MicroRNA Promoter Identification in Human With a Three-level Prediction Method.

Abstract

The accurate annotation of miRNA promoters is critical for the mechanistic understanding of miRNA gene regulation. Various computational methods have been developed for the prediction of miRNA promoters solely employing a single classifier. Most of these computational methods extract either sequence features or one-sided signal features, and the accuracy and reliability of predictions need to be improved. To address these issues, we present miPTP, a three-level prediction method that combines SVM, RF, and correlation coefficients. It is capable of identifying miRNA promoters based on both DNA sequence and ChIP-Seq data (RPol II). By sequentially integrating these two types of information sources with the three methods selected, miPTP can identify miRNA promoters with higher accuracy and sensitivity compared to specific existing methods. Finally, the reliability of miPTP is validated by examining the conservation, CpG content, and activating histone marks in the identified miRNA promoters.