Version Compatibility

Reference examples tested with: chrombpnet 0.1.7+, bpnet-lite 0.6+ (github.com/jmschrei/bpnet-lite), scBasset 0.1.0+ (basenji2 fork), tangermeme 0.1+, tfmodisco-lite 2.2+, DeepLIFT 0.6+, captum 0.7+, tensorflow 2.13+, pytorch 2.1+, kipoi 0.8+.

Verify before use:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws unexpected errors, introspect the installed package and adapt rather than retrying. Deep-learning tooling evolves rapidly; method papers post 2023 may have superseded reference implementations.

Sequence-Based Deep Learning for ATAC-seq

"Score the effect of a GWAS SNP on chromatin accessibility" -> Train (or use pre-trained) sequence-to-accessibility CNNs that take 1-5 kb DNA windows and predict per-base Tn5 cleavage profiles. Outputs include: bias-corrected accessibility, single-base mutation effect predictions, and DeepLIFT contribution scores convertible to motifs via TF-MoDISco.

• CLI: chrombpnet pipeline --bigwig signal.bw --bigwig-bias bias.bw ...
• Python: bpnet-lite for custom architectures; tangermeme for fast scoring
• Python (single-cell): scBasset for per-cell sequence-based predictions
• Python (long-context): Enformer pre-trained models via Kipoi