The organization Jackson Laboratory, a nonprofit biomedical research institution, used Neo4j to manage and analyze large amounts of genomic data. The researchers chose Neo4j due to its ability to scale with their data, efficient graph querying capabilities, and robust REST API for interacting with the graph. They built two prototypes, one using SQL databases and another using Python, but found that Neo4j's bulk import feature significantly reduced the time it took to build the graph. The researchers successfully integrated Neo4j with Google Storage and deployed a Community Edition instance on Compute Engine. Today, their graph contains around 3.6 billion relationships between genes, transcripts, variants, eQTLs, and other data sources. The graph is used by researchers to analyze genomic data, retrieve tables of links between genes, human genes getting chromosomes, and genes linked to variants in another species without an ortholog. The team plans to integrate the graph with geneweaver.org and deploy it externally. Neo4j's scalability, query efficiency, and REST API made it an ideal choice for their use case.