OmniBioAI is an AI-native computational biology platform that unifies bioinformatics workflows, scientific AI, biomedical knowledge, reproducible execution, and governed computational infrastructure.
It provides a common operating layer for moving from:
scientific question → data → workflow → computation → evidence → interpretation
across local workstations, HPC clusters, containerized infrastructure, and cloud execution environments.
Built for computational scientists, bioinformatics engineers, AI engineers, and research teams developing reproducible biomedical AI systems.
OmniBioAI Studio is the primary user-facing environment for accessing and operating the platform.
🌐 Website
https://omnibioai.org
🖥️ OmniBioAI Studio
https://github.com/OmniBioAI/omnibioai-studio
📚 Documentation
https://github.com/OmniBioAI/omnibioai-docs
📊 Platform Control Center
https://control.omnibioai.org
🧬 Workflows
https://github.com/OmniBioAI/omnibioai-workflow-bundles
📦 Containers
https://github.com/orgs/OmniBioAI/packages
🎥 Tutorials
https://github.com/OmniBioAI/omnibioai-videos
| Platform | Scale |
|---|---|
| Source repositories | 33 |
| Codebase | 5.2M+ |
| Automated tests | 65,000+ |
| Microservices / platform services | 28+ |
| Bioinformatics & AI/ML plugins | 500+ |
| Workflow bundles | 1,000+ |
| Execution / HPC / cloud tools | 12,000+ |
| Container artifacts | 1,500+ |
| PubMed corpus | 28M+ unique abstracts |
| Biomedical vector index | 75M+ vectors |
Public counts distinguish verified or usable platform resources from registry entries where appropriate.
Live architecture, service health, and platform metrics:
OmniBioAI connects the major layers required for modern computational biology:
Integrated computational workflows for:
- Genomics
- Transcriptomics
- Single-cell analysis
- Variant analysis
- Proteomics
- Functional annotation
- Pathway analysis
- Comparative genomics
- Biomedical knowledge integration
AI operates alongside deterministic scientific workflows rather than replacing them.
Capabilities include:
- Scientific workflow planning
- Biomedical RAG
- Literature-aware reasoning
- Biological knowledge retrieval
- Scientific hypothesis generation
- AI-assisted interpretation
- Agent-driven tool execution
- Model lifecycle management
- Human-in-the-loop scientific review
OmniBioAI integrates biomedical literature and structured biological knowledge into a local retrieval and reasoning layer.
Current infrastructure includes:
- 28M+ unique PubMed abstracts
- 75M+ biomedical vectors
- Domain-oriented biomedical indexes
- Semantic retrieval
- Evidence-linked RAG
- Biological knowledge services
- Literature-aware scientific agents
External biological resources can be integrated through governed platform services and APIs.
A common workflow architecture supports:
Nextflow · WDL · Snakemake · CWL
Workflows can execute through a unified computational layer rather than being tied to a single infrastructure backend.
Scientific workloads can run across:
Local · Slurm/HPC · AWS Batch · Azure Batch · Kubernetes · Containers
The Tool Execution Service (TES) separates scientific workflow intent from the infrastructure on which computation executes.
Reproducibility is a platform primitive rather than an afterthought.
OmniBioAI captures computational execution context including:
- Inputs and references
- Workflow definitions
- Tool and software versions
- Containers and environments
- Execution backend
- DAG and lineage
- Logs and metrics
- Output artifacts
Each execution can produce an auditable Run Bundle:
Run Bundle
├── Inputs
├── References
├── Workflow
├── Toolchain
├── Containers
├── Execution Backend
├── DAG / Lineage
├── Logs
├── Metrics
└── Outputs
This makes computational results easier to reproduce, inspect, trace, and audit.
AI-native computational biology architecture spanning scientific AI, bioinformatics workflows, execution infrastructure, provenance, security, governance, and observability.
| Repository | Responsibility |
|---|---|
| omnibioai | Scientific platform and plugin ecosystem |
| omnibioai-studio | Primary user-facing environment and stack orchestration |
| omnibioai-control-center | Operations, security, readiness, and observability |
| omnibioai-workbench | Scientific plugin and analysis execution |
| omnibioai-launcher | Jupyter, VS Code, and RStudio integration |
| omnibioai-sdk | Python platform SDK |
| Repository | Responsibility |
|---|---|
| omnibioai-tes | Unified local/HPC/cloud Tool Execution Service |
| omnibioai-toolserver | Governed scientific tool API |
| omnibioai-tool-runtime | Container execution runtime |
| omnibioai-tool-images | Bioinformatics and AI/ML execution images |
| omnibioai-workflow-bundles | Versioned reproducible scientific workflows |
| Repository | Responsibility |
|---|---|
| omnibioai-rag | Biomedical retrieval-augmented generation |
| omnibioai-dev-hub | Semantic development and AI intelligence services |
| omnibioai-model-registry | Governed model lifecycle and provenance |
| Repository | Responsibility |
|---|---|
| omnibioai-auth | Authentication and identity |
| omnibioai-api-gateway | Zero-trust API gateway |
| omnibioai-policy-engine | RBAC/ABAC policy enforcement |
| omnibioai-hpc-policy-engine | Computational quota governance |
| omnibioai-security-audit | Durable security-event processing |
| omnibioai-security-sdk | Shared security primitives |
| omnibioai-iam-client | IAM client SDK |
| omnibioai-usage-client | Usage-event SDK |
| Repository | Responsibility |
|---|---|
| omnibioai-lims | Biological sample and metadata management |
| omnibioai-data | Reference and example datasets |
| omnibioai-docs | Technical documentation |
| omnibioai-videos | Tutorials and onboarding |
OmniBioAI is engineered around zero-trust and least-privilege principles for biomedical computational environments.
The security architecture includes:
- Identity and Access Management
- JWT-based authentication
- RBAC / ABAC authorization
- Organization and tenant isolation
- SAML-based enterprise SSO
- API gateway enforcement
- Service-to-service identities
- Scoped infrastructure identities
- Audit-event pipelines
- Policy enforcement
- Security posture monitoring
- Evidence-backed readiness tracking
Security controls are tracked through separate lifecycle states:
Implementation → Testing → Deployment → Operational Verification
This prevents source-code implementation or successful unit tests from being treated as equivalent to production verification.
OmniBioAI includes HIPAA-aligned technical safeguards and evidence tracking designed to support environments handling sensitive biomedical data.
These include:
least-privilege access · audit logging · retention controls · integrity verification · backup and recovery · security monitoring · evidence-backed control tracking
OmniBioAI does not describe these controls as HIPAA certification.
Operational and organizational compliance depends on the deployment environment, policies, procedures, agreements, and other applicable requirements.
| Layer | Technologies |
|---|---|
| Scientific Computing | Python · R · Bioinformatics · Multi-omics |
| AI | LLMs · RAG · Vector Search · Knowledge Graphs · Scientific Agents |
| Workflow | Nextflow · WDL · Snakemake · CWL |
| Backend | FastAPI · Django · MySQL · Redis · Redis Streams |
| Frontend | React · TypeScript · Vite |
| Infrastructure | Docker · Apptainer/SIF · Kubernetes · Slurm · AWS Batch · Azure Batch |
| Security | IAM · JWT · RBAC · ABAC · SAML · Service Identities · Audit · Policy Enforcement |
Scientific results should carry enough execution context to be reproduced and audited.
AI assists scientific workflows, while deterministic computational tools remain the execution authority.
Scientific interpretation should remain connected to evidence and provenance.
Users and services receive only the permissions and computational resources required for their responsibilities.
AI-generated scientific interpretations remain subject to human review.
A security or operational control is not considered operational simply because its source code exists or its unit tests pass.
| Resource | Link |
|---|---|
| 🌐 Website | https://omnibioai.org |
| 📊 Control Center | https://control.omnibioai.org |
| 🐙 GitHub | https://github.com/OmniBioAI |
| 📚 Documentation | https://github.com/OmniBioAI/omnibioai-docs |
| 🧬 Workflows | https://github.com/OmniBioAI/omnibioai-workflow-bundles |
| 📦 Container Registry | https://github.com/orgs/OmniBioAI/packages |
| 🤗 Hugging Face | https://huggingface.co/omnibioai |
| 🎥 Tutorials | https://github.com/OmniBioAI/omnibioai-videos |
| 💬 Discord | https://discord.gg/Hu6vgfAFn |
| 🐦 X / Twitter | https://twitter.com/OmniBioAI |
Senior Computational Scientist · AI-Native Bioinformatics Engineer
19 years of experience spanning bioinformatics, multi-omics, computational biology, HPC, cloud computing, software engineering, and scientific AI across the United States, Qatar, Malaysia, Saudi Arabia, and India.
Creator and lead engineer of OmniBioAI.
Building computational systems at the intersection of:
Biology × AI × Software Engineering × HPC × Reproducibility
Build the computational operating layer where biological data, scientific workflows, reproducible execution, and artificial intelligence converge to accelerate biomedical discovery.
⭐ Explore the platform, architecture, workflows, and open-source ecosystem at https://omnibioai.org