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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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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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DPred_3S: identifying dihydrouridine (D) modification on three species epitranscriptome based on multiple

Jinjin Ren1,2, Xiaozhen Chen1, Zhengqian Zhang1

  • 1Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China.

Frontiers in Genetics
|January 3, 2024
PubMed
Summary

Dihydrouridine (D) is a crucial RNA modification found across life. A new machine learning model, DPred_3S, accurately predicts D sites in yeast and bacteria, advancing epitranscriptome research.

Keywords:
Escherichia coliSaccharomyces cerevisiaeSchizosaccharomyces pombedihydrouridinemachine learning

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Dihydrouridine (D) is a conserved RNA modification in tRNA across all life domains.
  • D modification impacts RNA structure, function, and is linked to diseases and evolution.
  • Emerging evidence suggests dihydrouridine also occurs on mRNA.

Purpose of the Study:

  • To develop a computational framework for identifying dihydrouridine (D) modifications in the epitranscriptome.
  • To leverage machine learning and epitranscriptome sequencing data for D site prediction.
  • To analyze D modification patterns across different species.

Main Methods:

  • Development of a machine learning-based prediction framework named "DPred_3S".
  • Utilized epitranscriptome sequencing data for training and validation for the first time.
  • Evaluated model performance using F-score and AUROC, comparing various machine learning algorithms.

Main Results:

  • DPred_3S achieved high prediction accuracy with AUROC scores of 0.955 for Saccharomyces cerevisiae, 0.946 for Escherichia coli, and 0.905 for Schizosaccharomyces pombe.
  • Optimal sequence features for D site prediction were identified.
  • Model performance varied across species, indicating potential limitations in cross-species prediction.

Conclusions:

  • Sequence context is a significant determinant for dihydrouridine site identification.
  • The DPred_3S framework demonstrates the feasibility of machine learning for epitranscriptome analysis.
  • Further research is needed to address challenges in cross-species epitranscriptome prediction.