MicroRNA prediction based on 3D graphical representation of RNA secondary structures

Abstract

: MicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an mRNA sequence. Therefore, it is quite involved to investigate miRNAs experimentally. Thus, machine learning (ML) isfrequently used to overcome such challenges. The key parts of a ML analysis largely depend on the quality of input data and the capacityof the features describing the data. Previously, more than 1000 features were suggested for miRNAs. Here, it is shown that using 36features representing the RNA secondary structure and its dynamic 3D graphical representation provides up to 98% accuracy values.In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classificationof known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naïve Bayes, and random forest. Although the methodis based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that thisapproach might be useful for the analysis of miRNAs from other species.