bio-clip-seq-clip-motif-analysis
Discover RBP binding motifs from CLIP-seq peaks or single-nucleotide crosslink sites using HOMER, MEME/STREME, kpLogo, mCross (CL-position-registered motifs), PEKA (positional k-mer enrichment), RBPamp (affinity), and RNA Bind-n-Seq (RBNS) cross-validation. Use when characterizing RBP sequence specificity, registering motifs to crosslink positions, validating in vivo CLIP motifs against in vitro RBNS Kd, reconciling motif disagreements across tools, or correcting for the uracil crosslinking bias that contaminates raw CLIP motif logos.
npx skills add BioTender-max/awesome-bio-agent-skills --skill clip-motif-analysis --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: HOMER 4.11+, MEME Suite 5.5+ (STREME, MEME-ChIP, FIMO), bedtools 2.31+, kpLogo 1.1+, mCross v1+, PEKA v1+, RBPamp 0.9+, ggseqlogo 0.1+, biopython 1.83+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `<tool> --version` then `<tool> --help` to confirm flags - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws unexpected errors, introspect the installed tool and adapt the example to match the actual API rather than retrying. # CLIP-seq Motif Analysis **"Find enriched RNA motifs at my RBP binding sites"** -> Discover the in vivo sequence preference of an RNA-binding protein from CLIP-seq peaks or single-nucleotide crosslink sites. The fundamental confound is the uracil bias of UV254 crosslinking: U is the most-crosslinked base (>50% of CL events), so naive motif logos centered on CL positions are U-enriched even for non-U-binding RBPs. Modern tools (mCross, PEKA) register motifs relative to the CL position and correct for this bias; legacy tools (HOMER, MEME) need careful background selection. - CLI (de novo, peak-based, HOMER RNA mode): `findMoti
- Version Compatibility
- Algorithmic Taxonomy
- Critical Choice: Peak-Based vs Crosslink-Site-Based Motif Discovery
- Uracil Crosslinking Bias
- Per-Tool Failure Modes
- HOMER -- Background mismatch inflates GC-biased motifs
- MEME -- Slow on large peak sets
- mCross -- Requires single-nt CL sites
- PEKA -- Background from same dataset
- RBPamp -- Slow convergence
- RBNS comparison -- in vitro vs in vivo divergence
- Known motif scan -- FIMO threshold too lenient
- Decision Tree by Scenario
- Reconciliation: When Motif Tools Disagree
Step 1: Extract peak sequences (use stringent peak set: log2 FC >= 3, -log10 p >= 3) bedtools getfasta -fi genome.fa -bed peaks.stringent.bed -s -fo peaks.fa Step 2: GC-matched background (random regions from expressed transcripts) expressed.bed = transcripts with TPM >= 1 in the same cell type shuffleBed -i peaks.stringent.bed -g chrom.sizes -incl expressed.bed -seed 42 > shuffled.bed bedtools getfasta -fi genome.fa -bed shuffled.bed -s -fo background.fa Step 3: HOMER de novo + known findMotifs.pl peaks.fa fasta homer_out \ Step 4: STREME for cross-validation streme --rna --oc streme_out -p peaks.fa -n background.fa --minw 5 --maxw 10
What does the bio-clip-seq-clip-motif-analysis skill do?
Discover RBP binding motifs from CLIP-seq peaks or single-nucleotide crosslink sites using HOMER, MEME/STREME, kpLogo, mCross (CL-position-registered motifs), PEKA (positional k-mer enrichment), RBPamp (affinity), and RNA Bind-n-Seq (RBNS) cross-validation. Use when characterizing RBP sequence specificity, registering motifs to crosslink positions, validating in vivo CLIP motifs against in vitro RBNS Kd, reconciling motif disagreements across tools, or correcting for the uracil crosslinking bias that contaminates raw CLIP motif logos.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill clip-motif-analysis --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.