[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-96197":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":9,"totalLinesOfCode":9,"stars":12,"forks":13,"watchers":14,"openIssues":14,"contributorsCount":9,"subscribersCount":14,"size":14,"stars1d":14,"stars7d":14,"stars30d":14,"stars90d":14,"forks30d":14,"starsTrendScore":14,"compositeScore":15,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":16,"fork":16,"defaultBranch":17,"hasWiki":16,"hasPages":16,"topics":9,"createdAt":9,"pushedAt":9,"updatedAt":18,"readmeContent":19,"aiSummary":20,"trendingCount":14,"starSnapshotCount":14,"syncStatus":21,"lastSyncTime":9,"discoverSource":22},96197,"alphagenome","google-deepmind\u002Falphagenome","google-deepmind","This API provides programmatic access to the AlphaGenome model developed by Google DeepMind.",null,"https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphagenome","Python",2082,291,0,56.4,false,"main","2026-09-20 04:01:32","![AlphaGenome header image](docs\u002Fsource\u002F_static\u002Fheader.png)\n\n# AlphaGenome API\n\n![PyPI Python version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002FAlphaGenome)\n![Presubmit Checks](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphagenome\u002Factions\u002Fworkflows\u002Fpresubmit_checks.yml\u002Fbadge.svg)\n\n[**Get API key**](https:\u002F\u002Fdeepmind.google.com\u002Fscience\u002Falphagenome) |\n[**Quick start**](#quick-start) | [**Installation**](#installation) |\n[**Documentation**](https:\u002F\u002Fwww.alphagenomedocs.com\u002F) |\n[**Community**](https:\u002F\u002Fwww.alphagenomecommunity.com) |\n[**Terms of Use**](https:\u002F\u002Fdeepmind.google.com\u002Fscience\u002Falphagenome\u002Fterms) |\n[**Model code**](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphagenome_research)\n\nThe AlphaGenome API provides access to AlphaGenome, Google DeepMind’s unifying\nmodel for deciphering the regulatory code within DNA sequences. This repository\ncontains client-side code, examples and documentation to help you use the\nAlphaGenome API.\n\nAlphaGenome offers multimodal predictions, encompassing diverse functional\noutputs such as gene expression, splicing patterns, chromatin features, and\ncontact maps (see [diagram below](#model_overview)). The model analyzes DNA\nsequences of up to 1 million base pairs in length and can deliver predictions at\nsingle base-pair resolution for most outputs. AlphaGenome achieves\nstate-of-the-art performance across a range of genomic prediction benchmarks,\nincluding numerous diverse variant effect prediction tasks (detailed in\n[Avsec et al.](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41586-025-10014-0)).\n\nAlphaGenome API also provides access to the ‘Atlas’ dataset. AlphaGenome Atlas\nis a resource of pre-computed variant effect score predictions, AlphaGenome\nVariant Impact (AVI) score, and feature importances across the entire human\ngenome. Learn more\n[here](https:\u002F\u002Fdeepmind.google.com\u002Fscience\u002Falphagenome\u002Fatlas).\n\nThe API is offered free of charge for\n[non-commercial use](https:\u002F\u002Fdeepmind.google.com\u002Fscience\u002Falphagenome\u002Fterms)\n(subject to the terms of use). Query rates vary based on demand – the\npre-computed Atlas predictions will typically have a larger query rate. Other\npredictions are better suited for smaller to medium-scale analyses such as\nanalysing a limited number of genomic regions or variants requiring 1000s of\npredictions, but is likely not suitable for large scale analyses requiring more\nthan 1 million predictions. Once you obtain your API key, you can easily get\nstarted by following our [Quick Start Guide](#quick-start), or watching our\n[AlphaGenome 101 tutorial](https:\u002F\u002Fyoutu.be\u002FXbvloe13nak).\n\n\u003Ca id='model_overview'>\n\n![Model overview](docs\u002Fsource\u002F_static\u002Fmodel_overview.png)\n\n\u003C\u002Fa>\n\nThe documentation also covers a set of comprehensive tutorials, variant scoring\nstrategies to efficiently score variant effects, and a visualization library to\ngenerate `matplotlib` figures for the different output modalities.\n\nWe cover additional details of the capabilities and limitations in our\ndocumentation. For support and feedback:\n\n-   Please submit bugs and any code-related issues on\n    [GitHub](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphagenome\u002Fissues).\n-   For general feedback, questions about usage, and\u002For feature requests, please\n    use the [community forum](https:\u002F\u002Fwww.alphagenomecommunity.com) – it’s\n    actively monitored by our team so you're likely to find answers and insights\n    faster.\n-   If you can't find what you're looking for, please get in touch with the\n    AlphaGenome team on alphagenome@google.com and we will be happy to assist\n    you with questions. We’re working hard to answer all inquiries but there may\n    be a short delay in our response due to the high volume we are receiving.\n\n## Terms\n\nAlphaGenome API is subject to the\n[AlphaGenome Terms of Service](https:\u002F\u002Fdeepmind.google.com\u002Fscience\u002Falphagenome\u002Fterms).\nOther than as expressly provided in the AlphaGenome Terms of Service (see\nreferences to Permissive Use Downloadable Artifacts for Commercial and\nNon-Commercial Use), outputs generated by AlphaGenome and other information\nprovided in AlphaGenome Atlas are for non-commercial use only and should not be\nused for the training of other machine learning models. Predictions are for\ntheoretical modelling and research purposes only. They must not be used for\nclinical decision-making or relied upon for medical or other professional\nadvice.\n\nAlphaGenome is also\n[available](https:\u002F\u002Fdocs.cloud.google.com\u002Fgemini-enterprise-agent-platform\u002Fmodels\u002Fopen-models\u002Falphagenome)\nfor commercial use via Google Cloud\n\n## Quick start\n\nThe quickest way to get started is to run our example notebooks in\n[Google Colab](https:\u002F\u002Fcolab.research.google.com\u002F). Here are some starter\nnotebooks:\n\n-   [Quick start](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fgoogle-deepmind\u002Falphagenome\u002Fblob\u002Fmain\u002Fcolabs\u002Fquick_start.ipynb):\n    An introduction to quickly get you started with using the model and making\n    predictions.\n-   [Visualizing predictions](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fgoogle-deepmind\u002Falphagenome\u002Fblob\u002Fmain\u002Fcolabs\u002Fvisualization_modality_tour.ipynb):\n    Learn how to visualize different model predictions using the visualization\n    libraries.\n\nAlternatively, you can dive straight in by following the\n[installation guide](#installation) and start writing code! Here's an example of\nmaking a variant prediction:\n\n```python\nfrom alphagenome.data import genome\nfrom alphagenome.models import dna_client\nfrom alphagenome.visualization import plot_components\nimport matplotlib.pyplot as plt\n\n\nAPI_KEY = 'MyAPIKey'\nmodel = dna_client.create(API_KEY)\n\ninterval = genome.Interval(chromosome='chr22', start=35677410, end=36725986)\nvariant = genome.Variant(\n    chromosome='chr22',\n    position=36201698,\n    reference_bases='A',\n    alternate_bases='C',\n)\n\noutputs = model.predict_variant(\n    interval=interval,\n    variant=variant,\n    ontology_terms=['UBERON:0001157'],\n    requested_outputs=[dna_client.OutputType.RNA_SEQ],\n)\n\nplot_components.plot(\n    [\n        plot_components.OverlaidTracks(\n            tdata={\n                'REF': outputs.reference.rna_seq,\n                'ALT': outputs.alternate.rna_seq,\n            },\n            colors={'REF': 'dimgrey', 'ALT': 'red'},\n        ),\n    ],\n    interval=outputs.reference.rna_seq.interval.resize(2**15),\n    # Annotate the location of the variant as a vertical line.\n    annotations=[plot_components.VariantAnnotation([variant], alpha=0.8)],\n)\nplt.show()\n```\n\n## Installation\n\n\u003C!-- mdformat off(disable for [!TIP] format) -->\n\n> [!TIP]\n> You may optionally wish to create a\n> [Python Virtual Environment](https:\u002F\u002Fdocs.python.org\u002F3\u002Ftutorial\u002Fvenv.html) to\n> prevent conflicts with your system's Python environment.\n\n\u003C!-- mdformat on -->\n\nTo install `alphagenome`, clone a local copy of the repository and run `pip\ninstall`:\n\n```bash\n$ git clone https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphagenome.git\n$ pip install .\u002Falphagenome\n```\n\nSee [the documentation](https:\u002F\u002Fwww.alphagenomedocs.com\u002Finstallation.html) for\ninformation on alternative installation strategies.\n\n## Contributing\n\nWe welcome bug fixes and some new features to this project. Before sending your\npull request, please read the [CONTRIBUTING](CONTRIBUTING.md) documentation,\nensure all tests pass, and that your contributions are formatted correctly.\n\nYou can check your changes by running the following from the `alphagenome` root\ndirectory:\n\n```shell\n$ python3 -m pip install hatch\n$ python3 -m hatch run check:all  # Runs linter and formatter.\n$ python3 -m hatch test\n```\n\n## Citing `alphagenome`\n\nIf you use AlphaGenome in your research, please cite using:\n\n\u003C!-- disableFinding(SNIPPET_INVALID_LANGUAGE) -->\n\n```bibtex\n@article{alphagenome,\n  title={Advancing regulatory variant effect prediction with {AlphaGenome}},\n  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 Wong, Lai Hong and Drot{\\'a}r, Pavol and Kosiorek, Adam and Senior, Andrew and Tanburn, Richard and Applebaum, Taylor and Basu, Souradeep and Hassabis, Demis and Kohli, Pushmeet},\n  journal={Nature},\n  volume={649},\n  number={8099},\n  pages={1206--1218},\n  year={2026},\n  doi={10.1038\u002Fs41586-025-10014-0},\n  publisher={Nature Publishing Group UK London}\n}\n```\n\n\u003C!-- enableFinding(SNIPPET_INVALID_LANGUAGE) -->\n\n## Acknowledgements\n\nAlphaGenome communicates with and\u002For references the following separate libraries\nand packages:\n\n*   [Abseil](https:\u002F\u002Fgithub.com\u002Fabseil\u002Fabseil-py)\n*   [anndata](https:\u002F\u002Fgithub.com\u002Fscverse\u002Fanndata)\n*   [fsspec](https:\u002F\u002Fgithub.com\u002Ffsspec\u002Ffilesystem_spec)\n*   [gRPC](https:\u002F\u002Fgithub.com\u002Fgrpc\u002Fgrpc)\n*   [immutabledict](https:\u002F\u002Fgithub.com\u002Fcorenting\u002Fimmutabledict)\n*   [intervaltree](https:\u002F\u002Fgithub.com\u002Fchaimleib\u002Fintervaltree)\n*   [jaxtyping](https:\u002F\u002Fgithub.com\u002Fpatrick-kidger\u002Fjaxtyping)\n*   [matplotlib](https:\u002F\u002Fmatplotlib.org\u002F)\n*   [ml_dtypes](https:\u002F\u002Fgithub.com\u002Fjax-ml\u002Fml_dtypes)\n*   [NumPy](https:\u002F\u002Fnumpy.org\u002F)\n*   [pandas](https:\u002F\u002Fpandas.pydata.org\u002F)\n*   [protobuf](https:\u002F\u002Fdevelopers.google.com\u002Fprotocol-buffers\u002F)\n*   [pyarrow](https:\u002F\u002Farrow.apache.org\u002F)\n*   [pyfaidx](https:\u002F\u002Fgithub.com\u002Fmdshw5\u002Fpyfaidx\u002F)\n*   [SciPy](https:\u002F\u002Fscipy.org\u002F)\n*   [seaborn](https:\u002F\u002Fseaborn.pydata.org\u002F)\n*   [tqdm](https:\u002F\u002Fgithub.com\u002Ftqdm\u002Ftqdm)\n*   [typeguard](https:\u002F\u002Fgithub.com\u002Fagronholm\u002Ftypeguard)\n*   [typing_extensions](https:\u002F\u002Fgithub.com\u002Fpython\u002Ftyping_extensions)\n*   [zstandard](https:\u002F\u002Fgithub.com\u002Findygreg\u002Fpython-zstandard)\n\nWe thank all their contributors and maintainers!\n\n## License and Disclaimer\n\nCopyright 2024 Google LLC\n\nAll software in this repository is licensed under the Apache License, Version\n2.0 (Apache 2.0); you may not use this except in compliance with the Apache 2.0\nlicense. You may obtain a copy of the Apache 2.0 license at:\nhttps:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0. For the avoidance of doubt, as\nnoted above, the API is offered free of charge for non-commercial use subject to\nthe [Terms of Use](https:\u002F\u002Fdeepmind.google.com\u002Fscience\u002Falphagenome\u002Fterms).\n\nExamples and documentation to help you use the AlphaGenome API are licensed\nunder the Creative Commons Attribution 4.0 International License (CC-BY). You\nmay obtain a copy of the CC-BY license at:\nhttps:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002Flegalcode.\n\nUnless required by applicable law or agreed to in writing, all software and\nmaterials distributed here under the Apache 2.0 or CC-BY licenses are\ndistributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\neither express or implied. See the licenses for the specific language governing\npermissions and limitations under those licenses.\n\nThis is not an official Google product.\n\n### Third-party software\n\nYour use of any third-party software, libraries or code referenced in the\nmaterials in this repository (including the libraries listed in the\n[Acknowledgments](#acknowledgements) section) may be governed by separate terms\nand conditions or license provisions. Your use of the third-party software,\nlibraries or code is subject to any such terms and you should check that you can\ncomply with any applicable restrictions or terms and conditions before use.\n\n### Reference Datasets\n\nA modified version of the GENCODE dataset (which can be found here:\nhttps:\u002F\u002Fwww.gencodegenes.org\u002Fhuman\u002Freleases.html) is released with the client\ncode package for illustrative purposes, and is available with reference to the\nfollowing:\n\n-   Copyright © 2024 EMBL-EBI\n-   The GENCODE dataset is subject to the EMBL-EBI terms of use, available at\n    https:\u002F\u002Fwww.ebi.ac.uk\u002Fabout\u002Fterms-of-use.\n-   Citation: Frankish A, et al (2018) GENCODE reference annotation for the\n    human and mouse genome.\n-   Further details about GENCODE can be found at\n    https:\u002F\u002Fwww.gencodegenes.org\u002Fhuman\u002Freleases.html, with additional citation\n    information at https:\u002F\u002Fwww.gencodegenes.org\u002Fpages\u002Fpublications.html and\n    further acknowledgements can be found at\n    https:\u002F\u002Fwww.gencodegenes.org\u002Fpages\u002Fgencode.html.\n","AlphaGenome 是 Google DeepMind 开发的统一基因组调控模型，旨在解析 DNA 序列中的功能调控编码。其核心功能包括对长达 100 万碱基对的 DNA 序列进行单碱基分辨率预测，覆盖基因表达、RNA 剪接、染色质特征及三维基因组接触图等多模态输出；同时提供全基因组预计算的 AlphaGenome Atlas 数据集（含 AVI 变异影响分和特征重要性）。项目以 Python API 形式开放，支持非商业用途的轻量级基因组分析。适用于功能基因组学研究、非编码变异效应评估、调控元件注释及生物医学假设生成等场景。",2,"trending"]