![[R] Genomic Large Language Models](https://preview.redd.it/ya4k6xwhmipg1.png?width=140&height=59&auto=webp&s=d3af77529fb2785c9eb49c58ed77b92a91e0f290) | Can a DNA language model find what sequence alignment can't? I've been exploring Evo2, Arc Institute's genomic foundation model trained on 9.3 trillion nucleotides, to see if its learned representations capture biological relationships beyond raw sequence similarity. The setup: extract embeddings from Evo2's intermediate layers for 512bp windows across 25 human genes, then compare what the model thinks is similar against what BLAST (the standard sequence alignment tool) finds. Most strong matches were driven by common repeat elements (especially Alu). But after stricter filtering, a clean pair remained: A section of the VIM (vimentin, chr10) gene and a section of the DES(desmin, chr2) gene showed very high similarity (cosine = 0.948), even though they have no detectable sequence match. Both regions are active promoters in muscle and connective tissue cells, share key regulatory proteins, and come from two related genes that are often expressed together. This suggests Evo2 is starting to learn to recognize patterns of gene regulation — not just the DNA letters themselves — even when the sequences look completely different. That said, this kind of meaningful signal is still hard to find. It only appears after heavy filtering, and many other matches remain noisy. Overall, Evo2 appears to capture some real biological information beyond sequence alignment, but making it practically useful will take more work. Would be curious to hear thoughts from others in genomics and AI. [link] [comments] |
[R] Genomic Large Language Models
Reddit r/MachineLearning / 3/17/2026
💬 OpinionIdeas & Deep AnalysisModels & Research
Key Points
- Evo2, Arc Institute's genomic foundation model trained on 9.3 trillion nucleotides, is being explored for its ability to capture regulatory relationships beyond raw sequence similarity.
- Researchers extracted embeddings from Evo2's intermediate layers for 512-base-pair windows across 25 human genes and compared the model's similarity to that found by BLAST.
- A notable finding is a highly similar region between VIM and DES genes (cosine similarity 0.948) with no detectable sequence match, both active promoters, suggesting the model may learn patterns of gene regulation.
- Most strong matches were driven by repeat elements such as Alu and many signals remain noisy, indicating the signal is not yet robust.
- Overall, the results show Evo2 captures some real biological information beyond sequence alignment, but turning this into practical tools will require more work and community input.
