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

Affiliations
  • 1Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, 100081, China. shaobinlx@gmail.com.
  • 2Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA. shaobinlx@gmail.com.
  • 3Independent researcher, 100 N Gushan Rd, Shanghai, 200135, China.

Published on:

October 31, 2024

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.

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