Kemal Yaylali
ae58e33fe2
Makes a real annotation runnable locally without the 25 GB VEP cache, which is what the demo needs and what a reviewer can reproduce in minutes. - params.vep_database (VEP_DATABASE=true) queries Ensembl's public database instead of a local cache. Slower per variant and fewer fields, so --everything is swapped for the flags the loader actually stores. Its cache placeholder is NO_CACHE, not NO_FILE: Nextflow rejects two staged inputs sharing a filename. - PIPELINE_DATABASE_URL is handed to the pipeline when set. The loader runs inside a container, where the API's own localhost URL would point at the container itself. - README: how to run the UI's annotate button locally against host Nextflow + Docker. Verified end to end on pipeline/tests/data/tiny.vcf: bcftools norm split the multiallelic record, VEP 113 annotated 4 variants live, the loader wrote them and marked the job succeeded, and the UI shows them. The deletion came back as 22:42126611 CT>C with exact VCF alleles, which is the case the audit's ID-tagging fix exists for. Tests: api 51, loader 16, stub run 3/3; ruff, mypy clean.
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
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 "Run VEP annotation" 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 sample's vcf_uri must sit under.
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.