What does the Deeptools Ngs Analysis AI skill do?

NGS CLI for ChIP/RNA/ATAC-seq. BAM→bigWig with RPGC/CPM/RPKM, sample correlation/PCA, heatmaps/profiles around features, fingerprints. For alignment use STAR/BWA; for peak calling use MACS2.

Why use Deeptools Ngs Analysis on TypingMind?

Because you install it once and use it with any model. Deeptools Ngs Analysis 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 Deeptools Ngs Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/genomics-bioinformatics/interval-ops/deeptools-ngs-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deeptools Ngs Analysis?

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 Deeptools Ngs Analysis?

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

Is the Deeptools Ngs Analysis AI skill free?

Yes. It is published on GitHub by jaechang-hits under the BSD-3-Clause license. You only pay your own AI provider for the tokens you use.

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.

Deeptools Ngs Analysis by jaechang-hits — AI Skill for Any LLM

deepTools — NGS Data Analysis Toolkit

Overview

deepTools is a command-line toolkit for processing and visualizing high-throughput sequencing data. It converts BAM alignments to normalized coverage tracks (bigWig), performs quality control (correlation, PCA, fingerprint), and generates publication-quality heatmaps and profile plots around genomic features. Supports ChIP-seq, RNA-seq, ATAC-seq, and MNase-seq.

When to Use

Converting BAM files to normalized bigWig coverage tracks
Comparing ChIP-seq treatment vs input control (log2 ratio tracks)
Assessing sample quality: replicate correlation, PCA, coverage depth
Evaluating ChIP enrichment strength (fingerprint plots)
Creating heatmaps and profile plots around TSS, peaks, or other genomic regions
Analyzing ATAC-seq data with Tn5 offset correction
Generating strand-specific RNA-seq coverage tracks
Use omics-plotting SKILL for custom figures from exported score matrices; standard signal heatmaps/profiles use deeptools' own plotHeatmap/plotProfile
For read alignment, use STAR, BWA, or bowtie2 instead
For peak calling, use MACS2 or HOMER instead
For BAM/VCF file manipulation, use pysam instead

Prerequisites

bash
pip install deeptools
# Verify installation
bamCoverage --version

Input requirements: BAM files must be sorted and indexed (.bai file present). Generate index with samtools index input.bam. BED files for genomic regions (genes, peaks) in standard 3+ column format.

Pre-flight Interview

Settle these with the user before writing any analysis code.

yaml
decisions:
  - id: D1
    param: normalizationMethod
    kind: required
    source: user
    ask: "How should coverage be scaled so tracks from different samples can be compared - by read depth, by genome coverage, or not at all?"
    default: "none - raw coverage, comparable only within one sample"

  - id: D2
    param: effectiveGenomeSize
    kind: derived
    source: upstream
    depends_on: [D1]
    ask: "Which assembly's mappable size should the coverage normalization use?"
    default: "looked up for the alignment reference"
    skip_if: "normalization does not require a genome size"

  - id: D3
    param: readExtension
    kind: required
    source: user
    ask: "Should reads be extended to the sequenced fragment length? Correct for ChIP and ATAC, wrong for spliced RNA."
    default: "not extended"

  - id: D4
    param: duplicateHandling
    kind: required
    source: user
    ask: "Should reads flagged as duplicates be ignored when building coverage?"
    default: "included"

  - id: D5
    param: mappingQualityFloor
    kind: required
    source: user
    ask: "Below what mapping confidence should reads be excluded from the signal?"
    default: "no filter"

  - id: D6
    param: binSize
    kind: optional
    source: user
    ask: "At what resolution should signal be summarized?"
    default: "50 bp"

  - id: D7
    param: comparisonOperation
    kind: optional
    source: user
    ask: "When comparing two samples, should the track hold a log ratio, a plain ratio, or a difference?"
    default: "log2 ratio"

  - id: D8
    param: profileAnchor
    kind: optional
    source: user
    ask: "Should profiles be anchored on feature starts, ends, centres, or scaled across whole features?"
    default: "feature start, 500 bp either side"

  - id: D9
    param: processors
    kind: never_ask
    source: data
    reason: "Affects runtime only, not the signal"
    default: "min(8, available_cores)"

D1 and D3 are the pair that makes tracks comparable or quietly misleading. Unnormalized tracks put the deeper-sequenced sample higher everywhere, which reads as biology on a genome browser; extending RNA-seq reads to a fragment length fills introns with signal that was never sequenced.

Quick Start

bash
# Convert BAM to normalized bigWig
bamCoverage --bam sample.bam --outFileName sample.bw \
    --normalizeUsing RPGC --effectiveGenomeSize 2913022398 \
    --binSize 10 --numberOfProcessors 8

# Create heatmap around TSS
computeMatrix reference-point -S sample.bw -R genes.bed \
    -b 3000 -a 3000 --referencePoint TSS -o matrix.gz
plotHeatmap -m matrix.gz -o heatmap.png --colorMap RdBu

Core API

1. BAM to Coverage Conversion

Convert BAM alignments to normalized coverage tracks (bigWig or bedGraph).

bash
# Basic conversion with RPGC normalization
bamCoverage --bam input.bam --outFileName output.bw \
    --normalizeUsing RPGC --effectiveGenomeSize 2913022398 \
    --binSize 10 --numberOfProcessors 8 \
    --extendReads 200 --ignoreDuplicates

# CPM normalization (simpler, no genome size needed)
bamCoverage --bam input.bam --outFileName output.bw \
    --normalizeUsing CPM --binSize 10 -p 8

# RNA-seq: strand-specific coverage
bamCoverage --bam rnaseq.bam --outFileName forward.bw \
    --filterRNAstrand forward --normalizeUsing CPM -p 8
# IMPORTANT: Never use --extendReads for RNA-seq (spans splice junctions)

2. Sample Comparison

Compare treatment vs control or generate ratio tracks.

bash
# Log2 ratio: treatment / control
bamCompare -b1 treatment.bam -b2 control.bam -o log2ratio.bw \
    --operation log2 --scaleFactorsMethod readCount \
    --extendReads 200 -p 8

# Subtract control from treatment
bamCompare -b1 treatment.bam -b2 control.bam -o subtract.bw \
    --operation subtract --scaleFactorsMethod readCount

3. Quality Control

Assess sample quality, replicate concordance, and enrichment strength.

bash
# Sample correlation heatmap
multiBamSummary bins --bamfiles rep1.bam rep2.bam rep3.bam \
    -o counts.npz --binSize 10000 -p 8
plotCorrelation -in counts.npz --corMethod pearson \
    --whatToShow heatmap -o correlation.png
# Good: replicates cluster with r > 0.9

# PCA of samples
plotPCA -in counts.npz -o pca.png --plotTitle "Sample PCA"

# ChIP enrichment fingerprint
plotFingerprint -b input.bam chip.bam -o fingerprint.png \
    --extendReads 200 --ignoreDuplicates
# Good ChIP: steep rise curve; flat diagonal = poor enrichment

# Coverage depth assessment
plotCoverage -b sample.bam -o coverage.png --ignoreDuplicates -p 8

# Fragment size distribution (paired-end)
bamPEFragmentSize -b sample.bam -o fragsize.png

4. Heatmaps and Profile Plots

Visualize signal around genomic features (TSS, peaks, gene bodies).

bash
# Reference-point mode: signal around TSS
computeMatrix reference-point -S chip.bw -R genes.bed \
    -b 3000 -a 3000 --referencePoint TSS -o matrix.gz -p 8

# Scale-regions mode: signal across gene bodies
computeMatrix scale-regions -S chip.bw -R genes.bed \
    -b 1000 -a 1000 --regionBodyLength 5000 -o matrix.gz -p 8

# Generate heatmap
plotHeatmap -m matrix.gz -o heatmap.png \
    --colorMap RdBu --kmeans 3 --sortUsing mean

# Generate profile plot
plotProfile -m matrix.gz -o profile.png \
    --plotType lines --colors blue red

# Multiple signal files: compare marks
computeMatrix reference-point -S h3k4me3.bw h3k27me3.bw -R genes.bed \
    -b 3000 -a 3000 --referencePoint TSS -o multi_matrix.gz
plotHeatmap -m multi_matrix.gz -o multi_heatmap.png

5. Read Filtering and Processing

Filter reads before analysis or correct for assay-specific biases.

bash
# Filter by mapping quality and fragment size
alignmentSieve --bam input.bam --outFile filtered.bam \
    --minMappingQuality 10 --minFragmentLength 150 \
    --maxFragmentLength 700

# ATAC-seq: apply Tn5 offset correction (+4/-5 bp shift)
alignmentSieve --bam atac.bam --outFile shifted.bam --ATACshift
# Then index: samtools index shifted.bam

# GC bias correction (only if significant bias detected)
computeGCBias -b input.bam --effectiveGenomeSize 2913022398 \
    -g genome.2bit --GCbiasFrequenciesFile gc_freq.txt -p 8
correctGCBias -b input.bam --effectiveGenomeSize 2913022398 \
    --GCbiasFrequenciesFile gc_freq.txt -o corrected.bam

6. Enrichment Analysis

Quantify signal enrichment at specific regions.

bash
# Signal enrichment at peak regions
plotEnrichment -b chip.bam input.bam --BED peaks.bed \
    -o enrichment.png --ignoreDuplicates -p 8

Key Concepts

Normalization Methods

Method	Formula	When to Use	Requires
RPGC	1× genome coverage	ChIP-seq, ATAC-seq	`--effectiveGenomeSize`
CPM	Counts per million	Any assay, quick comparison	Nothing
RPKM	Per kb per million	RNA-seq gene-level	Nothing
BPM	Bins per million	Similar to CPM	Nothing
None	Raw counts	Not recommended for comparison	Nothing

Rule: Use RPGC for ChIP-seq/ATAC-seq (accounts for genome size). Use CPM for quick comparisons. Use RPKM for RNA-seq gene-level analysis.

Effective Genome Sizes

Organism	Assembly	Effective Size
Human	GRCh38/hg38	2,913,022,398
Mouse	GRCm38/mm10	2,652,783,500
Zebrafish	GRCz11	1,368,780,147
Drosophila	dm6	142,573,017
C. elegans	ce10/ce11	100,286,401

computeMatrix Modes

Mode	Use When	Key Params
`reference-point`	Signal around a fixed point (TSS, peak summit)	`-b`, `-a`, `--referencePoint`
`scale-regions`	Signal across variable-length features (gene bodies)	`-b`, `-a`, `--regionBodyLength`

Common Workflows

Workflow: ChIP-seq QC and Visualization

bash
#!/bin/bash
# Complete ChIP-seq QC + visualization pipeline
CHIP="chip.bam"
INPUT="input.bam"
GENES="genes.bed"
PEAKS="peaks.bed"
GSIZE=2913022398
THREADS=8

# 1. QC: sample correlation
multiBamSummary bins --bamfiles $INPUT $CHIP -o summary.npz -p $THREADS
plotCorrelation -in summary.npz --corMethod pearson --whatToShow heatmap -o correlation.png

# 2. QC: enrichment fingerprint
plotFingerprint -b $INPUT $CHIP -o fingerprint.png --extendReads 200 --ignoreDuplicates

# 3. Convert to normalized bigWig
bamCoverage --bam $CHIP --outFileName chip.bw --normalizeUsing RPGC \
    --effectiveGenomeSize $GSIZE --extendReads 200 --ignoreDuplicates -p $THREADS

# 4. Log2 ratio track
bamCompare -b1 $CHIP -b2 $INPUT -o log2ratio.bw --operation log2 \
    --scaleFactorsMethod readCount --extendReads 200 -p $THREADS

# 5. Heatmap at TSS
computeMatrix reference-point -S chip.bw log2ratio.bw -R $GENES \
    -b 3000 -a 3000 --referencePoint TSS -o tss_matrix.gz -p $THREADS
plotHeatmap -m tss_matrix.gz -o tss_heatmap.png --colorMap RdBu --kmeans 3

# 6. Profile at peaks
computeMatrix reference-point -S chip.bw -R $PEAKS \
    -b 2000 -a 2000 -o peak_matrix.gz -p $THREADS
plotProfile -m peak_matrix.gz -o peak_profile.png

Workflow: ATAC-seq Analysis

bash
#!/bin/bash
ATAC="atac.bam"
PEAKS="atac_peaks.bed"
GSIZE=2913022398
THREADS=8

# 1. Apply Tn5 offset correction (+4/-5 bp)
alignmentSieve --bam $ATAC --outFile shifted.bam --ATACshift -p $THREADS
samtools index shifted.bam

# 2. Generate RPGC-normalized coverage
bamCoverage --bam shifted.bam --outFileName atac.bw \
    --normalizeUsing RPGC --effectiveGenomeSize $GSIZE \
    --binSize 5 --extendReads -p $THREADS

# 3. Check nucleosome periodicity (expect 200bp/400bp peaks)
bamPEFragmentSize -b shifted.bam -o fragsize.png \
    --maxFragmentLength 1000 --binSize 1

# 4. Heatmap at ATAC peaks
computeMatrix reference-point -S atac.bw -R $PEAKS \
    -b 2000 -a 2000 -o atac_matrix.gz -p $THREADS
plotHeatmap -m atac_matrix.gz -o atac_heatmap.png --colorMap Blues --kmeans 2

Key Parameters

Parameter	Tool(s)	Default	Range	Effect
`--normalizeUsing`	bamCoverage, bamCompare	None	RPGC, CPM, RPKM, BPM, None	Coverage normalization method
`--effectiveGenomeSize`	bamCoverage, bamCompare	—	See table above	Required for RPGC normalization
`--binSize`	bamCoverage, multiBamSummary	50	1–10000	Resolution in bp; smaller = larger files
`--extendReads`	bamCoverage, bamCompare	False	integer (bp)	Extend to fragment length (ChIP: YES, RNA: NO)
`--ignoreDuplicates`	Most tools	False	True/False	Remove PCR duplicates
`--numberOfProcessors`	Most tools	1	1–N cores	Parallel processing
`--operation`	bamCompare	log2	log2, ratio, subtract, add, mean, reciprocal_ratio	Sample comparison operation
`--referencePoint`	computeMatrix	TSS	TSS, TES, center	Anchor point for reference-point mode
`-b` / `-a`	computeMatrix	500	100–10000 bp	Upstream/downstream distance from reference
`--kmeans`	plotHeatmap	None	1–20	Number of clusters for heatmap rows
`--minMappingQuality`	Most tools	None	0–60	Minimum alignment quality filter

Best Practices

Always extend reads for ChIP-seq: Use --extendReads 200 (or actual fragment length) — ChIP fragments are longer than reads.
Never extend reads for RNA-seq: --extendReads would span splice junctions, creating artifacts.
Anti-pattern — comparing with different normalizations: Always use the same normalization method across all samples in a comparison.
Use --region for parameter testing: Test on a single chromosome (--region chr1:1-10000000) before running on the full genome — saves hours.
Always use --numberOfProcessors: Most tools parallelize well — use all available cores.
Anti-pattern — using RPGC without --effectiveGenomeSize: Will silently produce wrong results. Always specify the correct genome size.
Run QC before analysis: Check fingerprint and correlation before investing time in heatmaps/profiles. Poor enrichment means downstream visualizations will be noise.

Common Recipes

Recipe: Multi-Sample Correlation Matrix

bash
# Compare 6 samples across the genome
multiBamSummary bins --bamfiles sample{1..6}.bam \
    -o all_samples.npz --binSize 10000 -p 8 \
    --labels S1 S2 S3 S4 S5 S6

# Pearson correlation heatmap
plotCorrelation -in all_samples.npz --corMethod pearson \
    --whatToShow heatmap -o pearson_corr.png --plotNumbers

# Spearman correlation + PCA
plotCorrelation -in all_samples.npz --corMethod spearman \
    --whatToShow heatmap -o spearman_corr.png
plotPCA -in all_samples.npz -o pca.png

Recipe: Gene Body Coverage Profile

bash
# Scale-regions mode for gene body analysis
computeMatrix scale-regions -S sample.bw -R genes.bed \
    -b 1000 -a 1000 --regionBodyLength 5000 -o gene_body.gz -p 8
plotProfile -m gene_body.gz -o gene_body_profile.png \
    --plotType lines --perGroup

Troubleshooting

Problem	Cause	Solution
`BAM index not found`	Missing `.bai` file	Run `samtools index input.bam`
Out of memory	Large genome, small bin size	Increase `--binSize`; process with `--region chr1`
Very slow processing	Single-threaded execution	Add `-p 8` (or available cores)
bigWig files very large	Bin size too small	Increase `--binSize 50` or larger
Flat ChIP fingerprint	Poor ChIP enrichment	Biological issue — consider repeating ChIP experiment
RNA-seq artifacts at exon boundaries	`--extendReads` used with RNA-seq	Remove `--extendReads` for RNA-seq data
ATAC-seq signal offset	Missing Tn5 correction	Apply `alignmentSieve --ATACshift` before analysis
Mismatched genome assemblies	BAM and BED use different assemblies	Verify both use same genome build (hg38 vs hg19)

Related Skills

pysam-genomic-files — programmatic BAM/VCF manipulation for custom filtering before deepTools
matplotlib-scientific-plotting — customize deepTools output figures beyond built-in options

References

deepTools documentation — official user guide and tool reference
deepTools Galaxy — web-based interface
Ramirez et al. (2016) "deepTools2: a next generation web server for deep-sequencing data analysis" — Nucleic Acids Research

Publisher	jaechang-hits
Repository	`SciAgent-Skills`
Skill name	`deeptools-ngs-analysis`
Stars	381
Forks	45
Bundled files	Instructions only
License	BSD-3-Clause
Links	Skill source Repository Homepage

Deeptools Ngs Analysis

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

deepTools — NGS Data Analysis Toolkit

Overview

When to Use

Prerequisites

Pre-flight Interview

Quick Start

Core API

1. BAM to Coverage Conversion

2. Sample Comparison

3. Quality Control

4. Heatmaps and Profile Plots

5. Read Filtering and Processing

6. Enrichment Analysis

Key Concepts

Normalization Methods

Effective Genome Sizes

computeMatrix Modes

Common Workflows

Workflow: ChIP-seq QC and Visualization

Workflow: ATAC-seq Analysis

Key Parameters

Best Practices

Common Recipes

Recipe: Multi-Sample Correlation Matrix

Recipe: Gene Body Coverage Profile

Troubleshooting

Related Skills

References

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

deepTools — NGS Data Analysis Toolkit

Overview

When to Use

Prerequisites

Pre-flight Interview

Quick Start

Core API

1. BAM to Coverage Conversion

2. Sample Comparison

3. Quality Control

4. Heatmaps and Profile Plots

5. Read Filtering and Processing

6. Enrichment Analysis

Key Concepts

Normalization Methods

Effective Genome Sizes

computeMatrix Modes

Common Workflows

Workflow: ChIP-seq QC and Visualization

Workflow: ATAC-seq Analysis

Key Parameters

Best Practices

Common Recipes

Recipe: Multi-Sample Correlation Matrix

Recipe: Gene Body Coverage Profile

Troubleshooting

Related Skills

References

Frequently asked questions

Related AI Skills

Set up your own AI workspace now