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Related Concept Videos

RNA Editing02:23

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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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A Nonsequencing Approach for the Rapid Detection of RNA Editing
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A Nonsequencing Approach for the Rapid Detection of RNA Editing

Published on: April 21, 2022

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Benchmarking RNA Editing Detection Tools.

David Rodríguez Morales1, Sarah Rennie1, Shizuka Uchida2

  • 1Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark.

Biotech (Basel (Switzerland))
|September 27, 2023
PubMed
Summary
This summary is machine-generated.

Epitranscriptomics research is growing, with RNA editing being a key focus. This study benchmarks bioinformatic tools for detecting RNA editing sites (RES) from RNA sequencing data to guide researchers.

Keywords:
RNA editingRNA sequencingdatabasesepitranscriptomicstools

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Over 170 RNA modifications exist, creating the field of epitranscriptomics.
  • RNA editing, particularly adenosine to inosine (A-to-I) and cysteine to uracil (C-to-U), is frequent in mammalian transcriptomes.
  • RNA editing is detectable from RNA sequencing (RNA-seq) data, offering insights into the epitranscriptome.

Purpose of the Study:

  • To benchmark bioinformatic tools for detecting RNA editing sites (RES) from RNA-seq data.
  • To provide a comprehensive comparison of available tools for RES detection.
  • To offer recommendations for selecting appropriate tools for future studies.

Main Methods:

  • Benchmarking of various bioinformatic tools designed for RES detection.
  • Utilizing previously published RNA-seq datasets for performance evaluation.
  • Comparative analysis of tool performance based on established criteria.

Main Results:

  • Each bioinformatic tool exhibits distinct advantages and disadvantages.
  • Performance variations among tools were observed when analyzing RNA-seq data.
  • The study provides a detailed performance overview of each tool.

Conclusions:

  • Selecting the right bioinformatic tool for RES detection is crucial for researchers and clinicians.
  • This benchmarking study aids in choosing the most suitable tool for specific research needs.
  • Recommendations are provided to facilitate the effective utilization of RNA-seq data for epitranscriptomic analysis.