NVIDIA
NVIDIA
AtacWorks
Resource
NVIDIA
NVIDIA
AtacWorks

Deep learning toolkit for coverage track denoising and peak calling from low-coverage or low-quality ATAC-Seq data

AtacWorks Paper

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.

Link: https://www.biorxiv.org/content/10.1101/829481v2

AtacWorks pre-trained models

The list of pre-trained models mentioned in the paper above are all hosted on NGC. The one line command for downloading a model and all it's corresponding files can be found in CLI Command box in the catalog description when you click on the model.

All the details about how atac-seq data is conditioned before sending it as input to AtacWorks, all the different models trained and the data used for them etc, is documented here.

AtacWorks Code

AtacWorks is an open source project and we highly value collaborations and contributions from the community. If you'd like to contribute to the toolkit, here's the github repository.

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:

  1. docker
  2. nvidia-docker
  3. nvidia-container-runtime

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

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