Overview
Microbiology workflows move from processed community data to defensible biological findings in a single study workspace. Each use case below maps a common research question to the platform capabilities, data requirements, and suggested analysis path.
Use case index
| Use case | Research question | Key capabilities |
|---|---|---|
| Community profiling | Which microbes are present and how abundant are they across samples? | QC and database provenance, taxonomic profiling, stacked bar chart, relative abundance, prevalence |
| Diversity comparison | Do communities differ in diversity or composition between groups? | Alpha diversity + rarefaction, alpha group significance, beta diversity (Bray-Curtis/Jaccard), PCoA, PERMANOVA + PERMDISP |
| Core microbiome | Which taxa are consistent, recurring members of this community? | Prevalence/abundance thresholding, interactive threshold exploration |
| Differential abundance | Which taxa differ between two cohorts, accounting for compositionality? | CLR or ANCOM-BC differential abundance, prevalence and effect size, BH correction |
| Longitudinal community analysis | How does a community change within subjects over time? | Within-subject trajectories, first differences, mixed-effects trend test |
| Functional profiling | What functions or pathways differ between communities? | Pathway abundance, cohort comparison, measured-vs-inferred distinction |
| Genomic relatedness | Which isolates are genomically related, and what clusters do they form? | Pairwise SNP distance, single-linkage clustering, cross-area outbreak support |
| Cross-area microbiome | How do microbial features relate to host, immune, or clinical outcomes? | Shared sample identity, cross-area result linking, grounded interpretation |
Who these use cases serve
| Audience | Typical goals |
|---|---|
| Microbiome researchers | Reproducible community profiling, diversity, differential abundance, and functional analysis |
| Infectious disease researchers | Link microbial features to host response and clinical outcomes |
| Core facilities | Standardized intake, parameterized pipelines, QC, provenance, and reporting |
| Pharma and clinical trial teams | Cohort comparison, longitudinal response, and auditable microbial biomarkers |
Common data requirements
Most use cases start with a microbiology study containing:
- A feature table (taxa/ASV/OTU by sample) — the first-release ingest path
- Sample metadata with group, site/body-site, timepoint, and treatment columns
- A reference database and version recorded for provenance (e.g. SILVA 138.1)
Longitudinal analysis additionally needs subject and time columns in sample metadata; genomic relatedness needs a separate isolate sequence alignment rather than a feature table.
Raw-read denoising and shotgun read classification are later additions; the first release ingests processed feature and pathway tables for faster onboarding.
Choosing a starting point
| If your primary data is… | Start with… |
|---|---|
| A feature table with taxonomy | Community profiling |
| A feature table + grouped metadata | Diversity comparison |
| A feature table, asking "what's consistently present?" | Core microbiome |
| Two cohorts to contrast | Differential abundance |
| Repeated samples per subject over time | Longitudinal community analysis |
| A pathway/gene-family table | Functional profiling |
| An isolate sequence alignment | Genomic relatedness |
| Microbial + host/immune/clinical data | Cross-area microbiome |
What these use cases are not
These workflows support exploratory and translational research. They are not clinical diagnostic, antimicrobial-prescribing, or infection-control workflows, and they do not turn an association into a causal, diagnostic, or transmission claim. All AI interpretation outputs include research disclaimers and cite computed metrics only.