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 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 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 trees

rf_distance = tree.robinson_foulds(other_tree)

Important notes:

Use nj() for neighbor 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 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 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 normalization

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 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

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

Forum support: https://forum.qiime2.org (scikit-bio is part of QIIME 2 ecosystem)

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