A filtering method for identification of significant target mrnas of coexpressed and differentially expressed microrna clusters

Abstract

MicroRNA (miRNA) binding is primarily based on sequence,but structure-specific binding is also possible.Various prediction algorithms have been developed for predicting miRNA target genes; the results,however,have relatively high levels of false positives,and the degree of overlap between predicted targets from different methods is poor or null.We devised a new method for identifying significant miRNA target genes from an extensive list of predicted miRNA target gene relationships using hypergeometric distributions.We evaluated our method in statistical and semantic aspects using a common miRNA cluster from six solid tumors.Our method provides statistically and semantically significant miRNA target genes.Complementing target prediction algorithms with our proposed method may have a significant synergistic effect in finding and evaluating functional annotation and enrichment analysis for miRNA.