ArticleCell reports methods2026
GenART: Reading the genome's language with adaptive "words".
Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Tokenization is both a prerequisite and a central challenge for genomic language models, due to the inherent difficulty of delineating meaningful segments within continuous DNA sequences. We present GenART, a genomic language model framework that dynamically segments DNA into variable-length words directly from raw sequences without manual annotation. Its key adaptive tokenization module infers biologically meaningful boundaries from multi-scale contextual signals during pretraining. GenART demonstrates competitive performance across diverse tasks, outperforming leading approaches. Word boundaries derived from unsupervised tokenization show strong correspondence with base-resolution regulatory signals, such as DNA methylation, and with multi-nucleotide functional elements annotated in GENCODE v.49. These findings highlight adaptive tokenization as a powerful strategy for building interpretable and biologically responsive genomic language models.
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