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Related Concept Videos

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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RNA Splicing01:32

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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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RNA Stability01:53

RNA Stability

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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Bacterial RNA Polymerase00:43

Bacterial RNA Polymerase

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Unlike eukaryotes, bacteria use a single RNA Polymerase (RNAP) to transcribe all genes. The different subunits of bacterial RNAPhave distinct functions. The multisubunit structure of the bacterial RNAP helps the enzyme to maintain catalytic function, facilitate assembly, interact with DNA and RNA, and self-regulate its activity.
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RNA Editing02:23

RNA Editing

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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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Eukaryotic RNA Polymerases00:58

Eukaryotic RNA Polymerases

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RNA Polymerase (RNAP) is conserved in all animals, with bacterial, archaeal, and eukaryotic RNAPs sharing significant sequence, structural, and functional similarities. Among the three eukaryotic RNAPs, RNA Polymerase II is most similar to bacterial RNAP in terms of both structural organization and folding topologies of the enzyme subunits. However, these similarities are not reflected in their mechanism of action.
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Updated: Feb 11, 2026

Sequencing of mRNA from Whole Blood using Nanopore Sequencing
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Advanced deep learning strategies in nanopore RNA sequencing.

Crystal Ling1, Benjamin Lebeau1, Kwoh Chee Keong2

  • 1School of Biological Sciences, Nanyang Technological University, Singapore.

RNA Biology
|February 9, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence, especially deep learning, is revolutionizing the analysis of RNA modifications detected by nanopore sequencing. These advanced computational methods are crucial for understanding the epitranscriptome and its role in disease.

Keywords:
Deep learningRNA modificationsdirect RNA sequencingepitranscriptomenanopore sequencing

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • The epitranscriptome, consisting of RNA chemical modifications, is vital for gene regulation.
  • Dysregulation of RNA modifications is linked to various diseases, highlighting their potential as biomarkers and therapeutic targets.
  • Nanopore direct RNA sequencing offers single-molecule resolution for profiling these modifications.

Purpose of the Study:

  • To review the application of artificial intelligence, particularly deep learning, in interpreting nanopore direct RNA sequencing data for epitranscriptome analysis.
  • To highlight advancements in computational approaches for addressing challenges in RNA modification profiling.

Main Methods:

  • Review of deep learning architectures (CNNs, RNNs) applied to RNA modification detection.
  • Discussion of recent specialized learning frameworks and ensemble strategies.
  • Focus on computational interpretation of nanopore direct RNA sequencing signals.

Main Results:

  • Deep learning is essential for interpreting complex nanopore sequencing data.
  • Advanced DL methods improve resolution and address data challenges like scarcity and noise.
  • Specialized frameworks and ensemble strategies show promise for enhanced epitranscriptome characterization.

Conclusions:

  • AI and deep learning are pivotal for advancing epitranscriptome research using nanopore sequencing.
  • Future opportunities lie in multidisciplinary collaborations between AI and biology.
  • Accurate characterization of the epitranscriptome holds promise for disease biomarker and therapeutic development.