A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2

• DOI: 10.4161/rna.21725
• Publication Date: 2012-09-06T19:41:29Z
• Authors: Anastasis Oulas; Nestoras Karathan...; Annita Louloupi; Ioannis Iliopoulos; Kriton Kalantidis; Panayiota Poirazi
• Keywords: Gene Expression Regulation, Neoplastic; MicroRNAs; 0303 health sciences; 03 medical and health sciences; Sequence Analysis, RNA; Neoplasms; Cyclin D2; Humans; RNA, Neoplasm; Algorithms; HeLa Cells; Neoplasm Proteins

Computational methods for miRNA target prediction vary in the algorithm used; and while one can state opinions about strengths or weaknesses of each particular algorithm, fact matter is that they fall substantially short capturing full detail physical, temporal spatial requirements miRNA::target-mRNA interactions. Here, we introduce a novel tool called Targetprofiler utilizes probabilistic learning form hidden Markov model trained on experimentally verified targets. Using large scale protein downregulation data set validate our method compare its performance to existing tools. We find exhibits greater correlation between computational predictions predicts targets more accurately than three other Concurrently, use primer extension identify mature sequence gene recently identified within cancer associated genomic region predict potential Experimental verification ability this small RNA molecule regulate expression CCND2, with documented oncogenic activity, confirms functional role as miRNA. These findings highlight competitive advantage efficacy extracting biologically significant results.