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scikit-bio

Manipulate biological sequences, alignments, trees, and diversity metrics in Python.

13|3|Updated Jun 10, 2026
Search Tags:#data analysis#bioinformatics#alignment#sequence analysis#ordination#phylogenetics#diversity analysis#statistical testing
One-click install
npx skills add https://github.com/tassiovale/claude-code-kit --skill scikit-bio-tassiovale
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-bio
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/scikit-bio
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill scikit-bio-tassiovale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scikit-bio, numpy, matplotlib, seaborn, plotly, biom-format, polars, annadata, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Scikit-bio addresses the complexities of bioinformatics by providing powerful tools for biological data analysis, from sequence manipulation to diversity metrics, making it easier to work with biological data.

Core Features & Use Cases

  • Sequence Manipulation: Offers classes for DNA, RNA, and protein data, supporting various operations like sequence reading, slicing, concatenation, reverse complement, transcription, translation, and pattern searching.
  • Sequence Alignment: Includes versatile aligners for global, local, and semi-global alignment, with configurable scoring and convenient wrappers for BLASTN/BLASTP-like defaults.
  • Phylogenetic Trees: Facilitates tree construction, manipulation, rearrangement, and distance calculations, with Newick format I/O.
  • Diversity Analysis: Provides alpha and beta diversity metrics, phylogenetic diversity metrics, rarefaction, and subsampling for microbial ecology and community analysis.
  • Ordination: Supports PCoA, CA, CCA, RDA, and biplot projection for reducing high-dimensional data to lower-dimensional spaces.
  • Statistical Testing: Offers PERMANOVA, ANOSIM, PERMDISP, Mantel test, Bioenv, and differential abundance tests for ecological and biological data analysis.
  • File I/O and Format Conversion: Supports reading and writing 19+ biological file formats with automatic format detection.
  • Distance Matrices: Allows creation and manipulation of distance/dissimilarity matrices with statistical methods.
  • Biological Tables: Handles feature tables (OTU/ASV tables) common in microbiome research.
  • Protein Embeddings: Works with protein language model embeddings for downstream analysis.
  • Best Practices: Installation and performance considerations for efficient data processing and integration with the ecosystem.
  • Common Workflows: Provides examples of common workflows, such as microbiome diversity analysis, phylogenetic analysis, sequence processing, and comparative genomics.

Quick Start

To use scikit-bio for sequence alignment, execute the following command: pair_align_nucl(DNA('ATCGATCG'), DNA('ATCGGGGATCG')).

Frequently Asked Questions about scikit-bio

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate alpha and beta diversity metrics for microbiome data?▼

To calculate alpha and beta diversity metrics for microbiome data, you can use dedicated diversity analysis functions that also support phylogenetic diversity, rarefaction, and subsampling for community analysis.

Can I perform global and local sequence alignment with configurable scoring?▼

Yes, you can perform global, local, and semi-global sequence alignment with configurable scoring, utilizing convenient wrappers that provide BLASTN and BLASTP-like defaults for DNA and protein sequences.

What statistical tests are available for ecological community comparisons?▼

Available statistical tests for ecological community comparisons include PERMANOVA, ANOSIM, PERMDISP, Mantel test, and Bioenv, which are designed to analyze distance matrices and differential abundance.

Does this bioinformatics environment support reading and writing Newick and biom formats?▼

Yes, the environment supports reading and writing over 19 biological file formats, including Newick for phylogenetic trees and biom-format for biological feature tables, with automatic format detection.

How do I reduce high-dimensional biological data using ordination methods?▼

You can reduce high-dimensional biological data using ordination methods such as PCoA, CA, CCA, and RDA, which support biplot projection to visualize lower-dimensional spaces.

What is the best way to manipulate DNA and RNA sequences for genomic workflows?▼

The best way to manipulate DNA and RNA sequences is using specialized classes that support sequence reading, slicing, reverse complement, transcription, translation, and pattern searching for genomic workflows.

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