scikit-bio

Overview

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.

When to Use This Skill

This skill should be used when the user:

• Works with biological sequences (DNA, RNA, protein)
• Needs to read/write biological file formats (FASTA, FASTQ, GenBank, Newick, BIOM, etc.)
• Performs sequence alignments or searches for motifs
• Constructs or analyzes phylogenetic trees
• Calculates diversity metrics (alpha/beta diversity, UniFrac distances)
• Performs ordination analysis (PCoA, CCA, RDA)
• Runs statistical tests on biological/ecological data (PERMANOVA, ANOSIM, Mantel)
• Analyzes microbiome or community ecology data
• Works with protein embeddings from language models
• Needs to manipulate biological data tables

Core Capabilities

1. Sequence Manipulation

Work with biological sequences using specialized classes for DNA, RNA, and protein data.

Key operations:

• Read/write sequences from FASTA, FASTQ, GenBank, EMBL formats
• Sequence slicing, concatenation, and searching
• Reverse complement, transcription (DNA→RNA), and translation (RNA→protein)
• Find motifs and patterns using regex
• Calculate distances (Hamming, k-mer based)
• Handle sequence quality scores and metadata

Common patterns:

import skbio

# Read sequences from file
seq = skbio.DNA.read('input.fasta')

# Sequence operations
rc = seq.reverse_complement()
rna = seq.transcribe()
protein = rna.translate()

# Find motifs
motif_positions = seq.find_with_regex('ATG[ACGT]{3}')

# Check for properties
has_degens = seq.has_degenerates()
seq_no_gaps = seq.degap()

Important notes:

• Use DNA, RNA, Protein classes for grammared sequences with validation
• Use Sequence class for generic sequences without alphabet restrictions
• Quality scores automatically loaded from FASTQ files into positional metadata
• Metadata types: sequence-level (ID, description), positional (per-base), interval (regions/features)

2. Sequence Alignment

Perform pairwise and multiple sequence alignments using dynamic programming algorithms.

Key capabilities:

• Global (Needleman-Wunsch) and local (Smith-Waterman) pairwise alignment via the unified pair_align API
• Configurable scoring (match/mismatch tuple, named substitution matrix, affine gap costs)
• CIGAR string handling via PairAlignPath
• Multiple sequence alignment storage and manipulation with TabularMSA

Common patterns:

from skbio.alignment import pair_align, pair_align_nucl, pair_align_prot, TabularMSA
from skbio import DNA

# Pairwise alignment (0.7+ unified API). mode='global' (default) or 'local'.
seq1, seq2 = DNA('ATCGATCGATCG'), DNA('ATCGGGGATCG')
res = pair_align(seq1, seq2, mode='local')
print(res.score)
aligned = res.paths[0].to_aligned((seq1, seq2))  # tuple of aligned sequences

# Nucleotide / protein convenience wrappers with sensible defaults
res = pair_align_nucl(seq1, seq2)                       # DNA/RNA
# res = pair_align_prot(p1, p2, sub_score='BLOSUM62')   # protein

# Read multiple alignment from file
msa = TabularMSA.read('alignment.fasta', constructor=DNA)
consensus = msa.consensus()

Important notes:

• pair_align returns a named tuple (score, paths, matrices); paths is a list of PairAlignPath objects (up to max_paths).
• sub_score accepts a (match, mismatch) tuple, a named matrix string (e.g. 'BLOSUM62'), or a SubstitutionMatrix; gap_cost takes a single value (linear) or (open, extend) tuple (affine — recommended for biological sequences).
• The older local_pairwise_align_ssw, StripedSmithWaterman, and *_pairwise_align/AlignScorer interfaces were removed/deprecated in 0.6–0.7; use pair_align* instead.

3. Phylogenetic Trees

Construct, manipulate, and analyze phylogenetic trees representing evolutionary relationships.

Key capabilities:

• Tree construction from distance matrices (UPGMA, WPGMA, Neighbor Joining, GME, BME)
• Tree manipulation (pruning, rerooting, traversal)
• Distance calculations (patristic, cophenetic, Robinson-Foulds)
• ASCII visualization
• Newick format I/O

Common patterns:

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 tree topologies (Robinson-Foulds)
rf_distance = tree.compare_rfd(other_tree)

Important notes:

• Tree construction lives in skbio.tree: nj (neighbor joining), upgma (assumes a molecular clock), and gme/bme (greedy/balanced minimum evolution).
• Robinson-Foulds is tree.compare_rfd(other); the module-level rf_dists() computes pairwise RF distances across many trees. (robinson_foulds was renamed.)
• GME and BME are highly scalable for large trees.
• Trees can be rooted or unrooted; some metrics require specific rooting.

4. Diversity Analysis

Calculate alpha and beta diversity metrics for microbial ecology and community analysis.

Key capabilities:

• Alpha diversity: richness, Shannon entropy, Simpson index, Faith's PD, Pielou's evenness
• Beta diversity: Bray-Curtis, Jaccard, weighted/unweighted UniFrac, Euclidean distances
• Phylogenetic diversity metrics (require tree input)
• Rarefaction and subsampling
• Integration with ordination and statistical tests

Common patterns:

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, taxa=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, taxa=feature_ids)

# Get available metrics
from skbio.diversity import get_alpha_diversity_metrics
print(get_alpha_diversity_metrics())

Important notes:

• Counts must be integers representing abundances, not relative frequencies.
• The feature-ID parameter for phylogenetic metrics is taxa= (renamed from otu_ids= in 0.6; "OTU" terminology was replaced by "taxon" project-wide). Plain richness is observed_features, not the old observed_otus.
• Phylogenetic metrics (Faith's PD, UniFrac) require a tree plus taxa (feature) IDs that match the tree tips.
• Use partial_beta_diversity() for computing specific sample pairs only.
• Alpha diversity returns a pandas.Series; beta diversity returns a DistanceMatrix.

5. Ordination Methods

Reduce high-dimensional biological data to visualizable lower-dimensional spaces.

Key capabilities:

• PCoA (Principal Coordinate Analysis) from distance matrices
• CA (Correspondence Analysis) for contingency tables
• CCA (Canonical Correspondence Analysis) with environmental constraints
• RDA (Redundancy Analysis) for linear relationships
• Biplot projection for feature interpretation

Common patterns:

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: y = samples-by-features table, x = samples-by-constraints (environment)
cca_results = cca(feature_table, environmental_matrix)

# Save/load ordination results
pcoa_results.write('ordination.txt')
results = skbio.OrdinationResults.read('ordination.txt')

Important notes:

• PCoA works with any distance/dissimilarity matrix
• CCA reveals environmental drivers of community composition
• Ordination results include eigenvalues, proportion explained, and sample/feature coordinates
• Results integrate with plotting libraries (matplotlib, seaborn, plotly)

6. Statistical Testing

Perform hypothesis tests specific to ecological and biological data.

Key capabilities:

• PERMANOVA: test group differences using distance matrices
• ANOSIM: alternative test for group differences
• PERMDISP: test homogeneity of group dispersions
• Mantel test: correlation between distance matrices
• Bioenv: find environmental variables correlated with distances

Common patterns:

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]}")

Important notes:

• Permutation tests provide non-parametric significance testing
• Use 999+ permutations for robust p-values
• PERMANOVA sensitive to dispersion differences; pair with PERMDISP
• Mantel tests assess matrix correlation (e.g., geographic vs genetic distance)

7. File I/O and Format Conversion

Read and write 19+ biological file formats with automatic format detection.

Supported formats:

• Sequences: FASTA, FASTQ, GenBank, EMBL, QSeq
• Alignments: Clustal, PHYLIP, Stockholm
• Trees: Newick
• Tables: BIOM (HDF5 and JSON)
• Distances: delimited square matrices
• Analysis: BLAST+6/7, GFF3, Ordination results
• Metadata: TSV/CSV with validation

Common patterns:

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')

Important notes:

• Use generators for large files to avoid memory issues
• Format can be auto-detected when into parameter specified
• Some objects can be written to multiple formats
• Support for stdin/stdout piping with verify=False

8. Distance Matrices

Create and manipulate distance/dissimilarity matrices with statistical methods.

Key capabilities:

• Store symmetric (DistanceMatrix) or asymmetric (DissimilarityMatrix) data
• ID-based indexing and slicing
• Integration with diversity, ordination, and statistical tests
• Read/write delimited text format

Common patterns:

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)

Important notes:

• DistanceMatrix enforces symmetry and zero diagonal
• DissimilarityMatrix allows asymmetric values
• IDs enable integration with metadata and biological knowledge
• Compatible with pandas, numpy, and scikit-learn

9. Biological Tables

Work with feature tables (OTU/ASV tables) common in microbiome research.

Key capabilities:

• BIOM format I/O (HDF5 and JSON)
• Integration with pandas, polars, AnnData, numpy
• Data augmentation techniques (phylomix, mixup, compositional methods)
• Sample/feature filtering and normalization
• Metadata integration

Common patterns:

from skbio.table 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  # scipy sparse; .toarray() for dense

# Filter
filtered = table.filter(sample_ids_to_keep, axis='sample')

# To pandas (sparse by default)
df = table.to_dataframe(dense=True)

Important notes:

• Table is scikit-bio's re-export of the BIOM Table; import it from skbio.table (not the top-level skbio namespace).
• In BIOM convention observation = features (taxa/OTUs/ASVs), sample = samples; matrix_data is observations-by-samples sparse.
• Build from existing data via the Table(data, observation_ids, sample_ids) constructor or Table.from_tsv / from_json / from_hdf5 (there is no from_dataframe).
• BIOM tables are standard in QIIME 2 workflows; HDF5 is more efficient than JSON for large tables.

10. Protein Embeddings

Work with protein language model embeddings for downstream analysis.

Key capabilities:

• Store embeddings from protein language models (ESM, ProtTrans, etc.)
• Convert embeddings to distance matrices
• Generate ordination objects for visualization
• Export to numpy/pandas for ML workflows

Common patterns:

from skbio.embedding import (
    ProteinEmbedding, ProteinVector,
    embed_vec_to_distances, embed_vec_to_ordination, embed_vec_to_numpy,
)

# Per-residue embedding for one protein (e.g. an ESM output)
emb = ProteinEmbedding(embedding_array, sequence)

# One fixed-length vector per protein (e.g. a mean-pooled embedding)
vecs = [ProteinVector(vec, seq) for vec, seq in zip(vectors, sequences)]

# Module-level helpers operate on a collection of *Vector objects:
arr = embed_vec_to_numpy(vecs)                      # ndarray for ML
dm = embed_vec_to_distances(vecs, metric='euclidean')   # DistanceMatrix
ord_results = embed_vec_to_ordination(vecs)             # OrdinationResults (PCoA)

Important notes:

• Distinguish *Embedding (per-position matrix for a single sequence) from *Vector (one summary vector per sequence).
• The to_distances/to_ordination/to_numpy/to_dataframe conversions are module-level functions (embed_vec_to_*) over a list of vectors, not methods on the embedding objects.
• Outputs (DistanceMatrix, OrdinationResults) plug straight into scikit-bio's diversity/ordination/statistics ecosystem.

Best Practices

Installation

uv pip install "scikit-bio>=0.7,<0.8"   # examples here target the 0.7 API

The 0.6→0.7 line renamed several interfaces (otu_ids→taxa, observed_otus→observed_features, robinson_foulds→compare_rfd) and replaced the old pairwise-alignment functions with pair_align*. Pin if you depend on these.

Performance Considerations

• Use generators for large sequence files to minimize memory usage
• For massive phylogenetic trees, prefer GME or BME over NJ
• Beta diversity calculations can be parallelized with partial_beta_diversity()
• BIOM format (HDF5) more efficient than JSON for large tables

Integration with Ecosystem

• Sequences interoperate with Biopython via standard formats
• Tables integrate with pandas, polars, and AnnData
• Distance matrices compatible with scikit-learn
• Ordination results visualizable with matplotlib/seaborn/plotly
• Works seamlessly with QIIME 2 artifacts (BIOM, trees, distance matrices)

Common Workflows

1. Microbiome diversity analysis: Read BIOM table → Calculate alpha/beta diversity → Ordination (PCoA) → Statistical testing (PERMANOVA)
2. Phylogenetic analysis: Read sequences → Align → Build distance matrix → Construct tree → Calculate phylogenetic distances
3. Sequence processing: Read FASTQ → Quality filter → Trim/clean → Find motifs → Translate → Write FASTA
4. Comparative genomics: Read sequences → Pairwise alignment → Calculate distances → Build tree → Analyze clades

Reference Documentation

For detailed API information, parameter specifications, and advanced usage examples, refer to references/api_reference.md which contains comprehensive documentation on:

• Complete method signatures and parameters for all capabilities
• Extended code examples for complex workflows
• Troubleshooting common issues
• Performance optimization tips
• Integration patterns with other libraries

Additional Resources

• Official documentation: https://scikit.bio/docs/latest/
• GitHub repository: https://github.com/scikit-bio/scikit-bio
• Forum support: https://forum.qiime2.org (scikit-bio is part of QIIME 2 ecosystem)