pysam
Python module for reading, manipulating and writing genomic alignment formats (SAM/BAM/CRAM) and variant files (VCF/BCF). Wrapper for htslib.
Best use case
pysam is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Python module for reading, manipulating and writing genomic alignment formats (SAM/BAM/CRAM) and variant files (VCF/BCF). Wrapper for htslib.
Teams using pysam should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pysam/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pysam Compares
| Feature / Agent | pysam | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Python module for reading, manipulating and writing genomic alignment formats (SAM/BAM/CRAM) and variant files (VCF/BCF). Wrapper for htslib.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Pysam - Genomic Alignments
Used for high-throughput sequencing pipelines. It allows efficient access to billions of DNA fragments aligned to a reference genome.
## When to Use
- Processing next-generation sequencing (NGS) data.
- Analyzing genomic variants (SNPs, indels).
- Extracting reads from specific genomic regions.
- Building custom bioinformatics pipelines.
- Quality control of sequencing data.
## Core Principles
### Indexed Access
BAM files must be indexed (.bai) for efficient random access to genomic regions.
### Coordinate System
Genomic coordinates are 0-based (Python-style) for positions, but 1-based for ranges in some contexts.
### Read Attributes
Each read contains sequence, quality scores, alignment position, and flags.
## Quick Reference
### Standard Imports
```python
import pysam
```
### Basic Patterns
```python
# 1. Open BAM file
samfile = pysam.AlignmentFile("aligned_reads.bam", "rb")
# 2. Iterate over reads in a specific genomic region
for read in samfile.fetch("chr1", 10000, 10100):
print(f"Read: {read.query_name}, Quality: {read.mapping_quality}")
print(f"Sequence: {read.query_sequence}")
print(f"Position: {read.reference_start}")
# 3. Variant analysis (VCF)
vcf = pysam.VariantFile("mutations.vcf")
for rec in vcf.fetch("chr1", 10000, 10100):
print(f"Pos: {rec.pos}, Ref: {rec.ref}, Alt: {rec.alts}")
print(f"Genotype: {rec.samples['sample1']['GT']}")
# 4. Writing aligned reads
outfile = pysam.AlignmentFile("output.bam", "wb", template=samfile)
for read in samfile:
if read.mapping_quality > 30:
outfile.write(read)
outfile.close()
```
## Critical Rules
### ✅ DO
- **Always use indexed files** - Create index with `pysam.index("file.bam")` for fast access.
- **Check read flags** - Use `read.is_paired`, `read.is_unmapped` to filter reads.
- **Handle unmapped reads** - Unmapped reads have `reference_start = -1`.
- **Close files explicitly** - Use context managers or `.close()` to avoid resource leaks.
### ❌ DON'T
- **Don't iterate over entire BAM** - Use `fetch()` with regions for efficiency.
- **Don't ignore quality scores** - Low-quality bases can cause false variants.
- **Don't mix coordinate systems** - Be consistent with 0-based vs 1-based indexing.
## Advanced Patterns
### Counting Reads per Gene
```python
# Using a gene annotation file
genes = {} # gene_name -> (chr, start, end)
for read in samfile.fetch():
# Check if read overlaps any gene
for gene, (chr, start, end) in genes.items():
if read.reference_name == chr and start <= read.reference_start < end:
genes[gene]['count'] += 1
```
### Variant Filtering
```python
# Filter variants by quality and depth
for rec in vcf.fetch():
depth = rec.samples['sample1']['DP']
qual = rec.qual
if depth > 10 and qual > 20:
# Process high-quality variant
pass
```
Pysam provides the low-level access needed for genomic data processing, enabling researchers to work directly with the raw data of life itself.Related Skills
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