bio-chipseq-chip-deep-learning
Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores
npx skills add BioTender-max/awesome-bio-agent-skills --skill chip-deep-learning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: chrombpnet 0.1.7+, BPNet 0.0.23+, TF-MoDISco-lite 2.0+, EnFormer (Avsec lab Colab + DeepMind release), tensorflow 2.13+, pytorch 2.0+, JASPAR 2026 deep-learning collection (released 2025). # Deep Learning for ChIP-seq **"Predict TF binding from sequence and quantify variant effects on binding"** -> Train base-resolution convolutional / transformer models on ChIP-seq / ChIP-nexus / CUT&RUN profiles; predict reference and alternate-allele binding profiles for variants; extract motif syntax via TF-MoDISco from sequence-attribution scores. - Python (modern): chrombpnet (bias-factorized; ATAC/DNase/ChIP) - Python (canonical TF ChIP): BPNet (originally for ChIP-nexus; soft motif syntax) - Python (long-range): EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input window, ~100 kb effective receptive field; tissue-aggregated training) - Python (multi-task): DeepSEA (Zhou 2015; older but still used) - Precomputed: JASPAR 2026 Deep Learning collection (1259 BPNet ChIP models from ENCODE; 240 TFs) Deep-learning ChIP-seq models predict signal from sequence; their power is in counterfactual variant prediction (effect on binding from a SNP
- Version Compatibility
- Model Taxonomy
- Decision Tree: Which Model
- In Silico Mutagenesis Workflow
- TF-MoDISco for Soft Motif Syntax
- Training chromBPNet from Scratch
- EnFormer Application
- Using JASPAR 2026 Deep Learning Models (Precomputed)
- Per-Tool Failure Modes
- chromBPNet -- Bias model trained on wrong assay
- BPNet -- Trained on insufficient peaks
- TF-MoDISco -- Background sequences not representative
- In silico mutagenesis -- Variant outside training distribution
- EnFormer -- Tissue-aggregated predictions
Install pip install chrombpnet Train bias model (control regions without TF binding) chrombpnet bias pipeline \ Train main chromBPNet model chrombpnet pipeline \
What does the bio-chipseq-chip-deep-learning skill do?
Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill chip-deep-learning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.