Pathicular

Pathicular identifies regulatory path motifs by integrating perturbational expression data with transcriptional, protein-protein, and phosphorylation interaction networks to detect short paths that connect transcription factors to target genes more frequently than expected by chance.

Key Features:

• Data integration: Integrates perturbational expression data with transcriptional, protein-protein, and phosphorylation interaction networks.
• Regulatory path motif detection: Detects short, significant paths (regulatory path motifs) that link transcription factors to target genes.
• Statistical enrichment assessment: Identifies motifs that occur more frequently than expected based on random expectation.
• Modular organization: Arranges identified path motifs into a modular structure to group related regulatory paths.

Scientific Applications:

• Transcription factor perturbation analysis: Examines cellular responses to transcription factor perturbations by linking TFs to downstream targets through network paths.
• Regulatory mechanism discovery: Reveals biologically significant pathways and motifs underlying gene regulation and cellular function.
• Saccharomyces cerevisiae case study: Applied to S. cerevisiae where it identified eight distinct regulatory path motifs.

Methodology:

Integrates perturbational expression data with transcriptional, protein-protein, and phosphorylation interaction networks, detects short paths connecting transcription factors to target genes that are enriched relative to random expectation, and arranges the resulting path motifs into modular structures.