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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...

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Related Experiment Video

Updated: Jun 20, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

Assessing potential miRNA targets based on a Markov model.

Hao-Yue Fu¹, Ding-Yu Xue, Xiang-de Zhang

¹College of Sciences, Northeastern University, Shenyang, China. fuhaoyue@tom.com

Genetics and Molecular Research : GMR

|September 5, 2009

Summary

This summary is machine-generated.

Researchers developed a Markov model to score microRNA target predictions. This model effectively identifies accurate microRNA targets by analyzing base-pairing patterns, improving experimental validation efficiency.

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Last Updated: Jun 20, 2026

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Area of Science:

Molecular Biology
Bioinformatics
Genetics

Background:

MicroRNA (miRNA) research primarily focuses on gene identification, with limited experimental validation of mRNA targets.
Computational tools predict numerous miRNA targets, necessitating methods for accurate assessment.
Understanding miRNA-target interactions is crucial for gene regulation studies.

Purpose of the Study:

To develop and evaluate a Markov model for assessing the accuracy of predicted microRNA targets.
To analyze base-pairing binding patterns of known miRNA targets to build a predictive model.
To score potential miRNA targets identified by computational programs based on known patterns.

Main Methods:

Utilized a Markov model to analyze base-pairing patterns in experimentally validated miRNA targets.
Applied the model to assess potential human miRNA targets predicted by four established computational programs.
Assigned scores to each potential target based on consistency with known binding patterns.

Main Results:

The developed Markov model successfully identified predicted targets with base-pairing patterns consistent with known miRNA targets.
Potential targets scoring above a defined cutoff value were validated by the model.
The model demonstrated efficiency in evaluating the accuracy of computational miRNA target predictions.

Conclusions:

The Markov model provides an effective method for evaluating computational miRNA target predictions.
This approach enhances the reliability of predicted targets for experimental validation.
The study contributes to improving the accuracy of microRNA target identification in human species.