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scikit-bio

Compute sequence alignments, phylogenetic analyses, and microbial diversity statistics with scikit-bio.

21|2|Updated Dec 8, 2025

Search Tags:#bioinformatics #sequence analysis #ordination #phylogenetic trees #protein embeddings #permanova #microbiome diversity

Ready to run

npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill scikit-bio-silverstein

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scikit-bio
Source: https://github.com/silverstein/claude-scientific-skills-desktop/tree/main/corpus/scikit-bio
Command: npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill scikit-bio-silverstein

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you perform end-to-end biological data analysis—handling sequences, alignments, phylogenetic trees, and microbiome diversity/ordination—without stitching together many separate tools manually.

Core Features & Use Cases

Sequence processing and format I/O: Read/write common biological formats (FASTA/FASTQ/GenBank/Newick/BIOM), including efficient streaming for large files.
Alignment and phylogenetics: Compute local/global alignments, manage MSAs, build trees from distance matrices, and work with Newick I/O and tree metrics.
Microbial ecology statistics: Calculate alpha/beta diversity (including phylogenetic metrics like UniFrac), run ordination (PCoA/CCA/RDA), and perform distance-based tests (PERMANOVA/ANOSIM/Mantel).
Tabular and embedding workflows: Work with feature tables (BIOM), distance matrices, and protein embeddings for downstream ML-style analysis.

Quick Start

Use the scikit-bio skill to compute alpha and beta diversity from a BIOM feature table and a phylogenetic tree, then summarize results with PCoA coordinates.

Frequently Asked Questions about scikit-bio

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

FAQPage Schema

How do I compute alpha and beta diversity from a BIOM table and a phylogenetic tree?▼

To compute alpha and beta diversity, you process a BIOM feature table with a phylogenetic tree to calculate ecological diversity statistics, including phylogenetic measures like UniFrac, yielding distance matrices for downstream ordination.

What is the best way to run PERMANOVA distance tests on large microbiome datasets?▼

Running PERMANOVA distance tests on microbiome datasets requires a symmetric distance matrix and applies permutation-based testing to evaluate statistical significance, utilizing efficient reading and generators to handle large biological datasets.

Can I perform multiple sequence alignment and phylogenetic tree construction from FASTA files?▼

Yes, you can perform multiple sequence alignment and phylogenetic tree construction directly from FASTA files by computing local and global alignments, then building trees from distance matrices using Newick I/O support.

Does this approach support ordination methods like PCoA and CCA for microbial ecology?▼

Yes, this approach supports ordination methods like PCoA and CCA for microbial ecology by computing principal coordinates analysis, canonical correspondence analysis, and redundancy analysis directly on generated distance matrices.

How do I read and write common biological sequence formats like FASTQ and GenBank efficiently?▼

Reading and writing biological sequence formats like FASTQ and GenBank is handled through standard file I/O, utilizing efficient streaming and generators to process large datasets without loading entire files into memory.

What are the limitations when applying protein embeddings to downstream machine learning workflows?▼

Limitations when applying protein embeddings to downstream machine learning workflows include ensuring ID-consistent inputs and proper alignment with feature tables, as this processing focuses on biological data workflows rather than generating the embeddings themselves.