The pace of innovation continues to accelerate; this is as true in genomics as in any other sector. Sequencing is faster, more affordable, and more powerful than ever before – but these advances also place increasing demands on laboratory workflows. Researchers are under constant pressure to generate more data, from smaller samples, with higher accuracy and reproducibility.
At the center of these workflows lies a deceptively simple component: liquid handling. Whether preparing sequencing libraries, processing single-cell samples, or validating new assays, precise and reliable pipetting is the foundation for every successful experiment.
Over the years, I’ve seen how thoughtful workflow design and intelligent automation can transform productivity in genomics. Here are five practical tips that can help research teams streamline their operations and unlock more value from their data.
Embrace automation
Reproducibility is the cornerstone of credible science. Yet in genomics, even minor inconsistencies in liquid handling can lead to significant variability in downstream results. Automating repetitive or complex steps can help eliminate this source of error.
By reducing manual intervention, automation ensures uniform performance across runs and users, improving confidence in results and freeing scientists to focus on interpretation rather than repetition. The time saved is valuable, but the gain in data quality is even more important.
Think smaller
Miniaturization has become one of the most effective ways to make genomics workflows more efficient. Reducing reaction volumes cuts reagent consumption, shortens processing times, and lowers costs – all without compromising quality.
Smaller reactions also make it possible to increase throughput, explore more conditions, and improve sustainability by using fewer consumables. In an era where budgets and environmental impact are under scrutiny, miniaturization offers a clear win on both fronts.
Design workflows with integration in mind
Modern genomics workflows rarely operate in isolation. They span multiple instruments, chemistries, and data systems – each contributing to the bigger picture. When workflows are designed to integrate smoothly from sample preparation through to data analysis, labs gain efficiency, traceability, and consistency.
Integration isn’t just about connecting devices; it’s about ensuring that every step works together logically and reproducibly. Building flexibility into these connections allows labs to evolve as new technologies and analytical methods emerge.
Balance throughput with flexibility
Throughput is often seen as the measure of a successful workflow, but in practice, flexibility is just as important. Research teams need to adapt to changing priorities, varying sample inputs, or newly developed assays.
Automation and workflow design should support this adaptability. Systems that are intuitive to reconfigure, scalable in capacity, and open to customization allow scientists to innovate without being constrained by their tools. In my experience, flexibility is what distinguishes a good workflow from a future-ready one.
Prioritize sustainability in every step
As laboratories expand their sequencing and analysis capabilities, sustainability must remain front of mind. Small changes such as reducing plastic usage or optimizing reagent volumes can have a measurable impact on a lab’s environmental footprint.
Sustainable design also goes hand in hand with efficiency. Workflows that use fewer resources, generate less waste, and minimize redundant steps are not only greener, but more cost-effective in the long term. It’s about working smarter, not just faster.
Ultimately, optimizing genomics workflows is not a one-time exercise – it’s a process of continual refinement. Collaboration between scientists, engineers, and data specialists plays a crucial role in identifying where automation, integration, and miniaturization can have the greatest impact.
By sharing experiences and challenges across the community, we can collectively move closer to workflows that are not only faster and more reliable, but more accessible and sustainable. The smallest improvements in how we move liquid today can lead to some of the biggest breakthroughs in genomics tomorrow.
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