Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PKU-YuanGroup/OpenAI4S --skill bio-chip-seq-motif-analysisgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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-chip-seq-motif-analysis)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-chip-seq-motif-analysis"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chip-seq-motif-analysis/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-chip-seq-motif-analysis"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chip-seq-motif-analysis.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 | $0.00167 | $0.04419 |
| Opus 5 | $0.00084 | $0.02210 |
| Sonnet 5 | $0.00033 | $0.00884 |
| Haiku 4.5 | $0.00017 | $0.00442 |
Grade A, and why
bio-chipseq-motif-analysis 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 8d 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-chipseq-motif-analysis — 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 — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: HOMER 4.11+, MEME suite 5.5+ (STREME replaces DREME from 5.4+), monaLisa 1.10+, JASPAR 2024 CORE, HOCOMOCO v12, BioPython 1.83+, bedtools 2.31+.
DREME was removed from MEME suite 5.4+; use STREME instead. Some tutorials still reference DREME — verify the installed version via meme --version. JASPAR 2026 (released late 2025) integrates 1259 BPNet ChIP models in a Deep Learning collection; the CORE collection remains the standard PWM source.
Motif Analysis on ChIP-seq Peaks
"Find enriched DNA binding motifs in my ChIP-seq peaks" -> Discover de novo motif patterns and test for known TF motif enrichment in peak sequences, with appropriate background to control for compositional and positional biases.
- CLI (HOMER, fast):
findMotifsGenome.pl peaks.bed hg38 outdir/ -size 200 -p 8 - CLI (MEME-ChIP, comprehensive):
meme-chip -db JASPAR.meme peaks.fa - R (regression-based, selective enrichment):
monaLisa::calcBinnedMotifEnrR(seqs, bins, pwms) - CLI (deep-learning-derived motifs): TF-MoDISco on BPNet attribution scores (see chip-deep-learning)
Motif discovery is sensitive to background choice and peak quality. Hyper-ChIPable artifacts at rRNA / housekeeping loci often produce false-positive motifs (GC-rich or A-T-rich biases of those regions). Filter peaks against blacklists and inspect peak distribution before running motif discovery.
Tool Taxonomy
| Tool | Discovery type | Background handling | Strength | Fails when |
|---|---|---|---|---|
| HOMER findMotifsGenome.pl | De novo + known | GC-matched genomic regions (auto) | Fast (multi-core); integrated vertebrate/insect/plant DBs; one-command full report | Background can include unmasked repeats producing motif artifacts; -size given slow; auto background may include peaks themselves |
| MEME-ChIP | De novo (STREME, MEME) + central enrichment (CentriMo) + DB comparison (TOMTOM) + scanning (FIMO) | Markov order-2 from input; shuffled (preserves dinucleotide) | Comprehensive single command; rigorous statistics; HTML report | Slower; sequences must be 100-500 bp; central enrichment requires summit-centered peaks |
| STREME (MEME 5.4+) | De novo (replaced DREME) | Markov order-2 | Bailey 2021 benchmark: more accurate than DREME/HOMER/MEME/Peak-motifs; handles 3-30 bp; scales to 100k+ sequences | Memory-hungry for very long sequences (>1 kb) |
| MEME (classical) | De novo (long, gapped) | Markov | Long motifs; gapped motifs | Slow (no parallel); replaced by STREME for short motifs |
| DREME | De novo (short) | Shuffled | Historical; small fast | Removed from MEME 5.4+; use STREME |
| monaLisa (Stadler lab) | Binned enrichment regression | Native (binned scoring) | Modern; regression-based; selectivity (TF-specific in differential peaks) | R-only; less integrated with browsers |
| AME (MEME suite) | Known motif differential | Matched background set required | Designed for two-set comparison (e.g., peaks vs. control regions) | Requires user-provided background set |
| CentriMo | Known motif central enrichment | Auto from input | Tests positional enrichment relative to peak center | Requires summit-centered peaks (200-500 bp) |
| FIMO | Motif scanning | Markov model | Genome-wide scanning at user-set p-value | Many false positives at p ≤ 1e-4; tighten to 1e-5 for whole-genome |
| HOMER scanMotifGenomeWide.pl | Motif scanning | None | Genome-wide scanning at fixed score threshold | Less calibrated than FIMO; HOMER's PWM format |
| RSAT peak-motifs | De novo + known | k-mer comparison | Web-server; multi-tool ensemble | Web limits; less reproducible from CLI |
| TF-MoDISco | DL attribution-based | Implicit in model | Motifs from BPNet/chromBPNet attribution scores; captures soft motif syntax | Requires trained DL model; see chip-deep-learning |
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.
- 8d ago First seen · 279 lines · 167 tokens per session scan A 0ae2ddaafff2
bio-chipseq-motif-analysis is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed today), licensed MIT. It adds 167 tokens to every session and 4,419 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to bio-chipseq-motif-analysis, differing in 12 lines, and is treated as a copy.
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