CeNet Omnibus

CeNet Omnibus constructs and analyzes competing endogenous RNA (ceRNA) networks to study post-transcriptional regulation and identify candidate regulatory interactions and biomarkers.

Key Features:

• Unified Framework: Provides an integrated solution for constructing and analyzing ceRNA networks to investigate post-transcriptional regulatory mechanisms.
• Measurement Selection: Supports multiple association measures including Pearson correlation coefficient (PCC), mutual information (MI), and liquid association (LA) for identifying ceRNA pairs.
• Topological Analysis: Computes and examines network topological properties to characterize ceRNA network structure.
• Functional Module Detection: Detects functional modules within ceRNA networks to identify groups of co-regulated RNAs.
• Gene Enrichment Analysis: Performs gene enrichment analysis to link network components to biological functions and pathways.
• Survival Analysis: Integrates survival analysis to identify survival-associated network features and candidate biomarkers.
• Customizability and Expandability: Allows customization of analysis parameters and expansion of methods within the framework.

Scientific Applications:

• ceRNA regulation studies: Investigation of competing endogenous RNA interactions and post-transcriptional regulatory mechanisms.
• Biomarker discovery: Identification of survival-associated biomarkers from network features.
• Candidate regulator selection: Selection of candidate regulators of disease genes based on network relationships.
• Long noncoding RNA function prediction: Prediction of functions for long noncoding RNAs through network context and enrichment analysis.

Methodology:

Network construction using Pearson correlation coefficient (PCC), mutual information (MI), and liquid association (LA), followed by network topology analysis, functional module detection, gene enrichment analysis, and survival analysis.