This repository contains code for our paper "Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts". The models are implemented in PyTorch.

All associated data can be downloaded from here.

Untar the dnase.chr.packbited.tar.gz file (occupies ~30 Gb).

The stage 1 models predict accessibility across all training cell types from only sequence, and does not utilise RNA-seq profiles.

The model_zoo/stage1 directory contains models for the Vanilla, Factorized and our ResNet models.

To start training any of these models (say, ResNet), from the model_zoo/stage1 directory:

python resnet.py -cp /path/to/stage1/checkpoint/dir --dnase /path/to/dnase/packbited --rna_quants /path/to/rna_quants_1630tf.joblib

For other inputs, such as hyperparameters, refer

python resnet.py --help

The stage 2 models predict accessibility for each cell type, sequence pair and uses RNA-seq profiles.

The model_zoo/stage2 directory contains models for the stage 2 models, which may be trained with or without mean accessibility feature as input (explained in more detail in the paper).

To start training any of these models (say, ResNet, with mean), from the model_zoo/stage2 directory:

python simple.py -cp /path/to/stage2/checkpoint/dir --dnase /path/to/dnase/packbited --rna_quants /path/to/rna_quants_1630tf.joblib --stage1_file ../stage1/resnet.py --stage1_pretrained_model_path /path/to/stage1/checkpoint/dir --with_mean 1

The model loads weights from the best model from the stage 1 checkpoint directory. You may resume training from a previous checkpoint by adding the argument -rb 1 to the above command. To predict on the test set, add the arguments -rb 1 -ev 1 to the above command. This will generate a report of performance on the test set and also produce precision-recall plots.

For other inputs, such as hyperparameters, refer

python simple.py --help

If you use this code for your research, please cite our paper.