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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
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Updated: Jan 10, 2026

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Predict SARS-CoV-2 genome interactions based on RNA language models.

Weiwei Shen1,2,3, Yixue Li2,3,4,5,6, Liucun Zhu1

  • 1School of Life Sciences, Shanghai University, Shanghai 200444, China.

Biosafety and Health
|November 20, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA-RNA interactions using vRIC-Seq data. This computational approach enhances understanding of viral functions and aids therapeutic development.

Keywords:
DeepRAMOne-hot encodingRNA language modelRNA sequenceRNAErnieRibonucleic acid (RNA)-RNA interactionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomeWord2Vec

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Area of Science:

  • Virology
  • Computational Biology
  • Genomics

Background:

  • Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) relies on intricate RNA-RNA interactions for replication and host cell engagement.
  • Understanding these viral RNA interactions is key to deciphering viral regulation and immune evasion strategies.

Purpose of the Study:

  • To apply machine learning techniques for analyzing and predicting RNA-RNA interactions within the SARS-CoV-2 genome.
  • To evaluate the efficacy of various computational models in identifying viral RNA-RNA interactions.

Main Methods:

  • Utilized virion RNA in situ conformation sequencing (vRIC-Seq) data.
  • Employed machine learning algorithms including One-hot coding, Word2Vec, neural networks, and the RNAErnie language model.

Main Results:

  • Achieved significant predictive accuracy in identifying the presence or absence of RNA-RNA interactions.
  • Demonstrated the effectiveness of advanced computational models in analyzing complex viral RNA networks.

Conclusions:

  • The developed machine learning framework offers a novel approach for investigating RNA-RNA interactions in coronaviruses and other viruses.
  • This study opens new avenues for developing targeted antiviral therapies by enhancing the comprehension of viral biology.