miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences

• Published in: PloS one Vol. 10; no. 11; p. e0142753
• Main Authors: Cui, Haibo, Zhai, Jingjing, Ma, Chuang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.11.2015; Public Library of Science (PLoS)
• Subjects: Acids; Algorithms; Area Under Curve; Artificial intelligence; Base Sequence; Bioinformatics; Biological activity; Computational biology; Computer applications; Databases, Genetic; Feature extraction; Gene expression; Gene sequencing; Genetic research; Genomes; Identification; Innovations; Learning algorithms; Life sciences; Machine Learning; Mathematical models; MicroRNA; MicroRNAs; MicroRNAs - chemistry; MicroRNAs - metabolism; miRNA; Non-coding RNA; Plant growth; Plant species; Plants - genetics; Prediction models; Properties; Ribonucleic acid; RNA; RNA Precursors - genetics; ROC Curve; Software; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0142753

MicroRNAs (miRNAs) are a class of short, non-coding RNA that play regulatory roles in a wide variety of biological processes, such as plant growth and abiotic stress responses. Although several computational tools have been developed to identify primary miRNAs and precursor miRNAs (pre-miRNAs), very few provide the functionality of locating mature miRNAs within plant pre-miRNAs. This manuscript introduces a novel algorithm for predicting miRNAs named miRLocator, which is based on machine learning techniques and sequence and structural features extracted from miRNA:miRNA* duplexes. To address the class imbalance problem (few real miRNAs and a large number of pseudo miRNAs), the prediction models in miRLocator were optimized by considering critical (and often ignored) factors that can markedly affect the prediction accuracy of mature miRNAs, including the machine learning algorithm and the ratio between training positive and negative samples. Ten-fold cross-validation on 5854 experimentally validated miRNAs from 19 plant species showed that miRLocator performed better than the state-of-art miRNA predictor miRdup in locating mature miRNAs within plant pre-miRNAs. miRLocator will aid researchers interested in discovering miRNAs from model and non-model plant species.