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AlphaGenome is out in @nature.com today along with model weights! 🧬 📄 Paper: www.nature.com/articles/s41... 💻 Weights: github.com/google-deepm... Getting here wasn’t a straight path. We discussed the story behind the model, paper & API in the following roundtable: youtu.be/V8lhUqKqzUc

Highlighting two manuscript additions: 1. Data ablation (Supp. Fig 12): RNA-seq + ATAC/DNase proved essential for key evals. h/t @anshulkundaje.bsky.social for the suggestion! 2. Variant Analysis: We found evidence to upgrade a 3’ UTR VUS from Ellingford et al 2022 to "likely pathogenic."

Excited to launch our AlphaGenome API goo.gle/3ZPUeFX along with the preprint goo.gle/45AkUyc describing and evaluating our latest DNA sequence model powering the API. Looking forward to seeing how scientists use it! @googledeepmind

Big thanks to the amazing team! They went above and beyond to get us here. It's a big privilege to work with them. Also thanks to our early testers for their feedback.

Excited to launch our AlphaGenome API goo.gle/3ZPUeFX along with the preprint goo.gle/45AkUyc describing and evaluating our latest DNA sequence model powering the API. Looking forward to seeing how scientists use it! @googledeepmind

A single model can be more convenient to use and deploy - saying this as someone who worked on a model zoo before. SOTA performance across diverse tasks suggests that fine-tuning it on new data should work quite well. One could also extend it with new assays in the future.

Excited to launch our AlphaGenome API goo.gle/3ZPUeFX along with the preprint goo.gle/45AkUyc describing and evaluating our latest DNA sequence model powering the API. Looking forward to seeing how scientists use it! @googledeepmind

I see this model as a fusion of many different DNA sequence-to-function models tackling different steps of gene expression. It also has important new features like splice junction prediction to better capture the complexity of different splicing events.