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genomics-epigenomics

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Load when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics. Skip when calling peaks from BAM (run MACS / Genrich externally first) or when working with single-cell ATAC (use `scatac-preprocessing`).

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Source SKILL.md: https://github.com/TianGzlab/OmicsClaw/blob/HEAD/skills/genomics/genomics-epigenomics/SKILL.md

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genomics-epigenomics

When to use

The user has a peak file (BED, narrowPeak, or broadPeak) from ATAC-seq, ChIP-seq, or CUT&Tag and wants peak summary statistics: total peak count, median / mean width, per-chromosome distribution, optional score column statistics. The script consumes peak files — it does NOT call peaks from BAM. --method (macs2 / macs3 / homer / genrich) and --assay (chip-seq / atac-seq / cut-tag) are recorded as metadata only.

For single-cell ATAC processing use scatac-preprocessing.

Inputs & Outputs

InputFormatRequired
Peaks.bed (3-col or 6-col) or .narrowPeak (10-col); broadPeak (9-col) loads as BED6 with cols 7-9 droppedyes (unless --demo)
Assay metadata--assay {chip-seq,atac-seq,cut-tag} (default chip-seq)no
Caller metadata--method {macs2,macs3,homer,genrich} (default macs2)no
OutputPathNotes
Peaks summarytables/peaks_summary.csvper-peak start/end/width/score
Per-chromosometables/peaks_per_chromosome.csvpeaks count per chromosome
Reportreport.md + result.jsonalways; result.json["data"]["peaks_per_chrom"] mirrors the table

Flow

  1. Load peak file (--input <peaks.bed|narrowPeak>) or generate a demo at output_dir/demo_peaks.narrowPeak (genomics_epigenomics.py:211).
  2. Parse coordinates; compute per-peak width.
  3. Aggregate per-chromosome counts; per---assay expected-width range is added to the report (genomics_epigenomics.py:172-178).
  4. Write tables/peaks_summary.csv (genomics_epigenomics.py:352) + tables/peaks_per_chromosome.csv (:360) + report.md + result.json (:366).

Gotchas

  • No peak caller is invoked. This skill summarises an existing BED/narrowPeak file — it does NOT run MACS / Genrich. Run them upstream and feed the output here.
  • --method is metadata-only; --assay changes the report. --method is recorded in result.json only. --assay controls the per-assay expected-peak-width range injected into the summary (genomics_epigenomics.py:172-178) — chip-seq reports 200-2000 bp, atac-seq 150-500 bp, cut-tag 150-300 bp. Peak parsing itself is identical across assays.
  • --input REQUIRED unless --demo. genomics_epigenomics.py:334 raises ValueError("--input required when not using --demo"); non-existent paths raise FileNotFoundError at :337.
  • 3-column BED has no score column. Without a score (col 5 in BED6 / narrowPeak), the summary statistics for "score" are NaN. Pre-convert to narrowPeak or BED6 for score-aware stats. Note: broadPeak's "signalValue" (col 7) and qValue (col 9) are NOT read — the parser only handles up to BED6 plus the narrowPeak 10-col extension.
  • Coordinate convention is 0-based half-open (BED). Width = end - start. If your input uses 1-based closed coordinates, widths are off-by-one.
  • Demo BED has 500 fixed-pattern peaks. Useful for orchestrator smoke tests; not biologically meaningful.

Key CLI

# Demo
python omicsclaw.py run genomics-epigenomics --demo --output /tmp/epi_demo

# Real ATAC-seq peaks
python omicsclaw.py run genomics-epigenomics \
  --input sample_peaks.narrowPeak --output results/ \
  --assay atac-seq --method macs3

See also

  • references/parameters.md — every CLI flag
  • references/methodology.md — peak-file format conventions, score interpretation
  • references/output_contract.md — tables/peaks_summary.csv + per-chromosome
  • Adjacent skills: scatac-preprocessing (parallel — single-cell ATAC), genomics-alignment (upstream — BAMs feed peak callers), genomics-qc (upstream — FASTQ QC before alignment), bulkrna-de (parallel — bulk RNA-seq differential expression)