Venn-diaNet

Venn-diaNet prioritizes differentially expressed genes (DEGs) from multiple transcriptome experiments by combining Venn diagram segmentation with network propagation on protein-protein interaction networks (String DB) to identify influential genes and interpret synergistic effects.

Key Features:

• Venn Diagram-Based Framework: Integrates Venn diagram segmentation with network-based analysis to address gene ranking challenges in intersecting DEG segments.
• Network Propagation Analysis: Applies network propagation on protein-protein interaction networks using String DB to assess and rank gene influence.
• Seed Gene Selection from Venn Segments: Enables selection of seed genes from specific Venn diagram segments for downstream network propagation.
• Synergistic Effect Analysis: Facilitates analysis of phenotypic differences arising from combined or synergistic treatments by highlighting intersecting and condition-specific DEGs.
• Reproducibility and Validation: Has been used to reproduce findings from original studies comparing two, three, and eight experiments.

Scientific Applications:

• Comparative transcriptome analysis: Prioritizes DEGs across multiple biological conditions to support differential expression studies.
• Identification of treatment-responsive genes: Highlights candidate genes that may mediate responses to individual or combined treatments.
• Investigation of synergistic treatments: Aids interpretation of combinatorial treatment effects by focusing on intersecting and unique DEG sets.
• Mechanistic inference and replication: Supports interpretation of gene interactions and has been applied to reproduce published comparative analyses.

Methodology:

Seed genes are selected from DEG lists represented in Venn diagram segments and subjected to network propagation on protein-protein interaction networks (String DB) to rank genes by influence.