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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA (lncRNA)...
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...

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Related Experiment Video

Updated: Jun 1, 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

Finding cancer-associated miRNAs: methods and tools.

Anastasis Oulas1, Nestoras Karathanasis, Annita Louloupi

  • 1Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.

Molecular Biotechnology
|May 25, 2011
PubMed
Summary
This summary is machine-generated.

Computational methods accelerate the discovery of microRNAs (miRNAs), small RNA molecules implicated in cancer. These tools aid in identifying novel miRNA genes and their roles in tumor development, complementing experimental approaches.

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miRNA Expression Analyses in Prostate Cancer Clinical Tissues

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Last Updated: Jun 1, 2026

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

Published on: October 7, 2025

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An In Vitro Protocol for Evaluating MicroRNA Levels, Functions, and Associated Target Genes in Tumor Cells

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miRNA Expression Analyses in Prostate Cancer Clinical Tissues
11:29

miRNA Expression Analyses in Prostate Cancer Clinical Tissues

Published on: September 8, 2015

Area of Science:

  • Molecular Biology
  • Genetics
  • Bioinformatics

Background:

  • Cancer research historically focused on protein-coding genes.
  • The discovery of non-coding RNAs (ncRNAs), including microRNAs (miRNAs), reveals greater complexity in cancer biology.
  • miRNAs are investigated for their roles as tumor suppressors or oncogenes in carcinogenesis.

Purpose of the Study:

  • To review computational methods for identifying miRNA genes.
  • To provide an overview of the methodologies used by these tools.
  • To highlight the contribution of computational approaches to understanding miRNA roles in cancer.

Main Methods:

  • Review of existing computational tools for miRNA gene identification.
  • Analysis of methodologies focusing on miRNA biogenesis features.
  • Integration with high-throughput sequencing data.

Main Results:

  • Computational procedures effectively predict novel miRNA genes.
  • These methods identify key features characterizing miRNA regulatory units.
  • Computational approaches expedite and reduce the cost of experimental verification.
  • Combined with deep sequencing, they help discover molecular signatures of miRNA deregulation in human tumors.

Conclusions:

  • Computational methods are essential complements to experimental miRNA identification.
  • These tools enhance the speed and efficiency of discovering miRNA genes.
  • Computational approaches significantly contribute to unraveling the complex role of miRNAs in cancer development.