Edi-Set-Flow: detecting differential RNA-editing at scale
- 1The Walter and Eliza Hall Institute
Abstract
Background: Adenosine-to-inosine (A-to-I) RNA editing is a post-transcriptional modification catalysed by ADAR enzymes that can alter codons, splicing patterns, and RNA secondary structures. RNA editing is essential for innate immunity and nervous system development, and its dysregulation is implicated in neurological disorders, cancers, and autoimmune diseases. Despite its biological importance, accurately detecting RNA editing from RNA-seq data remains technically challenging, and robust inference of differential editing across experimental conditions is not straightforward. Findings: We have developed Edi-Set-Flow, a reproducible and scalable pipeline for transcriptome-wide A-to-I RNA editing analysis of bulk RNA-seq data. Implemented in Nextflow, the pipeline takes raw FASTQ files as input, trims reads and filters for quality, aligns reads to the reference genome, and identifies editing sites with JACUSA2. Common genetic variants are excluded using the gnomAD population database. Identified sites are annotated for genetic feature context and predicted functional consequences, and results are summarised in a user-friendly HTML report. The pipeline scales efficiently to hundreds or thousands of samples and is suitable for large datasets such as GTEx. An accompanying R package enables advanced analyses, including model fitting, hypothesis testing, false discovery rate control, and visualisation, thereby facilitating reliable statistical comparisons of editing across experimental groups. Applying Edi-Set-Flow to GTEx brain RNA-seq data, we uncovered distinct RNA editing signatures across brain tissues and recovered known protein-recoding events. Conclusions: Edi-Set-Flow provides researchers with a robust, user-friendly, scalable, end-to-end solution for efficiently identifying and interpreting biologically meaningful RNA editing events across diverse transcriptomic datasets, for both mouse and human.
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BibTeX
@misc{munro2026edisetflow,
title = {{Edi-Set-Flow: detecting differential RNA-editing at scale}},
author = {J. E. Munro and M. Bahlo and B. R. E. Ansell},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.02.763838},
url = {https://www.biorxiv.org/content/10.64898/2026.11.02.763838v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Edi-Set-Flow: detecting differential RNA-editing at scale
AU - J. E. Munro
AU - M. Bahlo
AU - B. R. E. Ansell
PY - 2026
DA - 2026/11/02
PB - bioRxiv
DO - 10.64898/2026.11.02.763838
UR - https://www.biorxiv.org/content/10.64898/2026.11.02.763838v1
AB - Background: Adenosine-to-inosine (A-to-I) RNA editing is a post-transcriptional modification catalysed by ADAR enzymes that can alter codons, splicing patterns, and RNA secondary structures. RNA editing is essential for innate immunity and nervous system development, and its dysregulation is implicated in neurological disorders, cancers, and autoimmune diseases. Despite its biological importance, accurately detecting RNA editing from RNA-seq data remains technically challenging, and robust inference of differential editing across experimental conditions is not straightforward. Findings: We have developed Edi-Set-Flow, a reproducible and scalable pipeline for transcriptome-wide A-to-I RNA editing analysis of bulk RNA-seq data. Implemented in Nextflow, the pipeline takes raw FASTQ files as input, trims reads and filters for quality, aligns reads to the reference genome, and identifies editing sites with JACUSA2. Common genetic variants are excluded using the gnomAD population database. Identified sites are annotated for genetic feature context and predicted functional consequences, and results are summarised in a user-friendly HTML report. The pipeline scales efficiently to hundreds or thousands of samples and is suitable for large datasets such as GTEx. An accompanying R package enables advanced analyses, including model fitting, hypothesis testing, false discovery rate control, and visualisation, thereby facilitating reliable statistical comparisons of editing across experimental groups. Applying Edi-Set-Flow to GTEx brain RNA-seq data, we uncovered distinct RNA editing signatures across brain tissues and recovered known protein-recoding events. Conclusions: Edi-Set-Flow provides researchers with a robust, user-friendly, scalable, end-to-end solution for efficiently identifying and interpreting biologically meaningful RNA editing events across diverse transcriptomic datasets, for both mouse and human.
ER -