Bioconductor Phyloseq logo

Bioconductor Phyloseq

Organization
bioMate-AI
bioconductor-phyloseq

phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.

Overview

PublisherbioMate-AI
Repositorybiomate-bioconductor-kb
Skill namebioconductor-phyloseq
Stars
804
Forks
66
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bioMate-AI on GitHub. Read the source before you install it.

Installation

Install the Bioconductor Phyloseq AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/metagenomics/phyloseq
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bioMate-AI/biomate-bioconductor-kb.git /tmp/biomate-bioconductor-kb
mkdir -p .claude/skills
cp -r /tmp/biomate-bioconductor-kb/skills/metagenomics/phyloseq .claude/skills/bioconductor-phyloseq
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bioconductor Phyloseq in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bioconductor Phyloseq on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bioconductor Phyloseq is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

phyloseq

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

  • Version: 1.56.0 · Bioconductor: 3.23 · R: ≥ 4.6
  • Imports: ade4, ape, Biobase, BiocGenerics, biomformat, Biostrings, cluster, data.table, foreach, ggplot2, igraph, multtest, plyr, reshape2, scales, vegan
  • Install: BiocManager::install("phyloseq")

When to Use

  • Importing and storing complex phylogenetic sequencing data (OTU tables, sample metadata, taxonomy, trees) into a single object using import().
  • Estimating and visualizing alpha diversity using plot_richness() with measures like "Chao1" or "Shannon".
  • Performing beta diversity ordination (e.g., PCoA, NMDS, CCA) using ordinate() and visualizing the results with plot_ordination().
  • Creating exploratory bar plots of taxonomic abundance across samples using plot_bar().

When NOT to Use

  • For advanced network analysis beyond basic ecological similarity, use igraph directly because plot_net() provides only a default Jaccard co-occurrence network.
  • For complex custom plotting beyond standard wrappers, use ggplot2 directly because phyloseq wrappers are built on it but may restrict advanced geoms.

Data Requirements

  • Pre-clustered phylogenetic sequencing data (OTU/ASV table).
  • Associated sample data (metadata), a phylogenetic tree, and/or taxonomic assignments.
  • Raw (untrimmed) OTU-clustered data is strictly required when performing richness estimates.

Key Parameters

  • measures: Alpha diversity estimators to calculate in plot_richness() (e.g., c("Observed", "Chao1", "Shannon")).
  • method: Ordination method for ordinate() (e.g., "PCoA", "NMDS", "CCA").
  • distance: Distance metric for ordinate() (e.g., "bray", "unifrac").
  • maxdist: Maximum distance required to create an edge in plot_net().
  • type: What to plot in plot_ordination() (e.g., "samples", "species", "biplot", "split").
  • color / shape / fill: Variables from sample_data or tax_table to map to aesthetics in plotting functions.

Best Practices

  • Use raw (untrimmed) OTU-clustered data when performing richness estimates with plot_richness(), as estimates depend heavily on singletons.
  • Prune absent taxa using prune_taxa() and taxa_sums() > 0 to remove artifacts before downstream analysis.
  • Subset large datasets using subset_taxa() before rendering computationally heavy plots like plot_heatmap() or plot_tree().
  • Use scree plots (plot_scree()) to evaluate the relative importance of axes after performing ordination.

Common Pitfalls

  • Richness estimates are skewed or incorrect: Using normalized or trimmed data removes singletons necessary for accurate alpha diversity estimation. Fix: Ensure you use raw, untrimmed counts in plot_richness().
  • plot_bar() is illegible: Plotting all taxa at once creates a solid block of color due to too many OTUs. Fix: Subset to the most abundant taxa or facet by taxonomic rank (e.g., facet_grid=~Genus).
  • UniFrac() takes too long: Calculating UniFrac distances on large datasets (like Global Patterns) is computationally intensive. Fix: Use parallelization or subset the data before running the distance calculation.

Alternatives

  • vegan: For underlying ecological distance and ordination calculations without the phyloseq S4 object overhead.
  • ape: For direct phylogenetic tree manipulation and plotting outside of microbiome contexts.
  • ggplot2: For building custom graphics from scratch when phyloseq plot wrappers are too constrained.

Citations

  • McMurdie PJ, Holmes S (2013). "phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data." PLoS ONE, 8(4): e61217.
  • Caporaso JG, et al. (2011). "Global Patterns of 16S rRNA Diversity at a Depth of Millions of Sequences Per Sample." PNAS, 108: 4516-22.

References


Run this on BioMate

This skill is the knowledge layer — when, why, and how to use phyloseq. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open phyloseq on BioMate →

Frequently asked questions

What does the Bioconductor Phyloseq AI skill do?

phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.

Why use Bioconductor Phyloseq on TypingMind?

Because you install it once and use it with any model. Bioconductor Phyloseq is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bioconductor Phyloseq in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/metagenomics/phyloseq. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bioconductor Phyloseq?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bioconductor Phyloseq?

As many as you like. As long as a model supports skills, you can use Bioconductor Phyloseq with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bioconductor Phyloseq AI skill free?

It is published on GitHub by bioMate-AI. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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