Awesome
AtacWorks
AtacWorks is a deep learning toolkit for coverage track denoising and peak calling from low-coverage or low-quality ATAC-Seq data.
Installation
System requirements
- Ubuntu 16.04+
- CUDA 9.0+
- Python 3.7.0+
- GCC 5+
- (Optional) A conda or virtualenv setup
- Any NVIDIA GPU that supports CUDA 9.0+
AtacWorks training and inference currently does not run on CPU.
1. Clone repository
Latest released version
This will clone the repo to the master branch, which contains code for latest released version
and hot-fixes.
git clone --recursive -b master https://github.com/clara-genomics/AtacWorks.git
Latest development version
This will clone the repo to the default branch, which is set to be the latest development branch. This branch is subject to change frequently as features and bug fixes are pushed.
git clone --recursive https://github.com/clara-genomics/AtacWorks.git
2. Install dependencies
Native Installation
-
Download
bedGraphToBigWigandbigWigToBedGraphbinaries and add $PATH to your bashrc.rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/bedGraphToBigWig <custom_path> rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/bigWigToBedGraph <custom_path> export PATH="$PATH:<custom_path> >> ~/.bashrc" -
Install pip dependencies
cd AtacWorks && pip install -r requirements.txt -
Optional -- Install macs2. Only required if you want to use macs2 subcommands to call peaks based on peak probabilities generated by AtacWorks.
pip install macs2==2.2.4 -
Install atacworks
pip install .
Docker Installation
Follow the instructions here for docker installation.
3. Tests
Run unit tests to verify that installation was successful
```
python -m pytest tests/
```
Workflow
AtacWorks trains a deep neural network to learn a mapping between noisy (low coverage/low cell count/low quality) ATAC-seq data and matching clean (high coverage, high cell count, and/or high quality) ATAC-seq data. Both noisy and clean data should be from the same cell type or tissue. Once this mapping is learned, the trained model can be applied to improve other noisy ATAC-Seq datasets.
1. Training an AtacWorks model
See Tutorial 1 for a workflow detailing the steps of model training and how to modify the parameters used in these steps.
2. Denoising and peak calling using a trained AtacWorks model
See Tutorial 2 for an advanced workflow detailing the prediction using a trained model, and how to modify the parameters used in these steps.
FAQ
- What's the preferred way for setting up the environment?
A virtual environment, conda installation or docker is preferred for running atacworks. Follow the instructions of setting up your preferred platforms. Once the env is setup, you can follow the Installation section above to install all the necessary dependencies.
Contributing to AtacWorks
This section is only for developers of atacworks. If you would like to contribute to atacworks codebase, take a look at the development guidelines here. You can read about our Continuous Integration (CI) test flow here.
Running CI Tests Locally
When a PR is submitted to the github, CI tests are triggered by github CI. If you would like to run those tests locally, follow the instructions below. !!CAUTION!! Please note, your git repository will be mounted to the container, any untracked files will be removed from it. Before executing the CI locally, stash or add them to the index.
Requirements:
Run the following command to execute the CI build steps inside a container locally:
bash ci/local/build.sh -r <Atacworks repo path>
ci/local/build.sh script was adapted from rapidsai/cudf The default docker image is clara-genomics-base:cuda10.1-ubuntu16.04-gcc5-py3.6.
Other images from gpuci/clara-genomics-base repository can be used instead, by using -i argument as shown below:
bash ci/local/build.sh -r <Atacworks repo path> -i gpuci/clara-genomics-base:cuda10.0-ubuntu18.04-gcc7-py3.6
Citation
Please cite AtacWorks as follows:
Lal, A., Chiang, Z.D., Yakovenko, N., Duarte, F.M., Israeli, J. and Buenrostro, J.D., 2019. AtacWorks: A deep convolutional neural network toolkit for epigenomics. BioRxiv, p.829481.