Skin Microbiome × Host Genes
✓ 已验证DADA2+MGnify ready · Host gene annotation verified
Your skin condition is a symbiotic result of genes and microbiome. 16S/ITS amplicon analysis reveals skin microbiome composition, ANNOVAR+HLA typing解析 host immune genotype, bidirectional joint mining discovers gene-microbiome interaction risks.
Understand skin condition isn't just skincare--genes and microbiome co-determine it--read "What is Skin Microbiome Joint Analysis" below.
Evaluate skin microbiome + host gene joint analysis capability--focus on "4-Step Pipeline", "Benchmarks".
Evaluate microbiome pipeline differentiation barriers--focus on "Differentiation", "Honest Boundaries".
To Beauty Seekers
Skin microbiome joint analysis is an exploratory research service, not a clinical diagnosis. Probiotic recommendations are still in the research stage, not clinical advice. We do not claim "probiotics improve skin"--causality is far from established. For skin diseases, consult a dermatologist.
Skin microbiome pipeline is deeply linked with the following services
What is Skin Microbiome Joint Analysis
Billions of microorganisms live on your skin surface--bacteria, fungi, viruses--forming the skin microbiome. These are not "dirt"; they work synergistically with your immune system, maintaining skin barrier function, resisting pathogen colonization, and regulating inflammatory responses.
But microbiome composition isn't random. Your HLA genotype determines how the immune system recognizes microbes, and your FLG genotype determines whether the skin barrier is intact--genes shape the microbiome's "niche." Conversely, microbiome metabolites also affect host immune phenotype. This is a bidirectional interaction system; looking at either side alone is incomplete.
DiVo's skin microbiome pipeline combines 16S/ITS microbiome analysis with host immune gene annotation, not producing two separate reports stitched together, but cross-mining correlations between genotype and microbiome composition, annotating interaction risks, and generating conceptual intervention plans.
People with different HLA genotypes have significantly different skin microbiome compositions. FLG mutations cause barrier defects, allowing specific microbes to colonize. Looking at microbiome without genes would misidentify gene-driven microbiome differences as "dysbiosis."
Microbiome data is not an isolated report; it forms a more complete causal chain together with genome (L1), proteome (L2), and aging trajectory (L3) as part of Digital Me.
DiVo Gen²AI's Role
The skin microbiome + host gene joint mining pipeline is DiVo's four-in-one service Pipeline C, connecting genome interpretation (Pipeline A) with aesthetic scenarios. We provide end-to-end computing from 16S/ITS amplicon analysis (DADA2 local + MGnify online fallback) to host immune gene annotation to joint analysis.
We do not do skin swab sampling (provided by partners), do not do clinical diagnosis, do not claim "probiotics improve skin" (causality far from established). We deliver gene-microbiome joint analysis results for professional institutions.
Core Capability · Pipeline C · 4 Steps
16S/ITS -> Host gene annotation -> Joint analysis -> Intervention suggestions
Differentiation
Core differences from standalone microbiome analysis or standalone genetic testing
Bidirectional Gene-Microbiome Joint Analysis
Not just looking at microbiome or genes alone, but cross-mining host HLA/FLG genotypes with skin microbiome composition--revealing how genes shape microbiome niches and how microbiome reciprocally affects host immune phenotype.
DADA2 Replaces QIIME2--More Precise
DADA2 uses ASV denoising instead of OTU clustering for higher resolution; SILVA 138.2 replaces Greengenes as taxonomy reference; MGnify online fallback for cross-validation. QIIME2 retained as reference only.
Deep Coupling with Digital Me
Microbiome data forms a more complete causal chain with genome, proteome, and aging trajectory data--not an isolated microbiome report, but one dimension of the four-in-one.
Benchmarks & Tool Chain
Databases and tools the pipeline depends on
| Name | Description | Status |
|---|---|---|
| DADA2 | ASV denoising analysis (local deployment) | ✓ 已验证 |
| MGnify API | EBI online analysis + cross-validation (fallback) | ✓ 已验证 |
| SILVA 138.2 | Taxonomy reference database (replaces Greengenes) | ✓ 已验证 |
| ANNOVAR | Deployed (dbNSFP42a 122-column annotation) | ✓ 已验证 |
| HLA Typing | OptiType+Polysolver dual-tool cross-validation | ✓ 已验证 |
| phyloseq | R diversity analysis + visualization | ✓ 已验证 |
Honest Boundaries
What we can and cannot do, clearly stated
What We Can Do
What We Don't Do
Glossary
Core terms in skin microbiome joint analysis
| Abbr. | Full Name | Translation | Explanation |
|---|---|---|---|
| 16S rRNA | 16S Ribosomal RNA | 16S Ribosomal RNA | Bacteria-specific ribosomal RNA gene fragment, used for bacterial species identification and abundance calculation |
| ITS | Internal Transcribed Spacer | Internal Transcribed Spacer | Spacer sequence in fungal ribosomal DNA, used for fungal species identification |
| QIIME2 | Quantitative Insights Into Microbial Ecology 2 | Microbial Ecology Quantitative Analysis Platform 2 | Most widely used open-source pipeline for microbiome analysis |
| DADA2 | Divisive Amplicon Denoising Algorithm 2 | Divisive Amplicon Denoising Algorithm 2 | Method for denoising amplicon sequencing reads into ASVs, more precise than OTU clustering |
| SILVA | SILVA Ribosomal RNA Database | SILVA Ribosomal RNA Database | High-quality ribosomal RNA sequence alignment and taxonomy reference database, v138.2 |
| ASV | Amplicon Sequence Variant | Amplicon Sequence Variant | Unique sequence after DADA2 denoising, equivalent to 100% similarity OTU, higher resolution |
| PICRUSt2 | Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 | Phylogenetic Community Function Prediction 2 | Predicts microbial community gene function profiles from 16S sequences, no whole-genome sequencing needed |
| HLA | Human Leukocyte Antigen | Human Leukocyte Antigen | Human MHC, determines immune recognition capacity, influences microbiome colonization |
| FLG | Filaggrin | Filaggrin | Key skin barrier protein; FLG mutations cause barrier defects, altering microbiome niches |
| α-diversity | Alpha Diversity | Alpha Diversity | Within-sample species diversity metric, e.g., Shannon index, Chao1 index |
| β-diversity | Beta Diversity | Beta Diversity | Between-sample species composition differences, e.g., Bray-Curtis distance, UniFrac distance |
CAPACITY TRACE
能力回溯
这项服务由哪些能力支撑——从硅片到你的场景
硅片(L1) → 模型(L2) → Agent(L3) → 管线(L4) → 你的场景