NetceRNA: An algorithm for construction of phenotype-specific regulation networks via competing endogenous RNAs

Abstract

By using the competing endogenous RNA (ceRNA) concept, we implemented a web-based application TraceRNA. TraceRNA allows us to interactively construct a regulation network for a specific phenotype by using a disease-specific transcriptome data. In this work, we further extend the TraceRNA with a novel algorithm implementation where we examined the microRNA expression derived from same disease type. The proposed algorithm, NetceRNA, finds an optimized network representation under a certain phenotype context by iteratively perturbing the network and measuring the network configuration change with respect to the original ceRNA network. The resulting algorithm outputs an improved network together with a ranked list of genes and miRNAs which are characteristic of the specific phenotype. To illustrate the utility of NetceRNA, gene expression and microRNA expression data of breast cancer study from The Cancer Genome Atlas (TCGA) were used.