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Related Experiment Video

Updated: Dec 13, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

High-Throughput Sequencing to Detect DNA-RNA Changes.

Claudio Lo Giudice¹, Graziano Pesole^1,2, Ernesto Picardi^3,4

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

Methods in Molecular Biology (Clifton, N.J.)

|July 31, 2020

Summary

This summary is machine-generated.

Researchers developed a simple computational method to identify RNA editing sites in humans. This protocol uses deep sequencing data (RNA-Seq and DNA-Seq) to analyze adenosine to inosine (A-to-I) RNA editing, enhancing transcriptome diversity studies.

Keywords:

DNA-Seq Epigenetics Next-generation sequencing RNA editing RNA-Seq Transcriptomics

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Last Updated: Dec 13, 2025

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Published on: April 21, 2022

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Deep sequencing technologies enhance the study of complex eukaryotic genomes and transcriptomes.
Posttranscriptional molecular mechanisms like alternative splicing and RNA editing are now studied at higher resolution.
Adenosine to inosine (A-to-I) RNA editing is prevalent in higher eukaryotes, contributing to transcriptome and proteome diversity.

Purpose of the Study:

To present an easy and reproducible computational protocol for identifying RNA editing sites.
To enable the investigation of A-to-I RNA editing in humans.

Main Methods:

Utilizing deep transcriptome sequencing (RNA-Seq) data.
Employing deep genome sequencing (DNA-Seq) data.
Developing a computational protocol for candidate site identification.

Main Results:

The protocol facilitates the identification of potential RNA editing sites.
The method is designed for human samples.
It leverages existing deep sequencing datasets.

Conclusions:

The presented protocol offers a straightforward approach to studying RNA editing.
This method aids in understanding transcriptome diversity.
It supports the investigation of A-to-I RNA editing in human genomics.