Biopython: Computational Molecular Biology in Python workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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name
biopython-v2
description
Biopython: Computational Molecular Biology in Python workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Biopython: Computational Molecular Biology in Python
Overview
This public intake copy packages plugins/antigravity-awesome-skills/skills/biopython from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Biopython: Computational Molecular Biology in Python
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Capabilities, Using This Skill, Common Patterns, Limitations.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
Working with biological sequences (DNA, RNA, or protein)
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
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Identify the relevant module based on the task description
Read the appropriate reference file using the Read tool
Extract relevant code patterns and adapt them to the user's specific needs
Combine multiple modules when the task requires it
Import modules explicitly
Set Entrez email when using NCBI databases
Use appropriate file formats - Check which format best suits the task
Imported Workflow Notes
Imported: 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):
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"
Imported: General Workflow Guidelines
Reading Documentation
When a user asks about a specific Biopython task:
Identify the relevant module based on the task description
Read the appropriate reference file using the Read tool
Extract relevant code patterns and adapt them to the user's specific needs
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:
Import modules explicitly
from Bio import SeqIO, Entrez
from Bio.Seq import Seq
Set Entrez email when using NCBI databases
Entrez.email = "your.email@example.com"
Use appropriate file formats - Check which format best suits the task
# Common formats: "fasta", "genbank", "fastq", "clustal", "phylip"
Handle files properly - Close handles after use or use context managers
withopen("file.fasta") as handle:
records = SeqIO.parse(handle, "fasta")
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
Handle errors gracefully - Network operations and file parsing can fail
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.
Imported: Summary
Biopython provides comprehensive tools for computational molecular biology. When using this skill:
Identify the task domain (sequences, alignments, databases, BLAST, structures, phylogenetics, or advanced)
Consult the appropriate reference file in the references/ directory
Adapt code examples to the specific use case
Combine multiple modules when needed for complex workflows
Follow best practices for file handling, error checking, and data management
The modular reference documentation ensures detailed, searchable information for every major Biopython capability.
Imported: Core Capabilities
Biopython is organized into modular sub-packages, each addressing specific bioinformatics domains:
Sequence Handling - Bio.Seq and Bio.SeqIO for sequence manipulation and file I/O
Alignment Analysis - Bio.Align and Bio.AlignIO for pairwise and multiple sequence alignments
Database Access - Bio.Entrez for programmatic access to NCBI databases
BLAST Operations - Bio.Blast for running and parsing BLAST searches
Structural Bioinformatics - Bio.PDB for working with 3D protein structures
Phylogenetics - Bio.Phylo for phylogenetic tree manipulation and visualization
Advanced Features - Motifs, population genetics, sequence utilities, and more
Examples
Example 1: Ask for the upstream workflow directly
Use @biopython-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @biopython-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @biopython-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @biopython-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Always read relevant reference documentation before writing code
Use grep to search reference files for specific functions or examples
Validate file formats before parsing
Handle missing data gracefully - Not all records have all fields
Cache downloaded data - Don't repeatedly download the same sequences
Respect NCBI rate limits - Use API keys and proper delays
Test with small datasets before processing large files
Imported Operating Notes
Imported: Best Practices
Always read relevant reference documentation before writing code
Use grep to search reference files for specific functions or examples
Validate file formats before parsing
Handle missing data gracefully - Not all records have all fields
Cache downloaded data - Don't repeatedly download the same sequences
Respect NCBI rate limits - Use API keys and proper delays
Test with small datasets before processing large files
Keep Biopython updated to get latest features and bug fixes
Use appropriate genetic code tables for translation
Document analysis parameters for reproducibility
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/biopython, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Imported Troubleshooting Notes
Imported: 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.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
To locate information in reference files, use these search patterns:
# 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
Imported: 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 filefor 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])
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
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")
Imported: Common Patterns
Pattern 1: Fetch Sequence from GenBank
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
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
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 sequencesfor 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
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)
Imported: 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.
