Clip Motif Analysis

What the source does

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Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/clip-seq/clip-motif-analysisThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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…

---
name: bio-clip-seq-clip-motif-analysis
description: 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.
tool_type: mixed
primary_tool: HOMER
---

## 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, 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): `findMotifs.pl peaks.fa fasta motif_out -rna -len 5,6,7,8 -p 4`
- CLI (de novo, peak-based, MEME-ChIP / STREME): `streme --rna --oc streme_out -p peaks.fa -n background.fa --minw 5 --maxw 10`
- CLI (positional, single-nt CL-registered, mCross): `mCross -i crosslinks.bed -g genome.fa -k 7 -o mcross_out` (jointly models motif + CL position)
- CLI (positional k-mer, no input control needed, PEKA): `peka --peak_file_name peaks.bed --crosslinks_file_name crosslinks.bed --genome_file_name genome.fa --regions_file_name regions.bed --kmer_length 5 --percentile 30 --outpath peka_out` (the short flags `-i/-x/-g/-r/-k/-p` shown in earlier docs are not all stable; use the long forms or check `peka --help`)
- CLI (affinity-weighted, RBPamp): `rbpamp run -i peaks.fa -k 7 -o rbpamp_out` (joint affinity + motif model)
- CLI (positional logo, kpLogo): `kpLogo crosslinks_with_kmer_scores.txt -o kplogo_out` (position-specific significance)

ENCODE eCLIP motif convention: extract sequences from peaks (CLIPper + SMInput stringent), use shuffled or expression-matched background, run HOMER `-rna -len 5,6,7,8`. The Yeo lab eCLIP papers typically report the top 1-3 HOMER hits per RBP and validate with mCross for CL-registered position. For ASB and variant-effect work, mCross is mandatory.

## Algorithmic Taxonomy

| Tool | Input | Background | CL-position aware | Output | Strength | Fails when |
|------|-------|------------|-------------------|--------|----------|------------|
| HOMER findMotifs.pl | Peak FASTA | Auto-shuffled or user-provided | No | PWM + de novo + known scan | Mature, fast, RNA mode (U instead of T) | Background generation is heuristic; uracil bias inflates U-content motifs |
| MEME (MEME Suite) | Peak FASTA | Optional Markov model | No | PWM | Statistically rigorous; broadly cited | Slow on large peak sets (> 1000 sequences); single-motif default |
| STREME (MEME Suite) | Peak FASTA | Shuffled or user-provided | No | PWM | Fast successor to MEME-ChIP; tolerant of large sets | Same uracil bias issue as HOMER |
| MEME-ChIP | Peak FASTA | Markov order 1-3 | No | Multiple motifs | Wrapper for MEME + CentriMo + Tomtom comparison | Designed for ChIP; for CLIP use STREME directly |
| kpLogo | k-mer scores with positions | NA (significance from kpLogo internals) | Yes (positional) | Position-specific logo | Visualizes position-specific signal; complements PWM | Requires k-mer-score input file; not a peak-to-motif tool by itself |
| mCross (Feng 2019) | Single-nucleotide CL sites + genome | Generated internally | Yes (jointly models motif + CL position) | Registered PWM + CL offset | The standard for in vivo RBP motif registration; SRSF1 example showed clustered GGA half-sites | Requires high-quality single-nt CL sites (PureCLIP or CTK CITS) |
| PEKA (Kuret 2022) | Peaks + crosslink BED | Low-count crosslinks within same dataset | Yes (positional k-mer) | Position-enriched k-mers with significance | No external input required; cross-validates with mCross | Less granular than mCross's jointly-modeled PWM |
| RBPamp (Jens 2022) | Peak FASTA or CL sites | Joint affinity model | No (affinity-based) | Affinity-weighted motif + Kd estimate | Provides Kd; compatible with RBNS calibration | Slow; small community; needs validation |
| RCAS | Peak BED + GTF | Auto | No | Motif + region annotation | One-stop for motif + region distribution | Less granular than per-tool dedicated runs |
| MEME FIMO (motif scan) | PWM + FASTA | Markov bg | No | Match locations and scores | Scan known motifs across genome | Not a de novo tool; complements HOMER known scan |
| RBPNet (Horlacher et al 2023) | Raw sequence | Trained model | Yes (sequence-to-signal at single nt) | Predicted CL count distribution | Deep learning; predicts per-base affinity profile | See clip-seq/clip-deep-learning |

Methodology evolves; verify motif comparison databases (CISBP-RNA, ATtRACT, oRNAment, mCrossBase, RBPDB) for known motif validation. RBNS in vitro Kd values (Lambert 2014; Dominguez 2018 for 78 RBPs) are the most reliable orthogonal reference for in vivo CLIP motifs.

## Critical Choice: Peak-Based vs Crosslink-Site-Based Motif Discovery

**Peak-based (HOMER, 

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/chaolinzhanglab/mCross
pip install peka
pip install rbpamp

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Clip Motif Analysis

• github.com/GPTomics/bioSkills/tree/main/clip-seq/clip-motif-anal — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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