A table with filters made the user do the work. Rare disease triage is a different task:
which few variants could explain *this* patient's phenotype, and why. The app now answers
that, and lets a reviewer act on the answer.
Domain
- a case is a proband: a VCF plus the HPO terms observed in the patient (samples -> cases)
- HPO's gene-to-phenotype annotations are loaded as reference data (scripts/load-hpo.py)
- each candidate can be shortlisted or dismissed with a reason and a note
Ranking (app/services/triage.py, 21 tests)
- weighted sum of phenotype match, rarity, consequence severity and the model's score,
with every component shown next to the candidate
- rarity and consequence filter; phenotype only ranks, because a real diagnosis can sit in
a gene nobody has annotated yet and filtering on it would hide exactly that case
- ClinVar is deliberately not an input: it appears beside the result as independent
confirmation, so nothing ranks highly merely because ClinVar already said pathogenic
UI
- the funnel is the headline: variants called -> rare -> coding candidates -> phenotype-matched
- ranked candidates with evidence chips, not a grid of everything; filters are demoted
- a variant panel showing the score breakdown, the matched HPO terms, the raw VEP record and
links out to Ensembl/gnomAD/ClinVar, with the decision controls
- a printable case report: phenotype, funnel, shortlisted variants with reasons, provenance
API: /cases with phenotypes, /cases/{id}/candidates (funnel + ranked + weights),
/variants/{id}, /variants/{id}/decision, /cases/{id}/report, /phenotypes for the picker.
Scoring moved under the case and now answers 503 with the reason when no model registry is
reachable, instead of a 500.
Verified end to end on a simulated proband (scripts/make-demo-case.sh: real GIAB HG002
background + one real ClinVar 2-star pathogenic NF2 variant). 13 variants called -> 1 coding
candidate, and the planted variant ranks first at 0.80 on phenotype 1.00, rarity 1.00 and
consequence 1.00, with ClinVar agreeing afterwards.
Tests: api 75, ml 18, loader 16, web 27; ruff, mypy, svelte-check, terraform validate, both
kustomize overlays and the Nextflow stub run all clean.
"running" for two and a half minutes tells the user nothing. The job page now shows a
spinner, the elapsed time, and the pipeline step Nextflow is actually on.
- the API streams the Nextflow output into jobs.log as it arrives, instead of keeping it
only when the run dies. Writes are throttled to one every 3s, or immediately when a new
process starts, and are skipped for a job that has already finished, so a late line
cannot overwrite the loader's result.
- web/src/lib/progress.ts formats the elapsed time and picks the latest [PROCESS] line.
No percentage: the pipeline cannot honestly estimate one.
- the spinner animates only under prefers-reduced-motion: no-preference.
Verified on a live run: the page showed "VEP (tiny)" for the duration, then the variant
table replaced it on success.
Tests: api 53, web 20; ruff, mypy, svelte-check clean.
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.
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.
Kemal Yaylali