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Arc Institute uses genome latent spaces to find candidate RNAs
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New from @arcinstitute: using the latent space representation of genomes from single-cell life to find new candidate functional RNAs. I think this is cool because: (1) it uses deep learning and model interpretability to productive ends on a totally different kind of "knowledge"; (2) it shows how agents may be able to accelerate discovery in vast combinatoric spaces; and (3) it's an example of the synergy we'll hopefully see in the coming years between computational techniques and wet lab…
pdhsu@pdhsu2d ago
Delighted to share a landmark preprint from our lab, using genome language models and AI agents for biological discovery and genome mining! The stretches of DNA between protein-coding genes are often called the genome’s “dark matter,” but they are far from empty. They encode RNA molecules that sense metabolites, regulate genes, and control some of molecular biology's most important tools (such as programmable genome editors e.g. CRISPR/bridge) Much of this biology is unexplored. One reason is that RNAs can retain similar structures and functions even as their DNA sequences change dramatically, making their relationships difficult to recognize with conventional computational tools