PlantMirP-Rice: An Efficient Program for Rice Pre-miRNA Prediction.

Huiyu Zhang,Yuangen Yao,Ming Yi,Hua Wang

doi:10.3390/genes11060662

Abstract

Rice microRNAs (miRNAs) are important post-transcriptional regulation factors and play vital roles in many biological processes, such as growth, development, and stress resistance. Identification of these molecules is the basis of dissecting their regulatory functions. Various machine learning techniques have been developed to identify precursor miRNAs (pre-miRNAs). However, no tool is implemented specifically for rice pre-miRNAs. This study aims at improving prediction performance of rice pre-miRNAs by constructing novel features with high discriminatory power and developing a training model with species-specific data. PlantMirP-rice, a stand-alone random forest-based miRNA prediction tool, achieves a promising accuracy of 93.48% based on independent (unseen) rice data. Comparisons with other competitive pre-miRNA prediction methods demonstrate that plantMirP-rice performs better than existing tools for rice and other plant pre-miRNA classification.

Highlights

MicroRNAs are an important type of short (approximately 20–24 nucleotides)small non-coding RNA, and they are involved extensively in post-transcriptional regulation of gene expression in animals, plants, and viruses [1]
Pre-miRNA, which is exported to the cytoplasm under the action of HASTY protein, is cleaved by Dicer-like (DCL) enzyme into a miRNA duplex, consisting of a miRNA and miRNA* strand. miRNA duplex is further processed into mature miRNA in the cytoplasm
Mature miRNA is included into RNA-induced silencing complex (RISC), and it mediates the degradation or transcription inhibition of messenger RNA through the principle of complementary base pairing [2,3,4,5]

Summary

Introduction

Small non-coding RNA (sRNA), and they are involved extensively in post-transcriptional regulation of gene expression in animals, plants, and viruses [1]. The primary transcript of miRNA gene (pri-miRNA) is mainly transcribed from intergenic regions of the genome by RNA polymerase II, and pri-miRNA is cleaved into miRNA precursor (pre-miRNA) with characteristic stem–loop (hairpin) structure. Wang et al validated that miR164a, as a general negative regulator, is involved in rice immunity against the blast fungus by targeting OsNAC60. They argued that the miR164a/OsNAC60 module may be considered as a common immune regulator for diverse pathogens [8].

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