OmniBioAI unifies reproducible multi-omics analysis, agentic AI reasoning, and enterprise-grade infrastructure — across local, HPC, and cloud environments.
Modern bioinformatics workflows are fragmented, slow, and difficult to reproduce across environments.
OmniBioAI automates end-to-end multi-omics analysis with reproducible, production-grade pipelines powered by AI reasoning.
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.
OmniBioAI is designed as a modular platform for advanced users who need custom workflows, reproducibility, and integration across research environments.
Each bioinformatics workflow is modular, allowing researchers to customize, extend, or replace analysis steps without breaking reproducibility.
Integrates with existing tools and environments including Python, R, Nextflow, WDL, and HPC systems for flexible deployment.
Runs consistently across local machines, Slurm-based HPC clusters, and cloud platforms like AWS and Azure with identical execution logic.
Every execution is tracked with full provenance, ensuring reproducibility from raw data to final biological insight.
LLM-powered reasoning assists in interpreting multi-omics outputs, summarizing results, and generating biologically meaningful insights to guide downstream analysis decisions.
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.
Whole-genome bisulfite sequencing and array-based methylation analysis with clinical reproducibility.
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 solves the reproducibility crisis in bioinformatics by combining distributed systems engineering with large language models.
The platform runs identically across local workstations, HPC clusters (Slurm/LSF), and cloud environments (AWS/Azure) — eliminating the "works on my machine" problem that plagues computational biology.
Built by researchers, for researchers — grounded in published science across genomics and precision medicine.
Join the private beta of OmniBioAI. Run reproducible multi-omics workflows, integrate genomic datasets, and accelerate discovery — without infrastructure setup.
Built by Manish Kumar — 18+ years in bioinformatics & AI systems