ceRNAnetsim

ceRNAnetsim simulates regulations of competing endogenous RNA (ceRNA) expression levels after expression level changes in one or more miRNAs or mRNAs within miRNA:target interaction networks.

Key Features:

• ceRNA incorporation: The methodology can incorporate any ceRNA type, including circRNA and lincRNA, into miRNA:target interaction networks.
• miRNA distribution modeling: The package distributes miRNA expression over available ceRNAs where each ceRNA attracts miRNAs proportional to its amount.
• miRNA effector parameters: Multiple parameters can modify miRNA effect on its target, including seed type, binding energy, and binding location.
• Graph-based computation: Functions handle the given dataset as a graph object and processes progress via edge and node variables.
• Network-wide perturbation analysis: The approach calculates network-wide effects of perturbations in expression levels of one or more nodes in the presence or absence of miRNA interaction factors.
• Sponge effect integration: The model takes the sponge effect into account to help unveil crosstalk between nodes in miRNA:target networks.
• Scalability: The tool is scalable and can be integrated with emerging miRNA effectors such as circRNAs and lncRNAs.

Scientific Applications:

• Cancer network analysis: Analysis of large-scale miRNA:target networks from breast cancer patients identified highly perturbing genes that coincide with breast cancer-associated genes and miRNAs.
• Unforeseen regulation discovery: The model reveals unforeseen regulations that are only evident in the network context through indirect and complex interactions.
• ceRNA crosstalk investigation: The approach helps unveil crosstalk between various types of non-coding RNA in humans through competing endogenous RNA regulations.

Methodology:

The package defines a network-based model that incorporates miRNA:ceRNA interactions with expression values. miRNA expression is distributed over available ceRNAs proportional to ceRNA amount, with optional modification by miRNA effector parameters. Processes progress via edge and node variables on graph objects.