ceRNA

ceRNA constructs competing endogenous RNA (ceRNA) networks from paired RNA-seq data to reveal lncRNA–miRNA–mRNA interactions that regulate gene expression.

Key Features:

• ceRNA network construction: Builds ceRNA networks from RNA-seq data by selecting biologically meaningful ceRNA crosstalks.
• Candidate identification: Identifies candidate ceRNA crosstalks based on a competition regulation mechanism between lncRNAs and mRNAs for miRNA binding.
• Competition scoring (PMI): Integrates a competition rule with pointwise mutual information (PMI) to calculate a competition score that quantifies association strength between RNA pairs.
• Significance-based selection: Selects ceRNA crosstalks with significant competition scores to form the final network.
• Biological mechanism: Explicitly models lncRNAs acting as molecular sponges that compete with mRNAs for miRNA binding.
• Total RNA expression consideration: Addresses total RNA expression characteristics when constructing ceRNA interactions from paired RNA-seq data.

Scientific Applications:

• Breast cancer analysis: Applied to paired RNA-seq data to capture regulatory mechanisms relevant to breast cancer.
• Disease mechanism discovery: Reveals ceRNA-mediated regulatory interactions that inform gene regulation in disease contexts.
• Therapeutic target identification: Identifies candidate regulatory axes (lncRNA–miRNA–mRNA) that may suggest potential therapeutic targets.
• General RNA-seq studies: Applicable to other RNA-seq datasets to investigate ceRNA interactions across diseases and biological conditions.

Methodology:

Identify candidate ceRNA crosstalks using a competition regulation mechanism; compute competition scores by integrating a competition rule with pointwise mutual information (PMI); and select crosstalks with significant competition scores to construct the final ceRNA network from paired RNA-seq data.