Pysam

pysam

Overview

Use pysam for low-level, streaming access to HTSlib-supported genomic formats:

• AlignmentFile and AlignedSegment for SAM/BAM/CRAM
• VariantFile, VariantHeader, and VariantRecord for VCF/BCF
• FastaFile for indexed FASTA and FastxFile for sequential FASTA/FASTQ
• TabixFile for BGZF-compressed, tabix-indexed BED/GFF/GTF/custom tables
• pysam.samtools and pysam.bcftools for wrapped command dispatchers

Current upstream baseline: pysam 0.24.0 (27 April 2026), wrapping HTSlib/samtools/bcftools 1.23.1. Read references/sources.md before updating version-specific guidance.

Installation

Use the pinned release for reproducible work:

uv pip install "pysam==0.24.0"

Confirm the runtime:

import pysam

print(pysam.__version__)           # 0.24.0
print(pysam.__samtools_version__)  # 1.23.1

Prebuilt wheels are available for supported macOS and Linux platforms. A source build needs a C compiler and HTSlib build dependencies; read the official installation guide linked from references/sources.md.

First Decide

Before writing code:

1. Identify the real format, compression, sort order, and available index.
2. Decide whether coordinates are numeric Python coordinates or a region string. Do not mix them.
3. For CRAM, identify the exact reference assembly and FASTA.
4. Prefer indexed region access; use sequential iteration only when intended.
5. Preserve headers when writing and write to a new path by default.
6. State filtering semantics: mapping/base quality, flags, overlap handling, duplicate handling, and pileup depth cap.

For unfamiliar files, start with the bundled read-only inspector:

python scripts/inspect_hts.py sample.bam
python scripts/inspect_hts.py cohort.vcf.gz
python scripts/inspect_hts.py reference.fa

Bundled Scripts

Script	Purpose	Typical call
scripts/inspect_hts.py	Metadata-only inspection for alignment, variant, FASTA, FASTQ, and tabix files	python scripts/inspect_hts.py sample.cram --reference ref.fa
scripts/alignment_qc.py	Streaming aggregate read/QC counts as JSON	python scripts/alignment_qc.py sample.bam --max-records 100000
scripts/variant_summary.py	Streaming variant, FILTER, and genotype summary as JSON	python scripts/variant_summary.py cohort.vcf.gz --region chr1:1-1000000
scripts/filter_alignments.py	Filter SAM/BAM/CRAM without changing record order	python scripts/filter_alignments.py input.bam output.bam --exclude-secondary

All scripts refuse to overwrite existing outputs. Run each with --help for coordinate, index, and privacy notes.

Coordinate Contract

Numeric coordinates accepted by pysam APIs are 0-based, half-open. This includes numeric AlignmentFile.fetch(), VariantFile.fetch(), FastaFile.fetch(), TabixFile.fetch(), and pileup() arguments.

Region strings are samtools-style: 1-based and inclusive.

# The same 100 bases:
bam.fetch("chr1", 99, 199)          # [99, 199)
bam.fetch(region="chr1:100-199")    # 1-based inclusive

VCF text uses 1-based POS, while record properties expose both systems:

record.pos    # 1-based
record.start  # 0-based inclusive
record.stop   # 0-based exclusive

Read references/coordinates_and_indexing.md for format conversions, overlap semantics, index choices, and contig-name checks.

Alignment Files

Use context managers and explicit modes:

import pysam

with pysam.AlignmentFile("sample.bam", "rb", threads=4) as bam:
    for read in bam.fetch("chr1", 1_000, 2_000):
        if (
            not read.is_unmapped
            and not read.is_secondary
            and not read.is_supplementary
            and read.mapping_quality >= 30
        ):
            print(read.query_name, read.reference_start, read.cigarstring)

Use fetch(until_eof=True) to stream every record in file order, including unplaced unmapped reads, without requiring an index:

with pysam.AlignmentFile("sample.bam", "rb") as bam:
    for read in bam.fetch(until_eof=True):
        ...

Important distinctions:

• fetch() returns alignment records overlapping a region.
• count() counts records and defaults to read_callback="nofilter".
• count_coverage() returns A/C/G/T base counts and defaults to base quality 15 plus read_callback="all".
• pileup() exposes per-column reads and has its own filtering, base-quality, overlap, orphan, and max_depth=8000 defaults.

For exact-region pileups, set truncate=True and explicit filters:

with pysam.FastaFile("reference.fa") as fasta, pysam.AlignmentFile(
    "sample.bam", "rb"
) as bam:
    for column in bam.pileup(
        "chr1",
        1_000,
        2_000,
        truncate=True,
        stepper="samtools",
        fastafile=fasta,
        min_mapping_quality=20,
        min_base_quality=20,
        max_depth=100_000,
    ):
        print(column.reference_pos, column.get_num_aligned())

Read references/alignment_files.md for flags, CIGAR operations, tags, modified bases, writing records, pileup details, and iterator lifetime.

Variant Files

Input format is auto-detected. Numeric fetch coordinates remain 0-based:

import pysam

with pysam.VariantFile("cohort.vcf.gz", threads=4) as variants:
    for record in variants.fetch("chr1", 999_999, 2_000_000):
        print(record.contig, record.pos, record.ref, record.alts)
        for sample_name, call in record.samples.items():
            print(sample_name, call.get("GT"))

Subset samples before retrieving records:

with pysam.VariantFile("cohort.bcf") as variants:
    variants.subset_samples(["sample_A", "sample_B"])
    for record in variants:
        ...

When changing a header, copy each record and translate it to the destination header before assigning newly declared INFO/FORMAT/FILTER fields. Do not manually clear and rebuild header.samples.

Read references/variant_files.md for safe headers, writing, sample subsetting, missing genotypes, symbolic alleles, filtering, translation, and indexing.

FASTA, FASTQ, and Tabix

Indexed FASTA uses numeric 0-based coordinates:

with pysam.FastaFile("reference.fa") as fasta:
    sequence = fasta.fetch("chr1", 999, 1_099)

FastxFile is sequential. persist=False is faster but yielded records become invalid after iteration advances:

with pysam.FastxFile("reads.fastq.gz", persist=False) as reads:
    for read in reads:
        qualities = read.get_quality_array()
        ...

Tabix input must be coordinate-sorted and BGZF-compressed, not ordinary gzip. Use a non-destructive two-step workflow:

pysam.tabix_compress("regions.bed", "regions.bed.gz")
pysam.tabix_index("regions.bed.gz", preset="bed")

with pysam.TabixFile("regions.bed.gz", parser=pysam.asBed()) as tbx:
    for interval in tbx.fetch("chr1", 1_000, 2_000):
        print(interval.contig, interval.start, interval.end)

Read references/sequence_files.md for FASTA/FASTQ records and safe tabix creation.

CRAM, Remote I/O, and Threads

pysam 0.24 changed inherited HTSlib behavior:

• Newly written CRAM defaults to CRAM 3.1, not 3.0.
• HTSlib no longer contacts the EBI reference server by default.
• Prefer reference_filename="reference.fa" for deterministic local reads and writes.

with pysam.AlignmentFile(
    "sample.cram",
    "rc",
    reference_filename="reference.fa",
    threads=4,
) as cram:
    for read in cram.fetch("chr1", 1_000, 2_000):
        ...

Only configure REF_PATH/REF_CACHE when reference-by-MD5 lookup is intentional. Do not assume a CRAM is self-contained. threads= accelerates compression/decompression; it does not parallelize Python analysis.

Read references/cram_and_performance.md before CRAM conversion, remote access, or concurrent iteration.

Wrapped samtools and bcftools

Import command modules explicitly. Pass each command-line token as a separate string:

import pysam.samtools
import pysam.bcftools

pysam.samtools.sort(
    "-@", "4", "-o", "sorted.bam", "input.bam", catch_stdout=False
)
pysam.samtools.index("-@", "4", "sorted.bam", catch_stdout=False)

pysam.bcftools.index("--csi", "variants.vcf.gz", catch_stdout=False)

Dispatchers capture stdout by default. For large or binary output, use the tool's -o option with catch_stdout=False, or save_stdout=..., rather than returning the complete output in memory.

try:
    pysam.samtools.quickcheck("-v", "sample.bam")
except pysam.SamtoolsError as error:
    messages = pysam.samtools.quickcheck.get_messages()
    raise RuntimeError(messages or str(error)) from error

Use the Python API for record-level logic and dispatchers for mature bulk operations such as sort, index, merge, view, and normalization. Never compose dispatcher arguments by splitting an untrusted shell command.

Writing Rules

• Copy or construct a valid header before opening output.
• Write to a new path; do not use force=True unless replacement is explicit.
• Preserve sort order if the output will be indexed.
• Set query_sequence before query_qualities.
• Prefer pysam.CIGAR_OPS enum members; top-level constants such as pysam.CMATCH are compatibility aliases slated for future removal.
• Validate outputs with pysam.samtools.quickcheck() for alignments and reopen variant/sequence outputs before downstream use.
• Use CSI rather than BAI/TBI when references or coordinates exceed legacy index limits.

Reference Map

Need	Read
Alignment API, flags, CIGAR, pileup, modified bases	references/alignment_files.md
VCF/BCF headers, records, samples, writing	references/variant_files.md
FASTA/FASTQ and tabix-indexed tables	references/sequence_files.md
Coordinate conversion and index selection	references/coordinates_and_indexing.md
CRAM references, remote I/O, threads, performance	references/cram_and_performance.md
Correct integrated analysis patterns	references/common_workflows.md
Compact current API signatures and defaults	references/api_reference.md
Upgrade notes for existing environments	references/migration_to_0_24.md
Official docs, specifications, and release sources	references/sources.md

Common Failure Modes

• Treating numeric VariantFile.fetch() coordinates as 1-based
• Using ordinary gzip where BGZF plus tabix/CSI is required
• Calling region fetch without an index
• Assuming fetch() includes unplaced unmapped alignments
• Forgetting truncate=True for an exact pileup interval
• Ignoring pileup defaults such as base quality 13 and depth cap 8000
• Sharing one file handle across active iterators or threads
• Decoding CRAM without its exact reference
• Assigning a new VCF field before declaring it in the output header
• Capturing large samtools/bcftools output in memory
• Using a SNP base-counting method for indels or symbolic alleles