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 the pair_align engine (introduced in scikit-bio 0.7.0), a versatile and efficient dynamic-programming aligner.

Key capabilities:

Global, local, and semi-global alignment (free ends configurable) in one function
Convenience wrappers pair_align_nucl (BLASTN-like) and pair_align_prot (BLASTP-like)
Configurable scoring: match/mismatch tuple or named substitution matrix; linear or affine gap penalties
PairAlignPath results carry CIGAR strings and convert to aligned sequences
Multiple sequence alignment storage and manipulation with TabularMSA

Common patterns:

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

# Nucleotide alignment with BLASTN-like defaults
seq1, seq2 = DNA('ACTACCAGATTACTTACGGATCAGG'), DNA('CGAAACTACTAGATTACGGATCTTA')
aln = pair_align_nucl(seq1, seq2)
aln.score                                  # alignment score (float)
path = aln.paths[0]                        # PairAlignPath (repr shows CIGAR)
aligned_seqs = path.to_aligned((seq1, seq2))  # list of gapped strings

# Build a TabularMSA from the alignment path + original sequences
msa = TabularMSA.from_path_seqs(path, (seq1, seq2))

# Customize the algorithm via pair_align (default mode='global')
aln = pair_align(seq1, seq2, mode='local')                       # Smith-Waterman
aln = pair_align(seq1, seq2, sub_score=(2, -3), gap_cost=(5, 2)) # affine gaps
aln = pair_align(seq1, seq2, sub_score='NUC.4.4', gap_cost=3)    # substitution matrix, linear gap

# Protein alignment (BLASTP-like, BLOSUM62)
aln = pair_align_prot(Protein('HEAGAWGHEE'), Protein('PAWHEAE'))

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

Important notes:

pair_align replaces the removed SSW wrapper (local_pairwise_align_ssw, StripedSmithWaterman) and the deprecated pure-Python aligners (global_pairwise_align, local_pairwise_align_nucleotide, etc.)
The result is a PairAlignResult that also unpacks as score, paths, matrices (use keep_matrices=True to retain the DP matrix)
sub_score accepts a (match, mismatch) tuple or a matrix name (e.g., 'NUC.4.4', 'BLOSUM62'); gap_cost accepts a single number (linear) or (open, extend) tuple (affine)
Parse external CIGAR strings with PairAlignPath.from_cigar('1I8M2D5M2I'); score an existing alignment with align_score(...) and build a distance matrix from an MSA with align_dists(...)

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 rearrangement with nearest neighbor interchange (nni)
Tree manipulation (pruning, rerooting, traversal)
Distance calculations (patristic via cophenet, Robinson-Foulds via compare_rfd)
ASCII visualization
Newick format I/O

Common patterns:

from skbio import TreeNode
from skbio.tree import nj, upgma, gme, bme, rf_dists

# 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.lca(['taxon1', 'taxon2'])

# Calculate distances
patristic_dist = tree.find('taxon1').distance(tree.find('taxon2'))
cophenetic_dm = tree.cophenet()           # patristic distance matrix among tips

# Compare two trees (Robinson-Foulds)
rf_distance = tree.compare_rfd(other_tree)
# Pairwise RF distances among many trees -> DistanceMatrix
rf_dm = rf_dists([tree, other_tree, third_tree])

Important notes:

Use nj() for neighbor joining (classic phylogenetic method)
Use upgma() for UPGMA/WPGMA (assumes molecular clock)
GME and BME are highly scalable for large trees; refine topology with nni()
cophenet() (formerly tip_tip_distances) returns the patristic distance matrix; compare_rfd() is the Robinson-Foulds method (compare_wrfd/compare_cophenet for weighted/cophenetic variants)
lca() is the lowest common ancestor; lowest_common_ancestor remains as an alias
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 (sobs, observed_features, chao1, ace), Shannon, Simpson, Hill numbers (hill), Faith's PD (faith_pd), generalized PD (phydiv), 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

# Alpha diversity (phylogenetic metrics take taxa= for tip-name mapping)
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 phylogenetic-metric argument is taxa= (renamed from otu_ids in 0.6.0; the old name is a deprecated alias); observed_otus is now observed_features (or sobs)
counts_matrix may be any table-like input (NumPy array, pandas/polars DataFrame, BIOM Table, or AnnData) via the dispatch system
Phylogenetic metrics (Faith's PD, UniFrac) require tree and taxa-to-tip mapping
Use partial_beta_diversity() for specific sample pairs, or block_beta_diversity() for large block-decomposed calculations
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
import skbio

# PCoA from distance matrix (limit dimensions for large matrices)
pcoa_results = pcoa(distance_matrix, dimensions=3)
pc1 = pcoa_results.samples['PC1']
pc2 = pcoa_results.samples['PC2']

# Built-in scatter plot colored by a metadata column
fig = pcoa_results.plot(sample_metadata, column='bodysite')

# 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')

Important notes:

PCoA works with any distance/dissimilarity matrix; pass dimensions as an int (count) or a float in (0, 1] (fraction of cumulative variance to retain)
OrdinationResults exposes pandas-based attributes: samples, features, eigvals, proportion_explained, biplot_scores, sample_constraints
CCA reveals environmental drivers of community composition
OrdinationResults.plot() produces a matplotlib figure; results also integrate with 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
Differential abundance: ancom, dirmult_ttest, and dirmult_lme (longitudinal mixed-effects) in skbio.stats.composition

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]}")

# Differential abundance on a feature table (raw counts recommended)
from skbio.stats.composition import dirmult_ttest
da = dirmult_ttest(counts_table, grouping, treatment='caseA', reference='control')

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)
Supply differential-abundance tests with raw counts, not pre-normalized proportions, to preserve magnitude information

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, hollow diagonal) or general pairwise (PairwiseMatrix) 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 a zero (hollow) diagonal; it is a subclass of SymmetricMatrix
PairwiseMatrix (renamed from DissimilarityMatrix, which is kept as a deprecated alias) allows general/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) via the native Table class
Table dispatch system (0.7.0+): functions accept any table_like input — BIOM Table, pandas/polars DataFrame, NumPy array, or AnnData — without explicit conversion
Data augmentation techniques (phylomix, mixup, aitchison_mixup, compos_cutmix)
Sample/feature filtering and normalization
Metadata integration

Common patterns:

from skbio import Table
from skbio.diversity import beta_diversity

# 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')

# Pass table-like objects directly to scikit-bio drivers (dispatch system)
import pandas as pd
df = pd.read_table('data.tsv', index_col=0)   # samples x features
bdiv = beta_diversity('braycurtis', df)         # no manual conversion needed

Important notes:

BIOM tables are standard in QIIME 2 workflows
Rows typically represent samples, columns represent features (OTUs/ASVs)
Supports sparse and dense representations
With the dispatch system, functions return the same format as their input, or a user-specified output format

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

# 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()

Important notes:

Embeddings bridge protein language models with traditional bioinformatics
Compatible with scikit-bio's distance/ordination/statistics ecosystem
SequenceEmbedding and ProteinEmbedding provide specialized functionality
Useful for sequence clustering, classification, and visualization

Best Practices

Installation

uv pip install scikit-bio

Requires Python 3.10+ and NumPy 2.0+. Pre-compiled wheels are published for each release since 0.7.0, so most platforms install without a compiler. Conda users can instead run conda install -c conda-forge scikit-bio.

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

Microbiome diversity analysis: Read BIOM table → Calculate alpha/beta diversity → Ordination (PCoA) → Statistical testing (PERMANOVA)
Phylogenetic analysis: Read sequences → Align → Build distance matrix → Construct tree → Calculate phylogenetic distances
Sequence processing: Read FASTQ → Quality filter → Trim/clean → Find motifs → Translate → Write FASTA
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
Changelog: https://github.com/scikit-bio/scikit-bio/blob/main/CHANGELOG.md
Reference paper: "scikit-bio: a fundamental Python library for biological omic data," Nature Methods (2025), https://www.nature.com/articles/s41592-025-02981-z
Forum support: https://forum.qiime2.org (scikit-bio is part of QIIME 2 ecosystem)

skills/scikit-bio

scikit-bio

Overview

When to Use This Skill

Core Capabilities

1. Sequence Manipulation

2. Sequence Alignment

3. Phylogenetic Trees

4. Diversity Analysis

5. Ordination Methods

6. Statistical Testing

7. File I/O and Format Conversion

8. Distance Matrices

9. Biological Tables

10. Protein Embeddings

Best Practices

Installation

Performance Considerations

Integration with Ecosystem

Common Workflows

Reference Documentation

Additional Resources