rarelens

A small, end-to-end variant interpretation platform for rare genetic disease research. A case is a proband: a VCF plus the patient's phenotype (HPO terms). A Nextflow workflow annotates the variants with Ensembl VEP, a model scores each one, and the app narrows thousands of variants to a handful of candidates ranked against that phenotype — each carrying the evidence for its rank, and each able to be shortlisted or dismissed with a reason that ends up in a case report.

This repository is a self-training lab. It exists so that one engineer can learn, in public, how a modern life-sciences platform is built end to end: full-stack application, scientific pipeline, ML serving, and cloud infrastructure, all in one monorepo. It is not a clinical tool and makes no diagnostic claims.

What is in the box

Layer	Technology	Directory
Pipeline	Nextflow DSL2, bcftools, Ensembl VEP, Docker, Google Batch	pipeline/
API	FastAPI, Pydantic v2, SQLAlchemy 2.0 (async), Alembic	api/
Database	PostgreSQL 16	docker-compose.yml
Frontend	SvelteKit, TypeScript	web/
ML	LightGBM pathogenicity scorer, MLflow registry	ml/
Orchestration	Cloud Run job (default), Argo Workflows + Argo Events, Pub/Sub	infra/terraform/, infra/argo-workflows/
Platform	Kubernetes (Kustomize), ArgoCD (GitOps)	infra/k8s/, infra/argocd/
Cloud	GCP: Cloud Run, Google Batch, GCS, Secret Manager, Artifact Registry; GKE Autopilot and Cloud SQL behind flags	infra/terraform/
CI/CD	GitHub Actions, Workload Identity Federation	.github/workflows/

Quick start (local)

make up          # postgres + api + web + mlflow via docker-compose
make migrate     # alembic upgrade head
make hpo         # HPO gene-to-phenotype annotations: what the ranking matches against
make demo-case   # a simulated proband: GIAB background + one ClinVar pathogenic variant
make published-case  # a real published patient: their reported phenotype and causal variant
make test        # api, ml, loader and web tests (no Docker needed for the DB tests)

Then open http://localhost:5173, create a case pointing at data/proband-simulated.vcf.gz, give it the phenotype of the planted disease (for the default NF2 case: bilateral vestibular schwannoma, sensorineural hearing impairment, tinnitus, meningioma, cataract), and analyse it. The planted variant should come back ranked first.

make published-case is the same idea with nothing invented. It builds a case from a GA4GH phenopacket curated from a peer-reviewed case report — by default the TGFBR2 proband from Loeys et al., Nat Genet 2005, 10.1038/ng1511, the paper that first described Loeys-Dietz syndrome. The patient's 30 reported HPO terms and their causal variant come straight from the publication; the background variants come from GIAB HG002, because the rest of that patient's genome is not public. It writes the phenotype list alongside the VCF, so the case can be created exactly as reported. See docs/data.md for the provenance and for what this case does and does not demonstrate.

The docker-compose API has no Nextflow, so "Analyse case" marks the job failed with the command to run instead. With Nextflow and Docker on the host, a VEP cache in pipeline/cache/vep and a VCF under data/ (see data/README.md):

make annotate JOB=<job id from the UI> VCF=data/example.vcf.gz
make pipeline VCF=data/example.vcf.gz   # dry run: annotate without touching the database

No cache? VEP_DATABASE=true queries Ensembl's public database instead. It is slow per variant and returns fewer fields, but it needs no 25 GB download, which is enough to demonstrate the pipeline on a handful of variants:

VEP_DATABASE=true make pipeline VCF=pipeline/tests/data/tiny.vcf

To make the UI's "Analyse case" button work, run the API on the host (where Nextflow and Docker are) rather than in docker-compose:

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.

To train and register a model (the API scores with models:/rarelens-pathogenicity@production):

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

What those metrics do and do not mean is in docs/data.md; the headline AUROC flatters a model whose strongest feature is the consequence class.

Local Kubernetes: make kind builds the images, loads them into a kind cluster and applies infra/k8s/overlays/local.

Deploying to GCP

Two tracks, same code. The serverless one is the default because it costs about £1/month idle; docs/cloud.md has the numbers.

Serverless (Cloud Run + Google Batch). The API and the UI scale to zero, and the Nextflow driver runs as a Cloud Run job only while a pipeline is running.

cd infra/terraform
terraform init -backend-config="bucket=<tfstate bucket>"
export TF_VAR_database_url='postgresql+asyncpg://user:pass@host/db?sslmode=require'  # e.g. Neon's free tier
terraform apply -var project=<project id>          # add -var deploy_cloud_sql=true to use Cloud SQL instead
cd ../.. && make serverless-deploy PROJECT=<project id> TAG=<commit sha>   # redeploy a new build

terraform output web_url is the URL to share; it serves the UI and proxies /api to the API, so there is one public address and no CORS. Upload the VEP cache to gs://<project>-rarelens-data/refs/vep before running a real annotation, and set -var model_uri=gs://<project>-rarelens-data/models/pathogenicity/1 to score without running an MLflow server. Set a billing budget first — the demo has no authentication.

Kubernetes (GKE + Argo + ArgoCD). Off by default; turn it on to demonstrate the GitOps path, then destroy it.

terraform apply -var project=<project id> -var deploy_kubernetes=true -var deploy_cloud_sql=true
make gcp-configure PROJECT=<project id>   # once; commit the result
make gcp-secrets PROJECT=<project id>

Then install Argo Workflows, Argo Events and ArgoCD, and kubectl apply -f infra/argocd/app.yaml. Every green CI run on main bumps image tags in the gcp overlay and ArgoCD deploys them. make serverless-destroy PROJECT=<project id> tears everything down.

Data

The demo runs on published, openly licensed human data: the NIST Genome in a Bottle HG002 benchmark genome as the background sample, ClinVar for labels, gnomAD for allele frequencies, the Human Phenotype Ontology's gene-to-phenotype annotations as what the ranking matches against, and GA4GH phenopackets curated from case reports for the published case. Sources, licences, citations and how the model should be evaluated honestly are in docs/data.md.

Architecture

See docs/architecture.md for the diagram and the reasoning behind each choice, and docs/cloud.md for why this deploys to Google Cloud rather than AWS.

Status

A self-training lab, built in the open. Working end to end:

1. Postgres, FastAPI, Nextflow VEP annotation on a public VCF, CI green
2. SvelteKit UI: cases, a phenotype-ranked candidate list showing the evidence behind each rank, shortlist/dismiss decisions, and a case report
3. Kubernetes manifests, kind, Argo Workflows trigger
4. Terraform for GCP: serverless Cloud Run + Batch by default, GKE and ArgoCD behind a flag
5. Pathogenicity model trained on ClinVar, MLflow registry, scoring endpoint
6. A demo case built from a published patient, with the citations behind it

Known gaps: allele frequencies need the 25 GB VEP cache, because VEP's database mode returns none, so the rarity term does no work without it; and the deployed demo has no authentication.

Licence

AGPL-3.0. Test data are public (ClinVar, gnomAD subsets); no patient data are used or accepted.