pysam

davila7/claude-code-templates · updated Apr 8, 2026

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$npx skills add https://github.com/davila7/claude-code-templates --skill pysam
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summary

Pysam is a Python module for reading, manipulating, and writing genomic datasets. Read/write SAM/BAM/CRAM alignment files, VCF/BCF variant files, and FASTA/FASTQ sequences with a Pythonic interface to htslib. Query tabix-indexed files, perform pileup analysis for coverage, and execute samtools/bcftools commands.

skill.md

Pysam

Overview

Pysam is a Python module for reading, manipulating, and writing genomic datasets. Read/write SAM/BAM/CRAM alignment files, VCF/BCF variant files, and FASTA/FASTQ sequences with a Pythonic interface to htslib. Query tabix-indexed files, perform pileup analysis for coverage, and execute samtools/bcftools commands.

When to Use This Skill

This skill should be used when:

  • Working with sequencing alignment files (BAM/CRAM)
  • Analyzing genetic variants (VCF/BCF)
  • Extracting reference sequences or gene regions
  • Processing raw sequencing data (FASTQ)
  • Calculating coverage or read depth
  • Implementing bioinformatics analysis pipelines
  • Quality control of sequencing data
  • Variant calling and annotation workflows

Quick Start

Installation

uv pip install pysam

Basic Examples

Read alignment file:

import pysam

# Open BAM file and fetch reads in region
samfile = pysam.AlignmentFile("example.bam", "rb")
for read in samfile.fetch("chr1", 1000, 2000):
    print(f"{read.query_name}: {read.reference_start}")
samfile.close()

Read variant file:

# Open VCF file and iterate variants
vcf = pysam.VariantFile("variants.vcf")
for variant in vcf:
    print(f"{variant.chrom}:{variant.pos} {variant.ref}>{variant.alts}")
vcf.close()

Query reference sequence:

# Open FASTA and extract sequence
fasta = pysam.FastaFile("reference.fasta")
sequence = fasta.fetch("chr1", 1000, 2000)
print(sequence)
fasta.close()

Core Capabilities

1. Alignment File Operations (SAM/BAM/CRAM)

Use the AlignmentFile class to work with aligned sequencing reads. This is appropriate for analyzing mapping results, calculating coverage, extracting reads, or quality control.

Common operations:

  • Open and read BAM/SAM/CRAM files
  • Fetch reads from specific genomic regions
  • Filter reads by mapping quality, flags, or other criteria
  • Write filtered or modified alignments
  • Calculate coverage statistics
  • Perform pileup analysis (base-by-base coverage)
  • Access read sequences, quality scores, and alignment information

Reference: See references/alignment_files.md for detailed documentation on:

  • Opening and reading alignment files
  • AlignedSegment attributes and methods
  • Region-based fetching with fetch()
  • Pileup analysis for coverage
  • Writing and creating BAM files
  • Coordinate systems and indexing
  • Performance optimization tips

2. Variant File Operations (VCF/BCF)

Use the VariantFile class to work with genetic variants from variant calling pipelines. This is appropriate for variant analysis, filtering, annotation, or population genetics.

Common operations:

  • Read and write VCF/BCF files
  • Query variants in specific regions
  • Access variant information (position, alleles, quality)
  • Extract genotype data for samples
  • Filter variants by quality, allele frequency, or other criteria
  • Annotate variants with additional information
  • Subset samples or regions

Reference: See references/variant_files.md for detailed documentation on:

  • Opening and reading variant files
  • VariantRecord attributes and methods
  • Accessing INFO and FORMAT fields
  • Working with genotypes and samples
  • Creating and writing VCF files
  • Filtering and subsetting variants
  • Multi-sample VCF operations

3. Sequence File Operations (FASTA/FASTQ)

Use FastaFile for random access to reference sequences and FastxFile for reading raw sequencing data. This is appropriate for extracting gene sequences, validating variants against reference, or processing raw reads.

Common operations:

  • Query reference sequences by genomic coordinates
  • Extract sequences for genes or regions of interest
  • Read FASTQ files with quality scores
  • Validate variant reference alleles
  • Calculate sequence statistics
  • Filter reads by quality or length
  • Convert between FASTA and FASTQ formats

Reference: See references/sequence_files.md for detailed documentation on:

  • FASTA file access and indexing
  • Extracting sequences by region
  • Handling reverse complement for genes
  • Reading FASTQ files sequentially
  • Quality score conversion and filtering
  • Working with tabix-indexed files (BED, GTF, GFF)
  • Common sequence processing patterns

4. Integrated Bioinformatics Workflows

Pysam excels at integrating multiple file types for comprehensive genomic analyses. Common workflows combine alignment files, variant files, and reference sequences.

Common workflows:

  • Calculate coverage statistics for specific regions
  • Validate variants against aligned reads
  • Annotate variants with coverage information
  • Extract sequences around variant positions
  • Filter alignments or variants based on multiple criteria
  • Generate coverage tracks for visualization
  • Quality control across multiple data types

Reference: See references/common_workflows.md for detailed examples of:

  • Quality control workflows (BAM statistics, reference consistency)
  • Coverage analysis (per-base coverage, low coverage detection)
  • Variant analysis (annotation, filtering by read support)
  • Sequence extraction (variant contexts, gene sequences)
  • Read filtering and subsetting
  • Integration patterns (BAM+VCF, VCF+BED, etc.)
  • Performance optimization for complex workflows

Key Concepts

Coordinate Systems

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

  • Start positions are 0-based (first base is position 0)
  • End positions are exclusive (not included in the range)
  • Region 1000-2000 includes bases 1000-1999 (1000 bases total)

Exception: Region strings in fetch() follow samtools convention (1-based):

samfile.fetch("chr1", 999, 2000)      # 0-based: positions 999-1999
samfile.fetch("chr1:1000-2000")       # 1-based string: positions 1000-2000

VCF files: Use 1-based coordinates in the file format, but VariantRecord.start is 0-based.

Indexing Requirements

Random access to specific genomic regions requires index files:

  • BAM files: Require .bai index (create with pysam.index())
  • CRAM files: Require .crai index
  • FASTA files: Require .fai index (create with pysam.faidx())
  • VCF.gz files: Require .tbi tabix index (create with pysam.tabix_index())
  • BCF files: Require .csi index

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

File Modes

Specify format when opening files:

  • "rb" - Read BAM (binary)
  • "r" - Read SAM (text)
  • "rc" - Read CRAM
  • "wb" - Write BAM
  • "w" - Write SAM
  • "wc" - Write CRAM

Performance Considerations

  1. Always use indexed files for random access operations
  2. Use pileup() for column-wise analysis instead of repeated fetch operations
  3. Use count() for counting instead of iterating and counting manually
  4. Process regions in parallel when analyzing independent genomic regions
  5. Close files explicitly to free resources
  6. Use until_eof=True for sequential processing without index
  7. Avoid multiple iterators unless necessary (use multiple_iterators=True if needed)

Common Pitfalls

  1. Coordinate confusion: Remember 0-based vs 1-based systems in different contexts
  2. Missing indices: Many operations require index files—create them first
  3. Partial overlaps: fetch() returns reads overlapping region boundaries, not just those fully contained
  4. Iterator scope: Keep pileup iterator references alive to avoid "PileupProxy accessed after iterator finished" errors
  5. Quality score editing: Cannot modify query_qualities in place after changing query_sequence—create a copy first
  6. Stream limitations: Only stdin/stdout are supported for streaming, not arbitrary Python file objects
  7. Thread safety: While GIL is released during I/O, comprehensive thread-safety hasn't been fully validated

Command-Line Tools

Pysam provides access to samtools and bcftools commands:

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

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

# View specific region
pysam.samtools.view("-b", "-o", "region.bam", "input.bam", "chr1:1000-2000")

# BCF tools
pysam.bcftools.view("-O", "z", "-o", "output.vcf.gz", "input.vcf")

Error handling:

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

Resources

references/

Detailed documentation for each major capability:

  • alignment_files.md - Complete guide to SAM/BAM/CRAM operations, including AlignmentFile class, AlignedSegment attributes, fetch operations, pileup analysis, and writing alignments

  • variant_files.md - Complete guide to VCF/BCF operations, including VariantFile class, VariantRecord attributes, genotype handling, INFO/FORMAT fields, and multi-sample operations

  • sequence_files.md - Complete guide to FASTA/FASTQ operations, including FastaFile and FastxFile classes, sequence extraction, quality score handling, and tabix-indexed file access

  • common_workflows.md - Practical examples of integrated bioinformatics workflows combining multiple file types, including quality control, coverage analysis, variant validation, and sequence extraction

Getting Help

For detailed information on specific operations, refer to the appropriate reference document:

  • Working with BAM files or calculating coverage → alignment_files.md
  • Analyzing variants or genotypes → variant_files.md
  • Extracting sequences or processing FASTQ → sequence_files.md
  • Complex workflows integrating multiple file types → common_workflows.md

Official documentation: https://pysam.readthedocs.io/

how to use pysam

How to use pysam on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add pysam
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/davila7/claude-code-templates --skill pysam

The skills CLI fetches pysam from GitHub repository davila7/claude-code-templates and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/pysam

Reload or restart Cursor to activate pysam. Access the skill through slash commands (e.g., /pysam) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.756 reviews
  • Omar Patel· Dec 28, 2024

    We added pysam from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Mia Diallo· Dec 16, 2024

    Registry listing for pysam matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Henry Bhatia· Dec 16, 2024

    Useful defaults in pysam — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Pratham Ware· Dec 12, 2024

    pysam reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Zara Ghosh· Dec 4, 2024

    Keeps context tight: pysam is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Benjamin Singh· Nov 23, 2024

    pysam reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Maya Diallo· Nov 23, 2024

    I recommend pysam for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Henry Kapoor· Nov 19, 2024

    Solid pick for teams standardizing on skills: pysam is focused, and the summary matches what you get after install.

  • Henry Jain· Nov 7, 2024

    pysam is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Arjun Haddad· Oct 26, 2024

    Keeps context tight: pysam is the kind of skill you can hand to a new teammate without a long onboarding doc.

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