OmniBioAI Studio is an AI-native bioinformatics platform designed to accelerate scientific discovery through intelligent automation, reproducible workflows, and scalable computational infrastructure.

It provides a unified environment for genomics, multi-omics, AI-driven biological reasoning, workflow orchestration, and reproducible computational research across local machines, HPC clusters, and cloud environments.

🚀 Built for researchers, computational scientists, and developers building the next generation of biomedical AI systems.

📊 Live platform metrics & health → — real-time architecture, codebase stats, and service status, not just claims.

OmniBioAI connects biological data, computational workflows, AI models, and scientific knowledge into a unified ecosystem.

Biological Data
      │
      ▼
 ┌──────────────────────┐
 │  OmniBioAI Platform  │
 └──────────────────────┘
      │
 ├── 🧬 Multi-Omics Analysis
 ├── 🤖 AI Agents & Scientific Reasoning
 ├── ⚙️ Workflow Orchestration
 ├── 🐳 Containerized Execution
 ├── 🖥 HPC & Cloud Compute
 ├── 📚 Knowledge Retrieval & RAG
 ├── 🔐 Security & Governance
 └── 🔬 Reproducible Research

Support for modern computational biology workflows:

• Genomics
• Transcriptomics
• Single-cell RNA sequencing
• Proteomics
• Variant analysis
• Functional annotation
• Biological pathway analysis
• Knowledge-driven discovery

OmniBioAI integrates AI systems designed for biological reasoning:

• AI-assisted workflow generation
• Biomedical knowledge retrieval
• Literature-aware analysis
• Scientific hypothesis generation
• Agent-driven computational workflows
• Model-assisted interpretation

Execute bioinformatics pipelines across multiple environments:

• Local workstation
• HPC clusters
• Slurm environments
• Cloud batch systems
• Containerized compute environments

616 versioned workflow bundles across:

• Nextflow
• WDL
• Snakemake
• CWL

Unified AI-Native Bioinformatics Platform — 9-layer architecture from client access through security, observability, and development environments.

Every analysis should be traceable:

• Input provenance
• Reference versions
• Tool versions
• Execution environment
• Workflow history
• Output artifacts

OmniBioAI follows:

✅ Deterministic execution before AI reasoning ✅ Evidence-based biological interpretation ✅ Human review for critical decisions ✅ Complete computational provenance

• Python
• FastAPI
• Django
• MySQL
• Redis Streams

• React
• TypeScript
• Vite
• Design systems

• Large Language Models
• Retrieval Augmented Generation
• Vector search
• Knowledge graphs
• Scientific agents

• Docker
• Kubernetes
• Slurm
• Cloud batch computing

🌐 Website https://omnibioai.org

📊 Live Platform Stats https://control.omnibioai.org

🐙 GitHub Organization https://github.com/OmniBioAI

📦 Container Registry (1,233 images) https://github.com/orgs/OmniBioAI/packages

🤗 Hugging Face https://huggingface.co/omnibioai

📚 Documentation See the omnibioai-docs repository

🎥 Tutorials See the omnibioai-videos repository

💬 Discord https://discord.gg/Hu6vgfAFn

🐦 Twitter https://twitter.com/OmniBioAI

Created by Manish Kumar

🧬 Senior Computational Scientist | 🤖 AI-Native Bioinformatics Engineer

18 years across 🇺🇸 USA, 🇶🇦 Qatar, 🇲🇾 Malaysia, 🇸🇦 Saudi Arabia, and 🇮🇳 India, building genomics and precision medicine infrastructure for research and clinical environments.

🏥 Currently at the University of Kansas Medical Center — genomics, single-cell, exome & proteomics pipelines for rare disease and translational research.

Building intelligent computational systems at the intersection of:

Biology × AI × Software Engineering × HPC

Build the operating layer where biological data, computational workflows, and artificial intelligence converge to accelerate discovery.

⭐ Star the repositories, explore the architecture, and join the future of AI-powered computational biology.