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.
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
import uuid
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from fastapi import APIRouter, Depends, Query
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from sqlalchemy import func, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.orm import selectinload
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from app.db import get_session
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from app.models import Prediction, Variant
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from app.schemas import VariantPage
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router = APIRouter()
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@router.get("", response_model=VariantPage)
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async def list_variants(
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job_id: uuid.UUID,
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gene: str | None = None,
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impact: str | None = Query(None, pattern="^(HIGH|MODERATE|LOW|MODIFIER)$"),
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max_af: float | None = Query(None, ge=0, le=1),
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min_score: float | None = Query(None, ge=0, le=1),
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limit: int = Query(50, le=500),
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offset: int = 0,
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session: AsyncSession = Depends(get_session),
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):
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stmt = select(Variant).where(Variant.job_id == job_id)
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if gene:
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stmt = stmt.where(Variant.gene == gene.upper())
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if impact:
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stmt = stmt.where(Variant.impact == impact)
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if max_af is not None:
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stmt = stmt.where((Variant.gnomad_af.is_(None)) | (Variant.gnomad_af <= max_af))
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if min_score is not None:
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stmt = stmt.join(Prediction, Prediction.variant_id == Variant.id).where(Prediction.score >= min_score)
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total = await session.scalar(select(func.count()).select_from(stmt.subquery()))
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rows = await session.scalars(
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stmt.options(selectinload(Variant.prediction))
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.order_by(Variant.chrom, Variant.pos)
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.limit(limit)
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.offset(offset)
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)
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return VariantPage(items=rows.all(), total=total or 0, limit=limit, offset=offset)
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