Software, Tools, and Methods Development for Single-Cell Transcriptomics

Advances in transcriptomics have transformed the study of gene expression, enabling a shift from low-throughput bulk RNA measurements to high-resolution, large-scale single-cell RNA-sequencing (scRNA-seq). This work refines existing methodologies and introduces new strategies for achieving precise, versatile, and scalable transcriptomic analyses across a broad spectrum of assays and biological contexts. On the computational front, this dissertation introduces new methods for adaptable preprocessing of sequencing reads, enabling the handling of very complex read structures. It refines existing strategies for efficiently querying large-scale transcriptomic datasets and enhances approaches for quantifying nascent and mature RNA species. A general framework is introduced for discovering and organizing biologically informative sequences directly from raw sequencing data, facilitating the detection of sample-specific or condition-specific variation. On the experimental front, a novel single-cell RNA sequencing method is presented that is cost-effective, open source, and scalable, supporting large-scale studies with substantial cell numbers and high per-cell resolution. These developments collectively expand the toolkit for transcriptomics, enabling more efficient and comprehensive exploration of RNA biology.