Initial release: rarelens platform skeleton (AGPL-3.0)

End-to-end variant interpretation platform for rare genetic disease research:
SvelteKit UI, FastAPI + PostgreSQL API, Nextflow/Ensembl VEP pipeline,
LightGBM pathogenicity scoring with MLflow, K8s/ArgoCD/GCP infrastructure.
Public test data only; no clinical claims.

api/app/models.py

@@ -0,0 +1,77 @@
+"""SQLAlchemy 2.0 declarative models.
+
+One sample -> many jobs; one job -> many variants; one variant -> one prediction (latest).
+"""
+from datetime import datetime
+import enum
+import uuid
+
+from sqlalchemy import DateTime, Enum, Float, ForeignKey, Integer, String, Text, func
+from sqlalchemy.dialects.postgresql import JSONB, UUID
+from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship
+
+
+class Base(DeclarativeBase):
+    pass
+
+
+class JobStatus(str, enum.Enum):
+    queued = "queued"
+    running = "running"
+    succeeded = "succeeded"
+    failed = "failed"
+
+
+class Sample(Base):
+    __tablename__ = "samples"
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    name: Mapped[str] = mapped_column(String(120), unique=True)
+    vcf_uri: Mapped[str] = mapped_column(Text)
+    assembly: Mapped[str] = mapped_column(String(10), default="GRCh38")
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
+    jobs: Mapped[list["Job"]] = relationship(back_populates="sample")
+
+
+class Job(Base):
+    __tablename__ = "jobs"
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    sample_id: Mapped[uuid.UUID] = mapped_column(ForeignKey("samples.id", ondelete="CASCADE"))
+    status: Mapped[JobStatus] = mapped_column(Enum(JobStatus), default=JobStatus.queued)
+    workflow_ref: Mapped[str | None] = mapped_column(String(200))   # Argo workflow name / nf run id
+    vep_version: Mapped[str | None] = mapped_column(String(40))
+    log: Mapped[str | None] = mapped_column(Text)
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
+    finished_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
+    sample: Mapped[Sample] = relationship(back_populates="jobs")
+    variants: Mapped[list["Variant"]] = relationship(back_populates="job")
+
+
+class Variant(Base):
+    __tablename__ = "variants"
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    job_id: Mapped[uuid.UUID] = mapped_column(ForeignKey("jobs.id", ondelete="CASCADE"), index=True)
+    chrom: Mapped[str] = mapped_column(String(10), index=True)
+    pos: Mapped[int] = mapped_column(Integer, index=True)
+    ref: Mapped[str] = mapped_column(Text)
+    alt: Mapped[str] = mapped_column(Text)
+    gene: Mapped[str | None] = mapped_column(String(60), index=True)
+    consequence: Mapped[str | None] = mapped_column(String(120))
+    impact: Mapped[str | None] = mapped_column(String(20))
+    hgvsc: Mapped[str | None] = mapped_column(Text)
+    hgvsp: Mapped[str | None] = mapped_column(Text)
+    gnomad_af: Mapped[float | None] = mapped_column(Float)
+    clinvar_sig: Mapped[str | None] = mapped_column(String(120))
+    annotations: Mapped[dict] = mapped_column(JSONB, default=dict)   # full VEP CSQ record
+    job: Mapped[Job] = relationship(back_populates="variants")
+    prediction: Mapped["Prediction | None"] = relationship(back_populates="variant", uselist=False)
+
+
+class Prediction(Base):
+    __tablename__ = "predictions"
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    variant_id: Mapped[int] = mapped_column(ForeignKey("variants.id", ondelete="CASCADE"), unique=True)
+    model_name: Mapped[str] = mapped_column(String(80))
+    model_version: Mapped[str] = mapped_column(String(40))
+    score: Mapped[float] = mapped_column(Float)         # P(pathogenic)
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
+    variant: Mapped[Variant] = relationship(back_populates="prediction")