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bioRxiv · 10.1101/2024.09.18.612131

Pangenome-Informed Language Models for Privacy-Preserving Synthetic Genome Sequence Generation

Huang, P.·Charton, F.·Schmelzle, J.-N. M.·Darnell, S. S.·Prins, P.·Garrison, E.·Suh, G. E.
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Abstract

Language Models (LM) have been extensively utilized for learning DNA sequence patterns and generating synthetic sequences. In this paper, we present a novel approach for the generation of synthetic DNA data using pangenomes in combination with LM. We introduce three innovative pangenome-based tokenization schemes that enhance DNA sequence generation. Our experimental results demonstrate the superiority of pangenome-based tokenization over classical methods in generating high-utility synthetic DNA sequences, highlighting significant improvements in training efficiency and sequence quality.

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Huang, P., Charton, F., Schmelzle, J.-N. M., Darnell, S. S., Prins, P., Garrison, E., Suh, G. E.. 2024-09-20. Pangenome-Informed Language Models for Privacy-Preserving Synthetic Genome Sequence Generation. https://doi.org/10.1101/2024.09.18.612131

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