The engineering behind a genomics company's custom assay design platform
Researchers design custom genotyping assays on this platform. Sequoia is its end to end engineering partner: the design pipelines, the reference databases they draw on, the web application researchers use and the APIs that tie it to the client's wider systems. Time to market for new assay products is up 50 percent and support costs are down 40 percent.
Platform functions
A genotyping assay is only as good as its probes. The platform lets a researcher pick the genomic regions they care about, then selects and optimises the probes for the assay, checks the design, and hands off files that manufacturing can build from. It has to be usable by a scientist who is not a bioinformatician.
The range of work that runs through it is wide. A sample:
| Field | What researchers design |
|---|---|
| Genotyping and genetic research | Custom assays for studying genetic variation across populations |
| Cancer research | Assays that pick out mutations and biomarkers tied to specific cancers |
| Plant and animal genomics | Plant and animal genome studies for breeding programmes and trait selection |
| Pharmacogenomics | Assays on how genetic variation shapes drug response |
| Infectious disease | Pathogen detection and host pathogen interaction studies |
Every one of those users expects the design to come back fast and the probes to be right, so the work sits inside our bioinformatics and genomics practice.
Engineering constraints
Probe selection is a balancing act. A probe has to be specific enough to hit only its target and sensitive enough to hit it reliably, and on a complex genome those two pull against each other. The pipeline has to make that trade well, at scale, for thousands of targets per design.
The platform also does not live alone. It has to exchange data with the client's other tools and with external bioinformatics platforms, and each of those integrations needs its own customisation. Large studies push high throughput data through the pipeline and expose computational bottlenecks that a small design would never hit. And some researchers need assay customisation that the standard flow was never built for.
Under all of that sits a codebase that had to keep serving customers while its foundations were replaced. There was no option to stop and rewrite it.
What Sequoia built
Sequoia came in as the end to end engineering partner, building the web application and the pipelines across several of the client's assay products. The work falls into three layers.
Design pipelines for genotyping products
New pipelines for new products, and customised pipelines for products already on the market. Each one was integrated with the client's cloud analytics platform for pipeline execution, then tuned for faster turnaround and better probe selection. Pipelines are written in Nextflow, which is the same tooling our GenXFlo accelerator is built around.
Reference databases shared across applications
Two reference stores that the pipelines and other applications all draw on. A reference SNP database generated by an ETL tool we wrote against the JSON files NCBI publishes for dbSNP. And a second reference database converted from flat TSV files into something queryable. Because they were built once, centrally, each new product pipeline started from a known baseline.
Web application and backend APIs
The web application is where researchers manage products, raise custom design requests, track orders and download generated design files. Behind it, a .NET backend exposes REST APIs for the UI, internal applications and pipeline execution.
Data lives in SQL Server. Deployments are staged, dev to test to production, and automated through Jenkins CI/CD. Testing is automated.
Maintaining the platform in production
The platform needed substantial framework upgrades while remaining live. Both the backend and front end were upgraded incrementally so the APIs used by other applications continued to work throughout. Compatibility and performance were tracked at each step rather than checked at the end.
Reference data grows and old versions rarely get deleted, so storage cost creeps. We introduced a tiered storage policy that pushes older reference databases to cheaper storage while keeping current data fast. Cost went down without anyone noticing a slower query.
This kind of upkeep decides whether the platform is still healthy in five years, and we bring the same discipline to our cloud and data platform work.
Project results
| Metric | Result |
|---|---|
| Time to market | Improved by 50 percent, through parallel development and close integration with the client's bioinformatics team |
| Support costs | Cut by 40 percent through automated design workflows |
| Customer turnaround | Faster path from design to delivery of custom assay panels for the client's customers |
| Team | Grew into a dedicated engineering team |
The engagement expanded as the client continued to bring more of the platform work to the team.
For genomics product teams
If your customers design products through your software, the platform is part of the product. A slow or brittle design tool costs you launches. The pattern that worked here was to treat pipelines, reference data and the application as one system with one team.
Reference databases deserve their own budget line. Build them centrally, version them, and give every pipeline the same baseline. Most of the 50 percent gain in time to market came from not rebuilding the same foundations for each new product.
Framework upgrades of this size only go smoothly when the APIs are well covered by automated tests before you start. If yours are not, that is the first piece of work, and it should be done before anyone touches a version number.
Common questions
What is a custom genotyping assay design platform?
It is the software a genomics company's customers use to design custom genotyping assays. A researcher chooses the genomic targets, the platform selects and optimises probes, and the resulting design files go to manufacturing. It is used across genetic research, cancer, plant and animal genomics, pharmacogenomics and infectious disease.
What Sequoia built
The design pipelines for new and existing genotyping products, reference databases built from NCBI dbSNP and from other flat file sources, the Angular web application, and the .NET REST APIs that serve the UI, the client's internal applications and pipeline execution. Sequoia is the end to end engineering partner on the platform.
What results did the engagement deliver?
Time to market for new assay products improved by 50 percent. Support costs fell by 40 percent through automated design workflows. The client's customers get their custom panels faster from design to delivery. The Sequoia team grew into a dedicated engineering team.
How were the platform upgrades handled?
The backend and front end were upgraded incrementally without breaking the applications that depend on the APIs. A tiered storage policy moved older reference data to cheaper storage to keep cost and performance in balance.
What is the technology stack?
Angular on the front end, .NET on the backend, SQL Server for data, Nextflow pipelines, and staged deployments from dev to test to production automated with Jenkins CI/CD. Testing is automated. If your platform runs on a similar stack, start a conversation and we will tell you where we would begin.
Running a product platform your customers design on?
Tell us the products, the pipelines and where the bottlenecks are. We will come back with an approach and a team.
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