deeptools-ngs-analysis

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

| 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

#!/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 --