biopython – skill Dữ liệu & Khoa học cho agent AI

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):

uv pip install biopython

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

import os
from Bio import Entrez
Entrez.email = "[email protected]"

# 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:

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:

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:

from Bio import Entrez
Entrez.email = "[email protected]"

# 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:

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:

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:

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:

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:

# 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

   from Bio import SeqIO, Entrez
   from Bio.Seq import Seq
   

2. Set Entrez email when using NCBI databases

   Entrez.email = "[email protected]"
   

3. Use appropriate file formats - Check which format best suits the task

   # Common formats: "fasta", "genbank", "fastq", "clustal", "phylip"
   

4. Handle files properly - Close handles after use or use context managers

   with open("file.fasta") as handle:
       records = SeqIO.parse(handle, "fasta")
   

5. Use iterators for large files - Avoid loading everything into memory

   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

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