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bioconductor-microbiome

Utilities for microbiome analysis.

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bioconductor-microbiome

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Use bioconductor-microbiome to create a targeted phyloseq-based microbiome analysis plan for [phyloseq taxonomic profiling data], covering exploration, statistical analysis, visualization, and reproducible best practices.

Required inputs

  • [phyloseq taxonomic profiling data]

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SKILL.md

name: bioconductor-microbiomedescription: Utilities for microbiome analysis.when_to_use: "Use when: Facilitating phyloseq-based exploration and analysis of taxonomic profiling data.; Performing manipulation, statistical analysis, and visualization of taxonomic profiling data.; Standardizing analyses and developing best practices for targeted microbiome analysis.. Not for: For new projects or multi-omics data analysis, use the miaverse project instead, as microbiome development has been discontinued.; For workflows based on the new TreeSummarizedExperiment data container, use miaverse packages instead of microbiome."user-invocable: false

microbiome

Dependencies & Environment

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

  • Version: 1.34.0 · Bioconductor: 3.23 · R: ≥ 4.6
  • Depends: phyloseq, ggplot2
  • Imports: Biostrings, compositions, dplyr, reshape2, Rtsne, scales, tibble, tidyr, vegan
  • Install: BiocManager::install("microbiome")

When to Use

  • Facilitating phyloseq-based exploration and analysis of taxonomic profiling data.
  • Performing manipulation, statistical analysis, and visualization of taxonomic profiling data.
  • Standardizing analyses and developing best practices for targeted microbiome analysis.

When NOT to Use

  • For new projects or multi-omics data analysis, use the miaverse project instead, as microbiome development has been discontinued.
  • For workflows based on the new TreeSummarizedExperiment data container, use miaverse packages instead of microbiome.

Data Requirements

  • Taxonomic profiling data in the independent phyloseq data format.

Key Parameters

  • No parameters are explicitly detailed in the provided vignette text.

Best Practices

  • Transition to the miaverse project and TreeSummarizedExperiment data container for added capabilities in multi-omics data analysis.
  • Use the package to facilitate scalable exploration of population cohorts and targeted case-control studies.
  • Rely on the independent phyloseq package and data structures for R-based microbiome analysis.

Common Pitfalls

  • Using discontinued software for new multi-omics projects: The microbiome package development is discontinued. Fix: Shift to miaverse tools based on TreeSummarizedExperiment.
  • Incompatibility with new data containers: Attempting to use TreeSummarizedExperiment objects directly in microbiome. Fix: Use phyloseq format or migrate to miaverse.
  • Missing general-purpose tools: Relying solely on microbiome for all analyses. Fix: Integrate with the independent phyloseq package and its data structures.

Alternatives

  • phyloseq: The independent package and data structure for R-based microbiome analysis that microbiome relies heavily upon.
  • miaverse: The recommended successor project based on the TreeSummarizedExperiment data container.

Citations

  • Leo Lahti et al. (Bioconductor, 2017-2020). Tools for microbiome analysis in R. Microbiome package version.

References

Run this on BioMate

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

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