Omics Integrator

Omics Integrator integrates high-throughput 'omic' datasets such as gene expression and phospho-proteomic data using network optimization algorithms to identify molecular pathways and condition-specific subnetworks.

Key Features:

• Data Integration: Accepts diverse 'omic' datasets and integrates them against a comprehensive molecular interaction network to identify subnetworks that explain observed signals.
• Subnetwork Identification: Discovers interpretable subnetworks that connect measured changes (e.g., gene expression, protein abundance, phospho-proteomics) to proteins not directly measured within large interaction networks.
• Pathway Discovery: Reveals unannotated molecular pathways not detectable through conventional pathway database searches by integrating multiple data types.
• Incorporation of Negative Evidence: Incorporates negative evidence to avoid bias toward unexpressed genes or highly-studied hub proteins unless strongly supported by the data.
• Modular Tools - Garnet and Forest: Includes Garnet and Forest modules that can be used independently or together to construct condition-specific subnetworks connecting changes across datasets.

Scientific Applications:

• Integrative systems biology: Integrates gene expression and phospho-proteomic data to elucidate molecular mechanisms and signaling pathways.
• Pathway and network discovery: Identifies novel and condition-specific molecular pathways across multiple omic layers.
• Drug discovery: Prioritizes pathway-level hypotheses and molecular connections relevant to drug targets and perturbations.
• Complex disease research: Supports analysis of complex diseases by integrating heterogeneous omic datasets to uncover multi-layer molecular responses.

Methodology:

Constructs a network model from molecular interactions and applies network optimization algorithms to identify high-confidence subnetworks that best explain the integrated omic datasets.