OmniBioAI unifies reproducible multi-omics analysis, agentic AI reasoning, and enterprise-grade infrastructure — across local, HPC, and cloud environments.
Every component is designed with clinical-grade reliability and reproducibility at its core.
LangGraph-based orchestration with RAG-powered assistants using Hugging Face and Ollama — bridging deterministic bio-computation with explainable AI interpretation.
Native support for WDL, Nextflow, Snakemake, and CWL. Cloud-agnostic Tool Execution Service ensures 1:1 parity between local and massive-scale genomic execution.
Optimized on NVIDIA PyTorch with CUDA on DGX Spark. High-performance GNNs for drug discovery and deep learning for single-cell transcriptomics.
Production-grade Model Registry and LIMS-X metadata system for total traceability — from raw FASTQ to drug-target intelligence and pathway enrichment.
98% average test coverage across 11 microservices. Sub-3ms latency for critical service handshakes. Built for clinical-grade stability from day one.
Extensible plugin architecture with OnboardAI documentation browser. 30,000+ lines of living documentation updated after every commit.
Each bioinformatics workflow is modular, allowing researchers to customize, extend, or replace analysis steps without breaking reproducibility.
Runs consistently across local machines, Slurm-based HPC clusters, and cloud platforms like AWS, Azure, and GCP with identical execution logic.
LangGraph-based agentic workflows dynamically plan, execute, and adapt bioinformatics pipelines, enabling multi-step reasoning across tools, datasets, and analysis stages.
From raw sequencing reads to biological insight — OmniBioAI covers the full spectrum of modern genomics.
QC, integration, clustering, trajectory inference, differential expression, TF network modeling, and pathway enrichment.
GATK variant calling, SnpEff annotation, SKAT-O burden testing, NMF, and decile prioritization pipelines.
LEV, SEV, and plasma sample analysis with GSEA, ssGSEA, GSVA, ReactomePA enrichment and volcano plots.
Graph neural networks for drug-target interaction prediction and pathway-level disease modeling.
Patient cohort analysis, variant burden testing, and clinical report generation for precision medicine.
OmniBioAI Studio runs on any modern Linux or macOS machine with Docker installed. One download, no command line needed.
Minimum: 16 GB RAM
Recommended: 32 GB RAM
With local LLM: 64 GB RAM
AppImage / DMG: ~84 MB
Docker images: ~5 GB (one-time pull)
Data + work dirs: 50–200 GB
Linux: Ubuntu 20.04+ (AppImage)
macOS: 12+ Apple Silicon + Intel
Windows: WSL2 + Docker
Docker Engine 24+ or Docker Desktop
Docker Compose v2 (included)
No other dependencies required
NVIDIA GPU + nvidia-container-toolkit
Required only for local LLM inference
Cloud API (Claude/GPT) works without
Internet required for first boot only
~10 GB pulled from ghcr.io automatically
Fully offline after first run
Approved researchers receive a platform-specific download link and onboarding support within 1–2 business days.
↗ Request Beta AccessLicense key included · 30-day free trial
Choose your platform and architecture. All installers include a 30-day free trial license.
Any ARM64 Linux
aarch64
A containerized, microservices-led environment built for massive scale, traceability, and high performance.
OmniBioAI Studio separates user experience orchestration from heavy-lifting workflow engines. It executes multi-omics routines natively, passing biological insights directly into automated reporting and visualization pipelines.
With strict isolation across its core service layers, researchers can safely deploy pipelines locally or easily burst to Slurm or cloud systems without code modifications.
Methods developed and powered by OmniBioAI platform architectures across transcriptomics and proteomics.
Get access to OmniBioAI Studio v0.2.0-beta. Accelerate your multi-omics data integration with robust, explainable AI workflows.