RNA’s Symphony: Harmonizing Splice Junctions and Exon Counts for a Novel Approach to Differential Splicing Analysis

Alternative Splicing (AS) plays a critical role in transcriptome complexity and cell-type-specific gene regulation, yet its analysis remains methodologically fragmented, especially in the context of noisy and sparse single-cell RNA sequencing (scRNA-seq) data. This dissertation addresses key computational challenges in AS detection by evaluating existing tools, developing integrative frameworks, and proposing new strategies for improving analysis accuracy in both bulk and single-cell contexts. In chapter 1, I present a comprehensive literature review of computational tools designed for detecting and quantifying AS from bulk and scRNA-seq data. This review outlines major methodological paradigms, including exon-based and splice junction-based approaches, and evaluates their underlying statistical models, highlighting limitations in resolution, interpretability, and scalability. Chapter 2 introduces GrASE, a novel splicing graph-based method that unifies exon fragment-based and splice junction-based approaches. This unified framework not only facilitates cross-method benchmarking but also reveals AS events consistent across methods, and method-specific biases using short-read RNA-seq data. Lastly, chapter 3 presents a comprehensive benchmarking framework for differential AS detection in scRNA-seq data. Three count structures: exon counts, splice junction counts, and a newly proposed adjacent exon count, are evaluated in combination with three statistical models: negative binomial, beta-binomial, and mixed binomial. This chapter assesses performance across methods and highlights the trade-offs between statistical power and false discovery. A pseudo-bulking strategy is also explored to mitigate noise and enhance detection sensitivity in single-cell datasets. Collectively, this work advances the methodological landscape for AS analysis by providing a unified modeling framework, benchmarking strategies, and practical guidance for robust detection of splicing variation at single-cell resolution.