GraphOmics

GraphOmics integrates transcriptomics, proteomics, and metabolomics data by mapping biological entities onto common biochemical reactions and Reactome pathways to enable horizontal multi-omics analysis.

Key Features:

• Data Integration: Performs horizontal integration of transcriptomics, proteomics, and metabolomics by mapping entities from different datasets to common biochemical reactions and pathways.
• Pathway Mapping (Reactome): Maps entities and pathways to Reactome to link molecular measurements to established biochemical pathways.
• Data Browser: Provides functionality to rank, sort, and filter mapped entities and pathways based on statistical significance (p-values) and fold changes.
• Heatmaps and Clustering: Generates heatmaps and applies clustering methods to reveal patterns and relationships across integrated omics data.
• Case Studies: Demonstrated on complex datasets including Zebrafish regeneration and Covid-19 human studies.

Scientific Applications:

• Systems Biology: Integration of multi-omics measurements to study molecular interactions and pathway-level relationships.
• Disease Mechanism Discovery: Identification of pathway perturbations and molecular changes relevant to disease states.
• Drug Discovery and Biomarker Identification: Prioritization of entities and pathways by statistical significance and fold change for translational research.
• Pathway Elucidation: Mapping experimental omics data to Reactome pathways to support hypothesis generation about biochemical mechanisms.

Methodology:

Entities from transcriptomics, proteomics, and metabolomics are mapped onto biochemical reactions and Reactome pathways for horizontal integration, with statistical ranking by p-values and fold changes and visualization via heatmaps and clustering.