Skip to content

PLAN.md

Latest commit

 

History

History
1100 lines (907 loc) · 41.2 KB

PLAN.md

File metadata and controls

1100 lines (907 loc) · 41.2 KB
Raw

AI GENOMICS LAB — Master Plan

Local-first platform for clinical genomic analysis. Bioinformatics pipelines + Knowledge Graph + AI Agents + LLM reasoning.

Table of Contents

  1. Vision & Scope
  2. System Architecture
  3. Technology Stack
  4. Repository Structure
  5. Infrastructure & Environments
  6. Bioinformatics Pipeline
  7. Data Storage Strategy
  8. Knowledge Graph
  9. AI & LLM Layer
  10. Agent System
  11. Clinical Data Model
  12. Frontend & Visualization
  13. Security & Compliance
  14. Development Roadmap
  15. Testing Strategy
  16. Risk Assessment
  17. Performance & Scalability
  18. Future Enhancements

1. Vision & Scope

Objective

Build a research platform capable of analyzing patient DNA samples, detecting genomic variants, linking them to known diseases through a biomedical knowledge graph, and generating AI-assisted clinical reports.

Scope

In Scope Out of Scope (v1)
FASTQ/BAM/CRAM input processing Clinical-grade diagnostics (FDA/CE-IVD)
Germline SNV/Indel detection Somatic tumor/normal paired analysis
Cancer gene panel analysis (BRCA, TP53, KRAS, EGFR) Whole-genome sequencing at scale
ClinVar-based variant classification Custom ML model training
Knowledge graph queries (gene → mutation → disease) Real-time patient monitoring
LLM-assisted variant interpretation Direct-to-patient reporting
Interactive web dashboard Mobile application

Target Users

  • Clinical researchers exploring variant-disease associations
  • Bioinformaticians running and validating pipelines
  • Oncologists reviewing AI-generated variant reports (research use only)

2. System Architecture

High-Level Overview

┌─────────────────────────────────────────────────────────────────┐
│                        FRONTEND (Next.js)                       │
│   Dashboard │ Case Wizard │ Variant Table │ Genome Browser      │
└────────────────────────────┬────────────────────────────────────┘
                             │ REST API
┌────────────────────────────▼────────────────────────────────────┐
│                      API LAYER (FastAPI)                         │
│   Auth │ Patients │ Cases │ Samples │ Variants │ Reports        │
│   Pipeline Runner │ Genome Indexing │ Storage Management        │
└────┬──────────┬──────────┬──────────┬──────────┬───────────────┘
     │          │          │          │          │
     ▼          ▼          ▼          ▼          ▼
┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────────┐
│PostgreSQL│ │ Neo4j  │ │ MinIO  │ │  LLM   │ │Bio-Pipeline│
│ (relat.) │ │(graph) │ │(object)│ │(DeepSeek/MIMO)│ │ (Nextflow) │
└────────┘ └────────┘ └────────┘ └────────┘ └────────────┘

Data Flow

Patient Sample (FASTQ)
    │
    ▼
[Genome Index Check] ──→ Reference genome indexed? (hg38)
    │
    ▼
[Bioinformatics Pipeline]
    │  strobealign → BAM → CRAM → bcftools mpileup → VCF
    │
    ▼
[Variant Storage]
    │  PostgreSQL (structured data)
    │  Neo4j (variant → gene → disease graph)
    │  MinIO (raw files + reports)
    │
    ▼
[AI Analysis]
    │  Graph Agent queries Neo4j for context
    │  Variant Agent classifies variants
    │  LLM generates clinical interpretation
    │
    ▼
[Clinical Report]
    │  Variant summary
    │  Disease associations
    │  Therapeutic implications
    │  Confidence scores

Core Design Principles

Principle Implementation
Local-first All services run in Docker on local workstation
Containerized Every service has its own Dockerfile and resource limits
Modular pipelines Nextflow DSL2 for reproducible bioinformatics workflows
Reproducible Versioned containers, pinned dependencies, deterministic pipelines
AI-assisted LLMs augment (never replace) bioinformatics evidence
Audit-ready All actions logged with timestamps and user context

3. Technology Stack

Backend

Component Technology Version Purpose
API Framework FastAPI 0.100+ Async REST API with auto-generated OpenAPI docs
Language Python 3.11+ Type hints, performance, ecosystem
ORM asyncpg 0.29+ Async PostgreSQL driver (raw SQL, no ORM overhead)
Auth JWT + Argon2 — Stateless authentication with GPU-resistant hashing
Pipeline Orchestration Nextflow 23+ Reproducible bioinformatics workflows

Databases

Component Technology Version Purpose
Relational PostgreSQL 15 Patients, cases, samples, variants, reports
Graph Neo4j 5.14 Community Gene-mutation-disease-drug knowledge graph
Object Storage MinIO latest FASTQ, BAM, CRAM, VCF, reference genomes

Bioinformatics

Tool Version Purpose
strobealign latest Read alignment (5-8x faster than BWA-MEM)
SAMtools 1.19+ SAM/BAM/CRAM processing and indexing
bcftools 1.19+ Variant calling (mpileup) and filtering
GATK 4.5.0+ Genome Analysis Toolkit (optional advanced callers)
htslib 1.19+ Low-level HTS file I/O
vmtouch latest RAM page cache preloading for reference genomes

AI & LLM

Component Technology Purpose
LLM Provider DeepSeek/MIMO Multi-model API gateway
Agent Framework Custom Python Domain-specific agents (no heavy framework dependency)
Graph Reasoning Neo4j Cypher Direct knowledge graph queries
Response Cache File-based with TTL Avoid redundant LLM calls

Frontend

Component Technology Version Purpose
Framework Next.js 16 SSR/SSG React framework
UI Library React 18 Component architecture
Styling Tailwind CSS 3 Utility-first CSS
State Management Zustand 5 Lightweight state management
Graph Visualization Cytoscape.js — Interactive knowledge graph rendering
Genome Browser IGV.js — Chromosomal variant visualization
Charts Recharts — Statistical dashboards
Component Dev Storybook 10 Component library documentation

4. Repository Structure

AI-Genomics-Lab/
├── api/                          # FastAPI backend
│   ├── main.py                   # App entry point + top-level endpoints
│   ├── v1/                       # Versioned API routes
│   │   ├── __init__.py           # Router registration
│   │   ├── patients.py           # Patient CRUD
│   │   ├── cases.py              # Clinical case management
│   │   ├── samples.py            # Sample management + FASTQ upload
│   │   ├── variants.py           # Variant querying with filters
│   │   ├── reports.py            # Clinical report CRUD
│   │   ├── pipeline_runs.py      # Pipeline execution management
│   │   ├── hospitals.py          # Hospital registry
│   │   └── clinical_catalogs.py  # Cancer types, stages, histology
│   ├── dependencies.py           # Auth dependencies
│   ├── requirements.txt          # Python dependencies
│   └── Dockerfile                # API container
│
├── services/                     # Core business logic
│   ├── auth_service.py           # JWT + Argon2 + RBAC + audit
│   ├── database_service.py       # PostgreSQL schema + CRUD
│   ├── minio_service.py          # MinIO object storage client
│   ├── neo4j_service.py          # Neo4j graph operations
│   ├── llm_client.py             # DeepSeek/MIMO API client
│   ├── bio_pipeline_client.py    # VCF parsing + pipeline integration
│   ├── nextflow_runner.py        # Nextflow execution via Docker + SSE
│   ├── cache_service.py          # LLM response caching
│   └── genome_sync_service.py    # Genome file synchronization
│
├── agents/                       # AI agent system
│   └── __init__.py               # VariantAgent, GraphAgent, LiteratureAgent, ReportAgent
│
├── core/                         # Shared dependencies
│   └── deps.py                   # FastAPI dependency injection (RBAC)
│
├── bio-pipeline/                 # Bioinformatics pipeline
│   ├── Dockerfile                # Bio container (Ubuntu 22.04 + all tools)
│   ├── genome_index_correct.nf   # Nextflow: genome download + index creation
│   ├── pipeline_1_genome_prep.nf # Nextflow: genome preparation from local file
│   ├── scripts/
│   │   ├── pipeline.sh           # Main analysis: FASTQ → CRAM → VCF
│   │   ├── index_genome.sh       # Reference genome indexing (bgzip + faidx + strobealign)
│   │   └── vcf_dashboard.sh      # VCF statistics generator
│   └── archive/                  # Historical pipeline variants (not for production)
│
├── graph/                        # Neo4j knowledge graph
│   └── schema.cypher             # Schema definition + seed data (genes, mutations, diseases, drugs)
│
├── frontend/                     # Next.js frontend
│   ├── src/
│   │   ├── app/                  # Next.js pages (dashboard, login, settings, cases)
│   │   ├── components/           # React components
│   │   │   ├── sections/         # Page sections (AlignGenome, Storage, Analysis, etc.)
│   │   │   ├── cases/            # Clinical case wizard (10 steps)
│   │   │   ├── ui/               # Design system (Button, Card, Input, Select, etc.)
│   │   │   ├── GraphView.tsx     # Cytoscape.js knowledge graph
│   │   │   ├── VariantTable.tsx  # Variant table with filters
│   │   │   └── GenomeBrowser.tsx # IGV.js genome browser
│   │   ├── stores/               # Zustand state management
│   │   ├── hooks/                # Custom React hooks
│   │   ├── types/                # TypeScript type definitions
│   │   └── design-system/        # Tokens, variants, utilities
│   ├── package.json
│   └── Dockerfile
│
├── docker/                       # Infrastructure
│   ├── docker-compose.yml        # Multi-service orchestration
│   └── dev.sh                    # Development helper script
│
├── scripts/                      # Utility scripts
│   └── init_database.py          # Database initialization
│
├── datasets/                     # Data directory (gitignored)
│   ├── fastq/                    # Patient FASTQ files
│   ├── bam/                      # Aligned BAM/CRAM files
│   ├── vcf/                      # Variant call files
│   ├── reference_genome/         # Reference genome (hg38)
│   ├── annotations/              # ClinVar, dbSNP annotation files
│   └── logs/                     # Pipeline execution logs
│
├── .env.example                  # Environment variable template
├── .gitignore                    # Comprehensive gitignore
├── PLAN.md                       # This document
├── README.md                     # Project documentation
├── ARCHITECTURE.md               # Technical architecture details
├── CONTRIBUTING.md               # Contribution guidelines
└── LICENSE                       # MIT License

5. Infrastructure & Environments

Development Environment

Resource Specification
CPU Intel i5-1335U (10 cores / 12 threads, 4.6GHz boost)
RAM 32 GB DDR4
Storage 500GB+ NVMe SSD (400GB allocated for project)
OS Ubuntu Linux (kernel 6.x)
Docker Docker Engine 24+ with Compose v2

Docker Services & Resource Allocation

Service Container Memory Limit CPU Ports
API ai-genomics-api 2 GB 2 cores 8000
Neo4j ai-genomics-neo4j 4 GB 2 cores 7474, 7687
PostgreSQL ai-genomics-postgres 2 GB 1 core 5432
MinIO ai-genomics-minio 1 GB 1 core 9000, 9001
Bio-Pipeline ai-genomics-bio 16 GB 8 cores —
Frontend ai-genomics-frontend 2 GB 1 core 3000
pgAdmin ai-genomics-pgadmin 512 MB 1 core 5050
Total ~27.5 GB 16 cores

Production Environment (Future)

Resource Development Production
CPU i5-1335U (10 cores) AMD EPYC / Intel Xeon (32-64 cores)
RAM 32 GB 128-256 GB
Storage 500 GB NVMe 4-10 TB NVMe RAID
GPU None NVIDIA A100 (ML inference)
Orchestration Docker Compose Kubernetes + Nextflow Tower
Database Local PostgreSQL PostgreSQL with read replicas
Storage Local MinIO MinIO cluster / AWS S3
LLM DeepSeek/MIMO API Self-hosted (Llama 3) or API
Auth JWT + local users OAuth2 + LDAP/SSO
Monitoring Docker logs Prometheus + Grafana + Loki

6. Bioinformatics Pipeline

Pipeline Architecture

The pipeline uses Nextflow DSL2 for workflow orchestration and strobealign for high-speed alignment.

Pipeline 1: Genome Indexing

Reference Genome URL
    │
    ▼
[Download] ──→ FASTA file
    │
    ▼
[BGZIP] ──→ .fa.gz (BGZF compressed)
    │
    ▼
[samtools faidx] ──→ .fa.gz.fai + .fa.gz.gzi
    │
    ▼
[strobealign index] ──→ .fa.gz.r150.sti
    │
    ▼
[Upload to MinIO] ──→ Persistent storage

Implementation: bio-pipeline/genome_index_correct.nf

Pipeline 2: Variant Analysis

FASTQ files (paired-end)
    │
    ▼
[strobealign] ──→ SAM (streamed via pipe)
    │
    ▼
[samtools sort] ──→ sorted BAM
    │
    ▼
[samtools view -C] ──→ CRAM (30-60% space saving)
    │
    ▼
[samtools index] ──→ .cram.crai
    │
    ▼
[bcftools mpileup] ──→ BCF (binary, fast I/O)
    │
    ▼
[bcftools call -mv] ──→ raw VCF
    │
    ▼
[bcftools filter] ──→ filtered VCF
    │
    ▼
[bcftools annotate] ──→ annotated VCF (ClinVar)

Implementation: bio-pipeline/scripts/pipeline.sh

Performance Optimizations

Optimization Impact
strobealign vs BWA-MEM 5-8x faster alignment
Streaming pipes (no SAM to disk) ~50% less I/O
CRAM output (vs BAM) 30-60% less storage
BCF binary intermediates Faster variant calling I/O
vmtouch RAM preloading Reference genome cached in RAM
Test mode with read limits Fast pipeline validation
Parallel sample processing Multiple samples concurrently

Supported Input Formats

Format Description Status
FASTQ.gz Compressed raw sequencing reads Primary input
FASTQ Uncompressed raw reads Supported (legacy)
BAM Aligned reads Accepted (skip alignment)
CRAM Compressed aligned reads Accepted

Reference Genomes

Genome Build Size (compressed) Status
Homo sapiens GRCh38 (hg38) ~3 GB Primary
Homo sapiens GRCh37 (hg19) ~3 GB Supported
E. coli — ~5 MB Testing

7. Data Storage Strategy

Three-Database Architecture

┌─────────────────────────────────────────────────────┐
│                    MinIO (Object Storage)            │
│  Raw files: FASTQ, BAM, CRAM, VCF, reference genome │
│  Reports: PDF, HTML clinical reports                 │
│  Bucket: genomics/                                   │
│    └── patient/{patient_id}/{case_id}/               │
│        ├── fastq/                                    │
│        ├── bam/                                      │
│        ├── vcf/                                      │
│        └── reports/                                  │
└─────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────┐
│                 PostgreSQL (Relational)              │
│  Structured data: patients, cases, samples,          │
│  variants, annotations, reports, audit logs          │
│  28 tables with foreign keys and constraints         │
└─────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────┐
│                    Neo4j (Graph)                     │
│  Knowledge: Gene → Mutation → Disease                │
│  Therapeutics: Drug → Gene targets                   │
│  Pathways: Gene → Pathway associations               │
│  7 node types, 6 relationship types                  │
└─────────────────────────────────────────────────────┘

PostgreSQL Schema (28 Tables)

Category Tables
Clinical patients, cases, samples_v2, variants, annotations, clinical_reports
Infrastructure hospitals, users, roles, user_roles, audit_logs
Pipeline pipeline_jobs, pipeline_runs, pipeline_settings, sequencing_runs, fastq_files
Reference genome_sources, species, indexed_genomes
Configuration ai_provider_settings, ui_preferences
Catalogs cancer_types, primary_sites, clinical_stages, histology_subtypes, clinical_modules

MinIO Path Structure

genomics/
└── patient/
    └── {patient_external_id}/
        └── {case_id}/
            ├── fastq/
            │   ├── sample_R1.fastq.gz
            │   └── sample_R2.fastq.gz
            ├── bam/
            │   ├── sample_sorted.cram
            │   └── sample_sorted.cram.crai
            ├── vcf/
            │   ├── sample_variants.vcf
            │   └── sample_filtered.vcf
            └── reports/
                └── clinical_report_2026-06-08.pdf

8. Knowledge Graph

Node Types

Node Key Properties Count (seed)
Gene symbol, chromosome, gene_type, omim_id 6
Mutation variant_id, hgvs, variant_type, clinical_significance 6
Disease name, category, icd10, prevalence 5
Protein uniprot_id, name, function —
Drug name, type, mechanism, fda_approval_year 3
Pathway kegg_id, name, description 4
Paper pmid, title, authors, journal —

Relationship Types

Relationship From → To Properties
HAS_MUTATION Gene → Mutation evidence_level
CAUSES Mutation → Disease risk_increase, penetrance
INTERACTS_WITH Gene → Gene type, context
TARGETS Drug → Gene mechanism
PARTICIPATES_IN Gene → Pathway role
CITED_IN Mutation → Paper relevance_score

Example Queries

// Find all pathogenic mutations in a gene
MATCH (g:Gene {symbol: 'BRCA1'})-[:HAS_MUTATION]->(m:Mutation)
WHERE m.clinical_significance = 'pathogenic'
RETURN m.variant_id, m.protein_change, m.description

// Find drugs targeting a mutated gene
MATCH (m:Mutation {variant_id: 'EGFR:L858R'})<-[:HAS_MUTATION]-(g:Gene)
MATCH (d:Drug)-[:TARGETS]->(g)
RETURN d.name, d.mechanism, d.approved_indications

// Find disease pathway for a variant
MATCH (m:Mutation)-[:CAUSES]->(d:Disease)
MATCH (g:Gene)-[:HAS_MUTATION]->(m)
MATCH (g)-[:PARTICIPATES_IN]->(p:Pathway)
RETURN m.variant_id, d.name, p.name, g.symbol

Seed Data Sources

Source URL Content
ClinVar ncbi.nlm.nih.gov/clinvar Variant-disease classifications
COSMIC cancer.sanger.ac.uk Cancer mutation database
gnomAD gnomad.broadinstitute.org Population allele frequencies
OMIM omim.org Mendelian disease associations
KEGG genome.jp/kegg Pathway definitions

9. AI & LLM Layer

LLM Integration

Component Implementation
Provider DeepSeek/MIMO API (multi-model gateway)
Models Configurable (GPT-4, Claude, Llama, etc.)
Caching File-based with TTL (avoids redundant API calls)
Data Privacy Patient data pseudonymized before LLM calls

AI Capabilities

Capability Input Output
Variant Interpretation Variant ID + gene context Clinical significance explanation
Mutation Impact Gene + mutation + pathway data Biological impact assessment
Literature Analysis Variant + disease context Research paper summary
Report Generation All variant data Structured clinical report

RAG (Retrieval-Augmented Generation)

User Query / Variant Detection
    │
    ▼
[Knowledge Retrieval]
    │  Neo4j: gene-mutation-disease graph
    │  ClinVar: variant classifications
    │  Literature: PubMed references
    │
    ▼
[Context Assembly]
    │  Structured prompt with evidence
    │
    ▼
[LLM Reasoning]
    │  Generate interpretation
    │
    ▼
[Response + Citations]
    │  Evidence-backed explanation

10. Agent System

Multi-Agent Architecture

Agent Responsibility Data Sources
VariantAgent Classify and interpret detected variants Neo4j + ClinVar + LLM
GraphAgent Query knowledge graph for relationships Neo4j
LiteratureAgent Retrieve and summarize research LLM + PubMed context
ReportAgent Generate structured clinical reports All agents' output + LLM
AnalysisOrchestrator Coordinate agent workflow All agents

Agent Workflow

VCF file with detected variants
    │
    ▼
[VariantAgent]
    │  For each variant:
    │  1. Query Neo4j for gene/mutation info
    │  2. Check ClinVar classification
    │  3. LLM generates interpretation
    │
    ▼
[GraphAgent]
    │  For significant variants:
    │  1. Find related genes (INTERACTS_WITH)
    │  2. Find pathway context
    │  3. Find known drug targets
    │
    ▼
[LiteratureAgent]
    │  Generate literature context via LLM
    │
    ▼
[ReportAgent]
    │  Combine all data into structured report:
    │  - Executive summary
    │  - Variant classification table
    │  - Disease associations
    │  - Therapeutic implications
    │  - Confidence scores
    │  - Limitations and disclaimers

11. Clinical Data Model

Entity Relationship

Hospital ──→ Patient ──→ Case ──→ Sample
                                      │
                                      ▼
                                Pipeline Run
                                      │
                                      ▼
                                 Variants
                                      │
                                      ▼
                              Annotations (ClinVar)
                                      │
                                      ▼
                              Clinical Report

Cancer Catalog Data (Seeded)

Catalog Count Examples
Cancer Types 28 Breast, Lung, Colorectal, Prostate, Ovarian...
Primary Sites 30 Lung, Colon, Breast, Liver, Brain...
Clinical Stages 12 I, II, III, IV (with substages)
Clinical Modules 7 SNV/Indel, CNV, SV, RNA Fusion, MSI/TMB, HRD, Germline

Clinical Modules

Module ID Description
SNV/Indel Detection A Single nucleotide variants and small insertions/deletions
Copy Number Variation B Gene amplifications and deletions
Structural Variants C Large genomic rearrangements
RNA Fusion Detection D Gene fusions (e.g., BCR-ABL, EML4-ALK)
MSI / TMB E Microsatellite instability and tumor mutational burden
HRD Score F Homologous recombination deficiency assessment
Germline Analysis G Hereditary cancer predisposition variants

12. Frontend & Visualization

Page Structure

Page Route Features
Dashboard / 9-tab overview (Home, Variants, Graph, Genomes, Samples, Analysis, Storage, Browser, Alignment)
Login /login JWT authentication
Cases /cases Case listing with filters
Case Detail /cases/[id] Full case timeline, variants, reports
New Case /cases/new 10-step clinical wizard
Settings /settings Platform configuration (7 tabs)

Case Creation Wizard (10 Steps)

  1. Hospital Selection — Choose clinical site
  2. Patient Selection — Select or create patient record
  3. Case Details — Cancer type, primary site, stage, histology
  4. Sample Registration — Sample type, tumor content, collection date
  5. Sequencing Run — Platform, kit, read length, coverage target
  6. FASTQ Upload — Drag-and-drop file upload to MinIO
  7. Pipeline Configuration — Select modules (A-G), parameters
  8. Pipeline Execution — Real-time log streaming via SSE
  9. Results Review — Variant table, quality metrics
  10. Report Generation — AI-assisted clinical report

Visualization Components

Component Technology Purpose
Knowledge Graph Cytoscape.js Interactive gene-mutation-disease network
Genome Browser IGV.js Chromosomal variant visualization
Variant Table React + custom Filterable, sortable variant list
Statistics Recharts Pipeline metrics, variant distributions

13. Security & Compliance

Authentication & Authorization

Feature Implementation
Password Hashing Argon2 (GPU/ASIC resistant)
Token Type JWT (stateless, 30min expiry)
Refresh Tokens 7-day expiry, rotated on use
Role-Based Access 4 roles: admin, analyst, researcher, viewer
Audit Logging All auth events + configuration changes logged

Role Permissions

Permission admin analyst researcher viewer
Manage users Yes — — —
Configure system Yes — — —
Run pipelines Yes Yes — —
Create cases Yes Yes Yes —
View variants Yes Yes Yes Yes
View reports Yes Yes Yes Yes
Export data Yes Yes Yes —

Data Protection

Measure Status Notes
HTTPS Planned TLS termination at reverse proxy
Data encryption at rest Planned PostgreSQL TDE + MinIO encryption
Patient pseudonymization Implemented External IDs used, no PII in LLM calls
Access control Implemented RBAC with granular permissions
Audit trail Implemented All mutations logged
GDPR compliance Partial Right to access, right to erasure framework
Data retention policy Planned Configurable retention periods

Production Security Checklist

  • Generate cryptographically secure JWT secret
  • Change all default passwords
  • Enable HTTPS with valid certificates
  • Configure CORS whitelist
  • Enable rate limiting
  • Set up intrusion detection
  • Implement data encryption at rest
  • Configure backup encryption
  • Set up log aggregation (ELK/Loki)
  • Enable database audit logging
  • Implement API key rotation
  • Set up secret management (Vault/SOPS)

14. Development Roadmap

Phase 1: Infrastructure ✅ COMPLETED

  • Docker Compose multi-service architecture
  • Service Dockerfiles (API, Bio-Pipeline, Frontend)
  • Development helper script (dev.sh)
  • Environment variable configuration
  • Volume management for persistent data

Phase 2: Bioinformatics Pipeline ✅ COMPLETED

  • Bio container with all tools (strobealign, SAMtools, bcftools, GATK)
  • Genome indexing pipeline (Nextflow DSL2)
  • Variant analysis pipeline (FASTQ → CRAM → VCF)
  • Streaming pipeline with observability
  • Test mode with configurable read limits
  • CRAM output for space efficiency

Phase 3: Data Layer ✅ COMPLETED

  • PostgreSQL schema (28 tables with seed data)
  • Neo4j knowledge graph (genes, mutations, diseases, drugs)
  • MinIO clinical path structure
  • Database initialization and seeding
  • Audit logging

Phase 4: API Layer ✅ COMPLETED

  • FastAPI with 50+ endpoints
  • JWT authentication with RBAC
  • Clinical data CRUD (patients, cases, samples, variants, reports)
  • Pipeline execution management
  • Genome indexing API with SSE streaming
  • Storage management API
  • OpenAPI documentation

Phase 5: AI Integration ✅ COMPLETED

  • DeepSeek/MIMO LLM client with caching
  • Multi-agent system (4 agents + orchestrator)
  • Knowledge graph integration
  • Variant interpretation pipeline
  • Report generation

Phase 6: Frontend ✅ COMPLETED

  • Next.js dashboard with 9 sections
  • Clinical case wizard (10 steps)
  • Cytoscape.js knowledge graph visualization
  • IGV.js genome browser
  • Variant table with filters
  • Real-time pipeline log streaming
  • Design system with Storybook

Phase 7: Clinical Validation 🔄 IN PROGRESS

  • Validate pipeline against GIAB benchmark samples
  • Compare variant calls with gold standard (Genome in a Bottle)
  • ClinVar concordance testing
  • Sensitivity/specificity metrics (target: >99%)
  • Performance benchmarking on reference hardware

Phase 8: Knowledge Ingestion 📋 PLANNED

  • Automated ClinVar data ingestion pipeline
  • gnomAD population frequency integration
  • dbSNP identifier mapping
  • COSMIC cancer mutation database
  • PubMed literature linking
  • Automated data refresh schedules

Phase 9: Production Hardening 📋 PLANNED

  • Alembic migration system (replace CREATE IF NOT EXISTS)
  • Health check endpoints with real service status
  • Prometheus metrics + Grafana dashboards
  • Log aggregation (Loki/ELK)
  • Automated backups with tested restore procedure
  • Load testing and capacity planning
  • Disaster recovery documentation

Phase 10: Advanced AI 📋 PLANNED

  • Fine-tuned variant classification model
  • Graph neural network for variant effect prediction
  • Multi-omics integration (transcriptomics, proteomics)
  • Drug response prediction
  • Patient-specific risk scoring

15. Testing Strategy

Test Levels

Level Scope Tools Status
Unit Individual functions pytest Planned
Integration API endpoints pytest + httpx Planned
Pipeline Bioinformatics workflow GIAB benchmark data In Progress
E2E Full user workflow Playwright Planned
Visual Component rendering Storybook + Chromatic Implemented
Performance Load and stress locust Planned

Pipeline Validation Plan

Benchmark Source Purpose
NA12878 (GIAB) genome-in-a-bottle.org SNV/Indel sensitivity/specificity
NA24385 (Ashkenazi) GIAB Trio analysis validation
Tumor cell lines SeraCare Somatic variant detection
Synthetic spike-ins SeraCor Known variant detection at various VAFs

Success Criteria

Metric Target Measurement
SNV Sensitivity >99% GIAB high-confidence calls
SNV Specificity >99% False positive rate
Indel Sensitivity >95% GIAB high-confidence calls
Pipeline Runtime (WES) <2 hours Reference hardware
Pipeline Runtime (Panel) <30 minutes Reference hardware
API Response Time <200ms 95th percentile
System Uptime >99.5% Monthly measurement

16. Risk Assessment

Technical Risks

Risk Severity Probability Mitigation
Disk space exhaustion during WGS High Medium CRAM compression, temp file cleanup, configurable storage limits
RAM exhaustion in alignment High Medium Memory limits in Docker, streaming pipeline, test mode
LLM hallucination in reports Critical High Evidence-backed prompts, ClinVar cross-reference, mandatory disclaimers
Reference genome corruption High Low SHA256 checksums, MinIO versioning
Pipeline failure mid-run Medium Medium Nextflow retry logic, checkpoint/resume, job tracking
Neo4j memory exhaustion Medium Medium Memory limits, pagination, query optimization

Clinical Risks

Risk Severity Mitigation
False negative variant call Critical Multi-tool validation, quality thresholds, manual review workflow
Incorrect disease association Critical ClinVar-only classifications, confidence scores, human-in-the-loop
Patient data exposure Critical Encryption, RBAC, audit logs, pseudonymization
Report misinterpretation High Clear disclaimers: "Research use only — not for clinical decisions"

Mitigation Strategy

┌─────────────────────────────────────────────────────────────┐
│                    RISK MITIGATION LAYERS                    │
├─────────────────────────────────────────────────────────────┤
│ Layer 1: Pipeline QC                                        │
│   → FastQC input validation                                 │
│ → Mapping quality thresholds (MAPQ >= 30)                   │
│   → Variant quality filtering (QUAL >= 30, DP >= 10)        │
├─────────────────────────────────────────────────────────────┤
│ Layer 2: Cross-Validation                                   │
│   → ClinVar classification comparison                       │
│   → Population frequency check (gnomAD)                     │
│   → Multiple caller concordance (future)                    │
├─────────────────────────────────────────────────────────────┤
│ Layer 3: AI Augmentation                                    │
│   → Evidence-backed LLM prompts                             │
│   → Confidence scoring                                      │
│   → Mandatory limitations section                           │
├─────────────────────────────────────────────────────────────┤
│ Layer 4: Human Review                                       │
│   → Variant review workflow                                 │
│   → Sign-off requirement for reports                        │
│   → Audit trail for all decisions                           │
└─────────────────────────────────────────────────────────────┘

17. Performance & Scalability

Development Hardware Performance Estimates

Operation Data Size Estimated Time Memory
Genome indexing (hg38) 3 GB compressed 15-30 min 8-12 GB
Alignment (WES) 10-15 GB FASTQ 30-60 min 8-16 GB
Alignment (Panel) 1-2 GB FASTQ 5-15 min 4-8 GB
Variant calling CRAM → VCF 10-30 min 4-8 GB
LLM interpretation Per variant 2-5 sec <100 MB
Report generation Full case 10-30 sec <100 MB

Scalability Path

Development (Now)           Production (Future)
─────────────────           ────────────────────
1 workstation               Kubernetes cluster
Docker Compose              Helm charts
1 bio-pipeline              Auto-scaling workers
Local MinIO                 Distributed MinIO / S3
Single PostgreSQL           PostgreSQL + read replicas
Single Neo4j                Neo4j Causal Cluster
File-based cache            Redis cluster
DeepSeek/MIMO API              Self-hosted + API hybrid

18. Future Enhancements

Short-Term (3-6 months)

  • Multi-caller variant validation (GATK + DeepVariant)
  • Automated ClinVar/gnomAD data ingestion
  • PDF report export
  • Batch analysis (multiple samples)
  • API rate limiting and throttling

Medium-Term (6-12 months)

  • Somatic variant detection (tumor/normal pairs)
  • Graph neural network for variant pathogenicity prediction
  • Multi-omics integration (RNA-seq, proteomics)
  • Patient cohort analysis
  • HL7 FHIR integration for hospital systems

Long-Term (12+ months)

  • Drug response prediction models
  • Personalized treatment recommendations
  • Federated learning across institutions
  • Real-time sequencing integration (Nanopore)
  • Regulatory pathway exploration (CE-IVD)

Appendix A: Environment Variables

# ============================================
# DATABASE
# ============================================
DATABASE_URL=postgresql://genomics:genomics@postgres:5432/genomics

# ============================================
# NEO4J
# ============================================
NEO4J_URI=bolt://neo4j:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=genomics

# ============================================
# MINIO
# ============================================
MINIO_ENDPOINT=minio:9000
MINIO_ACCESS_KEY=genomics
MINIO_SECRET_KEY=genomics

# ============================================
# LLM
# ============================================
DEEPSEEK_API_KEY=your_openrouter_api_key_here

# ============================================
# AUTHENTICATION
# ============================================
JWT_SECRET_KEY=your-secret-key-change-in-production
JWT_ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30
REFRESH_TOKEN_EXPIRE_DAYS=7

# ============================================
# PIPELINE
# ============================================
REFERENCE_GENOME=/datasets/reference_genome/Homo_sapiens.GRCh38.dna_sm.toplevel.fa
REFERENCE_GENOME_GZ=/datasets/reference_genome/Homo_sapiens.GRCh38.dna_sm.toplevel.fa.gz
FASTQ_DIR=/datasets/fastq
DATASETS_DIR=/datasets

# ============================================
# FRONTEND
# ============================================
NEXT_PUBLIC_API_URL=http://localhost:8000

Appendix B: Quick Start Commands

# Clone and setup
git clone https://github.com/rendergraf/AI-Genomics-Lab.git
cd AI-Genomics-Lab
cp .env.example .env
# Edit .env with your DEEPSEEK_API_KEY

# Start all services
cd docker && ./dev.sh start

# Access services
# Frontend:   http://localhost:3000
# API Docs:   http://localhost:8000/docs
# Neo4j:      http://localhost:7474
# MinIO:      http://localhost:9001
# pgAdmin:    http://localhost:5050

# Default credentials
# Frontend:   admin@company.com / admin123
# Neo4j:      neo4j / genomics
# MinIO:      genomics / genomics
# pgAdmin:    admin@genomics.com / genomics

# Run bioinformatics test
./docker/dev.sh shell-bio
cd /bio-pipeline && bash scripts/pipeline.sh

# Stop services
./docker/dev.sh stop

Appendix C: API Endpoint Summary

Category Endpoints Auth
Health GET /, GET /health No
Auth POST /api/auth/login, POST /api/auth/logout, GET /api/auth/me Mixed
Patients GET/POST/PUT/DELETE /api/v1/patients Yes
Cases GET/POST/PUT/DELETE /api/v1/cases Yes
Samples GET/POST/PUT/DELETE /api/v1/samples Yes
Variants GET /api/v1/variants Yes
Reports GET/POST /api/v1/reports Yes
Pipeline POST /api/v1/pipeline-runs, GET /api/v1/pipeline-runs Yes
Genome POST /genome/index, GET /genome/indexed Yes
Storage GET /storage/genomes, POST /storage/sync/genomes Yes
Graph GET /graph/genes/{gene}, GET /graph/mutations/{mutation} Yes
Agents POST /agents/analyze, POST /agents/report Yes
Settings GET/PUT /api/settings/* Admin

Document Version: 2.0 Last Updated: June 2026 Author: Xavier Araque License: MIT