The thesis will address the development of a new targeted enrichment method using a hybrid selection-based approach. While targeted enrichment approaches have been used routinely by many research and clinical labs, the project here addressed several areas in need of improvement in order to increase assay sensitivity, specificity, and provide even coverage across the exome for somatic applications. The goal of this work was to achieve these improvements in a streamlined laboratory workflow optimized for a variety of sample types. It will explore the beginning to end process: from the entirety of the universal hybrid selection product creation, including challenges of early research and development, to implementation of a streamlined hybrid selection method at large-scale production at a clinical level, to data analysis, and the clinical applications for precision medicine. Large-scale genomic sequencing involves both the innovative scientific methods developed in research and overcoming operational challenges to produce repeatable, robust data at scale. The thesis will further discuss how laboratory operations balance custom, complex cancer projects with various sample types, challenging sample inputs such as formalin-fixed paraffin-embedded (FFPE) samples, and low quantity deoxyribose nucleic acid (DNA) samples processed through unique molecular indices (UMI) library preparation, sequencing, and data analysis, while also maintaining quality for the large-scale research and clinical projects that consist of thousands of samples.