A pre-microRNA classifier by structural and thermodynamic motifs

Abstract

MicroRNAs (miRNAs) have been found in diverse organisms and play critical role in gene expression regulations of many essential cellular processes. Discovery of miRNAs and identification of their target genes are fundamental to the study of such regulatory circuits. To distinguish the real pre-miRNA from other stem loop hairpins with similar stem loop (pseudo pre-miRNA) is an important task in molecular biology. From the analysis of experimentally proved pre-miRNAs, we identified 17 parameters for miRNA formation. These parameters are grouped into two categories: structural and thermodynamic properties of the pre-miRNAs. A set of feature vector was formed from the pre-miRNA-like hairpins of human, mouse and rat. A feed forward multi layer perceptron Artificial Neural Network (ANN) classifier is trained by these feature vectors. This classifier is an application program, that decide whether a given sequence is a pre-miRNA like hairpin sequence or not. If the sequence is a pre-miRNA like hairpin, then the ANN classifier will predict whether it is a real pre-miRNA or a pseudo premiRNA. The approach can classify correctly the precursors of Human Mouse and Rat, with an average sensitivity of 97.40% and specificity of 95.85%. When compared with previous approaches, MiPred, mR-abela, ProMiR and Triplet SVM classifier, current approach was greater in total accuracy.