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
• 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

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.

Quick Start

# 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).

# 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.

# 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.

# 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).

# 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.

# 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.

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

Key Concepts

Normalization Methods

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

computeMatrix Modes

Common Workflows

Workflow: ChIP-seq QC and Visualization

#!/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

#!/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

Best Practices

1. Always extend reads for ChIP-seq: Use --extendReads 200 (or actual fragment length) — ChIP fragments are longer than reads.

2. Never extend reads for RNA-seq: --extendReads would span splice junctions, creating artifacts.

3. Anti-pattern — comparing with different normalizations: Always use the same normalization method across all samples in a comparison.

4. Use --region for parameter testing: Test on a single chromosome (--region chr1:1-10000000) before running on the full genome — saves hours.

5. Always use --numberOfProcessors: Most tools parallelize well — use all available cores.

6. Anti-pattern — using RPGC without --effectiveGenomeSize: Will silently produce wrong results. Always specify the correct genome size.

7. 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

# 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

# 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

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