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bio-multi-omics-microbiome-metabolomics

Integrate paired microbiome and metabolomics data to rank microbe-metabolite associations.

7|Updated Apr 5, 2026
Search Tags:#metabolomics#multi-omics#microbiome#mmvec#clr-transform#scfa-quantification#bile-acids
One-click install
npx skills add https://github.com/dailycafi/metabolism-skills --skill bio-multi-omics-microbiome-metabolomics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-multi-omics-microbiome-metabolomics
Source: https://github.com/dailycafi/metabolism-skills/tree/main/skills/multi-omics/microbiome-metabolomics
Command: npx skills add https://github.com/dailycafi/metabolism-skills --skill bio-multi-omics-microbiome-metabolomics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Integrate paired microbiome (16S/shotgun) and metabolomics datasets to reveal microbe–metabolite interactions, quantify SCFAs and bile acids, and map tryptophan pathway activity for biological interpretation and downstream modeling.

Core Features & Use Cases

  • Co-occurrence modeling and correlation analysis: Prepares BIOM or tabular inputs, applies CLR to compositional taxa and log transforms to metabolites, computes Spearman correlations with FDR correction, and ranks associations using mmvec neural co-occurrence probabilities.
  • Targeted assay workflows and pathway scoring: Guides SCFA calibration and internal standard normalization, classifies and summarizes bile acid groups, and computes tryptophan pathway branch scores and the kynurenine-to-tryptophan ratio (KTR).
  • Multi-omics integration & visualization: Demonstrates sparse PLS (mixOmics) for feature selection, generates paired heatmaps and correlation networks, and exports ranked microbe-metabolite pairs for follow-up experiments.
  • Use case: Match fecal 16S abundance to targeted LC-MS bile acid profiles to identify taxa associated with secondary bile acid production and visualize robust associations for hypothesis generation.

Quick Start

Integrate your sample-matched OTU/ASV table and metabolite concentration matrix to compute CLR-transformed taxa, log-transform metabolites, run mmvec or sPLS to identify top microbe-metabolite associations, and export the ranked results.

Frequently Asked Questions about bio-multi-omics-microbiome-metabolomics

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

FAQPage Schema
How do I integrate microbiome and metabolomics data to identify microbe-metabolite associations?▼

To integrate microbiome and metabolomics data, you match sample OTU/ASV tables with metabolite concentration matrices, apply CLR-transforms to taxa and log-transforms to metabolites, then use mmvec or sPLS to rank microbe-metabolite associations.

What is mmvec co-occurrence modeling used for in multi-omics analysis?▼

Mmvec co-occurrence modeling is used in multi-omics analysis to compute neural co-occurrence probabilities that rank microbe-metabolite associations, working alongside Spearman correlations with FDR correction to identify robust interactions.

Can I use this workflow for SCFA quantification and bile acid profiling from 16S and LC-MS data?▼

Yes, you can use this workflow for SCFA quantification and bile acid profiling by matching fecal 16S abundance to targeted LC-MS profiles, applying internal standard normalization, and classifying bile acid groups to find associated taxa.

Do I need BIOM format files to run sparse PLS integration on my microbiome data?▼

You need BIOM format files or tabular inputs to run sparse PLS integration, as the workflow requires compositional transforms on taxa tables and log transforms on metabolite matrices before executing mixOmics feature selection.

How do I map tryptophan pathway activity using paired microbiome and metabolomics datasets?▼

To map tryptophan pathway activity, you integrate paired microbiome and metabolomics datasets to compute branch scores and the kynurenine-to-tryptophan ratio (KTR), revealing microbial associations with tryptophan metabolism.

What's the best way to visualize ranked microbe-metabolite pairs for hypothesis generation?▼

The best way to visualize ranked microbe-metabolite pairs is generating paired heatmaps and correlation networks from sPLS feature selection, allowing you to identify robust associations for downstream follow-up experiments.

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