Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or Enformer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
How to install deep-learning-atac?
gptomics/bioskills/deep-learning-atac
$npx -y skills add gptomics/bioskills --skill deep-learning-atacInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/gptomics/bioskills" --skill "gptomics/bioskills/deep-learning-atac"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/gptomics/bioskills" that are relevant to the current task. Run `npx skills add "https://github.com/gptomics/bioskills"` and select the relevant skills, then follow their instructions.
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