Samtools Bam Processing

samtools — SAM/BAM/CRAM Alignment Toolkit

Overview

samtools is the standard command-line toolkit for processing sequence alignment files in SAM, BAM, and CRAM formats. It handles the complete alignment file lifecycle: format conversion, coordinate sorting, index creation, quality control statistics, read filtering, duplicate marking, and multi-file merging. samtools is a near-universal component of NGS pipelines between alignment (STAR, BWA) and downstream analysis (variant calling, peak calling, coverage).

When to Use

• Sorting BAM files by coordinate after alignment (required before indexing)
• Indexing sorted BAM files for random access and region queries
• Converting between SAM, BAM, and CRAM formats to save storage
• Generating alignment QC metrics: mapping rates, insert sizes, per-chromosome stats
• Filtering reads by mapping quality, FLAG bits, or genomic regions
• Marking or removing PCR duplicates before variant calling
• Merging multiple BAM files from different lanes or samples
• Calculating per-base depth or coverage breadth for target regions
• Use pysam instead for Python-native BAM manipulation in custom scripts
• Use deeptools bamCoverage instead when you need normalized bigWig coverage tracks
• Use mosdepth instead for whole-genome per-base depth (faster, parallelized)

Prerequisites

• Installation: samtools 1.17+ recommended
• Input requirements: SAM/BAM/CRAM files; CRAM requires FASTA reference
• Companion tools: samtools faidx for FASTA indexing; samtools sort before samtools index

Check before installing: The tool may already be available in the current environment (e.g., inside a pixi / conda env). Run command -v samtools first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via pixi run samtools rather than bare samtools.

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# Bioconda (recommended)
conda install -c bioconda samtools

# Homebrew (macOS)
brew install samtools

# Verify
samtools --version | head -1

Pre-flight Interview

Settle these with the user before writing any analysis code.

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decisions:
  - id: D1
    param: mappingQualityFloor
    kind: required
    source: user
    ask: "Below what mapping confidence should reads be dropped? Everything counted downstream inherits this cut."
    default: "0 - keep every alignment, including ambiguously placed ones"

  - id: D2
    param: flagFilter
    kind: required
    source: user
    ask: "Which read classes should be excluded - unmapped, secondary, supplementary, failed-QC, duplicates?"
    default: "keep all classes"

  - id: D3
    param: duplicateHandling
    kind: required
    source: user
    ask: "Should duplicates be marked and left in place, or physically removed from the file?"
    default: "marked, not removed - downstream tools can then choose"

  - id: D4
    param: sortOrder
    kind: derived
    source: upstream
    ask: "Does the next step need coordinate order, or name order for mate pairing?"
    default: "coordinate"

  - id: D5
    param: outputFormat
    kind: optional
    source: user
    ask: "Write BAM, or CRAM against a reference to save space?"
    default: "BAM"

  - id: D6
    param: opticalDuplicateDistance
    kind: optional_conditional
    source: data
    ask: "Should optical duplicates be distinguished from PCR duplicates, using this platform's pixel distance?"
    default: "not distinguished"

  - id: D7
    param: threadsAndMemory
    kind: never_ask
    source: data
    reason: "Compression threads and per-thread sort memory affect runtime and peak RAM, not the records"
    default: "available cores, 768M per thread"

D1 and D2 look like plumbing and are not. A MAPQ floor silently removes multi-mapping regions - paralogs, recent duplications, repeat-adjacent genes - from everything computed afterwards, and a run that keeps secondary alignments counts the same fragment several times. Neither is visible in the output file.

Quick Start

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# Typical post-alignment workflow: sort → index → QC
samtools sort -@ 8 -o sorted.bam input.bam
samtools index sorted.bam
samtools flagstat sorted.bam

Core API

Module 1: BAM/SAM I/O and Format Conversion

Convert between SAM/BAM/CRAM formats and extract subsets.

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# SAM → BAM (saves ~75% disk space)
samtools view -b -h input.sam -o output.bam

# BAM → CRAM (saves additional 40-50%)
samtools view -C -T reference.fa input.bam -o output.cram

# Filter: mapping quality ≥20, exclude unmapped (-F 4)
samtools view -q 20 -F 4 input.bam -o filtered.bam

# Extract specific region (requires index)
samtools view -h sorted.bam "chr1:1000000-2000000" -o region.bam

# Count reads matching filter
samtools view -c -F 4 input.bam
# Output: 45231923 (number of mapped reads)

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# Extract reads as FASTQ (for realignment or de novo assembly)
samtools fastq -@ 4 -1 R1.fastq.gz -2 R2.fastq.gz -0 unpaired.fastq.gz input.bam

# Extract reads as FASTA
samtools fasta input.bam > reads.fasta

# Filter by read group
samtools view -r SAMPLE_001 multi_rg.bam -o sample001.bam

Module 2: Sorting and Indexing

Organize BAM files for efficient random access.

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# Sort by coordinate (required before indexing)
samtools sort -@ 8 -m 2G input.bam -o sorted.bam

# Sort by read name (required for fixmate/markdup)
samtools sort -n -@ 8 input.bam -o namesorted.bam

# Index sorted BAM (creates sorted.bam.bai)
samtools index sorted.bam

# For chromosomes > 512 Mbp: use CSI index instead
samtools index -c sorted.bam

# Group reads by name (fast, for fixmate — no full sort needed)
samtools collate -o collated.bam input.bam

Module 3: Quality Control and Statistics

Generate alignment QC metrics and coverage reports.

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# Quick summary: total, mapped, paired, properly paired
samtools flagstat sorted.bam
# Example output:
# 50000000 + 0 in total (QC-passed reads + QC-failed reads)
# 48523111 + 0 mapped (97.05% : N/A)
# 50000000 + 0 paired in sequencing
# 48490234 + 0 properly paired (96.98% : N/A)

# Per-chromosome mapped/unmapped read counts
samtools idxstats sorted.bam
# chr1  248956422  12345678  0
# chr2  242193529  11234567  0

# Comprehensive stats (insert sizes, GC content, base quality)
samtools stats -r reference.fa sorted.bam > full_stats.txt
grep "^SN" full_stats.txt | cut -f2,3  # Summary Numbers only

# Coverage report (min/max/mean per region/chromosome)
samtools coverage sorted.bam

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# Per-base read depth for specific regions
samtools depth -b target_regions.bed sorted.bam > depth.txt
# Output: chr  pos  depth (e.g., chr1  1000  45)

# Statistics split by read group
samtools stats -S RG sorted.bam > per_rg_stats.txt

Module 4: Read Filtering and FLAG Operations

Filter reads using SAM FLAG bits for specific subsets.

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# FLAG reference — common masks:
# 1    = paired         4  = unmapped
# 2    = proper pair    8  = mate unmapped
# 16   = reverse strand 64 = R1 (first in pair)
# 128  = R2             256= secondary alignment
# 1024 = PCR duplicate  2048= supplementary

# Extract properly paired, mapped reads (FLAG 2 set, 4 unset)
samtools view -f 2 -F 4 sorted.bam -o proper_pairs.bam

# Extract R1 reads only
samtools view -f 64 sorted.bam -o R1.bam

# Remove secondary and supplementary alignments
samtools view -F 2304 sorted.bam -o primary.bam

# Extract reads from BED file regions
samtools view -L regions.bed -b sorted.bam -o regions.bam

Module 5: Duplicate Handling

Mark or remove PCR duplicates before variant calling.

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# Full duplicate marking workflow (collate → fixmate → sort → markdup)
samtools collate -@ 8 -o collated.bam input.bam
samtools fixmate -m -@ 8 collated.bam fixmated.bam
samtools sort -@ 8 -o sorted.bam fixmated.bam
samtools markdup -@ 8 sorted.bam marked.bam
samtools index marked.bam

# Check duplication rate
samtools flagstat marked.bam | grep "duplicates"
# Output: 2345678 + 0 duplicates (4.83%)

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# NovaSeq optical duplicate detection (2500 pixel distance)
samtools markdup -d 2500 sorted.bam marked_novaseq.bam

# Remove duplicates instead of marking
samtools markdup -r sorted.bam deduped.bam

# Get duplication stats without writing output
samtools markdup -s sorted.bam /dev/null

Module 6: Multi-file Operations and Region Analysis

Merge BAM files and perform region-level analysis.

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# Merge multiple BAM files (all must be sorted)
samtools merge -@ 8 merged.bam lane1.bam lane2.bam lane3.bam

# Merge files listed in a text file (one per line)
samtools merge -b bam_list.txt -@ 8 merged.bam

# Merge with read group tags from filenames
samtools merge -r merged.bam sample1.bam sample2.bam

# Extract specific chromosome region from merged output
samtools view -h merged.bam chr1 -b -o chr1.bam

Key Concepts

SAM FLAG Bits

FLAGS encode read properties as a sum of bit values. Common filtering patterns:

CRAM vs BAM vs SAM

Use CRAM for long-term storage; BAM for active analysis.

Common Workflows

Workflow 1: Post-Alignment QC and Preparation

Goal: Convert aligner output to analysis-ready BAM with QC metrics.

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#!/bin/bash
SAMPLE="sample_001"
REF="reference.fa"
THREADS=8

# 1. Sort and index (aligner often outputs unsorted SAM/BAM)
samtools sort -@ $THREADS -o ${SAMPLE}.sorted.bam ${SAMPLE}.bam
samtools index ${SAMPLE}.sorted.bam

# 2. QC metrics
samtools flagstat ${SAMPLE}.sorted.bam > ${SAMPLE}.flagstat.txt
samtools stats -r $REF ${SAMPLE}.sorted.bam > ${SAMPLE}.stats.txt
samtools coverage ${SAMPLE}.sorted.bam > ${SAMPLE}.coverage.txt

# 3. Per-chromosome stats
samtools idxstats ${SAMPLE}.sorted.bam > ${SAMPLE}.idxstats.txt

echo "QC complete: $(grep 'mapped (' ${SAMPLE}.flagstat.txt | head -1)"

Workflow 2: Full Duplicate-Marking Pipeline

Goal: Prepare BAM for GATK or other variant callers requiring deduplicated input.

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#!/bin/bash
INPUT="aligned.bam"
FINAL="deduped.bam"
THREADS=8

# Collate → fixmate → sort → markdup
samtools collate -@ $THREADS -o collated.bam $INPUT
samtools fixmate -m -@ $THREADS collated.bam fixmated.bam
samtools sort -@ $THREADS -o sorted.bam fixmated.bam
samtools markdup -@ $THREADS -s sorted.bam $FINAL

# Clean up intermediates
rm collated.bam fixmated.bam sorted.bam

# Index and verify
samtools index $FINAL
samtools flagstat $FINAL | grep "duplic"
# Expected: 3-15% duplicates (WGS); 10-30% for amplicon

Key Parameters

Best Practices

1. Always sort before indexing: samtools index requires coordinate-sorted input. Attempting to index an unsorted BAM will fail or produce incorrect results.

2. Use -@ for all production runs: Most samtools commands are I/O-bound. Adding -@ 8 provides near-linear speedup for compression/decompression with minimal overhead.

3. Run flagstat before any analysis: samtools flagstat runs in seconds and catches alignment failures (low mapping rate, unexpected paired-end rates) before wasting time on downstream steps.

4. Use the collate → fixmate → sort → markdup pipeline: Running samtools markdup directly on coordinate-sorted BAM without fixmate produces incorrect duplicate detection. The mate information added by fixmate -m is essential.

5. Prefer CRAM for archiving: CRAM reduces storage 40-50% vs BAM with no loss. Always store the reference FASTA alongside CRAM files.

6. Use -L bed_file for targeted analyses: Restricting samtools view to BED-defined target regions (WES capture, amplicons) dramatically reduces I/O for downstream steps.

Common Recipes

Recipe: Batch Flagstat for Multiple Samples

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# Process all BAM files in directory
for bam in *.sorted.bam; do
    echo "=== $bam ==="
    samtools flagstat $bam | grep -E "mapped|properly paired|duplicates"
done

Recipe: Extract Unmapped Reads for De Novo Assembly

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# Pull both unmapped reads (useful for pathogen detection)
samtools view -f 4 -b input.bam -o unmapped.bam
samtools fastq -@ 4 -1 unmapped_R1.fastq -2 unmapped_R2.fastq unmapped.bam
echo "Unmapped pairs ready for de novo assembly"

Recipe: Downsample BAM to Target Coverage

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# Estimate current depth, then subsample to ~30×
TOTAL=$(samtools flagstat input.bam | grep "mapped (" | head -1 | awk '{print $1}')
GENOME_SIZE=3100000000  # hg38
READ_LEN=150
CURRENT_COV=$(echo "scale=1; $TOTAL * $READ_LEN / $GENOME_SIZE" | bc)
TARGET_FRAC=$(echo "scale=3; 30 / $CURRENT_COV" | bc)
echo "Current: ${CURRENT_COV}×; subsample fraction: $TARGET_FRAC"
samtools view -b -s $TARGET_FRAC input.bam -o downsampled.bam
samtools index downsampled.bam

Troubleshooting

Related Skills

• deeptools-ngs-analysis — normalized bigWig coverage tracks and ChIP-seq visualization downstream of samtools
• pysam-genomic-files — Python API for BAM manipulation in custom scripts
• bedtools-genomic-intervals — genomic interval operations on BAM/BED files produced by samtools

References

• samtools documentation — official man pages and command reference
• GitHub: samtools/samtools — source, releases, issue tracker
• Danecek et al. (2021) "Twelve years of SAMtools and BCFtools" — GigaScience 10(2)
• SAM format specification — FLAG bits, CIGAR strings, optional tags