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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...

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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...

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Related Experiment Video

Updated: Jun 18, 2026

Enrichment of Native Lipoprotein Particles with microRNA and Subsequent Determination of Their Absolute/Relative microRNA Content and Their Cellular Transfer Rate

Enrichment of Native Lipoprotein Particles with microRNA and Subsequent Determination of Their Absolute/Relative microRNA Content and Their Cellular Transfer Rate

Published on: May 9, 2019

A personalized microRNA microarray normalization method using a logistic regression model.

Bin Wang¹, Xiao-Feng Wang, Paul Howell

¹Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA.

Bioinformatics (Oxford, England)

|November 26, 2009

Summary

This summary is machine-generated.

A new logistic regression model effectively normalizes microRNA (miRNA) array data, improving accuracy for cancer research. This method addresses limitations of traditional approaches for analyzing small non-coding RNA expression profiling.

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Published on: October 28, 2011

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

MicroRNAs (miRNAs) are small non-coding RNAs regulating critical biological processes like cell proliferation and tumorigenesis.
Traditional normalization methods for messenger RNA (mRNA) are unsuitable for miRNA expression profiling due to low miRNA numbers and lack of endogenous controls.
New adaptive normalization methods are crucial for accurate miRNA analysis.

Purpose of the Study:

To develop and validate a novel normalization method for miRNA microarray data.
To improve the accuracy of miRNA expression profiling analysis.

Main Methods:

Utilized Locked Nucleic Acid (LNA)-based miRNA arrays for profiling.
Employed Taqman-based quantitative real-time polymerase chain reaction (qRT-PCR) for pre-evaluation.
Developed a logistic regression model based on qRT-PCR results for array data normalization.
Validated normalized data using 20 additional miRNAs.

Main Results:

The logistic regression model effectively calibrated variance across arrays.
The proposed normalization method significantly improved miRNA microarray discovery accuracy compared to existing methods.
Expression levels of selected miRNAs were successfully post-validated.

Conclusions:

A logistic regression-based normalization approach offers a robust solution for miRNA microarray analysis.
This method enhances the reliability of miRNA expression profiling in biological and clinical research.
The developed model and datasets are publicly available for use.