Jelard Aquino, Ph.D. Candidate

School of Life Sciences 

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

Advisory Committee Members: 

Mira Han, Ph.D.,  Committee Chair

Jingchun Chen, Ph.D.,  Committee Member

Qian Liu, Ph.D.,  Committee Member

Edwin Oh, Ph.D.,  Graduate College Representative

Vikram Chhatre, Ph.D.,  Outside Committee Member

Abstract

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. Currently, AS analysis in RNA sequencing data falls into two approaches: splice junction-based approach and exon-based approach. This dissertation 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. Additionally, I will present 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 that expands on the GrASE framework, are evaluated in combination with three statistical models: negative binomial, beta-binomial, and mixed binomial. I will discuss performance across methods and highlight 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.