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Improved methods for storing and processing large-scale genetic variation data
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Current large-scale genetic variation datasets have enormous potential for advancing human health, but the storage and analysis of these datasets present major challenges to existing data formats. For example, performing a simple query to find the genome coordinates of all genetic variants can take several hours to complete. This is a major hindrance to researcher productivity as well as a substantial economic cost. We develop improved computational methods to store genetic data in a form that much faster and cheaper to store and process, and showcase these improvements on the 100,000 Genomes dataset.
