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Related Concept Videos

MicroRNAs01:22

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...
MicroRNAs01:22

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...
MicroRNAs01:22

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...
DNA Microarrays02:34

DNA Microarrays

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 12, 2026

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
09:06

MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method

Published on: October 7, 2025

Detecting microRNA activity from gene expression data.

Stephen F Madden1, Susan B Carpenter, Ian B Jeffery

  • 1School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.

BMC Bioinformatics
|May 21, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method to identify microRNAs (miRNAs) linked to specific diseases by analyzing gene expression data. The approach successfully pinpointed biologically significant miRNAs across various datasets.

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MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
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Area of Science:

  • Bioinformatics
  • Molecular Biology
  • Genomics

Background:

  • MicroRNAs (miRNAs) are key regulators of gene expression, influencing translation and mRNA degradation.
  • Computational methods are crucial for identifying miRNA targets and understanding their regulatory roles.
  • miRNAs play significant roles in various diseases and biological conditions.

Purpose of the Study:

  • To develop and apply a novel computational technique for predicting disease-associated miRNAs.
  • To integrate predicted miRNA-gene interactions with gene expression microarray data.
  • To identify specific miRNAs implicated in diseases and biological conditions.

Main Methods:

  • Combined correspondence analysis, between-group analysis, and co-inertia analysis (CIA).
  • Utilized miRNA target predictions from multiple databases (TargetScan, PicTar, miRanda).
  • Analyzed gene expression levels from microarray datasets.

Main Results:

  • Developed a method to rank miRNAs based on their association with gene expression differences.
  • Successfully identified biologically relevant miRNAs in papillary thyroid carcinoma, LPS-treated macrophages, and multi-tissue datasets.
  • Demonstrated the method's effectiveness across diverse biological contexts.

Conclusions:

  • Presented an integrated approach combining gene expression data and multi-source miRNA target predictions.
  • The developed technique enables the identification of disease-associated miRNAs.
  • This method offers a powerful tool for miRNA research in various biological and medical fields.