bio-chipseq-motif-analysis
Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between cond
npx skills add BioTender-max/awesome-bio-agent-skills --skill 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 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-ChIPa
- Version Compatibility
- Tool Taxonomy
- Background Selection — The Biggest Source of Error
- Window Around Summit Matters
- HOMER Workflow
- MEME-ChIP Workflow
- monaLisa Workflow (R, Regression-Based)
- Per-Tool Failure Modes
- HOMER -- Background includes peaks themselves
- HOMER / MEME -- Repeat-derived false-positive motifs
- STREME -- Memory failure on long sequences
- CentriMo -- No central enrichment due to wrong centering
- FIMO -- Massive false positives at default p ≤ 1e-4
- monaLisa -- GC bins not respected
De novo + known motif discovery, repeat-masked, GC-matched background
findMotifsGenome.pl peaks.narrowPeak hg38 homer_out/ \
With user-supplied background (e.g., control peaks or random genomic)
Known motifs only (skip de novo; faster)
findMotifsGenome.pl peaks.narrowPeak hg38 homer_known_only/ \
Differential motif analysis: peaks gained in condition A vs condition B
findMotifsGenome.pl gained_in_A.bed hg38 differential_motifs/ \
Center peaks to ±100 bp around summit (column 10 in narrowPeak)
awk 'BEGIN{OFS="\t"} {summit = $2 + $10; print $1, summit - 100, summit + 100, $4, $5, $6}' \
peaks.narrowPeak > peaks_centered.bedWhat does the bio-chipseq-motif-analysis skill do?
Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between cond
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill 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.