Excited to release AlphaGenome Atlas 🧬 We used AlphaGenome to predict the regulatory impact of all 9B possible SNVs in the human genome. We collaborated with amazing scientists to analyze and apply it, and developed a portal to browse the genome using this new lens 🔬 🌐 Portal: alphagenome.google/atlas 📖 Blog: goo.gle/4heuCvn 📄 Preprint: deepmind.google/blog/alphage… 🧵 1/6

Happy to see our open source interpretation tools (released pre pre-print/publication) for deep learning regulatory sequence models being used by others to create such fantastic resources. Listing a few pointers below for others interested. 1/

Excited to be a part of this collaboration! The AlphaGenome Atlas will be a huge accelerant in our field for understanding regulatory encoding.

Happy to have contributed to this work! Amazing to see cis-regulatory motif maps across so many cell types readily available. Get a bird’s eye view of the cis-regulatory code or dive in deep into specific details, I highly recommend it!

Prioritizing and interpreting disease-associated genetic variants remains one of the greatest challenges in human genetics. Today, we’re thrilled to introduce AlphaGenome Atlas 🧬, a genome-wide platform providing precomputed predictions for the regulatory effects of all ~9 billion possible single-letter changes and >100M observed indels in the human genome. Here is what Atlas delivers: 1. Variant Prioritization via AVI To prioritize variants, we developed the AlphaGenome Variant Impact (AVI) score. AVI predicts a unified score per variant, where higher values indicate greater disruption. It achieves state-of-the-art performance across diverse benchmarks. As a proof-of-concept with our collaborators at @broadinstitute, AVI prioritized a deep-intronic variant in DNM1, helping solve a previously unexplained rare epileptic encephalopathy case by revealing a brain-specific cryptic splice site.   2. Multi-Layer Molecular Interpretation Variant prioritization is only half the battle; researchers also need to understand why a variant matters. Atlas decomposes variant effects across multiple interpretable layers: •Feature Attributions which decompose each variant’s score into specific biological modalities driving the impact.  •Cell-Type Specificity: Precomputed predictions with AlphaGenome across hundreds of biosamples reveal the exact cellular context in which a variant acts.  •Regulatory Grammar: Over 2,600 de novo DNA motifs (and >250B genome-wide instances) show when variants directly disrupt critical regulatory binding "words".   Explore the resource: 🌐 Interactive browser & precomputed data: alphagenome.google/atlas - 🎥 Video: piped.video/U0aToL5C-bQ - 📖 Blog: goo.gle/4heuCvn - 📄 Preprint: storage.googleapis.com/deepm…

We’re launching AlphaGenome Atlas: an AI-powered searchable database mapping the predicted impact of all 9 billion possible single-letter DNA changes. Here’s how it could help researchers better understand our biology 🧵

AlphaGenome Atlas is an interactive resource, mapping the predicted impact of all 9 billion DNA variants. It works in a regular web browser, without any coding required, and is free for academic researchers. Excited for the discoveries to come.

1/5 🧬 Today, our team @GoogleDeepMind is taking another step on our mission of deciphering the genome. We are releasing AlphaGenome Atlas, a massive (petabyte-scale) resource containing AlphaGenome predictions for every possible single-letter DNA change in the human genome — 9 billion in total. deepmind.google/blog/alphage…

Very excited to announce ENCODE GRAMMAR (Genomic Regulatory Atlas of sequence Models, Motifs, Annotations & Rules): 3,865 experiment-specific deep learning model sets and sequence annotations for decoding human regulatory DNA. 1/

Agents for computational biology are advancing rapidly, but evals are lagging. Current benchmarks can be overly prescriptive. Full analysis vignettes are hard to verify. We introduce CompBioBench: 100 diverse, challenging, verifiable tasks + benchmark general-purpose agents. 1/9

Check out a vetted Pytorch port of AlphaGenome by GenomicsxAI collaborative team. See QT thread (with links to code & blogpost). Various fine tuning modules + tutorials coming next. Community building announcements coming soon as well. Follow blog for latest updates.

We built a lightweight wrapper to fine-tune AlphaGenome on genome-wide assays (RNA-seq, ATAC-seq, ChIP-seq) and MPRAs. To keep it accessible, we included blog posts, markdown guides, and notebooks, so you can use whichever format works best for you! Links in the thread:

We’ve been working with AlphaGenome for a while — today we’re sharing two updates: • alphagenome-ft: a lightweight structured fine-tuning wrapper • MPRA fine-tuning results with SOTA performance Adapting AlphaGenome's JAX/Haiku stack to new assays isn’t trivial. 1/8