Local development

make up          # postgres + api + web + mlflow via docker-compose
make migrate     # alembic upgrade head
make hpo         # HPO annotations: what the ranking matches against
make published-case   # a real published patient
make test        # api, ml, loader and web tests

Then open http://localhost:5173.

Making the "Analyse case" button work

docker-compose has no Nextflow, so the button marks the job failed with the command to run instead. To get the real thing, run the API on the host, where Nextflow and Docker are:

docker compose up -d db
cd api && DATABASE_URL=postgresql+asyncpg://rarelens:rarelens@localhost:5432/rarelens \
  PIPELINE_DATABASE_URL=postgresql+asyncpg://rarelens:[email protected]:5432/rarelens \
  LOCAL_DATA_ROOT=$PWD/.. VEP_DATABASE=true \
  uv run --extra dev uvicorn app.main:app --port 8000

PIPELINE_DATABASE_URL is what the loader container gets. Inside it, the API's own localhost would be the container itself.
LOCAL_DATA_ROOT is the directory a case's vcf_uri must sit under. This is a security boundary, not a convenience — see Gotchas.

Ports

Service	Port	Note
web	5173
api	8000
postgres	5432
MLflow	5001	not 5000: macOS AirPlay Receiver owns 5000 and answers 403

Training a model

make training-set   # ClinVar-derived table, ~370k labelled variants
make train          # fits, reports held-out metrics by gene split, moves the production alias

The API scores with models:/rarelens-pathogenicity@production. Setting MODEL_URI to an artifact path skips the registry entirely, which is how the serverless deployment scores with no MLflow server running.

What the metrics do and do not mean is in Benchmarks. Read that before quoting them.

rarelens

Understanding it

Working on it

Running it

When it goes wrong