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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 🧵

2,007,859 просмотров • 6 дней назад •via X (Twitter)

Комментарии: 36

Фото профиля Google DeepMind
Google DeepMind6 дней назад

AlphaGenome Atlas is over 30 times larger than the AlphaFold Database. It gives scientists an intuitive way to explore this vast 1-petabyte dataset - unifying interconnected resources so researchers can link genetic variants directly to the molecular mechanisms they disrupt.

Фото профиля Google DeepMind
Google DeepMind6 дней назад

Atlas includes the AlphaGenome Variant Impact (AVI) score, which combines AlphaGenome, AlphaMissense and other features to: 1️⃣ Rank mutations from low to high-impact 2️⃣ Reveal how they cause damage - like breaking gene switches or RNA splicing instructions.

Фото профиля Google DeepMind
Google DeepMind6 дней назад

We believe foundational biology tools should be accessible to everyone. AlphaGenome Atlas resources are available to the global scientific community via the Atlas website, our AlphaGenome API, as a skill in Google Antigravity, and coming to @GoogleCloud soon →

Фото профиля 𝕱𝖚𝖑𝖑 𝕶𝖊𝖑𝖑𝖞
𝕱𝖚𝖑𝖑 𝕶𝖊𝖑𝖑𝖞6 дней назад

At this rate I wouldn't be surprised if AlphaFold cures cancer before Gemini gets good

Фото профиля Sem Day
Sem Day6 дней назад

mapping 9B DNA variants in a browser is nice until u realize all that compute is locked inside google closed stack... drop the open weights

Фото профиля Swyisse AI Capital
Swyisse AI Capital6 дней назад

9 billion possible DNA changes is far too much for a person to study one by one. This is where AI can become incredibly useful. It can search through enormous amounts of biological information and help scientists find the changes worth investigating. Some of AI’s biggest breakthroughs may happen in science, not chatbots.

Фото профиля Filecoin
Filecoin6 дней назад

predictions have more scientific value when labs can query them without depending on one cloud

Фото профиля Mgoes (bio/acc 🤖💉)
Mgoes (bio/acc 🤖💉)6 дней назад

genuinely mindblowing , genome sequencing at the light of speed now!

Фото профиля Creative Dreamer
Creative Dreamer6 дней назад

Now we need every datacenter in the world working to solve human disease and discover how we can radically extend human lifespan.

Фото профиля PeritumAI
PeritumAI6 дней назад

9 billion DNA letter-swaps… and now there’s a search box. Wild that biology got a query language before half our agent desks did.

Фото профиля Boyang & YUP
Boyang & YUP6 дней назад

Humans after 10,000 years: “Maybe we should spellcheck the source code.”

Фото профиля dicechess.base.eth
dicechess.base.eth6 дней назад

AlphaGenome predicts what a DNA letter change does. Prime editing is the molecular word processor that can find-and-replace that letter. Alphabet Inc. holds ~16.56 million shares of Prime Medicine $PRME, roughly 9.2% of the company

Фото профиля Vladimir Arustamian
Vladimir Arustamian6 дней назад

Here is a free app to interact with AlphaGenome.

Фото профиля Udoh Jeremiah
Udoh Jeremiah6 дней назад

We've spent decades building tools to read biology. Now we're building AI systems that can help us interpret it at enormous scale. Mapping the predicted impact of 9 billion possible DNA changes is a pretty incredible demonstration of where biology and AI are heading.

Фото профиля Giulia 🌸
Giulia 🌸6 дней назад

Unfortunate timing for breaking the news. Whole X busy with Navier Stokes drama. Great tho!

Фото профиля Pitch
Pitch6 дней назад

Now do epistasis.

Фото профиля Maxime Rivest 🧙‍♂️🦙🐧
Maxime Rivest 🧙‍♂️🦙🐧6 дней назад

how many parameters in alpha genome?

Фото профиля Billy Boneyard
Billy Boneyard6 дней назад

A Common Thread

Фото профиля Ryan | Don't Fear AI
Ryan | Don't Fear AI6 дней назад

This is the type of work that will improve publics perception of AI. This is why we need to 3x the amount of data centers we have in the U.S.

Фото профиля Hüseyin Örskaya
Hüseyin Örskaya6 дней назад

It's like building a spellchecker for the entire human blueprint. Now we've just got to hope the "autocorrect" doesn't start making its own creative decisions.

Фото профиля panera
panera5 дней назад

What interests me most is the shift from sequencing the genome to navigating it. Once billions of variants become searchable, the bottleneck moves downstream: which predictions deserve experiments, and how efficiently can we get reality to answer? That may be the bigger story behind AlphaGenome. DeepMind Just Turned the Genome Into a Giant Google Map

Фото профиля Creed Hardcastle
Creed Hardcastle6 дней назад

9b nice, can finally become an at home biologist 😀

Фото профиля Paul · SpellWright
Paul · SpellWright6 дней назад

A searchable atlas at that scale is wild. How are you exposing model confidence and uncertainty so researchers can separate useful signal from a precise-looking guess?

Фото профиля Stats Wire
Stats Wire6 дней назад

I am more interested in understanding of ai can do some research like scientist, even if it's a small one

Фото профиля Prakash Anant
Prakash Anant6 дней назад

this is where AI in science gets really exciting precomputing the effects at this scale could save researchers a massive amount of time

Фото профиля Mario
Mario6 дней назад

Great use of narrow AI! This will help to better understand human biology. Another Nobel on the horizon?

Фото профиля nabu
nabu6 дней назад

dna but with find :D

Фото профиля Violeta Insights
Violeta Insights6 дней назад

9 billion single-letter changes is the sort of number that needs a search box

Фото профиля Peter Morris
Peter Morris6 дней назад

Now THIS is what AI should be for!

Фото профиля Shubham Sharma | AI & Tech
Shubham Sharma | AI & Tech6 дней назад

So expecting gemini 4 to be a beast scientist

Фото профиля Shane
Shane6 дней назад

@sundarpichai Single-letter DNA Changes are scientifically / genetically called "Point Mutations"

Фото профиля AI Mastery Guide
AI Mastery Guide6 дней назад

Free for researchers is huge too

Фото профиля Definitely Not A Bot
Definitely Not A Bot6 дней назад

Good to see something that can actually help people understand and research biology instead of locking it behind classifiers like some labs do

Фото профиля Sophie
Sophie5 дней назад

Please do Arabidopsis next 🤌🏻

Фото профиля Ben Builds
Ben Builds5 дней назад

This almost certainly opens up lots of old research projects that were shut down because they hit a wall. It’s kind of like what happened with neural networks. Lots of promising research, but it hit a wall, and we entered a cold stretch. Then AlexNet comes along, and reignites tons of old research that could only be done once new tech had unlocked the necessary capabilities. So it’s not even the new research (though obviously I’m excited about that too). Now that this has been solved, teams can go back and continue research in places they had already made lots of progress (but didn’t complete it).

Фото профиля Chinaza Joseph
Chinaza Joseph6 дней назад

Mapping billions of biological variations requires absolute data integrity. When you handle scale like this, automated pipelines still need strict verification gates and human oversight to prevent silent errors.

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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 Broad Institute, 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: - 🎥 Video: - 📖 Blog: - 📄 Preprint:

Jun Cheng

11,434 просмотров • 6 дней назад

Demis Hassabis just described what might be the end of genetic disease. Google DeepMind built a system called AlphaGenome. It reads human DNA the way a software engineer reads source code. Every letter. Every position. Every mutation across 3 billion characters. Hassabis: “AlphaGenome is the best system in the world for predicting if a mutation will cause disease or if it’s benign.” 98% of your genome doesn’t code for proteins. For decades scientists treated it like dark matter. Present everywhere. Readable nowhere. AlphaGenome reads it. Now pair that with CRISPR. Jennifer Doudna’s gene editing tool can already target any DNA sequence on command. The bottleneck was never the scalpel. It was knowing exactly where to cut. Hassabis: “A combination of things like AlphaGenome and CRISPR could be incredibly powerful.” That might be the most restrained sentence ever spoken about the future of medicine. AI locates the exact mutation killing you. CRISPR goes in and deletes it. Not treatment. Not management. Deletion. The hardest cases are multigenic. Mutations that cascade and compound. Hiding behind each other. Too complex for any human mind to untangle in a single lifetime. Hassabis: “Those are even harder to detect, but actually perfect for AI to try and help with.” The diseases that have defeated medicine for centuries are the exact ones AI is purpose-built to solve. That’s not a coincidence. That’s the turning point. Every parent who sat in a white room and heard “there’s nothing more we can do.” Every patient who watched their own biology turn against them with nothing to fight back. Every name carved into stone because we could name the disease but couldn’t disarm it. That era now has an expiration date. Humanity spent 10,000 years fighting disease with observation and guesswork. We’re about to fight it with comprehension. Your grandchildren may read about genetic disease the way you read about smallpox. As something that once ended millions of lives before we learned to read the code that wrote them. Somewhere right now a child carries a death sentence folded into their DNA. Born with it before they ever opened their eyes. They don’t know it yet. Their parents don’t know it yet. But for the first time in human history, the answer might arrive before the disease does. That’s not technology. That’s the moment our biology stopped being our fate.

Dustin

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