Scikit Bio

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 analyses 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)
• Analyses 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 specialised 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 alignment (Needleman-Wunsch with semi-global variant)
• Local alignment (Smith-Waterman)
• Configurable scoring schemes (match/mismatch, gap penalties, substitution matrices)
• CIGAR string conversion
• Multiple sequence alignment storage and manipulation with TabularMSA

Common patterns:

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()

Important notes:

• Use local_pairwise_align_ssw for local alignments (faster, SSW-based)
• Use StripedSmithWaterman for protein alignments
• Affine gap penalties recommended for biological sequences
• Can convert between scikit-bio, BioPython, and Biotite alignment formats

3. Phylogenetic Trees

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

Key capabilities:

• Tree construction from distance matrices (UPGMA, WPGMA, Neighbour Joining, GME, BME)
• Tree manipulation (pruning, rerooting, traversal)
• Distance calculations (patristic, cophenetic, Robinson-Foulds)
• ASCII visualisation
• 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 trees
rf_distance = tree.robinson_foulds(other_tree)

Important notes:

• Use nj() for neighbour joining (classic phylogenetic method)
• Use upgma() for UPGMA (assumes molecular clock)
• 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, 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())

Important notes:

• Counts must be integers representing abundances, not relative frequencies
• Phylogenetic metrics (Faith's PD, UniFrac) require tree and OTU ID mapping
• Use partial_beta_diversity() for computing specific sample pairs only
• Alpha diversity returns Series, beta diversity returns DistanceMatrix

5. Ordination Methods

Reduce high-dimensional biological data to visualisable 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 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
• 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 normalisation
• Metadata integration

Common patterns:

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)

Important notes:

• BIOM tables are standard in QIIME 2 workflows
• Rows typically represent samples, columns represent features (OTUs/ASVs)
• Supports sparse and dense representations
• Output format configurable (pandas/polars/numpy)

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 visualisation
• 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 specialised functionality
• Useful for sequence clustering, classification, and visualisation

Best Practices

Installation

uv pip install scikit-bio

Performance Considerations

• Use generators for large sequence files to minimise 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 visualisable with matplotlib/seaborn/plotly
• Works seamlessly with QIIME 2 artefacts (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 → Analyse 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 optimisation 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)