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scikit-bio

Compute diversity, ordination, and statistical summaries from sequence-derived inputs.

Updated May 24, 2026
Search Tags:#bioinformatics#scikit-bio#phylogenetic trees#microbiome diversity#PERMANOVA#PCoA ordination
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill scikit-bio-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-bio
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/scikit-bio
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill scikit-bio-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

scikit-bio helps you turn biological sequence and community ecology data into analysis-ready objects for alignment, phylogenetics, diversity metrics, ordination, and permutation-based statistics.

Core Features & Use Cases

  • Sequence I/O and manipulation: Load and transform biological sequences using FASTA/FASTQ/GenBank/Newick and other supported formats, including reverse-complement, transcription, translation, motif/regex matching, and metadata handling.
  • Alignments and phylogenetic workflows: Run pairwise and multiple sequence alignments, build phylogenetic trees from distance matrices, and compute tree distances and comparisons.
  • Microbiome/community statistics: Compute alpha and beta diversity (including UniFrac), perform ordination (PCoA/CCA/RDA), and run group-difference tests like PERMANOVA with permutation control.
  • File formats and distance/ordination integration: Use consistent distance/dissimilarity matrices and rich ordination result objects for downstream visualization and interpretation.

Quick Start

Use the scikit-bio skill to compute PCoA from a microbiome beta-diversity distance matrix stored as a delimited text file.

Frequently Asked Questions about scikit-bio

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

FAQPage Schema
How do I calculate UniFrac beta diversity for microbiome data?▼

UniFrac beta diversity is computed by applying scikit-bio's diversity functions to sequence-derived inputs and phylogenetic trees, requiring correct ID mapping between your table and tree objects to generate a distance matrix.

What biological sequence file formats are supported for reading and manipulation?▼

Supported biological sequence file formats include FASTA, FASTQ, GenBank, and Newick, allowing you to load sequences for reverse-complement, transcription, translation, and motif matching operations.

How do I run PERMANOVA group-difference tests on a distance matrix?▼

PERMANOVA group-difference tests are executed using scikit-bio's permutation-based statistics on a distance matrix, controlling permutations to assess whether community composition differences are statistically significant.

Can I perform PCoA ordination directly from a microbiome beta-diversity distance matrix?▼

PCoA ordination can be performed directly from a delimited text file containing beta-diversity distance matrices, producing rich ordination result objects for downstream visualization and interpretation.

Does scikit-bio support building phylogenetic trees from multiple sequence alignments?▼

Phylogenetic trees can be built from distance matrices derived from pairwise and multiple sequence alignments, enabling tree distance computations and phylogenetic workflow comparisons.

How do I handle ID mapping errors when computing alpha and beta diversity?▼

ID mapping errors are resolved by ensuring consistent identifiers across scikit-bio's data model for sequences, tables, and distance matrices, which is required for successful diversity calculations.

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