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MicroRNAs

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...

MicroRNAs

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...

MicroRNAs

MicroRNAs

MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...

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Related Experiment Video

Updated: May 16, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees.

Philip H Williams¹, Rod Eyles, Georg Weiller

¹Division of Plant Sciences, Research School of Biology, College of Medicine, Biology & Environment, The Australian National University, Canberra, ACT 0200, Australia.

Journal of Nucleic Acids

|December 5, 2012

Summary

This summary is machine-generated.

A new machine learning model predicts plant microRNAs (miRNAs) without requiring read counts, enabling analysis of diverse sequence data. This cross-species predictor achieves high accuracy, advancing miRNA discovery in plants.

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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

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Published on: January 21, 2020

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Last Updated: May 16, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

MicroRNAs (miRNAs) are small noncoding RNAs regulating protein production.
Existing miRNA prediction tools often require deep-sequencing read counts, limiting their application.
Novel miRNA discovery necessitates methods applicable to various sequence sources like genomic and transcriptomic data.

Purpose of the Study:

To develop a cross-species plant miRNA predictor.
To create a model that does not rely on read counts for prediction.
To enable miRNA identification from diverse sequence data, including short sequences.

Main Methods:

Supervised machine learning utilizing a decision-tree model.
Training the predictor on a cross-section of different plant species.
Utilizing sequence features such as miRNA:miRNA(∗) duplex energy and mismatches.

Main Results:

A novel miRNA-predictive decision-tree model was developed.
The predictor achieves 84.08% sensitivity and 98.53% specificity.
Leave-one-out cross-validation confirmed the model's robust performance across species.

Conclusions:

The developed model accurately predicts plant miRNAs independent of read counts.
This predictor expands the scope of miRNA discovery to various sequence data types.
The approach facilitates efficient identification of novel plant miRNAs.