Thesis (Ph.D.)--University of Washington, 2022 ; This thesis presents methods of data retrieval in DNA data storage systems. While DNA has been considered a promising data storage medium for decades for its data density, durability, programmability, and eternal relevance, the cost has, until recently, been much too high for implementation. Now that the cost of DNA synthesis and sequencing has fallen significantly, the field has begun to examine DNA data storage at meaningful scale. This thesis includes scaling up PCR-based random access, evaluating PCR-based random access performance under increasingly sparse and complex conditions, exploring the feasibility of implementing a CRISPR-Cas9-based similarity-search data retrieval method, and finally, comparing DNAarchival methods to successfully retrieve data after long periods of time. The work presented here is part of a small but quickly growing body of work showing the scalability and viability of DNA data storage, with broader application to the world of synthetic biology and the many other fields where DNA is present.