Edi-Set-Flow: detecting differential RNA-editing at scale

Edi-Set-Flow enables transcriptome-wide detection of A-to-I RNA editing from bulk RNA-seq at scale, using JACUSA2 with gnomAD filtering and producing differential-ready results.

• Why it matters: Accurate inference of differential RNA editing from RNA-seq is technically challenging, limiting robust comparisons across experimental conditions despite links to immunity, neural development, and disease.
• What they did: The Nextflow pipeline processes raw FASTQ through trimming, quality filtering, genome alignment, and JACUSA2 site calling, excluding common variants via gnomAD, then annotates sites and summarizes outputs in HTML.
• The result: Applied to GTEx brain RNA-seq, it reveals distinct editing signatures across brain tissues and recovers known protein-recoding events, supporting statistical comparisons with an accompanying R package.

AI summary of the abstract · may contain errors · not medical advice · read the paper.