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RNA Interference01:23

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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.
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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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Circular RNA-MicroRNA-MRNA interaction predictions in SARS-CoV-2 infection.

Yılmaz Mehmet Demirci1, Müşerref Duygu Saçar Demirci2

  • 1Faculty of Engineering, Engineering Science Department, Abdullah Gül University, 38080Kayseri, Turkey.

Journal of Integrative Bioinformatics
|March 16, 2021
PubMed
Summary

This study developed a machine learning model to analyze human microRNA (miRNA) expression during SARS-CoV-2 infection. The research identified potential antiviral mechanisms involving miRNAs targeting viral messenger RNAs (mRNAs).

Keywords:
SARS-CoV-2circRNAgene regulationmachine learningmiRNA

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Noncoding RNAs, including microRNAs (miRNAs) and circular RNAs (circRNAs), regulate gene expression post-transcriptionally.
  • These molecules play roles in cellular processes, especially during infections and host-pathogen interactions.
  • Human miRNAs are investigated for their potential to target SARS-CoV-2 mRNAs as an antiviral defense.

Purpose of the Study:

  • To develop a machine learning workflow for predicting differential expression patterns of human miRNAs during SARS-CoV-2 infection.
  • To explore potential targeting interactions between human circRNAs and miRNAs.
  • To investigate interactions between human miRNAs and SARS-CoV-2 viral mRNAs.

Main Methods:

  • A machine learning-based analysis workflow was designed for miRNA expression profiling.
  • Thirty-six features were defined based on miRNA hairpin secondary structures for graphical representation.
  • Computational methods were used to predict targeting interactions between RNAs.

Main Results:

  • The study successfully developed a machine learning workflow for analyzing miRNA expression patterns.
  • Potential targeting interactions between human miRNAs and SARS-CoV-2 mRNAs were identified.
  • Interactions between human circRNAs and miRNAs were also investigated.

Conclusions:

  • Machine learning approaches can effectively analyze miRNA expression during viral infections like SARS-CoV-2.
  • Human miRNAs show potential as antiviral agents by targeting viral mRNAs.
  • Further research into circRNA-miRNA and miRNA-viral mRNA interactions is warranted.