Google has developed AlphaGenome, an AI system that evaluates every possible one-base change in genomes to identify functional non-coding DNA sequences, aiding biologists in understanding genetic functions.
<p>On Tuesday, Google <a href="https://deepmind.google/blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/">announced AlphaGenome Atlas</a>, a resource that attempts to predict the consequences of every possible single-base variant in the human genome. The human genome is about 3 billion bases long, so trying the other three DNA bases that <em>don't</em> appear in our reference genome means sending a total of 9 billion bases through AlphaGenome software.</p>
<p>AlphaGenome is designed to identify potential functions of non-coding DNA, which does not encode proteins but makes up the vast majority of the human genome. Some of this non-coding DNA is essential for controlling the activity of the protein-coding portion—it tells the cell where and when to make messenger RNAs, how to process them into mature protein-coding forms, and so on. But much of it appears to be little more than the remains of viruses and other molecular parasites.</p>
<p>Being able to identify the functional portion is very useful, as is having all the analysis done by a single software package. But until biologists start to use it heavily (assuming they do), it won't be clear what AlphaGenome offers beyond what we could have gotten out of its training data.</p><p><a href="https://arstechnica.com/science/2026/09/googles-ai-genome-system-evaluates-every-possible-one-base-change/">Read full article</a></p>
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# Google's AI genome system evaluates every possible one-base change
Source: [https://arstechnica.com/science/2026/09/googles-ai-genome-system-evaluates-every-possible-one-base-change/](https://arstechnica.com/science/2026/09/googles-ai-genome-system-evaluates-every-possible-one-base-change/)
But most of the non\-coding DNA is junk—the remains of ancient viral infections, DNA\-level parasites, genes that have been inactivated by mutation, and so on\. Figuring out what’s useful and what’s not has been an ongoing challenge for biologists for many reasons\.
First, the proteins that interact with DNA aren’t that picky about the sequences they stick to, potentially binding at random throughout the genome and tolerating a certain degree of mutation\. Many of these proteins are also cell\-type specific; there’s a different population of DNA\-binding proteins in liver cells, nerve cells, immune cells, and so on\. In many cases, having many different protein binding sites in a compact space matters more than the presence of any one of them\.
We’ve developed various software tools that identify individual sites of interest in non\-coding DNA\. But this is exactly the sort of problem that AI is good at solving: one involving probabilities that are imprecise and rely heavily on context\. So Google developed[the AlphaGenome AI system](https://www.nature.com/articles/s41586-025-10014-0), which evaluates sequences for their potential function\.
\(For the biology geeks that don’t want to sort through the paper, AlphaGenome attempts to identify “gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chromatin contact maps, splice site usage, and splice junction coordinates and strength\.”\)
The system is currently limited to sequences from mice and humans\. It has also only been trained on a limited number of cell types that biologists have studied exhaustively\. Still, it can be useful\. If a researcher studying a gene finds changes in its non\-coding regions, it can be difficult to tell whether they’re likely to be significant\. AlphaGenome can provide a hint about their significance, along with a hypothesis about why\. And its predictions are generally as good as or better than those from specialized software tools\.
DeepMind introduces AlphaGenome, an AI model that predicts how DNA sequence variants impact gene regulation and biological processes across diverse cell types and tissues. The model processes up to 1 million base pairs and is available via API for non-commercial research, with the full paper published in Nature.
Google DeepMind introduces AlphaGenome Atlas, a platform with AI predictions for all 9 billion single-letter DNA variants in the human genome, accelerating biological research and disease understanding.
Google DeepMind's AlphaGenome Atlas is an AI-powered map of all possible single-letter DNA mutations, providing a 1-petabyte dataset for researchers to explore genetic variants via a free web interface and API.
Google DeepMind releases AlphaGenome Atlas, an AI tool that provides predictive maps of all possible DNA variations in the human genome to accelerate disease research.
Google DeepMind introduces the AlphaGenome Atlas, using AI models to predict the impact of human genome variants, and provides AVI scores to accelerate genomics research and understand the language of life.