OmniRE-seq: A Dual-Method Framework for Decoding RNA Editing Dynamics in Disease and Therapy

Abstract

RNA editing is a widespread post-transcriptional modification involved in transcriptome diversity and disease regulation. However, its quantification remains technically challenging. We present OmniRE-seq, a dual-framework combining RE-seq for bulk and scRE-seq for single-cell RNA-seq data, enabling multi-level quantification, differential analysis, and functional annotation of RNA editing. Using RE-seq, we profile editing alterations across diverse contexts -- including murine sepsis, MDS, drug-treated melanoma, and colorectal cancer -- demonstrating the utility of locus-, gene-, and burden-level metrics in capturing biologically meaningful differences. scRE-seq further resolves cell-type-specific editing patterns by aggregating cells of shared identity into pseudo-bulk units (Spots), enhancing editing signal while preserving cellular resolution in colorectal cancer. Integrated bulk and single-cell analysis identified convergent editing signals and therapeutic targets, exemplified in a mouse model of thymic atrophy treated with Ganoderma lucidum spores. Together, OmniRE-seq offers a standardized and scalable toolkit for dissecting RNA editing landscapes across resolution levels, advancing both mechanistic discovery and translational application in precision medicine.