Alternative polyadenylation (APA) is a post-transcriptional mechanism that generates diverse mRNA isoforms with distinct 3' untranslated regions (UTRs), influencing mRNA stability, localization, and protein synthesis. The complexity and dynamics of APA patterns across different human tissues underscore its pivotal role in gene expression regulation and its implications in development, differentiation, and disease. Despite its significance, comprehensive analysis and understanding of APA events at a tissue-specific level is largely unknown. This project leverages high-performance computing (HPC) platforms, utilizing both CPU and GPU resources, to explore the tissue-, celltype- and disease-specific APA envent. By combining HPC with state-of-the-art bioinformatics tools, we aim to elucidate the APA landscape in human tissues more efficiently and accurately. Developing a novel bioinformatic tool is possible if it's need for the project. The significance of this project lies in its potential to provide comprehensive insights into the APA mechanism's role in human biology and its impact on disease. By using the power of HPC, we are able to process and analyze genomic data at an unprecedented scale, contributing to the advancement of computational biology and the understanding of complex regulatory mechanisms in human genetics.