Genomic Language Models Achieve Competitive DNA Compression Using Tokenization Strategies
Researchers developed DNAGPT2, a family of GPT-2-based language models trained on DNA sequences, achieving 1.47 bits per base compression of the human genome — ranking fourth on a standard benchmark and outperforming all general-purpose compressors. The study uses compression performance as an objective measure of how well a model has learned the statistical structure of DNA, exploiting the mathematical equivalence between probabilistic modeling and data compression. The findings raise questions about standard tokenization choices in genomic AI and provide a nucleotide-level map of information content across different functional regions of the human genome.
A preprint posted to bioRxiv introduces DNAGPT2, a set of ten GPT-2-small language models pretrained on a multi-species DNA corpus, differing only in their byte-pair encoding (BPE) vocabulary size. Using arithmetic coding — a technique that links probabilistic predictions directly to compression efficiency — the best-performing model compressed the telomere-to-telomere (T2T) human genome to 1.47 bits per base, placing fourth on the Cobilab compression benchmark and surpassing all general-purpose compression tools. A notable finding is that a small 32-token BPE vocabulary outperformed larger vocabularies, suggesting that NLP-style tokenization strategies commonly borrowed for genomic models may not be optimal for DNA. The study also found that published long-context genomic language models underperformed the shorter-context DNAGPT2, though the authors caution this is not a controlled comparison since those models differ in architecture, training data, and parameter count as well. Additionally, the researchers generated a per-nucleotide information-content map of the human genome, demonstrating that exons, introns, intergenic regions, and Alu repeats each carry statistically distinct information profiles, which could have implications for understanding genomic structure and function.
What's missing
It is unclear how the per-nucleotide information-content map performs across diverse human populations or non-human genomes beyond the training corpus.
What different sources said
- bioRxivCenter
DNA Compression with Genomic Language Models: Tokenization, Benchmarking, and an Information-Content Map
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