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The AlphaGenome API provides access to AlphaGenome, Google DeepMind’s unifying model for deciphering the regulatory code within DNA sequences. This repository contains client-side code, examples and documentation to help you use the AlphaGenome API.

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. AlphaGenome achieves state-of-the-art performance across a range of genomic prediction benchmarks, including numerous diverse variant effect prediction tasks (detailed in Avsec et al. 2025).

The API is offered free of charge for non-commercial use (subject to the terms of use). Query rates vary based on demand – it is well suited for smaller to medium-scale analyses such as analysing a limited number of genomic regions or variants requiring 1000s of predictions, but is likely not suitable for large scale analyses requiring more than 1 million predictions. Once you obtain your API key, you can easily get started by following our Quick Start Guide.

The documentation also covers a set of comprehensive tutorials, variant scoring strategies to efficiently score variant effects, and a visualization library to generate matplotlib figures for the different output modalities.

We cover additional details of the capabilities and limitations in our documentation. For support and feedback:

• Please submit bugs and any code-related issues on GitHub.
• For general feedback, questions about usage, and/or feature requests, please use the community forum – it’s actively monitored by our team so you're likely to find answers and insights faster.
• If you can't find what you're looking for, please get in touch with the AlphaGenome team on alphagenome@google.com and we will be happy to assist you with questions. We’re working hard to answer all inquiries but there may be a short delay in our response due to the high volume we are receiving.

The quickest way to get started is to run our example notebooks in Google Colab. Here are some some starter notebooks:

• Quick start: An introduction to quickly get you started with using the model and making predictions.
• Visualizing predictions: Learn how to visualize different model predictions using the visualization libraries.

Alternatively, you can dive straight in by following the installation guide and start writing code! Here's an example of making a variant prediction:

from alphagenome.data import genome
from alphagenome.models import dna_client
from alphagenome.visualization import plot_components
import matplotlib.pyplot as plt API_KEY = 'MyAPIKey'
model = dna_client.create(API_KEY) interval = genome.Interval(chromosome='chr22', start=35677410, end=36725986)
variant = genome.Variant( chromosome='chr22', position=36201698, reference_bases='A', alternate_bases='C',
) outputs = model.predict_variant( interval=interval, variant=variant, ontology_terms=['UBERON:0001157'], requested_outputs=[dna_client.OutputType.RNA_SEQ],
) plot_components.plot( [ plot_components.OverlaidTracks( tdata={ 'REF': outputs.reference.rna_seq, 'ALT': outputs.alternate.rna_seq, }, colors={'REF': 'dimgrey', 'ALT': 'red'}, ), ], interval=outputs.reference.rna_seq.interval.resize(2**15), # Annotate the location of the variant as a vertical line. annotations=[plot_components.VariantAnnotation([variant], alpha=0.8)],
)
plt.show()

To install alphagenome, clone a local copy of the repository and run pip install:

$ git clone https://github.com/google-deepmind/alphagenome.git
$ pip install ./alphagenome

See the documentation for information on alternative installation strategies.

If you use AlphaGenome in your research, please cite using:

@misc{alphagenome, title={AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model}, author={Avsec, {\v Z}iga and Latysheva, Natasha and Cheng, Jun and Novati, Guido and Taylor, Kyle R. and Ward, Tom and Bycroft, Clare and Nicolaisen, Lauren and Arvaniti, Eirini and Pan, Joshua and Thomas, Raina and Dutordoir, Vincent and Perino, Matteo and De, Soham and Karollus, Alexander and Gayoso, Adam and Sargeant, Toby and Mottram, Anne and Hong Wong, Lai and Drot\'ar, Pavol and Kosiorek, Adam and Senior, Andrew and Tanburn, Richard and Applebaum, Taylor and Basu, Souradeep and Hassabis, Demis and Kohli, Pushmeet}, url={https://storage.googleapis.com/deepmind-media/papers/alphagenome.pdf}, year={2025},
}

AlphaGenome communicates with and/or references the following separate libraries and packages:

We thank all their contributors and maintainers!

Copyright 2024 Google LLC

All software in this repository is licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use this except in compliance with the Apache 2.0 license. You may obtain a copy of the Apache 2.0 license at: https://www.apache.org/licenses/LICENSE-2.0.

Examples and documentation to help you use the AlphaGenome API are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode.

Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.

This is not an official Google product.

Your use of any third-party software, libraries or code referenced in the materials in this repository (including the libraries listed in the Acknowledgments section) may be governed by separate terms and conditions or license provisions. Your use of the third-party software, libraries or code is subject to any such terms and you should check that you can comply with any applicable restrictions or terms and conditions before use.

A modified version of the GENCODE dataset (which can be found here: https://www.gencodegenes.org/human/releases.html) is released with the client code package for illustrative purposes, and is available with reference to the following: