Biology is a very noisy field. Experiments are difficult to reproduce, the mechanisms behind life are not well understood, and data that we do obtain is difficult to make sense of. Much like traditional engineering fields where engineers draw from a library of reusable parts for their designs, experimental and synthetic biologists have designed biological circuits by drawing from a library of genetic constructs. However, these so-called genetic parts are poorly understood and are therefore limited in their usefulness. Additionally, there are hundreds of thousands of parts and sequences that have been either created or discovered. For my thesis, I filter through this biological noise to provide genetic circuit designers a powerful way to search for and access the genetic parts that are useful to them. This thesis is focused on creating SBOLExplorer, a system that is used to provide intuitive search within the SynBioHub genetic design repository. SynBioHub integrates genetic construct data from various sources and transforms and stores this data in a standardized data model. By tackling the intricate data mining and data infrastructure problems associated with large-scale semi-structured and noisy data, the search, transformation, and storage of data in genetic design repositories can be enhanced. In particular, this thesis focuses on improving the usability of genetic part repositories’ search capabilities. By clustering SynBioHub’s genetic parts into many derived collections, duplicate parts are merged. From there, a graph analysis algorithm is used to rank collections of parts by popularity and usefulness. Finally, data infrastructure challenges relating to indexing, storing, serving, and distributed search are solved. The end goal of SBOLExplorer is to integrate these findings into SynBioHub and other genetic design repositories’ data representation, search functionality, and data infrastructure.