Skip to content
cccskills
FreePublished on GitHub

atac-seq

ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.

The English summary is being prepared. Showing the original description.

See how to install

Files included (3)

  • SKILL.md3.8 KB
  • commands_and_thresholds.md575 B
  • technical_reference.md759 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

ATAC Seq

Version Compatibility

Reference examples assume:

  • macs3 3.0+
  • samtools 1.18+
  • deepTools 3.5+

Verify the runtime first:

  • CLI: macs3 --version, samtools --version, bamCoverage --version

Overview

Use this skill when the user needs:

  • bulk ATAC-seq QC
  • peak calling
  • accessibility counting
  • differential accessibility
  • motif deviation or footprint follow-up

When To Use This Skill

  • the task is bulk ATAC-seq rather than ChIP-seq
  • TSS enrichment, fragment periodicity, or FRiP need review
  • the output should include peaks, counts, and downstream accessibility summaries

Quick Route

  • paired-end bulk ATAC: use BAMPE
  • call peaks without control using ATAC-specific settings
  • if TSS enrichment is poor, stop and flag data quality before interpretation

Progressive Disclosure

Prerequisites

CheckGuidance
uniquely mapped reads>= 20M preferred for strong bulk ATAC
TSS enrichment> 7 acceptable, > 10 strong
FRiP> 0.2 often strong for good bulk ATAC

Expected Inputs

  • paired-end ATAC BAM or FASTQ
  • reference genome
  • sample groups for comparisons

Expected Outputs

  • results/peaks/sample_peaks.narrowPeak
  • results/matrix/consensus_peak_counts.tsv
  • results/diff_accessibility.tsv
  • figures/tss_enrichment.pdf
  • figures/fragment_size_distribution.pdf

Starter Pattern

macs3 callpeak \
  -t atac.bam \
  -f BAMPE \
  -g hs \
  -n sample \
  --nomodel \
  --shift -100 \
  --extsize 200 \
  -q 0.01 \
  --outdir results/peaks

Key Parameters

ParameterTypical valueNotes
-fBAMPEpaired-end ATAC should use fragment-aware mode
--nomodelonstandard for ATAC
--shift-100common Tn5 offset convention
--extsize200common first-pass extension
-q0.01starting FDR threshold

Workflow

1. Validate assay QC

Review:

  • TSS enrichment
  • fragment size periodicity
  • duplication
  • mapped read depth

2. Call peaks with ATAC-specific settings

Use fragment-aware paired-end mode and Tn5-aware shifting or equivalent settings.

3. Build a consensus peak matrix

Merge peaks across samples, count fragments into consensus intervals, then produce a peak-by-sample matrix.

4. Test differential accessibility

Use replicate-aware statistics and report both effect size and adjusted significance.

5. Run motif or footprint follow-up

Only after peak quality and read depth support it.

Output Artifacts

results/
├── peaks/
│   ├── sample_peaks.narrowPeak
│   └── sample_summits.bed
├── matrix/
│   └── consensus_peak_counts.tsv
└── diff_accessibility.tsv
qc/
├── tss_enrichment.tsv
└── fragment_metrics.tsv
figures/
├── tss_enrichment.pdf
└── fragment_size_distribution.pdf

Quality Review

  • TSS enrichment below 7 should trigger caution.
  • Strong nucleosome periodicity supports a good bulk ATAC library.
  • FRiP below 0.1 is usually weak and needs scrutiny.
  • Footprinting should not be trusted on low-depth or poor-quality libraries.

Anti-Patterns

  • using generic ChIP peak-calling defaults for ATAC
  • running footprinting on weak libraries
  • skipping TSS enrichment review
  • merging peaks from mixed reference builds

Related Skills

  • ChIP Seq
  • Gene Regulatory Networks
  • Multiome And scATAC

Optional Supplements

  • deeptools
  • pysam

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.

The English summary is being prepared. Showing the original description.

BioTender-max/awesome-bio-agent-skills204Updated Jul 2, 2026

Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.

The English summary is being prepared. Showing the original description.

BioTender-max/awesome-bio-agent-skills204Updated Jul 2, 2026

AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.

The English summary is being prepared. Showing the original description.

BioTender-max/awesome-bio-agent-skills204Updated Jul 2, 2026

Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.

The English summary is being prepared. Showing the original description.

BioTender-max/awesome-bio-agent-skills204Updated Jul 2, 2026

End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.

The English summary is being prepared. Showing the original description.

BioTender-max/awesome-bio-agent-skills204Updated Jul 2, 2026

Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).

The English summary is being prepared. Showing the original description.

BioTender-max/awesome-bio-agent-skills204Updated Jul 2, 2026

See all skills by BioTender-max

Report a problem with this skill