You understand the biology. You shouldn't need a CS degree to understand your omics data.
You can design elegant experiments. You understand statistical significance. You know exactly what biological questions to ask. So why do you spend 6 months debugging instead of discovering?
No code reviews. No one to debug with. Your scripts work (maybe), but are they reproducible? Accurate? Even you can't tell six months later.
Broken dependencies. Deprecated packages. Documentation that's three versions behind. You spend more time debugging than analyzing data.
You know what test you need, but implementing it correctly? Different story. And machine learning across multi-omics data? That's challenging even with dedicated teams.
Test how well your polygenic risk scores perform in different ancestry groups. Get calibration plots, performance metrics, and publication-ready statistics.
Use multi-omics clustering to identify disease subtypes in your cohort. Get survival curves, pathway signatures, and clinical correlations.
Run pathway enrichment on your differential expression results. Get network visualizations, driver genes, and pathway crosstalk analysis.
Focus on your hypotheses, not command line syntax. Ask questions the way you think about them.
Proper corrections, appropriate tests, confidence intervals that actually mean something.
Stop waiting for pipelines to work. Get from hypothesis to publication-ready results in hours.
Share analyses with colleagues, reviewers, and collaborators. Everything is reproducible by default.
Join leading research institutions who've stopped debugging and started discovering. Whether you're a single PI or a major genomics center, let's build something together.