Metagenomics on the platform

Metagenomics tells you which organisms are present in a sample and how their communities differ between conditions — running the full microbiome pipeline from raw-read taxonomic classification through diversity and differential-abundance statistics. Reach for it when your biology lives in a microbial community rather than a single genome, such as profiling a stool sample or comparing treatment versus control microbiomes. Where genomics queries curated databases for a known gene, metagenomics classifies unknown sequencing reads and can hand abundant species back to genomics for functional annotation. See what you can achieve for the full set.

Metagenomics overview

The Metagenomics toolkit provides a complete pipeline for microbiome analysis, from raw read classification through statistical comparison of community composition. All tools run as GPU-accelerated Modal jobs. Classification and profiling typically take 5-45 minutes depending on read count; downstream diversity and differential abundance analysis take 3-20 minutes.

Architecture

User Input (FASTQ reads, abundance table, sample metadata)
  --> Intent Detection
       |- Taxonomic classification --> metagenomics_classify
       |- Community profiling --> metagenomics_profile
       |- Diversity analysis --> microbiome_diversity
       +- Differential abundance --> microbiome_differential
  --> Modal GPU Compute (classification) or Direct Compute (stats)
  --> Structured Results with Abundance Tables and Statistics

Tool inventory

Capabilities

Taxonomic classification

metagenomics_classify assigns taxonomic labels to individual reads (method: kraken2, centrifuge, or kaiju, against a standard, silva, or greengenes database). metagenomics_profile instead produces a quantitative, per-sample community summary (method: metaphlan, bracken, or motu) — read-level classification and quantitative profiling are two separate tools, not two modes of one tool.

Community profiling

The metagenomics_profile tool generates a per-sample community summary (richness, evenness, dominant taxon, and phylum-level abundances) at a configurable taxonomic resolution (species, genus, or family). Run it once per sample, then pass the results into microbiome_diversity or microbiome_differential to compare across samples.

Alpha diversity

Alpha diversity quantifies the species diversity within individual samples. microbiome_diversity always returns these four metrics per sample:

Beta diversity

Beta diversity measures compositional differences between samples, returned as a distance to every other sample (beta_distances). beta_method selects the distance:

There's no ordination (PCoA/NMDS) step — beta_distances is a distance map, not a set of plotting coordinates.

Differential abundance

The microbiome_differential tool identifies taxa with significantly different abundances between experimental groups, all methods designed for compositional microbiome data:

When to use each tool

When to use other tools instead

Licensing

Citations

Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257. doi: 10.1186/s13059-019-1891-0

Blanco-Miguez A, et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat Biotechnol. 2023;41:1633-1644. doi: 10.1038/s41587-023-01688-w

Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11:3514. doi: 10.1038/s41467-020-17041-7

Chat Usage

Example Prompts

• "Classify my stool metagenome FASTQ reads with Kraken2"
• "Generate a genus-level abundance profile from my samples"
• "Calculate Shannon and Simpson diversity for all my samples"
• "What taxa are enriched in the treatment group vs control?"
• "Compare community beta diversity using Bray-Curtis PCoA"
• "Run MetaPhlAn species-level profiling on my paired-end reads"

Workflow Chaining

• Feeds into: Literature search (microbiome-disease associations), Systems Biology (community metabolic modeling), Genomics (functional gene annotation of abundant species), Target Passport (pathogen-derived targets)
• Accepts from: Data upload (FASTQ files, abundance tables), Literature search (study design metadata), Genomics (reference genome annotations)

FAQ

Read next