/scikit-bio

name: scikit-bio
description: Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
license: BSD-3-Clause license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.10+ and scikit-bio 0.7+ (uv pip install scikit-bio). NumPy 2.0+ is required. Optional matplotlib/seaborn/plotly for plotting; biom-format for BIOM tables; polars/anndata for table interoperability.
metadata:
  version: "1.2"
  skill-author: K-Dense Inc.

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 distan