This repository is a companion page for a research paper submitted to the 18th Machine Learning in Computational Biology conference MLCB-2023. It provides code and experiments to demonstrate the value of foundation models of chromatin state for accurate prediction of human introns that are subject to intron retention (IR). We used two approaches for leveraging the Sei architecture for predicting retained introns: The Fine-tuned Sei version uses the representation learned by the Sei convolutional layers, adding a convolutional block plus pooling and fully connected and output layers. Alternatively, the Sei-outputs version uses the chromatin targets learned by Sei (transcription factor binding, histone modifications, and chromatin accessibility) and uses these as input to a logistic regression or light-GBM classifier. Furthermore, this repository uses a novel approach to score the relevance of each transcription factor in the Jaspar Database in intron retention, using Integrated Gradients (IG). The data was obtained from here. You can find a description on the number of sequences in each class, the sequence length, and the number of sequences with ambiguous bps in the notebook Data_description.ipynb.
You can clone this repository using the command:
git clone https://github.com/Addaoud/IntronRetention.git
You can create a python virtual environment or an anaconda environment to install the dependencies using pip or conda:
pip install -r requirements.txt
conda install -r requirements.txt
Additionally, you need to install a pytorch version compatible with your cuda version. You can follow the steps in here to install the latest pytorch version or you can refer to previous versions to install an old pytorch version.
- Follow the steps in here to download the pre-trained Sei framework and the target.names file. Please put both files under the main directory.
- Preprocess_data_to_numpy.py can be used to map the DNA sequences to Sei targets, and save the results in numpy files. This data can be used subsequently as input to the Logistic Regression or Light GBM models. You can use the following command line:
python3 Preprocess_data_to_numpy.py --dir directory_where_to_save_the_numpy_files
- You can train the Logistic Regression by building a new model or loading an existing one using the following command lines:
python3 LR.py --json path_to_LR_json -n
python3 LR.py --json path_to_LR_json -m existing_model_path
- You can train the LGBM model (and optionally perform a Bayesian optimization to search for the best hyperparameters) using the following command lines:
python3 LR.py --json path_to_LR_json --optimize
- You can train the Fine-tuned Sei by building a new model or loading an existing one using the following command lines:
python3 FSei.py --json path_to_LR_json -n
python3 FSei.py --json path_to_LR_json -m existing_model_path
Contributions to the IR Package are welcome! If you find any bugs, have suggestions for new features, or want to improve the existing code, please create an issue or submit a pull request. You can post in the Github issues or e-mail Ahmed Daoud (ahmed0daoudad@gmail.com)