官方在TenK10K这张覆盖28种原发性免疫细胞的单细胞图谱上比较后写到，AlphaGenome 2稳定识别出的因果变异是30%，上一代在单细胞数据上微调后的AlphaGenome以及其他专门工具是16%；方向性基因效应是43%对23%。

这组数字来自Google DeepMind 2026年11月11日的博客。作者是Adam Gayoso、Natasha Latysheva、Pushmeet Kohli和Žiga Avsec。他们把结果概括为：与此前最先进的模型相比，AlphaGenome 2找出的、会改变细胞中基因表达的变异，数量接近两倍。

同一段DNA，在心肌细胞和正在对抗感染的免疫细胞里，会打开或关闭不同的基因。作者写到，要预测细微的DNA变异怎样改变这些细胞里的基因表达，目前仍未解决，而这和人类性状、疾病都有关。全球项目如人类细胞图谱，正在为数百万个细胞绘制基因表达，但这一步预测还没有被解决。

页面顶部有一张等距网格图：深色背景上排列着半透明的粉色立方体，细分程度各不相同。

训练数据写得更具体：超过1.07亿个单独的细胞图谱，覆盖68种组织。对外可用范围是：非商业研究可通过AlphaGenome API使用，商业研究目前是私有预览。文末再次强调，AlphaGenome和AlphaGenome 2仅供研究使用，尚未通过临床或诊断用途的验证或批准。

从一次并行预测所有组织，改成按条件逐个预测

去年发布的AlphaGenome，预测的是DNA变异在较宽组织尺度上的分子活性。他们后来还做了AlphaGenome Atlas，给出人类基因组里全部约90亿种单字母变化的预测，用来帮助研究者排序和解释变异效应，并推动罕见病、复杂性状和基础生物学里的发现。

博客说，第一代模型接收100万个字母的DNA序列，然后同时预测来自许多组织的数千项实验。这样效率高，但模型能力被摊薄，也很难抓住相近细胞类型之间的细微差别。

AlphaGenome 2改成了条件模型：一次只针对一个条件做预测，条件可以是组织、细胞类型或细胞状态。除了这段100万字母的DNA序列，目标生物学条件会进入架构的每个阶段，用来学习同一段DNA在不同细胞类型里会被怎样不同地读取。

博客配了一段说明图，对比两代模型怎样处理细胞类型。第一代从100万字母的序列并行输出批量组织和细胞系的结果。第二代是条件模型，除了DNA序列，还接收一个特定生物学条件：既可以从细胞类型参考目录里选，也可以通过他们的细胞编码器，从科学家自己的单细胞实验里即时生成。图片的替代文本写成“比较两代模型预测性能的图”，图注写的是结构对比，这里按图注理解这张图。

条件化还带来跨模态补全。模型把细胞的生物学状态和输出分开。如果研究者只输入一种测量，比如单细胞基因活性，AlphaGenome 2可以预测尚未测量的性质，例如染色质可及性。作者认为，这可以帮助决定下一步该做哪些实验。

训练数据同时包含人和小鼠的批量数据与单细胞数据。它预测的核心读数与上一代相同，包括基因表达、组蛋白修饰和染色质可及性，并新增了DNA甲基化。博客称这是一项被广泛要求的读数。

人类细胞图谱联盟联合创始人、剑桥大学干细胞医学教授Sarah Teichmann的评价是：AlphaGenome 2让她感到兴奋的地方，是它能直接适应新的单细胞数据，而不需要重新训练；哪怕只有一小部分细胞样本，也能预测DNA变异在新刻画的细胞状态里如何起作用。这是她的个人评价，不是模型的官方性能指标。

它复现过一项肠道巨噬细胞里的IBD发现

官方还写了细胞类型特异性的改进：在训练数据之外的基因组区域上，AlphaGenome 2能解释显著更多的、同一组织内不同细胞类型之间的基因表达差异，并跟踪连续变化，例如人类造血干细胞成熟为红细胞和免疫谱系时的变化。

他们举了一个已发表的研究：一个位于远处调控开关上的变异，通过在肠道免疫细胞（巨噬细胞）里过度激活ETS2基因，推动IBD。AlphaGenome 2在肠道多种免疫细胞类型上做预测，从计算上复现了这一发现。它标出了那个因果变异，并显示它选择性地在肠道巨噬细胞里增强ETS2，对其他细胞类型和基因大体没有影响。这是对已有实验发现的计算复现，不是一项新的临床试验。

配图是三张热图，比较观察到的基因表达与两代模型的预测。对象是正在成熟的红细胞，也就是造血过程。左图是按行标准化（Z分数）后的观察值，中间是AlphaGenome 2的预测，右图是在单细胞数据上微调后的AlphaGenome 1。图注写：每张热图的横向是不同基因，纵向是造血分化的连续阶段；官方说明是AlphaGenome 2的预测更接近观察到的基因表达。图片替代文本补充说，热图包含7个基因、9种造血细胞类型，从上到下从红细胞系到B细胞，颜色从蓝到红表示表达从低到高。图注原文把造血过程写成了hematopoesis。

不必重新训练，也能看新的细胞状态

科学家可以上传只有几十个细胞的单细胞RNA测序快照。这些基因活性会被压缩成细胞状态表示，再用来条件化模型，以处理未见过的状态。官方写到，AlphaGenome 2可以达到或超过直接在那些数据集上训练或微调的模型。

这被用来预测新细胞状态里的变异效应和基因调控，例如疾病或疫苗接种后出现的状态。一个例子是：在一份此前未见过的COVID-19 mRNA疫苗研究的单细胞数据上，AlphaGenome 2区分了疫苗激活的免疫T细胞和静息T细胞。它还显示，一个与重症COVID-19相关的变异，在细胞处于这种激活状态时，对基因活性的干扰更强。这个效应如果只看未激活的细胞，会被漏掉。

预先算好的分数，用来连接GWAS和单细胞地图

为了让研究者不必自己做大规模计算，他们预先计算了与性状相关的变异在数百种人体细胞和组织条件下的预测，针对两个问题。

第一，把增强子和基因的连接画到真实的人体细胞类型里。许多疾病相关变异位于远处的调控开关，也就是增强子，效应不容易追踪。实验性的CRISPR筛选大多用稳定的实验室细胞系，研究者往往缺少人体内特定细胞的接线关系。AlphaGenome 2把增强子-基因作图扩展到原代人体细胞类型，例如支持和保护神经元的脑星形胶质细胞。

第二，在全基因组关联研究（GWAS）标出的较宽DNA区段里，更有信心地找出可能的因果变异、目标基因，以及它们起作用的细胞类型。官方写到，在从身高到2型糖尿病等若干复杂性状上，预先计算的变异分数帮助锁定最可能相关的变异和细胞类型。在有把握时，模型还能准确预测一个变异是提高还是降低某个基因的活性。这被写成理解该基因如何参与疾病的一条关键线索。

Genomics Limited首席执行官Peter Donnelly的评价是：人类遗传学是药物发现中最有力的工具之一，GWAS是其中的核心，但从关联信号走到药物项目真正需要的信息仍然困难。DNA序列预测模型是填补这一差距的重要工具，他认为AlphaGenome 2是该领域一项令人印象深刻的进展，并表示期待把它纳入他们的Mystra平台。这是他的表态，不是独立评测结论。

今天能用的范围，以及明确不能做的事

他们还在与Wellcome Sanger Institute等地的研究者合作，希望刻画更广的人体细胞类型、发育谱系和受扰动的细胞状态。AlphaGenome 2以及用于表达条件化的交互式Colab教程，今天已通过AlphaGenome API向非商业研究开放，商业研究是私有预览。官方还写到，很快将在智能体开发平台Google Antigravity中发布AlphaGenome 2 Skills。他们把这类专用模型和Antigravity中的Gemini 4等科学工具放在一起，认为可以帮助科学家加快研究。

Google DeepMind的官方账号同一天发了介绍帖。博客还链向一份AlphaGenome 2论文PDF。这篇改写依据的是博客正文，没有把论文全文一并收入。

如果要试用，官方给出的入口是非商业研究的AlphaGenome API，以及即将到来的Antigravity技能。边界也写在同一页上：这些模型还没有被验证或批准用于临床或诊断。今天能做的，是按细胞类型去提出和排序研究假设，而不是把预测直接当成诊断依据。

AlphaGenome 2: Deciphering how cells read DNA to better understand disease

Adam Gayoso, Natasha Latysheva, Pushmeet Kohli and Žiga Avsec

November 11, 2026
Science
URL: https://deepmind.google/blog/alphagenome-2-deciphering-how-cells-read-dna-to-better-understand-disease/

Our next-generation DNA sequence model bridges single-cell biology and human genetics, predicting how genetic variants affect hundreds of human cell types.

Today, we are announcing AlphaGenome 2, our next-generation model that predicts how DNA sequences and their variations impact specific human cell types, giving scientists a powerful new tool to uncover the cellular drivers of disease.

Cells are the building blocks of life. Every cell in our body carries the same DNA blueprint, yet each cell type and state—like a heart muscle cell or an immune cell fighting an infection—switches different genes on or off to do its job. Global efforts like the Human Cell Atlas are mapping this gene expression for millions of cells, but it is still an unsolved challenge to predict how subtle DNA variations affect this expression in cells, which is a key driver of human traits and disease.

Last year, we launched AlphaGenome to predict how DNA variants affect molecular activity across broad tissues. We expanded its reach with AlphaGenome Atlas, a platform containing predictions for all 9 billion possible single-letter changes in the human genome. These tools help researchers prioritize and interpret variant effects, driving discoveries in rare diseases, complex traits, and fundamental biology.

By zooming in from broad tissue averages to individual cell types and cell states, and redesigning the model’s architecture, AlphaGenome 2 represents a step-change improvement in variant predictive performance. Importantly, it allows researchers to predict variant effects in disease-relevant cells that would be missed in broad-tissue models, as well as predictions for cell and disease states that the model hasn’t seen before.

AlphaGenome 2 identifies nearly twice as many variants that change gene expression in cells, compared to our previous state-of-the-art model. Trained on more than 100 million individual cell profiles across 68 tissues, the model turns static cell atlases into a predictive engine for human genetics. AlphaGenome 2 is available today for non-commercial research via the AlphaGenome API, and in private preview for commercial research. A technical paper (PDF) is linked from the post.

Redesigning AlphaGenome for specific cell types

Comparing how AlphaGenome 1 and 2 treat cell types. AlphaGenome produces fixed parallel outputs of bulk tissues and cell lines from a 1-million-letter DNA sequence. AlphaGenome 2 is a conditional model, taking a specific biological condition alongside an input DNA sequence, either chosen from a reference catalog of cell types or generated on the fly from a scientist’s own single-cell experiment via our cell encoder.

The first AlphaGenome model took a million-letter DNA sequence and predicted properties across thousands of experiments from many tissues all at once. While efficient, this parallel approach spread the model’s capacity thin and made it hard to capture subtle differences between closely related cell types.

To improve this, we fundamentally redesigned how cell types are treated by the model. Instead of predicting in parallel, AlphaGenome 2 is a conditional model that predicts DNA effects for one condition - defined as a tissue, cell type or state - at a time. Alongside the million-letter DNA sequence, it uses the target biological condition as input at every stage of its architecture. This allows it to learn how the same stretch of DNA is read differently across cell types.

A conditional model also allows cross-modality imputation, enabling AlphaGenome 2 to predict missing experimental data. The model separates a cell’s biological state from its outputs, so if a scientist inputs just one measurement, like single-cell gene activity, AlphaGenome 2 can then predict unmeasured properties, such as chromatin accessibility. This can then help researchers prioritize which experiments to do next.

We trained AlphaGenome 2 on both bulk and single-cell data from human and mouse samples, including over 107 million individual cell profiles. Moving beyond the bulk tissue averages of the original model, AlphaGenome 2 can resolve activity inside specialized cells—such as distinct heart muscle or blood immune cells—to help uncover crucial DNA variants that act only in specific cell types.

The model predicts the same core experimental readouts of DNA function as AlphaGenome — such as gene expression, histone modifications and chromatin accessibility — but also DNA methylation, a widely requested readout.

What makes AlphaGenome 2 exciting is how it adapts directly to new single-cell data without retraining, predicting how DNA variants act in newly profiled cell states from even a small sample of cells

Professor Sarah Teichmann, Co-founder of the Human Cell Atlas consortium and Professor of Stem Cell Medicine at the University of Cambridge

State-of-the-art cell-type predictions and variant recall

AlphaGenome 2 is significantly better at predicting gene regulation in specific cells, helping researchers pinpoint disease-relevant variants.

• Improved cell type specificity. On genome regions outside its training data, AlphaGenome 2 explains significantly more of how gene expression differs across cell types within a tissue—tracking continuous changes in gene expression, such as when human blood stem cells mature into red blood cells and immune lineages.
• Improved variant effect prediction. On the TenK10K dataset, a large single-cell map of 28 primary immune cell types, AlphaGenome 2 reliably identified nearly twice as many causal variants (30% vs. 16%) and directional gene effects (43% vs. 23%) as the original AlphaGenome fine-tuned on single-cell data and other specialized tools. This precision helps researchers find overlooked candidate variants for new discoveries.
• Pinpointing disease-relevant variants. A recent landmark study showed that a variant in a distant regulatory switch drives IBD by overactivating the ETS2 gene in gut immune cells (macrophages). Making predictions across various immune cell types in the gut, AlphaGenome 2 reproduced this finding computationally. It singled out the causal variant and showed that it selectively boosts the ETS2 gene in gut macrophages, leaving other cell types and genes largely unaffected.

AlphaGenome2’s predictions are closer to observed gene expression. A heatmap of gene expression for maturing red blood cells, or hematopoesis, displaying row-normalized (Z-scored) observations (left), AG2 predictions (middle), and AG1 fine tuned on single cell data predictions (right). For each heatmap, different genes are on the horizontal row, while sequential stages in hematopoietic differentiation are on the vertical row.

Analyzing new cell types and states without retraining

AlphaGenome 2 can also generalize to entirely new single-cell experiments without needing to be retrained. Scientists can upload a single-cell RNA-seq snapshot of gene activity from just a few dozen cells; this gene activity is then compressed into a cell state representation, before being used to condition the model on unseen states. AlphaGenome 2 can match or outperform models trained or fine-tuned directly on those datasets.

This enables AlphaGenome 2 to predict variant effects and gene regulation for new cell states, such as those that occur from disease or vaccination. For example, given unseen single-cell data from a COVID-19 mRNA vaccine study, AlphaGenome 2 distinguished vaccine-activated immune T cells from resting ones. It also revealed that a variant linked to severe COVID-19 disrupts gene activity more strongly when cells are in this activated state — an effect that would be missed in unactivated cells.

Bridging human genetics and single-cell biology

One way to better understand disease is by linking human genetics to cell biology. An important part of building this link requires connecting the thousands of trait and disease-associated variants found in genome-wide association studies (GWAS) to the growing single-cell maps of the human body.

To help researchers make these connections without running large-scale computations themselves, we precomputed AlphaGenome 2 predictions for trait-associated variants across hundreds of human cell and tissue conditions, tackling two major challenges:

• Mapping enhancer–gene links in real human cell types: Many disease-linked variants sit in distant regulatory switches called enhancers, making their effects hard to trace. Because experimental CRISPR screens mostly use stable, lab-grown cell lines, scientists often lack these wiring diagrams for specific cells in the human body. AlphaGenome 2 extends enhancer-gene mapping to primary human cell types — such as brain astrocytes, which support and protect neurons — helping researchers connect regulatory variants to their target genes in the specific cells where disease unfolds.
• Pinpointing causal GWAS variants and the cells where they act: Genome-wide association studies flag broad stretches of DNA linked to disease, but researchers can’t always then find the exact causal variant, target gene, and cell type with high confidence. Across a number of complex traits, ranging from height to type 2 diabetes, AlphaGenome 2’s precomputed variant scores help pinpoint the specific variants and cell types most likely to be involved. When confident, the model also accurately predicts whether a variant turns a gene’s activity up or down — a critical clue for understanding how that gene contributes to disease.

Human genetics is one of the most powerful tools in drug discovery. GWAS is central to that but it is difficult to move from an association signal to what a drug programme needs. DNA sequence prediction models are an important tool in helping bridge that gap, and AlphaGenome 2 represents an impressive advance in the field. We look forward to incorporating it in our Mystra platform.

Peter Donnelly, CEO of Genomics Limited

Looking ahead with the scientific community

As scientists map more of the human body at the single-cell level, AI models will continue to become more powerful. Through our ongoing collaborations, including with researchers at the Wellcome Sanger Institute, we are working to profile an even wider spectrum of human cell types, developmental lineages, and perturbed cellular states. We look forward to seeing how researchers from across the community use AlphaGenome 2 and its improved cell-type specific predictions to uncover how genetic variants act across cells, and what that reveals about basic biology and disease.

AlphaGenome 2, and interactive Colab tutorials for expression conditioning are available today for non-commercial research via the AlphaGenome API and in private preview for commercial research. And soon, we will release AlphaGenome 2 Skills in Google Antigravity, our agentic development platform. The combination of specialized models, like AlphaGenome 2, with powerful agentic science tools like Gemini 4 in Antigravity, will help scientists accelerate research and make new discoveries.

Please note that AlphaGenome and AlphaGenome 2 are intended for research use only and have not been validated or approved for clinical or diagnostic purposes.

Acknowledgements

The core research work was done thanks to the contributions of Adam Gayoso, Anne Mottram, Alexander Karollus, Guido Novati, Pavol Drotár, John Reid, Laetitia Meng-Papaxanthos, Adam Kosiorek, Eirini Arvaniti, Vincent Dutordoir, Soham De, Tom Ward, Lai Hong Wong, Matteo Perino, Kyle Taylor, Jun Cheng, Natasha Latysheva, Joshua Pan, Dhavanthi Hariharan, Clare Bycroft, Lauren Nicolaisen, Demis Hassabis, Pushmeet Kohli and Žiga Avsec.

We wish to thank the following people for their support and feedback including Gabriella Stanton, Charlie Taylor, Raina Thomas, Lucas Tenório, Antonio García, Joseph Powell, Angli Xue, Trygve Bakken, Nelson Johansen, Junyue Cao, Ziyu Lu, Jacob Hepkema, Leopold Parts, Irina Voineagu, Souradeep Basu, Michal Zielinski, Jeff Cao, Ioannis Sarropoulos, Sarah Teichmann, Amanda Stafford, Eliseo Papa.

Finally we wish to thank Evie Gray, Adriana Fernandez Lara, Alex Wilkins, Danielle Breen, Dimple Vijaykumar, Muinat Abdul, Chris Kwaku-Abdullahi, Kathryn Seager, Ryan Smith, Ross West and Gaby Pearl for their expertise in communicating this work.