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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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Updated: Jun 16, 2026

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

Published on: April 6, 2012

MTar: a computational microRNA target prediction architecture for human transcriptome.

Vinod Chandra¹, Reshmi Girijadevi, Achuthsankar S Nair

¹Centre for Bioinformatics, University of Kerala, Thiruvananthapuram, India. vinodchandrass@gmail.com

BMC Bioinformatics

|February 4, 2010

Summary

We developed MTar, a machine learning model for microRNA (miRNA) target prediction. MTar accurately identifies miRNA:mRNA interactions using 16 features and an Artificial Neural Network, improving upon existing methods.

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Last Updated: Jun 16, 2026

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

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Published on: April 6, 2012

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

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Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells

Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells

Published on: September 28, 2011

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

MicroRNAs (miRNAs) are crucial regulators of gene expression, inhibiting target messenger RNAs (mRNAs).
Identifying miRNA:mRNA interactions is vital for understanding cellular functions.
There is a need for computational methods to predict miRNA targets.

Purpose of the Study:

To develop an efficient machine learning model for predicting miRNA targets.
To unravel the complex relationships between miRNAs and their target mRNAs.

Main Methods:

Developed MTar, a novel computational architecture for miRNA target prediction.
Utilized 16 positional, thermodynamic, and structural parameters from validated miRNA:mRNA pairs.
Incorporated an Artificial Neural Network (ANN) trained on experimentally verified microRNA targets.

Main Results:

MTar achieved 94.5% sensitivity and 90.5% specificity in miRNA target prediction.
Identified numerous previously unknown targets for various miRNA families.
The model detects all three types of miRNA targets (5' seed-only, 5' dominant, 3' canonical), unlike methods focusing only on 5' complementarity.

Conclusions:

MTar is an effective ANN-based architecture for identifying functional miRNA-mRNA interactions.
The integration of a thermodynamic model and target accessibility enhances prediction accuracy.
MTar offers a more comprehensive approach to miRNA target prediction, particularly for the human transcriptome, compared to existing methods.