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
metadata	{"skill-author":"K-Dense Inc."}
verified	false
lastVerifiedAt	"2026-02-19T05:29:09.098Z"
source	builtin
trust_score	100
provenance_sha	6af3e2f264233de4

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