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MicroRNAs01:22

MicroRNAs

3.0K
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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pre-mRNA Processing02:01

pre-mRNA Processing

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In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
Once about 20-40 ribonucleotides have been joined together by RNA polymerase, a group of enzymes adds a “cap” to the 5’ end of the growing transcript. In this process, a 5’ phosphate is replaced by modified guanosine that has a methyl group attached to it (7-Methyl...
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siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

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Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the...
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RNA Interference01:23

RNA Interference

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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
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Small interfering RNAs (siRNA)02:30

Small interfering RNAs (siRNA)

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Pre-mRNA Processing: Modification of pre-mRNA Ends01:35

Pre-mRNA Processing: Modification of pre-mRNA Ends

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In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
Once about 20-40 ribonucleotides have been joined together by RNA polymerase, a group of enzymes adds a cap to the 5' end of the growing transcript. In this process, a 5' phosphate is replaced by modified guanosine that has a methyl group attached (7-methyl guanosine). This 5' cap helps...
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Related Experiment Video

Updated: Jun 6, 2025

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants
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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

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Description Generation Using Variational Auto-Encoders for Precursor microRNA.

Marko Petković1,2, Vlado Menkovski2,3

  • 1Department of Applied Physics and Science Education, Eindhoven University of Technology, 5612AZ Eindhoven, The Netherlands.

Entropy (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new machine learning framework using Variational Auto-Encoders (VAEs) to identify micro RNA (miRNA) genes. The method provides an interpretable description of precursor miRNA (pre-miRNA) structures, improving disease-associated gene identification.

Keywords:
description generationgenerative modelsinterpretabilitymicroRNA

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Micro RNAs (miRNAs) are crucial non-coding RNAs regulating gene expression, implicated in diseases like cancer.
  • Accurate identification of precursor miRNA (pre-miRNA) genomes is vital for understanding disease mechanisms.
  • Current experimental methods for pre-miRNA detection are costly and complex.

Purpose of the Study:

  • To develop a novel computational framework for identifying pre-miRNA.
  • To create an interpretable machine learning model for pre-miRNA structural description.
  • To improve the accuracy and efficiency of miRNA gene identification and classification.

Main Methods:

  • Utilized generative modeling with Variational Auto-Encoders (VAEs) to uncover latent generative factors of pre-miRNA.
  • Developed a decision tree classifier on the lower-dimensional latent space derived from VAEs.
  • Applied the framework to miRNA classification tasks.

Main Results:

  • Achieved high reconstruction performance in modeling pre-miRNA.
  • Demonstrated high classification performance for miRNA identification.
  • Generated an accurate and interpretable structural description of pre-miRNA.

Conclusions:

  • The proposed VAE-based framework offers an effective and interpretable approach for pre-miRNA identification.
  • This method enhances the understanding of miRNA gene regulation and its association with diseases.
  • The framework provides a valuable tool for computational biology and genomic research.