3Omics

3Omics integrates human transcriptomic, proteomic, and metabolomic data and provides correlation, coexpression, phenotype linkage, pathway enrichment, and Gene Ontology analyses to support systems biology investigations.

Key Features:

• Multi-omics integration: Integrates inter- and intra-transcriptomic, proteomic, and metabolomic datasets for combined analysis.
• PubMed text-mining supplementation: Supplements a missing omics dataset by text-mining relevant information from PubMed when only two omics types are provided.
• Correlation networking: Generates inter-omic correlation networks to visualize relationships among transcripts, proteins, and metabolites across conditions or time points.
• Coexpression analysis: Identifies shared functions and co-regulated genes, proteins, and metabolites across omics datasets.
• Phenotype linkage (OMIM): Integrates transcriptomic or proteomic data with Online Mendelian Inheritance in Man (OMIM) to link molecular data with phenotypic traits.
• Pathway enrichment (KEGG/HumanCyc): Performs pathway enrichment analysis using KEGG and HumanCyc databases to identify enriched metabolic pathways from metabolomics data.
• Gene Ontology enrichment: Conducts statistical analyses to detect significantly overrepresented Gene Ontology (GO) terms in transcriptomic datasets.
• Single-omics analysis: Supports analysis of individual transcriptomic, proteomic, or metabolomic datasets independent of multi-omics integration.
• Visualization: Produces visual representations of networks, correlations, and enrichment results for interpretation of multi-omics relationships.

Scientific Applications:

• Systems biology: Enables integrative analysis to study system-level interactions among transcripts, proteins, and metabolites.
• Genomics, proteomics, and metabolomics research: Supports domain-specific analyses within each omics discipline and cross-omics comparisons.
• Disease mechanism analysis: Facilitates holistic investigation of biological processes and disease mechanisms through integrated omics data.
• Biomarker discovery: Aids identification of candidate molecular biomarkers by integrating and correlating multi-omics measurements.
• Therapeutic target identification: Assists in prioritizing potential therapeutic targets by linking molecular changes to pathways and phenotypes.
• Personalized medicine: Supports analyses that contribute to stratification and mechanistic understanding relevant to individualized treatment approaches.

Methodology:

Computational methods explicitly include dataset integration, inter-omic correlation network generation, coexpression analysis, PubMed text-mining to supplement missing omics, integration with OMIM for phenotype linkage, pathway enrichment using KEGG and HumanCyc, and statistical Gene Ontology enrichment analyses.