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Bio Chipseq Peak Calling

OrganizationPopular
FreedomIntelligence
bio-chipseq-peak-calling

ChIP-seq peak calling using MACS3 (or MACS2). Call narrow peaks for transcription factors or broad peaks for histone modifications. Supports input control, fragment size modeling, and various output formats including narrowPeak and broadPeak BED files. Use when calling peaks from ChIP-seq alignments.

Overview

PublisherFreedomIntelligence
RepositoryOpenClaw-Medical-Skills
Skill namebio-chipseq-peak-calling
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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 Chipseq 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-chipseq-peak-calling .claude/skills/bio-chipseq-peak-calling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bio Chipseq 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 Chipseq 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 Chipseq 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: MACS2 2.2+, MACS3 3.0+

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.

Peak Calling with MACS3

"Call peaks from my ChIP-seq data" → Identify significantly enriched regions (narrow peaks for TFs, broad peaks for histone marks) by comparing IP signal to input control.

  • CLI: macs3 callpeak -t chip.bam -c input.bam -f BAM -g hs -n sample

MACS3 is the actively developed successor to MACS2. Commands are identical except the binary name. MACS2 is in maintenance mode.

Basic Peak Calling

Goal: Call enriched regions from ChIP-seq alignments with input control normalization.

Approach: Compare treatment BAM signal against input control using MACS3 local Poisson model.

bash
# Call peaks with input control (recommended)
macs3 callpeak -t chip.bam -c input.bam -f BAM -g hs -n sample --outdir peaks/

# For MACS2 (legacy), replace 'macs3' with 'macs2' - syntax is identical

Without Input Control

Goal: Call peaks without a matched input/control sample.

Approach: Use MACS3 with genomic background estimation only (less accurate than with control).

bash
# Not recommended, but possible
macs3 callpeak -t chip.bam -f BAM -g hs -n sample --outdir peaks/

Narrow Peaks (TF, H3K4me3, H3K27ac)

Goal: Call sharp, well-defined peaks typical of transcription factors and active histone marks.

Approach: Use default narrow peak mode with q-value filtering and genome size correction.

bash
macs3 callpeak \
    -t chip.bam \
    -c input.bam \
    -f BAM \
    -g hs \                        # hs=human, mm=mouse, ce=worm, dm=fly
    -n sample_narrow \
    --outdir peaks/ \
    -q 0.05                        # q-value threshold

Broad Peaks (H3K36me3, H3K27me3, H3K9me3)

Goal: Call diffuse, broad enrichment domains typical of repressive or elongation-associated histone marks.

Approach: Enable broad peak mode which links nearby enriched regions into broader domains.

bash
macs3 callpeak \
    -t chip.bam \
    -c input.bam \
    -f BAM \
    -g hs \
    -n sample_broad \
    --outdir peaks/ \
    --broad \                      # Broad peak mode
    --broad-cutoff 0.1             # Broad peak q-value

Paired-End Data

Goal: Call peaks from paired-end sequencing using actual fragment sizes instead of modeled estimates.

Approach: Use BAMPE format so MACS3 calculates fragment size from mate pairs directly.

bash
# MACS3 uses BAMPE format for paired-end
macs3 callpeak \
    -t chip.bam \
    -c input.bam \
    -f BAMPE \                     # Paired-end BAM
    -g hs \
    -n sample_pe \
    --outdir peaks/

Multiple Replicates

Goal: Call peaks from multiple biological replicates pooled together for increased statistical power.

Approach: Provide all replicate BAMs to MACS3, which internally pools reads before peak calling.

bash
# Pool replicates (MACS3 handles internally)
macs3 callpeak \
    -t rep1.bam rep2.bam rep3.bam \
    -c input.bam \
    -f BAM \
    -g hs \
    -n pooled \
    --outdir peaks/

Custom Genome Size

Goal: Call peaks for non-model organisms without a built-in genome size shortcut.

Approach: Provide the effective genome size as a numeric value instead of a species abbreviation.

bash
# For non-model organisms or custom genomes
macs3 callpeak \
    -t chip.bam \
    -c input.bam \
    -f BAM \
    -g 2.7e9 \                     # Effective genome size in bp
    -n sample \
    --outdir peaks/

Common Genome Sizes

GenomeFlagEffective Size
Humanhs2.7e9
Mousemm1.87e9
C. elegansce9e7
D. melanogasterdm1.2e8

Fixed Fragment Size

Goal: Call peaks when fragment size modeling fails or a specific extension size is needed.

Approach: Bypass model building and specify a fixed read extension size manually.

bash
# If modeling fails or for ATAC-seq
macs3 callpeak \
    -t chip.bam \
    -c input.bam \
    -f BAM \
    -g hs \
    --nomodel \                    # Skip model building
    --extsize 200 \                # Fixed extension size
    -n sample \
    --outdir peaks/

Generate Signal Tracks

Goal: Produce normalized signal tracks for genome browser visualization alongside peak calls.

Approach: Enable bedGraph output with signal-per-million-reads normalization, then convert to bigWig.

bash
# Generate bedGraph and bigWig files
macs3 callpeak \
    -t chip.bam \
    -c input.bam \
    -f BAM \
    -g hs \
    -n sample \
    --outdir peaks/ \
    -B \                           # Generate bedGraph
    --SPMR                         # Signal per million reads

# Convert to bigWig (requires UCSC tools)
sort -k1,1 -k2,2n peaks/sample_treat_pileup.bdg > peaks/sample.sorted.bdg
bedGraphToBigWig peaks/sample.sorted.bdg chrom.sizes peaks/sample.bw

Local Lambda for Broad Marks

Goal: Improve broad peak calling by disabling the genome-wide lambda estimate.

Approach: Use --nolambda to rely solely on local background estimation for very broad domains.

bash
# Recommended for very broad marks
macs3 callpeak \
    -t chip.bam \
    -c input.bam \
    -f BAM \
    -g hs \
    --broad \
    --nolambda \                   # Use local lambda only
    -n sample \
    --outdir peaks/

Cutoff Analysis

Goal: Evaluate how different significance thresholds affect the number of called peaks.

Approach: Run MACS3 cutoff analysis mode to generate a table of peak counts at various q-value cutoffs.

bash
# Test different q-value cutoffs
macs3 callpeak \
    -t chip.bam \
    -c input.bam \
    -f BAM \
    -g hs \
    --cutoff-analysis \            # Generate cutoff analysis file
    -n sample \
    --outdir peaks/

Output Files

FileDescription
*_peaks.narrowPeakPeak coordinates (BED6+4)
*_peaks.broadPeakBroad peak coordinates
*_summits.bedPeak summit positions
*_model.rR script for model visualization
*_treat_pileup.bdgTreatment signal (with -B)
*_control_lambda.bdgControl signal (with -B)

narrowPeak Format

chr1  100  200  peak_1  100  .  5.2  10.5  8.3  50

Columns: chr, start, end, name, score, strand, signalValue, pValue, qValue, peak

Filter Peaks

Goal: Post-filter called peaks by statistical significance or signal strength.

Approach: Use awk on narrowPeak columns to apply q-value or signal-value cutoffs.

bash
# Filter by q-value
awk '$9 > 2' peaks.narrowPeak > peaks.filtered.narrowPeak  # -log10(q) > 2 means q < 0.01

# Sort by signal strength
sort -k7,7nr peaks.narrowPeak > peaks.sorted.narrowPeak

Key Parameters

ParameterDefaultDescription
-trequiredTreatment BAM file(s)
-cnoneControl BAM file(s)
-fAUTOFormat (BAM, BAMPE, BED)
-ghsGenome size
-nNAOutput prefix
-q0.05Q-value cutoff
-pnoneP-value cutoff (overrides -q)
--broadfalseBroad peak calling
--nomodelfalseSkip model building
--extsize200Extension size (with --nomodel)
-BfalseGenerate bedGraph
--SPMRfalseSignal per million reads

Related Skills

  • peak-annotation - Annotate peaks to genes
  • differential-binding - Compare peaks between conditions
  • alignment-files - Prepare BAM files
  • chipseq-visualization - Visualize peaks

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 Chipseq Peak Calling AI skill do?

ChIP-seq peak calling using MACS3 (or MACS2). Call narrow peaks for transcription factors or broad peaks for histone modifications. Supports input control, fragment size modeling, and various output formats including narrowPeak and broadPeak BED files. Use when calling peaks from ChIP-seq alignments.

Why use Bio Chipseq Peak Calling on TypingMind?

Because you install it once and use it with any model. Bio Chipseq 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 Chipseq 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-chipseq-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 Chipseq 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 Chipseq Peak Calling?

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

Is the Bio Chipseq 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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