Nebula Bio Technologies develops computational tools and AI-driven pipelines that accelerate discovery in bioinformatics, genomics, and molecular biology. We turn complex biological data into clear, actionable knowledge.
Trusted by researchers worldwide
Our interdisciplinary team works at the intersection of biology, computer science, and artificial intelligence to solve the most pressing challenges in modern life sciences.
High-throughput sequence analysis, structural bioinformatics, and computational pipelines for multi-omics data integration. We build tools that transform raw biological data into actionable insights at scale.
Whole-genome sequencing, variant calling, and population genetics. We specialize in scalable methods for genome assembly, annotation, and comparative genomic analysis across diverse species and populations.
Custom neural architectures tailored for biological data. From protein structure prediction to drug-target interaction modeling, we apply state-of-the-art deep learning to the most challenging problems in biology.
Nebula Bio Technologies is a research-driven company headquartered in Las Vegas, Nevada with operations in San Francisco, California. Founded with the conviction that the next great breakthroughs in biology will be computational, we develop next-generation tools that help researchers unlock the full potential of biological data.
Our team combines deep expertise in molecular biology, statistical genetics, and machine learning. We work at the frontier where terabytes of sequencing data meet sophisticated algorithms capable of revealing patterns invisible to traditional analysis.
From whole-genome analysis to AI-powered protein modeling, we partner with academic institutions, biotech companies, and pharmaceutical organizations to push the boundaries of what's possible in computational biology. Our open-science philosophy means many of our tools and pipelines are made available to the broader research community.
We believe that meaningful scientific progress requires more than clever algorithms. It demands rigor, reproducibility, openness, and a deep respect for the complexity of living systems.
Every pipeline we build is containerized, version-controlled, and documented to a standard that any lab on the planet can reproduce our results independently.
We publish our methods, share our code, and contribute to the open-source bioinformatics ecosystem. Science advances fastest when knowledge flows freely.
Our best work happens when biologists, statisticians, and engineers sit at the same table. We actively cultivate diverse perspectives in every research initiative.
We don't just apply off-the-shelf ML to biological data. We design architectures that encode biological priors, evolutionary constraints, and domain knowledge from the ground up.
From raw sequencing reads to publication-ready figures and biological interpretation, our capabilities cover the full spectrum of modern computational biology.
We invest heavily in infrastructure and tooling so our researchers can focus on science, not systems administration. Every analysis runs on reproducible, scalable, and auditable pipelines.
Our platform is built on cloud-native infrastructure with GPU-accelerated compute nodes for deep learning workloads. We use containerized workflows (Nextflow, Snakemake) orchestrated across distributed clusters, ensuring that analyses scale from a single sample to population-level cohorts seamlessly.
Data security and compliance are non-negotiable. All patient-derived data is processed in HIPAA-aligned environments with full encryption at rest and in transit. We maintain strict access controls and comprehensive audit logs.
Nextflow, Snakemake, and custom DAG-based schedulers for complex multi-step bioinformatics pipelines with automatic retry and checkpointing.
PyTorch and JAX for custom architectures. We maintain internal libraries for graph neural networks on molecular data and attention-based sequence models.
GCP and AWS with Kubernetes-orchestrated GPU clusters. Petabyte-scale object storage with tiered archival. Terraform-managed infrastructure as code.
Every project follows a structured methodology that balances scientific rigor with the agility to adapt as new findings emerge.
We begin every engagement with a deep dive into the biological question, existing literature, and available data. This phase ensures we're solving the right problem with the right approach before writing a single line of code.
Raw data undergoes rigorous QC including adapter trimming, contamination screening, batch effect detection, and normalization. We believe that clean data is the foundation of reliable results.
We apply appropriate statistical and machine learning methods, from established bioinformatics tools to custom-built neural networks. Multiple approaches are compared and validated against each other.
Results are validated through cross-validation, independent datasets, and where possible, experimental confirmation. We translate statistical findings into biologically meaningful narratives.
Deliverables include publication-ready figures, interactive dashboards, reproducible code repositories, and comprehensive documentation. We ensure our collaborators can build on the work independently.
A selection of recent publications, preprints, and technical reports from our research programs.
Whether you have a specific research challenge, a dataset that needs analysis, or a collaboration idea, we'd love to hear from you.
If you're an academic researcher interested in accessing our tools or exploring a collaboration, reach out at research@nebula-bio.com. We offer reduced-rate computational support for academic groups and non-profits.