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+![AlphaGenome header image](https://raw.githubusercontent.com/google-deepmind/alphagenome/refs/heads/main/docs/source/_static/header.png)
+
+# AlphaGenome
+[**Overview**](#overview) | [**Use Cases**](#use-cases) | [**Documentation**](#documentation) | [**Pricing**](#pricing) | [**Quick start**](#quick-start)
+
+## Overview
+**Disclaimer:** *Experimental*.
+
+*The AlphaGenome Private Preview is a "Pre-GA Offering" subject to the "Pre-GA
+Offerings Terms" in the General Service Terms section of the Google Cloud
+[Service Specific Terms](https://cloud.google.com/terms/service-terms). It is
+also a “Generative AI Preview Product” as defined in and subject to the
+[Additional Terms for Generative AI Preview Products](https://cloud.google.com/trustedtester/aitos?e=48754805&hl=en).
+Pre-GA products are available "as is" and might have limited support. For more
+information, see the [launch stage](https://cloud.google.com/products?e=48754805#product-launch-stages)
+descriptions.*
+
+Access to the AlphaGenome model capabilities requires application and approval.
+Users must be added to an allowlist to use the service.
+If you are interested in applying to the program, **Request Access** above.
+
+&nbsp;
+
+AlphaGenome is Google DeepMind’s unifying model for deciphering the regulatory
+code within DNA sequences.
+
+AlphaGenome offers multimodal predictions, encompassing diverse functional
+outputs such as gene expression, splicing patterns, chromatin features, and
+contact maps (see diagram below). The model analyzes DNA sequences of up to 1
+million base pairs in length and can deliver predictions at single base-pair
+resolution for most outputs.
+
+Training data was sourced from large public consortia including
+[ENCODE](http://encodeproject.org/), [GTEx](https://www.gtexportal.org/),
+[4D Nucleome](https://4dnucleome.org/) and
+[FANTOM5](https://fantom.gsc.riken.jp/5/), which experimentally measured these
+properties covering important modalities of gene regulation across hundreds of
+human and mouse cell types and tissues.
+
+![Diagram showing an overview of the AlphaGenome model architecture and its inputs/outputs](https://www.alphagenomedocs.com/_images/model_overview.png)
+
+## Use Cases
+*   **Predict outputs for a DNA sequence:** AlphaGenome is a model that makes
+    predictions from DNA sequences. AlphaGenome predicts multiple 'tracks' per
+    output type, covering a wide variety of tissues and cell-types.
+
+*   **Open-vocabulary object retrieval:** AlphaGenome can make predictions for
+    a human reference genome sequence specified by a genomic interval.
+    For example, let's predict RNA-seq for tissue 'Right liver lobe' in a 1MB
+    region of Chromosome 19 around the gene CYP2B6, which encodes an enzyme
+    involved in drug metabolism, and is primarily expressed in the liver.
+
+*   **Predict variant effects:** AlphaGenome can predict the effect of a
+    variant on a specific output type and tissue by making predictions for the
+    reference (REF) and alternative (ALT) allele sequences.
+
+*   **Scoring the effect of a genetic variant:** AlphaGenome can score the
+    effect of a genetic variant by making predictions for the REF and ALT
+    sequences and aggregating the track signal. To highlight which regions in a
+    DNA sequence are functionally important for a final variant prediction,
+    AlphaGenome can help you to perform an in silico mutagenesis (ISM) analysis
+    by scoring all possible single nucleotide variants in a specific interval.
+
+*   **Human and mouse predictions:** AlphaGenome can generate predictions for
+    both humans and mouse.
+
+## Documentation
+This API provides access to AlphaGenome, Google DeepMind's unifying model for
+deciphering the regulatory code within DNA sequences. AlphaGenome offers
+multimodal predictions, encompassing diverse functional outputs including gene
+expression, splicing patterns, chromatin features, and contact maps (see diagram
+below). The model analyzes up to 1 million base pairs of DNA sequence and can
+deliver predictions at single base-pair resolution for most modalities.
+AlphaGenome achieves state-of-the-art performance across a range of genomic
+prediction benchmarks, including diverse variant effect prediction tasks.
+
+The Google Cloud API for AlphaGenome provides a way for Google Cloud customers
+to explore the AlphaGenome API for commercial use cases. This API is in private
+preview (Request Access above). Once allowlisted, customers can access the API
+directly or use the [colab](cloudai_alphagenome_vai_quickstart.ipynb).
+
+### Acknowledgements
+
+*Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., ... Kohli, P. (2025). AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model. bioRxiv.* [https://doi.org/10.1101/2025.06.25.661532](https://doi.org/10.1101/2025.06.25.661532)
+
+### Contact
+If you have any questions on using these models on Google Cloud please contact:
+[alphagenome-cloud-external@google.com](mailto:alphagenome-cloud-external@google.com) or join the community [Discourse](https://www.alphagenomecommunity.com/) for more generic questions on AlphaGenome.
+
+### Links
+
+*   Read our [paper](https://doi.org/10.1101/2025.06.25.661532)
+*   Read our [blog post](https://deepmind.google/discover/blog/alphagenome-ai-for-better-understanding-the-genome)
+*   Join the [community](https://www.alphagenomecommunity.com/)
+*   Check out the [AlphaGenome 101 Video](https://youtu.be/Xbvloe13nak)
+
+## Pricing
+Access to AlphaGenome on Vertex AI is currently restricted.
+To utilize these models via this service:
+
+*   You must **Request Access** using your Google contact.
+*   Your application will be reviewed, and if approved, you will be **added to
+    an allowlist**.
+*   Only allowlisted users can access the API
+*   **Pricing information** will be shared directly with users upon approval
+    and placement on the allowlist.
+
+## Quick start
+The quickest way to get started with the AlphaGenome in Google Cloud Platform is to run [our example notebook](cloudai_alphagenome_vai_quickstart.ipynb) in [Google Colab](https://colab.research.google.com/).