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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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Master Transcription Regulators02:23

Master Transcription Regulators

Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...

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Related Experiment Video

Updated: Jul 9, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

MiRformer: a dual-transformer-encoder framework for predicting microRNA-mRNA interactions from paired sequences.

Jiayao Gu1,2, Can Chen2, Yue Li1,2

  • 1School of Computer Science, McGill University, Montréal, QC, H3A 0G4, Canada.

Bioinformatics (Oxford, England)
|July 7, 2026
PubMed
Summary
This summary is machine-generated.

MiRformer, a novel transformer framework, accurately predicts microRNA-messenger RNA interactions and pinpoints binding/cleavage sites. This tool enhances understanding of post-transcriptional regulation and RNA therapeutics development.

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A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools
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Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
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Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

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Related Experiment Videos

Last Updated: Jul 9, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools
09:29

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
07:23

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Published on: June 15, 2016

Area of Science:

  • Computational Biology
  • Genomics
  • Molecular Biology

Background:

  • MicroRNAs (miRNAs) are key regulators of gene expression, impacting translation and mRNA stability.
  • Precise identification of miRNA-mRNA interactions and binding/cleavage sites is crucial for RNA biology and therapeutics.
  • Current computational methods face limitations in scalability, feature engineering, and interpretability for long mRNA sequences.

Purpose of the Study:

  • To develop a novel computational framework for accurate prediction and localization of miRNA-mRNA interactions.
  • To improve the understanding of post-transcriptional gene regulation mechanisms.
  • To provide an interpretable tool for RNA therapeutics research.

Main Methods:

  • Developed MiRformer, a transformer-based framework utilizing a dual-encoder architecture.
  • Implemented a sliding-window attention mechanism to efficiently process kilobase-long mRNA sequences at nucleotide resolution.
  • Validated performance on interaction prediction, binding-site, and cleavage-site identification using experimental data.

Main Results:

  • MiRformer achieved state-of-the-art performance in predicting miRNA-mRNA interactions and localizing binding and cleavage sites.
  • Attention mechanisms in MiRformer provided biological interpretability by highlighting miRNA seed regions and interaction signals.
  • Joint prediction of binding and cleavage sites revealed frequent co-localization, supporting miRNA-mediated degradation.

Conclusions:

  • MiRformer offers a powerful and interpretable deep learning approach for analyzing miRNA-mRNA interactions.
  • The framework advances the prediction of critical regulatory sites, aiding in the development of RNA-based therapies.
  • MiRformer's ability to model long sequences and provide interpretable results addresses limitations of existing methods.