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Chip Seq

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BioTender-max
chip-seq

ChIP-seq peak calling and downstream interpretation with MACS3, signal track export, annotation, motif analysis, and differential binding review.

Overview

PublisherBioTender-max
Repositoryawesome-bio-agent-skills
Skill namechip-seq
Stars
204
Forks
36
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 BioTender-max on GitHub. Read the source before you install it.

Installation

Install the Chip Seq 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.
    https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw/chip-seq
  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/BioTender-max/awesome-bio-agent-skills.git /tmp/awesome-bio-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-bio-agent-skills/skills/bioclaw/chip-seq .claude/skills/chip-seq
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chip Seq 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 Chip Seq 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 Chip Seq 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.

ChIP Seq

Version Compatibility

Reference examples assume:

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

Before using commands, verify the installed environment:

  • CLI: macs3 --version, samtools --version, bamCoverage --version
  • If flags differ, inspect --help and adapt rather than forcing the example unchanged.

Overview

Use this skill for:

  • narrow or broad peak calling
  • input-normalized signal tracks
  • peak annotation
  • motif follow-up
  • differential binding review when replicates exist

When To Use This Skill

  • the user has aligned ChIP and optional input BAM files
  • the deliverable includes peaks, browser tracks, or motif results
  • the assay is TF ChIP or histone-mark ChIP and needs standard peak-centric processing

Quick Route

  • TF or narrow marks: use narrow peak mode first.
  • H3K27me3, H3K36me3, or other broad marks: use --broad.
  • Paired-end BAM: prefer -f BAMPE.
  • No input control: still possible, but report the limitation explicitly.

Progressive Disclosure

Prerequisites

RequirementNarrow TF-styleBroad histone-style
usable uniquely mapped reads>= 10M>= 20M
matched input recommendedyesyes
biological replicates recommended>= 2>= 2

Expected Inputs

  • chip.bam
  • input.bam when available
  • reference genome build
  • chromosome sizes if bigWig export is needed

Expected Outputs

  • results/peaks/sample_peaks.narrowPeak or .broadPeak
  • results/peaks/sample_summits.bed
  • results/tracks/sample_treat_pileup.bw
  • results/annotation/peak_annotation.tsv
  • qc/chip_qc_summary.tsv

Starter Pattern

bash
macs3 callpeak \
  -t chip.bam \
  -c input.bam \
  -f BAMPE \
  -g hs \
  -n sample \
  -q 0.01 \
  --outdir results/peaks

Key Parameters

ParameterTypical valueMeaning
-fBAM or BAMPEpaired-end should use BAMPE
-ghs, mm, or numericeffective genome size
-q0.01 or 0.05FDR cutoff for narrow peaks
--broadbroad marks onlybroad peak mode
--broad-cutoff0.1broad-peak FDR cutoff
-B --SPMRenabled for tracksbedGraph for normalized signal

Workflow

1. Validate BAMs and replicate structure

Check:

  • mapped read counts
  • duplicate burden
  • whether input control exists
  • whether the mark is narrow or broad

2. Call peaks with MACS3

  • narrow marks: -q 0.01 is a good starting point
  • broad marks: use --broad --broad-cutoff 0.1
  • paired-end: -f BAMPE

3. Export signal tracks

Use -B --SPMR, sort the resulting bedGraph, then convert to bigWig for browser use.

4. Annotate and inspect peaks

Map peaks to promoters, gene bodies, or distal intervals and review top loci in a genome browser or track plot.

5. Run motif or differential follow-up

Only after peak quality looks credible and replicate structure supports the downstream question.

Output Artifacts

text
results/
├── peaks/
│   ├── sample_peaks.narrowPeak
│   ├── sample_summits.bed
│   └── sample_model.r
├── tracks/
│   ├── sample_treat_pileup.bdg
│   └── sample_treat_pileup.bw
└── annotation/
    └── peak_annotation.tsv
qc/
└── chip_qc_summary.tsv

Quality Review

  • TF ChIP-seq FRiP:
    • < 0.01 poor
    • 0.01-0.05 usable but weak
    • > 0.05 generally solid
  • Histone broad-mark FRiP often differs; compare within assay type rather than against TF expectations.
  • Use replicate concordance when available. Do not trust a single noisy replicate just because peaks were called.
  • Check that top peaks occur in plausible loci and not only blacklisted or artifactual regions.

Anti-Patterns

  • treating broad and narrow marks with the same peak-calling setup
  • calling peaks on unsorted or low-quality BAMs
  • presenting motif hits without showing peak quality
  • hiding that no input control was available

Related Skills

  • ATAC Seq
  • Methylation Analysis
  • Gene Regulatory Networks

Optional Supplements

  • deeptools
  • pysam

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 Chip Seq AI skill do?

ChIP-seq peak calling and downstream interpretation with MACS3, signal track export, annotation, motif analysis, and differential binding review.

Why use Chip Seq on TypingMind?

Because you install it once and use it with any model. Chip Seq 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 Chip Seq in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw/chip-seq. 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 Chip Seq?

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 Chip Seq?

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

Is the Chip Seq AI skill free?

It is published on GitHub by BioTender-max. 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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