The rapid growth in biomedical research has generated vast amounts of data, including genomic, molecular, imaging, and clinical information from humans and other species. Leveraging this data is essential for groundbreaking scientific discoveries and a deeper understanding of health and disease across different species. However, the complexity and volume of these datasets present significant computational challenges, limiting their potential.This dissertation addresses two key challenges in biomedical data analysis: the efficient evaluation of sequencing data and the effective management and analysis of gene sets. By focusing on these areas, we develop innovative computational methods that enable the rapid, scalable, and accurate processing of large-scale biomedical data. For sequencing data, we create algorithms that enhance the speed and precision of data evaluation, making it feasible to manage the increasing volume of sequences generated by modern technologies. For gene sets, we devise tools for their efficient management and analysis, allowing researchers to draw meaningful insights from complex genetic information. Through this research, we aim to contribute to the development of new analytical tools and methods, ultimately supporting the advancement of precision medicine and personalized healthcare for both human and veterinary applications.