MiRNAs

Predicting miRNA targets utilizing an extended profile HMM

Jan Grau, Daniel Arend, Ivo Grosse, Artemis G. Hatzigeorgiou, Jens Keilwagen, Manolis Maragkakis, Claus Weinholdt, and Stefan Posch

Abstract

The regulation of many cellular processes is influenced by miRNAs, and bioinformatics approaches for predicting miRNA targets evolve rapidly. Here, we propose conditional profile HMMs that learn rules of miRNA-target site interaction automatically from data. We demonstrate that conditional profile HMMs detect the rules implemented into existing approaches from their predictions. And we show that a simple UTR model utilizing conditional profile HMMs predicts target genes of miRNAs with a precision that is competitive compared to leading approaches, although it does not exploit cross-species conservation.

Graphical representation of learned models

• Model learned on miRanda predictions
• Model learned on TargetScan predictions
• Model learned on RNAhybrid predictions
• Model learned on verified targets from mirecords
• Model used for predicting targets