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SKILL.md
npx skills add davila7/claude-code-templates --skill scikit-bio
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
npx skills add davila7/claude-code-templates --skill scikit-bio
scikit-bio is a comprehensive Python library for working with biological data. Apply this skill for bioinformatics analyses spanning sequence manipulation, alignment, phylogenetics, microbial ecology, and multivariate statistics.
This skill should be used when the user:
Work with biological sequences using specialized classes for DNA, RNA, and protein data.
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DNA, RNA, Protein classes for grammared sequences with validationSequence class for generic sequences without alphabet restrictionsPerform pairwise and multiple sequence alignments using dynamic programming algorithms.
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TabularMSACommon patterns:
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local_pairwise_align_ssw for local alignments (faster, SSW-based)StripedSmithWaterman for protein alignmentsConstruct, manipulate, and analyze phylogenetic trees representing evolutionary relationships.
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nj() for neighbor joining (classic phylogenetic method)upgma() for UPGMA (assumes molecular clock)Calculate alpha and beta diversity metrics for microbial ecology and community analysis.
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partial_beta_diversity() for computing specific sample pairs onlyReduce high-dimensional biological data to visualizable lower-dimensional spaces.
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Perform hypothesis tests specific to ecological and biological data.
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Read and write 19+ biological file formats with automatic format detection.
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into parameter specifiedverify=FalseCreate and manipulate distance/dissimilarity matrices with statistical methods.
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Work with feature tables (OTU/ASV tables) common in microbiome research.
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Work with protein language model embeddings for downstream analysis.
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partial_beta_diversity()For detailed API information, parameter specifications, and advanced usage examples, refer to references/api_reference.md which contains comprehensive documentation on:
from skbio.alignment import local_pairwise_align_ssw, TabularMSA
# Pairwise alignment
alignment = local_pairwise_align_ssw(seq1, seq2)
# Access aligned sequences
msa = alignment.aligned_sequences
# Read multiple alignment from file
msa = TabularMSA.read('alignment.fasta', constructor=skbio.DNA)
# Calculate consensus
consensus = msa.consensus()from skbio import TreeNode
from skbio.tree import nj
# Read tree from file
tree = TreeNode.read('tree.nwk')
# Construct tree from distance matrix
tree = nj(distance_matrix)
# Tree operations
subtree = tree.shear(['taxon1', 'taxon2', 'taxon3'])
tips = [node for node in tree.tips()]
lca = tree.lowest_common_ancestor(['taxon1', 'taxon2'])
# Calculate distances
patristic_dist = tree.find('taxon1').distance(tree.find('taxon2'))
cophenetic_matrix = tree.cophenetic_matrix()
# Compare trees
rf_distance = tree.robinson_foulds(other_tree)from skbio.diversity import alpha_diversity, beta_diversity
import skbio
# Alpha diversity
alpha = alpha_diversity('shannon', counts_matrix, ids=sample_ids)
faith_pd = alpha_diversity('faith_pd', counts_matrix, ids=sample_ids,
tree=tree, otu_ids=feature_ids)
# Beta diversity
bc_dm = beta_diversity('braycurtis', counts_matrix, ids=sample_ids)
unifrac_dm = beta_diversity('unweighted_unifrac', counts_matrix,
ids=sample_ids, tree=tree, otu_ids=feature_ids)
# Get available metrics
from skbio.diversity import get_alpha_diversity_metrics
print(get_alpha_diversity_metrics())from skbio.stats.ordination import pcoa, cca
# PCoA from distance matrix
pcoa_results = pcoa(distance_matrix)
pc1 = pcoa_results.samples['PC1']
pc2 = pcoa_results.samples['PC2']
# CCA with environmental variables
cca_results = cca(species_matrix, environmental_matrix)
# Save/load ordination results
pcoa_results.write('ordination.txt')
results = skbio.OrdinationResults.read('ordination.txt')from skbio.stats.distance import permanova, anosim, mantel
# Test if groups differ significantly
permanova_results = permanova(distance_matrix, grouping, permutations=999)
print(f"p-value: {permanova_results['p-value']}")
# ANOSIM test
anosim_results = anosim(distance_matrix, grouping, permutations=999)
# Mantel test between two distance matrices
mantel_results = mantel(dm1, dm2, method='pearson', permutations=999)
print(f"Correlation: {mantel_results[0]}, p-value: {mantel_results[1]}")import skbio
# Read with automatic format detection
seq = skbio.DNA.read('file.fasta', format='fasta')
tree = skbio.TreeNode.read('tree.nwk')
# Write to file
seq.write('output.fasta', format='fasta')
# Generator for large files (memory efficient)
for seq in skbio.io.read('large.fasta', format='fasta', constructor=skbio.DNA):
process(seq)
# Convert formats
seqs = list(skbio.io.read('input.fastq', format='fastq', constructor=skbio.DNA))
skbio.io.write(seqs, format='fasta', into='output.fasta')from skbio import DistanceMatrix
import numpy as np
# Create from array
data = np.array([[0, 1, 2], [1, 0, 3], [2, 3, 0]])
dm = DistanceMatrix(data, ids=['A', 'B', 'C'])
# Access distances
dist_ab = dm['A', 'B']
row_a = dm['A']
# Read from file
dm = DistanceMatrix.read('distances.txt')
# Use in downstream analyses
pcoa_results = pcoa(dm)
permanova_results = permanova(dm, grouping)from skbio import Table
# Read BIOM table
table = Table.read('table.biom')
# Access data
sample_ids = table.ids(axis='sample')
feature_ids = table.ids(axis='observation')
counts = table.matrix_data
# Filter
filtered = table.filter(sample_ids_to_keep, axis='sample')
# Convert to/from pandas
df = table.to_dataframe()
table = Table.from_dataframe(df)from skbio.embedding import ProteinEmbedding, ProteinVector
# Create embedding from array
embedding = ProteinEmbedding(embedding_array, sequence_ids)
# Convert to distance matrix for analysis
dm = embedding.to_distances(metric='euclidean')
# PCoA visualization of embedding space
pcoa_results = embedding.to_ordination(metric='euclidean', method='pcoa')
# Export for machine learning
array = embedding.to_array()
df = embedding.to_dataframe()uv pip install scikit-bio