miRNAFinder: A Comprehensive Web Resource for Plant Pre-microRNA Classification

Abstract

microRNAs (miRNAs) are known as one of the small non-coding RNA molecules that control the expression of genes at the RNA level, while some operate at the DNA level. They typically range from 20-24 nucleotides in length and can be found in the plant and animal kingdoms as well as in some viruses. Computational approaches have overcome the limitations of the experimental methods and have performed well in identifying miRNAs. Compared to mature miRNAs, precursor miRNAs (pre-miRNAs) are long and have a hairpin loop structure with structural features. Therefore, most in-silico tools are implemented for pre-miRNA identification. This study presents a multilayer perceptron (MLP) based classifier implemented using 180 features under sequential, structural, and thermodynamic feature categories for plant pre-miRNA identification. This classifier has a 92% accuracy, a 94% specificity, and a 90% sensitivity. We have further tested this model with other small non-coding RNA types and obtained 78% accuracy. Furthermore, we introduce a novel dataset to train and test machine learning models, addressing the overlapping data issue in the positive training and testing datasets presented in PlantMiRNAPred for the classification of real and pseudo-plant pre-miRNAs. The new dataset and the classifier that can be used with any plant species are deployed on a web server freely accessible at http://mirnafinder.shyaman.me/.