LQF_Machine_Learning_E…
LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine…
Primary retained Python toolkit for molecular biology sequence work. Preferred for sequence manipulation, FASTA/FASTQ/GenBank parsing, Bio.Entrez, BLAST workflows, alignments, structures, and phylogenetics. For biological database evidence lookup, use bio-database-evidence. For
Claude Code
$ npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill biopython --agent claude-code
Codex
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill biopython -a codex -y
Cursor
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill biopython -a cursor -y
Gemini CLI
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill biopython -a gemini-cli -y
GitHub Copilot
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill biopython -a github-copilot -y
OpenCode
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill biopython -a opencode -y
Windsurf
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill biopython -a windsurf -y
name: biopython description: "Primary retained Python toolkit for molecular biology sequence work. Preferred for sequence manipulation, FASTA/FASTQ/GenBank parsing, Bio.Entrez, BLAST workflows, alignments, structures, and phylogenetics. For biological database evidence lookup, use bio-database-evidence. For single-cell workflows use scanpy. For direct literature REST API, use pubmed-database."
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.
Use this skill when:
Do not use this skill as a catch-all for single-cell analysis, bulk RNA-seq differential expression, biological database evidence tables, protein language model training, metabolic flux modeling, or flow-cytometry file parsing. Those surfaces are either owned by another retained bio-science skill or intentionally no longer exposed as separate bundled route owners.
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
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"
This skill provides comprehensive documentation organized by functionality area. When working on a task, consult the relevant reference documentation:
**Reference:** `references/sequence_io.md`
Use for:
**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")**Reference:** `references/alignment.md`
Use for:
**Quick example:**
from Bio import Align
# Pairwise alignment
aligner = Align.PairwiseAligner()
aligner.mode = 'global'
alignments = aligner.align("ACCGGT", "ACGGT")
print(alignments[0])**Reference:** `references/databases.md`
Use for:
**Quick example:**
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")**Reference:** `references/blast.md`
Use for:
**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}")**Reference:** `references/structure.md`
Use for:
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