Your Genome.Sequenced, annotated, and ready to explore.

.genome is an open file format for genome data, designed to be read and interpreted by AI systems. It uses the same underlying data as a VCF, but restructures it so models can process it reliably, with clear provenance and without relying on partial parsing or guesswork.

Under the hood it's not one flat file but a structured, queryable bundle (format version .genome/1.0). Your variants are stored in fast columnar tables alongside the annotations that give them meaning — trait associations, the supporting research behind them, gene-level context, polygenic scores, and pharmacogenomics — so a tool can answer questions against your whole genome directly.

A whole-genome sequence fills it out completely (~4.3M variants); uploads from array-based tests like 23andMe or Ancestry come in smaller (~600k). Either way, it's a single portable file that travels with you.

Compared to raw VCF files, .genome reduces token usage by 3–10× while reducing factual errors by 10–20×.

A .genome file is a portable, AI-readable container for your genetic data. The point is that it travels with you and works inside any tool that can read a file, so you're not locked into one platform's interface.

Concretely, you can:

• Drop it into an AI coding/agent tool — Codex, Claude Code, Cursor, or anything that can ingest a file — and treat your genome as queryable context. The file format (.genome/1.0) is structured so the model can parse variants, archetype data, and phenotype context directly.
• Ask open-ended questions — "What does my genotype suggest about caffeine metabolism?", "Which of my variants relate to sleep?", "How should I read my stress-recovery axis?" — and get answers grounded in your actual data rather than generic advice.
• Test hypotheses — pull in a paper or a new GWAS finding and ask the tool to check it against your specific genotypes, so you can see whether a result actually applies to you.
• Stay current — re-run analysis as new research lands (ClinVar/PharmGKB updates, new associations) without re-sequencing. The same file gets re-interpreted against newer knowledge.

All three are true whole-genome sequencing — they read your entire genome, not a fixed chip of ~650K sites like an array. The "x" is depth: how many times, on average, each position in your genome gets read. More depth = more confidence, especially on rare variants.

3x — reads your whole genome lightly. Confident on the common-variant layer that powers your identity, polygenic scores, ancestry, and trait insights — and already far beyond any array. It can see rarer variants but not yet call them with clinical confidence. A real entry into whole-genome.

30x — the clinical-grade standard. Enough depth to confidently call rare and pathogenic variants: carrier status, actionable ClinVar findings, full pharmacogenomics. This is "your real genome" with no imputation caveats — the sweet spot for almost everyone.

100x — research-grade, maximum confidence. Diminishing returns for everyday genomics, but it pulls ahead on the hardest cases: low-frequency and mosaic variants, structural variation, and difficult-to-read regions. The deepest, most future-proof read.

Whichever depth you choose, it converts into the same .genome bundle — same format, same tools. The difference is how much of your genome answers with confidence: 3x lights up your identity and trait layer; 30x adds the full clinical and pharmacogenomic layer; 100x maxes out certainty on the rarest signals.