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A long-context language model for deciphering and generating bacteriophage genomes

Author

Listed:
  • Bin Shao

    (Beijing Institute of Technology
    Harvard University)

  • Jiawei Yan

    (Independent researcher)

Abstract

Inspired by the success of large language models (LLMs), we develop a long-context generative model for genomes. Our multiscale transformer model, megaDNA, is pre-trained on unannotated bacteriophage genomes with nucleotide-level tokenization. We demonstrate the foundational capabilities of our model including the prediction of essential genes, genetic variant effects, regulatory element activity and taxonomy of unannotated sequences. Furthermore, it generates de novo sequences up to 96 K base pairs, which contain potential regulatory elements and annotated proteins with phage-related functions.

Suggested Citation

  • Bin Shao & Jiawei Yan, 2024. "A long-context language model for deciphering and generating bacteriophage genomes," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53759-4
    DOI: 10.1038/s41467-024-53759-4
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    References listed on IDEAS

    as
    1. Amin Espah Borujeni & Jing Zhang & Hamid Doosthosseini & Alec A. K. Nielsen & Christopher A. Voigt, 2020. "Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage," Nature Communications, Nature, vol. 11(1), pages 1-18, December.
    2. Travis L. LaFleur & Ayaan Hossain & Howard M. Salis, 2022. "Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
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