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Related Experiment Video

Updated: Jan 30, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences.

Ting Zhang^1,2, Lie Ju³, Jingjing Zhai^1,2

¹State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Yangling, China.

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

|February 1, 2019

Summary

This summary is machine-generated.

This study introduces miRLocator, a computational tool using machine learning to accurately predict microRNA (miRNA) locations within precursor miRNAs (pre-miRNAs). The freely available Python implementation and web interface facilitate miRNA identification for researchers.

Keywords:

Machine learning Plant Pre-miRNA Prediction Random forest Secondary structure miRNA-miRNA* duplex miRNAs

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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

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

MicroRNAs (miRNAs) are crucial noncoding regulatory RNAs involved in gene expression.
Computational methods are essential for large-scale miRNA identification alongside experimental techniques.
Accurate prediction of miRNA localization within precursor structures (pre-miRNAs) is vital for understanding their function.

Purpose of the Study:

To present a detailed protocol for the miRLocator tool.
To provide a user-friendly Python implementation and web interface for miRNA prediction.
To enable automated training of machine learning models for specific miRNA sets.

Main Methods:

Utilizing machine learning algorithms for miRNA prediction.
Extracting informative features from miRNA-miRNA* duplexes.
Employing an optimized ratio of positive and negative samples for model training.
Developing a Python implementation and web interface for the miRLocator tool.

Main Results:

miRLocator accurately predicts the localization of most likely miRNAs within their pre-miRNAs.
The machine learning model can be automatically trained on user-defined miRNA sets.
A functional Python implementation and web interface are now available.

Conclusions:

miRLocator offers a powerful and accessible computational approach for miRNA identification.
The tool enhances genomic-scale miRNA discovery through accurate localization prediction.
Freely available resources (source code, web interface) support academic research in miRNA biology.