pysam skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Python/HTSlib workflows for genomic files. Use when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.

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Install the pysam skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/pysam ~/.claude/skills/pysam

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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:

string. Do not mix them.

Identify the real format, compression, sort order, and available index.
Decide whether coordinates are numeric Python coordinates or a region

duplicate handling, and pileup depth cap.

For CRAM, identify the exact reference assembly and FASTA.
Prefer indexed region access; use sequential iteration only when intended.
Preserve headers when writing and write to a new path by default.
State filtering semantics: mapping/base quality, flags, overlap handling,

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

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/coordinatesandindexing.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:

15 plus read_callback="all".

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

overlap, orphan, and max_depth=8000 defaults.

pileup() exposes per-column reads and has its own filtering, base-quality,

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:

writes.

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

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