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