Biopython
This skill utilizes Biopython for parsing and manipulating biological data like DNA sequences, streamlining bioinformatics workflows and analysis.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add moosegoose0701-biopython npx -- -y @trustedskills/moosegoose0701-biopython
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"moosegoose0701-biopython": {
"command": "npx",
"args": [
"-y",
"@trustedskills/moosegoose0701-biopython"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill provides access to Biopython, a powerful suite of tools for computational biology. It allows AI agents to analyze biological sequences (DNA, RNA, protein), perform sequence alignments, and work with phylogenetic trees. The skill enables tasks like identifying genes within a DNA sequence or comparing the similarity between two proteins.
When to use it
- Analyzing genomic data: When you need to extract information from raw DNA or RNA sequences.
- Protein structure comparison: To determine similarities and differences in protein structures for research purposes.
- Phylogenetic analysis: Building evolutionary trees based on sequence comparisons.
- Identifying potential drug targets: Analyzing the genetic makeup of pathogens to find vulnerabilities.
Key capabilities
- Sequence alignment (global, local)
- DNA/RNA sequence parsing and manipulation
- Protein structure analysis
- Phylogenetic tree construction
- Database searching for homologous sequences
Example prompts
- "Analyze this DNA sequence: [sequence] and identify any potential gene start codons."
- "Compare the amino acid sequences of these two proteins: [protein1], [protein2] and calculate their similarity score."
- “Build a phylogenetic tree based on these nucleotide sequences: [sequences].”
Tips & gotchas
- Requires familiarity with biological sequence formats (FASTA, GenBank).
- Biopython can be computationally intensive for very large datasets.
Tags
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