DNA_Insight

Upload your Raw DNA safely and locally(ran on the web app so your data isn't saved) for extra insight

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Genome Full App

DNA analysis web app with a Flask backend and Next.js (App Router) frontend. Handles parent DNA uploads, child trait/health predictions, PDF reports, an optional AI explainer, and a demo Height Polygenic Score (PGS).

Prerequisites

  • Python 3.10+ (backend)
  • Node 18+ (frontend / Next.js 14)
  • Git (Git LFS if you choose to store large datasets)

Environment

Create a .env (or .env.local) in the repo root using .env.example:

OPENAI_API_KEY=your_openai_api_key
OPENAI_MODEL=gpt-4o-mini

The AI explainer is optional; without a key, that endpoint returns 503.

Backend setup (Flask)

cd backend
python -m venv .venv
.\.venv\Scripts\activate   # Windows PowerShell
pip install -r requirements.txt
python app.py

The server listens on port 5000.

Reference data (not committed)

Large files like backend/clinvar.gz, backend/nih/clinvar.gz, and backend/nih/dbsnp.gz are ignored. Place them under backend/ locally or use Git LFS if needed.

Uploads

User uploads live under backend/upload/ and backend/uploads/ (ignored by git).

Frontend setup (Next.js)

cd frontend
npm install
npm run dev

Dev server runs on port 3000. Ensure the backend (port 5000) is running for API calls.

Features

  • Parent DNA upload + child trait/health prediction (/parents, /child-results)
  • Punnett-style visualizations and genotype heatmaps
  • PDF report generation
  • In-memory results cache: only a small childResultId is stored in sessionStorage; large payloads stay in memory
  • Optional AI summary (/api/explain-results) when OPENAI_API_KEY is set
  • Height Polygenic Score demo (/height_pgs backend, /height frontend)

Basic workflow

1) Start backend (port 5000). 2) Start frontend (port 3000). 3) Upload parent DNA files; view child results. 4) Optionally generate a PDF or request an AI explanation. 5) For height PGS, open /height, upload a single raw DNA file, and view the bell-curve card.

Height PGS

  • Backend endpoint: POST /height_pgs with form-data file, optional sex (male/female/unspecified), and optional global_ancestry JSON (e.g. {"AFR":0.6,"EUR":0.4}).
  • Demo weights: backend/nih/height_demo_weights.csv (swap with real weights as needed; supports beta_afr, beta_eur, etc columns).
  • Output: raw/z scores, percentile, predicted height with CI90/CI95, coverage, confidence tier, warnings, ancestry breakdown, and ancestry component scores.
  • Frontend page: /height with upload + sex/ancestry inputs and visualization.
  • If global_ancestry is omitted, the backend attempts to infer it from an AIMs panel at backend/nih/height_ancestry_aims.csv (populate with reference frequencies).

Height Calibration Engine

  • Configurable calibration lives in backend/utils/height_calibration/config.yaml.
  • Training: python -m utils.height_calibration.train --input data/train.csv --config backend/utils/height_calibration/config.yaml --output-model data/height_calibration.joblib --output-metrics data/height_calibration_metrics.json.
  • Inference: python -m utils.height_calibration.infer --config backend/utils/height_calibration/config.yaml --input-json data/sample_input.json.

Height SNP Catalog

  • Build unified catalog: python -m utils.height_catalog.ingest --config backend/utils/height_catalog/config.json --output-tsv data/height_catalog.tsv --output-report data/height_catalog_report.json.
  • Configure GWAS inputs by populating sources in backend/utils/height_catalog/config.json (GIANT, UKBB, PAGE, MVP, H3Africa, GWAS Catalog).

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Troubleshooting

  • Storage/Quota: Large genomes are never stored in browser storage; only small IDs are. Reloading drops in-memory caches—re-upload to regenerate results.
  • Push rejected for large files: keep datasets/caches out of git; use LFS or download scripts if needed.
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