Kemal Yaylali
11fb6b3d73
An end-to-end audit found the repo could not build, test or run as shipped. This fixes every finding, then adds a Cloud Run track so the demo costs about £1/month idle instead of ~£150. CI (red on its first run) - api: setuptools could not build the package (flat layout with app/ and alembic/) - web: missing @types/node; `vitest run` exited 1 with no test files - pipeline: the stub run needed a gitignored VCF, and no process had a stub block - ruff pinned, mypy configured, DB tests on real Postgres (pgserver locally, service in CI) ML serving (scores were meaningless) - the registered model now carries its own feature engineering and returns predict_proba, so serving sends raw columns and cannot drift from training - resolve by registry alias (stages are deprecated in MLflow 3) and record the real version; re-scoring upserts instead of failing on the unique constraint - ClinVar labels parsed from VEP's lowercase terms Pipeline - exact ref/alt recovered from a CHROM_POS_REF_ALT VCF ID; loading is idempotent - job status reaches running/failed/succeeded, so the UI stops polling dead jobs - DATABASE_URL travels in the environment or a Nextflow secret, never on a command line - VEP cache and plugins staged as inputs; the gcp profile runs tasks on Google Batch Deployment - the API serves /api (matching the ingress); the web app reads its API URL at runtime - migrations run in an init container under a Postgres advisory lock - terraform: custom VPC shared with Batch, private Cloud SQL, API enablement, Workload Identity bindings, Secret Manager, deletion protection - serverless track, now the default: Cloud Run services scaling to zero, a Cloud Run job for the Nextflow driver, and Neon or Cloud SQL behind one DATABASE_URL secret. GKE and Argo remain, behind -var deploy_kubernetes=true. See docs/cloud.md. Correctness and security - 409 on duplicate sample names, 422 on bad paging, natural chromosome ordering, wider VEP text columns, enum dropped on downgrade, the sample's assembly actually used - vcf_uri restricted to gs:// objects or files under the data root, blocking option injection - CORS restricted to configured origins; `make down` no longer deletes volumes Data - docs/data.md records the peer-reviewed, openly licensed sources (GIAB HG002, ClinVar, gnomAD) with citations and an honest evaluation plan; `make data` fetches a chr22 slice Verified: api 50 tests, ml 18, loader 16, web 12; ruff, mypy, svelte-check, terraform validate and both kustomize overlays clean.
5.4 KiB
rarelens
A small, end-to-end variant interpretation platform for rare genetic disease research. Scientists upload a VCF, a Nextflow workflow annotates it with Ensembl VEP, a machine learning model scores each variant, and results are browsable in a web app.
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)
make up # postgres + api + web + mlflow via docker-compose
make migrate # alembic upgrade head
make data # real public data: GIAB HG002 + ClinVar, chr22 (needs bcftools)
make test # api, ml, loader and web tests (no Docker needed for the DB tests)
Then open http://localhost:5173.
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):
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
To train and register a model (the API scores with models:/rarelens-pathogenicity@production):
cd ml && MLFLOW_TRACKING_URI=http://localhost:5000 \
uv run python -m rarelens_ml.train --tsv ../pipeline/results/<sample>.vep.tsv --register
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 sample, ClinVar for labels, gnomAD for allele frequencies. 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
Work in progress. Milestones, in order:
- Skeleton, Postgres, FastAPI, Nextflow VEP annotation on a public VCF, CI green
- SvelteKit UI: sample list, variant table with filters, job status
- Kubernetes manifests, kind, Argo Workflows trigger
- Terraform for GCP, ArgoCD GitOps deploy
- Pathogenicity model, MLflow registry, prediction endpoint
Licence
AGPL-3.0. Test data are public (ClinVar, gnomAD subsets); no patient data are used or accepted.