---
name: biopython
description: "Biopython is a comprehensive set of freely available Python tools for biological computation. It provides functionality for sequence manipulation, file I/O, database access, structural bioinformatics, phylogenetics, and many other bioinformatics tasks."
license: Unknown
metadata:
    skill-author: K-Dense Inc.
risk: critical
source: "https://github.com/biopython/biopython"
date_added: "2026-03-06"
---
## Bundled upstream support

Supporting files are included at skill-relative paths. See [UPSTREAM-NOTICE.md](UPSTREAM-NOTICE.md) for their pinned sources, licenses, hashes, and import changes.

# Biopython: Computational Molecular Biology in Python

## Overview

Biopython is a comprehensive set of freely available Python tools for biological computation. It provides functionality for sequence manipulation, file I/O, database access, structural bioinformatics, phylogenetics, and many other bioinformatics tasks. Examples span several Biopython releases. Record and pin the version actually used; do not treat the old 1.85 snapshot as the current release. Verify the matching Python/NumPy requirements and API documentation before running analysis.

## When to Use This Skill

Use this skill when:

- Working with biological sequences (DNA, RNA, or protein)
- Reading, writing, or converting biological file formats (FASTA, GenBank, FASTQ, PDB, mmCIF, etc.)
- Accessing NCBI databases (GenBank, PubMed, Protein, Gene, etc.) via Entrez
- Running BLAST searches or parsing BLAST results
- Performing sequence alignments (pairwise or multiple sequence alignments)
- Analyzing protein structures from PDB files
- Creating, manipulating, or visualizing phylogenetic trees
- Finding sequence motifs or analyzing motif patterns
- Calculating sequence statistics (GC content, molecular weight, melting temperature, etc.)
- Performing structural bioinformatics tasks
- Working with population genetics data
- Any other computational molecular biology task

## Core Capabilities

Biopython is organized into modular sub-packages, each addressing specific bioinformatics domains:

1. **Sequence Handling** - Bio.Seq and Bio.SeqIO for sequence manipulation and file I/O
2. **Alignment Analysis** - Bio.Align and Bio.AlignIO for pairwise and multiple sequence alignments
3. **Database Access** - Bio.Entrez for programmatic access to NCBI databases
4. **BLAST Operations** - Bio.Blast for running and parsing BLAST searches
5. **Structural Bioinformatics** - Bio.PDB for working with 3D protein structures
6. **Phylogenetics** - Bio.Phylo for phylogenetic tree manipulation and visualization
7. **Advanced Features** - Motifs, population genetics, sequence utilities, and more

## Installation and Setup

Install Biopython using pip (requires Python 3 and NumPy):

```bash
uv pip install biopython
```

For NCBI database access, always set your email address (required by NCBI):

```python
import os
from Bio import Entrez
Entrez.email = "your.email@example.com"

# Optional: API key for higher rate limits (10 req/s instead of 3 req/s)
Entrez.api_key = os.environ.get("NCBI_API_KEY")
```

## Using This Skill

This skill provides comprehensive documentation organized by functionality area. When working on a task, consult the relevant reference documentation:

### 1. Sequence Handling (Bio.Seq & Bio.SeqIO)

**Reference:** `references/sequence_io.md`

Use for:
- Creating and manipulating biological sequences
- Reading and writing sequence files (FASTA, GenBank, FASTQ, etc.)
- Converting between file formats
- Extracting sequences from large files
- Sequence translation, transcription, and reverse complement
- Working with SeqRecord objects

**Quick example:**
```python
from Bio import SeqIO

# Read sequences from FASTA file
for record in SeqIO.parse("sequences.fasta", "fasta"):
    print(f"{record.id}: {len(record.seq)} bp")

# Convert GenBank to FASTA
SeqIO.convert("input.gb", "genbank", "output.fasta", "fasta")
```

### 2. Alignment Analysis (Bio.Align & Bio.AlignIO)

**Reference:** `references/alignment.md`

Use for:
- Pairwise sequence alignment (global and local)
- Reading and writing multiple sequence alignments
- Using substitution matrices (BLOSUM, PAM)
- Calculating alignment statistics
- Customizing alignment parameters

**Quick example:**
```python
from Bio import Align

# Pairwise alignment
aligner = Align.PairwiseAligner()
aligner.mode = 'global'
alignments = aligner.align("ACCGGT", "ACGGT")
print(alignments[0])
```

### 3. Database Access (Bio.Entrez)

**Reference:** `references/databases.md`

Use for:
- Searching NCBI databases (PubMed, GenBank, Protein, Gene, etc.)
- Downloading sequences and records
- Fetching publication information
- Finding related records across databases
- Batch downloading with proper rate limiting

**Quick example:**
```python
from Bio import Entrez
Entrez.email = "your.email@example.com"

# Search PubMed
handle = Entrez.esearch(db="pubmed", term="biopython", retmax=10)
results = Entrez.read(handle)
handle.close()
print(f"Found {results['Count']} results")
```

### 4. BLAST Operations (Bio.Blast)

**Reference:** `references/blast.md`

Use for:
- Running BLAST searches via NCBI web services
- Running local BLAST searches
- Parsing BLAST XML output
- Filtering results by E-value or identity
- Extracting hit sequences

**Quick example:**
```python
from Bio.Blast import NCBIWWW, NCBIXML

# Run BLAST search
result_handle = NCBIWWW.qblast("blastn", "nt", "ATCGATCGATCG")
blast_record = NCBIXML.read(result_handle)

# Display top hits
for alignment in blast_record.alignments[:5]:
    print(f"{alignment.title}: E-value={alignment.hsps[0].expect}")
```

### 5. Structural Bioinformatics (Bio.PDB)

**Reference:** `references/structure.md`

Use for:
- Parsing PDB and mmCIF structure files
- Navigating protein structure hierarchy (SMCRA: Structure/Model/Chain/Residue/Atom)
- Calculating distances, angles, and dihedrals
- Secondary structure assignment (DSSP)
- Structure superimposition and RMSD calculation
- Extracting sequences from structures

**Quick example:**
```python
from Bio.PDB import PDBParser

# Parse structure
parser = PDBParser(QUIET=True)
structure = parser.get_structure("1crn", "1crn.pdb")

# Calculate distance between alpha carbons
chain = structure[0]["A"]
distance = chain[10]["CA"] - chain[20]["CA"]
print(f"Distance: {distance:.2f} Å")
```

### 6. Phylogenetics (Bio.Phylo)

**Reference:** `references/phylogenetics.md`

Use for:
- Reading and writing phylogenetic trees (Newick, NEXUS, phyloXML)
- Building trees from distance matrices or alignments
- Tree manipulation (pruning, rerooting, ladderizing)
- Calculating phylogenetic distances
- Creating consensus trees
- Visualizing trees

**Quick example:**
```python
from Bio import Phylo

# Read and visualize tree
tree = Phylo.read("tree.nwk", "newick")
Phylo.draw_ascii(tree)

# Calculate distance
distance = tree.distance("Species_A", "Species_B")
print(f"Distance: {distance:.3f}")
```

### 7. Advanced Features

**Reference:** `references/advanced.md`

Use for:
- **Sequence motifs** (Bio.motifs) - Finding and analyzing motif patterns
- **Population genetics** (Bio.PopGen) - GenePop files, Fst calculations, Hardy-Weinberg tests
- **Sequence utilities** (Bio.SeqUtils) - GC content, melting temperature, molecular weight, protein analysis
- **Restriction analysis** (Bio.Restriction) - Finding restriction enzyme sites
- **Clustering** (Bio.Cluster) - K-means and hierarchical clustering
- **Genome diagrams** (GenomeDiagram) - Visualizing genomic features

**Quick example:**
```python
from Bio.SeqUtils import gc_fraction, molecular_weight
from Bio.Seq import Seq

seq = Seq("ATCGATCGATCG")
print(f"GC content: {gc_fraction(seq):.2%}")
print(f"Molecular weight: {molecular_weight(seq, seq_type='DNA'):.2f} g/mol")
```

## General Workflow Guidelines

### Reading Documentation

When a user asks about a specific Biopython task:

1. **Identify the relevant module** based on the task description
2. **Read the appropriate reference file** using the Read tool
3. **Extract relevant code patterns** and adapt them to the user's specific needs
4. **Combine multiple modules** when the task requires it

Example search patterns for reference files:
```bash
# Find information about specific functions
grep -n "SeqIO.parse" references/sequence_io.md

# Find examples of specific tasks
grep -n "BLAST" references/blast.md

# Find information about specific concepts
grep -n "alignment" references/alignment.md
```

### Writing Biopython Code

Follow these principles when writing Biopython code:

1. **Import modules explicitly**
   ```python
   from Bio import SeqIO, Entrez
   from Bio.Seq import Seq
   ```

2. **Set Entrez email** when using NCBI databases
   ```python
   Entrez.email = "your.email@example.com"
   ```

3. **Use appropriate file formats** - Check which format best suits the task
   ```python
   # Common formats: "fasta", "genbank", "fastq", "clustal", "phylip"
   ```

4. **Handle files properly** - Close handles after use or use context managers
   ```python
   with open("file.fasta") as handle:
       records = SeqIO.parse(handle, "fasta")
   ```

5. **Use iterators for large files** - Avoid loading everything into memory
   ```python
   for record in SeqIO.parse("large_file.fasta", "fasta"):
       # Process one record at a time
   ```

6. **Handle errors gracefully** - Network operations and file parsing can fail
   ```python
   try:
       handle = Entrez.efetch(db="nucleotide", id=accession)
   except HTTPError as e:
       print(f"Error: {e}")
   ```

## Common Patterns

### Pattern 1: Fetch Sequence from GenBank

```python
from Bio import Entrez, SeqIO

Entrez.email = "your.email@example.com"

# Fetch sequence
handle = Entrez.efetch(db="nucleotide", id="EU490707", rettype="gb", retmode="text")
record = SeqIO.read(handle, "genbank")
handle.close()

print(f"Description: {record.description}")
print(f"Sequence length: {len(record.seq)}")
```

### Pattern 2: Sequence Analysis Pipeline

```python
from Bio import SeqIO
from Bio.SeqUtils import gc_fraction

for record in SeqIO.parse("sequences.fasta", "fasta"):
    # Calculate statistics
    gc = gc_fraction(record.seq)
    length = len(record.seq)

    # Find ORFs, translate, etc.
    protein = record.seq.translate()

    print(f"{record.id}: {length} bp, GC={gc:.2%}")
```

### Pattern 3: BLAST and Fetch Top Hits

```python
from Bio.Blast import NCBIWWW, NCBIXML
from Bio import Entrez, SeqIO

Entrez.email = "your.email@example.com"

# Run BLAST
result_handle = NCBIWWW.qblast("blastn", "nt", sequence)
blast_record = NCBIXML.read(result_handle)

# Get top hit accessions
accessions = [aln.accession for aln in blast_record.alignments[:5]]

# Fetch sequences
for acc in accessions:
    handle = Entrez.efetch(db="nucleotide", id=acc, rettype="fasta", retmode="text")
    record = SeqIO.read(handle, "fasta")
    handle.close()
    print(f">{record.description}")
```

### Pattern 4: Build Phylogenetic Tree from Sequences

```python
from Bio import AlignIO, Phylo
from Bio.Phylo.TreeConstruction import DistanceCalculator, DistanceTreeConstructor

# Read alignment
alignment = AlignIO.read("alignment.fasta", "fasta")

# Calculate distances
calculator = DistanceCalculator("identity")
dm = calculator.get_distance(alignment)

# Build tree
constructor = DistanceTreeConstructor()
tree = constructor.nj(dm)

# Visualize
Phylo.draw_ascii(tree)
```

## Best Practices

1. **Always read relevant reference documentation** before writing code
2. **Use grep to search reference files** for specific functions or examples
3. **Validate file formats** before parsing
4. **Handle missing data gracefully** - Not all records have all fields
5. **Cache downloaded data** - Don't repeatedly download the same sequences
6. **Respect NCBI rate limits** - Use API keys and proper delays
7. **Test with small datasets** before processing large files
8. **Pin and record Biopython and external-tool versions**; test migrations before upgrading a reproducible analysis
9. **Use appropriate genetic code tables** for translation
10. **Document analysis parameters** for reproducibility

## Troubleshooting Common Issues

### Issue: "No handlers could be found for logger 'Bio.Entrez'"
**Solution:** Configure Python logging for a missing-handler warning. Set Entrez.email separately for NCBI identification; it does not fix logging-handler configuration.

### Issue: "HTTP Error 400" from NCBI
**Solution:** Check that IDs/accessions are valid and properly formatted.

### Issue: "ValueError: EOF" when parsing files
**Solution:** Verify file format matches the specified format string.

### Issue: Alignment fails with "sequences are not the same length"
**Solution:** Ensure sequences are aligned before using AlignIO or MultipleSeqAlignment.

### Issue: BLAST searches are slow
**Solution:** Use local BLAST for large-scale searches, or cache results.

### Issue: PDB parser warnings
**Solution:** Investigate parser warnings and structure quality before optionally suppressing understood messages. QUIET does not repair malformed or missing structural data.

## Additional Resources

- **Official Documentation**: https://biopython.org/docs/latest/
- **Tutorial**: https://biopython.org/docs/latest/Tutorial/
- **Cookbook**: https://biopython.org/docs/latest/Tutorial/ (advanced examples)
- **GitHub**: https://github.com/biopython/biopython
- **Mailing List**: biopython@biopython.org

## Quick Reference

To locate information in reference files, use these search patterns:

```bash
# Search for specific functions
grep -n "function_name" references/*.md

# Find examples of specific tasks
grep -n "example" references/sequence_io.md

# Find all occurrences of a module
grep -n "Bio.Seq" references/*.md
```

## Summary

Biopython provides comprehensive tools for computational molecular biology. When using this skill:

1. **Identify the task domain** (sequences, alignments, databases, BLAST, structures, phylogenetics, or advanced)
2. **Consult the appropriate reference file** in the `references/` directory
3. **Adapt code examples** to the specific use case
4. **Combine multiple modules** when needed for complex workflows
5. **Follow best practices** for file handling, error checking, and data management

The modular reference documentation ensures detailed, searchable information for every major Biopython capability.

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

## Data and interpretation boundaries

Use local synthetic or authorized public data first. Remote BLAST, Entrez search terms/IDs, structure downloads and web services transmit data; obtain authorization before sending private, patient, proprietary or access-controlled material. Restrict output paths and preserve originals. Sequence matches, motifs, structural distances and inferred trees are research evidence, not clinical diagnoses or proof of orthology, causation or biosafety. Record dataset/accession versions, parameters, uncertainty and licensing.