PlantMirP2: An Accurate, Fast and Easy-To-Use Program for Plant Pre-miRNA and miRNA Prediction.

Dashuai Fan,Yuangen Yao,Ming Yi

doi:10.3390/genes12081280

Dashuai Fan, Yuangen Yao + Show 1 more

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https://doi.org/10.3390/genes12081280

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Abstract

MicroRNAs (miRNAs) are a kind of short non-coding ribonucleic acid molecules that can regulate gene expression. The computational identification of plant miRNAs is of great significance to understanding biological functions. In our previous studies, we have put firstly forward and further developed a set of knowledge-based energy features to construct two plant pre-miRNA prediction tools (plantMirP and riceMirP). However, these two tools cannot be used for miRNA prediction from NGS (Next-Generation Sequencing) data. In addition, for further improving the prediction performance and accessibility, plantMirP2 has been developed. Based on the latest dataset, plantMirP2 achieves a promising performance: 0.9968 (Area Under Curve, AUC), 0.9754 (accuracy), 0.9675 (sensitivity) and 0.9876 (specificity). Additionally, the comparisons with other plant pre-miRNA tools show that plantMirP2 performs better. Finally, the webserver and stand-alone version of plantMirP2 are available.

Highlights

Genes 2021, 12, 1280. https://MicroRNAs are small noncoding RNAs with a length of about 20–24 nucleotides [1]
MdmiR285N microRNA is involved in the biotic stress response, plant growth and reproductive development in apple (Malus × domestica) and Arabidopsis thaliana [3]
We argue that the combination of high-throughput sequencing-based with machine learning-based methods will further boost the performance of miRNA prediction, narrow the range of selection and reduce the false-positive rate

Summary

Introduction

Genes 2021, 12, 1280. https://MicroRNAs (miRNAs) are small noncoding RNAs with a length of about 20–24 nucleotides [1]. Plant miRNAs play important functions in plant growth, development and responses to abiotic and biotic stresses [2]. MdmiR285N microRNA is involved in the biotic stress response, plant growth and reproductive development in apple (Malus × domestica) and Arabidopsis thaliana [3]. Many methods have been developed to this area. These methods are roughly divided into two categories: One is based on biological experiments [6,7,8,9], and the other is based on computational prediction. Traditional experimental methods are usually time-consuming, laborious and inefficient, and may even miss miRNAs with low expression levels. Computational methods can make up for these shortages of traditional experimental methods, and attract more and more attention

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