Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4Snpx agentmods add skills/pku-yuangroup/openai4s/bio-atac-seq-deep-learning-atacWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-atac-seq-deep-learning-atac)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-atac-seq-deep-learning-atac"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-atac-seq-deep-learning-atac/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-atac-seq-deep-learning-atac"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-atac-seq-deep-learning-atac.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00104 | $0.05015 |
| Opus 5 | $0.00052 | $0.02508 |
| Sonnet 5 | $0.00021 | $0.01003 |
| Haiku 4.5 | $0.00010 | $0.00502 |
Grade A, and why
bio-atac-seq-deep-learning-atac scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to bio-atac-seq-deep-learning-atac — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto 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-litefor custom architectures;tangermemefor fast scoring - Python (single-cell):
scBassetfor per-cell sequence-based predictions - Python (long-context): Enformer pre-trained models via Kipoi
Sequence models are NOT a replacement for MACS+TOBIAS at every step. They excel at three specific tasks where classical pipelines struggle: (1) Tn5 bias correction in low-complexity sequence contexts, (2) variant effect prediction in non-genic regions, (3) cell-type-specific motif discovery beyond what JASPAR provides.
Algorithmic Taxonomy
| Tool | Architecture | Training | Output | Strength | Fails when |
|---|---|---|---|---|---|
| chromBPNet (Pampari 2024 bioRxiv) | Two-track CNN: bias model + accessibility model; bias trained on naked-DNA control or k-mer baseline, accessibility trained on chromatin signal | Per-cell-type, paired bias track | Bias-corrected per-base profile + total counts | Strongest bias correction of the compared tools; established in Kundaje lab pipelines | Requires GPU, ~24h training per cell type; needs >= 50M reads |
| BPNet (Avsec 2021 Nat Genet 53:354) | Original counts + profile dual-head CNN | TF ChIP-seq or ATAC | Per-base profile prediction | Foundational; widely cited; bpnet-lite reimpl maintained | Less polished than chromBPNet for ATAC; bias correction needs separate model |
| scBasset (Yuan & Kelley 2022) | Basenji2-derived CNN, per-cell projection layer | Single-cell ATAC | Per-cell sequence-derived peak score | First sequence model that predicts per-cell accessibility; outperforms chromVAR for cluster discrimination | Fixed architecture, hard to extend; benchmarks evolving |
| Enformer (Avsec 2021 Nat Methods 18:1196) | Long-context Transformer (196 kb input) | Reference epigenome (DNase + histones + CAGE) | Per-bin epigenome prediction | Best for distal regulation modeling; pre-trained available | Pre-trained models cell-line specific; finetuning on custom data is expensive |
| Borzoi (Linder 2025 Nat Genet) | Enformer extension trained on RNA + ATAC | Multi-tissue paired data | Sequence -> RNA + chromatin | Current best benchmark for variant effect on RNA via ATAC linkage | Newer; benchmarks still emerging |
| DeepATAC / Basset (legacy) | Earlier CNN architectures | -- | Binary peak prediction | Historical context; cited in older literature | Superseded by chromBPNet + Enformer; do not use for new work |
| tangermeme | Inference-only fast wrapper | Use any saved model | Marginal scoring of variants | Speeds up variant effect prediction 100x; works with chromBPNet/BPNet outputs | Inference only; cannot train |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 303 lines · 104 tokens per session scan A b588803cc1ab
bio-atac-seq-deep-learning-atac is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed today), licensed MIT. It adds 104 tokens to every session and 5,015 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to bio-atac-seq-deep-learning-atac, differing in 12 lines, and is treated as a copy.
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