---
title: "Google DeepMind Unveils AlphaGenome 2: AI That Reads DNA at the Single-Cell Level"
description: "Google DeepMind has introduced AlphaGenome 2, an AI model that predicts gene activity at the single-cell level, accounting for tissue type. The tool is now available to researchers via API."
date: 2026-11-11T21:41:30.180Z
lang: en
url: https://xab.info/en/posts/google-deepmind-alphagenome-2-dna-single-cell
tags: [alpha-genome, google-deepmind, dna, genomics, ai, single-cell]
author: "Alexander Buran"
publisher: "XAB.info"
---

# Google DeepMind Unveils AlphaGenome 2: AI That Reads DNA at the Single-Cell Level

![Graphic representation of a DNA strand, connected cells, and a digital brain model on a display.](https://xab.info/media/2026/11/11/google-deepmind-alphagenome-2-chitaet-dnk-na-urovne-kletok/google-deepmind-alphagenome-2-chitaet-dnk-na-urovne-kletok-1.webp)

## Key Points

- AlphaGenome 2 is a conditional AI model that considers cell and tissue types when predicting gene activity.
- Input includes DNA up to 1 million nucleotides and biological context; trained on 107+ million cell profiles (68 tissue types).
- The model handles unknown cell types without fine-tuning and turns cell atlases into predictive tools.
- Available for non-commercial research via API; clinical application in humans is not currently permitted.

Possessing the same DNA does not mean that genes influence all cell types in the same way. Gene activity is largely individual, depends on the tissue and the cell's developmental stage, and is susceptible to malfunctions that can lead to genetic and other diseases. This is the exact problem that Google DeepMind's new model, AlphaGenome 2, unveiled in November 2026, aims to solve. Unlike classical approaches, it operates at the single-cell level and is capable of detecting not just sequences, but how specific DNA segments regulate gene activity within a specific cellular environment.

### From Static Sequence to the Cell's Living Context

The primary task of AlphaGenome is to learn how individual DNA segments control gene function. The genome contains not only protein-coding sequences but also numerous regulatory regions that determine where, when, and how actively genes are switched on. Alterations in these areas can affect cell performance and be linked to disease development. The first version of the model, released a year earlier, analyzed multiple tissues and cell lines simultaneously but did not account for the biological context as precisely as is required for accurate diagnostics.

### What's New in AlphaGenome 2: A Conditional Model Incorporating Cell Type

The key distinction of AlphaGenome 2 is the use of additional information about the cell type to account for gene expression nuances in specific tissues or cellular environments. In developer terminology, this is a conditional model. The input consists of a DNA sequence of up to one million nucleotides and a description of the biological context: tissue, cell type, or developmental stage. This allows the model to evaluate how the same sequence regulates gene activity in different cells, rather than providing an averaged result across all tissues at once.

### Training on 107 Million Cell Profiles

To train the model, the developers used over 107 million individual cell profiles from humans and mice, covering 68 types of tissue. This volume of data enabled the model to capture subtle differences in gene regulation between cellular states. AlphaGenome 2 predicts several characteristics of genome activity, including gene expression, giving researchers a more complete picture of how genetic information is realized in a specific cell.

### Predictions Without Fine-Tuning and Transforming Atlases into Tools

Another feature of AlphaGenome 2 is its ability to work with previously unknown cell types without additional training. Researchers can upload single-cell RNA sequencing data, obtained from even a few dozen cells, and the model translates the information about gene activity into a description of the cellular state for future predictions. DeepMind claims the model effectively turns static cell atlases—huge data repositories that detail and catalog individual cells—into a "mechanism for predicting human genetics."

### Access, Limitations, and Clinical Status

AlphaGenome 2 is already available for non-commercial scientific research via API, while commercial use is currently limited to preliminary closed access. The developers emphasize that the new version does not replace the first: more detailed analysis of individual cell states requires additional computational resources, so the original AlphaGenome will continue to be a less resource-intensive tool for large-scale genome research. Furthermore, these models are not yet approved for clinical research: experiments on animals are permitted, but application on humans is not.

## Fact-Check & Sources

- [Google научила ИИ читать ДНК на уровне отдельных клеток — это поможет искать корни болезней](https://3dnews.ru/1151170) - Primary source: AlphaGenome 2 description, training data volume (107+ million profiles, 68 tissue types), API access, and clinical limitations.
- [Google научила ИИ читать ДНК на уровне отдельных клеток — это поможет искать корни болезней](https://3dnews.ru/1151170/google-nauchila-ii-chitat-dnk-na-urovne-otdelnih-kletok-eto-pomoget-iskat-korni-bolezney) - Duplicate link to the same material; content matches the base of the article.

## FAQ

### How does AlphaGenome 2 differ from the first AlphaGenome?

The first version analyzed multiple tissues simultaneously, whereas AlphaGenome 2 uses additional cell type information (a conditional model) to account for gene function nuances in specific environments.

### What data was AlphaGenome 2 trained on?

Developers used over 107 million individual human and mouse cell profiles, covering 68 different tissue types.

### Can AlphaGenome 2 be used to treat people?

No. These models are not currently approved for clinical research: animal experiments are allowed, but use in humans is not. The model is available for non-commercial scientific research via API.

### Does AlphaGenome 2 replace the first version?

No. The developers emphasize that the new version is more computationally demanding, meaning the original AlphaGenome remains a less resource-intensive tool for large-scale genomic research.