Version Compatibility

Reference examples tested with: pysam 0.22+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

BAM Statistics

"Get alignment statistics and coverage from my BAM file" → Generate read counts, mapping rates, per-chromosome statistics, depth profiles, and coverage summaries.

• CLI: samtools flagstat, samtools stats, samtools depth, samtools coverage (samtools)
• Python: pysam.AlignmentFile with pileup() and get_index_statistics() (pysam)

Generate alignment statistics using samtools and pysam.

Quick Summary Commands

Command	Output	Speed
flagstat	Read counts by category	Very fast
idxstats	Per-chromosome counts	Very fast (needs index)
stats	Comprehensive statistics	Moderate
depth	Per-position depth	Slow (full scan)
coverage	Per-region coverage	Fast (needs index)

samtools flagstat

Fast summary of alignment flags.

samtools flagstat input.bam

Output:

10000000 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 secondary
50000 + 0 supplementary
0 + 0 duplicates
9800000 + 0 mapped (98.00% : N/A)
9950000 + 0 paired in sequencing
4975000 + 0 read1
4975000 + 0 read2
9700000 + 0 properly paired (97.49% : N/A)
9750000 + 0 with itself and mate mapped
100000 + 0 singletons (1.01% : N/A)
25000 + 0 with mate mapped to a different chr
10000 + 0 with mate mapped to a different chr (mapQ>=5)

Multi-threaded

samtools flagstat -@ 4 input.bam

Output to File

samtools flagstat input.bam > flagstat.txt

samtools idxstats

Per-chromosome read counts (requires index).

samtools idxstats input.bam

Output format: chrom length mapped unmapped

chr1    248956422    5000000    1000
chr2    242193529    4800000    800
chrM    16569        50000      100
*       0            0          150000

Parse idxstats

# Total mapped reads
samtools idxstats input.bam | awk '{sum += $3} END {print sum}'

# Mitochondrial percentage
samtools idxstats input.bam | awk '
    /^chrM/ {mt = $3}
    {total += $3}
    END {print mt/total*100 "% mitochondrial"}'

samtools stats

Comprehensive statistics including insert size, base quality, and more.

samtools stats input.bam > stats.txt

View Summary Numbers

samtools stats input.bam | grep "^SN"

Key summary fields:

• raw total sequences - Total reads
• reads mapped - Mapped reads
• reads mapped and paired - Properly paired
• insert size average - Mean insert size
• insert size standard deviation - Insert size spread
• average length - Mean read length
• error rate - Mismatch rate

Generate Plots (with plot-bamstats)

samtools stats input.bam > stats.txt
plot-bamstats -p plots/ stats.txt

Stats for Specific Region

samtools stats input.bam chr1:1000000-2000000 > region_stats.txt

samtools depth

Per-position read depth.

Basic Depth

samtools depth input.bam > depth.txt

Output: chrom position depth

Depth at Specific Positions

samtools depth -r chr1:1000-2000 input.bam

Include Zero-Depth Positions

samtools depth -a input.bam > depth_with_zeros.txt

Maximum Depth Cap

samtools depth -d 0 input.bam  # No cap (default 8000)

Depth from BED Regions

samtools depth -b regions.bed input.bam

Calculate Mean Depth

samtools depth input.bam | awk '{sum += $3; n++} END {print sum/n}'

samtools coverage

Per-chromosome or per-region coverage statistics (faster than depth).

samtools coverage input.bam

Output columns:

• #rname - Reference name
• startpos - Start position
• endpos - End position
• numreads - Number of reads
• covbases - Bases with coverage
• coverage - Percentage of bases covered
• meandepth - Mean depth
• meanbaseq - Mean base quality
• meanmapq - Mean mapping quality

Coverage for Specific Region

samtools coverage -r chr1:1000000-2000000 input.bam

Coverage from BED

samtools coverage -b regions.bed input.bam

Histogram Output

samtools coverage -m input.bam

pysam Python Alternative

Count Reads

import pysam

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    total = mapped = paired = proper = 0
    for read in bam:
        total += 1
        if not read.is_unmapped:
            mapped += 1
        if read.is_paired:
            paired += 1
        if read.is_proper_pair:
            proper += 1

    print(f'Total: {total}')
    print(f'Mapped: {mapped} ({mapped/total*100:.1f}%)')
    print(f'Properly paired: {proper} ({proper/paired*100:.1f}%)')

Per-Chromosome Counts

import pysam

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for stat in bam.get_index_statistics():
        print(f'{stat.contig}: {stat.mapped} mapped, {stat.unmapped} unmapped')

Calculate Depth at Position

import pysam

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for pileup in bam.pileup('chr1', 1000000, 1000001):
        print(f'Position {pileup.pos}: depth {pileup.n}')

Mean Depth in Region

import pysam

def mean_depth(bam_path, chrom, start, end):
    depths = []
    with pysam.AlignmentFile(bam_path, 'rb') as bam:
        for pileup in bam.pileup(chrom, start, end, truncate=True):
            depths.append(pileup.n)

    if depths:
        return sum(depths) / len(depths)
    return 0

depth = mean_depth('input.bam', 'chr1', 1000000, 2000000)
print(f'Mean depth: {depth:.1f}x')

Coverage Statistics

Goal: Compute coverage breadth and depth for a genomic region from a BAM file.

Approach: Iterate pileup columns in the region, count covered positions and accumulate depth, then derive percentages and means.

Reference (pysam 0.22+):

import pysam

def coverage_stats(bam_path, chrom, start, end):
    covered = 0
    total_depth = 0

    with pysam.AlignmentFile(bam_path, 'rb') as bam:
        for pileup in bam.pileup(chrom, start, end, truncate=True):
            covered += 1
            total_depth += pileup.n

    length = end - start
    pct_covered = covered / length * 100
    mean_depth = total_depth / length if length > 0 else 0

    return {
        'length': length,
        'covered_bases': covered,
        'pct_covered': pct_covered,
        'mean_depth': mean_depth
    }

stats = coverage_stats('input.bam', 'chr1', 1000000, 2000000)
print(f'Coverage: {stats["pct_covered"]:.1f}%')
print(f'Mean depth: {stats["mean_depth"]:.1f}x')

Insert Size Distribution

Goal: Compute the insert size distribution to assess library preparation quality.

Approach: Iterate properly paired read1 records, accumulate template lengths into a Counter, then compute summary statistics.

Reference (pysam 0.22+):

import pysam
from collections import Counter

insert_sizes = Counter()

with pysam.AlignmentFile('input.bam', 'rb') as bam:
    for read in bam:
        if read.is_proper_pair and read.is_read1 and read.template_length > 0:
            insert_sizes[read.template_length] += 1

sizes = list(insert_sizes.keys())
mean_insert = sum(s * c for s, c in insert_sizes.items()) / sum(insert_sizes.values())
print(f'Mean insert size: {mean_insert:.0f}')
print(f'Min: {min(sizes)}, Max: {max(sizes)}')

Quick Reference

Task	Command
Quick counts	samtools flagstat input.bam
Per-chrom counts	samtools idxstats input.bam
Full stats	samtools stats input.bam
Coverage summary	samtools coverage input.bam
Per-position depth	samtools depth input.bam
Mean depth	samtools depth input.bam | awk '{sum+=$3;n++}END{print sum/n}'

Common Metrics

Metric	Good	Concerning
Mapping rate	>95%	<80%
Proper pair rate	>90%	<70%
Duplicate rate	<20%	>40%
Error rate	<1%	>2%
Coverage uniformity	<2x CV	>3x CV

Related Skills

• sam-bam-basics - View alignment files
• alignment-indexing - idxstats requires index
• duplicate-handling - Check duplicate rates
• alignment-filtering - Filter before stats
• sequence-io/sequence-statistics - FASTA/FASTQ statistics