AlphaGenome Atlas: Molecular predictions for 9 Billion human DNA variants — Google DeepMindSkip to main content Explore our next generation AI systems Explore models Gemini GeminiBuild intelligent agents Gemini OmniCreate anything from anything Nano BananaCreate and edit detailed images Gemini AudioTalk, create and control audio Specialized models VeoGenerate cinematic video with audio ImagenGenerate high-quality images from text LyriaGenerate high fidelity music and audio World models & physical AI Genie 3Generate and explore interactive worlds Gemini RoboticsPerceive, reason, use tools and interact Open models GemmaBuild responsible AI applications at scale Our latest AI breakthroughs and updates from the lab Explore research Breakthroughs SIMA 2An agent that plays, reasons, and learns with you Genie 3Generate and explore interactive worlds AlphaGoMastering the game of Go Gemini RoboticsPerceive, reason, use tools and interact Learn more Evals Publications Responsibility Frontier safety Unlocking a new era of discovery with AI Explore science Breakthroughs AlphaFoldPredict protein structures with high accuracy AlphaGenomeUsing AI to understand the human genome WeatherNextFast and accurate AI weather forecasting AlphaEarthMap our planet in unprecedented detail AlphaEvolveDesign advanced algorithms for math and applications in computing Learn more Gemini for Science Experimental Tools Science Skills Our mission is to build AI responsibly to benefit humanity About Google DeepMind ResponsibilityEnsuring AI safety through proactive security, even against evolving threats NewsDiscover our latest AI breakthroughs, projects, and updates CareersWe’re looking for people who want to make a real, positive impact on the world Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Models Explore our next generation AI systems Explore models Gemini GeminiBuild intelligent agents Gemini OmniCreate anything from anything Nano BananaCreate and edit detailed images Gemini AudioTalk, create and control audio Specialized models VeoGenerate cinematic video with audio ImagenGenerate high-quality images from text LyriaGenerate high fidelity music and audio World models & physical AI Genie 3Generate and explore interactive worlds Gemini RoboticsPerceive, reason, use tools and interact Open models GemmaBuild responsible AI applications at scale Research Our latest AI breakthroughs and updates from the lab Explore research Breakthroughs SIMA 2An agent that plays, reasons, and learns with you Genie 3Generate and explore interactive worlds AlphaGoMastering the game of Go Gemini RoboticsPerceive, reason, use tools and interact Learn more Evals Publications Responsibility Frontier safety Science Unlocking a new era of discovery with AI Explore science Breakthroughs AlphaFoldPredict protein structures with high accuracy AlphaGenomeUsing AI to understand the human genome WeatherNextFast and accurate AI weather forecasting AlphaEarthMap our planet in unprecedented detail AlphaEvolveDesign advanced algorithms for math and applications in computing Learn more Gemini for Science Experimental Tools Science Skills About Our mission is to build AI responsibly to benefit humanity About Google DeepMind Learn more Education Our National Partnerships for AI Accelerator programs The Podcast ResponsibilityEnsuring AI safety through proactive security, even against evolving threats NewsDiscover our latest AI breakthroughs, projects, and updates CareersWe’re looking for people who want to make a real, positive impact on the world Build with Gemini Try Gemini Google DeepMind Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Google Research Explore our research Products and apps Gemini app Chat with Gemini Google AI Studio Build with our next-gen AI models Google Antigravity Our agentic development platform Models Research Science About Build with Gemini Try Gemini September 8, 2026 ScienceAlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genomeAlphaGenome Atlas team Share CopiedHow predicting the molecular impact of every possible single-letter DNA variant in the human genome will help accelerate our understanding of biology.Today, we are introducing AlphaGenome Atlas: a platform containing predictions for the effects of 9 billion single-nucleotide variants — every single-letter change possible — in the human genome. It is the most comprehensive catalogue of how genetic mutations affect molecular biology, and it is available for academic research through an intuitive and free-to-use website portal.DNA is the language of life. Mastering it is a grand challenge that could transform our ability to understand biology and treat disease. But progress has been limited by a fundamental problem: interpreting how genetic variations impact biology at a molecular level. With roughly 9 billion possible single-letter mutations in the human genome, testing each one in the lab is practically impossible.Google DeepMind has already made progress on this challenge with AlphaGenome, an artificial intelligence (AI) model that can predict how genetic variants impact biological processes. AlphaGenome is helpful for analyzing specific variants and has found widespread use in research, but we wanted to show researchers a big-picture view of variants across the entire genome.By precomputing AlphaGenome’s predictions at scale, we have created an easily accessible resource that vastly expands the model's reach. Just as an atlas is a collection of maps, linking together features of the land like altitude and location, AlphaGenome Atlas charts the molecular effects of DNA variants across the genome.To help scientists quickly find the most impactful genetic changes, we are also releasing the AlphaGenome Variant Impact (AVI) score. The AVI combines the strengths of AlphaGenome and AlphaMissense — our model for predicting the impact of protein-altering DNA variants — condensing both models’ predictions into a single number. Now, researchers can rapidly rank variants and interpret their molecular effects at the same time.Our trusted external collaborators have already used AlphaGenome Atlas to identify and experimentally verify key variants in unsolved rare disease research and find rare variants associated with common traits.AlphaGenome Atlas is available today through an intuitive website portal, our AlphaGenome API, and as a skill in Google Antigravity.AlphaGenome AtlasAlphaGenome Atlas is a massive 1-petabyte dataset, more than 30 times larger than the AlphaFold Database. When we expanded the AlphaFold Database in 2022, we grew the 3D structure information available from around 190K experimental structures to more than 200M structure predictions — covering nearly all catalogued proteins known to science. The database provided a portal that researchers with no coding experience could use, providing intuitive visualizations and making it easier to do large-scale protein structure analysis. It quickly became a crucial resource that drove discoveries across the life sciences and continues to accelerate researchers’ important work in countless fields.In building AlphaGenome Atlas, we also aspire to make predictions more accessible and give scientists an intuitive way to explore a vast dataset.AlphaGenome Atlas provides several powerful, interconnected resources, allowing researchers to link variants directly to the functional DNA sequences they disrupt.Molecular effect predictions Atlas contains thousands of molecular effect predictions for each variant, across multiple important aspects of gene regulation, spanning hundreds of human and mouse cell types and tissues. This serves as the starting point for further resources.AVI score A single number describing the impact for each genetic variant.AVI feature attributions Each AVI score is also linked to distinct biological features driving it, such as the aspects of gene regulation predicted by AlphaGenome or the protein impact score from AlphaMissense.DNA sequence motifs A comprehensive collection of over 2,500 recurrent DNA sequences — the "words" of the genome — and their locations.Together, these resources support researchers for a wide range of genetic research tasks, from rapid variant ranking to deep dives into variant functions.Extensive community collaboration guided the design of AlphaGenome Atlas. The AVI score helps researchers rapidly score and rank variants based on their potential impact. Crucially, it works for both coding regions (the 2% of the genome that codes for proteins) and non-coding regions (the remaining 98%), which orchestrates gene activity and houses most trait-associated variants.Our testing shows that the AVI score provides best-in-class performance across many variant pathogenicity and rare disease benchmarks. To help interpret these scores, we also calculated AVI feature attributions that highlight which molecular processes — like RNA splicing or gene expression — are predicted to be most disrupted by each variant. Your browser does not support the video tag. Your browser does not support the video tag.Overview of the AlphaGenome Atlas.(1) Precomputed effects are generated genome-wide for over 9 billion single-nucleotide variants. (2) From this, an allelic-resolution AlphaGenome Variant Impact (AVI) score is derived for each variant. To facilitate variant interpretation, AlphaGenome Atlas then decomposes the AVI score into additive feature contributions across interpretable categories such as chromatin accessibility, splicing, and conservation. (3) The precomputed variant effects, AVI score and the AVI feature attributions are linked, together with a compendium of genome-wide de novo motifs, which enables high-resolution mechanistic insights into variant function.Real-world impact: From rare diseases to population genetics and molecular biologyAlphaGenome Atlas provides a high-resolution, global view of the genome. These large-scale predictions become most useful when applied to targeted research questions. By translating this data into actionable biological insights, our academic partners are already uncovering links between genetic variation and disease.Understanding unsolved rare diseases. A major hurdle in understanding rare diseases is the daunting task of pinpointing the few causal variants hidden among thousands of candidates. In collaboration with the GREGoR Consortium, researchers applied the AVI score to prioritize these needle-in-a-haystack genetic variants for unsolved rare disease research. When Laura Covill and Anne O’Donnell-Luria from the Broad Institute and their colleagues used the AVI score to prioritize variants, driving a rare disease, that were overlooked in previous research, the team discovered a variant affecting a gene called DNM1, which is strongly linked to epileptic encephalopathy.Crucially, the AlphaGenome predictions underlying the AVI score showed exactly how the variant functioned: it created an incorrect splice site (a mistake in the cell’s genetic instructions) that led to an abnormal extension of the resulting protein. Experimental screens validated the research prediction and found nearby variants with similar effects, showing that Atlas is a powerful tool for understanding impactful genomic variation.Mapping rare variants associated with protein levels and complex traits. Moving beyond individual rare disease research, AlphaGenome Atlas can help uncover the genetic architecture of common traits in the general population. Identifying which rare, non-coding variants are associated with a specific trait or disease is notoriously difficult because the sheer volume of harmless genetic changes creates a statistical 'background noise'.To test how AlphaGenome Atlas can improve our ability to find non-coding variants affecting human traits, Gareth Hawkes, a Medical Research Council fellow at the University of Exeter, applied AlphaGenome Atlas to whole-genome data from over 54,000 UK Biobank participants, which made these elusive signals more obvious. By grouping rare variants based on their predicted molecular effects, Hawkes uncovered 22% more non-coding genetic associations, which would otherwise have not been detectable in the statistical noise. This let Hawkes pinpoint specific regulatory variants driving the abundance of critical proteins circulating in the human body, including PLA2G7 (linked to aging) and EGLN1 (a vital cellular oxygen sensor).Taking this approach even further, Hawkes used AlphaGenome Atlas to look at how hundreds of millions of non-coding variants in the UK Biobank might be linked to body mass index. By focusing on the 1% of non-coding variants which Atlas predicts to be most impactful, he identified 19 genetic regions, which could help direct the next stage of targeted research into this trait.Identifying the regulatory ‘words’ of the genome. Atlas can also be used to identify which recurring short sequences, or motifs, are driving different molecular processes in different cell types for different genes. These motifs can provide key clues, such as locating binding sites of transcription factors (proteins that turn genes on or off) and providing additional interpretation of non-coding variants. Julia Zeitlinger and Melanie Weilert at the Stowers Institute for Medical Research used this resource, for example, to categorize which transcription factors only affect the accessibility of DNA versus which ones are also able to turn genes on and off.Accelerating genomic discoveryWith AlphaGenome Atlas we are creating new layers of information that will help further our understanding of the human genetic code. We hope that this will be a valuable resource for scientists, but we also view it as a baseline rather than an endpoint. As our AI models like AlphaGenome improve, our maps of the entire human genome will become increasingly comprehensive and precise.AlphaGenome Atlas is powerful in isolation, but it also represents a step towards our vision of a broad, unified solution for biologists. Its resources can be integrated into our broader agentic systems, like Google Antigravity, to help enhance end-to-end scientific workflows.It is also important that AlphaGenome Atlas’ scientific knowledge is widely available, so we have made it accessible for non-commercial use through our website from today, as well as for commercial use on Google Cloud soon. (The AlphaGenome base model is already available for academic use on GitHub and via the AlphaGenome API, and also is available for commercial use on Cloud via Model Garden).Together, these tools will enable researchers and industry partners to accelerate the pace of biological discovery: finding novel therapeutic targets, better understanding genetic disorders, and driving the next wave of targeted experimental validation. Explore AlphaGenome Atlas Use the AlphaGenome Atlas skill Connect with AlphaGenome users Read our paper AcknowledgementsWe are grateful to our research collaborators at University of Exeter, Broad Institute, Boston Children’s Hospital, Stowers Institute for Medical Research, Harvard University, Memorial Sloan Kettering Cancer Center, Center for Genomic Medicine at Massachusetts General Hospital, and the University of Kansas Medical Center.This work was done thanks to the contributions of Jun Cheng, Kyle R. Taylor, Lauren Nicolaisen, Joshua Pan, Clare Bycroft, Matteo Perino, Tom Ward, Raina W. Thomas, Natasha Latysheva, Gareth Hawkes, Laura E. Covill, Melanie Weilert, Maile J. Hirschmann, Xi Dawn Chen, Robin N Beaumont, V Kartik Chundru, Michael N Weedon, Simon Bourdareau, Hoyin Chu, Dhavi Hariharan, Thais Kagohara, Lucas Tenório, Yosuke Ushigome, Amanda Stafford, Courtney A. Shearer, Barbara Ikica, Ada Fang, Mouad Naciri, Victoria Johnston, Richard Green, Elisa Lai Hong Wong, Vincent Dutordoir, Anne Mottram, Adam Gayoso, Eirini Arvaniti, Guido Novati, Heidi L. Rehm, Fei Chen, Caleb A. Lareau, Caroline F Wright, Anne O'Donnell-Luria, Julia Zeitlinger, Pushmeet Kohli, Žiga Avsec.We wish to thank our teammates and collaborators for their technical support, feedback, including Kathryn Tunyasuvunakool, Alexander Karollus, Risha Patel, Francesca Pietra, Alisha Eastep, Doga Fadillioglu, Charlie Taylor, Raphael Aboyeji, Uchechi Okereke, Gemma Gibbs, Olufemi Duduyemi, Juan Mateos-Garcia, Mariana Felix, Sahar Abdulrahman, Antonia Mould, Rachael Tremlett, Chang Yun, Salil Deshpande, Anshul Kundaje, Samantha Bryen, Greg Findlay, Phoebe Dace, Kinga Bujakowska, Emma Sherrill, Aubrie Soucy Verran, Boxun Zhao, Tim Yu, Francesca Pietra, Brendah Namugamba, Cassie Gray, Daniel MacArthur, Lingyi Wang, Marc Mansour, Mohamad Hajjari, Mounica Vallurupalli, Philip Montgomery, Phoebe Dace, Roisin Sullivan, Sam Bryen, Teresa Niccoli,Finally we wish to thank Evie Gray, Adriana Fernandez Lara, Alex Wilkins, Danielle Breen, Mariana Montes, Inês Ayer, Ryan Smith, Ross West and Gaby Pearl for their expertise in communicating this work.The information provided by AlphaGenome Atlas is not intended to be a substitute for professional medical advice, diagnosis, or treatment, and does not constitute medical or other professional advice. AlphaGenome has not been validated for, and is not approved for, any clinical use.Related postsAlphaGenome Learn more AlphaGenome: AI for better understanding the genomeJune 2025Science Learn more Follow us Sign up for updates on our latest innovationsI accept Google's Terms and Conditions and acknowledge that my information will be used in accordance with Google's Privacy Policy. Sign up Build AI responsibly to benefit humanityModels Gemini Gemini Omni Nano Banana Gemini Audio Gemma Genie Lyria Veo Research Gemini Robotics Breakthroughs Evals Publications Frontier safety Responsibility Science AlphaFold AlphaGenome WeatherNext AlphaEarth AlphaEvolve Products Gemini app Google AI Studio Google Antigravity Learn more About News Careers National Partnerships for AI Accelerator programs The Podcast About Google Google products Privacy Terms Cookies management controls

AlphaGenome Atlas is an expansive platform developed to predict the molecular consequences of every possible single-letter DNA variant across the human genome, aiming to accelerate the understanding of biology and disease by interpreting genetic variations at the molecular level. The core challenge addressed by this initiative stems from the impossibility of experimentally testing the effects of roughly nine billion potential single-nucleotide mutations in the human genome. Google DeepMind developed AlphaGenome as an artificial intelligence model capable of predicting how these genetic variants impact biological processes. To provide a comprehensive, high-level view for researchers, the Atlas extends these single-variant predictions across the entire genome, serving as a centralized resource that links genetic variations directly to functional DNA sequences.

The platform is structured around several powerful, interconnected resources designed to facilitate diverse research tasks. Central to this framework is the AlphaGenome Variant Impact (AVI) score, which synthesizes the predictive power of AlphaGenome and AlphaMissense—a model for predicting the impact of protein-altering variants—into a single numerical metric. This score allows researchers to rapidly rank genetic variants by their potential molecular effect. Furthermore, the Atlas decomposes the AVI score into feature attributions that pinpoint which specific biological processes, such as gene regulation or splicing, are predicted to be most disrupted by each variant. This decomposition is linked to a comprehensive collection of recurrent DNA sequence motifs, representing the "words" of the genome, providing high-resolution mechanistic insights into how variants function across different cell types and tissues.

The data foundation for the Atlas constitutes a massive one-petabyte dataset, significantly larger than the AlphaFold Database expansion. This resource provides thousands of molecular effect predictions for each variant, spanning hundreds of human and mouse cell types, which serves as the foundational starting point for deeper investigations.

The real-world impact of AlphaGenome Atlas spans critical areas of biological discovery. In the context of rare disease research, the AVI score has been utilized by collaborators to prioritize elusive genetic variants. For instance, researchers have used this methodology to pinpoint variants associated with conditions like epileptic encephalopathy, where the underlying predictions revealed that a variant caused an incorrect splice site, leading to abnormal protein extension. Beyond rare diseases, the Atlas aids in mapping the genetic architecture of complex traits in the general population by addressing the statistical noise inherent in non-coding genetic variation. Studies, such as those conducted by Gareth Hawkes, have applied the Atlas to large-scale genomic data to uncover 22% more non-coding genetic associations related to protein abundance, enabling the identification of specific regulatory variants influencing critical proteins. Additionally, the platform helps identify the regulatory "words" of the genome by identifying recurring motifs that dictate various molecular processes, such as the binding sites for transcription factors, thereby offering crucial context for interpreting non-coding variation.

The availability of this comprehensive knowledge is facilitated through intuitive portals, an API, and integration into agentic systems like Google Antigravity. This accessibility allows academic researchers and industry partners to leverage the Atlas for tasks ranging from rapid variant scoring and deep functional analysis to uncovering novel therapeutic targets and driving targeted experimental validation across molecular biology. The work underlines a commitment not only to scientific knowledge but also to making this foundational data accessible, positioning AlphaGenome Atlas as a powerful tool for accelerating genomic discovery.