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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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Quantifying RNA Editing in Deep Transcriptome Datasets.

Claudio Lo Giudice1, Domenico Alessandro Silvestris2, Shalom Hillel Roth3

  • 1Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Bari, Italy.

Frontiers in Genetics
|March 27, 2020
PubMed
Summary
This summary is machine-generated.

Accurate RNA editing detection is crucial for understanding its role in disease. This study highlights how bioinformatics steps significantly impact RNA editing profiling using RNAseq data.

Keywords:
Alu editing indexRNA editingRNAseqdeep sequencingtranscriptome

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • RNA editing, specifically adenosine-to-inosine conversion by ADAR enzymes, is prevalent in human transcriptomes.
  • Dysregulation of RNA editing is linked to various diseases, including autoimmune disorders, neurological conditions, and cancer.
  • Accurate quantification of RNA editing is essential for studying its biological functions and implications in disease.

Purpose of the Study:

  • To investigate the impact of different bioinformatics processing and alignment steps on RNA editing detection using RNAseq data.
  • To identify key metrics for quantifying RNA editing activity and assess their reliability.
  • To improve the accuracy of inosinome repertoire calling for robust RNA editing analysis.

Main Methods:

  • Analysis of real RNAseq data.
  • Evaluation of various bioinformatics pipelines for transcriptome processing.
  • Development and application of metrics to quantify RNA editing levels.

Main Results:

  • Bioinformatics choices significantly influence the detection and quantification of RNA editing events.
  • Specific processing and alignment strategies yield more accurate RNA editing profiles.
  • Established metrics effectively quantify RNA editing activity across different samples.

Conclusions:

  • Optimizing bioinformatics workflows is critical for reliable RNA editing profiling.
  • Accurate RNA editing detection using RNAseq data is achievable with careful methodology.
  • This work provides a framework for robust investigation of RNA editing's role in human health and disease.