RNA editing enzymes, such as those in the APOBEC and ADAR families, modify RNA transcripts with nucleotide-level specificity, playing critical roles in cellular differentiation and function. Beyond their endogenous roles, these enzymes have been repurposed as tools for identifying RNA-binding protein (RBP) binding sites when fused to RBPs of interest. However, the lack of efficient computational methods to identify editing events and distinguish them from off-target effects, genetic variations, and sequencing errors has posed a significant challenge. In the first chapter of this thesis, we introduce FLARE, a Snakemake-based computational pipeline for analyzing RNA editing enrichment. FLARE identifies statistically enriched regions for various types of RNA editing, including cytosine-to-uracil (C-to-U) and adenosine-to-inosine (A-to-I) conversions. Applied to C-to-U data from an RBFOX2-APOBEC1 STAMP experiment, FLARE demonstrates high specificity in detecting RBFOX2 binding sites. Additionally, FLARE effectively identifies regions of endogenous A-to-I hyperediting, underscoring its versatility.
In the second chapter, we present MARINE (Multi-Core Algorithm for Rapid Identification of Nucleotide Edits), a fast and efficient Python-based tool for analyzing RNA
editing across diverse sequencing data types. MARINE is optimized for single-cell resolution, enabling more detailed, cell type-specific mapping of the RNA editing landscape. Using MARINE to analyze an immune cell dataset, we uncover a potential role for RNA editing in the proliferation and differentiation of B cells. I also demonstrate MARINE's utility in assessing RBFOX2-APOBEC1 fusion-directed editing in single cells, in the context of the STAMP technology developed by the Yeo lab. MARINE empowers granular investigations into transcriptomic diversity, enabling insights into the impact of RNA editing in both health and disease contexts.