From raw sequence to readable interpretation, one traceable pipeline
Follow your sample as sequencing reads become quality-controlled features, taxonomic and functional profiles, and finally the Scores and Panels you read in ATLAS™.
Between a sample and a report sits a sequence of well-defined bioinformatics steps — quality control, taxonomic assignment, functional annotation, and feature engineering — that transform raw sequencing output into structured, sequencing-derived microbial features. VAMS ATLAS™ then organizes those features into Scores, Panels, and confidence indicators. Every step preserves lineage, so each interpretation can be traced back to the data it came from. The pipeline is built for transparency and reproducibility; it summarizes microbial patterns for educational interpretation and does not perform diagnosis.
Why bioinformatics is where trust is built
From millions of raw reads to something you can read.
The credibility of any microbiome interpretation depends on how carefully the underlying data is processed. Reads must be quality-filtered, features assigned with defined methods, and results contextualized against reference cohorts before any narrative is drawn. Our pipeline applies consistent, documented processing so that interpretation depth never outruns data quality — and so the confidence indicators shown in ATLAS™ genuinely reflect what the sequencing data supports. This is method-supported interpretation with visible limits, not clinical-grade testing, and it describes associations rather than causation.
Because lineage is preserved end to end, the same sample processed under the same method should yield consistent, reproducible features — the foundation of longitudinal tracking.
Derived from sequencing data. For educational and informational use only; not intended for diagnosis or treatment.
Raw Data Processing
Raw Data Processing explains the scientific role this topic plays within VAMS BIOME’s microbiome intelligence architecture.
Raw Data Processing describes how raw sequencing data is transformed into structured microbial features, taxonomic profiles, functional annotations, scores, panels, and longitudinal signals.
Derived from sequencing data. For educational and informational use only; not intended for diagnosis or treatment.
Taxonomic Assignment
Taxonomic Assignment explains the scientific role this topic plays within VAMS BIOME’s microbiome intelligence architecture.
Taxonomic Assignment describes how raw sequencing data is transformed into structured microbial features, taxonomic profiles, functional annotations, scores, panels, and longitudinal signals.
Functional Annotation
Functional Annotation explains the scientific role this topic plays within VAMS BIOME’s microbiome intelligence architecture.
Functional Annotation describes how raw sequencing data is transformed into structured microbial features, taxonomic profiles, functional annotations, scores, panels, and longitudinal signals.
Feature Engineering
Feature Engineering explains the scientific role this topic plays within VAMS BIOME’s microbiome intelligence architecture.
Feature Engineering describes how raw sequencing data is transformed into structured microbial features, taxonomic profiles, functional annotations, scores, panels, and longitudinal signals.
Score Generation
Score Generation explains the scientific role this topic plays within VAMS BIOME’s microbiome intelligence architecture.
Score Generation describes how raw sequencing data is transformed into structured microbial features, taxonomic profiles, functional annotations, scores, panels, and longitudinal signals.
Panel Construction
Panel Construction explains the scientific role this topic plays within VAMS BIOME’s microbiome intelligence architecture.
Panel Construction describes how raw sequencing data is transformed into structured microbial features, taxonomic profiles, functional annotations, scores, panels, and longitudinal signals.
Longitudinal Analytics
Longitudinal Analytics explains the scientific role this topic plays within VAMS BIOME’s microbiome intelligence architecture.
Longitudinal Analytics describes how raw sequencing data is transformed into structured microbial features, taxonomic profiles, functional annotations, scores, panels, and longitudinal signals.
Follow your sample from sequence to Score
Move from scientific understanding to the next practical step in the VAMS BIOME ecosystem.
See how sequencing reads become quality-controlled features and then the Scores, Panels, and confidence indicators presented in your report.
- ✓ Read quality control
- ✓ Taxonomic assignment
- ✓ Functional annotation
- ✓ Feature engineering
- ✓ Score and panel generation
The pipeline produces sequencing-derived microbial features for educational interpretation and wellness context. It is method-supported, describes associations, and is not a substitute for personal medical care.
Instrumented reality
The pipeline begins where instruments end.
Sequencers and analysers produce raw signal, not answers. The bioinformatics pipeline exists to carry that signal through quality control, curated references and versioned analysis before anything is interpreted.
Pipeline, illustrated
A gate, not a straight line.
Quality control is drawn as a real decision point: data that does not pass the QC and method-fit checks is held and returned with the reasons — not silently analysed.
- Accepted input Raw reads or already-processed tables, with the method and run details that produced them. FASTQ readsProcessed tables
- QC and method fit Read quality, depth, completeness and metadata are checked, and the data is tested against what the method can support. Does not pass: held and returned with the QC reasons, rather than analysed with a caveat.
- Analysis Taxonomic or functional assignment, run on a recorded pipeline version.
- Structured output Tables and figures returned with the method, pipeline version and the limits that apply to them.