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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...

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 ends...

piRNA - Piwi-interacting RNAs

piRNA - Piwi-interacting RNAs

PIWI-interacting RNAs, or piRNAs, are the most abundant short non-coding RNAs. More than 20,000 genes have been found in humans that code for piRNAs while only 2000 genes have been found for miRNAs. piRNAs can act at the transcriptional and post-transcriptional levels and have a vital role in silencing transposable elements present in germ cells. They are also involved in epigenetic silencing and activation. Previously, they were thought to function only in germ cells but new evidence suggests...

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Related Experiment Video

Updated: May 15, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

MiRmat: mature microRNA sequence prediction.

Chenfeng He¹, Ying-Xin Li, Guangxin Zhang

¹The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, School of Life Science, Nanjing University, Nanjing, China.

|January 10, 2013

Summary

A new method, MiRmat, accurately predicts mature microRNA sequences by analyzing RNA structure and using Random Forest. This tool aids in identifying microRNAs and understanding gene regulation across vertebrates.

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Area of Science:

Bioinformatics
Computational Biology
Molecular Biology

Background:

MicroRNAs (miRNAs) are crucial regulators of gene expression, processed from primary transcripts by Drosha and Dicer enzymes.
Mature miRNA sequences, particularly the seed region, dictate target mRNA binding and specificity.
Accurate prediction of mature miRNA sequences is essential for discovering novel miRNAs, their targets, and mapping genome-wide post-transcriptional regulatory networks.

Purpose of the Study:

To develop and validate a computational method for accurately predicting mature microRNA sequences.
To improve the identification of Drosha and Dicer processing sites in microRNA biogenesis.
To provide a tool for advancing the study of miRNA function and regulation in vertebrates.

Main Methods:

Developed MiRmat, a two-part method for predicting Drosha and Dicer processing sites.
Utilized the conserved free energy distribution patterns of vertebrate microRNA hairpin structures.
Employed the Random Forest algorithm for sequence prediction based on structural features.

Main Results:

MiRmat achieved high accuracy in predicting Drosha (77.8%) and Dicer (92.8%) processing sites within 2 nt deviation on a vertebrate test set.
Demonstrated robust performance on novel microRNA families, with identification rates of 71.9% for Drosha and 87.2% for Dicer sites.
Outperformed existing state-of-the-art methods in predicting mature microRNA sequences.

Conclusions:

MiRmat effectively predicts mature microRNA sequences using RNA stem-loop free energy distributions and Random Forest.
The method exhibits superior performance compared to current tools and is applicable across vertebrate species.
MiRmat is freely accessible online for research use.