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sequence-alignment

@benchflow-ai/skillsbench
15
0

Pairwise and multiple sequence alignment algorithms. Use when comparing biological sequences, finding conserved regions, or computing sequence similarity and identity scores using tools like MUSCLE, ClustalW, or pairwise alignment algorithms.

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SKILL.md

name sequence-alignment
description Pairwise and multiple sequence alignment algorithms. Use when comparing biological sequences, finding conserved regions, or computing sequence similarity and identity scores using tools like MUSCLE, ClustalW, or pairwise alignment algorithms.

Sequence Alignment

Tools for aligning biological sequences.

Pairwise Alignment

from Bio import pairwise2
from Bio.pairwise2 import format_alignment

seq1 = "ACCGGT"
seq2 = "ACGT"

# Global alignment (Needleman-Wunsch)
alignments = pairwise2.align.globalxx(seq1, seq2)
print(format_alignment(*alignments[0]))

# Local alignment (Smith-Waterman)
alignments = pairwise2.align.localxx(seq1, seq2)

# With scoring parameters
alignments = pairwise2.align.globalms(seq1, seq2,
                                       match=2,
                                       mismatch=-1,
                                       open=-0.5,
                                       extend=-0.1)

Substitution Matrices

from Bio.Align import substitution_matrices

# Load BLOSUM62
blosum62 = substitution_matrices.load("BLOSUM62")

# Access scores
score = blosum62["A", "A"]  # Match score
score = blosum62["A", "R"]  # Mismatch score

Multiple Sequence Alignment

from Bio.Align.Applications import MuscleCommandline
from Bio import AlignIO

# Run MUSCLE alignment
muscle_cline = MuscleCommandline(input="sequences.fasta",
                                  out="aligned.fasta")
muscle_cline()

# Read alignment
alignment = AlignIO.read("aligned.fasta", "fasta")
print(f"Alignment length: {alignment.get_alignment_length()}")
print(f"Number of sequences: {len(alignment)}")

# Calculate conservation
from Bio.Align import AlignInfo
summary = AlignInfo.SummaryInfo(alignment)
consensus = summary.dumb_consensus()