Designing for petabytes: clinical-grade interfaces on top of genomic data.
Seven Bridges (now Velsera) is a cloud workspace where biomedical teams store, analyse and jointly interpret genomic data. This is a read of the product architecture I worked inside: how cohort discovery, reproducible pipelines and lab intelligence get turned into surfaces a wet-lab researcher can actually use.
No product screenshots. The platform is under NDA and handles regulated patient data, so nothing in this study shows the real interface. Every visual below is a diagram I drew for this write-up to explain the architecture, the flows and the numbers. Figures quoting scale or timing come from Seven Bridges' published benchmarks.
- The problem: genomic data is too large to move, too complex to browse, and too regulated to guess at. Three constraints, one interface.
- The design work: a shared analysis loop, an interactive-speed cohort builder, and auto-generated GUIs that let developers ship interfaces without designers in the loop.
- Worth stealing: reproducibility rendered as interface, honest performance, and progressive disclosure from Run button down to API.
Three constraints that decide every screen.
Genomics UX is shaped by physics before it is shaped by taste. A single whole-genome sample is hundreds of gigabytes; a study is petabytes. That means the data cannot come to the user, so the analysis has to go to the data, and the interface has to make a remote, long-running, expensive computation feel like a document you opened.
The second constraint is comprehension. A phenotypic store with 21,600 fields is not browsable. Nobody scrolls to a cohort. The interface has to be a search-and-refine instrument, and it has to answer fast enough that defining a cohort feels like thinking rather than like submitting a job.
The third is trust. This work ends up in clinical diagnostics and regulatory submissions. If a researcher cannot see which tool version produced a result, the result is not usable, no matter how good the chart is.
Four people who need the same objects in different clothes.
The hard part is not that these personas want different features. It is that they want different renderings of the same underlying object, and a handoff between them is where projects die.
Move the analysis to the data, and make reproducibility a visible object on screen. Everything else is a rendering decision.
Three loops that carry the whole product.
The platform looks enormous from the outside. In practice almost all value passes through three loops, and each one has a single moment where the design either holds or collapses.
What the UX work actually consisted of.
Reproducibility rendered as interface. Revision numbers, CWL version, contributors and app IDs live in the page header rather than in documentation. A scientist can answer "what produced this?" without leaving the screen, which is the precondition for the result being citable at all.
One saturated call to action per surface. These pages are dense with metadata, and density is fine, as long as there is exactly one thing that is obviously the next action. Run is the only strong colour on the page.
Progressive disclosure from button to API. The same objects are reachable through GUI, CLI, notebooks and API. A team can start in the browser and graduate to code without re-learning the model or migrating projects.
Lab intelligence as a dashboard, not a report. For diagnostics work the interface had to show sample flow, QC metrics and turnaround in near real time, so a lab manager can spot a stalled batch in the morning rather than in a weekly PDF.
Honest performance. Publishing the slow path next to the fast ones sets expectations before the spinner does. In scientific tooling, credibility is a feature, and overclaiming costs more than a fifteen-minute export.
An honest read, including the parts I would redo.
Treating "the data never moves" as a UX decision rather than an infrastructure one removes the worst step in genomics work instead of decorating it.
Versions and IDs as visible objects gave a regulated audience something to trust, and gave support a shared vocabulary with users.
Generated GUIs and a public app library convert bioinformatician output into researcher-usable surfaces without a handoff.
Platform, ARIA, GRAF and RHEO is an org chart projected onto users. Nothing told a new team which product to start with. A persona-by-task chooser was the obvious fix and never shipped.
The best interface in the product, the reproducibility chrome, is the one thing competitors cannot copy, and it was almost invisible outside the app. Marketing surfaces led with abstract heroes instead.
Power users were well served, first-week users much less so. The gap between "I have an account" and "I ran something meaningful" stayed wider than it should have.
Four principles I carried out of this domain.
In data-heavy products, the constraint chooses the interaction model. Design the interface after you have decided where the compute lives.
Provenance, versioning and audit are UI work, not compliance paperwork. If a user cannot see why a number is true, they will rebuild it elsewhere.
The most valuable thing I saw here was an interface generated from code. Removing a step between two personas beats designing a smoother step.
Publish where the time goes. Users forgive a long operation they chose; they do not forgive an exploratory click that stalls.
Confidentiality note: Seven Bridges and Velsera work is covered by NDA and involves regulated patient data, so this case study contains no product screenshots. All figures are original diagrams drawn to explain the architecture and flows. Quantitative claims come from Seven Bridges' publicly published platform benchmarks and product pages.