The development of next generation sequencing (NGS) technologies has allowed rapid and cost-effective sequencing of large amounts of DNA or RNA, enabling systematic analysis of biological and pathological processes. Interpretation of genomic and transcriptomic data became crucial in both research and clinical settings, particularly for investigating complex human diseases. This thesis employed various computational analysis methods in three projects that utilized different types of NGS data: (1) Constructing genomic trajectory for the progression process of multiple hematopoietic malignancies using panel sequencing and karyotype data; (2) Mathematical modeling of the mutation accumulation history in bladder cancer on a whole-organ level; and (3) Identifying endogenous immunogenetic double-stranded RNA species by classic bioinformatic analysis of RNA-seq data. The research conducted in this thesis revealed genomic evolution dynamics in cancer development providing important reference for cancer progression monitoring and intervention, and shed light on RNA therapeutic targets in autoimmune diseases.