Key points
- The Wellcome Sanger Institute is undertaking a large-scale operational transformation to unify specialized lab systems into a consolidated, fit-for-purpose operational data foundation.
- Scientific operations at Sanger support its scientific programs with a vast range of activities, making the move from bespoke systems to operational consistency a strategic priority, not an administrative cleanup.
- The approach is discovery-led, mapping real workflows and engaging researchers and operational teams early to build shared understanding and long-term adoption.
- The long-term vision positions operational data as “reusable evidence,” supporting continuous improvement, defensible analytics and AI-enabled capabilities over time.
- Built on the Sapio Sciences platform, the transformation aims to provide unified visibility across lab workflows, sample management and operational data outputs within a single governed environment.
At SapioCon 2026, Amy Yeung, head of cellular services within scientific operations at the Wellcome Sanger Institute, made a practical case for why scientific ambition now depends on operational foundations. At Sanger’s scale, operations are not a support function in the background. Instead, they are, as Yeung described them, the enablers of science.
Yeung shared two numbers that make the point hard to ignore. Sanger has generated more than 54 petabases of sequencing data. Furthermore, the Institute now generates sequencing output at a rate equivalent to a reference human genome every 12 minutes. Scientific operations support a wide range of work, from high-throughput genomics to fast-moving cellular modeling. The challenge, however, is not simply volume. It is keeping sample lineage, execution context, and metadata coherent enough that results remain usable across teams, programs, and time.
The limits of bespoke excellence
For years, Sanger’s operational infrastructure grew organically. Different teams developed specialized, highly effective systems to manage sample receipt, storage and lab execution. Those local solutions helped the Institute move quickly and adapt as the science evolved.
Over time, complexity accumulated in the places where workflows converge. In practice, preparing data for publication became a stitching exercise, pulling together sample lineage, storage history, run metadata, and experimental context from multiple systems and expert teams. Yeung’s point was not that homegrown tooling lacks sophistication. It reflects deep institutional expertise. The issue is that as data volume and complexity increase, the cost of fragmentation rises. As collaboration scales, the number of coordination channels needed starts to work against the pace of science.
A foundation built with scientific rigor
Sanger’s response has been deliberately methodical. Rather than patching individual pain points, Yeung described an effort to define what good operations should look like across the Institute, with consistent workflows and a unified view spanning samples, storage, and execution.
The team ran 21 workshops and captured 455 requirements across 14 operational areas before selecting a platform. Yeung emphasized that discovery is as much about people as it is about process. The goal was not to force uniformity across programs. Instead, it was to surface differences early, build shared understanding, and create the trust needed for long-term adoption.
Operations as a strategic capability
The transition to the Sapio Sciences platform is intended to provide consistent lab workflows and consolidated operational visibility. But Yeung framed the ambition as more than efficiency. She described the goal as turning operational data, structured and trustworthy from the point of collection, into “reusable evidence.” In practice, that means operational information is reusable for continuous improvement and productivity analytics and, over time, more defensible AI-enabled insights.
At an institution generating output at Sanger’s scale, unifying operations is not aspirational; it is an operating requirement. The point is not to centralize for its own sake. Rather, it is to reduce preventable handoffs, make context easier to retrieve, and ensure the Institute can scale global science without constantly rebuilding the operational scaffolding underneath it.
Amy Yeung presented at SapioCon 2026. Sanger’s operational transformation is being built on the Sapio Sciences platform, which provides unified visibility across lab workflows, sample management, and data outputs within a single system.