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---
name: biopython
description: "Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database."
---
# 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. The current version is **Biopython 1.85** (released January 2025), which supports Python 3 and requires NumPy.
## 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):
```python
uv pip install biopython
```
For NCBI database access, always set your email address (required by NCBI):
```python
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 = "your_api_key_here"
```
## 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. **Keep Biopython updated** to get latest features and bug fixes
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:** This is just a warning. Set Entrez.email to suppress it.
### 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:** Use `PDBParser(QUIET=True)` to suppress warnings, or investigate structure quality.
## 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.