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Article summary:

1. AtacWorks is a deep learning-based toolkit that denoises low-coverage or low-quality ATAC-seq data and improves the resolution of chromatin accessibility signal and identification of regulatory elements.

2. The neural network used in AtacWorks is based on the ResNet architecture, which allows for deeper and more accurate models to be trained.

3. AtacWorks can be applied to subsampled bulk ATAC-seq, single-cell ATAC-seq, and low-signal-to-noise ATAC-seq datasets, as well as adapted for cross-modality prediction of transcription factor footprints and ChIP-seq peaks from low-input ATAC-seq.

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