Alphagenome Single Variant Analysis
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
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)…
$ npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-motif-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-clip-seq-clip-motif-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/clip-seq/clip-motif-analysis .claude/skills/bio-clip-seq-clip-motif-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "bio-clip-seq-clip-motif-analysis" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clip-seq/clip-motif-analysis into .claude/skills/bio-clip-seq-clip-motif-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clip-seq-clip-motif-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/GPTomics/bioSkills/tree/main/clip-seq/clip-motif-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-motif-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-clip-seq-clip-motif-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/clip-seq/clip-motif-analysis .agents/skills/bio-clip-seq-clip-motif-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bio-clip-seq-clip-motif-analysis" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clip-seq/clip-motif-analysis into .agents/skills/bio-clip-seq-clip-motif-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clip-seq-clip-motif-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-motif-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-clip-seq-clip-motif-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/clip-seq/clip-motif-analysis .cursor/skills/bio-clip-seq-clip-motif-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bio-clip-seq-clip-motif-analysis" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clip-seq/clip-motif-analysis into .cursor/skills/bio-clip-seq-clip-motif-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clip-seq-clip-motif-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/GPTomics/bioSkills.git --path clip-seq/clip-motif-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-motif-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-clip-seq-clip-motif-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/clip-seq/clip-motif-analysis .gemini/skills/bio-clip-seq-clip-motif-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bio-clip-seq-clip-motif-analysis" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clip-seq/clip-motif-analysis into .gemini/skills/bio-clip-seq-clip-motif-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clip-seq-clip-motif-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install GPTomics/bioSkills bio-clip-seq-clip-motif-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-motif-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/clip-seq/clip-motif-analysis .github/skills/bio-clip-seq-clip-motif-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bio-clip-seq-clip-motif-analysis" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clip-seq/clip-motif-analysis into .github/skills/bio-clip-seq-clip-motif-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clip-seq-clip-motif-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-motif-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GPTomics/bioSkills bio-clip-seq-clip-motif-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/clip-seq/clip-motif-analysis .opencode/skills/bio-clip-seq-clip-motif-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bio-clip-seq-clip-motif-analysis" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clip-seq/clip-motif-analysis into .opencode/skills/bio-clip-seq-clip-motif-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clip-seq-clip-motif-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
bio-clip-seq-clip-motif-analysisDiscover 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)…
Bio Clip Seq Clip Motif Analysis is an agent skill from GPTomics/bioSkills. 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…
Its SKILL.md is about 5.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/find_motifs.sh` and `usage-guide.md`).
It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.
Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships script files (Shell), which the agent can run.
Shell commands in SKILL.md call:
pipFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Bio Clip Seq Clip Motif Analysis loads about 5.5k tokens when it runs. Until then it costs about 143 tokens; SKILL.md has 2,562 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 2,562 words, ~5,501 tokens.
.claude/skills/bio-clip-seq-clip-motif-analysis/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.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:
<tool> --version then <tool> --help to confirm flagspip show <package> then help(module.function) to check signaturesIf code throws unexpected errors, introspect the installed tool and adapt the example to match the actual API rather than retrying.
"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.
findMotifs.pl peaks.fa fasta motif_out -rna -len 5,6,7,8 -p 4streme --rna --oc streme_out -p peaks.fa -n background.fa --minw 5 --maxw 10mCross -i crosslinks.bed -g genome.fa -k 7 -o mcross_out (jointly models motif + CL position)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)rbpamp run -i peaks.fa -k 7 -o rbpamp_out (joint affinity + motif model)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.
| 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.
Peak-based (HOMER, MEME/STREME, RBPamp): Extract sequences from peak BED, find enriched k-mers / PWMs. Produces classical motif logos. Fast and intuitive. Captures broad binding zones.
Crosslink-site-based (mCross, PEKA, kpLogo): Use single-nucleotide CL positions; analyze sequence in a window around each CL position. Produces motif logos REGISTERED to the CL site. Required for understanding the structural relationship between motif and crosslink.
The fundamental difference: peak-based logos show "what sequence is enriched near binding"; CL-registered logos show "what sequence the RBP contacts at the crosslink position." For most RBPs they agree on the core motif but diverge on flanking context.
UV254 crosslinking is U-biased: single-nt CL events are strongly enriched at U residues (Konig 2010; Sugimoto 2012). Consequence: motif logos centered on raw CL positions are U-enriched at the center even for non-U-binding RBPs (e.g., PUM2 binds UGUANAUA but peak-center is shifted; HNRNPK binds C-rich tracts but centers can show spurious U).
Mitigations:
--markov-order 1 does).For naive HOMER/MEME output, examine the central column of the PWM: if it is U-skewed, suspect CL bias rather than true U preference. Cross-check against published RBNS Kd if available.
Trigger: HOMER auto-generates a shuffled background that preserves only nucleotide frequency, not GC distribution per region; CLIP peaks come disproportionately from 3' UTRs which are AU-rich.
Mechanism: AU-rich foreground vs GC-shuffled background produces "AU-rich motif" as the top hit for any 3' UTR-binding RBP, regardless of biology.
Symptom: Top HOMER motif looks like the 3' UTR mean composition (AU-rich, no clear specificity); known motif scan (-known) finds the RBP's canonical motif at lower rank than the spurious "AU" motif.
Fix: Provide a GC-matched background: extract sequences from random 3' UTR regions of expressed transcripts matched by length distribution. Use -bg matched_bg.fa flag. Or use STREME with -n matched_bg.fa.
Trigger: MEME run on > 1000 peak sequences.
Mechanism: MEME is O(N^2) in sequence count; classical OOPS/ZOOPS/ANR models do not scale.
Symptom: Runtime > 12h; out-of-memory on large machines.
Fix: Use STREME (MEME Suite >= 5.0); it is the modern fast successor (sub-quadratic). Or restrict to top-1000 peaks by score.
Trigger: mCross run on a peak BED instead of crosslink BED.
Mechanism: mCross's model is motif_PWM x CL_position_offset_PMF. Without single-nt CL positions there is no offset axis; the model degenerates.
Symptom: mCross output looks identical to a peak-centered logo; the diagnostic CL-offset histogram is flat.
Fix: Provide CL sites from PureCLIP, CTK CITS, or PARalyzer. The Yeo lab and Zhang lab use the convention of PureCLIP CL sites as mCross input.
Trigger: Concerned that PEKA's "low-count crosslinks within same dataset" background is biased.
Mechanism: PEKA does NOT need external input. It uses low-count crosslinks (below a quantile threshold) as the background and high-count crosslinks (above) as the foreground. This is by design (Kuret 2022) and cross-validated against mCross.
Symptom: False positive concern; user wants to verify.
Fix: Run BOTH PEKA and mCross; cross-validate. The PEKA paper reports high concordance with mCross for ENCODE RBPs; treat them as orthogonal confirmation.
Trigger: Large peak set (> 5000); RBPamp jointly optimizes affinity + motif iteratively.
Mechanism: Joint optimization is slow; some RBPs have multimodal binding (multiple motifs) that confuse single-PWM RBPamp.
Symptom: Runtime > 24h; output PWM disagrees with HOMER top motif.
Fix: Down-sample to top-2000 peaks; or run HOMER for initial PWM and use it as RBPamp seed.
Trigger: Comparing CLIP-derived motif to RBNS Kd-ranked motif (Dominguez 2018) and finding divergence (e.g., CLIP top motif rank 3 in RBNS; RBNS top motif weakly enriched in CLIP).
Mechanism: In vivo binding is shaped by structure accessibility (RNA secondary structure), cooperativity, and competing factors. RBNS measures intrinsic affinity in vitro. They diverge for:
Symptom: CLIP motif logo plausible but rank-3 in RBNS; or RBNS top motif missing from CLIP peaks.
Fix: Accept the divergence as informative. Cite both. Use SHAPE-eCLIP or icSHAPE-MaP to test structural-accessibility hypothesis. For variant-effect studies, prefer RBNS-derived PWM (RBPamp Kd) over CLIP motif for absolute affinity prediction.
Trigger: Used FIMO with default -thresh 1e-4 and got matches everywhere.
Mechanism: Default threshold is too lenient for in vivo binding; CLIP background sequences contain many low-affinity matches.
Symptom: FIMO match rate > 50% of peaks AND > 20% of random background; no enrichment signal.
Fix: Use -thresh 1e-5 or -thresh 1e-6; or use CentriMo for local enrichment around peak center (testing positional enrichment, not threshold).
| Scenario | Tool | Why |
|---|---|---|
| Quick de novo motif from CLIPper peaks, ENCODE-comparable | HOMER -rna -len 5,6,7,8 with GC-matched bg | Fast, mature, ENCODE convention |
| Motif registered to crosslink position (for variant effect) | mCross on PureCLIP single-nt sites | The 2019 Feng paper is the canonical reference |
| Confirm mCross result with independent method | PEKA on same peaks + crosslinks | Cross-validation; PEKA does not need input control |
| Affinity-weighted motif with Kd estimate | RBPamp | Provides quantitative Kd; integrates with RBNS comparison |
| Compare to in vitro RBNS Kd | RBPamp + Dominguez 2018 / Lambert 2014 tables | RBNS is the orthogonal in vitro standard |
| RBP with known motif - validation scan | FIMO -thresh 1e-5 against CISBP-RNA / ATtRACT PWM | Lower-confidence motifs need tight threshold |
| Multiple binding modes (SRSF1 GGA clusters) | mCross + manual inspection of CL-offset histogram | mCross reveals the multimodal structure |
| Position-specific logo (5' vs 3' of CL) | kpLogo with k-mer scores | Position-specific significance visualization |
| AU-rich 3' UTR binders (HuR, AUF1) - validate U bias is real | Compare HOMER motif center vs PUM2-style register | RBP-specific tradition |
| Allele-specific motif (variant effect) | mCross PWM + DeepRiPe/RBPNet for variant scoring | See clip-seq/clip-deep-learning |
| snoRNA / structural RNA - sequence motif less meaningful | Skip motif analysis; emphasize structural context (icSHAPE) | snoRNA binders read structure, not linear sequence |
| Pattern | Likely cause | Action |
|---|---|---|
| HOMER and STREME agree; mCross disagrees on flanking | mCross's CL-offset registers a flanking base that's identical in peak; HOMER sees both flanking bases as motif | Trust mCross for CL-registered position; HOMER is "what's nearby" |
| Top HOMER motif AU-rich; RBP knows to bind structure | GC-matched background not used | Re-run HOMER with -bg matched_bg.fa |
| HOMER finds canonical motif at rank 1; RBNS Kd ranks at rank 3 | In vitro vs in vivo specificity divergence | Both valid; cite RBNS for absolute affinity, CLIP for in vivo prevalence |
| mCross PWM has multiple CL-position peaks | Multiple binding modes (SRSF1 GGA half-sites; PTBP1 multimer) | Treat as biologically meaningful; report both |
| Skipper window-level motifs differ from CLIPper peak-level | Window size dilutes signal; peak boundaries sharpen | Both valid; report at the level of the downstream analysis |
| PAR-CLIP motifs cleaner than iCLIP for same RBP | PAR-CLIP T->C registers exactly; iCLIP truncates -1 from CL | Both correct; PAR-CLIP is the sharper single-nt method when 4SU labeling tolerated |
| HOMER finds different motif on top vs bottom strand | Strand information lost in upstream BED | Verify BED column 6 (strand) is preserved; HOMER respects strand |
Operational rule for high-confidence motif reporting: Report (a) HOMER top de novo motif with GC-matched background, (b) mCross PWM with CL-position offset histogram, (c) PEKA k-mer enrichment for orthogonal confirmation, (d) FIMO scan against published RBP PWM (CISBP-RNA / ATtRACT) for known-motif validation, (e) RBNS Kd ranking from Dominguez 2018 if available. Three independent methods agreeing on the core motif is the bar.
Goal: Produce a publication-grade motif report combining peak-based de novo discovery, CL-position-registered motif (mCross), and known-motif validation.
Approach: Extract sequences with strand preservation, build a GC-matched 3' UTR background, run HOMER + STREME for de novo, mCross for CL-registered, FIMO scan against CISBP-RNA, and report all three views with information-content QC.
# 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 \
-rna -len 5,6,7,8 -p 8 \
-fasta background.fa
# Step 4: STREME for cross-validation
streme --rna --oc streme_out -p peaks.fa -n background.fa --minw 5 --maxw 10
# Step 5: mCross requires single-nt CL sites (PureCLIP output)
# crosslinks.bed = PureCLIP -o sites.bed (single-nt CL positions, see clip-seq/crosslink-site-detection)
mCross \
-i crosslinks.bed -g genome.fa -k 7 -n 5 -o mcross_out
# Step 6: PEKA orthogonal positional k-mer
peka --peak_file_name peaks.stringent.bed --crosslinks_file_name crosslinks.bed --genome_file_name genome.fa --regions_file_name regions.bed --kmer_length 5 --percentile 30 --outpath peka_out
# Step 7: Compare to RBNS in vitro Kd (Dominguez 2018 supplementary)
# Manual cross-reference to RBNS Kd-ranked top-5 motifs for the target RBPimport numpy as np
import pandas as pd
from Bio import motifs
def parse_homer_motif(motif_file):
'''Parse HOMER motif PWM and return information content'''
pwm = []
with open(motif_file) as f:
for line in f:
if line.startswith('>'):
continue
pwm.append([float(x) for x in line.strip().split()])
pwm = np.array(pwm)
# Information content per position (bits)
epsilon = 1e-10
ic = np.sum(pwm * np.log2(pwm / 0.25 + epsilon), axis=1)
return {
'length': pwm.shape[0],
'mean_IC_per_position': np.mean(ic),
'max_IC': np.max(ic),
'min_IC': np.min(ic),
'is_low_complexity': np.mean(ic) < 0.5
}
# A high-quality CLIP motif has mean IC per position > 1.0 bit
# Mean IC < 0.5 suggests no real motif - check for background mismatch| Metric | Good | Investigate |
|---|---|---|
| Mean IC per position (HOMER PWM) | > 1.0 bit | < 0.5 = no real motif or background mismatch |
| Motif center U content | matches RBP biology | > 80% U in central position = U-CL bias suspect |
| FIMO scan rate in peaks vs background | peaks 3-10x bg | < 2x = motif too weak; > 50x = motif too narrow |
| mCross CL-offset histogram | single mode at offset N | flat = mCross unable to register |
| RBNS rank of CLIP top motif | top 3 | > 10 = strong in vivo / in vitro divergence; investigate |
| Replicate motif overlap | top motif identical in 2/2 reps | discordance = under-powered or one rep failed |
| Error / symptom | Cause | Solution |
|---|---|---|
| HOMER top motif is "ATCC" or similar TruSeq-adapter-like | Adapter trim incomplete in preprocessing | Re-run cutadapt with correct adapter; verify trimming completed before peak calling |
| mCross "no crosslinks found" or output identical to peak-centered logo | Passed peak BED instead of single-nt crosslink-site BED | mCross requires PureCLIP or CTK CITS single-nt site BED; not a CLIPper peak BED. See clip-seq/crosslink-site-detection |
| mCross CL-offset histogram is flat | Strand information missing OR alignment soft-clipped 5' base | Verify BED column 6 (strand); confirm upstream --alignEndsType EndToEnd |
| All HOMER motifs are AU-rich, no specificity | Default shuffled background AU-rich for 3' UTR peaks | Provide GC-matched background from expressed 3' UTRs |
| mCross input "no crosslinks found" | Provided peak BED instead of single-nt CL BED | Pass PureCLIP / CTK CITS single-nt BED instead |
| MEME "out of memory" or > 12h runtime | Peak set too large | Switch to STREME; or down-sample to top-1000 peaks |
| FIMO match in nearly every peak | Threshold too lenient | Tighten to 1e-5 or 1e-6 |
| Motif width > 10 nt looks chimeric | HOMER reported overlapping co-motifs as one | Re-run with -len 5,6,7,8 (force narrower); inspect rank 2-5 motifs |
| PWM info content < 0.5 bits | Background mismatch or peaks have no specificity | Verify peak set is stringent (log2 FC >= 3); regenerate background |
| Strand-specific motif different on +/- | BED column 6 missing or wrong | Verify strand encoding; re-extract with bedtools getfasta -s |
| Multiple distinct motifs per RBP | Biological (multimodal RBPs) OR contaminating multi-RBP CLIP | Validate with mCross multimode + compare to literature |
© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files in clip-seq/clip-motif-analysis of GPTomics/bioSkills.
Open the folder on GitHubat commit d91ed3d
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.
Bio Clip Seq Clip Motif Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Bio Clip Seq Clip Motif Analysis this skillGPTomics/bioSkills | 1.2k | 2 repos | ~5.5k | Automated safety check: Pass | MIT | |
| Alphagenome Single Variant Analysisgoogle-deepmind/science-skills | 3.2k | 2 repos | ~3k | Automated safety check: Notes | Apache-2.0 | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Clinvar Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.9k | Automated safety check: Notes | Apache-2.0 | |
| Metabolic Study Planneraiming-lab/AutoResearchClaw | 15k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Dbsnp Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.4k | Automated safety check: Notes | Apache-2.0 |
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
K-Dense-AI/scientific-agent-skills
Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.
google-deepmind/science-skills
A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…
aiming-lab/AutoResearchClaw
Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.
google-deepmind/science-skills
A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.
aiming-lab/AutoResearchClaw
Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.
GPTomics/bioSkills
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.
GPTomics/bioSkills
Installs the bioSkills collection of 425 bioinformatics skills in one step, or only chosen categories, so sequencing, RNA-seq, single-cell and variant tasks get specialized help.
GPTomics/bioSkills
Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.
GPTomics/bioSkills
Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.
GPTomics/bioSkills
Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.
GPTomics/bioSkills
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.
Categories
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)…. Bio Clip Seq Clip Motif Analysis is an agent skill from GPTomics/bioSkills. 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.
Bio Clip Seq Clip Motif Analysis fits situations like: characterizing RBP sequence specificity; registering motifs to crosslink positions; validating in vivo CLIP motifs against in vitro RBNS Kd; reconciling motif disagreements across tools.
Run `npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-motif-analysis -a claude-code`. Or copy the skill folder (clip-seq/clip-motif-analysis in GPTomics/bioSkills) into .claude/skills/bio-clip-seq-clip-motif-analysis in your project. Claude Code loads it when a task matches its description.
Run `npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-motif-analysis -a codex`. Or copy the skill folder (clip-seq/clip-motif-analysis in GPTomics/bioSkills) into .agents/skills/bio-clip-seq-clip-motif-analysis in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-motif-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-clip-seq-clip-motif-analysis, .gemini/skills/bio-clip-seq-clip-motif-analysis, .github/skills/bio-clip-seq-clip-motif-analysis and .opencode/skills/bio-clip-seq-clip-motif-analysis in your project.
Going by SKILL.md and its folder, Bio Clip Seq Clip Motif Analysis needs a shell for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: A Bash shell.
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Bio Clip Seq Clip Motif Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.5k tokens (SKILL.md is roughly 22k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Bio Clip Seq Clip Motif Analysis: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.
Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.