From Fragmentation to Flow
Clinical genomics has never moved faster or felt more constrained. New assays, new modalities, expanding capacity needs, and increasingly complex workflows mean most laboratories end up stitching processes together across liquid handlers, semi-automated workcells, and manual benches.
The result?
- Data lives in different systems.
- Process fidelity varies by person, instrument, and shift.
- Capacity planning feels more like guesswork than science.
Enter orchestration.
Orchestration turns fragmented lab activity – manual, semi-automated, or fully robotic – into a single, coordinated operational system.
If you haven’t already, start with this primer and then come back here for a deeper look at its impact on clinical genomics.
What Orchestration Really Means for Clinical Genomics
While automation focuses on individual or clustered instrument control, orchestration focuses on everything:
The flow of work, the assignment of resources, guidance for operators, and real-time visibility of what happened when.
Where most labs today rely on tribal knowledge and disconnected tools, orchestration makes it possible to operate like a unified production system, even when that ‘system’ includes people, freezers, barcode readers, benchtop instruments, and increasingly sophisticated robots.
Below are three problem spaces where orchestration doesn’t just help - it transforms performance.
1. Traceability: Every Sample, Every Step, Every Time
The Problem Today
Clinical genomics labs are responsible for the ultimate chain of custody. But beyond the high-throughput automation islands, traceability is often manual:
- Notebooks, fragmented files and spreadsheets.
- Paper checklists or SOP binders.
- Partially implemented electronic documentation completed on a Friday afternoon.
- Workarounds for instruments not designed for regulated workflows.
As complexity increases with more samples, more assays, more operators, the risk increases with it - lost samples, rework, incomplete audit trails, and inconsistent execution.
How Orchestration Solves It
Orchestration captures every action, across:
- Automated systems and Semi-automated tools, like liquid handling stations
- Manual steps and human-in-the-loop decisions are integrated to a complete workflow
Operators receive guided instructions (step-by-step), and orchestration records:
- Who touched the sample
- Which resource was used
- When and how
- The resulting data outputs
The result:
A complete forensic record for every sample, from receipt to sequencing, without slowing the lab down.
Traceability becomes automatic, precise, and universal, not something you “remember to document” and includes all the additional metadata which automation takes for granted.
Video 1. A short demonstration of a workflow combining manual and automated tasks, where the user is prompted to take action to move plates and operate devices.
2. Redundancy: Flexibility Without Risk
The Problem Today
Labs buy instruments to meet specific demands, for example, we buy individual analyzers or liquid handlers with overlapping capabilities but are dedicated and reserved for single tasks. They rarely function as a true network:
- A liquid handler specific to extraction fails? The run collapses.
- An automation specialist is absent? The workflow halts.
- A process must be rerouted to a different instrument? Someone must manually decide how, and what impact this might have.
Most labs could route samples to alternate resources, but can’t do it without sacrificing time, traceability, predictability, or validation confidence.
How Orchestration Solves It
With orchestration, workflows are resource aware:
- There can be multiple paths to complete the same job using dynamic combinations of instruments
- Defined rules for when to use which instrument
- Built-in operator guidance, with full tracking, to fill the gaps when systems are offline
- If a system breaks? Orchestration can reroute samples to another qualified device while maintaining full chain of custody.
If demand spikes? Tasks distribute across available resources, manual or automated, while remaining compliant and standardized.
Redundancy becomes a strength, not a liability.
Figure 1. The Prime NGS Workstation, featuring the HighRes Prime Liquid Handler and including integrated instruments like the Formulatrix Tempest, BlueCatBio BlueWasher, Inheco ODTC, and more.
3. Utilization: Maximize the Assets
The Problem Today
Clinical genomics labs are packed with capability and instruments designed for specific tasks, but also those that could span so much more. Take the automated workcells or liquid handlers as examples. They are built from a generic set of tools and then specialized for a task, such as nucleic acid extraction, library prep or quantitation and normalization. Those generic capabilities are often there, and yet we rarely look at using systems for multiple functions.
Most systems run one task, one workflow - even though many overlap in capability.
This creates:
- Underused hardware
- Capital inefficiency
- “Islands of automation” instead of coordinated throughput
How Orchestration Solves It
Orchestration evaluates:
- What plates need to be processed
- What instruments are available
- What steps require what capabilities
- The optimal distribution for maximized utilization
Suddenly:
- Pre-PCR tasks can overflow onto temporarily under-utilized extraction systems
- Normalization and pooling systems can take on extra post-PCR prep steps when capacity opens (or vice versa)
- People and stand-alone instruments operate as integrated capacity, not ‘the last resort’ when all else fails.
It becomes possible to use all your assets dynamically, not just the subset tied to a single script. Utilization moves from static to strategic, maximizing throughput without adding hardware.
It All Adds Up to Operational Excellence
Orchestration isn’t about robots replacing people. It’s about connecting: People + Instruments + Data + Process into a seamless, resilient production engine capable of scaling with clinical demand.
Labs gain:
- Higher throughput and redundancy without additional headcount or capital
- Better chain-of-custody documentation
- Faster troubleshooting and fewer errors
- Predictable turnaround time
- Process reproducibility across shifts, sites, and people
And most importantly: the confidence to innovate, without breaking the system every time science advances.
Summary
Clinical genomics is undergoing a shift, from automating isolated tasks to orchestrating whole laboratories.
The labs that scale sequencing capacity, adapt to assay evolution, and protect sample integrity across every step are those that treat orchestration as core infrastructure, not an optional upgrade.
Automation gets work done. Orchestration ensures all work gets done, correctly, efficiently, and repeatably, no matter the instruments, workflows, or complexity involved.