ChromBERT is a pre-trained deep learning model designed to capture the genome-wide co-association patterns of approximately one thousand transcription regulators, thereby enabling accurate representations of context-specific transcriptional regulatory networks (TRNs). As a foundational model, ChromBERT can be fine-tuned to adapt to various biological contexts through transfer learning. This significantly enhances our understanding of transcription regulation and offers a powerful tool for a broad range of research and clinical applications in different biological settings.
ChromBERT is compatible with Python versions 3.8 or higher and requires PyTorch 2.0 or above, along with FlashAttention-2. These dependencies must be installed prior to ChromBERT.
Follow the detailed instructions on PyTorch’s official site to install PyTorch according to your device and CUDA version specifications.
Execute the following commands to install the requried packages and FlashAttention-2
# install the required packages for FlashAttention-2
pip install packaging
pip install ninja
pip install flash-attn<=2.4 --no-build-isolation # FlashAttention-3 is not supported yet, please install FlashAttention-2Clone the repository and install ChromBERT using the commands below:
git clone git@github.com:TongjiZhanglab/ChromBERT.git
cd ChromBERT
pip install .
chrombert_prepare_env # download required pre-trained model and annotation data files from Hugging Face to ~/.cache/chrombert/dataTo verify installation, execute the following command:
import chrombertFor detailed information on usage, please checkout the documentations and tutorials at chrombert.readthedocs.io.
ChromBERT has been initially trained on the human Cistrome-Human-6K dataset at 1-kb resolution. Plans for further training on the mouse Cistrome-Mouse-5K dataset at the same resolution are underway. Currently available pre-trained models include:
| Model Name | Description | Download Link |
|---|---|---|
| Human-6K-1kb | Pre-trained on Cistrome-Human-6K dataset at 1-kb resolution | Download |
Note: Models can also be downloaded via the chrombert_prepare_env command, as outlined in the installation section.
Explore detailed examples of how to fine-tune ChromBERT for downstream tasks such as prompt-enhanced fine-tuning for generative prediction, and analyses focused on locus specificities and cellular dynamics of TRNs, by visiting our examples page at chrombert.readthedocs.io.
Our work is ongoing and contributions are continually being made.