pwOmics

pwOmics performs pathway-based integrative analysis of coupled human proteomic and genomic/transcriptomic datasets to compare biological levels and identify regulatory interactions.

Key Features:

• Input data: Accepts pre-analyzed lists of differential genes, transcripts, and proteins as the basis for comparative analysis.
• Pathway-Based Analysis: Compares omics datasets at the pathway level to evaluate how changes in gene expression or protein abundance affect biological processes.
• Cross-Platform Consensus Analysis: Integrates proteomic and genomic/transcriptomic data to examine pathways, transcription factors (TFs), and genes/transcripts for consensus functional insights.
• Upstream and Downstream Analyses: Performs downstream proteomics analyses including pathway identification, enrichment analysis, TF identification, and target gene discovery, and upstream analyses from genes/transcripts to upstream regulators and TFs.
• Network Reconstruction and Inference: Uses Steiner tree algorithms and dynamic Bayesian network inference to generate consensus graphical networks representing inferred regulatory interactions.
• Visualization: Provides visualization options to explore regulatory networks and pathway dynamics derived from integrated omics data.

Scientific Applications:

• Systems biology and functional genomics: Integrates multi-omics data to characterize biological processes and their regulation.
• Regulatory mechanism investigation: Identifies transcriptional and post-transcriptional regulatory effects and TF-target relationships across conditions or time points.
• Cross-level signaling analysis: Examines interplay between pathways, TFs, and gene/protein expression to elucidate multi-level signaling dynamics.

Methodology:

Compares log fold changes from coupled datasets against public database-derived pathway and interaction models and reconstructs consensus networks using Steiner tree algorithms and dynamic Bayesian network inference.