TimeXNet Web

TimeXNet Web reconstructs time-dependent gene and protein response networks from time-course omics datasets to identify signaling pathways and potential regulators.

Key Features:

• Multi-omics integration: Integrates time-course transcriptomic, proteomic, and phospho-proteomic datasets with an interaction network.
• Time-dependent network extraction: Classifies genes and proteins into groups based on timing of peak activity and extracts probable paths connecting these groups across consecutive time points.
• Novel regulator identification: Enriches the extracted sub-network with activated genes and proteins to highlight regulators that may lack observable changes in the input data.
• Functional enrichment analysis: Performs functional enrichment of the derived response network to identify involved biological processes and pathways.
• Cross-species support: Uses high-quality, weighted protein-protein interaction networks for 12 model organisms.

Scientific Applications:

• Dynamic response characterization: Elucidates the temporal dynamics of cellular responses to stimuli using time-course omics data.
• Signaling pathway discovery: Identifies signaling pathways connecting temporally ordered molecular events.
• Regulatory element identification: Reveals key regulatory elements, including putative novel regulators not directly observed as differentially active.
• Condition-specific target discovery: Supports discovery of condition-specific regulators and potential therapeutic targets from time-resolved data.

Methodology:

Integrates user-provided time-course transcriptomic, proteomic, and phospho-proteomic data with an input interaction network, classifies genes and proteins into time-dependent groups based on activity peaks, and identifies probable paths connecting these groups across consecutive time points to extract an enriched response sub-network and highlight activated components and putative novel regulators.