Development of computational methods for long read sequencing data analysis in the omics era

This doctoral thesis focuses on the development of computational methods for the analysis of third-generation sequencing data across multiple omic layers, including genomics, epigenomics, and transcriptomics. In particular, the work addresses key methodological challenges related to the detection of structural variants, the characterization of differential DNA methylation, and the analysis of RNA modifications from nanopore sequencing data. The proposed approaches aim to improve the robustness, interpretability, and generalizability of long-read sequencing analyses through novel strategies for signal representation, segmentation, and annotation. Overall, the thesis contributes to advancing the analytical maturity of third-generation sequencing technologies, supporting their future application in research and clinical contexts.