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Bio Atac Seq Atac Peak Calling

OrganizationPopular
FreedomIntelligence
bio-atac-seq-atac-peak-calling

Call accessible chromatin regions from ATAC-seq data using MACS3 with ATAC-specific parameters. Use when identifying open chromatin regions from aligned ATAC-seq BAM files, different from ChIP-seq peak calling.

Overview

PublisherFreedomIntelligence
RepositoryOpenClaw-Medical-Skills
Skill namebio-atac-seq-atac-peak-calling
Stars
3K
Forks
410
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by FreedomIntelligence on GitHub. Read the source before you install it.

Installation

Install the Bio Atac Seq Atac Peak Calling AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills.git /tmp/OpenClaw-Medical-Skills
mkdir -p .claude/skills
cp -r /tmp/OpenClaw-Medical-Skills/skills/bio-atac-seq-atac-peak-calling .claude/skills/bio-atac-seq-atac-peak-calling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bio Atac Seq Atac Peak Calling in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bio Atac Seq Atac Peak Calling on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bio Atac Seq Atac Peak Calling is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Version Compatibility

Reference examples tested with: Bowtie2 2.5.3+, MACS3 3.0+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

  • CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

ATAC-seq Peak Calling

"Call peaks from my ATAC-seq data" → Identify open chromatin regions using ATAC-specific parameters (no input control, shifted Tn5 cut sites, paired-end mode).

  • CLI: macs3 callpeak -t atac.bam -f BAMPE -g hs --nomodel --shift -75 --extsize 150

Basic MACS3 for ATAC-seq

Goal: Identify open chromatin regions from ATAC-seq data using ATAC-specific peak calling parameters.

Approach: Run MACS3 in paired-end mode with Tn5 shift correction, no model building, and duplicate retention since ATAC-seq generates natural duplicates at accessible sites.

bash
# Standard ATAC-seq peak calling
macs3 callpeak \
    -t sample.bam \
    -f BAMPE \
    -g hs \
    -n sample \
    --outdir peaks/ \
    -q 0.05 \
    --nomodel \
    --shift -75 \
    --extsize 150 \
    --keep-dup all \
    -B

Key ATAC-seq Parameters

bash
# Explained parameters
macs3 callpeak \
    -t sample.bam \        # Treatment BAM
    -f BAMPE \             # Paired-end BAM (uses fragment size)
    -g hs \                # Genome size: hs (human), mm (mouse)
    -n sample \            # Output name prefix
    --nomodel \            # Don't build shifting model
    --shift -75 \          # Shift reads to center on Tn5 cut site
    --extsize 150 \        # Extend reads to this size
    --keep-dup all \       # Keep duplicates (ATAC has natural duplicates)
    -B \                   # Generate bedGraph for visualization
    --call-summits         # Call peak summits

Why These Parameters?

ParameterReason
--nomodelATAC doesn't have control, can't build model
--shift -75Centers on Tn5 insertion site
--extsize 150Smooths signal around cut sites
--keep-dup allTn5 creates duplicate cuts at accessible sites
-f BAMPEUses actual fragment size from paired-end

Paired-End vs Single-End

bash
# Paired-end (recommended for ATAC)
macs3 callpeak -f BAMPE -t sample.bam ...

# Single-end (less common)
macs3 callpeak -f BAM -t sample.bam \
    --nomodel --shift -75 --extsize 150 ...

Call Peaks on NFR Only

Goal: Call peaks using only nucleosome-free fragments for sharper regulatory element detection.

Approach: Filter BAM to fragments <100 bp (NFR), then call peaks with adjusted shift/extsize parameters matching the shorter fragment size.

bash
# First, filter to nucleosome-free reads (<100bp fragments)
samtools view -h sample.bam | \
    awk 'substr($0,1,1)=="@" || ($9>0 && $9<100) || ($9<0 && $9>-100)' | \
    samtools view -b > nfr.bam

# Call peaks on NFR
macs3 callpeak \
    -t nfr.bam \
    -f BAMPE \
    -g hs \
    -n sample_nfr \
    --nomodel \
    --shift -37 \
    --extsize 75 \
    --keep-dup all \
    -q 0.01

Broad Peaks (Optional)

bash
# For broader accessible regions
macs3 callpeak \
    -t sample.bam \
    -f BAMPE \
    -g hs \
    -n sample_broad \
    --nomodel \
    --shift -75 \
    --extsize 150 \
    --broad \
    --broad-cutoff 0.1

Batch Processing

Goal: Call peaks on multiple ATAC-seq samples in one pass.

Approach: Loop over BAM files and run MACS3 with consistent ATAC-specific parameters for each sample.

bash
#!/bin/bash
GENOME=hs  # hs for human, mm for mouse
OUTDIR=peaks

mkdir -p $OUTDIR

for bam in *.bam; do
    sample=$(basename $bam .bam)
    echo "Processing $sample..."

    macs3 callpeak \
        -t $bam \
        -f BAMPE \
        -g $GENOME \
        -n $sample \
        --outdir $OUTDIR \
        --nomodel \
        --shift -75 \
        --extsize 150 \
        --keep-dup all \
        -q 0.05 \
        -B \
        --call-summits
done

Output Files

FileDescription
_peaks.narrowPeakPeak locations (BED-like)
_summits.bedPeak summit positions
_peaks.xlsPeak statistics (Excel format)
_treat_pileup.bdgSignal track (bedGraph)
_control_lambda.bdgBackground (if control provided)

narrowPeak Format

chr1  100  500  peak1  500  .  10.5  50.2  45.1  200

Columns: chrom, start, end, name, score, strand, signalValue, pValue, qValue, summit_offset

Convert to BigWig

bash
# Sort bedGraph
sort -k1,1 -k2,2n sample_treat_pileup.bdg > sample.sorted.bdg

# Convert to BigWig
bedGraphToBigWig sample.sorted.bdg chrom.sizes sample.bw

Merge Replicates

bash
# Pool BAMs before peak calling (recommended for final peaks)
samtools merge -@ 8 merged.bam rep1.bam rep2.bam rep3.bam

# Call peaks on merged
macs3 callpeak -t merged.bam -f BAMPE -g hs -n merged ...

IDR for Replicate Consistency

Goal: Identify reproducible peaks across biological replicates using the Irreproducible Discovery Rate framework.

Approach: Call peaks on each replicate independently, then run IDR to score peak reproducibility and filter to a high-confidence set.

bash
# Call peaks on each replicate
macs3 callpeak -t rep1.bam -f BAMPE -g hs -n rep1 ...
macs3 callpeak -t rep2.bam -f BAMPE -g hs -n rep2 ...

# Run IDR
idr --samples rep1_peaks.narrowPeak rep2_peaks.narrowPeak \
    --input-file-type narrowPeak \
    --output-file idr_peaks.txt \
    --plot

# Filter by IDR threshold
awk '$5 >= 540' idr_peaks.txt > reproducible_peaks.bed

Related Skills

  • read-alignment/bowtie2-alignment - Align ATAC-seq reads
  • atac-seq/atac-qc - Quality control
  • chip-seq/peak-calling - ChIP-seq comparison
  • genome-intervals/bed-file-basics - Work with peak files

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Bio Atac Seq Atac Peak Calling AI skill do?

Call accessible chromatin regions from ATAC-seq data using MACS3 with ATAC-specific parameters. Use when identifying open chromatin regions from aligned ATAC-seq BAM files, different from ChIP-seq peak calling.

Why use Bio Atac Seq Atac Peak Calling on TypingMind?

Because you install it once and use it with any model. Bio Atac Seq Atac Peak Calling is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bio Atac Seq Atac Peak Calling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-atac-seq-atac-peak-calling. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Bio Atac Seq Atac Peak Calling?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bio Atac Seq Atac Peak Calling?

As many as you like. As long as a model supports skills, you can use Bio Atac Seq Atac Peak Calling with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bio Atac Seq Atac Peak Calling AI skill free?

It is published on GitHub by FreedomIntelligence. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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