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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+library_name: alphagenome-pytorch
+tags:
+  - genomics
+  - biology
+  - dna
+  - deep-learning
+  - regulatory-genomics
+  - chromatin-accessibility
+  - gene-expression
+pipeline_tag: other
+---
+
+# AlphaGenome PyTorch
+
+A PyTorch implementation of [AlphaGenome](https://www.nature.com/articles/s41586-025-10014-0), the DNA sequence model from Google DeepMind that predicts hundreds of genomic tracks at single base-pair resolution from sequences up to 1M bp.
+
+This is an accessible, readable, and hackable implementation for integrating into existing PyTorch pipelines, fine-tuning on custom datasets, and building on top of.
+
+## Model Details
+
+- **Parameters**: 450M
+- **Input**: One-hot encoded DNA sequence
+- **Organisms**: Human, Mouse
+- **Weights**: Converted from the official JAX checkpoint
+
+## Download Weights
+
+Available weight files:
+- `model.safetensors` - trained on all data (recommended)
+- `model_fold_0.safetensors` through `model_fold_3.safetensors` - individual CV folds
+
+```bash
+# Using huggingface-cli
+huggingface-cli download gtca/alphagenome_pytorch model.safetensors --local-dir .
+
+# Or using Python
+pip install huggingface_hub
+python -c "from huggingface_hub import hf_hub_download; hf_hub_download('gtca/alphagenome_pytorch', 'model.safetensors', local_dir='.')"
+```
+
+## Usage
+
+```python
+from alphagenome_pytorch import AlphaGenome
+from alphagenome_pytorch.utils.sequence import sequence_to_onehot_tensor
+import pyfaidx
+import torch
+
+model = AlphaGenome.from_pretrained("model.safetensors")
+model.eval()
+
+with pyfaidx.Fasta("hg38.fa") as genome:
+    sequence = str(genome["chr1"][1_000_000:1_131_072])
+
+dna_onehot = sequence_to_onehot_tensor(sequence).unsqueeze(0)
+
+preds = model.predict(dna_onehot, organism_index=0)  # 0=human, 1=mouse
+
+# Access predictions by head name and resolution:
+# - preds['atac'][1]: 1bp resolution, shape (batch, 131072, 256)
+# - preds['atac'][128]: 128bp resolution, shape (batch, 1024, 256)
+```
+
+## Model Outputs
+
+| Head | Tracks | Resolutions | Description |
+|------|--------|-------------|-------------|
+| atac | 256 | 1bp, 128bp | Chromatin accessibility |
+| dnase | 384 | 1bp, 128bp | DNase-seq |
+| procap | 128 | 1bp, 128bp | Transcription initiation |
+| cage | 640 | 1bp, 128bp | 5' cap RNA |
+| rnaseq | 768 | 1bp, 128bp | RNA expression |
+| chip_tf | 1664 | 128bp | TF binding |
+| chip_histone | 1152 | 128bp | Histone modifications |
+| contact_maps | 28 | 64x64 | 3D chromatin contacts |
+| splice_sites | 5 | 1bp | Splice site classification (D+, A+, D−, A−, None) |
+| splice_junctions | 734 | pairwise | Junction read counts |
+| splice_site_usage | 734 | 1bp | Splice site usage fraction |
+
+## Installation
+
+```bash
+pip install alphagenome-pytorch
+```
+
+## License
+
+The model weights were created by Google DeepMind and are the property of Google LLC.
+They are released under the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0),
+consistent with the [official release on Kaggle](https://www.kaggle.com/models/google/alphagenome).
+
+## Links
+
+- [GitHub Repository](https://github.com/kundajelab/alphagenome-pytorch)
+- [Reference JAX Implementation](https://github.com/google-deepmind/alphagenome_research) (official Google DeepMind code)
+- [Original AlphaGenome Paper](https://www.nature.com/articles/s41586-025-10014-0)
+- [AlphaGenome Documentation](https://www.alphagenomedocs.com/)
+
+## Citation
+
+```bibtex
+@article{avsec2026alphagenome,
+  title={Advancing regulatory variant effect prediction with AlphaGenome},
+  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 others},
+  journal={Nature},
+  volume={649},
+  number={8099},
+  pages={1206--1218},
+  year={2026},
+  publisher={Nature Publishing Group UK London}
+}
+```