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Google’s Atlas of the human genome could pave the way for new treatments

Google DeepMind launches AlphaGenome Atlas, a catalog of predicted molecular effects for roughly nine billion single-letter DNA changes across the human genome.

Google DeepMind unveiled AlphaGenome Atlas, a roughly 1-petabyte dataset predicting how each of about nine billion possible single-nucleotide variants affects molecular biology, including non-coding regions that regulate gene behavior. It builds on the AlphaGenome model released in 2025 and adds a Variant Impact Score (AVI) to help researchers rank variants. The catalog is available for noncommercial research via a web portal, the Antigravity platform, and the AlphaGenome interface, with commercial access on Google Cloud planned.

The Verge · AI · 8d agoAI industry 2 sources

Google DeepMind Releases AlphaGenome Atlas With Precomputed Molecular Effect Predictions and AVI Scores for 9 Billion Human DNA Variants

Google DeepMind launched AlphaGenome Atlas, precomputing molecular effect predictions and AVI impact scores for ~9 billion human single-nucleotide variants in a 1-petabyte catalogue.

Google DeepMind released AlphaGenome Atlas, a 1-petabyte catalogue of precomputed molecular effect predictions for roughly 9 billion possible single-nucleotide variants in the human genome. It introduces the AlphaGenome Variant Impact (AVI) score, combining AlphaGenome regulatory predictions with AlphaMissense, plus per-variant feature attributions and over 2,500 recurrent DNA sequence motifs. DeepMind reports best-in-class AVI performance on variant pathogenicity and rare disease benchmarks. Early users at the Broad Institute, University of Exeter, and Stowers Institute demonstrated rare-disease variant reprioritization and 22% more non-coding associations across 54,000+ UK Biobank genomes.

MarkTechPost · 8d agoAI research2

Deepmind's AlphaGenome Atlas maps every possible DNA change in the human genome

DeepMind's AlphaGenome Atlas precomputes impact predictions for ~9 billion human DNA variants in a 1-petabyte dataset; its AVI score beats CADD in benchmarks

Google DeepMind released the AlphaGenome Atlas, precomputing functional-effect predictions for roughly 9 billion human genome variants (about 27,000 prediction values per variant) in a one-petabyte dataset more than 30 times the size of the AlphaFold database. The accompanying AlphaGenome Variant Impact Score (AVI), a small neural network combining AlphaGenome, AlphaMissense and evolutionary conservation features (18 inputs versus CADD's 150+), outperformed existing tools on clinically classified variants, ranking causal variants in the top 50 candidates for 29.5% of solved GREGoR cases versus 12.5% for CADD. A GREGoR epilepsy case illustrates the impact: AVI elevated a previously unclear DNM1 splice variant that lab experiments confirmed as likely disease-causing. The atlas is available for noncommercial use via web portal, API and a Google Antigravity skill, with a commercial version planned through Google Cloud.

The Decoder · 7d agoAI research

Google DeepMind Releases AlphaGenome Atlas

Google DeepMind released AlphaGenome Atlas, a 1-petabyte database pre-computing effects of all 9 billion single-nucleotide variants in the human genome, with a unified AVI score.

Google DeepMind launched AlphaGenome Atlas, a database that predicts the regulatory impact of every possible single nucleotide variant across the roughly 3 billion base pairs of the human genome, yielding a 1-petabyte dataset. It introduces the AlphaGenome Variant Impact (AVI) score, combining coding and non-coding predictions for rapid variant prioritization. Broad Institute researchers used it to support solving an unsolved rare disease case via a predicted DNM1 splice variant, and analysis of 54,000+ UK Biobank participants uncovered 22% more non-coding genetic associations, including 19 regions linked to BMI. The Atlas is available through a no-code web portal.

Hacker News · AI · 8d agoAI researchHN 44↑ · 4 comments

How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules

OpenAI profiles César de la Fuente's lab using ChatGPT and Codex alongside deep-learning models to accelerate antimicrobial molecule discovery.

OpenAI published a case study on bioengineer César de la Fuente's lab, which uses ChatGPT and Codex for hypothesis brainstorming, code writing, dataset processing, and bridging knowledge gaps across biology, chemistry, and computer science. The lab's deep-learning models scan genome and protein databases for antimicrobial peptide candidates, potentially cutting initial searches from years to hours. Bacterial antimicrobial resistance was associated with about five million deaths in 2021, a toll projected to roughly double by 2050.

OpenAI News · 6d agoAI industry1

Google's AI genome system evaluates every possible one-base change

Google's AlphaGenome AI system predicts functional effects of non-coding DNA variants in humans and mice.

Google's AlphaGenome AI system evaluates genomic sequences to predict gene expression, transcription factor binding, chromatin accessibility, splice site usage, and related genomic features. The system is currently limited to human and mouse sequences and a limited set of well-studied cell types, but its predictions generally match or exceed specialized software tools. Researchers can use it to assess whether non-coding variants are likely significant and generate hypotheses about their function.

Ars Technica · AI · 7d agoAI research

Convergent Emergence of In-Context Learning Across Modalities

Controlled experiments show few-shot in-context learning emerges across six modalities including language, genomes, images, and proteins, partially supporting a convergence hypothesis.

The paper tests the Convergent Emergence Hypothesis: that few-shot in-context learning, when it emerges, shares a common cross-modality difficulty profile. A controlled framework instantiated the same task suite across six modalities: language, genome, integer sequences, time series, images, and proteins. Paired-mapping ICL emerged in all six modalities, surpassed controlled baselines, and showed correlated per-task effects in five of them, providing partial support for the hypothesis.

Hugging Face daily papers · 5d agoAI research

AI Is Learning to Write Genetic Code

AI models generated viable bacteriophage genomes, with 16 designs successfully replicating and some outperforming the original virus at attacking E. coli.

Two AI models generated complete genome designs for bacteriophages modeled on ΦX174, a virus known to infect E. coli. Researchers produced about 700,000 candidate designs, selected 285 promising ones, and synthesized DNA inserted into E. coli, yielding 16 viable viruses. Some of the newly generated viruses proved more effective at attacking E. coli than the original bacteriophage.

Schneier on Security · 26d agoAI research

Building AI to accelerate science and improve lives

Google highlights AI-for-science advances: AlphaGenome Atlas mapping 9 billion genetic variants, WeatherNext 3 weather model, and global health AI tools.

Google detailed AI advances across science and health, including AlphaGenome Atlas, which mapped all 9 billion possible single-letter genetic changes in the human genome and was made openly available. WeatherNext 3 delivers 50% more accurate precipitation forecasts a day or more ahead and is already in products. AlphaFold is used by 4 million researchers in 190 countries, TB chest X-ray screening has processed 25,000+ scans across six nations, and the diabetic retinopathy model has supported 1.15 million screenings. Google also released its AI & Economy ATLAS global usage insights.

Google · AI · 1d agoAI industry

4.1 Million Impacted by AdaptHealth Data Breach

AdaptHealth disclosed a breach affecting 4,115,802 people after a socially engineered attacker stole health and insurance data from cloud-based patient systems.

A threat actor used social engineering to hijack a user session at a third-party contractor and gained access to AdaptHealth cloud applications, including patient management and document storage systems, in early June. Names, contact and demographic information, and health and health insurance data were exfiltrated; Social Security numbers and financial information were not affected. AdaptHealth reported 4,115,802 affected individuals to HHS, whose breach portal listed the incident this week; Baylor Genetics separately reported 2,810,878 individuals affected in a related June healthcare breach.

SecurityWeek · 6d agoData breach

Getting ahead of ‘harvest-now-decrypt-later’: Post-quantum cryptography planning

Opinion piece urges organizations to begin post-quantum cryptography migration now, citing harvest-now-decrypt-later risk and NIST deadlines.

CSO Online outlines why harvest-now-decrypt-later makes long-lived sensitive data a current risk even before quantum computers exist. It cites NIST IR 8547 timelines deprecating RSA-2048 and ECC P-256 by 2030 and removing them by 2035, finalized FIPS standards ML-KEM, ML-DSA, and SLH-DSA, upcoming FN-DSA (FIPS 206), NSA requirements for national security systems from 2027, and UK NCSC phased guidance through 2035. The author recommends cryptographic discovery, crypto-agility, and prioritizing long-confidentiality data and TLS endpoints.

CSO Online · 6d agoResearch

ICE Collecting DNA Samples

ICE's DNA collection program projects detainee samples to reach 33% of CODIS offender index by 2030, raising privacy concerns.

A Schneier on Security blog post discusses ICE collecting DNA samples from detainees, citing Georgetown Law research. DHS detainee samples are projected to constitute 33% of the FBI's CODIS offender index in 2030, up from 0.2% in 2019. The post raises concerns that samples collected under civil authority are being searched against crime scenes indefinitely, potentially without legal cause under Fourth Amendment standards.

Schneier on Security · 28d agoPolicy & legal