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mirMachine: A One-Stop Shop for Plant miRNA Annotation

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Protocols for miRNA Target Prediction in Plants.

Gaurav Sablok^1,2, Kun Yang³, Xiaopeng Wen³

¹Finnish Museum of Natural History (Botany), University of Helsinki, Helsinki, Finland. sablokg@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)

|April 10, 2019

Summary

Next-generation sequencing aids in identifying microRNAs (miRNAs) and their stress response roles. This study details a protocol for pinpointing miRNA targets using six computational prediction tools.

Keywords:

Arabidopsis thaliana Cleaveland MicroRNA TAPIR TarHunter Target prediction psRNATarget psRobot

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Last Updated: Jan 26, 2026

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Plant molecular biology
Genomics
Bioinformatics

Background:

MicroRNAs (miRNAs) are key regulators of plant gene expression at the posttranscriptional level.
miRNA-mRNA interactions influence plant adaptation to abiotic and biotic stresses.
Understanding miRNA targets is crucial for deciphering plant systems biology.

Purpose of the Study:

To present a detailed protocol for identifying microRNA targets in plants.
To evaluate the efficacy of six different miRNA target prediction tools.

Main Methods:

Utilizing next-generation sequencing data for miRNA identification.
Employing six distinct computational tools for in silico miRNA target prediction.
Comparative analysis of prediction tool outputs.

Main Results:

The study provides a standardized protocol for miRNA target identification.
Highlights the utility of multiple prediction tools for robust target discovery.
Facilitates the identification of miRNAs involved in plant stress responses.

Conclusions:

Accurate identification of miRNA targets is essential for understanding plant stress adaptation.
The presented protocol enables comprehensive miRNA target discovery.
This methodology supports advancements in plant genomics and breeding.