts patterns of signalling dynamics including network rewiring between two or more biological conditions.
• Cross-species Compatibility: Handles biological instances from any species, validated on Arabidopsis thaliana and Solanum tuberosum datasets.

Scientific Applications:

• Network Rewiring Detection: Facilitates detection of network-rewiring events in response to perturbations.
• Gene Prioritisation: Supports gene prioritisation for future experimental design.
• Signalling Dynamics Capture: Captures dynamics of complex biological systems through differential network analysis.
• Plant Signalling Research: Reveals hidden patterns of plant signalling dynamics in multi-conditional experiments.

Methodology:

DiNAR implements a differential network approach with embedded network analysis to identify condition-specific responses and network rewiring between biological conditions.