Week 1: Onboarding & Foundations

Goals

• Orientation: project scope, domain context, biotech data types, compliance frameworks.
• Environment Setup: local dev environment, HPC/cloud access, code repositories.
• Docs & Tools: wikis, Jira, Git, communication channels.

Key Tasks

• Project Kickoff: HIPAA, GDPR, 21 CFR Part 11 basics; biotech data standards (VCF, FASTQ, BAM, EHR schemas).
• High-Level Architecture: Microservices approach (front-end, ingestion, HPC orchestration, ML, compliance logs).
• Dev Setup: Install Docker, K8s CLI, Python, HPC client tools (Slurm commands).

Deliverables

• System diagram or high-level architecture sketch
• Local environment configured (Docker, credentials)
• Checklist of compliance guidelines

Next Steps

Prepare to dive into the system build!

Week 2: Data Ingestion & Validation Framework

Goals

• Build a robust ingestion service for multiple biotech file formats (FASTQ, BAM, VCF).
• Implement validation (schema checks, metadata normalization).

Key Tasks

• Data Ingestion Microservice: POST /ingest endpoint, store files in object storage or HPC filesystem.
• Schema & Format Validation: Check sample IDs, read lengths, detect corrupt files.
• Metadata Repository: PostgreSQL for file metadata; minimal UI for upload & validation statuses.

Tech Stack

• Backend: Python (FastAPI/Flask) or Node.js in Docker
• Data Storage: S3-compatible, PostgreSQL
• Validation: Pydantic or custom logic

Deliverables

• Data ingestion microservice container
• Basic UI/CLI for uploading files
• Validation of test data (FASTQ/VCF)

Next Steps

HPC pipeline integration in Week 3.

Week 3: HPC Integration & Workflow Orchestration

Goals

• Connect ingestion layer to HPC pipeline for large-scale data transformations.
• Demonstrate end-to-end data flow: upload → HPC job → results storage.

Key Tasks

• HPC Scheduling Setup: Slurm/PBS or Kubernetes Jobs for HPC tasks.
• Pipeline Logic: Launch indexing/alignment using reference genomes, store outputs (sorted BAM, QC metrics).
• Orchestration & Status Tracking: Airflow/Prefect to define tasks, monitor HPC states, update metadata DB.

Tech Stack

• Workflow Orchestration: Airflow, Prefect, or Luigi
• HPC: Slurm or K8s HPC cluster
• Scripting: Bash, Python

Deliverables

• Automated HPC pipeline for sample data
• Job-monitoring dashboard (Airflow/Prefect)

Next Steps

Advanced transformations & compliance logging in Week 3.

Week 4: Data Transformation & Compliance Logging

Goals

• Implement variant calling, annotation, normalization; track all steps in regulatory logs.
• Ensure data traceability and an audit trail (21 CFR Part 11).

Key Tasks

• Advanced Data Processing: GATK or bcftools for variant calling on HPC; annotation merges with dbSNP, ClinVar.
• Audit & Logging Microservice: Tamper-proof logs, HPC job submissions, user IDs, timestamps.
• Compliance Event Triggers: Auto-quarantine incomplete data, generate data lineage reports.

Tech Stack

• Processing Tools: GATK, bcftools
• Logging: Elasticsearch + Kibana or audited PostgreSQL

Deliverables

• Variant calling & annotation pipeline on HPC
• Centralized audit log capturing HPC usage
• Automated compliance “exception” workflow

Next Steps

Front-end dashboards in Week 4.

Week 5: Front-End Dashboards & Visualization

Goals

• Create interactive dashboards for researchers/clinicians to explore processed data.
• Monitor HPC pipelines, compliance logs, and visualize results effectively.

Key Tasks

• Dashboard Design: HPC job queue/status, QC metrics, audit/compliance events.
• Visual Analytics: D3.js, Plotly, or Highcharts for data charts, possibly a mini genome browser.
• Access Control: User roles (admin, researcher, compliance) to restrict sensitive data.

Tech Stack

• Front-End: React / Angular / Vue
• Charting: D3.js, Plotly, or Highcharts

Deliverables

• Functional dashboards showing HPC pipelines, data stats
• Verified role-based UI restrictions

Next Steps

Security & encryption in Week 6.

Week 6: Security & Encryption Implementation

Goals

• Enforce HIPAA/GDPR-grade security controls at rest and in transit.
• Lock down data with IAM, RBAC, and intrusion detection.

Key Tasks

• Encryption at Rest & in Transit: Server-side encryption for object storage, TLS/SSL for microservices.
• IAM & RBAC: Integrate LDAP/AD for user management, fine-grained role-based HPC/data access.
• Intrusion Detection & Monitoring: SIEM tools (Splunk, Datadog), alert for suspicious activity.

Tech Stack

• Secrets Mgmt: Vault or AWS KMS
• Monitoring: Prometheus, Grafana, Splunk
• Reverse Proxies: Nginx, Envoy

Deliverables

• End-to-end encrypted data flows
• Central identity management (HPC & microservices)
• SIEM solution integrated

Next Steps

ML pipeline integration in Week 6.

Week 7: Machine Learning & Model Serving

Goals

• Extend HPC data transformations into ML pipelines for classification/regression models.
• Set up model training, versioning, real-time/batch inference.

Key Tasks

• Model Development: Use HPC outputs (variants, QC metrics) as ML features in PyTorch/TensorFlow.
• ML Orchestration: Airflow/Kubeflow for training, hyperparam tuning, scheduled re-trains.
• Model Serving & Deployment: Containerize inference microservice (FastAPI, Seldon Core, or MLflow).

Tech Stack

• ML Libraries: PyTorch, TensorFlow, scikit-learn
• Orchestration: Airflow/Kubeflow, MLflow for versioning

Deliverables

• Working ML pipeline integrated with HPC
• Inference endpoint (real-time/batch)

Next Steps

Regulatory validation (GxP) in Week 7.

Week 8: Regulatory Validation & GxP Alignment

Goals

• Ensure compliance with 21 CFR Part 11, GxP, and electronic record regulations.
• Prepare for validated lab or clinical usage if needed.

Key Tasks

• Gap Analysis: Map existing features (audit logs, version control) to GxP and Part 11 requirements.
• Validation Protocols: Draft IQ/OQ/PQ, outline official test scripts, acceptance criteria.
• Electronic Signatures & Approval Flows: eSignature for final data sign-offs stored in logs.

Tech Stack

• Documentation: GxP compliance docs, e-signature library

Deliverables

• Formal GxP compliance plan (IQ/OQ/PQ, UAT tests)
• E-signature mechanism for data release

Next Steps

Advanced DevOps (CI/CD) in Week 9.

Week 9: DevOps, CI/CD & Multi-Environment Deployments

Goals

• Automate container builds, testing, and deployments across dev, QA, and production.
• Prepare multi-region or multi-cluster usage if needed.

Key Tasks

• CI/CD Pipeline: Jenkins/GitLab/GitHub Actions for builds/tests, container registry integration.
• Environments & Promotion: Dev → QA → Prod pipelines, environment-specific configs.
• Multi-Cluster / Multi-Region: HPC replication across sites, cross-region object storage sync.

Tech Stack

• CI/CD: Jenkins, GitLab, or GitHub Actions
• Infra as Code: Terraform, Ansible

Deliverables

• Automated build & deploy pipelines for each microservice
• Documented multi-region architecture approach

Next Steps

Stress & performance testing in Week 9.

Week 10: Stress Testing & Performance Optimization

Goals

• Identify bottlenecks with large data sets and concurrency.
• Optimize HPC usage, container resources, DB queries, etc.

Key Tasks

• Load/Stress Testing: Locust/JMeter or custom HPC tests; focus on ingestion spikes, HPC concurrency, ML model loads.
• Profiling & Optimization: HPC parallelization, DB indexing, caching, container CPU/memory tuning.
• Auto-Scaling: K8s horizontal pod autoscaling, HPC cluster elasticity in cloud or on-prem.

Tech Stack

• Load Testing: Locust, JMeter
• Monitoring: Grafana, Prometheus, Splunk

Deliverables

• Performance test results & optimization improvements
• Updated HPC/microservice resource configs

Next Steps

Final UI polishing & user acceptance in Week 10.

Week 11: Advanced UI Enhancements & User Acceptance Testing

Goals

• Refine user experience with intuitive data exploration and HPC monitoring tools.
• Conduct UAT with domain experts (biologists, clinicians) to ensure usability.

Key Tasks

• UI Enhancements: Advanced filtering for large variant sets, tooltips, drag-and-drop HPC pipeline triggers.
• Collaboration & Reporting: Annotate HPC outputs, generate PDF/HTML summary reports.
• UAT: Domain experts run typical workflows. Collect feedback on performance, correctness, ease of use.

Tech Stack

• Front-End: React/Angular/Vue with advanced charting
• Collaboration: Real-time or comment-based features

Deliverables

• Polished front-end with interactive visualizations
• Documented UAT feedback & final backlog items

Next Steps

Final compliance verification in Week 11.

Week 12: Final Compliance Audit & Pre-Production Validation

Goals

• Ensure all HIPAA, GDPR, 21 CFR Part 11, GxP requirements are fully met.
• Confirm system stability and security for real biotech data usage.

Key Tasks

• Compliance Audit: Re-check HIPAA/GDPR/21 CFR Part 11 alignment; re-run IQ/OQ/PQ tests.
• Security Penetration Testing: Internal or external pentests. Confirm encryption, no open ports.
• Disaster Recovery Drill: Simulate HPC or data store failures, validate backups & failover procedures.

Tech Stack

• Pen Testing Tools: custom or external services
• Compliance Documentation & e-signoffs

Deliverables

• Final compliance sign-off reports
• Pen test results & remediation plan
• Verified backup/restore plan

Next Steps

Go-live in Week 12.

Week 13: Production Launch & Post-Launch Handover

Goals

• Roll out the platform to production or a production-like environment.
• Conduct final knowledge transfer and define maintenance processes.

Key Tasks

• Production Deployment: Official rollout to HPC cluster(s), domain config, SSL certs, user access for real usage.
• Post-Launch Monitoring: Monitor logs, HPC usage, error rates. Establish escalation policies.
• Handover & Next Steps: Transfer runbooks/SOPs, gather backlog for future improvements.

Deliverables

• Fully live production system
• Handover docs & maintenance schedule
• Final presentation or “graduation” of the project

Final Words of Encouragement

By completing these 13 weeks, you've delivered a robust, enterprise-grade biotech workflow platform: HPC-based transformations, compliance logging, and advanced ML pipelines. This foundation positions you to tackle extended AI features, multi-tenant usage, or additional assay types—truly a remarkable achievement in a high-stakes, regulated industry.