Genome Language Models: From DNA Sequences to Biological Foundation Models

Recent advances in protein language models have greatly transformed protein structure prediction, functional annotation, and biomolecular design. In contrast, genome language models aim to learn directly from DNA sequences, which represent a more upstream layer of biological information encoding genes, regulatory logic, variant effects, and evolutionary signals. In this talk, I will introduce the basic motivation and recent progress of DNA and genome language models, including DNABERT, DNABERT-2, HyenaDNA, Evo, Evo 2, and AlphaGenome. I will discuss how different model architectures and tokenization strategies address the challenges of genomic sequence modeling, such as long-range dependencies, multi-scale biological structure, and genome-scale context.