Jetomics bridges the gap between massive sequencing data and biological insight. We build tools that let biologists analyze their own data without learning to code or compromising privacy.
Why we started
In 2025, we noticed a growing disconnect in modern genomics: while sequencing technologies have become faster and more affordable, data analysis remains expensive, slow, and inaccessible to many researchers. Generating RNA-seq data is no longer the bottleneck, interpreting it is.
Today, most RNA-seq analysis workflows force researchers into one of two suboptimal paths. The first is relying on specialized bioinformaticians, often shared across labs or core facilities, resulting in long queues, limited iteration, and a loss of scientific momentum. Simple changes, adjusting thresholds, excluding samples, or testing alternative models can take days or weeks.
The second option is cloud-based platforms. While powerful, they introduce their own challenges: high and unpredictable costs, mandatory data uploads, complex configuration, and serious privacy and compliance concerns when working with sensitive or unpublished datasets. For many labs, especially in academia, these platforms are financially unsustainable and operationally restrictive.
Command-line tools offer flexibility, but they come with a steep learning curve. Installing dependencies, managing environments, and debugging pipelines often requires substantial computational expertise, time and effort that most biologists did not sign up for and should not be forced to acquire just to analyze their own data.
We created Jetomics to offer a third option. A platform that combines the rigor and transparency of established R and Python workflows with the usability of modern software without the cloud, without long waits, and without compromising data ownership.
Jetomics runs entirely on your machine. Your data never leaves your laptop. By carefully optimizing and packaging well-validated RNA-seq algorithms, we enable fast, reproducible analysis on standard hardware.
Our goal is simple: to give researchers direct, immediate control over their data analysis, shorten the path from sequencing to insight, and make high-quality RNA-seq analysis accessible to anyone, not just those with dedicated computational support.