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bioRxiv · 10.1101/2021.02.17.431713

Compression for population genetic datathrough finite-state entropy

Chen, W.·Elliott, L. T.
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Abstract

We improve the efficiency of population genetic file formats and GWAS computation by leveraging the distribution of sample ordering in population-level genetic data. We identify conditional exchangeability of these data, recommending finite state entropy algorithms as an arithmetic code naturally suited to population genetic data. We show between 10% and 40% speed and size improvements over dictionary compression methods for population genetic data such as Zstd and Zlib in computation and and decompression tasks. We provide a prototype for genome-wide association study with finite state entropy compression demonstrating significant space saving and speed comparable to the state-of-the-art.

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Chen, W., Elliott, L. T.. 2021-02-18. Compression for population genetic datathrough finite-state entropy. https://doi.org/10.1101/2021.02.17.431713

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