Pysam genomic files

Pysam — Genomic File Toolkit

Overview

Pysam provides a Pythonic interface to htslib for reading, manipulating, and writing genomic data files. It handles SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences with efficient region-based random access. Also exposes samtools and bcftools as callable Python functions.

When to Use

• Reading and querying BAM/CRAM alignment files (region extraction, read filtering)
• Analyzing VCF/BCF variant files (genotype access, variant filtering, annotation)
• Extracting reference sequences from indexed FASTA files
• Calculating per-base coverage and pileup statistics
• Building custom bioinformatics pipelines that combine alignment + variant + sequence data
• Quality control of NGS data (mapping quality, flag filtering, coverage)
• For alignment from FASTQ (read mapping), use STAR, BWA, or minimap2 instead
• For variant calling from BAM, use GATK or DeepVariant instead

Prerequisites

pip install pysam

Note: Requires htslib C library (bundled with pip install on most platforms). On some Linux systems, may need libhts-dev or equivalent. Index files (.bai, .tbi, .fai) required for random access — create with pysam.index(), pysam.tabix_index(), or pysam.faidx().

Quick Start

import pysam

# Read BAM file, fetch reads in a region
with pysam.AlignmentFile("sample.bam", "rb") as bam:
    for read in bam.fetch("chr1", 1000, 2000):
        print(f"{read.query_name}: pos={read.reference_start}, mapq={read.mapping_quality}")
    print(f"Total reads in region: {bam.count('chr1', 1000, 2000)}")

Core API

1. Alignment Files (SAM/BAM/CRAM)

Read, query, and write aligned sequencing reads.

import pysam

# Open BAM (binary) or SAM (text) file
bam = pysam.AlignmentFile("sample.bam", "rb")  # rb=read BAM, r=read SAM, rc=read CRAM

# Fetch reads overlapping a region (requires .bai index)
for read in bam.fetch("chr1", 10000, 20000):
    print(f"Name: {read.query_name}")
    print(f"  Position: {read.reference_start}-{read.reference_end}")
    print(f"  MAPQ: {read.mapping_quality}")
    print(f"  CIGAR: {read.cigarstring}")
    print(f"  Sequence: {read.query_sequence[:30]}...")
    break

# Count reads in region (fast, no iteration needed)
n_reads = bam.count("chr1", 10000, 20000)
print(f"Reads in region: {n_reads}")

# Filter reads by quality and flags
for read in bam.fetch("chr1", 10000, 20000):
    if read.mapping_quality >= 30 and not read.is_unmapped and not read.is_duplicate:
        pass  # Process high-quality, mapped, non-duplicate reads

bam.close()

# Write filtered reads to a new BAM file
with pysam.AlignmentFile("input.bam", "rb") as inbam:
    with pysam.AlignmentFile("filtered.bam", "wb", header=inbam.header) as outbam:
        for read in inbam.fetch("chr1", 10000, 20000):
            if read.mapping_quality >= 30:
                outbam.write(read)

# Index the output
pysam.index("filtered.bam")
print("Created filtered.bam + filtered.bam.bai")

2. Coverage and Pileup Analysis

Calculate per-base coverage statistics.

import pysam
import numpy as np

bam = pysam.AlignmentFile("sample.bam", "rb")

# Pileup: per-base coverage with read-level detail
for pileup_col in bam.pileup("chr1", 10000, 10100, min_mapping_quality=30):
    bases = [p.alignment.query_sequence[p.query_position]
             for p in pileup_col.pileups if not p.is_del and p.query_position is not None]
    print(f"Pos {pileup_col.reference_pos}: depth={pileup_col.nsegments}, bases={''.join(bases[:5])}")

# Quick coverage count per region (faster than pileup)
coverage = bam.count_coverage("chr1", 10000, 10100, quality_threshold=20)
# Returns tuple of 4 arrays (A, C, G, T counts per position)
total_cov = np.array(coverage).sum(axis=0)
print(f"Mean coverage: {total_cov.mean():.1f}x")

bam.close()

3. Variant Files (VCF/BCF)

Read, query, and filter genetic variants.

import pysam

# Open VCF/BCF file
vcf = pysam.VariantFile("variants.vcf.gz")

# Iterate all variants
for record in vcf.fetch("chr1", 10000, 50000):
    print(f"{record.chrom}:{record.pos} {record.ref}>{','.join(record.alts or [])}")
    print(f"  QUAL={record.qual}, FILTER={list(record.filter)}")
    print(f"  INFO: {dict(record.info)}")

    # Access genotypes per sample
    for sample in record.samples:
        gt = record.samples[sample]["GT"]
        print(f"  {sample}: GT={gt}")
    break

vcf.close()

# Filter variants and write to new VCF
with pysam.VariantFile("variants.vcf.gz") as vcf_in:
    with pysam.VariantFile("filtered.vcf.gz", "wz", header=vcf_in.header) as vcf_out:
        for record in vcf_in:
            if record.qual and record.qual >= 30 and "PASS" in record.filter:
                vcf_out.write(record)

pysam.tabix_index("filtered.vcf.gz", preset="vcf")
print("Created filtered.vcf.gz + filtered.vcf.gz.tbi")

4. Sequence Files (FASTA/FASTQ)

Random access to reference sequences and sequential reading of raw reads.

import pysam

# FASTA: random access (requires .fai index)
fasta = pysam.FastaFile("reference.fasta")
seq = fasta.fetch("chr1", 10000, 10050)
print(f"Sequence ({len(seq)} bp): {seq}")
print(f"Available contigs: {fasta.references[:5]}")
print(f"Contig lengths: {dict(zip(fasta.references[:3], fasta.lengths[:3]))}")
fasta.close()

# Create FASTA index if needed
# pysam.faidx("reference.fasta")

# FASTQ: sequential reading
with pysam.FastxFile("reads.fastq.gz") as fq:
    for i, entry in enumerate(fq):
        print(f"Read {entry.name}: {len(entry.sequence)} bp, mean_qual={sum(entry.get_quality_array())/len(entry.sequence):.1f}")
        if i >= 2:
            break

5. Read Groups and Sample Information

Extract and filter reads by read group (essential for multi-sample BAM files).

import pysam

bam = pysam.AlignmentFile("multisample.bam", "rb")

# Access read group information from BAM header
print("Read groups in file:")
for rg_dict in bam.header.get("RG", []):
    print(f"  ID: {rg_dict['ID']}, Sample: {rg_dict.get('SM', 'N/A')}, Library: {rg_dict.get('LB', 'N/A')}, Platform: {rg_dict.get('PL', 'N/A')}")

# Get all samples in the BAM (from RG headers)
samples = set()
for rg_dict in bam.header.get("RG", []):
    if "SM" in rg_dict:
        samples.add(rg_dict["SM"])
print(f"Samples in BAM: {sorted(samples)}")

bam.close()

# Filter reads by read group ID
def extract_reads_by_rg(bam_path, rg_id, output_path):
    """Extract all reads from a specific read group.

    WARNING: Uses fetch(until_eof=True), which scans the entire BAM sequentially.
    Multi-sample BAMs can be tens to hundreds of GB — this may be slow.
    For large files, prefer region-based filtering:
        for read in bam.fetch("chr1", start, end): ...
    Or use the samtools CLI equivalent (faster for one-off extractions):
        samtools view -b -r <rg_id> input.bam -o output.bam
    """
    with pysam.AlignmentFile(bam_path, "rb") as bam_in:
        with pysam.AlignmentFile(output_path, "wb", header=bam_in.header) as bam_out:
            for read in bam_in.fetch(until_eof=True):
                if read.has_tag("RG") and read.get_tag("RG") == rg_id:
                    bam_out.write(read)
    pysam.index(output_path)
    print(f"Extracted reads from RG:{rg_id} → {output_path}")

extract_reads_by_rg("multisample.bam", "SAMPLE_001_LaneA", "sample001_laneA.bam")

from collections import defaultdict
import pysam

# Count reads per sample
def reads_per_sample(bam_path):
    """Count reads per sample from read group information.

    Two distinct "unknown" cases are tracked separately:
    - "no_sm_field":  RG header entry exists but is missing the SM (sample name) field.
    - "undefined_rg": A read carries an RG tag not declared in the BAM header.
    """
    counts = defaultdict(int)
    rg_to_sample = {}

    with pysam.AlignmentFile(bam_path, "rb") as bam:
        # Build RG → sample mapping from header
        for rg_dict in bam.header.get("RG", []):
            rg_id = rg_dict["ID"]
            # (a) RG header entry lacks SM field
            rg_to_sample[rg_id] = rg_dict.get("SM", "no_sm_field")

        # Count reads per resolved sample name
        for read in bam.fetch(until_eof=True):
            if read.has_tag("RG"):
                rg_id = read.get_tag("RG")
                # (b) Read's RG tag is not declared in the header
                sample = rg_to_sample.get(rg_id, "undefined_rg")
                counts[sample] += 1

    return dict(counts)

sample_counts = reads_per_sample("multisample.bam")
for sample, count in sorted(sample_counts.items()):
    print(f"  {sample}: {count:,} reads")

6. Samtools/Bcftools CLI Access

Call samtools and bcftools commands from Python.

import pysam

# Sort BAM file
pysam.sort("-o", "sorted.bam", "input.bam")

# Index BAM
pysam.index("sorted.bam")

# View region as BAM
pysam.view("-b", "-o", "region.bam", "sorted.bam", "chr1:1000-2000")

# BCFtools: compress and index VCF
pysam.bcftools.view("-O", "z", "-o", "output.vcf.gz", "input.vcf")
pysam.tabix_index("output.vcf.gz", preset="vcf")

# Error handling
try:
    pysam.sort("-o", "output.bam", "nonexistent.bam")
except pysam.SamtoolsError as e:
    print(f"samtools error: {e}")

CLI equivalents (for reference — use Python API in automated pipelines):

# These are equivalent to the Python calls above:
samtools sort -o sorted.bam input.bam
samtools index sorted.bam
samtools view -b -o region.bam sorted.bam chr1:1000-2000
bcftools view -O z -o output.vcf.gz input.vcf

Key Concepts

Coordinate Systems

Critical: pysam uses 0-based, half-open coordinates (Python convention):

System	Start	End	Example: "bases 1000-2000"
pysam Python API	0-based	exclusive	fetch("chr1", 999, 2000)
samtools region string	1-based	inclusive	fetch("chr1:1000-2000")
VCF file format	1-based	—	record.pos = 1-based, record.start = 0-based
BED format	0-based	exclusive	chr1\t999\t2000

Index File Requirements

File Type	Index Extension	Create With
BAM	.bai	pysam.index("file.bam")
CRAM	.crai	pysam.index("file.cram")
FASTA	.fai	pysam.faidx("file.fasta")
VCF.gz	.tbi	pysam.tabix_index("file.vcf.gz", preset="vcf")
BCF	.csi	pysam.tabix_index("file.bcf", preset="bcf")

Without an index, use fetch(until_eof=True) for sequential reading.

File Mode Strings

Mode	Format	Direction
"rb"	BAM (binary)	Read
"r"	SAM (text)	Read
"rc"	CRAM	Read
"wb"	BAM	Write
"w"	SAM	Write
"wz"	VCF.gz (compressed)	Write

Common Workflows

Workflow 1: Coverage Analysis for Target Regions

Goal: Calculate coverage statistics for a set of target regions (e.g., exome capture targets).

import pysam
import numpy as np

def coverage_for_regions(bam_path, regions, min_mapq=30):
    """Calculate coverage stats for a list of (chrom, start, end) regions."""
    results = []
    with pysam.AlignmentFile(bam_path, "rb") as bam:
        for chrom, start, end in regions:
            cov = np.array(bam.count_coverage(chrom, start, end,
                                               quality_threshold=min_mapq))
            total = cov.sum(axis=0)
            results.append({
                "region": f"{chrom}:{start}-{end}",
                "mean_cov": total.mean(),
                "min_cov": total.min(),
                "pct_above_20x": (total >= 20).mean() * 100,
            })
    return results

regions = [("chr1", 10000, 10500), ("chr1", 20000, 20500), ("chr2", 5000, 5500)]
stats = coverage_for_regions("sample.bam", regions)
for s in stats:
    print(f"{s['region']}: mean={s['mean_cov']:.1f}x, min={s['min_cov']}x, ≥20x={s['pct_above_20x']:.1f}%")

Workflow 2: Variant Annotation with Read Support

Goal: For each variant in a VCF, count supporting reads from the BAM.

import pysam

def annotate_variants_with_reads(vcf_path, bam_path, output_path):
    """Add read support counts to each variant."""
    with pysam.VariantFile(vcf_path) as vcf_in:
        # Add INFO field to header
        vcf_in.header.add_line(
            '##INFO=<ID=READ_SUPPORT,Number=1,Type=Integer,Description="Reads supporting alt allele">'
        )
        with pysam.VariantFile(output_path, "w", header=vcf_in.header) as vcf_out:
            with pysam.AlignmentFile(bam_path, "rb") as bam:
                for record in vcf_in:
                    alt_count = 0
                    for col in bam.pileup(record.chrom, record.start, record.stop,
                                           min_mapping_quality=30, truncate=True):
                        if col.reference_pos == record.start:
                            for p in col.pileups:
                                if (not p.is_del and p.query_position is not None and
                                    p.alignment.query_sequence[p.query_position] in (record.alts or [])):
                                    alt_count += 1
                    record.info["READ_SUPPORT"] = alt_count
                    vcf_out.write(record)

annotate_variants_with_reads("variants.vcf", "sample.bam", "annotated.vcf")
print("Created annotated.vcf with READ_SUPPORT field")

Key Parameters

Parameter	Module	Default	Range / Options	Effect
mode string	AlignmentFile, VariantFile	—	"rb", "r", "rc", "wb", "w", "wz"	File format and read/write direction
min_mapping_quality	pileup()	0	0–60	Filter reads below this MAPQ
quality_threshold	count_coverage()	15	0–40	Minimum base quality to count
truncate	pileup()	False	True/False	Truncate pileup to exact region (True) vs include overlapping reads (False)
until_eof	fetch()	False	True/False	Read all records sequentially without index
multiple_iterators	fetch()	False	True/False	Allow multiple simultaneous iterators (slight overhead)
preset	tabix_index()	—	"vcf", "bed", "gff", "sam"	File format for tabix indexing

Best Practices

1. Always use context managers (with statement) for automatic file cleanup. Unclosed files can leak file descriptors.

2. Create and verify index files first: Most random-access operations fail silently or raise cryptic errors without indexes. Check for .bai/.tbi/.fai files before queries.

3. Use count() instead of iterating to count reads: bam.count("chr1", 1000, 2000) is much faster than sum(1 for _ in bam.fetch(...)).

4. Use count_coverage() for coverage, pileup() for base-level detail: count_coverage() is faster when you only need depth numbers. Use pileup() only when you need per-read, per-base information.

5. Anti-pattern — mixing 0-based and 1-based coordinates: Always double-check coordinate systems when combining pysam (0-based) with VCF files (1-based POS), BED files (0-based), or region strings (1-based). See Key Concepts table.

6. Anti-pattern — forgetting truncate=True in pileup: Without truncate=True, pileup() extends to the full extent of overlapping reads, which can be much larger than the requested region.

Common Recipes

Recipe: Extract Gene Sequences from Reference

import pysam

def get_gene_sequence(fasta_path, chrom, start, end, strand="+"):
    """Extract gene sequence, reverse-complement if on minus strand."""
    with pysam.FastaFile(fasta_path) as fasta:
        seq = fasta.fetch(chrom, start, end)
        if strand == "-":
            complement = str.maketrans("ACGTacgt", "TGCAtgca")
            seq = seq.translate(complement)[::-1]
        return seq

seq = get_gene_sequence("reference.fasta", "chr1", 10000, 11000, strand="-")
print(f"Gene sequence ({len(seq)} bp): {seq[:50]}...")

Recipe: BAM Statistics Summary

import pysam

def bam_summary(bam_path):
    """Quick summary statistics for a BAM file."""
    with pysam.AlignmentFile(bam_path, "rb") as bam:
        stats = {"total": 0, "mapped": 0, "unmapped": 0, "duplicates": 0, "mapq_ge30": 0}
        for read in bam.fetch(until_eof=True):
            stats["total"] += 1
            if read.is_unmapped:
                stats["unmapped"] += 1
            else:
                stats["mapped"] += 1
                if read.is_duplicate:
                    stats["duplicates"] += 1
                if read.mapping_quality >= 30:
                    stats["mapq_ge30"] += 1
        return stats

summary = bam_summary("sample.bam")
for k, v in summary.items():
    print(f"  {k}: {v:,}")

Troubleshooting

Problem	Cause	Solution
ValueError: could not open alignment file	Missing file or wrong mode string	Check file path; use "rb" for BAM, "r" for SAM
ValueError: fetch called on bamfile without index	No .bai index file	Run pysam.index("file.bam") first
Region returns unexpected reads	Reads overlapping boundaries are included	Use truncate=True in pileup() or filter by read.reference_start >= start
Coordinate off-by-one errors	Mixing 0-based (pysam) with 1-based (VCF, samtools)	See Key Concepts coordinate table; record.pos is 1-based, record.start is 0-based
PileupProxy accessed after iterator finished	Pileup iterator went out of scope	Store needed data from pileup columns immediately, don't save PileupProxy references
SamtoolsError from CLI calls	Invalid arguments or missing input	Wrap in try/except pysam.SamtoolsError; check samtools docs for argument syntax
Very slow iteration	Iterating all reads without region query	Use fetch("chr1", start, end) for targeted queries; use indexed files
Read group filter returns 0 reads	RG tag missing or wrong ID specified	Verify RG tag exists: read.has_tag("RG"); list available RGs from bam.header.get("RG", [])

Related Skills

• biopython-molecular-biology — sequence I/O and alignment; complementary for non-BAM sequence formats
• pydeseq2-differential-expression — downstream analysis of read counts from BAM coverage data
• scanpy-scrna-seq — single-cell analysis; pysam handles the upstream BAM processing

References

• Pysam documentation — official API reference
• htslib — underlying C library for genomic file formats
• SAM format specification — SAM/BAM format details
• Li et al. (2009) "The Sequence Alignment/Map format and SAMtools" — Bioinformatics