— 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: unknown source: "https://github.com/biopython/biopython" — # 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 = "[email protected]" # 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 = "[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:** “`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 = "[email protected]" “` 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 = "[email protected]" # 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 = "[email protected]" # 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**: [email protected] ## 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.