AI-Interactive Genomic Visualization + Variant Risk Screening (Research-Grade / Informational)
Genome Copilot is a privacy-first genomic application that lets users upload their DNA/genetic data, explore an interactive genome viewer, and chat with an AI copilot that explains variants and jumps to the exact genomic region being discussed.
Important Disclaimer Genome Copilot is not a medical device and does not provide medical diagnosis, treatment recommendations, or clinical decision support. Outputs are informational / research-grade and should be reviewed with qualified clinicians and confirmatory testing when relevant.
- Upload VCF (baseline required for robust analysis)
- Upload 23andMe / Ancestry raw genotype TXT/CSV (auto-converted to internal variant format)
- File format detection, integrity hashing, schema validation
- Encrypted-at-rest storage, versioning per upload
- Data deletion workflow (hard delete + retention policy)
- Normalize variant records:
chrom,pos,ref,alt - Annotate variants with:
- gene(s)
- transcript impact
- predicted consequence (missense, nonsense, frameshift, splice, etc.)
- population frequency (when available)
- known clinical assertions (when available)
- Classification buckets aligned with common 5-tier terminology:
- Pathogenic / Likely Pathogenic / VUS / Likely Benign / Benign
- Focused screening tracks:
- Rare disease high-impact Mendelian-associated variants
- Hereditary cancer risk indicators (configurable gene panels)
Ask questions like:
- “Do I have any cancer-related variants?”
- “What does this BRCA1 variant mean?”
- “Why was this flagged?”
AI responses are structured:
- Summary
- What we found (variants from your dataset)
- Evidence (why flagged)
- Confidence + uncertainty
- Suggested next steps (educational + “consider confirmatory testing”)
When the AI references a variant, it renders:
- “View in Genome” (button or chip)
- Clicking navigates the viewer to:
- chromosome + coordinate
- optional region window (±50–200 bp)
- highlighted variant track
- Interactive genome viewer (recommended: igv.js)
- Features:
- coordinate browsing
- gene tracks + variant overlay
- click-to-inspect variants
- jump-to coordinate search
- highlight + focus
- Rare Disease Screening Summary
- Hereditary/Cancer Indicator Summary
- Filterable “All Variants” view
- Export:
- PDF summaries (planned)
- JSON results (advanced users)
Workspace Layout (Core)
- Left: AI Copilot chat
- Center: Genome viewer (IGV-style)
- Right: Findings panel (filters + search)
Variant Detail Drawer
- gene
- coordinate
- consequence
- classification tier (P/LP/VUS/LB/B)
- evidence blocks
- actions:
- “View in Genome”
- “Ask AI about this”
Critical Interaction Model
- Chat references variant(s)
- Variants appear as clickable chips
- Clicking a chip:
- navigates IGV
- opens detail drawer
- optionally pins evidence context
This repo can be implemented as a modern web app with async genomics processing.
Frontend
- Next.js (TypeScript)
- igv.js embedded viewer
- Chat UI with tool-calling for
navigateTo(chrom, pos, windowBp) - Virtualized lists for large variant tables
Backend
- API service for uploads, jobs, results, chat orchestration
- Async job queue for analysis (VCF/WES/WGS can take minutes)
- Evidence + annotation resolvers
Data Stores
- Object storage for uploads (encrypted)
- Relational DB for entities + indexed variant querying
- Audit logs for sensitive access
Core entities:
UserGenomeUploadVariantAnnotationFindingChatSessionChatMessageAuditLog
Example variant fields:
chrom,pos,ref,altgenes[]consequence,transcriptzygosity(if inferable)classification_tier(P/LP/VUS/LB/B)flags(rare_disease, hereditary_cancer, high_impact)
Typical endpoints for V1:
POST /api/uploadsGET /api/uploads/:id/status
POST /api/analysis/:uploadId/runGET /api/analysis/:uploadId/status
GET /api/variants?uploadId=...&gene=...&tier=...GET /api/findings?uploadId=...&category=rare_disease|hereditary_cancer
POST /api/chat- grounded on: user dataset + panels + conversation context
- returns structured answer + variant chips + navigation actions
Expose a single navigation API to unify chat + UI interactions:
/**
* Navigate genome viewer to a location and highlight the target locus.
*/
function navigateTo(chrom: string, pos: number, windowBp = 100): void
Chat messages should render variant chips that call navigateTo(...).
⸻
Security & Privacy
Genome Copilot is designed to be privacy-first:
• Encryption in transit (TLS) and at rest
• Access control per user workspace
• Audit logs for data access
• Clear deletion workflow (“Delete my data”)
• Minimal retention + automated purging policy
⸻
Performance & Reliability Targets
• Upload validation: immediate feedback
• Processing time expectations:
• Genotyping TXT: seconds → minutes
• VCF (WES/WGS): minutes (async)
• Responsive viewer + paginated/virtualized findings table
• Retries for annotation failures
• Health checks + metrics + job observability
⸻
AI Safety & Hallucination Controls
The copilot must:
• Never invent variants not present in the uploaded dataset
• If asked about a variant not found → respond: “Not found in uploaded data”
• Avoid medical diagnosis / certainty beyond evidence
• Treat VUS as uncertain with explicit uncertainty language
• Recommend confirmatory clinical testing for high-risk findings
⸻
QA & Acceptance Criteria
Upload & Processing
• ✅ Upload accepts VCF under size limits and validates header
• ✅ Genotyping TXT converts successfully (supported vendors)
• ✅ Analysis completes and produces findings list
Viewer Navigation
• ✅ “View in Genome” navigates to correct chrom/pos
• ✅ Variant highlight visible and consistent
AI Safety
• ✅ No diagnosis or overstated certainty
• ✅ No invented findings
• ✅ VUS responses include uncertainty language
Reporting
• ✅ Summaries include disclaimers + top findings
• ✅ PDF export includes selected findings (Phase 2)
⸻
Roadmap
Phase 1 (MVP)
• Upload VCF + genotype TXT
• Basic annotation + findings list
• Grounded chat copilot
• igv.js integration + jump-to navigation
Phase 2
• Improved evidence blocks + more sources
• Filtering (panels, severity, gene search)
• PDF export
• Additional tracks (gene models, reference annotations)
Phase 3
• Optional BAM/CRAM support
• Advanced disease panels
• Family history guided intake (non-diagnostic)
• Optional true 3D genome visualization module
⸻
Risks & Mitigations
Risk: Over-medicalization / user panic
Mitigation: Strong disclaimers, careful language, non-alarming UX, confirmatory testing prompts
Risk: Misclassification or inconsistent interpretations
Mitigation: Informational labeling, transparent evidence, reanalysis + reclassification support
Risk: Data privacy concerns
Mitigation: Encryption, deletion tools, user control, minimal retention
⸻
Contributing
Contributions are welcome, especially in:
• VCF normalization & parsing improvements
• Annotation pipeline integrations
• Evidence transparency UX
• IGV track configuration + performance
• Safe structured output enforcement for AI copilot
⸻
License
MIT License