alphafold
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) P…
Maintainer Kuan-lin Huang · Last updated April 1, 2026
Phylogenetic analysis reconstructs the evolutionary history of biological sequences (genes, proteins, genomes) by inferring the branching pattern of descent. This skill covers the standard pipeline: 1. **MAFFT** — Multiple sequence alignment 2. **IQ-TREE 2** — Maximum likelihood tree inference with model selection 3. **FastTree** — Fast approximate maximum likelihood (for large datasets) 4. **ETE3** — Python library….
Original source
https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/phylogenetics
Skill Snapshot
Source Doc
Use phylogenetics when:
import subprocess
import os
def run_mafft(input_fasta: str, output_fasta: str, method: str = "auto",
n_threads: int = 4) -> str:
"""
Align sequences with MAFFT.
Args:
input_fasta: Path to unaligned FASTA file
output_fasta: Path for aligned output
method: 'auto' (auto-select), 'einsi' (accurate), 'linsi' (accurate, slow),
'fftnsi' (medium), 'fftns' (fast), 'retree2' (fast)
n_threads: Number of CPU threads
Returns:
Path to aligned FASTA file
"""
methods = {
"auto": ["mafft", "--auto"],
"einsi": ["mafft", "--genafpair", "--maxiterate", "1000"],
"linsi": ["mafft", "--localpair", "--maxiterate", "1000"],
"fftnsi": ["mafft", "--fftnsi"],
"fftns": ["mafft", "--fftns"],
"retree2": ["mafft", "--retree", "2"],
}
cmd = methods.get(method, methods["auto"])
cmd += ["--thread", str(n_threads), "--inputorder", input_fasta]
with open(output_fasta, 'w') as out:
result = subprocess.run(cmd, stdout=out, stderr=subprocess.PIPE, text=True)
if result.returncode != 0:
raise RuntimeError(f"MAFFT failed:\n{result.stderr}")
# Count aligned sequences
with open(output_fasta) as f:
n_seqs = sum(1 for line in f if line.startswith('>'))
print(f"MAFFT: aligned {n_seqs} sequences → {output_fasta}")
return output_fasta
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