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microbiome-amplicon

Automate amplicon microbiome workflows from raw reads to abundance summaries.

25|5|Updated Mar 22, 2026
Search Tags:#taxonomy#diversity#denoising#differential-abundance#microbiome#amplicon
One-click install
npx skills add https://github.com/zongtingwei/Bioclaw_Skills_Hub --skill microbiome-amplicon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: microbiome-amplicon
Source: https://github.com/zongtingwei/Bioclaw_Skills_Hub/tree/main/skills/metagenomics-and-microbiome/microbiome-amplicon
Command: npx skills add https://github.com/zongtingwei/Bioclaw_Skills_Hub --skill microbiome-amplicon

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Amplicon microbiome analysis often requires chaining multiple steps (denoising, taxonomic assignment, diversity analyses) across disparate tools, which can be error-prone and time-consuming. This skill provides a cohesive, repeatable workflow to move from raw reads to interpretable abundance tables and diversity summaries with clear guidance and provenance.

Core Features & Use Cases

  • Denoising and Amplicon Inference: generate ASV/OTU-level counts from raw marker-gene data.
  • Taxonomy Assignment: assign taxonomic labels using suitable reference databases.
  • Diversity Analysis: compute alpha and beta diversity metrics and visualize group differences.
  • Differential Abundance: support group-level comparisons with appropriate statistical approaches.
  • Use Case: analyze 16S rRNA gene data to compare microbial communities across treatment groups.

Quick Start

Use the starter pattern to process a raw amplicon dataset and generate ASV tables and diversity plots.

Frequently Asked Questions about microbiome-amplicon

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

FAQPage Schema
How do I automate amplicon microbiome analysis from raw reads to abundance tables?▼

Automate amplicon microbiome analysis by chaining denoising, taxonomy assignment, and diversity analysis steps into a cohesive workflow that processes raw 16S/ITS reads to generate ASV/OTU tables and diversity reports.

Can I use QIIME2-style pipelines for 16S rRNA gene data to compare microbial communities across treatment groups?▼

Yes, QIIME2-style pipelines support 16S rRNA gene data analysis to compare microbial communities across treatment groups by generating ASV tables, computing diversity metrics, and performing differential abundance testing.

What is the best way to assign taxonomy and compute alpha and beta diversity for amplicon datasets?▼

Assign taxonomy and compute alpha and beta diversity for amplicon datasets by applying denoising to generate ASV counts, matching against reference databases for taxonomic labels, and visualizing group differences with diversity reports.

Does amplicon microbiome denoising require pandas and scikit-bio to produce ASV tables?▼

Yes, amplicon microbiome denoising requires pandas and scikit-bio alongside QIIME2-style pipelines to process raw marker-gene data and produce ASV tables, taxonomic labels, and interpretable diversity summaries.

How do I run differential abundance analysis on ASV tables for microbiome data?▼

Run differential abundance analysis on ASV tables by applying appropriate statistical approaches to compare group-level microbial abundance counts, supporting group comparisons within the amplicon microbiome analysis workflow.

What are the limitations of using ASV versus OTU approaches for amplicon microbiome denoising?▼

ASV approaches for amplicon microbiome denoising provide exact sequence resolution but may be more sensitive to sequencing errors, while OTU clustering groups similar sequences, potentially reducing sensitivity to true biological variants.

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