v0.3.4

v0.3.4 Release

v0.3.1 - Hotfix for v0.3.0

Hotfix: Fix a bug related to dataloader

AtacWorks 0.3.0 release

Key Features:

• Simplified user interface. Single command interface.
• Improved documentation. Launched webpage : https://clara-parabricks.github.io/AtacWorks/index.html
• Improved test coverage
• Package upload to PyPI

hotfix for release v0.2.0

Bug fixes:
* Custom config files are now being picked up.
* Tutorials are updated to match expected outputs and output folders

hotfix for release v0.2.0

Update bugs related to tutorials.

hotfix for release v0.2.0

hotfix. This Release updates the tutorials to use single GPU by default and adds instructions to use multi-GPU training/inference.
Also, fixes a classification mode related bug.

AtacWorks 0.2.0 release

AtacWorks v0.2.0 release. The main features of this release include :

• Tutorials for training and inference.
• Addition of end-to-end tests.
• Addition of setup scripts.
• updating documentation, linting of code.
• Ability to provide multiple input channels for training, inference.
• Introduced config files for setting up training parameters and for defining model structure.

AtacWorks 0.2.0 release candidate 1

This is a pre-release candidate for AtacWorks v0.2.0. The main features of the pre-release include :

• Addition of end-to-end tests.
• Addition of setup scripts.
• updating documentation, linting of code.
• Ability to provide multiple input channels for training, inference.
• Introduced config files for setting up training parameters and for defining model structure.

AtacWorks Release 0.1.1

Hotfix Fixed an issue with generating regression bigwg files when some intervals had all 0 valued outputs.

AtacWorks Release 0.1.0

This is the first official release of AtacWorks. AtacWorks is a deep learning-based toolkit for denoising and peak calling from noisy ATAC-Seq data. While currently tested only on ATAC-Seq, AtacWorks can also be applied to other epigenomic data types such as ChIP-Seq or DNase-Seq. A detailed description and results for several use cases are given in the preprint: https://www.biorxiv.org/content/10.1101/829481

The main components of this release are:

1. Data reading and writing from BED, BEDGRAPH and BigWig formats
2. Training deep learning models using a customizable resnet architecture
3. Pre-trained models that can be applied to new data
4. Inference using a newly trained or provided model, producing a denoised ATAC-Seq signal and peak calls
5. Evaluating model performance on denoising and peak calling tasks.