https://github.com/genentech/grelu
gReLU is a python library to train, interpret, and apply deep learning models to DNA sequences.
https://github.com/genentech/grelu
Last synced: 2 months ago
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gReLU is a python library to train, interpret, and apply deep learning models to DNA sequences.
- Host: GitHub
- URL: https://github.com/genentech/grelu
- Owner: Genentech
- License: mit
- Created: 2024-04-18T18:01:46.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2026-03-26T17:11:56.000Z (2 months ago)
- Last Synced: 2026-03-26T20:41:45.268Z (2 months ago)
- Language: Python
- Homepage: https://genentech.github.io/gReLU/
- Size: 71.9 MB
- Stars: 318
- Watchers: 8
- Forks: 52
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Authors: AUTHORS.md
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README
[](https://doi.org/10.5281/zenodo.15627611)
# gReLU
gReLU is a Python library to train, interpret, and apply deep learning models to DNA sequences. Code documentation is available [here](https://genentech.github.io/gReLU/).
## Breaking Changes in v1.1.0
**Model Zoo Migration:** The gReLU model zoo has moved from Weights & Biases to [HuggingFace](https://huggingface.co/collections/Genentech/grelu-model-zoo). The `grelu.resources` API has changed:
```python
# Old API (wandb) - still available at grelu.resources.wandb but will be removed in future
grelu.resources.load_model(project="human-atac-catlas", model_name="model")
# New API (HuggingFace)
grelu.resources.load_model(repo_id="Genentech/human-atac-catlas-model", filename="model.ckpt")
```
Browse the zoo at https://huggingface.co/collections/Genentech/grelu-model-zoo and see the [Model Zoo Tutorial](docs/tutorials/6_model_zoo.ipynb) for updated usage.
## Installation
To install from source:
```shell
git clone https://github.com/Genentech/gReLU.git
cd gReLU
pip install .
```
To install using pip:
```shell
pip install gReLU
```
Typical installation time including all dependencies is under 10 minutes.
To train or use transformer models containing flash attention layers, [flash-attn](https://github.com/Dao-AILab/flash-attention) needs to be installed first:
```shell
conda install -c conda-forge cudatoolkit-dev -y
pip install torch ninja
pip install flash-attn --no-build-isolation
pip install gReLU
```
## Contributing
See our [contribution guide](https://genentech.github.io/gReLU/contributing.html).
## Additional requirements
If you want to use genome annotation features through the function `grelu.io.genome.read_gtf`, you will need to install the following UCSC utilities: `genePredToBed`, `genePredToGtf`, `bedToGenePred`, `gtfToGenePred`, `gff3ToGenePred`.
If you want to create bigWig files through the function `grelu.data.preprocess.make_insertion_bigwig`, you will need to install the following UCSC utilities: `bedGraphToBigWig`.
UCSC utilities can be installed from `http://hgdownload.cse.ucsc.edu/admin/exe/`, for example using the following commands:
```shell
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/bedGraphToBigWig /usr/bin/
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/genePredToBed /usr/bin/
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/genePredToGtf /usr/bin/
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/bedToGenePred /usr/bin/
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/gtfToGenePred /usr/bin/
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/gff3ToGenePred /usr/bin/
```
or via bioconda:
```shell
conda install -y \
bioconda::ucsc-bedgraphtobigwig \
bioconda::ucsc-genepredtobed \
bioconda::ucsc-genepredtogtf \
bioconda::ucsc-bedtogenepred \
bioconda::ucsc-gtftogenepred \
bioconda::ucsc-gff3togenepred
```
If you want to create ATAC-seq coverage bigWig files using `grelu.data.preprocess.make_insertion_bigwig`, you will need to install bedtools. See https://bedtools.readthedocs.io/en/latest/content/installation.html for instructions.
## Citation
Please cite our paper: https://www.nature.com/articles/s41592-025-02868-z
Lal, A., Gunsalus, L., Nair, S. et al. gReLU: a comprehensive framework for DNA sequence modeling and design. Nat Methods (2025). https://doi.org/10.1038/s41592-025-02868-z