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Compression for population genetic data through finite-state entropy

Winfield Chen, View ORCID ProfileLloyd T. Elliott
doi: https://doi.org/10.1101/2021.02.17.431713
Winfield Chen
1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, V5A 1S6, Canada
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  • For correspondence: winfield.chen@sfu.ca lloyd.elliott@sfu.ca
Lloyd T. Elliott
1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, V5A 1S6, Canada
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  • ORCID record for Lloyd T. Elliott
  • For correspondence: winfield.chen@sfu.ca lloyd.elliott@sfu.ca
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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.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 18, 2021.
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Compression for population genetic data through finite-state entropy
Winfield Chen, Lloyd T. Elliott
bioRxiv 2021.02.17.431713; doi: https://doi.org/10.1101/2021.02.17.431713
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Compression for population genetic data through finite-state entropy
Winfield Chen, Lloyd T. Elliott
bioRxiv 2021.02.17.431713; doi: https://doi.org/10.1101/2021.02.17.431713

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