DFHiC

Installation

DFHiC can be downloaded by

git clone https://github.com/BinWangCSU/DFHiC

Enviroment setup

Dependency

• Python==3.6.10
• Tensorflow-gpu==1.10.0
• Tensorlayer==1.9.1
• numpy==1.14.5
• scikit-image==0.14.5
• scikit-learn==0.19.2

The code is compatible with both TensorFlow v1 and TensorLayer. Our models are trained with GPUs. See environment.yml for all prerequisites, and you can also install them using the following command.

conda env create -f environment.yml

Instructions

We provide detailed step-by-step instructions for running DFHiC model for reproducing the results in the original paper and processed train data and test data be provided here.

Download raw aligned sequencing reads

We download alighed sequencing reads(GSE62525) from Rao et al. 2014 (e.g. GSM1551550_HIC001_merged_nodups.txt.gz ), and you can donwlaod data using the raw_data_download_script.sh script. You will download data to CELL folder, such as GM12878.

bash raw_data_download_script.sh GM12878 

Data preprocessing

We preprocess Hi-C data from alighed sequencing reads using preprocess.sh and generate_train_data.py. One can directly downsample raw data and generate raw Hi-C contacts matrix by using preprocess.sh, and finally save the data in this folder.

bash preprocess.sh GM12878 10000 juicer_tools.jar 

Data for training and evaluating the model can be obtained by directly runing generate_train_data.py, and the resulting training and test sets are saved in preprocess/data/CELL folder. We provide training files in here.

python generate_train_data.py GM12878 16

Train DFHiC model

To train:

python run_train.py [GPU_ID] [CHECKPOINT_PATH] [GRAPH_PATH] [BLOCK_SIZE]
python run_train.py 0 checkpoint/ graph/ 40

To evaluate DFHiC model on test data:

python run_test.py [GPU_ID]
python run_test.py -1

We provide pretained weights for DFHiC model.

Predicting

We can directly enhance the entire chromosome Hi-C matrix by run_prediction.py, and you can also enhance your own data through DFHiC:

python run_predict.py [GPU_ID] [CHROME_ID]
python run_predict.py -1 22

License

This project is licensed under the MIT License - see the LICENSEfile for details