# 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 | Argo Workflows + Argo Events (pipeline), Pub/Sub (events) | `infra/argo-workflows/` | | Platform | Kubernetes (Kustomize), ArgoCD (GitOps) | `infra/k8s/`, `infra/argocd/` | | Cloud | GCP: GKE Autopilot, Cloud SQL, GCS, Batch, Secret Manager, Artifact Registry | `infra/terraform/` | | CI/CD | GitHub Actions, Workload Identity Federation | `.github/workflows/` | ## Quick start (local) ```bash 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](https://doi.org/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](docs/data.md) for the provenance and for what this case does and does not demonstrate. The docker-compose API has no Nextflow, so "Run VEP annotation" 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](data/README.md)): ```bash make annotate JOB= 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: ```bash VEP_DATABASE=true make pipeline VCF=pipeline/tests/data/tiny.vcf ``` To make the UI's "Run VEP annotation" button work, run the API on the host (where Nextflow and Docker are) rather than in docker-compose: ```bash docker compose up -d db cd api && DATABASE_URL=postgresql+asyncpg://rarelens:rarelens@localhost:5432/rarelens \ PIPELINE_DATABASE_URL=postgresql+asyncpg://rarelens:rarelens@host.docker.internal: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 sample's `vcf_uri` must sit under. To train and register a model (the API scores with `models:/rarelens-pathogenicity@production`): ```bash 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](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](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. ```bash cd infra/terraform terraform init -backend-config="bucket=" export TF_VAR_database_url='postgresql+asyncpg://user:pass@host/db?sslmode=require' # e.g. Neon's free tier terraform apply -var project= # add -var deploy_cloud_sql=true to use Cloud SQL instead cd ../.. && make serverless-deploy PROJECT= TAG= # 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://-rarelens-data/refs/vep` before running a real annotation, and set `-var model_uri=gs://-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. ```bash terraform apply -var project= -var deploy_kubernetes=true -var deploy_cloud_sql=true make gcp-configure PROJECT= # once; commit the result make gcp-secrets PROJECT= ``` 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=` tears everything down. ## Data The demo runs on published, openly licensed human data: the NIST Genome in a Bottle HG002 benchmark genome as the sample, ClinVar for labels, gnomAD for allele frequencies. Sources, licences, citations and how the model should be evaluated honestly are in [docs/data.md](docs/data.md). ## Architecture See [docs/architecture.md](docs/architecture.md) for the diagram and the reasoning behind each choice, and [docs/cloud.md](docs/cloud.md) for why this deploys to Google Cloud rather than AWS. ## Status Work in progress. Milestones, in order: 1. Skeleton, Postgres, FastAPI, Nextflow VEP annotation on a public VCF, CI green 2. SvelteKit UI: sample list, variant table with filters, job status 3. Kubernetes manifests, kind, Argo Workflows trigger 4. Terraform for GCP, ArgoCD GitOps deploy 5. Pathogenicity model, MLflow registry, prediction endpoint ## Licence AGPL-3.0. Test data are public (ClinVar, gnomAD subsets); no patient data are used or accepted.